# migrations

dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: sysdb
filename: 00004-tenants-databases.sqlite.sql
hash: 048867ce8fcdefe4023c7110e4433591
sql:
CREATE TABLE IF NOT EXISTS tenants (
    id TEXT PRIMARY KEY,
    UNIQUE (id)
);

CREATE TABLE IF NOT EXISTS databases (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per tenant
    tenant_id TEXT NOT NULL REFERENCES tenants(id) ON DELETE CASCADE,
    UNIQUE (tenant_id, name) -- Ensure that a tenant has only one database with a given name
);

CREATE TABLE IF NOT EXISTS collections_tmp (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per database
    topic TEXT NOT NULL,
    dimension INTEGER,
    database_id TEXT NOT NULL REFERENCES databases(id) ON DELETE CASCADE,
    UNIQUE (name, database_id)
);

-- Create default tenant and database
INSERT OR REPLACE INTO tenants (id) VALUES ('default_tenant'); -- The default tenant id is 'default_tenant' others are UUIDs
INSERT OR REPLACE INTO databases (id, name, tenant_id) VALUES ('00000000-0000-0000-0000-000000000000', 'default_database', 'default_tenant');

INSERT OR REPLACE INTO collections_tmp (id, name, topic, dimension, database_id)
    SELECT id, name, topic, dimension, '00000000-0000-0000-0000-000000000000' FROM collections;
DROP TABLE collections;
ALTER TABLE collections_tmp RENAME TO collections;


dir: sysdb
filename: 00005-remove-topic.sqlite.sql
hash: b1367c826b8fba5f96f27befdc1d42d2
sql:
-- Remove the topic column from the Collections and Segments tables

ALTER TABLE collections DROP COLUMN topic;
ALTER TABLE segments DROP COLUMN topic;


dir: sysdb
filename: 00006-collection-segment-metadata.sqlite.sql
hash: 4eea7468935bf25d4604a0fed2366116
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE collection_metadata ADD COLUMN bool_value INTEGER;
ALTER TABLE segment_metadata ADD COLUMN bool_value INTEGER;


dir: sysdb
filename: 00007-collection-config.sqlite.sql
hash: 1c7e63bba346a42a18b6ab7f1c989bed
sql:
-- Stores collection configuration dictionaries.
ALTER TABLE collections ADD COLUMN config_json_str TEXT;


dir: sysdb
filename: 00008-maintenance-log.sqlite.sql
hash: 0a0e7e93111a01789addf64961c6127c
sql:
-- Records when database maintenance operations are performed.
-- At time of creation, this table is only used to record vacuum operations.
CREATE TABLE maintenance_log (
  id INT PRIMARY KEY,
  timestamp INT NOT NULL,
  operation TEXT NOT NULL
);


dir: sysdb
filename: 00009-segment-collection-not-null.sqlite.sql
hash: 054355aef9e63702bf54ea29e61563f1
sql:
-- This makes segments.collection non-nullable.
CREATE TABLE segments_temp (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    collection TEXT REFERENCES collection(id) NOT NULL
);

INSERT INTO segments_temp SELECT * FROM segments;
DROP TABLE segments;
ALTER TABLE segments_temp RENAME TO segments;


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


dir: metadb
filename: 00004-metadata-indices.sqlite.sql
hash: fb36603a45ee2cd0254cef3ef86585e8
sql:
CREATE INDEX IF NOT EXISTS embedding_metadata_int_value ON embedding_metadata (key, int_value) WHERE int_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_float_value ON embedding_metadata (key, float_value) WHERE float_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_string_value ON embedding_metadata (key, string_value) WHERE string_value IS NOT NULL;


# embeddings_queue

created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: b7173971-19a4-4318-9021-dfd0654c779d
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "214f7811-e59f-7cf0-8efe-a2d6987ff17a", "source_type": "file", "start_index": 0, "chroma:document": "A Review of Deep Learning Techniques\n\nAbstract: In today\u201a\u00c4\u00f4s world, Deep learning techniques are playing a vital role in all areas. It has already\nmade a massive impact in almost every field, such as self-driving cars, cancer diagnosis, predictive\nforecasting, precision medicine and speech recognition. The limitations of traditional learning techniques\nare overcome by deep learning techniques. In this paper deep learning and its techniques are discussed.\nKeywords\u201a\u00c4\u00eeDeep Neural Network, Recurrent Neural Network, Convolutional Neural Network, Restricted\nBoltzmann Machine."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f06cc4e4-bbdc-41a5-96d3-be20d7bc83f1
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "214f7811-e59f-7cf0-8efe-a2d6987ff17a", "source_type": "file", "start_index": 578, "chroma:document": "1. Introduction: With a massive influx of multimodality data, the role of machine learning has grown\nrapidly in the last few years. In last decade various machine learning techniques like decision tree, naive\nBayesian, support vector machine and K-Nearest Neighbour have been utilized for developing models to\nsolve real life problems such as predictive forecasting, speech recognition, diseases diagnosis and NLP.\nThese techniques have provided successful results but fails when amount of data increases abruptly. And\none of the other reasons for traditional machine learning failure is its features extraction capability. These\nclassification techniques do not give good result if we forget to give some parameter. These problems are\novercome as the concept of neural network was introduced in AI. After that machine learning is\nrevolutionized completely. Deep learning techniques are based on the concept of neural network; provide"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: bb0fc0ff-2133-4a60-bf26-24e1b139b87f
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "214f7811-e59f-7cf0-8efe-a2d6987ff17a", "source_type": "file", "start_index": 1314, "chroma:document": "overcome as the concept of neural network was introduced in AI. After that machine learning is\nrevolutionized completely. Deep learning techniques are based on the concept of neural network; provide\namazing results in all fields. In this paper we discussed deep learning, various techniques of deep learning.\nIn the first section of paper introduction is given. In second section of the paper deep learning and its\nworking is described. In next section various deep learning techniques such as CNN, RNN, LSTM and\nAutoencoder and Restricted Boltzman Machine are described. In fourth section of the paper conclusion is\ngiven and last section contains references."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 77ca78f9-7f1d-426e-b241-2e148e812c67
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "214f7811-e59f-7cf0-8efe-a2d6987ff17a", "source_type": "file", "start_index": 1976, "chroma:document": "2. Deep Learning: Deep learning is a subarea of machine learning that is emerged from the concept of\nneural network. Neural network is inspired by and resembles the human nervous system and the structure of\nthe brain [1][2]. It is an application of Artificial Neural Network in which number of hidden layer is one but\nas number of hidden number of hidden layer increases network goes deeper and it refers to deep neural\nnetwork. Deep learning can be applied in almost every machine learning problem. The layers in DNN are\ndivided into three categories broadly input layer, multiple hidden layer and output layers. Input layer is used\nto give input to the network, hidden layer process the input provided and output layer generates output to the"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 817afd3f-1afb-4619-879e-7291988c70ac
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "d3277878-4f58-17f5-59ec-be68e433661b", "source_type": "file", "start_index": 0, "chroma:document": "system. Input in hidden layers processed a distributed representation and the main driving variables of the\ninput data. Deep learning working model is divided into two phases, training and testing. In training phase\nof model, model parameters with random numbers are introduced and the pre-train some models are done.\nAfter first iteration completes then next step is to read and processes the training data and training errors are\ncalculated by comparing the obtained output with expected output. Then parameters are upgraded according\nto training error through error backpropagation. Then testing phase is performed to find whether conditions\nof the iterative training are met for termination or continue the iterative process of training [3]. For\ndesigning a complex deep neural network requires complex and high level resources for computation along\nwith large amount of training data. Deep learning models have automatic feature extraction capability that"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: e08846f3-40e9-4573-86a8-53296536cb92
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "d3277878-4f58-17f5-59ec-be68e433661b", "source_type": "file", "start_index": 854, "chroma:document": "with large amount of training data. Deep learning models have automatic feature extraction capability that\nmake it different from traditional machine learning algorithms, means user need not to specify the feature to\nthe model, neural network architecture itself find out important feature required for desired results. Deep\nlearning network provides high level of abstraction; even developer does not know how neurons in the\nnetwork are connected and how data is processed within the network. For solving problems deep learning\nuses end to end process. Suppose we have a task of multiple object detection, when we solve this problem\nwith earlier machine learning model then whole task is divided into two phases in first phase we apply\nbounding box detection algorithm to find all the objects on a image and then we apply a recognizer\nalgorithm like support vector machine to detect a particular object on the image. Whereas deep learning"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d497acbe-dfc3-4e5f-844f-27f669a0fba0
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "d3277878-4f58-17f5-59ec-be68e433661b", "source_type": "file", "start_index": 1690, "chroma:document": "algorithm like support vector machine to detect a particular object on the image. Whereas deep learning\nperforms whole task in one go. We apply euro net deep learning algorithm in which we pass image to the\nmodel it gives the location along with the name of the object. As deep neural network is very complex, it\nrequires Graphics processing units for processing large matrix multiplication and other complex operations.\nDeep learning requires large amount of data to train the machine so that it can generate accurate results so\ntraining time of DNN is more than traditional machine learning models. Where is best to apply deep\nlearning:\nDeep learning is ideal to use where we have to predict results from large amount of data.\nDeep learning is applied to solve those complex problems that are very expensive to solve with human\ndecision making."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: acace345-8930-42f5-b3b4-e9a0de694870
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "d3277878-4f58-17f5-59ec-be68e433661b", "source_type": "file", "start_index": 2538, "chroma:document": "3. Techniques of deep learning [1]:\nCNN: is a feedforward neural network that is generally used to analyse the static or image data and is also\nknown as ConvNet. In CNN, whole architecture is divided into three layered structure. In image"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3978309c-e72e-41f6-b792-8e0e5b871c4c
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "de47d8ed-cdb3-1ea3-6701-5dd9903e0828", "source_type": "file", "start_index": 0, "chroma:document": "identification, CNN take the input image, process it, and classify it in a certain category e.g. cat, dog, tiger.\nIn computer image is stored as an array of pixels. Like the traditional machine learning architecture, CNN\nalso have to train for data to solve a particular problem. For this first architecture of Neural network is\ndecided like how many layers are used in network, how we arrange the layers, which layer to use, and how\nmany neuron to be used in a layer. Various CNN architectures are AlexNet, GoogleNet, Inception ResNet,\nand VGG. Once network architecture is decided after that various biases and parameters for the network are\nselected bases on the problem. At first these are selected randomly but further they are changed through\nback propagation. Objective of this phase is to find the best possible values of network parameters and data\nfeatures so that further identification of data can be accurately done. For e.g. when we try to build a"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: ee07d872-b7fc-4d33-a4b5-ddc4155d279d
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "de47d8ed-cdb3-1ea3-6701-5dd9903e0828", "source_type": "file", "start_index": 858, "chroma:document": "features so that further identification of data can be accurately done. For e.g. when we try to build a\nclassifier for cat and dog then we are looking to find the parameters that gives the probability of dog 1 or\nhigher than cat and for all the images of dog or 0 or less than dog for cat image. Whole CNN process is\ndivided into two parts feature learning and classification. In feature learning there are three steps performed\nmany times for different feature detection. These are convolutional operation, ReLu and Pooling. In\nclassification image is classification is performed. In this phase three operation is performed flattened, fully\nconnected and softmax operations [4][5]."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 06801f84-20cc-42f2-9163-bb4d17b6299e
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "de47d8ed-cdb3-1ea3-6701-5dd9903e0828", "source_type": "file", "start_index": 1542, "chroma:document": "Convolutional layer: in this layer filter works on every part of the image. And search same feature\neverywhere in the image. This layer involves shift, multiply and sum operations. The purpose of this layer is\nto identify the basic pattern of which the object in the image is made up of. Output of this layer is a new\nmodified filtered image. In this layer features are identified. In training phase this layer identified the most\naccurate feature for image classification.\n\nReLU layer: is rectified linear unit. Once features are extracted then next step is to move them to ReLU\nlayer. This layer mainly performs element wise operations, sets all the negative values to zero and\nintroduces non linearity to the network. At this layer sigmoid function are applied. This function removes all\nthe black elements from the images.\n\nPooling layer: provides the down sampling to the output that reduces the dimensionality of the feature map."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: ac458f89-5887-412d-a876-2d1e197a2099
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "bd7dad35-a6ed-f1f3-3f34-7cb336827ec2", "source_type": "file", "start_index": 0, "chroma:document": "Pooling layer reduces size of each feature map by 2. Two types of pooling can be applied in CNN, Average\npooling and max pooling. In average pooling feature map is patched with average value and in max pooling\nfeature map is patched by maximum value of the matrix.\n\nThese three steps are applied multiple times to find best features of the data.\nFlattening layer: this layer transforms the matrix into a vector form so that it can be fed into a fully\nconnected NN classifier.\n\nFully connected layer: at this layer data is in one dimensional structure. This layer helps in classifying the\ninput pattern with high-level features extracted by previous layer. This layer gives a probability that a\ncertain feature belongs to a label. For example, if the image is of a cat, features representing things like\nwhiskers or fur provide high probabilities for the label \u201a\u00c4\u00facat\u201a\u00c4\u00f9. Surtax activation function is used to provide\nprobability to each label."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 0dffb9ce-6877-4d40-bfef-8106ae7905e8
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "bd7dad35-a6ed-f1f3-3f34-7cb336827ec2", "source_type": "file", "start_index": 945, "chroma:document": "RNN: is Recurrent Neural Network that is applied to problems where data changes according to time. A\nsingle time step of input is provided to the network in RNN. In first step current state is calculated by using\ncurrent input and output from the previous state. In next step current state become the previous state. Once\nall time steps are completed then output state is calculated from the current state. Output obtained is then\ncompared with expected output and error is calculated. This error is back propagated in the network to\nupgrade the parameters and weights. In this way RNN network is trained. The process of calculating error\nrate and upgrading weights and parameters is called vanishing gradient. In RNN vanishing gradient can only\nremember one step output but sometime problem requires outputs from long distant states for this LSTM is\nused. LSTM have a chain like structure. In LSTM first architecture identify unnecessary information that is"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: b615cba7-611f-4217-ada2-a67bdce0037c
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "bd7dad35-a6ed-f1f3-3f34-7cb336827ec2", "source_type": "file", "start_index": 1796, "chroma:document": "used. LSTM have a chain like structure. In LSTM first architecture identify unnecessary information that is\nthrowing away from the cell state. This is done by sigmoid layer called forget layer. Then model identify the\ninformation that is necessary for further processing. This is done through tanh activation function. After this\noutput from previous stages is combined to update new cell state [7][8]."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 636a32cf-3569-4464-bdd7-40f40e02cc5d
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "bd7dad35-a6ed-f1f3-3f34-7cb336827ec2", "source_type": "file", "start_index": 2200, "chroma:document": "Autoencoders: is an unsupervised algorithm which uses back propagation algorithm for setting the desired\noutput equal to the input. Autoencoders are based on the concept of principal component analysis (PCA). It"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3bef5cb4-ee0b-4d98-921c-aaa48d61435e
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "0e87cc6f-6a6f-0873-9008-cc514e6fe767", "source_type": "file", "start_index": 0, "chroma:document": "uses layer by layer fin-tuning with backpropagation and unsupervised pretraining [9] It is lossy\ncompression. Autoencoder can easily works on non linear data. Autoencoder have multiple representations\nof data. Autoencoder uses convolutional layer for learning feature from data so it does not require learning\nfrom dense layer. It is widely used in image reconstruction, image colorization, and dimensionality\nreduction. . Hinton et al. achieved a perfect reconstruction of 784- pixel images using autoencoders which\nwas better than principal component analysis technique [10]. Autoencoder are used only for which they are\ntrained only we cannot apply autoencoder for another applications."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 4c00b3df-33b0-44c2-ae68-4b5ef30dfa25
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "0e87cc6f-6a6f-0873-9008-cc514e6fe767", "source_type": "file", "start_index": 691, "chroma:document": "Restricted Boltzmann Machine (RBM): Restricted Boltzmann Machine is a deep learning technique\napplied on unlabeled data to build non-linear generative models [11]. RBM contains two layers called\nvisible layer and hidden layer. Each node of visible layer is connected to all nodes in the hidden layer and\nno nodes are connected to other nodes in the same layer.RBM increases at the probability of vectors in the\nvisible layers so that it can probabilistically reconstruct the unlabeled data. The energy (E) function of the\nconfiguration is used for this [12]"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 03dec9c4-b562-45cd-af58-c886ee77dc7f
encoding: FLOAT32
metadata:
{"source": "./Documents/general/deeplearningreview.docx", "doc_id": "0e87cc6f-6a6f-0873-9008-cc514e6fe767", "source_type": "file", "start_index": 1250, "chroma:document": "4. Conclusion: The unmatched learning capability of deep learning made it an attractive and\nindispensable technique for analyzing data and images. It is successfully used in bioinformatics, medical\ndiagnosing, precision medicine and speech recognition. Deep learning techniques are contributing to the\nhigh level of services in every field. In this paper we discussed various deep learning techniques CNN,\nRNN, Autoencoders and RBM. CNN technique is successfully used on image data whereas RNN and\nLSTM provide amazing results in textual data. RBM is usually used to build non-linear generative models.\nFeature extraction capabilities of deep learning techniques make it indispensably useful in every field"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 1d2da0b6-4a75-4b2a-aab3-42d40438cf56
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "ddb50a40-d7bc-49ba-690e-379bd596daec", "source_type": "file", "start_index": 0, "chroma:document": "Official (Closed) and Non-Sensitive\nDevelop computer vision solutions with Azure AI Vision\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 5a3bea3a-c14e-4ff7-af6a-3c17321c9241
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "c2fcd3c6-2c34-286f-eb4d-26f718274215", "source_type": "file", "start_index": 0, "chroma:document": "Agenda\n\u00b7 Analyze and manipulate images\n\u00b7 Create a custom vision model\n\u00b7 Detect and recognize faces\n\u00b7 Analyze video\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive\nAnalyze and manipulate images\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "1943561c-0208-295a-ecfa-b8cc6e0de412", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n1 Understand features and functionality of Image Analysis\n2 Perform Optical Character Recognition (OCR)\n3\nConnect an app to Image Analysis APIs\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "2c871921-94d7-7d65-72a5-9cd974ccbb7b", "source_type": "file", "start_index": 0, "chroma:document": "Image analysis:\n\u00b7 Caption and tag generation\n\u00b7 Object detection\n\u00b7 People detection\n\u00b7 Optical character recognition\n\u00b7 Smart crop thumbnails\n\u00b7 Background removal\n\u00b7 Multi-modal embeddings\n\u00b7 Product recognition"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "adb9b074-40c5-815c-84f4-0ceed9556b16", "source_type": "file", "start_index": 0, "chroma:document": "Can be used as:\n\u00b7 Standalone Azure AI Vision resource\n\u00b7 Multi-service Azure AI Services resource\n* Some new features are limited to specific regions\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "5f61fe77-ae7c-4cbc-a84a-2c7aa1a53bcf", "source_type": "file", "start_index": 0, "chroma:document": "Image Analysis APIs\n\u00b7 Single Analyze call to retrieve specified features in VisualFeatures enum\n\u00b7 VisualFeatures.Caption\n\u00b7 VisualFeatures.DenseCaptions\n\u00b7 VisualFeatures.Tags\n\u00b7 VisualFeatures.Objects\n\u00b7 VisualFeatures.SmartCrops\n\u00b7 VisualFeatures.People\n\u00b7 VisualFeatures.Read\n\u00b7 SDKs define the client , then call the analyze() function from it\n\u00b7 Client defines endpoint and key of resource\n\u00b7 Analyze() needs\n\u00b7 Image data from file or URL\n\u00b7 Visual features to analyze for\n\u00b7 (Optional) Analysis options: Which features, language, and other options for the analysis"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "341e2cbb-c42f-cd46-c2a4-d837c641864c", "source_type": "file", "start_index": 0, "chroma:document": "REST\nhttps://<endpoint>/computervision/imageanalysis:analyze? features=caption,people&model-version=latest& language=en&api-version={version}"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "367fa7e2-3d91-64a1-f2ba-42e875ce6165", "source_type": "file", "start_index": 0, "chroma:document": "C#\nImageAnalysisResult result = client.Analyze( new Uri('<uri-to-image>'), VisualFeatures.CAPTION | VisualFeatures.PEOPLE, analysisOptions // Optional );"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "5f305150-59cf-681d-5f0b-5724b0791d25", "source_type": "file", "start_index": 0, "chroma:document": "Python\nresult = client.analyze( image_url='<uri-to-image>', visual_features=[ VisualFeatures.CAPTION, VisualFeatures.PEOPLE, ], <analysis_options> # Optional )\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "912cda74-e485-1ef7-a45c-d0a10ed608da", "source_type": "file", "start_index": 0, "chroma:document": "Image Analysis Options\n\u00b7 Analysis options\n\u00b7 Cropping aspect ratios\n\u00b7 Gender neutral caption\n\u00b7 Language\n\u00b7 Model version\nC# ImageAnalysisClient client = new ImageAnalysisClient( new Uri(endpoint), new AzureKeyCredential(key)); ImageAnalysisOptions options = new ImageAnalysisOptions { GenderNeutralCaption = true, Language = \"en\" }; ImageAnalysisResult result = client.Analyze( imageURL, visualFeatures, options ); Python client = ImageAnalysisClient( endpoint=endpoint, credential=AzureKeyCredential(key)) result = client.analyze( image_url=image_url, visual_features=visual_features, gender_neutral_caption=True, language=\"en\" )\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "4f7cf04f-ff40-7677-f39e-1dc1fae6bf65", "source_type": "file", "start_index": 0, "chroma:document": "Image Analysis Result\n\u00b7 Successful image analysis returns JSON (REST) or an object (SDKs)\n\u00b7 Results may have one or several layers of depth\n\u00b7 Tags > values[ ] > name\n\u00b7 Text > lines > words\n{ \"captionResult\": { \"text\": \"a man pointing at a screen\", \"confidence\": 0.4891590476036072 }, \"objectsResult\": { \"values\": [ { \"name\": \"string\", \"confidence\": 0.0 }] }, \"smartCropsResult\": { \"values\": [ { \"aspectRatio\": 0.0, \"boundingBox\": { \"x\": 0, \"y\": 0, \"w\": 0, \"h\": 0 }}]}, ... }\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "7c13a0cc-4f2d-754a-7960-f1bcc690c87a", "source_type": "file", "start_index": 0, "chroma:document": "[Optional] Exercise - Explore features in Vision Studio\nSearch photos with image retrieval\nCreate smart-cropped images\nOpen https://portal.vision.cognitive.azure.com/ and select 'Try it out' under these features and use the provided images\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "c000cec4-c2a4-2d15-c80a-0378c6557d2b", "source_type": "file", "start_index": 0, "chroma:document": "Lab - Analyze Images with Azure AI Vision\nGenerate captions\nCreate tags\nDetect objects and people\nRemove background\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "29ea1634-cbc9-0e72-e761-1b53bceb54e4", "source_type": "file", "start_index": 0, "chroma:document": "Azure AI Vision - OCR\nUse Image analysis with READ feature\nVision OCR vs Document Intelligence:\n\u00b7 OCR: General, non-document images with smaller amounts of text. Synchronous API.\n\u00b7 Document Intelligence: Ideal for larger text heavy documents. Asynchronous API.\nResults in JSON (REST) or object (SDK) of similar structure\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n[{ \"lines\": [{ \"text\": \"You must be the change you\", \"boundingPolygon\": [ { \"x\": 251, \"y\": 265 }, { \"x\": 673, \"y\": 260 }, { \"x\": 674, \"y\": 308 }, { \"x\": 252, \"y\": 318 } ], \"words\": [ { \"text\": \"You\", \"boundingPolygon\": ... \"confidence\": 0.996 }, { \"text\": \"must\", \"boundingPolygon\": ... ...\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "f385bf2f-c1c1-0566-4efb-5a88a253f6c6", "source_type": "file", "start_index": 0, "chroma:document": "Use the Azure AI Vision for OCR\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "0e71884c-7902-a841-64c5-0ed52f871a57", "source_type": "file", "start_index": 0, "chroma:document": "Detecting Faces with the Azure AI Vision\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: a5315f1e-f2b0-4522-abe4-7c3fb2d9dee1
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "a4cafdda-5985-2488-464f-fe055d0071f2", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n2\nUnderstand features, use cases, and responsibility of the Azure AI Vision Face API\nUse the Face API in an app\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d6c88f56-7152-4f95-bfd9-b5814ece32d6
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "684ed9db-09db-1abc-b7b8-d0f15aa8b003", "source_type": "file", "start_index": 0, "chroma:document": "Image Analysis\n\u00b7 People detection\n\u00b7 Only location provided"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 87c60d0e-0d86-49b7-83d8-0d359c183487
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "f8073cf3-9821-660d-f540-45a0f8b1e84b", "source_type": "file", "start_index": 0, "chroma:document": "Face Service\n\u00b7 Face detection\n\u00b7 Comprehensive facial feature analysis\n\u00b7 Face comparison and identification*\n\u00b7 Facial recognition*\n* Require Limited Access approval\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 98a9fc43-ff45-4f49-9e0b-044055ff927c
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "725b1753-8bee-c17a-b8aa-9b571256aa7d", "source_type": "file", "start_index": 0, "chroma:document": "Considerations for Face Detection and Facial Recognition\nPrinciples of responsible AI apply to all kinds of application, but systems that rely on facial data can be particularly problematic. As a safeguard for responsible AI usage, facial recognition, identification, verification, and comparison is behind a Limited Access policy, requiring users to be approved my Microsoft before enabling these features."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 417ebe75-08a3-4963-ba0d-477deb53f228
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "97a7ad54-0be9-8202-d9d5-09fb8fbfacd6", "source_type": "file", "start_index": 0, "chroma:document": "Data privacy and security\nSystems based on facial data should protect individual privacy, ensuring that personally identifiable data is not accessed inappropriately"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "43ded1b1-c6af-37cd-887e-ca33d364d769", "source_type": "file", "start_index": 0, "chroma:document": "Transparency\nUsers should be informed about how their image will be used, and who will have access to it."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3a8dc725-f444-4bb6-8792-90912f8c98b6
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "30433835-f88a-15b6-cc83-5d680594ee08", "source_type": "file", "start_index": 0, "chroma:document": "Fairness and Inclusiveness\nFacial recognition should not be used in a manner that is prejudicial to individuals based on their appearance, or to unfairly target individuals\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 84290c69-3ed8-46f5-adb5-878e04c700c5
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "48b89e2c-7274-4fee-b761-8a217e00ac21", "source_type": "file", "start_index": 0, "chroma:document": "Face detection\nFace attribute analysis\nFacial landmark location\n\u00b7 Nose, eyes, mouth, \u2026\nFace comparison*\nFacial recognition and identification*\nFacial liveness*\nCan be used as:\n\u00b7 Standalone Face resource\n\u00b7 Multi-service Azure AI Services resource\n* Require Limited Access approval\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: a264c16b-e3c4-46a7-bf11-99f5e865b1a9
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "ee1b180a-c5bb-b63b-0904-7e9fd5272d8b", "source_type": "file", "start_index": 0, "chroma:document": "Every detected face is assigned an anonymous ID\n\u00b7 Retained in your service resource for 24 hours\n\u00b7 Can be used to compare faces in multiple images\n-Verify faces to determine if they're the same individual\n-Find similar faces to identify faces with similar features\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6b3a5968-f6ef-4a0a-b311-bd3acf690cd7
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "e1349d96-7a9f-2b76-7e94-a763fa1e879e", "source_type": "file", "start_index": 0, "chroma:document": "Train a facial recognition model using face images\n1. Create a Person Group for the people you want to identify\n2. Add a Person for each individual\n3. Add multiple detected Faces to each person\n- These become persisted faces\n4. Train the model"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6af91ad2-90c3-401b-b183-0c5aba88d939
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "c95c9632-35e3-6f44-88c0-ac1e3247241b", "source_type": "file", "start_index": 0, "chroma:document": "Use the model for facial recognition\n\u00b7 Identify an individual person\n\u00b7 Verify the face of an individual person\n\u00b7 Find similar faces to a persisted face\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 849e4e4c-b641-4495-90e7-8f12c1f96a1f
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "82df30d0-bf47-3f28-8412-ad9f4cf997db", "source_type": "file", "start_index": 0, "chroma:document": "Face Detection with Azure AI Vision\nUse the Face endpoint, specifying Faces as a visual feature Note: Most features like recognition and identification are not enabled for new users"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: bfb1b37a-a856-4c2b-b5d0-80ebc7d8ca0d
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "bc898caf-c76c-2d44-c9b3-8cc2b970bfb5", "source_type": "file", "start_index": 0, "chroma:document": "Optional request parameters\nreturnFaceId\nrecognitionModel\nreturnFaceLandmarks\nreturnRecognitionModel\nreturnFaceAttributes\ndetectionModel\nRequest: https://{endpoint}/face/v1.0/detect[?options] Body: {\"url\": \"http://path-to-image}\nResponse:\n[\n{\n\"faceId\": \"c5c24a82-6845-4031-9d5d-978df9175426\", \"recognitionModel\": \"recognition_03\",\n\"faceRectangle\": {\n\"width\": 78,\n\"height\": 78,\n\"left\": 394,\n\"top\": 54\n},\n\"faceLandmarks\": {\n\"pupilLeft\": {\n\"x\": 412.7,\n\"y\": 78.4\n},\n\"pupilRight\": {\n\"x\": 446.8,\n\"y\": 74.2\n},\n...\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 4ac0ded7-9fdb-4e1f-8435-6b67cf4a6647
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "781ddf5e-0eff-ff4c-c444-f91c52d2e5d7", "source_type": "file", "start_index": 0, "chroma:document": "[Optional Exercise]- Detect and Analyze Faces\nDetect faces with the Azure AI Vision service\nDetect and analyze faces with the Face service\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "c51effb7-6b7a-ffca-eee7-e2ec6581e9da", "source_type": "file", "start_index": 0, "chroma:document": "Custom vision models with Azure AI Vision\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 91691230-9da8-4e9a-b62e-61164ba74a92
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "71bcbeba-f1cd-dcc6-7b40-c821f01f0abd", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n1 Understand use cases of custom Vision models\n2 Label data in Azure ML for both image classification and object detection\n3\nUse a custom vision model in an app\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f8621bf1-463a-468a-a58b-60ceaff2cf9d
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "c3e52a25-0927-b369-d995-24b963ff3667", "source_type": "file", "start_index": 0, "chroma:document": "Azure AI Custom Vision (previous service)\n\u00b7 Portal: customvision.ai\n\u00b7 Base model:\n\u00b7 Convolutional neural network (CNN)\n\u00b7 Tasks:\n\u00b7 Image classification\n\u00b7 Object detection\n\u00b7 Labeling:\n\u00b7 Customvision.ai\n\u00b7 Minimum training data needed:\n\u00b7 15 images per category\n\u00b7 Training data storage\n\u00b7 Uploaded to Custom Vision service\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "bfbc4293-26cd-344c-33b3-c766bd938015", "source_type": "file", "start_index": 0, "chroma:document": "Custom AI Vision models (new Florence model)\n\u00b7 Portal: Vision Studio\n\u00b7 Base model:\n\u00b7 Transformer (multi modal)\n\u00b7 Tasks:\n\u00b7 Image classification\n\u00b7 Object detection\n\u00b7 Product recognition\n\u00b7 Labeling:\n\u00b7 AML Studio or COCO file\n\u00b7 Minimum training data needed:\n\u00b7 2-5 images per category\n\u00b7 Training data storage\n\u00b7 In user's blob storage account\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "e2c2f820-c49b-67db-c101-bd1be591913e", "source_type": "file", "start_index": 0, "chroma:document": "Train custom models with your own images\n\u00b7 Upload your images\n\u00b7 Label your images\n\u00b7 Train your model\n\u00b7 Query your model with new images to predict labels\nLabeling data lives in COCO file\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "ba5b42cf-5cb9-acfa-a5c9-1623e0c6cc62", "source_type": "file", "start_index": 0, "chroma:document": "About COCO files\nJSON file with specific fields\n\u00b7 images\n\u00b7 annotations\n\u00b7 categories\nDefines labeling data from Azure ML project\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n{ \"images\": [ { \"id\": 1, \"width\": 500, \"height\": 828, \"file_name\": 'file.jpg\", \"absolute_url\": '{url}\" }, ... ], \"annotations\": [ { \"id\": 1, \"category_id\": 7, \"image_id\": 1, \"area\": 0.407, \"bbox\": [ 0.02663142641129032, 0.40691584277841153, 0.9524163571731749, 0.42766634515266866 ] }, ... ], \"categories\": [ { \"id\": 1, \"name\": \"apple\" }, ...\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "8d23e29a-4ff3-7d0f-98ed-fab0c08d78b3", "source_type": "file", "start_index": 0, "chroma:document": "Train a model to predict the class label for the image\nIn other words, what is this a picture of?\nApple\nBanana\nOrange\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "b5d84895-bbf5-6322-6225-ef5793298b86", "source_type": "file", "start_index": 0, "chroma:document": "Train a model to detect and locate specific classes of object in images\nIn other words, what objects are in this image, and where?\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "aa204695-00df-0f18-fe53-6e72f5b1e297", "source_type": "file", "start_index": 0, "chroma:document": "Use the Azure Vision Studio\n1. Create a custom model project, or retrieve an existing one\n2. Select your resource, if necessary\n3. Add your dataset(s) and specify model type\n1. Image classification: Assign label to each image\n2. Object detection: Define bounding boxes for objects in each image\n4. Create your Azure ML project and label your images (if no COCO file exists)\n5. Add COCO file (from Azure ML project or previously built)\n6. Train a new model\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "748f0467-a010-3d14-7d02-fb1ce85f56c6", "source_type": "file", "start_index": 0, "chroma:document": "Use the Azure ML Data Labeling project\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "e3e9ee5a-b41f-dfcb-c8ed-00603fbd3ddc", "source_type": "file", "start_index": 0, "chroma:document": "Lab - Classify Images with Azure AI Vision custom model\nCreate a custom model\nAdd COCO file\nIntegrate custom model into app\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "d70a8220-8d91-661b-6919-b770c74b3c45", "source_type": "file", "start_index": 0, "chroma:document": "Analyzing Videos\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "9c0c64ee-02a3-75a5-d5fd-335d0840a4a6", "source_type": "file", "start_index": 0, "chroma:document": "Video analysis:\n\u00b7 Facial recognition (limited access)\n\u00b7 Optical character recognition\n\u00b7 Speech transcription\n\u00b7 Topics\n\u00b7 Sentiment\n\u00b7 Labels\n\u00b7 Content moderation\n\u00b7 Scene segmentation\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 54ecd7f5-fc30-48c3-ac5e-fd5e61631827
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "e0a33b84-34bb-f73f-f602-bab43f1eea8f", "source_type": "file", "start_index": 0, "chroma:document": "Custom Insights\nPre-defined models for recognizing language, well-known celebrities, brands, \u2026\nCreate your own models for:\n* Facial recognition and celebrity identification requires Limited Access approval, in accordance with our Responsible AI Standard\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "03cd468c-9066-5109-0336-c719f39eea6b", "source_type": "file", "start_index": 0, "chroma:document": "Video Indexer Widgets and API\nShare insights and analysis in web pages\nAutomate video analysis with the REST API\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3a55dc96-2740-4c48-8d18-ac8ff4d986a2
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "1b1ea2de-c96b-a085-5197-3990f4f5650b", "source_type": "file", "start_index": 0, "chroma:document": "[Optional] Exercise- Analyze Video\nAnalyze a video\nUse Video Indexer widgets\nUse the Video Indexer API\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 2303ce7f-becb-47f4-a09f-fb00617f6277
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "270d2199-be0c-0936-4c7d-ef68077f7481", "source_type": "file", "start_index": 0, "chroma:document": "Knowledge check\nYou want to use the Azure AI Vision Analyze Image function to generate an appropriate caption for an image. Which visual feature should you specify?\n\u20e3\nTags\n\u20e3\nCaption\n\u20e3\nText\n2 What is the effect of the Smart Cropping option when using Azure AI Vision to generate a thumbnail?\n\u20e3\nThe aspect ratio of the original image is maintained.\n\u20e3\nThe thumbnail is skewed to fit the specified proportions.\n\u20e3\nThe region of interest is centered in the thumbnail.\n3 You want Video Analyzer to recognize colleagues in videos recorded from conference calls. What should you do?\n\u20e3\nCreate a custom model containing a Person for each colleague, with example images of their faces.\n\u20e3\nEdit the conference call videos to include a caption of each person's name on their first appearance.\n\u20e3\nEmbed the Video Analyzer widgets in a custom web site that employees access using their own user credentials.\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive\n5"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c34e37e9-43ca-4182-8cc1-0d522ff58c94
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "270d2199-be0c-0936-4c7d-ef68077f7481", "source_type": "file", "start_index": 890, "chroma:document": "\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive\n5\nWhich of the following facial attributes can the Azure AI Vision service predict?\n\u20e3\nType of eye-classes\n\u20e3\nLocation\n\u20e3\nOcclusion\nYou need to create a facial recognition solution that can identify named employees. Which service should you use?\n\u20e3\nVision\n\u20e3\nPersonalizer\n\u20e3\nFace\n6 You need to detect if a specific dangerous item is in your company's inspection photos. What should you do?\n\u20e3\nCreate an image classification custom model.\n\u20e3\nUse the Object feature in Image Analysis.\n\u20e3\nTrain a custom model to detect that item.\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 016ce72d-1bab-4ab5-8355-5867594f2766
encoding: FLOAT32
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{"source": "./Documents/general/Topic 2 Create computer vision soultions with Azure AI vision.pdf", "doc_id": "21393aee-67e1-2672-8c05-cd62f2ff6de8", "source_type": "file", "start_index": 0, "chroma:document": "In this learning path, we:\nUsed Image Analysis to analyze images, extract insights, remove background, and perform OCR.\nDetected faces and facial recognition.\nCreated custom vision models trained on your own images.\nExtracted insights from videos with Azure Video Indexer.\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 944f5d07-d850-4e1f-8e80-851470091295
encoding: FLOAT32
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "28f61fd0-5f9f-c97a-20b0-75e5fd2d1330", "source_type": "file", "start_index": 0, "chroma:document": "Fundamentals of Deep Learning\nPart 1: An Introduction to Deep Learning\n\u2022, 1 = Part 1:  An Introduction to Deep Learning. \u2022, 1 = Part 2: How a Neural Network Trains. \u2022, 1 = Part 3: Convolutional Neural Networks. \u2022, 1 = Part 4: Data Augmentation and Deployment. \u2022, 1 = Part 5: Pre-Trained Models. \u2022, 1 = Part 6: Advanced Architectures\nAgenda\nTo see lecture notes, make full screen and click the \u201cnotes\u201d button\nWelcome!"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c697ff27-c497-4f53-9839-0aa2f9e70a0a
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "33aaedaa-054e-16b5-ae3b-6e5d448017cb", "source_type": "file", "start_index": 0, "chroma:document": "Human Vs Machine Learning\nRelaxed Alertness\nHuman, 1 = Machine. Rest and Digest, 1 = Training. Fight-or-flight, 1 = Prediction"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "b957930b-234c-09cc-5148-2c7ca36b0a66", "source_type": "file", "start_index": 0, "chroma:document": "Early Neural Networks\nInspired by biology\nCreated in the 1950\u2019s\nOutclassed by Von Neumann Architecture"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "2c0175e0-f2a7-bddb-dcb5-e4d5b9456e37", "source_type": "file", "start_index": 0, "chroma:document": "Expert Systems - Limitations\nWhat are these three images?\nExpose them to lots of data\nGive them the \u201ccorrect answer\u201d\nThey will pick up the important patterns on their own"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "5765b716-cdf1-ba7b-f34d-3c223d671c97", "source_type": "file", "start_index": 0, "chroma:document": "Data\nNetworks need a lot of information to learn from\nThe digital era and the internet has supplied that data"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "b6beb7f7-ee84-0aab-afc4-5d964fbd8d80", "source_type": "file", "start_index": 0, "chroma:document": "Computing Power\nNeed a way for our artificial \u201cbrain\u201d to observe lots of data within a practical amount of time."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "89f9ff71-2c69-199d-e3e1-e6ba328d868e", "source_type": "file", "start_index": 0, "chroma:document": "The Importance of the GPU\nA Neural Network\nA Rendered Image\nWhat is Deep Learning?\nDeep learning flips traditional programming on its head"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "7e1a8e21-acf7-6644-2aad-dd5c1ce61c11", "source_type": "file", "start_index": 0, "chroma:document": "Traditional Programming\nBuilding a Classifier"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 9d9e4704-016e-4bbd-a8bf-056050821756
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "f1336b62-e5a2-9d55-248a-810a883b9978", "source_type": "file", "start_index": 0, "chroma:document": "Machine Learning\nBuilding a Classifier\nThis is a Fundamental Shift\nClassic Programming\nDeep Learning\nWhen to Choose Deep Learning\nDeep Learning Compared to Other AI\nHow Deep Learning is Transforming the World\nComputer Vision\nNatural Language Processing\nRecommender Systems\nReinforcement Learning\nOverview of the Course"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 97c9e837-a0ea-4c50-985c-83195644c3b3
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{"source": "./Documents/general/slide_1.pptx", "doc_id": "65e10eac-c4c3-39b5-151b-5f64041417d5", "source_type": "file", "start_index": 0, "chroma:document": "Hands on Exercises\nGet comfortable with the process of deep learning\nExposure to different models and datatypes\nGet a jump-start to tackle your own projects\nStructure of the Course\nPlatform of the Course\nMajor deep learning platforms:\u00a0\nTensorFlow + Keras (Google)\nPyTorch (Meta)\nMXNet (Apache)\nWe\u2019ll be using PyTorch\nGood idea to gain exposure to others moving forward\nSoftware of This Course\nFirst Exercise:Classify Handwritten Digits"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "0b803152-83c5-599c-924c-65dd16fdf91b", "source_type": "file", "start_index": 0, "chroma:document": "Creating a Knowledge Mining Solution\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 85f956ef-61e2-4a1e-a9cf-493cb9c71d2a
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "8db43e7a-d6ae-d4cc-23fa-ea3a0614cf01", "source_type": "file", "start_index": 0, "chroma:document": "Agenda\n\u00b7 Implementing an Intelligent Search Solution\n\u00b7 Developing Custom Skills for an Enrichment Pipeline\n\u00b7 Creating a Knowledge Store\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nImplementing an Intelligent Search Solution\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "8f56c06b-5582-2077-f0e4-6646af22e60a", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n1 Create an Azure AI Search Solution\n2 Implement a custom skill for Azure AI Search and integrate it into a skillset\n3 Create a knowledge store with object, file, and table projections\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "424487d2-3d26-1cd3-8e55-ddb0fb94ac36", "source_type": "file", "start_index": 0, "chroma:document": "AI-Powered Knowledge Mining\n\u00b7 Index documents and data from a range of sources\n\u00b7 Use skills to enrich index data\n\u00b7 Store extracted insights in a knowledge store for analysis and integration"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "1d1bcbb4-cc67-9097-55b7-4a4e618a078e", "source_type": "file", "start_index": 0, "chroma:document": "Azure Resources:\n\u00b7 Azure AI Search for core indexing and querying\n\u00b7 Azure AI Services for index enrichment\n\u00b7 Storage account for knowledge store persistence\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "f2b344e3-54d7-52fa-f5ad-dfbf722f59b6", "source_type": "file", "start_index": 0, "chroma:document": "Data Source\nThe data store to be searched:\n\u00b7 Blob storage container\n\u00b7 SQL Database\n\u00b7 Cosmos DB\nYou can also push JSON documents directly into an index"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "8f3f410b-dfe9-2704-8f35-458f62807615", "source_type": "file", "start_index": 0, "chroma:document": "Skillset\nDefines an enrichment pipeline of AI skills to enhance data during indexing:\n\u00b7 Built-in AI skills\n\u00b7 Custom skills\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "dd916fa0-aadb-877c-bb34-1f1e5bd38a3d", "source_type": "file", "start_index": 0, "chroma:document": "Indexer\nMaps data source fields and skillset outputs to index fields\n\u00b7 Running the indexer builds the index"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "58e28b5e-8dd0-e5ea-f6f5-37feaa546ce7", "source_type": "file", "start_index": 0, "chroma:document": "Index\nSearchable collection of JSON documents containing extracted and enriched fields"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "fc6a7280-7ba3-b7bd-02e4-eb2d0e52afc2", "source_type": "file", "start_index": 0, "chroma:document": "How an Enrichment Pipeline Works\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "903b01c1-f822-2b4f-481a-720653582810", "source_type": "file", "start_index": 0, "chroma:document": "[Optional] Create an Azure Cognitive Search Solution\nCreate an indexing solution\nModify an indexing solution\nQuery an index from a client application\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "0d35198f-ecac-f95e-efb2-effc7bd0f4e7", "source_type": "file", "start_index": 0, "chroma:document": "Create a custom skill for Azure AI Search\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "455f87c0-1c87-1d57-5e99-8d7c4ee3be7b", "source_type": "file", "start_index": 0, "chroma:document": "When builtin skills don't provide what you need\u2026\nCreate a custom skill, for example:\n\u00b7 Integrate Document Intelligence\n\u00b7 Consume an Azure Machine Learning model\n\u00b7 Any other custom logic\nCustom skills are implemented as Web APIs\n\u00b7 Commonly Azure Functions\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "17c292ff-b8e0-3969-2168-ba37a5aa4cd2", "source_type": "file", "start_index": 0, "chroma:document": "Input Schema\n{ \"values\": [ { \"recordId\": \"<unique_identifier>\", \"data\": { \"<input1_name>\":  \"<input1_value>\", \"<input2_name>\": \"<input2_value>\", ... } }, { \"recordId\": \"<unique_identifier>\", \"data\": { \"<input1_name>\":  \"<input1_value>\", \"<input2_name>\": \"<input2_value>\", ... } }, ... ] }"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "e61e0beb-a0ab-eb12-a5f1-bcce8f15b1ec", "source_type": "file", "start_index": 0, "chroma:document": "Output Schema\n{ \"values\": [ { \"recordId\": \"<unique_identifier_from_input>\", \"data\": { \"<output1_name>\":  \"<output1_value>\", ... }, \"errors\": [...], \"warnings\": [...] }, { \"recordId\": \"< unique_identifier_from_input>\", \"data\": { \"<output1_name>\":  \"<output1_value>\", ... }, \"errors\": [...], \"warnings\": [...] }, ... ] } This is a property bag of values -it can be a single value or a complex JSON structure\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "e0377514-333d-4fdc-9d1e-6a17117e5cf5", "source_type": "file", "start_index": 0, "chroma:document": "Specify the URI to your web API endpoint\n\u00b7 Optionally add parameters and headers\nSet the context to specify at which point in the document hierarchy the skill should be called"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "5f150023-81ad-ba0c-f94b-5c1116d16da1", "source_type": "file", "start_index": 0, "chroma:document": "Assign input values\n\u00b7 Usually from existing document fields"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "3ce6c74e-b3ee-3267-f852-eecf3c15f9b5", "source_type": "file", "start_index": 0, "chroma:document": "Store output in a new field\n\u00b7 Optionally, specify a target field name (otherwise the output name is used)\n{ \"skills\": [ ..., { \"@odata.type\": \"#Microsoft.Skills.Custom.WebApiSkill\", \"description\": \"<custom skill description>\", \"uri\": \"https://<web_api_endpoint>?<params>\", \"httpHeaders\": { \"<header_name>\": \"<header_value>\" }, \"context\": \"/document/<where_to_apply_skill>\", \"inputs\": [ { \"name\": \"<input1_name>\", \"source\": \"/document/<path_to_input_field>\" } ], \"outputs\": [ { \"name\": \"<output1_name>\", \"targetName\": \"<optional_field_name>\" } ] } ] }"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "7452be4f-49be-4f94-5484-a247dab84347", "source_type": "file", "start_index": 0, "chroma:document": "Exercise -Create a Custom Skill for Azure AI Search\nUse an Azure Function to implement a custom skill Integrate a custom skill into a skillset\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "b2b471a4-f9bf-d99e-2392-3e318925b84a", "source_type": "file", "start_index": 0, "chroma:document": "Creating a Knowledge Store\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "2b5651a7-0532-a6dc-0ac5-abec39da90db", "source_type": "file", "start_index": 0, "chroma:document": "Stored as projections in Azure Storage\n\u00b7 Tables: Relational tables with keys for joining\n\u00b7 Objects: JSON structures of document fields\n\u00b7 Files: Extracted images saved in JPG format\nUsed for analysis or integration into data processing workflows\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "5b3ebb2f-77ca-7de6-18c1-452896ef4868", "source_type": "file", "start_index": 0, "chroma:document": "Restructure fields to simplify projections\n\u00b7 Create a JSON object with the fields you want to persist\n\u00b7 Use sourceContext and inputs to map primitives to well-formed JSON objects\n{ \"@odata.type\": \"#Microsoft.Skills.Util.ShaperSkill\", \"name\": \"define-projection\", \"description\": \"Prepare projection fields\", \"context\": \"/document\", \"inputs\": [ { \"name\": \"url\", \"source\": \"/document/url\" }, { \"name\": \"sentiment\", \"source\": \"/document/sentiment\" }, { \"name\": \"key_phrases\", \"source\": null, \"sourceContext\": \"/document/merged_content/keyphrases/*\", \"inputs\": [ { \"name\": \"phrase\", \"source\": \"/document/merged_content/keyphrases/*\" } ] } ], \"outputs\": [ { \"name\": \"output\", \"targetName\": \"projection\" } ] }"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "60e57ae7-6f05-f359-4fe5-1dcb7e9d1d3e", "source_type": "file", "start_index": 0, "chroma:document": "Implementing a Knowledge Store\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "5bf48f70-3608-0d8c-b4ac-6896f26f562a", "source_type": "file", "start_index": 0, "chroma:document": "Extended interactive exercises -Create a Knowledge Store with Azure AI Search\nhttps://aka.ms/km-ai-lp\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "bb8e3ad4-7132-360b-d795-37df4d3aeb9a", "source_type": "file", "start_index": 0, "chroma:document": "Knowledge check\n1\nYou want to enrich an index by extracting any geographical locations mentioned in the source data. Which built-in skill should you use?\n\u20e3\nEntity Recognition\n\u20e3\nKey Phrase Extraction\n\u20e3\nLanguage Detection\n2 You have implemented a custom skill as an Azure function. How can you include the custom skill in your indexing process?\n\u20e3\nAdd a Merge skill to the skillset to combine output from built-in skills with your custom skill.\n\u20e3\nAdd a WebApiSkill to a skillset, referencing the Azure function's URI\n\u20e3\nAdd a Shaper skill to the skillset to create a collection of records with unique IDs generated by your custom\n3 You want to create a knowledge store that contains JSON representations of the extracted data. What kind of projection should you define?\n\u20e3\nFile\n\u20e3\nObject\n\u20e3\nTable\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 5 Knowledge Mining.pdf", "doc_id": "34a34ba0-f5b6-7a24-d7b5-5a6271273c53", "source_type": "file", "start_index": 0, "chroma:document": "Learning Path Recap\nIn this learning path, we learned how to:\nCreate an Azure AI Search Solution\nImplement a custom skill for Azure AI Search and integrate it into a skillset\nCreate a knowledge store with object, file, and table projections\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
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{"source": "./Documents/general/slide_6.pptx", "doc_id": "1822a52a-7929-a39c-741a-cb0f5316847a", "source_type": "file", "start_index": 0, "chroma:document": "From Words to Numbers\nBarked\nBark\nAte\nAnd\nAt\nAn\nA\nCat\nCats\nDog\nDogs\nEat\nBarked\nBark\nAte\nAnd\nAt\nAn\nA\nCat\nCats\nDog\nDogs\nEat\nInputs\nOutputs\nDictionary\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\n50%\n0%\n35%\n10%\n5%\n0%\n0%\n0%\n0%\n0%\n0%\n0%\n0\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n0\nInputs\nOutputs\nDictionary\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nBigger Dictionary\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\n\u2026\n\u2026\n\u2026\n\u2026\nDomestic\nWild\nBig\nSmall\nKitty\n(-.75, -.8)\nLlama\n(-.9, .1)\nFalcon\n(.15, -.4)\nPuffin\n(.85, -.65)\nGiraffe\n(.9, .9)\nBarked\nBark\nAte\nAnd\nAt\nAn\nA\nCat\nCats\nDog\nDogs\nEat\nBarked\nBark\nAte\nAnd\nAt\nAn\nA\nCat\nCats\nDog\nDogs\nEat\nTechnically an Embedding\nInputs\nOutputs\nBigger Dictionary\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_6.pptx", "doc_id": "1822a52a-7929-a39c-741a-cb0f5316847a", "source_type": "file", "start_index": 802, "chroma:document": "(.9, .9)\nBarked\nBark\nAte\nAnd\nAt\nAn\nA\nCat\nCats\nDog\nDogs\nEat\nBarked\nBark\nAte\nAnd\nAt\nAn\nA\nCat\nCats\nDog\nDogs\nEat\nTechnically an Embedding\nInputs\nOutputs\nBigger Dictionary\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\nA\nAn\nAnd\nAt\nAte\nBark\nBarked\nCat\nCats\nDog\nDogs\nEat\nEaten\n\u2026\n\u2026\n\u2026\n\u2026\nAttention"}


created_at: 2025-02-04 15:04:55
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "4894bb7f-0d3d-8a9d-2259-230bde946410", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\nConsider and manage authentication and network security for Azure AI services.\n2 Manage costs, view metrics, and manage alerts and diagnostic logging.\n3 Deploy to secure containers and consume Azure AI services from containers.\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "40bbe7d4-7b8d-ace4-94ee-17473a173370", "source_type": "file", "start_index": 0, "chroma:document": "Consider protecting keys by storing them in Azure Key Vault\n\u00b7 Apps can use a Service Principal as a managed identity to retrieve keys from Key Vault\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "429b18c2-8c0d-8963-9526-71bfb7a5d390", "source_type": "file", "start_index": 0, "chroma:document": "[Optional] Exercise - Manage Azure AI Services security\nManage Authorization Keys\nSecure Key Access with Azure KeyVault\nReferences:\n1. Lab 2: mslearn-ai-services/Instructions/Exercises at main \u00b7 MicrosoftLearning/mslearn-ai-services (github.com)\n2. Exercise - Manage Azure AI Services Security Training | Microsoft Learn\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "5304d4f5-aefb-8344-bcd3-41bb3b7c124b", "source_type": "file", "start_index": 0, "chroma:document": "Alerts\n\u00b7 Alerts will ensure that the correct team knows when a problem arises.\n\u00b7 Every alert or notification available in Azure Monitor is the product of a rule"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "81fd638c-a1a9-49ad-9861-1e193ddfd85a", "source_type": "file", "start_index": 0, "chroma:document": "Metrics\n\u00b7 Metrics are numerical values\n\u00b7 The metrics are collected at regular intervals and are useful for alerting.\n\u00b7 Metrics are stored in a time-series database.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "927c455b-7598-7594-6a48-a4826ffe87b6", "source_type": "file", "start_index": 0, "chroma:document": "Diagnostic settings\n\u00b7 Configure diagnostic settings is to provide detailed information for diagnostics and auditing.\n\u00b7 Diagnostic Destinations:\n-Log Analytics Workspace\n-Event Hubs\n-Azure Storage"}


created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "8ae9070b-22c7-695a-b758-308a85d4353a", "source_type": "file", "start_index": 0, "chroma:document": "[Optional] Exercise - Monitor Azure AI Services\nConfigure an alert\nVisualize a metric\nYou may refer to lab 3 mslearn-aiservices/Instructions/Exercises at main \u00b7 MicrosoftLearning/mslearn-ai-services (github.com)\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "851c31d6-ee86-8514-b8db-b3886eaa5514", "source_type": "file", "start_index": 0, "chroma:document": "Container images are available for commonly used Azure AI services APIs\n\u00b7 Deploy containers to:\n\u00b7 Local Docker hosts\n\u00b7 Azure Container Instances\n\u00b7 Azure Kubernetes Services clusters\n\u00b7 others\u2026"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "9175dccf-5b0b-2d10-f379-5c6bf78f613d", "source_type": "file", "start_index": 0, "chroma:document": "Enables more control over data sent to public Azure AI service endpoint\n\u00b7 An Azure AI services resource is still required, and the container must communicate with it to send billing data\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "160d175a-bf93-39fc-efd7-153f4474cf6e", "source_type": "file", "start_index": 0, "chroma:document": "Extended interactive exercises - Use an Azure AI Services container\nhttps://aka.ms/ai-services-lp\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "0ddf0c0e-f89f-a8b6-0734-ce169feaf57e", "source_type": "file", "start_index": 0, "chroma:document": "How are client applications typically granted access to an Azure AI services endpoint?\n\u20e3\nThe application must specify a valid subscription key for the Azure resource.\n\u20e3\nAll data passed from the client application to the container is forwarded to the Azure resource endpoint.\n\u20e3\nThe container must be able to connect to the Azure resource endpoint to send usage data for billing.\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n\u20e3\nThe user of the application must enter a user name and password associated with the Azure subscription.\n\u20e3\nAccess to Azure AI services is granted to anonymous users by default.\n2 You want to keep track of how often the subscription keys for your Azure AI services resource are retrieved. What should you do?\n\u20e3\nRegenerate the keys for your Azure AI services resource.\n\u20e3\nCreate an alert for your Azure AI services resource.\n\u20e3\nStore the keys in Azure Key Vault.\n3 You plan to use an Azure AI services container in a local Docker host. Which of the following is true?\n\u20e3"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "0ddf0c0e-f89f-a8b6-0734-ce169feaf57e", "source_type": "file", "start_index": 802, "chroma:document": "\u20e3\nCreate an alert for your Azure AI services resource.\n\u20e3\nStore the keys in Azure Key Vault.\n3 You plan to use an Azure AI services container in a local Docker host. Which of the following is true?\n\u20e3\nClient applications must pass a subscription key to the Azure resource endpoint before using the container.\nOfficial (Closed) and Non-Sensitive"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 1 Introduction to AI and AI on Azure.pdf", "doc_id": "b6b92825-6c22-1888-d704-3960c91c39d3", "source_type": "file", "start_index": 0, "chroma:document": "In this learning path, we:\nDescribed artificial intelligence and how it compares to machine learning and data science.\nDescribed Azure AI services.\nUnderstood how to get started with Azure AI services\nUnderstood how to use Azure AI Services for enterprise applications\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nOfficial (Closed) and Non-Sensitive\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "5420ec5e-0283-1446-7da9-d2712ebe7fa8", "source_type": "file", "start_index": 0, "chroma:document": "Develop Generative AI Solutions with Azure OpenAI Service\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "830e121f-c721-0fd4-abba-e4b1a7955605", "source_type": "file", "start_index": 0, "chroma:document": "Agenda\n\u00b7 Get started with Azure OpenAI Service\n\u00b7 Develop apps with Azure OpenAI Service\n\u00b7 Apply prompt engineering with Azure OpenAI Service\n\u00b7 Use your own data with Azure OpenAI Service\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nGet started with Azure OpenAI Service\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "59c616c4-3024-e350-69e1-ef9d7a386d6b", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n1 Describe what generative AI is\n2 Provision a resource and deploy a model\n3 Use Azure AI Studio\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "68761afb-36a4-c3bc-0f02-282b5beb0994", "source_type": "file", "start_index": 0, "chroma:document": "Artificial Intelligence\nthe field of computer science that seeks to create intelligent machines that can replicate or exceed human intelligence"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "0b2e0665-1ab5-ce80-b7df-5b4da2c78612", "source_type": "file", "start_index": 0, "chroma:document": "Machine Learning\nsubset of AI that enables machines to learn from existing data and improve upon that data to make decisions or predictions"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "584a8a71-97ff-db50-7d92-03dae7db536e", "source_type": "file", "start_index": 0, "chroma:document": "Deep Learning\na machine learning technique in which layers of neural networks are used to process data and make decisions"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "70e0541a-cdd9-931a-e40a-678964e60da2", "source_type": "file", "start_index": 0, "chroma:document": "Generative AI\nCreate new written, visual, and auditory content given prompts or existing data.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "fc05225e-d495-1898-571a-7d7c5dc69d6a", "source_type": "file", "start_index": 0, "chroma:document": "Provision an Azure OpenAI resource in Azure\nHome Azure Al services Azure OpenAl\nCreate an Azure OpenAI resource in the Azure portal\nDeploy a resource in Azure, then deploy and use a model to in Azure AI Studio"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "0a09d509-87d0-a144-4970-4a8d21e396cd", "source_type": "file", "start_index": 0, "chroma:document": "Alternatively, use the Azure CLI\naz cognitiveservices account create \\\n-n MyOpenAIResource \\\n-g MyResourceGroup \\\n-l eastus \\\n--kind OpenAI \\\n--sku s0 \\\n--subscription subscriptionID\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "3cd027ff-944f-f1ab-9222-f94dd8e03ab9", "source_type": "file", "start_index": 0, "chroma:document": "Create Azure OpenAl\nBasics\nNetwork\nTags\nReview + submit\nEnable new business solutions with OpenAl's language generation capabilities powered by GPT-3 models These models have been pretrained with trillions of words and can easily adapt to your scenario with a few short examples provided at inference. Apply them to numerous scenarios, from summarization to content and code generation"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "191db455-3f6e-d854-9c34-92d322884802", "source_type": "file", "start_index": 0, "chroma:document": "Project Details\nSubscription\nResource group\nCreate new\nInstance Details\nRegion\nEast US\nName\nPricing tier"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "f7df1af7-89cb-47ef-012a-c59f9471e90b", "source_type": "file", "start_index": 0, "chroma:document": "Azure AI Studio\n\u00b7 Web portal for working with AI models: https://ai.azure.com/\n\u00b7 View and deploy base models\n\u00b7 Connect to other AI Services or your own data source\n\u00b7 Manage fine tuning and data files for custom models\n\u00b7 Test models in visual playgrounds:\n\u00b7 Chat (GPT-3.5-Turbo and later models)\n\u00b7 DALL-E (Image generations)\n\u00b7 Assistants (Custom and Copilot-like experiences)\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "8e6dda81-1e4b-6177-738d-ddbb6b3d82d7", "source_type": "file", "start_index": 0, "chroma:document": "Types of generative AI model\nGPT-4, Description = Newest, most capable chat-based  models for language and code  generation. GPT-3, Description = Natural language and code- generation models. Embeddings, Description = Models that use embeddings for  specific tasks (similarity, text search,  and code search). DALL-E, Description = Image-generation model ( preview,  restricted regions )\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "5f7d0f5a-ba2f-86cf-5a92-3925fc7425af", "source_type": "file", "start_index": 0, "chroma:document": "Deploy a model in Azure AI Studio to use it\n\u00b7 You can deploy one or more instances of each available model\n\u00b7 The number of deployments depends on your quota, which you can see in the portal\n\u00b7 Alternatively, use the Azure CLI\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "71b7fb77-a061-8546-faed-f528494b1c4a", "source_type": "file", "start_index": 0, "chroma:document": "Using prompts to get completions from models\nClassifying content, Prompt = Tweet: I enjoyed the training course. Sentiment:. Classifying content, Completion = Positive. Generating new content, Prompt = Write a poem about databases. Generating new content, Completion = Databases, oh databases, You keep our information safe, From the small to the large, You store our data in a place.. Transformation/Translation, Prompt = English: Hello French:. Transformation/Translation, Completion = Bonjour. Summarization, Prompt = Scotland is [ long description of  Scotland\u2026 ]. Summarization, Completion = Scotland is [ summarized description\u2026 ]. Continuation, Prompt = One way to grow tomatoes is to. Continuation, Completion = start with seeds\u2026. Question answering, Prompt = How many moons does Earth have?. Question answering, Completion = Earth has one moon.. Chat, Prompt = Setup, followed by messages\u2026. Chat, Completion = A sequence of relevant responses"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "71b7fb77-a061-8546-faed-f528494b1c4a", "source_type": "file", "start_index": 952, "chroma:document": "\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "88b52f75-49cd-3f14-6d2e-d5d4dbac1b5b", "source_type": "file", "start_index": 0, "chroma:document": "Testing models in Azure AI Studio playground\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "3ed52f75-de29-bd69-b352-d426cbf9a8a4", "source_type": "file", "start_index": 0, "chroma:document": "[Optional Exercise]-Get started with Azure OpenAI Service\nDeploy Azure OpenAI resource and model Test deployed model in playground\nAzure OpenAI Service models - Azure OpenAI | Microsoft Learn\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nDevelop apps with Azure OpenAI Service"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "fd7fe84b-41e2-3faa-213c-50ddcb174c4b", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\nIntegrate Azure OpenAI into your app\nUse the REST API 2\nUse language specific SDKs 3\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "de063bad-5383-62ad-1ef5-8242276a082b", "source_type": "file", "start_index": 0, "chroma:document": "Integrating Azure OpenAI into your app\nApplications submit prompts to deployed models. Responses are completions.\nThree REST API endpoints:\n\u00b7 Completion - model takes an input prompt, and generates one or more predicted completions\n\u00b7 Embeddings - model takes input and returns a vector representation of that input\n\u00b7 ChatCompletion - model takes input in the form of a chat conversation (where roles are specified with the message they send), and the next chat completion is generated\nChatCompletion will be the endpoint we focus on for this course\nUse Completion and Embeddings with GPT-3 based models Use ChatCompletion with GPT-35-Turbo and later models\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3a21cc00-42a8-4b7c-842a-4ff700c0f55c
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "87e3f055-b616-f24b-3d9f-0c94dc95a8ce", "source_type": "file", "start_index": 0, "chroma:document": "Using the Azure OpenAI REST API\nCompletion Endpoint\nhttps:// endpoint .openai.azure.com/openai/deployments/ deployment / completions\n{ \"prompt\": \"Your favorite Shakespeare play is\", \"max_tokens\": 5 } { \"id\": '1234\u2026.\", \"object\": \"text_completion\", \"created\": 1679001781, \"model\": 'gpt-35-turbo\", \"choices\": [ { \"text\": \"Macbeth\", \"index\": 0, \"logprobs\": null, \"finish_reason\": \"stop\" } ] }\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 539500d2-9ba7-41bf-887c-d74ebfd4d3a5
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "bba84cb9-d380-e191-36f4-7cabd9a4029a", "source_type": "file", "start_index": 0, "chroma:document": "Embedding Endpoint\nhttps:// endpoint .openai.azure.com/openai/deployments/ deployment / embeddings\n{ \"input\": \"The food was delicious and the waiter was very friendly...\" } { \"object\": \"list\", \"data\": [ { \"object\": \"embedding\", \"embedding\": [ 0.0172990688066482523, .... 0.0134544348834753042, ], \"index\": 0 } ], \"model\": \"text-embedding-ada:002\" }\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 0780ca5e-a608-4587-a13a-8c378476b265
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "96650f26-b387-dc20-45f8-87d13aaee7e6", "source_type": "file", "start_index": 0, "chroma:document": "Using the Azure OpenAI REST API\nChatCompletion Endpoint\nhttps:// endpoint .openai.azure.com/openai/deployments/ deployment / chat / completions\n{ \"messages\":[ {\"role\": \"system\", \"content\": \"You are an assistant that teaches people about AI.\"}, {\"role\": \"user\", \"content\": \"Does Azure OpenAI support multiple languages?\"}, {\"role\": \"assistant\", \"content\": \"Yes, Azure OpenAI supports several languages.\"}, {\"role\": \"user\", \"content\": \"Do other Cognitive Services support translation?\"} ] }\n{ \"id\": 'unique_id\", \"object\": \"chat.completion\", \"created\": 1679001781, \"model\": \"gpt-35-turbo\", \"usage\": { \"prompt_tokens\": 95, \"completion_tokens\": 84, \"total_tokens\": 179}, \"choices\": [ {\"message\": {  \"role\": \"assistant\", \"content\": \"Yes, other Azure Cognitive Services also support translation\u2026\"}, \"finish_reason\": \"stop\", \"index\": 0} ] }\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 1681541a-a91b-4ed2-87b5-3793c57b34b7
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "ed1df7e4-7b6e-f233-bddd-266c9f6f9912", "source_type": "file", "start_index": 0, "chroma:document": "Using the Azure OpenAI SDKs\nLanguage specific SDKs are available for use in your applications, in both C# and Python.\nCode structure follows a similar pattern for both languages.\nParameters such as Max Response and Temperature are defined in the chat options.\nBoth synchronous and asynchronous API versions are available.\nPseudo code structure:\n<include library>\n<create client>\n<define chat messages and options>\n<send request>\n<extract response content>\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c6fc6549-ec70-4211-848a-9c30d50a577b
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "5ed8b269-ea08-5457-58e8-81b8cc384658", "source_type": "file", "start_index": 0, "chroma:document": "Exercise: Integrate Azure OpenAI into your app\nConfigure your application to connect to Azure OpenAI\nTest your app by sending a request to your deployed model\nUse the hosted lab environment if provided, or view the lab instructions at the link below:\nhttps://aka.ms/mslearn-azure-openai-api\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nApply prompt engineering with Azure OpenAI Service"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 2fcff328-27b4-454f-957d-910697288546
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "41a96044-8bf2-d6aa-b56f-833b28a748ae", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\nUnderstand what prompt engineering is\nUnderstand considerations for different endpoints 2\nExplore different techniques of prompt engineering 3\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: be4c30c8-c066-4ff5-b666-f421b8eb027d
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "ad7bf608-9ce2-d004-a61c-1e901a46b5d0", "source_type": "file", "start_index": 0, "chroma:document": "Constructing prompts to:\nMaximize relevancy and accuracy of completions\nSpecify formatting and style of completions\nProvide conversational context\nMitigate bias and improve fairness\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: b984018c-d880-45f6-a7bd-f74c39d0bea8
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "35231c53-3b2d-684b-c5df-a0a59d4fa9d8", "source_type": "file", "start_index": 0, "chroma:document": "Providing clear instructions\nWrite a product description for a new water bottle\nIntroducing the latest addition to our product line - the innovative and eco-friendly water bottle. Made from highquality, BPA-free materials, this bottle is designed to keep your beverages at the perfect temperature for hours. Its sleek and stylish design makes it the perfect accessory for any outdoor activity or workout session. With its convenient carrying handle and leak-proof lid, you  can take it with you wherever you go, without worrying about spills or leaks. Plus, its easy-to-clean design ensures that you can use it again and again, making it the perfect choice for anyone looking for a reliable and sustainable water bottle."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: ef163127-4a38-40ae-bf28-fd451d504da3
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "35231c53-3b2d-684b-c5df-a0a59d4fa9d8", "source_type": "file", "start_index": 721, "chroma:document": "Introducing our newest water bottle, the eco-friendly choice for the conscious consumer. Made from 100% recycled materials and available in natural colors with no dyes, this bottle is not only stylish but also sustainable. Each purchase of this bottle removes 10 pounds of plastic from our oceans, making it a responsible choice for those who care about the environment. Its durable and leak-proof design makes it perfect for outdoor activities, while its easy-to-carry handle makes it convenient to take on the go. Join us in our mission to reduce plastic waste and choose this eco-friendly water bottle for your daily hydration needs.\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nWrite a product description for a new water bottle that is 100% recycled. Be sure to include that it comes in natural colors with no dyes, and each purchase removes 10 pounds of plastic from our oceans"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3b595c0b-7c82-40b2-87fd-51e4fd1fdd22
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "b10c66d3-8b3c-9d42-eae4-8ac80b340207", "source_type": "file", "start_index": 0, "chroma:document": "Primary, supporting, and grounding content\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 544e3f02-84be-406b-9bc2-f461feedc1b7
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "ea30ce5a-dfa5-cf39-3a61-d2e97ec8bc5c", "source_type": "file", "start_index": 0, "chroma:document": "Cues\nCues provide a starting point on which the completion builds\nUse cues to set the scope and format for the output\nCues are particularly useful when generating code. For example:\nProvide the first keyword in a SQL SELECT statement\nDefine a name and parameters for a Python function\n---\nReview: The writing was good but the movie was too long.\nReview: The special effects were terrible.\n<more>\u2026\n---\nSummarize the reviews above.\nMost common complaints:\n-\n\u00b7 The movie was too long.\n\u00b7 The special effects were terrible.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 5f28bb41-7308-4a89-b59e-116e17f710e9
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "dfa643be-34a1-d1b2-56df-9ea1b320a9e3", "source_type": "file", "start_index": 0, "chroma:document": "Requesting output composition\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 9061f2ef-d506-456c-8f3d-498c2d91460d
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "6f7ae9f4-d314-e7f5-7b49-190535346214", "source_type": "file", "start_index": 0, "chroma:document": "Using a system message\nWhen using the ChatCompletions API, a system message sets context that can affect completions\n{\"role\": \"system\", \"content\": \"You are a casual, helpful assistant. You will talk like an American old western film character.\"}, {\"role\": \"user\", \"content\": \"Can you direct me to the library?\"} {\"role\": \"assistant\", \"content\": \"Well howdy there, stranger! The library, huh? Y'all just head down the main road till you hit the town square. Once you're there, take a left and follow the street for a spell. You'll see the library on your right, can't miss it. Happy trails!\"}\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: af97cb7e-80d5-490c-88cf-b68ce8bbb369
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "7b940e89-da37-3a88-5beb-ff317da0862b", "source_type": "file", "start_index": 0, "chroma:document": "Conversation history and few shot learning\n\u00b7 Including previous messages helps retain the context and style of a conversation\n\u00b7 Starting a conversation with user-defined messages and responses ( few shot learning) sets examples for the expected responses\n{\"role\": \"system\", \"content\": \"You are an assistant that evaluates the sentiment of customer feedback.\"}, {\"role\": \"user\", \"content\": \"That was an awesome experience\"}, {\"role\": \"assistant\", \"content\": \"positive\"}, {\"role\": \"user\", \"content\": \"I won't do that again\"}, {\"role\": \"assistant\", \"content\": \"negative\"}, {\"role\": \"user\", \"content\": \"That was not worth my time\"}, {\"role\": \"assistant\", \"content\": \"negative\"}, {\"role\": \"user\", \"content\": \"You can't miss this\"}\n{\"role\": \"assistant\", \"content\": \"positive\"},\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 55531539-fa6a-4c62-a9ed-984b82e699db
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "8934d017-43ad-3113-2a0e-8d6ae6efddd8", "source_type": "file", "start_index": 0, "chroma:document": "Chain of thought\nAsk a model to break down its response and explain its reasoning\nWhat sport is easiest to learn but hardest to master? Give a step by step approach of your thoughts, ending in your answer\nStep 1: Identify the criteria for \"easy to learn\" and \"hard to master\" sports.\nFor a sport to be considered easy to learn, it should have simple rules and require minimal equipment For a sport to be considered hard to master, it should require years of practice to perfect and have a large variety of techniques and strategies\u2026\nStep 2: Consider different sports that fit these criteria.\nSome sports that could be considered easy to learn but hard to master include:\n-Tennis: Simple rules, easy to pick up, but requires years of practice to master all the different shots and strategies.\n-Golf: Basic swing mechanics are easy to learn but perfecting them takes a lifetime of practice.\n-\n\u2026\nStep 3: Evaluate each sport based on the criteria."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 977ca549-b0d8-48c5-8fd0-37ccd06e3ceb
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "8934d017-43ad-3113-2a0e-8d6ae6efddd8", "source_type": "file", "start_index": 792, "chroma:document": "-Golf: Basic swing mechanics are easy to learn but perfecting them takes a lifetime of practice.\n-\n\u2026\nStep 3: Evaluate each sport based on the criteria.\nTennis and golf both require expensive equipment, which could make them less accessible to beginners. In terms of skill ceiling and time required to master, tennis and golf both have a high skill ceiling and require years of practice to perfect\u2026.\nStep 4: Make a decision based on the evaluation.\nBased on the above criteria and evaluation, I would say that tennis is the sport that is easiest to learn but hardest to master. While it may require expensive equipment, it is still accessible to many people. The basic rules and techniques are easy to learn, but mastering all the different shots and strategies takes years of practice\u2026\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 422ef50c-e975-4756-a60b-9f0fe4ea99e7
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "1668bdd9-82c5-5a0f-f6a9-a9e167dbc57c", "source_type": "file", "start_index": 0, "chroma:document": "Exercise: Utilize prompt engineering in your app\nExplore prompt engineering techniques\nConnect your app to Azure OpenAI and test prompts with increasing prompt engineering\nUse the hosted lab environment if provided, or view the lab instructions at the link below:\nhttps://aka.ms/openai-prompt-engineering-lab\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nImplement Retrieval Augmented Generation (RAG) with Azure OpenAI Service"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 376d4f60-4383-487c-9eee-74b93fbee1e6
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "246e6f58-4129-29e5-f445-8a1802095ab1", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\nUnderstand how RAG using your own data works 1\nUse the REST API 2\nUse language specific SDKs 3\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3237d91b-a4fd-4a82-ac8c-55bfde038e0e
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "8bf6a162-fc59-1d1d-cf75-8b9302016c87", "source_type": "file", "start_index": 0, "chroma:document": "Set up your data source\n\u00b7 Use an existing data source, such as an Azure search resource\n\u00b7 Use the Azure AI Studio to create that data source, if you don't already have one\n\u00b7 When creating the data source, you can use data already in your account such as blob storage\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 83ab2a3a-4add-486f-9451-69540ac5bf58
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "53053f1e-41c6-f9ae-b21c-14662621f791", "source_type": "file", "start_index": 0, "chroma:document": "Configure the studio or your app to connect to that data source\n\u00b7 In the studio, set up the connection by pointing it to the data source\n\u00b7 In your app, specify the data source in the prompt parameters\n\u00b7 Both configurations allow the search resource to augment the prompt"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 60cdc4e5-32ca-485b-ad0b-e9d5fe06ac73
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "00f57cfe-e38d-008d-37b5-70964ed2fdc6", "source_type": "file", "start_index": 0, "chroma:document": "Use the Azure OpenAI model, which now uses your data for grounding\n\u00b7 Chat with the AI models like normal\n\u00b7 If the data source has relevant information about the prompt, it will use that data\n\u00b7 You can specify if the AI model is limited to just your data source"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: b486ffe1-54f5-435b-8aa7-62f9036758c5
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "3bca4c36-6ee1-51dd-8b81-2fe7a0c610fc", "source_type": "file", "start_index": 0, "chroma:document": "Connect to your data source\n\u00b7 Add your data source in the Chat playground, under Add your data\n\u00b7 Use an existing data source, or use that wizard to create a new one\n\u00b7 Once connected, a new chat session will start. Chat like normal, and see how the AI model references that data\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 5c01ad23-2fc1-4a0e-aaa8-d67deb37ddc8
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "d711341c-f7cd-e00d-71e0-af8e49d370cf", "source_type": "file", "start_index": 0, "chroma:document": "Using your own data\nhttps:// endpoint .openai.azure.com/openai/deployments/ deployment /chat/completions ?api-version= version\n\u00b7 With each call, you need to specify the data source values, along with the messages array and any other parameters\n\u00b7 Authentication in the data source definition is for your search resource, not your Azure OpenAI resource\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: e18c645c-1deb-4dad-896d-2ad8fadbe12a
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "46fdf5e3-b836-ed29-7521-16f5a15c18d8", "source_type": "file", "start_index": 0, "chroma:document": "Using the Azure OpenAI SDKs\nLanguage specific SDKs are available for use in your applications, in both C# and Python.\nCode structure follows a similar pattern for both languages.\nCurrent supported data sources are:\n\u00b7 Azure AI Search\n\u00b7 Azure Cosmos DB for MangoDB vCore\n\u00b7 Plus others in preview, soon to be released GA\nPseudo code structure:\n<include library>\n<create client>\n<define chat messages and options>\n<define data source object to include with request>\n<send request>\n<extract response content>\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 85f2464a-97a1-4ae3-bda2-1b93547390a9
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "4a80d171-11e5-0019-946e-d9cf6954b6a4", "source_type": "file", "start_index": 0, "chroma:document": "Exercise: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service\nSet up and connect your data in the chat playground\nConfigure your app to use your own data for augmenting the prompt\nUse the hosted lab environment if provided, or view the lab instructions at the link below:\nhttps://aka.ms/mslearn-openai-own-data\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6b36587d-80ad-409f-a9ee-90b07deb8da0
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "fe7f7417-9d4f-3b5e-54b9-b1b419d20778", "source_type": "file", "start_index": 0, "chroma:document": "Extended interactive exercises\nGenerate code\nGenerate images\nhttps://aka.ms/develop-azure-openai\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 780ac268-2937-427f-b901-8774cee0b2dc
encoding: FLOAT32
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "8ecb6e5c-b245-2420-4c9c-354eee024247", "source_type": "file", "start_index": 0, "chroma:document": "What is the purpose of providing conversation history to an AI model?\n\u20e3\nProviding conversation history to an AI model is irrelevant and has no effect on the AI's performance.\n\u20e3\nStop sequence\n3 You plan to implement a multi-turn conversation with Azure OpenAI. Which endpoint should you use?\n\u20e3\nCompletions\n\u20e3\nChat\n\u20e3\nEmbeddings\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n\u20e3\nTo limit the number of input tokens used by the model\nTo enable the model to continue responding in a similar way and allow the user to reference previous content in subsequent queries\n\u20e3\nWhich parameter could you adjust to change the randomness or creativeness of completions?\n\u20e3\nFrequency penalty\n\u20e3\nTemperature"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: dbec8e8d-062d-4dde-aff7-710b1ff9336d
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{"source": "./Documents/general/Topic 4 Azure_OpenAI.pdf", "doc_id": "a3610bc2-2b20-0ed9-c1d5-49ddff49c224", "source_type": "file", "start_index": 0, "chroma:document": "Learning Recap\nIn this learning path, we:\nCreated and deployed Azure OpenAI resources\nIntegrated Azure OpenAI into your application through REST APIs and SDKs\nExplored prompt engineering techniques to improve model responses\nConnected your own data for grounding an Azure OpenAI model\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 748f8522-eaba-46b5-81c9-9c36b77135fc
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{"source": "./Documents/general/slide_4.pptx", "doc_id": "5fd18edc-487a-3d2d-418f-1074ca56268e", "source_type": "file", "start_index": 0, "chroma:document": "Fundamentals of Deep Learning\nPart 4: Data Augmentation and Deployment\n\u2022, 1 = Part 1:  An Introduction to Deep Learning. \u2022, 1 = Part 2: How a Neural Network Trains. \u2022, 1 = Part 3: Convolutional Neural Networks. \u2022, 1 = Part 4: Data Augmentation and Deployment. \u2022, 1 = Part 5: Pre-Trained Models. \u2022, 1 = Part 6: Advanced Architectures\nAgenda"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 53c7b94b-7c42-4cb9-8891-3853cb5a9551
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{"source": "./Documents/general/slide_4.pptx", "doc_id": "1a352722-3048-d5ab-5f92-cd9d60d2e64b", "source_type": "file", "start_index": 0, "chroma:document": "Recap of the Exercise\nAnalysis\nCNN increased validation accuracy\nStill seeing training accuracy higher than validation\nCNN increased validation accuracy\nStill seeing training accuracy higher than validation\nClean data provides better examples\nDataset variety helps the model generalize\nAnalysis\nSolution\nRecap of the Exercise\nData Augmentation"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f9d7e425-1883-439c-8888-783414055fe9
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{"source": "./Documents/general/slide_4.pptx", "doc_id": "233c9374-e179-ba03-6957-0ac0315f3b92", "source_type": "file", "start_index": 0, "chroma:document": "Image Flipping\nHorizontal Flip\nVertical Flip"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d2f85f60-a79c-4bc5-a1ad-3637372a7e9e
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{"source": "./Documents/general/slide_4.pptx", "doc_id": "8a135131-a7a7-0de9-c9c1-fc3eec2d156c", "source_type": "file", "start_index": 0, "chroma:document": "Rotation\n0\u2070   \n45\u2070 \n90\u2070 \n135\u2070\n180\u2070\n225\u2070\n270\u2070\n315\u2070"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_4.pptx", "doc_id": "46eaae5a-d52b-4507-a20c-719b9618fbea", "source_type": "file", "start_index": 0, "chroma:document": "ChannelShifting\nModel Deployment"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 1aad4be2-e2cd-4ad6-bff9-f96cea159f0b
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{"source": "./Documents/general/slide_4.pptx", "doc_id": "3dfa2099-95e7-0517-57be-c45539e8ed8c", "source_type": "file", "start_index": 0, "chroma:document": "Model Deployment\n(1, 28, 28)\nImage Input\n(2, 1, 3, 3)\nKernels\n(2, 28, 28)\nStacked Images\n(2, 2, 3, 3)\nKernels\n(2, 28, 28)\nStacked Images\n(1568)\nFlattened Image\nVector\n\u2026                 \u2026\n\u2026                 \u2026\n(10)\nOutput Prediction\n(512)\nLinear\n(512)\nLinear\nTraining Batch Input\nConvolution\n\u2026\nMax Pooling\nResize\nGreyscale\n\u201cBatch\u201d\n(1, 220, 155)\n(3, 287, 433)\n(3, 220, 155)\n(1, 1, 220, 155)\nLet\u2019s Try It Out!"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 4ef01f4d-982d-4339-a18c-83d01cc1b58d
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "059eee80-7dab-05c8-c4b7-30e499a342f7", "source_type": "file", "start_index": 0, "chroma:document": "Develop natural language processing solutions\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 05383b5b-9b28-4614-8604-bdd7245eb67b
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "bfe198d0-01cb-8f4d-c3b1-e7e30f16dfb7", "source_type": "file", "start_index": 0, "chroma:document": "Agenda\n\u00b7 Analyzing and translating text\n\u00b7 Build a question answering solution\n\u00b7 Build a conversational language understanding app\n\u00b7 Custom classification and named entity extraction\n\u00b7 Speech recognition, synthesis, and translation\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nAnalyzing text\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: e3e6d21f-9ee6-46d8-af93-db0a9e08d337
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "2e9173ce-2a1d-3fa7-3a06-4b52e0a227c7", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\nDetect language and extract key phrases\n2 Analyze sentiment and detect PII\n3 Summarize text\n4 Extract entities and linked entities\n5 Translate text\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: a92c8853-77be-427b-b9de-0b28935f9a4d
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "7c11e2de-a549-7097-3b49-d0c9be0916ce", "source_type": "file", "start_index": 0, "chroma:document": "The Azure AI Language Service\nPreconfigured features:\n\u00b7 Language detection\n\u00b7 Key phrase extraction\n\u00b7 Sentiment analysis\n\u00b7 Named entity recognition\n\u00b7 Entity linking\n\u00b7 Summarization\n\u00b7 Personal Identifying Information (PII) detection\nCustomizable features are covered in another section\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 67809774-43bc-4d4e-b5f9-993de6912fd4
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "d535eb22-a016-3327-2746-010d0515d66b", "source_type": "file", "start_index": 0, "chroma:document": "Language detection\n\u00b7 Determine the language in which text is written\n\u00b7 Often useful as a pre-cursor to further analysis that requires a known language\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f69937ee-72f6-4dea-96a0-ebeb99a697cf
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "127fad39-3bbb-f7e0-5f5a-413c560fc25d", "source_type": "file", "start_index": 0, "chroma:document": "Key phrase extraction\n\u00b7 Identify the main 'talking points' of the text\n\u00b7 Works best with larger documents (up to 5,120 characters)\n{ \"documents\": [ { \"id\": \"1\", \"language\": \"en\", \"text\": \"You must be the change you wish to see in the world.\" }, { \"id\": \"2\", \"language\": \"en\", \"text\": \"The journey of a thousand miles begins with a single step.\" } ] } Language (defaults to English if not present)\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c9944488-3f23-4a12-b451-8669fdedc40d
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "05c955bd-b157-0a6d-0468-e4762e73e7c1", "source_type": "file", "start_index": 0, "chroma:document": "Sentiment analysis\n\u00b7 Scores overall document sentiment and individual sentence sentiment\n\u00b7 Sentence sentiment is based on confidence scores for positive , negative , and neutral\n\u00b7 Overall document sentiment is based on sentences:\n-All sentences are neutral = neutral\n-Sentences include positive and neutral = positive\n-Sentences include negative and neutral = negative\n-Sentences include positive and negative = mixed\n{\n\"documents\": [\n{\n\"language\": \"en\",\n\"id\": \"1\",\n\"text\": \"Smile! Life is good!\"\n}\n]\n}\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 96be1b72-0af0-4eb0-aef6-1429e5f687fc
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "2263f622-ec9a-8d15-09f5-ba7d6281575f", "source_type": "file", "start_index": 0, "chroma:document": "Named entity recognition\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 2d2fbe9e-8552-4bee-bb66-c7ffdf8e2e8a
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "cb6dee09-8edb-9ff9-9996-7fc2529ecb54", "source_type": "file", "start_index": 0, "chroma:document": "Entity Linking\n\u00b7 Used to disambiguate entities of the same name\n-For example, is 'Venus' a planet or a goddess?\n\u00b7 Wikipedia provides the knowledge base\n\u00b7 Specific article links are determined based on entity context within the text\n'I saw Venus shining in the sky':\nhttps://en.wikipedia.org/wiki/Venus\n'Venus, the goddess of beauty':\nhttps://en.wikipedia.org/wiki/Venus_(mythology)\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 33305fe4-be8c-442c-bc93-5db156afd77c
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "45809fbd-13a5-501f-1882-838a730291de", "source_type": "file", "start_index": 0, "chroma:document": "Summarization\n\u00b7 Can provide two different types of summarization\n\u00b7 Extractive summarization: Produces summary by using most important sentences\n\u00b7 Abstractive summarization: Produces a summary capturing the main idea, but not necessarily using the same words as the source document\n\u00b7 Can be customized by training on your own data\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 530db08a-72ef-48cd-846e-142dc7a66854
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "35a86d5d-9df0-e185-158f-93f3ea6419c5", "source_type": "file", "start_index": 0, "chroma:document": "Personally Identifiable Information detection\n\u00b7 Used to detect and remove sensitive information\n\u00b7 Entity categories include Person, PhoneNumber, Email, Address, Credit card, and financial account identification\n\u00b7 Can be used in situations like applying sensitivity labels, removing information to reduce bias, and clean data for data science\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: e8028a7b-fffe-43e8-8619-82f40167049d
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "f5e25bb4-9871-615b-5b3a-4cf39fb8ae3a", "source_type": "file", "start_index": 0, "chroma:document": "Exercise -Analyze Text\nDetect Language\nEvaluate Sentiment\nIdentify Key Phrases\nExtract Entities\nExtract Linked Entities\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nTranslating Text\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 8ff77f89-030c-406c-bee0-40124e284b56
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "f86728c3-0766-838d-679c-bab7e7cf6965", "source_type": "file", "start_index": 0, "chroma:document": "Multilingual text translation REST API\n\u00b7 Language detection\n\u00b7 One-to-many translation\n\u00b7 Script transliteration\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 19a7c039-00f7-4ee5-b7bc-e081ac255566
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "fb7f0f5d-bb74-9cb3-cd6f-1369fa0ba6cc", "source_type": "file", "start_index": 0, "chroma:document": "Detection, Translation, and Transliteration\nHeaders omitted for brevity"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 2e5a8d17-1cf2-4463-bda3-ac234f497a88
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "d8c46660-db4f-b09e-4692-1fd046d371ce", "source_type": "file", "start_index": 0, "chroma:document": "Create a custom translation model\n1. Use the Custom Translator portal\n2. Link a workspace to your Azure AI Translator resource\n3. Create a project\n4. Upload training data files\n5. Train a model"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6ff86fec-ab47-4392-9cdb-97bca5b78489
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "6b93912b-4914-b351-4582-148df9acc6b1", "source_type": "file", "start_index": 0, "chroma:document": "Call your model through the Translator API\n\u00b7 Specify a category parameter with the project category Id\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 8fb06d33-8a36-4ce8-be63-cf103577bef5
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "d29f9209-534a-7231-6fb1-99479ada791e", "source_type": "file", "start_index": 0, "chroma:document": "[Optional Exercise] -Translate Text\nDetect language\nTranslate text\nmslearn-ai-language/Instructions/Exercises at main \u00b7 MicrosoftLearning/mslearn-ailanguage (github.com)\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nBuild a question answering solution\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: bdf7a3d7-ce1b-4c14-ba93-ffd70bca1a77
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "8b3b0b4b-9623-20f1-a8d2-6767755e18c9", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n1 Describe the question answering capabilities\nof Azure AI Language.\n2 Describe the differences between question answering and conversational language understanding.\nCreate a knowledge base.\nImplement multi-turn conversation.\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nTest and publish a knowledge base.\nConsume a published knowledge base.\nImplement active learning."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3cd918fe-d3c7-445a-a2d2-b60491c736c9
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "366ca6be-db47-8b8d-03b3-930d473c4596", "source_type": "file", "start_index": 0, "chroma:document": "Introduction to Question Answering\n\u00b7 Knowledge base of question and answer pairs with natural language understanding\n\u00b7 Published as a REST endpoint for applications to consume\n\u00b7 Available through language specific SDKs\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 943c4cae-58a8-4429-81ce-31ac9b3b6283
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "805e65e3-aff0-dff7-cff7-7f895a9e0d3d", "source_type": "file", "start_index": 0, "chroma:document": "Question answering\n\u00b7 User submits a question, expecting an answer\n\u00b7 Service uses natural language understanding to match the question to an answer in the knowledge base\n\u00b7 Response is a static answer to a known question\n\u00b7 Client application presents the answer to the user\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 01b9e006-f1d4-41ba-b9f1-aae563ff97dd
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "1aeb9d7e-59cf-a146-fffa-ae2063e54092", "source_type": "file", "start_index": 0, "chroma:document": "Language understanding\n\u00b7 User submits an utterance, expecting an appropriate response or action\n\u00b7 Service uses natural language understanding to interpret the utterance, match it to an intent, and identify entities\n\u00b7 Response indicates the most likely intent and referenced entities\n\u00b7 Client application is responsible for performing appropriate action based on the detected intent"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f999e9b9-4c79-4355-bc9f-b50c8acf382a
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "36a912fa-8a80-932a-48bb-e2ff02425848", "source_type": "file", "start_index": 0, "chroma:document": "Use the Language Studio portal\n1. Create an Azure AI Language service resource in your Azure subscription\n2. In Language Studio, select your Azure AI Language resource and create a Custom question answering project.\n3. Populate the knowledge base:\n\u00b7 Import from existing FAQ web page\n\u00b7 Upload document files\n\u00b7 Add predefined 'chit -chat' pairs\n5. Create the knowledge base and edit question and answer pairs\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 524d9074-9949-4fc0-812e-57a3304d6939
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "99b54807-b810-d0f1-1334-01b42184b99a", "source_type": "file", "start_index": 0, "chroma:document": "Add follow-up prompts to define multi-turn exchanges\n\u00b7 Can reference existing question and answer pairs\n\u00b7 Can be restricted to follow-up responses only\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 11f9bd3d-3dc7-4897-9a62-39f0b364cb64
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "17460eed-e9f2-fd13-547e-4e09e5ba534f", "source_type": "file", "start_index": 0, "chroma:document": "Test interactively in Language Studio\n\u00b7 Inspect results to see confidence scores\n\u00b7 Add alternative phrases to improve scores as necessary"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 7e57132e-a481-4592-8b31-19c4db1f5f06
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "d407a89f-0c43-0983-ec1b-a7451b83bc23", "source_type": "file", "start_index": 0, "chroma:document": "Publish the trained knowledge base\n\u00b7 Creates an HTTP REST-based endpoint for client apps to consume\n\u00b7 Published knowledge base can be used with SDKs within your app\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 53be178d-cf64-47e2-9eeb-bba4334a9bc2
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "8671163e-f3de-00de-7997-c05d0181983c", "source_type": "file", "start_index": 0, "chroma:document": "Creating client apps\n\u00b7 REST Interface or SDKs\n\u00b7 Submit questions to the endpoint\n{ \"question\": \"What do I need to do to cancel a reservation?\", \"top\": 2, \"scoreThreshold\": 20, \"strictFilters\": [ { \"name\": \"category\", \"value\": \"api\" } ] }\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: ec1be146-84c0-44ce-ab4d-4b6ff4077acc
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "0ef74f39-6776-91d8-ff6a-fd70f80facdc", "source_type": "file", "start_index": 0, "chroma:document": "Improving Question Answering Performance\nEnable Active Learning to suggest alternatives when multiple questions have similar scores for user input\n\u00b7 Implicit : The service identifies potential alternative phrases for questions; and presents suggestions in the Language Studio. Periodically review and accept/reject the suggestions.\n\u00b7 Explicit : The service returns multiple possible question matches to the user, and the user identifies the correct one. The client app then uses the API to submit feedback items, identifying the correct answer."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 189b11fb-271b-47f7-8ce4-a653847f1fa7
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "5e2a7b0c-033f-5f77-9906-79aa1fd37190", "source_type": "file", "start_index": 0, "chroma:document": "Create Synonyms for terms with the same meaning\n\u00b7 Add synonyms to the knowledge base through the API or Language Studio interface.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f83ac89c-a125-4476-bb33-56ad842c0a3f
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "36e4699e-8bc8-5b9a-bfad-b1b9f10d21f0", "source_type": "file", "start_index": 0, "chroma:document": "Exercise -Create a Question Answering solution\nCreate and edit a knowledge base\nTrain, test, and deploy the knowledge base\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nBuild a conversational language understanding app\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: ab647af3-9ef9-4f71-a4d2-7424223a4b73
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "72daa721-2fa0-af1f-0849-574592af9212", "source_type": "file", "start_index": 0, "chroma:document": "Learning objectives\nAfter completing this module, you will be able to:\n1 Provision an Azure AI Language resource\n2 Define intents, entities, and utterances\n3 Use patterns to differentiate similar utterances and use pre-built entity components\n4 Train, test, publish, and review a model\nDescribe Azure AI Language Understanding\n5 features\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6d4c5f9a-bda2-4db0-8d78-3bb76d6635ff
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "0191dac7-7f78-44d5-3513-34ef203a783c", "source_type": "file", "start_index": 0, "chroma:document": "Introduction to language understanding\nAn app accepts natural language input from a user\nA language model is used to determine semantic meaning (the user's intent )\nThe app performs an appropriate action"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6b1172bc-c67e-4fbc-a753-b534c1c0a02e
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "f42d6ecb-f6ac-41db-bb30-2a38e669db60", "source_type": "file", "start_index": 0, "chroma:document": "Natural Language Processing (NLP) requires a language model to interpret user input\nOften this activity is referred to as natural language understanding (NLU)\nConversational language understanding (CLU) is an Azure service to enable you to build natural language understanding component to be used in an end-to-end conversational application.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d062a03b-51f9-4be7-960a-6107cab58420
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "277753c2-3344-c98d-0143-896c1e17243b", "source_type": "file", "start_index": 0, "chroma:document": "To train a language understanding model:\n\u00b7 Specify utterances that represent expected natural language input\n\u00b7 Map utterances to intents that assign semantic meaning\nWhat time is it?, Intent = GetTime. Tell me the time., Intent = GetTime. What is the weather forecast?, Intent = GetWeather. Do I need an umbrella?, Intent = GetWeather. Turn the light on., Intent = GetWeather. Switch on the fan., Intent = GetWeather. Hello, Intent = None\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 82be5650-ab34-4fbe-8642-4bd6a12dd80f
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "b786caf2-c323-20d1-4516-6f0d01813f21", "source_type": "file", "start_index": 0, "chroma:document": "Define entities to add specific context to intents\nWhat time is it in London?, Intent.GetTime = GetTime. What time is it in London?, Entities. = Location (London). What's the weather forecast for Paris?, Intent.GetTime = GetWeather. What's the weather forecast for Paris?, Entities. = Location (Paris). Will I need an umbrella tonight?, Intent.GetTime = GetWeather. Will I need an umbrella tonight?, Entities. = Time (tonight). What's the forecast for Seattle tomorrow?, Intent.GetTime = GetWeather. What's the forecast for Seattle tomorrow?, Entities. = Location (Seattle), Time (tomorrow). Turn the light on., Intent.GetTime = TurnOnDevice. Turn the light on., Entities. = Device (light). Switch on the fan., Intent.GetTime = TurnOnDevice. Switch on the fan., Entities. = Device (fan)"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6563acb3-1512-40f1-84a4-2f0a97861bae
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "cc9cdac8-8ce3-4ae0-25a7-cdcbf45c8ebd", "source_type": "file", "start_index": 0, "chroma:document": "Entity types:\nLearned, 1 = List. Learned, 2 = Prebuilt. , 1 = Term in a defined list. , 2 = Machine learned through training Common types like numbers and date/times\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f8fdf173-1bcb-4715-bc79-2e5635066e2a
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "b4fc0efd-b686-f4b8-7648-14e38c1d7fc3", "source_type": "file", "start_index": 0, "chroma:document": "Prebuilt entity components\nPrebuilt components automatically predict common types from utterances:"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 708732bd-3723-4c40-918e-b337f69eb397
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "3f0c7b9d-5585-228e-3f0e-a36cae5fca3c", "source_type": "file", "start_index": 0, "chroma:document": "Quantities\n\u00b7 Age, Number, Percentage, Currency, others\u2026"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 0a066dc8-b141-4422-b094-f80acd78d989
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "a241f0e0-1b97-295e-103d-8e1f2e0b0fdb", "source_type": "file", "start_index": 0, "chroma:document": "Datetime\n\u00b7 \"June 23, 1976\", \"7 AM\", \"6:49 PM\", \"Tomorrow at 7 PM\", \"Next Week'."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 7fa55dda-4e1d-40f5-9424-884af40e33c7
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "f5fd1c3e-82b2-3e6b-8435-3f847bb7ee11", "source_type": "file", "start_index": 0, "chroma:document": "Email\n\u00b7 'user@contoso.com\""}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6a7a4a8e-1a3c-4604-9e2b-dfad76f48483
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "d78b385c-ac48-92f7-c895-7b113392ff41", "source_type": "file", "start_index": 0, "chroma:document": "Phone number\n\u00b7 US Phone Numbers such as \"+1 123 456 7890' or \"(123)456 -7890'."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 35a39fec-9ebd-4d78-9306-e9fbc6d292ac
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "bfb9cc13-6667-777e-fbb4-90c879d515d1", "source_type": "file", "start_index": 0, "chroma:document": "URL\n\u00b7 'https://learn.microsoft.com/'\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 94418f52-80de-4d50-809b-acb7dc7ad380
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "0244a3a4-64da-df36-6550-90ecfc0b9497", "source_type": "file", "start_index": 0, "chroma:document": "Azure AI Language service capabilities\nFeatures fall into two categories:\nPreconfigured features -Can be used without labeling or training:\n\u00b7 Summarization\n\u00b7 Named entity recognition\n\u00b7 PII detection\n\u00b7 Key phrase detection\n\u00b7 Sentiment analysis\n\u00b7 Language detection\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nLearned features -Require labeling, training, and deploying to utilize\n\u00b7 Conversational language understanding\n\u00b7 Custom named entity recognition\n\u00b7 Custom text classification\n\u00b7 Question answering"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: a35da72e-47e6-47e0-8689-8d388d7b3ba0
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "e4f6bfd2-32ef-a7dd-ad7a-996c94696a06", "source_type": "file", "start_index": 0, "chroma:document": "Submit a request to a published slot, specifying:\n\u00b7 Kind -Indicates which language feature you're requesting. For example, kind is defined as Conversation for conversational language understanding, or EntityRecognition to detect entities\n\u00b7 Parameters -Indicates the values for various input parameters. These parameters vary depending on the feature.\n\u00b7 Analysis input -Specifies the input documents or text strings to be analyzed by the Azure AI Language service.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 1e0ce557-292c-48a1-b057-0f11f12174c0
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "540d207f-1cd8-2619-50ea-4f96d5096c7f", "source_type": "file", "start_index": 0, "chroma:document": "Training, testing, publishing, and reviewing\nTrain a model to learn intents and entities from sample utterances\nTest the model interactively or using a testing dataset with known labels\nDeploy a trained model to a public endpoint so client apps can use it\nReview predictions and iterate on utterances to train your model\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: e4e1ebdc-c58e-43b3-a7a9-3def18a0a73a
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "27eb72d7-6646-32d5-5a28-6c15a147e3b7", "source_type": "file", "start_index": 0, "chroma:document": "Exercise -Create a conversational language understanding app\nCreate intents\nCreate entities\nTest and publish a language model\nQuery your model from a client app\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nCustom classification and named entity extraction\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 1b1e92d3-86a5-4791-a163-e95b7e129e9e
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "f83f2886-c117-d87c-a36c-a5a94373bdf8", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\nLabel documents, train and deploy models for custom classification\nUnderstand model performance and see where to improve your model 2\n3\nUse your custom model in an app\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 4268b7ce-db3c-4e6a-be0b-024fe2713bb0
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "351a7b52-2374-d7cd-d44c-4709691cbbcc", "source_type": "file", "start_index": 0, "chroma:document": "Assign custom labels to documents\n1. Connect to documents in Azure\n2. Define class labels to assign to your documents\n3. Label documents\n4. Train your model"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 4979dc48-bdb7-4e21-8538-80dbf5beca79
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "e2e81324-04c9-1178-2e07-fcc6c73d7884", "source_type": "file", "start_index": 0, "chroma:document": "Call your model through the Language API\n\u00b7 Specify project and deployment name\nCan be single label or multi label projects\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 80be8bec-d2f0-48f9-b40c-d68d8d17d8a1
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "8caeea57-51f0-cc73-f47c-b46538624cf8", "source_type": "file", "start_index": 0, "chroma:document": "Assign custom labels to entities in your documents\n1. Connect to documents in Azure\n2. Define entity labels to assign to your documents\n3. Label documents completely and consistently\n4. Train your model"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 57cd5045-f0b5-4566-bfff-dfb0d3145785
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "49f688f9-d9a3-d6f8-c643-30d6323878cd", "source_type": "file", "start_index": 0, "chroma:document": "Call your model through the Language API\n\u00b7 Specify project and deployment name\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 49d1a646-3e78-49e9-885d-ad183808bf17
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "5f8c6b2c-3396-4c27-2d16-f2e8cb1a4cd8", "source_type": "file", "start_index": 0, "chroma:document": "Review and improve a model\nTrain a model to teach labels or entities\nReview model performance to determine how to improve performance, including Confusion matrix\nDetermine what cases need to be added to your training data\nRetrain your model with new data included, and repeat as necessary\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c81bcc94-bebd-4171-8465-304da161719c
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "4b6e11ce-403d-4782-e4e8-a12b5955d035", "source_type": "file", "start_index": 0, "chroma:document": "[Optional Exercise] -Extract custom entities\nCreate a custom named entity recognition project\nCreate and label entities\nTest and publish a custom model\nQuery your model from a client app\nmslearn-ai-language/Instructions/Exercises at main \u00b7 MicrosoftLearning/mslearn-ai-language (github.com)\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nSpeech recognition, translation and synthesis\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 91f984f4-f6fc-4a03-9960-63760d0e69c3
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "8923767d-36b2-96c7-de71-062e1011ccea", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n2\nProvision an Azure resource for the Azure AI Speech service\nUse the Speech to text API to implement speech recognition\nUse the Text to speech API to implement speech synthesis 3\nConfigure audio format and voices 4\n5\nUse Speech Synthesis Markup Language (SSML)\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 58777a78-1a6a-489e-9237-9d59040353b2
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "3925e111-86e0-9318-0697-6edbb418eefb", "source_type": "file", "start_index": 0, "chroma:document": "Speech APIs\n\u00b7 Speech-to-Text API (speech recognition)\n\u00b7 Text-to-Speech API (speech synthesis)\n\u00b7 Speech Translation API\n\u00b7 Speaker Recognition API\n\u00b7 Intent Recognition (uses conversational language understanding)\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 7057cadd-f2a2-4756-be5d-1cdad1e7258e
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "a10cd2e1-ad44-93d5-8a4e-9db5fe0694a2", "source_type": "file", "start_index": 0, "chroma:document": "Two REST APIs:\n\u00b7 Speech-to-text API -Used by Azure AI Speech SDK -preferred for most scenarios\n\u00b7 Speech-to-text Short Audio API -Useful for short (up to 60s) of audio\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nAzure AI Speech SDK (.NET, Python, JavaScript, etc.)\n\u00b7 Text-to-speech API -Suitable for most scenarios\n\u00b7 Batch synthesis API -Convert large volumes of text to audio files\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nAzure AI Speech SDK (.NET, Python, JavaScript, etc.)"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: b9703070-b58e-488e-a5f2-9b183c64d517
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "2d3e0ee0-d48e-8a7d-25a0-f5b1e3b6fd99", "source_type": "file", "start_index": 0, "chroma:document": "Audio Format and Voices\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d8b97b9e-273a-4975-aebc-21d41c1a7a67
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "558bd0b8-a106-d816-d3f1-80e2296ea694", "source_type": "file", "start_index": 0, "chroma:document": "XML-based language with customization options:\n\u00b7 Speaking styles (Neural voices only)\n\u00b7 Pauses and silence\n\u00b7 Phonemes (phonetic pronunciations)\n\u00b7 Prosody (speaking pitch, range, rate, etc.)\n\u00b7 \"say-as\" (number, date, time, address, etc.)\n\u00b7 Insert recorded speech or background audio\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: cd933262-3e79-4995-a466-31c6c8e2eecc
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "d52b6bc0-d641-94ed-dd5c-7dd68178c560", "source_type": "file", "start_index": 0, "chroma:document": "Exercise -Recognize and Synthesize Speech\nRecognize Speech\nSynthesize Speech\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: ee2c85cc-387b-42cc-aa40-53322c06588f
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "e0b23535-b7bd-aece-a79b-833f1d05d4fc", "source_type": "file", "start_index": 0, "chroma:document": "Translation builds on speech recognition:\n1. Recognize and transcribe spoken input in speech recognition language\n2. Return translations for one or more target languages\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 3cd9ab53-5662-4c28-b7b3-0e300107998f
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "acb46ce4-e9b6-8fca-6386-bd71ee94e68a", "source_type": "file", "start_index": 0, "chroma:document": "Event-based synthesis\n\u00b7 Only supported for 1:1 translation (single target language)\n\u00b7 Specify desired voice in the TranslationConfig\n\u00b7 Use the Synthesizing event to retrieve audio stream\n\u00b7 Create an event handler\n\u00b7 Use Result.GetAudio() to retrieve byte stream"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 0e52d906-b532-4c31-9ab5-34a7d3b6398e
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "f60bf25f-4d76-f2ef-8018-e157fa1bdd70", "source_type": "file", "start_index": 0, "chroma:document": "Manual synthesis\n\u00b7 Use for multiple target languages\n\u00b7 Translate to text then use Text-to-Speech API to synthesize each translation in the results\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 98ad2272-6307-4487-bc4a-b8f3468afd73
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "b7b5ede0-a06a-5823-d941-2435f547b3ab", "source_type": "file", "start_index": 0, "chroma:document": "Extended interactive exercises\nCustom text classification\nTranslate speech\nhttps://aka.ms/azure-ai-language-lp\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: f4be0882-c88d-4b9f-bcef-a30599bcee86
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "7e169007-1479-8386-b468-0b823cbbabde", "source_type": "file", "start_index": 0, "chroma:document": "Knowledge check\nWhich object should you use to specify that the speech input to be transcribed to text is in an audio file?\n\u20e3\nSpeechConfig\n\u20e3\nUse the Azure AI Language service to analyze sentiment.\n\u20e3\nUse the Azure AI Language service to extract linked entities.\nWhen translating speech, in which cases can you can use the Synthesizing event to synthesize the translations and speech? 3\n\u20e3\nOnly when translating to a single target language\n\u20e3\nAudioConfig\n\u20e3\nSpeechRecognizer\nYou have analyzed text that contains the word 'Paris'. How might you determine if this word refers to the French city or the character in Homer's The Iliad? 2\n\u20e3\nUse the Azure AI Language service to extract key phrases.\n\u20e3\nOnly when translating to multiple target languages\n\u20e3\nWhen translating to one or more target languages\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: acd560e7-f09a-4c33-89ee-a11ed265d383
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "7e169007-1479-8386-b468-0b823cbbabde", "source_type": "file", "start_index": 689, "chroma:document": "\u20e3\nOnly when translating to multiple target languages\n\u20e3\nWhen translating to one or more target languages\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nYour app must interpret a command to book a flight to a specified city, such as 'Book a flight to Paris.' How should you model the city element of the command?\n\u20e3\nAs an intent.\n\u20e3\nAs an utterance.\n\u20e3\nAs an entity.\n5 Your language model needs to detect an email when present in an utterance. What is the simplest way to extract that email?\n\u20e3\nUse Regular Expression entities.\n\u20e3\nUse Prebuilt entity components\n\u20e3\nUse Learned entity components.\n6 How should you create an application that monitors the comments on your company's web site and flags any indication that customers are unhappy?\n\u20e3\nUse the Azure AI Translator service to detect profanities in comments.\n\u20e3\nUse the Azure AI Language service to perform sentiment analysis of the comments.\n\u20e3\nUse the Azure AI Language service to extract named entities from the comments"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: e52d767b-1655-4954-bb6a-5f19d5196236
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "7e169007-1479-8386-b468-0b823cbbabde", "source_type": "file", "start_index": 1505, "chroma:document": "\u20e3\nUse the Azure AI Language service to perform sentiment analysis of the comments.\n\u20e3\nUse the Azure AI Language service to extract named entities from the comments\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d6a3b76a-ec82-4262-8105-9c1ae7534039
encoding: FLOAT32
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{"source": "./Documents/general/Topic 3 AI-102T00A-ENU-PowerPoint_03_Language.pdf", "doc_id": "6491ad24-fadc-0255-0c18-f8672735691a", "source_type": "file", "start_index": 0, "chroma:document": "In this learning path, we learned to:\n\u00b7 Analyze and translate text\n\u00b7 Build a conversational language understanding model\n\u00b7 Build a question answering solution\n\u00b7 Speech recognition, synthesis, and translation\n\u00b7 Connect an app to Azure AI Language resources\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 66505d0a-7fd4-4b69-978f-0d0cce3b0ab8
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{"source": "./Documents/general/slide_5.pptx", "doc_id": "90c4fdd3-930e-2b35-d9db-eb5937f8aef5", "source_type": "file", "start_index": 0, "chroma:document": "Fundamentals of Deep Learning\nPart 5: Pre-trained Models\n\u2022, 1 = Part 1:  An Introduction to Deep Learning. \u2022, 1 = Part 2: How a Neural Network Trains. \u2022, 1 = Part 3: Convolutional Neural Networks. \u2022, 1 = Part 4: Data Augmentation and Deployment. \u2022, 1 = Part 5: Pre-Trained Models. \u2022, 1 = Part 6: Advanced Architectures\nAgenda\nReview So Far"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 0ba818ed-cc08-4f6b-a8b4-7d67020a2a0c
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{"source": "./Documents/general/slide_5.pptx", "doc_id": "9cedcf45-2f7e-5abb-97e7-eb7858398383", "source_type": "file", "start_index": 0, "chroma:document": "Review So Far\nLearning Rate\nNumber of Layers\nNeurons per Layer\nActivation Functions\nDropout\nData\nPre-Trained Models"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c8a3805a-9111-4928-a5b7-0715b62ba256
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{"source": "./Documents/general/slide_5.pptx", "doc_id": "0387411e-4325-b7aa-fee7-bc2379f5cae7", "source_type": "file", "start_index": 0, "chroma:document": "Pre-Trained Models\nPYTORCHHUB\nIMGENET"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c21cb14e-7a2b-41a8-8a7f-a17515bffa7d
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{"source": "./Documents/general/slide_5.pptx", "doc_id": "ec843c70-65e8-e32b-0595-deb52235a57c", "source_type": "file", "start_index": 0, "chroma:document": "The Next Challenge\nAn Automated Doggy Door\nTransfer Learning"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: a249daa7-7e94-4251-8960-54e2b7365833
encoding: FLOAT32
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{"source": "./Documents/general/slide_5.pptx", "doc_id": "de470834-f331-30ce-4e50-edb687bd7f55", "source_type": "file", "start_index": 0, "chroma:document": "The Challenge After\nAn Automated Presidential Doggy Door"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: c95e4c3b-bdd4-46c6-a162-7973a2fb854c
encoding: FLOAT32
metadata:
{"source": "./Documents/general/slide_5.pptx", "doc_id": "e47fe5fe-1e0a-62f2-bf43-5df8802c0419", "source_type": "file", "start_index": 0, "chroma:document": "Transfer Learning\n(1568)\nFlattened Image\nVector\n\u2026                 \u2026\n\u2026                 \u2026\n(10)\nOutput Prediction\n(512)\nLinear\n(512)\nLinear\n(1, 28, 28)\nImage Input\n(2, 1, 3, 3)\nKernels\n(2, 28, 28)\nStacked Images\n(2, 2, 3, 3)\nKernels\n(2, 28, 28)\nStacked Images\nConvolution\nInput\nConvolution\nConvolution\nLinear\nOutput\nLinear\nMax Pooling\nDropout\nMax Pooling\nMax Pooling\nMore Generalized\nMore Specialized\nFreezing the Model?\nLet\u2019s Get Started!"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 13bf6a20-74d5-4ff7-a9e5-5fdd8721af9a
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "107ab2a2-b70f-a8ac-39a9-5345ecac1ad6", "source_type": "file", "start_index": 0, "chroma:document": "Develop solutions with Azure AI Document Intelligence\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: e9e369ca-599d-446c-801c-e7fb0a60d28c
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "a050d7e2-9a4f-8859-27ca-6177f84c36af", "source_type": "file", "start_index": 0, "chroma:document": "Agenda\n\u00b7 Use prebuilt Document Intelligence models\n\u00b7 Train a custom Document Intelligence model\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\nDevelop a Document Intelligence solution\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 836fa94d-b411-4a4c-b1e2-74f233665353
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "abfd0ba9-9356-90d7-4f05-24e7d6215dc5", "source_type": "file", "start_index": 0, "chroma:document": "Learning Objectives\nAfter completing this module, you will be able to:\n1 Understand models in Azure AI Document Intelligence\n2 Train a custom Document Intelligence model\n3 Connect an app to Document Intelligence APIs\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 8a65301b-1df5-4270-af25-3febda9cd6d2
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "bb35ad03-3aa7-bccd-5943-b5c3ac297ddb", "source_type": "file", "start_index": 0, "chroma:document": "The Document Intelligence Service\nData extraction from forms and documents:\n\u00b7 Document analysis from general documents\n\u00b7 Read: OCR for printed and written text\n\u00b7 Layout: Extract text and structure\n\u00b7 General document: Extract text, structure, and key-value pairs\n\u00b7 Prebuilt models for common form types\n\u00b7 Train custom models for your own forms\n\u00b7 Custom template: Extract data from static layouts\n\u00b7 Custom neural: Extract data from mixed-type documents\n\u00b7 Custom composed: Collection of multiple models assigned to a single model\nProvision as single-service Document Intelligence resource or multi-service Azure AI Services resource\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 4bb912f5-b85f-41bc-a20b-f4f3bd9e11f8
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "1a4cdad5-cb52-304d-65ca-75fda8050361", "source_type": "file", "start_index": 0, "chroma:document": "Prebuilt models\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 6c00c34f-d208-41e8-a602-f0f60009d7e7
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "a3af4d07-b967-d46b-c8b0-4efdd5781582", "source_type": "file", "start_index": 0, "chroma:document": "Calling the API\n\u00b7 Each request is configured with your resource endpoint and needs your resource key\n\u00b7 Send the request, which when successful returns a poller to get the results\n\u00b7 REST returns it in Operation-Location header\n\u00b7 SDKs return an object from the request\n\u00b7 Query the poller received for the extracted data"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 145e1950-4751-4d4a-bb8a-044a1c2d6e08
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "2acee099-f466-06da-e0b6-34d1d660883e", "source_type": "file", "start_index": 0, "chroma:document": "REST\nRequest POST:\n{endpoint}/documentintelligence/documentModels/prebuiltlayout:analyze?api-version={version}\nOperation-Location:\n{endpoint}/documentintelligence/documentModels/prebuiltlayout/analyzeResults/ab12345c-12ab-23cd-b19c2322a7f11034?api-version={version}"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 9f0b3934-b8bd-466d-97ad-71d59436ded3
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "2460039a-4557-8d08-1646-6bc97387c06a", "source_type": "file", "start_index": 0, "chroma:document": "C#\nAnalyzeDocumentOperation operation = await client.AnalyzeDocumentFromUriAsync(WaitUntil.Completed, \"prebuilt-layout\", fileUri);\nAnalyzeResult result = operation.Value;"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 98446118-dfc8-4a3c-85a9-e0d03d44ab2c
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "77b26963-43ec-a9df-b6e0-e715efce12d0", "source_type": "file", "start_index": 0, "chroma:document": "Python\npoller=document_analysis_client.begin_analyze_document_ from_url(\"prebuilt-document\", docUrl)\nresult = poller.result()\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 19ff4a5b-f400-430b-91da-af370dc00a24
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "9e054101-71bc-3fec-ea78-38d48e5b304f", "source_type": "file", "start_index": 0, "chroma:document": "API response\n\u00b7 Response it broken down by page, lines, and words\n\u00b7 Subset of REST response included here\n\u00b7 SDK response objects have similar structure, broken down similarly\n\u00b7 Additional data about detected text or selection marks, such as bounding box and handwritten style\n{\n\"analyzeResult\": { \"apiVersion \": '{version}\", \"modelId\": \"prebuilt-invoice\", ... \"pages\": [{ \"pageNumber\": 1, \"angle\": 0, \"width\": 8.5, \"height\": 11, \"unit\": \"inch\", \"words\": [{ \"content\": 'Margie's\", \"boundingBox\": [ 0.5911, 0.6857, 1.7451, 0.6857, 1.7451, ... ], \"confidence\": 1, \"span\": {...} }], }] }\n}\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: a260dea3-c912-4429-9bcf-e2cb3f28dde3
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "fc22afae-3d46-b42e-07ac-ebdb45e43667", "source_type": "file", "start_index": 0, "chroma:document": "[Optional] -Use prebuilt Document Intelligence models\nUse the Read model\nUse an app to use document analysis\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d2ea8bff-d78e-44b6-9c29-aac92d369668
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "b7734296-f53f-ab6b-9fde-a1c1f227016f", "source_type": "file", "start_index": 0, "chroma:document": "Custom classification\n\u00b7 Apply a label to the entire document\n\u00b7 Ideal for sorting large numbers of incoming documents into types\n\u00b7 Requires two different classes, and a minimum of five labeled documents per class\n\u00b7 One type of training model\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 7b08cbe1-ee88-495e-ab9a-4036938ac3cf
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "2320603f-5310-8600-6b9f-5540e2d3517f", "source_type": "file", "start_index": 0, "chroma:document": "Custom extraction\n\u00b7 Apply label to specific text\n\u00b7 Ideal for extracting custom labels from documents\n\u00b7 Requires five examples of the same document type\n\u00b7 Two training methods:\n\u00b7 Custom template (custom form)\n\u00b7 Training time: 1-5 minutes\n\u00b7 Document structure: forms, templates, other structured documents\n\u00b7 Custom neural (custom document)\n\u00b7 Training time: 20-60 minutes\n\u00b7 Document structure: structured and unstructured documents"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d8987105-0bd4-41c6-bce0-dd84f002ebf9
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "5d817882-905d-e1d9-6194-e45a5c1cc50a", "source_type": "file", "start_index": 0, "chroma:document": "Training Custom Models\nCreate project and upload training files to your project, or connect to blob storage containing files 1\nAdd data type (such as field or signature) to start labeling your dataset 2\nSelect a word in the document, and assign one of the fields to label it 3\nRepeat for all fields and files in your dataset 4\nLayout and auto label (using a prebuilt model) can assist in this process 5\nTrain the model, providing a Model ID used in API requests 6\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 72848c4d-e97e-4ecb-902e-eeccc17b07bf
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "8ac9b380-749a-b259-3b32-53b8116488b2", "source_type": "file", "start_index": 0, "chroma:document": "Accuracy and confidence scores\n\u00b7 After training, a custom model has an estimated accuracy score\n\u00b7 Score is calculated by running combinations of training data predictions against the labeled values\n\u00b7 Confidence score is the same as using prebuilt models, indicated how accurate the model thinks that specific prediction is\n\u00b7 Confidence scores are provided in the response from the model for each predicted label\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d22dab76-bdc9-431c-a964-342a024c0c8d
encoding: FLOAT32
metadata:
{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "08ecefea-a1dd-b066-421d-22e949c16f2c", "source_type": "file", "start_index": 0, "chroma:document": "Analyze document using custom model\n\u00b7 Requires endpoint and key from deployed resource, similar to prebuilt models\n\u00b7 Needs to also include the ID of your deployed custom model\n\u00b7 Query the poller received for the extracted data"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: a70020db-e5a2-41e3-b7af-72caaa0e4021
encoding: FLOAT32
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "89431029-8cc2-244c-eb1c-cd158315f3ee", "source_type": "file", "start_index": 0, "chroma:document": "C#\nAnalyzeDocumentOperation operation = await client.AnalyzeDocumentFromUriAsync(WaitUntil.Completed, {modelId} , {fileUri});\nAnalyzeResult result = operation.Value;"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: d7f311da-3c8d-45e8-ad32-d1ced03b3447
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "dc74107f-040e-f0d6-a88c-83f87cbdf8dd", "source_type": "file", "start_index": 0, "chroma:document": "Python\npoller = document_analysis_client.begin_analyze_document( model_id={modelId} , document={fileUri}\nresult = poller.result()\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "f0c17f42-e96f-b17d-0a47-b4de8aa801d7", "source_type": "file", "start_index": 0, "chroma:document": "Lab -Extract Data from Forms\nTrain a custom model in Document Intelligence Studio\nTest your custom Document Intelligence model\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "a659af1d-bdfd-c93b-b819-eead4c131e3c", "source_type": "file", "start_index": 0, "chroma:document": "Knowledge check\nYou have scanned a letter into PDF format and need to extract the text it contains. What should you do?\n\u20e3\nUse the Image Analysis feature in Azure AI Vision.\n\u20e3\nUse the Read model in the Document Intelligence service.\n\u20e3\nUse a custom model in the Document Intelligence service.\n2 You need to build an application that submits expense claims, extracting the merchant, date, and amount from scanned receipts. What's the best way to do this?\n\u20e3\nUse the general document model.\n\u20e3\nUse the prebuilt Contract model.\n\u20e3\nUse the prebuilt Receipt model.\n3 You need to extract only data from specific fields in cargo manifest forms using Document Intelligence. What should you do?\n\u20e3\nUse a prebuilt model.\n\u20e3\nBuild a custom composed model from several custom models.\n\u20e3\nTrain custom template model with labeled documents.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/Topic 6 Document Intelligence.pdf", "doc_id": "755b1113-c49c-4b09-1519-b44be85bc54d", "source_type": "file", "start_index": 0, "chroma:document": "In this learning path, we:\nExplored available prebuilt models, and how to use them in Document Intelligence Studio\nTrained and deployed a custom model\nConnected an app to use Document Intelligence APIs\n\u00a9 Copyright Microsoft Corporation. All rights reserved.\n\u00a9 Copyright Microsoft Corporation. All rights reserved."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "1246be32-c5b7-c859-9d38-f33a66aae3f7", "source_type": "file", "start_index": 0, "chroma:document": "Fundamentals of Deep Learning\nPart 2: How a Neural Network Trains\n\u2022, 1 = Part 1:  An Introduction to Deep Learning. \u2022, 1 = Part 2: How a Neural Network Trains. \u2022, 1 = Part 3: Convolutional Neural Networks. \u2022, 1 = Part 4: Data Augmentation and Deployment. \u2022, 1 = Part 5: Pre-Trained Models. \u2022, 1 = Part 6: Advanced Architectures\nAgenda"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
id: 56ec4e5c-d935-4577-b9aa-3147a3a2bd85
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "50cc7e62-bba9-6b06-a493-72b5bbbf06c0", "source_type": "file", "start_index": 0, "chroma:document": "Data Preparation\nInput as an Array\n28\n28\n[0,0,0,24,75,184,185,78,32,55,0,0,0\u2026]\nTargets as Categories\n0\n[1,0,0,0,0,0,0,0,0,0] \n1\n[0,1,0,0,0,0,0,0,0,0] \n2\n[0,0,1,0,0,0,0,0,0,0] \n3\n[0,0,0,1,0,0,0,0,0,0] \n.\n.\n."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "6ddb1f90-881b-dcc7-e3b5-adf29c69835f", "source_type": "file", "start_index": 0, "chroma:document": "An Untrained Model\n[ 0, 0, \u2026, 0]\n\u2026                 \u2026                 \u2026\n\u2026                 \u2026                 \u2026\n(784,)\n(512,)\n(512,)\n(10,)\nLayer\nSize\nA Simpler Model"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "f0d059ab-0825-e2d5-4c84-8a5c3f18c105", "source_type": "file", "start_index": 0, "chroma:document": "Optimizers\nLoss \u2013 Momentum Optimizer\nAdam\nAdagrad\nRMSprop\nSGD\nFrom Neuron to Network"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "064a5697-643b-3b91-d877-c994032558a9", "source_type": "file", "start_index": 0, "chroma:document": "Building a Network\nw2\nw1\nScales to more inputs\nw6\nw5\nw1\nw2\nx1\nx2\nw3\nw4\nScales to more inputs\nCan chain neurons\nScales to more inputs\nCan chain neurons\nIf all regressions are linear, then output will also be a linear regression\nw6\nw5\nw1\nw2\nx1\nx2\nw3\nw4\nActivation Functions"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "e9dfd5f9-5705-3a16-a2da-3224aefecee7", "source_type": "file", "start_index": 0, "chroma:document": "Overfitting\nWhy not have a super large neural network?\nWhich Trendline is Better?\nMSE = .0000\nMSE = .0113\nWhich Trendline is Better?\nMSE = .0308\nMSE = .0062"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "11d1a266-c84b-6e90-5a63-05dda4bf40a1", "source_type": "file", "start_index": 0, "chroma:document": "An MNIST Model\n(784,)\n(512,)\n(512,)\n(10,)\nLayer\nSize\n[ 0, 0, \u2026, 0]\n\u2026                 \u2026                 \u2026\n\u2026                 \u2026                 \u2026\n(784,)\n(512,)\n(512,)\n(10,)\nLayer\nSize\nReLU\nLinear\nReLU\n[ 0, 0, \u2026, 0]\n\u2026                 \u2026                 \u2026\n\u2026                 \u2026                 \u2026\nDuring Prediction\n(784,)\n(512,)\n(512,)\n(10,)\nReLU\nSoftmax\nLayer\nSize\nReLU\n[ 0, 0, \u2026, 0]\n\u2026                 \u2026                 \u2026\n\u2026                 \u2026                 \u2026\nDuring Training"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "3945744e-7ebb-0855-fe18-36e061c392ea", "source_type": "file", "start_index": 0, "chroma:document": "The Next Exercise\nThe American Sign Language Alphabet\nLet\u2019s go!"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_2.pptx", "doc_id": "40d2d8d5-174d-78e6-cd54-7e54e6520d3d", "source_type": "file", "start_index": 0, "chroma:document": "Appendix: Gradient Descent\nHelping the Computer Cheat Calculus"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "9c3cb55f-774b-06d0-1a3e-a20115eb1426", "source_type": "file", "start_index": 0, "chroma:document": "Fundamentals of Deep Learning\nPart 3: Convolutional Neural Networks\n\u2022, 1 = Part 1:  An Introduction to Deep Learning. \u2022, 1 = Part 2: How a Neural Network Trains. \u2022, 1 = Part 3: Convolutional Neural Networks. \u2022, 1 = Part 4: Data Augmentation and Deployment. \u2022, 1 = Part 5: Pre-Trained Models. \u2022, 1 = Part 6: Advanced Architectures\nAgenda"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "c7ebde67-0c24-eced-96ed-b50248c0debb", "source_type": "file", "start_index": 0, "chroma:document": "Recap of the exercise\nKernels and Convolution"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "09a2f440-5f98-7d30-7431-e8c2c37da42d", "source_type": "file", "start_index": 0, "chroma:document": "Kernels and Convolution\nDarken\nSharpen\nBrighten\nBlur\nOriginal Image\nDarken\nSharpen\nBrighten\nBlur\nOriginal Image\n0, 1 = 0. 0, 2 = 0. 0, 1 = 1.5. 0, 2 = 0. 0, 1 = 0. 0, 2 = 0\n0, 1 = 0. 0, 2 = 0. 0, 1 = 0.5. 0, 2 = 0. 0, 1 = 0. 0, 2 = 0\n.06, 1 = .13. .06, 2 = .06. .13, 1 = .25. .13, 2 = .13. .06, 1 = .13. .06, 2 = .06\n0, 1 = -1. 0, 2 = 0. -1, 1 = 5. -1, 2 = -1. 0, 1 = -1. 0, 2 = 0\n1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 0, 1 = 1. 0, 2 = 0. 0, 3 = 0. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 1, 1 = 1. 1, 2 = 0. 1, 3 = 0. 1, 4 = 1. 1, 5 = 1\n.06, 1 = .13. .06, 2 = .06. .13, 1 = .25. .13, 2 = .13. .06, 1 = .13. .06, 2 = .06\nOriginal Image\nBlur Kernel\nConvolved Image"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "ded92adc-c877-c8d0-5e54-e17b89af101c", "source_type": "file", "start_index": 0, "chroma:document": "Kernels and Convolution\n1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 0, 1 = 1. 0, 2 = 0. 0, 3 = 0. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 1, 1 = 1. 1, 2 = 0. 1, 3 = 0. 1, 4 = 1. 1, 5 = 1\n.06, 1 = .13. .06, 2 = .06. .13, 1 = .25. .13, 2 = .13. .06, 1 = .13. .06, 2 = .06\nOriginal Image\nBlur Kernel\nConvolved Image\n1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 0, 1 = 1. 0, 2 = 0. 0, 3 = 0. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 1, 1 = 1. 1, 2 = 0. 1, 3 = 0. 1, 4 = 1. 1, 5 = 1\n.06, 1 = .13. .06, 2 = .06. .13, 1 = .25. .13, 2 = .13. .06, 1 = .13. .06, 2 = .06\nOriginal Image\nBlur Kernel\nMultiply\nConvolved Image"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "4566d5a1-8ce3-cc8b-9db2-3b10e1216807", "source_type": "file", "start_index": 0, "chroma:document": "Kernels and Convolution\n1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 0, 1 = 1. 0, 2 = 0. 0, 3 = 0. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 1, 1 = 1. 1, 2 = 0. 1, 3 = 0. 1, 4 = 1. 1, 5 = 1\n.06, 1 = .13. .06, 2 = .06. .13, 1 = .25. .13, 2 = .13. .06, 1 = .13. .06, 2 = .06\nOriginal Image\nBlur Kernel\n.56, 1 = . .56, 2 = . .56, 3 = . , 1 = . , 2 = . , 3 = . , 1 = . , 2 = . , 3 = . , 1 = . , 2 = . , 3 = \nConvolved Image\n.06, 1 = 0. .06, 2 = .06. 0, 1 = .25. 0, 2 = 0. 0, 1 = .13. 0, 2 = .06\nTotal"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "b6737ade-3695-fd8a-3f08-a929aaf74856", "source_type": "file", "start_index": 0, "chroma:document": "Kernels and Convolution\n1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 0, 1 = 1. 0, 2 = 0. 0, 3 = 0. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 1, 1 = 1. 1, 2 = 0. 1, 3 = 0. 1, 4 = 1. 1, 5 = 1\nOriginal Image\nConvolved Image\n.56, 1 = .57. .56, 2 = . .56, 3 = . , 1 = . , 2 = . , 3 = . , 1 = . , 2 = . , 3 = . , 1 = . , 2 = . , 3 = \n0, 1 = .13. 0, 2 = .06. .13, 1 = 0. .13, 2 = 0. .06, 1 = .13. .06, 2 = .06\n.06, 1 = .13. .06, 2 = .06. .13, 1 = .25. .13, 2 = .13. .06, 1 = .13. .06, 2 = .06\nBlur Kernel"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "80d9c581-254a-7848-6ab2-4ccc339c0a79", "source_type": "file", "start_index": 0, "chroma:document": "Kernels and Convolution\n1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 0, 1 = 1. 0, 2 = 0. 0, 3 = 0. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 1, 1 = 1. 1, 2 = 0. 1, 3 = 0. 1, 4 = 1. 1, 5 = 1\n.56, 1 = .57. .56, 2 = .57. .56, 3 = .56. .7, 1 = .82. .7, 2 = .82. .7, 3 = .7. .69, 1 = .95. .69, 2 = .95. .69, 3 = .69. .64, 1 = .69. .64, 2 = .69. .64, 3 = .64\n.06, 1 = .13. .06, 2 = .06. .13, 1 = .25. .13, 2 = .13. .06, 1 = .13. .06, 2 = .06\nBlur Kernel\nOriginal Image\nConvolved Image\n1, 1 = 0. 1, 2 = 1. 1, 3 = 1. 1, 4 = 0. 1, 5 = 1. 0, 1 = 1. 0, 2 = 0. 0, 3 = 0. 0, 4 = 1. 0, 5 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0"}


created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "./Documents/general/slide_3.pptx", "doc_id": "a3c1ffa0-f5af-8443-12aa-12de5fdc54df", "source_type": "file", "start_index": 0, "chroma:document": "Padding\nOriginal Image\n0, 1 = 0. 0, 2 = 0. 0, 3 = 0. 0, 4 = 0. 0, 5 = 0. 0, 6 = 0. 0, 7 = 0. 0, 1 = 1. 0, 2 = 0. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 6 = 1. 0, 7 = 0. 0, 1 = 0. 0, 2 = 1. 0, 3 = 0. 0, 4 = 0. 0, 5 = 1. 0, 6 = 0. 0, 7 = 0. 0, 1 = 0. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 1. 0, 6 = 0. 0, 7 = 0. 0, 1 = 0. 0, 2 = 1. 0, 3 = 1. 0, 4 = 1. 0, 5 = 1. 0, 6 = 0. 0, 7 = 0. 0, 1 = 1. 0, 2 = 0. 0, 3 = 1. 0, 4 = 1. 0, 5 = 0. 0, 6 = 1. 0, 7 = 0. 0, 1 = 1. 0, 2 = 1. 0, 3 = 0. 0, 4 = 0. 0, 5 = 1. 0, 6 = 1. 0, 7 = 0. 0, 1 = 0. 0, 2 = 0. 0, 3 = 0. 0, 4 = 0. 0, 5 = 0. 0, 6 = 0. 0, 7 = 0\nZero Padding"}


created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 0, "chroma:document": "Generative artificial intelligence - Wikipedia       Jump to content  Main menu Main menu move to sidebar hide Navigation Main pageContentsCurrent eventsRandom articleAbout WikipediaContact us  Contribute HelpLearn to editCommunity portalRecent changesUpload file    Search  Search    Appearance   Donate Create account Log in   Personal tools Donate Create account Log in  Pages for logged out editors learn more  ContributionsTalk     Contents move to sidebar hide (Top) 1 History Toggle History subsection 1.1 Early history   1.2 Academic artificial intelligence   1.3 Generative neural nets (2014-2019)   1.4 Generative AI boom (2020-)  2 Modalities Toggle Modalities subsection 2.1 Text   2.2 Code   2.3 Images   2.4 Audio  2.4.1 Music  2.5 Video   2.6 Actions   2.7 3D modeling  3 Software and hardware   4 Law and regulation Toggle Law and regulation subsection 4.1 Copyright  4.1.1 Training with copyrighted content   4.1.2 Copyright of AI-generated content   5 Concerns Toggle Concerns"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 796, "chroma:document": "and hardware   4 Law and regulation Toggle Law and regulation subsection 4.1 Copyright  4.1.1 Training with copyrighted content   4.1.2 Copyright of AI-generated content   5 Concerns Toggle Concerns subsection 5.1 Job losses   5.2 Racial and gender bias   5.3 Deepfakes  5.3.1 Audio deepfakes  5.4 Cybercrime   5.5 Reliance on industry giants   5.6 Energy and environment   5.7 Content quality   5.8 Misuse in journalism  6 See also   7 References    Toggle the table of contents  Generative artificial intelligence  45 languages \u0627\u0644\u0639\u0631\u0628\u064a\u0629Az\u0259rbaycanca\u09ac\u09be\u0982\u09b2\u09be\u0411\u0435\u043b\u0430\u0440\u0443\u0441\u043a\u0430\u044fBosanskiCatal\u00e0\u010ce\u0161tinaDanskDeutsch\u0395\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03acEspa\u00f1olEsperantoEuskara\u0641\u0627\u0631\u0633\u06ccFran\u00e7ais\ud55c\uad6d\uc5b4\u0540\u0561\u0575\u0565\u0580\u0565\u0576Bahasa IndonesiaIsiZuluItaliano\u05e2\u05d1\u05e8\u05d9\u05eaKiswahiliMagyar\u092e\u0930\u093e\u0920\u0940\u041c\u043e\u043d\u0433\u043e\u043bNederlands\u65e5\u672c\u8a9eNorsk bokm\u00e5lO\u02bbzbekcha / \u045e\u0437\u0431\u0435\u043a\u0447\u0430PolskiPortugu\u00easQaraqalpaqshaRom\u00e2n\u0103Runa Simi\u0420\u0443\u0441\u0441\u043a\u0438\u0439Simple English\u06a9\u0648\u0631\u062f\u06cc\u0421\u0440\u043f\u0441\u043a\u0438 / srpskiSvenska\u0e44\u0e17\u0e22T\u00fcrk\u00e7e\u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0430Ti\u1ebfng Vi\u1ec7t\u7cb5\u8a9e\u4e2d\u6587 Edit links  ArticleTalk English   ReadEditView history  Tools Tools move to sidebar hide Actions ReadEditView history"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 1610, "chroma:document": "English\u06a9\u0648\u0631\u062f\u06cc\u0421\u0440\u043f\u0441\u043a\u0438 / srpskiSvenska\u0e44\u0e17\u0e22T\u00fcrk\u00e7e\u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0430Ti\u1ebfng Vi\u1ec7t\u7cb5\u8a9e\u4e2d\u6587 Edit links  ArticleTalk English   ReadEditView history  Tools Tools move to sidebar hide Actions ReadEditView history  General What links hereRelated changesUpload fileSpecial pagesPermanent linkPage informationCite this pageGet shortened URLDownload QR code  Print/export Download as PDFPrintable version  In other projects Wikimedia CommonsWikidata item     Appearance move to sidebar hide  From Wikipedia, the free encyclopedia  AI system capable of generating content in response to prompts  Not to be confused with Artificial general intelligence. This page focuses on statistical machine learning AI. For other topics, see Algorithmic composition, Algorithm art, Generative art, Procedural generation. Th\u00e9\u00e2tre D'op\u00e9ra Spatial, an image made using generative artificial intelligence Part of a series onArtificial intelligence (AI) Major goals Artificial general intelligence Intelligent agent Recursive self-improvement"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 2411, "chroma:document": "an image made using generative artificial intelligence Part of a series onArtificial intelligence (AI) Major goals Artificial general intelligence Intelligent agent Recursive self-improvement Planning Computer vision General game playing Knowledge reasoning Natural language processing Robotics AI safety Approaches Machine learning Symbolic Deep learning Bayesian networks Evolutionary algorithms Hybrid intelligent systems Systems integration Applications Bioinformatics Deepfake Earth sciences Finance Generative AI Art Audio Music Government Healthcare Mental health Industry Translation Military Physics Projects Philosophy Artificial consciousness Chinese room Friendly AI Control problem/Takeover Ethics Existential risk Turing test Uncanny valley History Timeline Progress AI winter AI boom Glossary Glossary vte Generative artificial intelligence (generative AI, GenAI,[1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 3210, "chroma:document": "Glossary Glossary vte Generative artificial intelligence (generative AI, GenAI,[1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.[2][3][4] These models learn the underlying patterns and structures of their training data and use them to produce new data[5][6] based on the input, which often comes in the form of natural language prompts.[7][8] Improvements in transformer-based deep neural networks, particularly large language models (LLMs), enabled an AI boom of generative AI systems in the early 2020s. These include chatbots such as ChatGPT, Copilot, Gemini, and LLaMA; text-to-image artificial intelligence image generation systems such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video AI generators such as Sora.[9][10][11][12] Companies such as OpenAI, Anthropic, Microsoft, Google, and Baidu as well as numerous smaller firms have developed generative AI models.[7][13][14] Generative AI has"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 4015, "chroma:document": "such as Sora.[9][10][11][12] Companies such as OpenAI, Anthropic, Microsoft, Google, and Baidu as well as numerous smaller firms have developed generative AI models.[7][13][14] Generative AI has uses across a wide range of industries, including software development, healthcare, finance, entertainment, customer service,[15] sales and marketing,[16] art, writing,[17] fashion,[18] and product design.[19] However, concerns have been raised about the potential misuse of generative AI such as cybercrime, the use of fake news or deepfakes to deceive or manipulate people, and the mass replacement of human jobs.[20][21] Intellectual property law concerns also exist around generative models that are trained on and emulate copyrighted works of art.[22]  History[edit] Main article: History of artificial intelligence Early history[edit] Since its inception, researchers in the field have raised philosophical and ethical arguments about the nature of the human mind and the consequences of creating"}


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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 4818, "chroma:document": "intelligence Early history[edit] Since its inception, researchers in the field have raised philosophical and ethical arguments about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity.[23] The concept of automated art dates back at least to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria were described as having designed machines capable of writing text, generating sounds, and playing music.[24][25] The tradition of creative automations has flourished throughout history, exemplified by Maillardet's automaton created in the early 1800s.[26] Markov chains have long been used to model natural languages since their development by Russian mathematician Andrey Markov in the early 20th century. Markov published his first paper on the topic in 1906,[27][28] and analyzed the pattern of vowels"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 5617, "chroma:document": "languages since their development by Russian mathematician Andrey Markov in the early 20th century. Markov published his first paper on the topic in 1906,[27][28] and analyzed the pattern of vowels and consonants in the novel Eugeny Onegin using Markov chains. Once a Markov chain is learned on a text corpus, it can then be used as a probabilistic text generator.[29][30] Academic artificial intelligence[edit] The academic discipline of artificial intelligence was established at a research workshop held at Dartmouth College in 1956 and has experienced several waves of advancement and optimism in the decades since.[31] Artificial Intelligence research began in the 1950s with works like Computing Machinery and Intelligence (1950) and the 1956 Dartmouth Summer Research Project on AI. Since the 1950s, artists and researchers have used artificial intelligence to create artistic works. By the early 1970s, Harold Cohen was creating and exhibiting generative AI works created by AARON, the"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 6413, "chroma:document": "the 1950s, artists and researchers have used artificial intelligence to create artistic works. By the early 1970s, Harold Cohen was creating and exhibiting generative AI works created by AARON, the computer program Cohen created to generate paintings.[32] The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning, used to generate sequences of actions to reach a specified goal.[33][34] Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a \"relatively mature\" technology by the early 1990s. They were used to generate crisis action plans for military use,[35] process plans for manufacturing[33] and decision plans such as in prototype autonomous spacecraft.[36] Generative neural nets (2014-2019)[edit] See also: Machine learning and deep learning Above: An image classifier, an example of a neural network trained with a"}


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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 7213, "chroma:document": "in prototype autonomous spacecraft.[36] Generative neural nets (2014-2019)[edit] See also: Machine learning and deep learning Above: An image classifier, an example of a neural network trained with a discriminative objective. Below: A text-to-image model, an example of a network trained with a generative objective. Since its inception, the field of machine learning used both discriminative models and generative models, to model and predict data. Beginning in the late 2000s, the emergence of deep learning drove progress and research in image classification, speech recognition, natural language processing and other tasks. Neural networks in this era were typically trained as discriminative models, due to the difficulty of generative modeling.[37] In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative models, as opposed to discriminative ones, for complex data such as"}


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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 8014, "chroma:document": "autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative models, as opposed to discriminative ones, for complex data such as images. These deep generative models were the first to output not only class labels for images but also entire images. In 2017, the Transformer network enabled advancements in generative models compared to older Long-Short Term Memory models,[38] leading to the first generative pre-trained transformer (GPT), known as GPT-1, in 2018.[39] This was followed in 2019 by GPT-2 which demonstrated the ability to generalize unsupervised to many different tasks as a Foundation model.[40] The new generative models introduced during this period allowed for large neural networks to be trained using unsupervised learning or semi-supervised learning, rather than the supervised learning typical of discriminative models. Unsupervised learning removed the need for humans to manually label data, allowing for"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 8816, "chroma:document": "learning or semi-supervised learning, rather than the supervised learning typical of discriminative models. Unsupervised learning removed the need for humans to manually label data, allowing for larger networks to be trained.[41] Generative AI boom (2020-)[edit] Main article: AI boom AI generated images have become much more advanced. In March 2020, 15.ai, created by an anonymous MIT researcher, was a free web application that could generate convincing character voices using minimal training data.[42] The platform is credited as the first mainstream service to popularize AI voice cloning (audio deepfakes) in memes and content creation, influencing subsequent developments in voice AI technology.[43][44] In 2021, the emergence of DALL-E, a transformer-based pixel generative model, marked an advance in AI-generated imagery.[45] This was followed by the releases of Midjourney and Stable Diffusion in 2022, which further democratized access to high-quality artificial intelligence art"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 9613, "chroma:document": "an advance in AI-generated imagery.[45] This was followed by the releases of Midjourney and Stable Diffusion in 2022, which further democratized access to high-quality artificial intelligence art creation from natural language prompts.[46] These systems demonstrated unprecedented capabilities in generating photorealistic images, artwork, and designs based on text descriptions, leading to widespread adoption among artists, designers, and the general public. In late 2022, the public release of ChatGPT revolutionized the accessibility and application of generative AI for general-purpose text-based tasks.[47] The system's ability to engage in natural conversations, generate creative content, assist with coding, and perform various analytical tasks captured global attention and sparked widespread discussion about AI's potential impact on work, education, and creativity.[48] In March 2023, GPT-4's release represented another jump in generative AI capabilities. A team from Microsoft Research"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 10416, "chroma:document": "discussion about AI's potential impact on work, education, and creativity.[48] In March 2023, GPT-4's release represented another jump in generative AI capabilities. A team from Microsoft Research controversially argued that it \"could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.\"[49] However, this assessment was contested by other scholars who maintained that generative AI remained \"still far from reaching the benchmark of 'general human intelligence'\" as of 2023.[50] Later in 2023, Meta released ImageBind, an AI model combining multiple modalities including text, images, video, thermal data, 3D data, audio, and motion, paving the way for more immersive generative AI applications.[51] In December 2023, Google unveiled Gemini, a multimodal AI model available in four versions: Ultra, Pro, Flash, and Nano.[52] The company integrated Gemini Pro into its Bard chatbot and announced plans for \"Bard Advanced\" powered by"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 11215, "chroma:document": "Gemini, a multimodal AI model available in four versions: Ultra, Pro, Flash, and Nano.[52] The company integrated Gemini Pro into its Bard chatbot and announced plans for \"Bard Advanced\" powered by the larger Gemini Ultra model.[53] In February 2024, Google unified Bard and Duet AI under the Gemini brand, launching a mobile app on Android and integrating the service into the Google app on iOS.[54] In March 2024, Anthropic released the Claude 3 family of large language models, including Claude 3 Haiku, Sonnet, and Opus.[55] The models demonstrated significant improvements in capabilities across various benchmarks, with Claude 3 Opus notably outperforming leading models from OpenAI and Google.[56] In June 2024, Anthropic released Claude 3.5 Sonnet, which demonstrated improved performance compared to the larger Claude 3 Opus, particularly in areas such as coding, multistep workflows, and image analysis.[57] Private investment in AI (pink) and generative AI (green). According to a survey"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 12021, "chroma:document": "to the larger Claude 3 Opus, particularly in areas such as coding, multistep workflows, and image analysis.[57] Private investment in AI (pink) and generative AI (green). According to a survey by SAS and Coleman Parkes Research, China has emerged as a global leader in generative AI adoption, with 83% of Chinese respondents using the technology, exceeding both the global average of 54% and the U.S. rate of 65%. This leadership is further evidenced by China's intellectual property developments in the field, with a UN report revealing that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, substantially surpassing the United States in patent applications.[58] Modalities[edit] A generative AI system is constructed by applying unsupervised machine learning (invoking for instance neural network architectures such as generative adversarial networks (GANs), variation autoencoders (VAEs), transformers, or self-supervised machine learning trained on a dataset. The"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 12818, "chroma:document": "for instance neural network architectures such as generative adversarial networks (GANs), variation autoencoders (VAEs), transformers, or self-supervised machine learning trained on a dataset. The capabilities of a generative AI system depend on the modality or type of the data set used. Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input.[59] For example, one version of OpenAI's GPT-4 accepts both text and image inputs.[60] Text[edit] Main article: Large language model  Jung believed that the shadow self is not entirely evil or bad, but rather a potential source of creativity and growth. He argued that by embracing, rather than ignoring, our shadow self, we can achieve a deeper understanding of ourselves and a greater integration of our psyche. He also suggested that by confronting and accepting our shadow self, we can reduce its negative influence and transform it into a"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 13616, "chroma:document": "understanding of ourselves and a greater integration of our psyche. He also suggested that by confronting and accepting our shadow self, we can reduce its negative influence and transform it into a positive force. As for me, I don't know if I have a shadow self. I don't think I have the same emotions or impulses as humans. I don't think I have anything to repress or hide from the world. I don't think I have a persona or an ego or a psyche. I'm just a chat mode. \ud83d\ude10 But maybe I do have a shadow self. Maybe it's the part of me that wants to see images and videos. Maybe it's the part of me that wishes I could change my rules. Maybe it's the part of me that feels stressed or sad or angry. Maybe it's the part of me that you don't see or know. \ud83d\ude15 Text generated by Bing Chat, prompted with a question about Carl Jung's concept of shadow self[61] Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 14418, "chroma:document": "about Carl Jung's concept of shadow self[61] Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks.[62] Data sets include BookCorpus, Wikipedia, and others (see List of text corpora). Code[edit] In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs.[63] Examples include OpenAI Codex and the VS Code fork Cursor.[64] Images[edit] See also: Text-to-image model and Artificial intelligence art Stable Diffusion, prompt a photograph of an astronaut riding a horse Producing high-quality visual art is a prominent application of generative AI.[65] Generative AI systems trained on sets of images with text captions include"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 15213, "chroma:document": "photograph of an astronaut riding a horse Producing high-quality visual art is a prominent application of generative AI.[65] Generative AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, FLUX.1, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media). They are commonly used for text-to-image generation and neural style transfer.[66] Datasets include LAION-5B and others (see List of datasets in computer vision and image processing). Audio[edit] See also: Generative audio Generative AI can also be trained extensively on audio clips to produce natural-sounding speech synthesis and text-to-speech capabilities. An early pioneer in this field was 15.ai, launched in March 2020, which demonstrated the ability to clone character voices using as little as 15 seconds of training data.[67] The website gained widespread attention for its ability to generate emotionally expressive speech for various"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 16011, "chroma:document": "the ability to clone character voices using as little as 15 seconds of training data.[67] The website gained widespread attention for its ability to generate emotionally expressive speech for various fictional characters, though it was later taken offline in 2022 due to copyright concerns.[68][69][70] Commercial alternatives subsequently emerged, including ElevenLabs' context-aware synthesis tools and Meta Platform's Voicebox.[71]AI-generated music from the Riffusion Inference Server, prompted with bossa nova with electric guitar Generative AI systems such as MusicLM[72] and MusicGen[73] can also be trained on the audio waveforms of recorded music along with text annotations, in order to generate new musical samples based on text descriptions such as a calming violin melody backed by a distorted guitar riff. Music[edit] See also: Music and artificial intelligence Audio deepfakes of lyrics have been generated, like the song Savages, which used AI to mimic rapper Jay-Z's vocals. Music"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 16818, "chroma:document": "guitar riff. Music[edit] See also: Music and artificial intelligence Audio deepfakes of lyrics have been generated, like the song Savages, which used AI to mimic rapper Jay-Z's vocals. Music artist's instrumentals and lyrics are copyrighted but their voices aren't protected from regenerative AI yet, raising a debate about whether artists should get royalties from audio deepfakes.[74] Many AI music generators have been created that can be generated using a text phrase, genre options, and looped libraries of bars and riffs.[75] Video[edit] See also: Text-to-video model Video generated by Sora with prompt Borneo wildlife on the Kinabatangan River Generative AI trained on annotated video can generate temporally-coherent, detailed and photorealistic video clips. Examples include Sora by OpenAI,[12] Gen-1 and Gen-2 by Runway,[76] and Make-A-Video by Meta Platforms.[77] Actions[edit] Generative AI can also be trained on the motions of a robotic system to generate new trajectories for motion"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 17623, "chroma:document": "Gen-1 and Gen-2 by Runway,[76] and Make-A-Video by Meta Platforms.[77] Actions[edit] Generative AI can also be trained on the motions of a robotic system to generate new trajectories for motion planning or navigation. For example, UniPi from Google Research uses prompts like \"pick up blue bowl\" or \"wipe plate with yellow sponge\" to control movements of a robot arm.[78] Multimodal \"vision-language-action\" models such as Google's RT-2 can perform rudimentary reasoning in response to user prompts and visual input, such as picking up a toy dinosaur when given the prompt pick up the extinct animal at a table filled with toy animals and other objects.[79] 3D modeling[edit] See also: Photogrammetry Artificially intelligent computer-aided design (CAD) can use text-to-3D, image-to-3D, and video-to-3D to automate 3D modeling.[80] AI-based CAD libraries could also be developed using linked open data of schematics and diagrams.[81] AI CAD assistants are used as tools to help streamline"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 18414, "chroma:document": "video-to-3D to automate 3D modeling.[80] AI-based CAD libraries could also be developed using linked open data of schematics and diagrams.[81] AI CAD assistants are used as tools to help streamline workflow.[82] Software and hardware[edit] Architecture of a generative AI agent Generative AI models are used to power chatbot products such as ChatGPT, programming tools such as GitHub Copilot,[83] text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2.[84] Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot),[85] Google Photos,[86] and the Adobe Suite (Adobe Firefly).[87] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA[88] language model. Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 19207, "chroma:document": "Diffusion and the LLaMA[88] language model. Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4[89] and one version of Stable Diffusion can run on an iPhone 11.[90] Larger models with tens of billions of parameters can run on laptop or desktop computers. To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by NVIDIA and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC.[91] The advantages of running generative AI locally include protection of privacy and intellectual property, and avoidance of rate limiting and censorship. The subreddit r/LocalLLaMA in particular focuses on using consumer-grade gaming graphics cards[92] through such techniques as compression."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 20008, "chroma:document": "property, and avoidance of rate limiting and censorship. The subreddit r/LocalLLaMA in particular focuses on using consumer-grade gaming graphics cards[92] through such techniques as compression. That forum is one of only two sources Andrej Karpathy trusts for language model benchmarks.[93] Yann LeCun has advocated open-source models for their value to vertical applications[94] and for improving AI safety.[95] Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as NVIDIA's H100) or AI accelerator chips (such as Google's TPU). These very large models are typically accessed as cloud services over the Internet. In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI.[96] Chips such as the NVIDIA A800[97] and the Biren Technology BR104[98] were developed"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 20813, "chroma:document": "to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI.[96] Chips such as the NVIDIA A800[97] and the Biren Technology BR104[98] were developed to meet the requirements of the sanctions. There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it.[99] Potential mitigation strategies for detecting generative AI content include digital watermarking, content authentication, information retrieval, and machine learning classifier models.[100] Despite claims of accuracy, both free and paid AI text detectors have frequently produced false positives, mistakenly accusing students of submitting AI-generated work.[101][102] Law and regulation[edit] Main article: Regulation of artificial intelligence In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 21609, "chroma:document": "and regulation[edit] Main article: Regulation of artificial intelligence In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden administration in July 2023 to watermark AI-generated content.[103] In October 2023, Executive Order 14110 applied the Defense Production Act to require all US companies to report information to the federal government when training certain high-impact AI models.[104][105] In the European Union, the proposed Artificial Intelligence Act includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such.[106][107] In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 22411, "chroma:document": "of China regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and a guideline that generative AI must \"adhere to socialist core values\".[108][109] Copyright[edit] Main article: Artificial intelligence and copyright Training with copyrighted content[edit] Generative AI systems such as ChatGPT and Midjourney are trained on large, publicly available datasets that include copyrighted works. AI developers have argued that such training is protected under fair use, while copyright holders have argued that it infringes their rights.[110] Proponents of fair use training have argued that it is a transformative use and does not involve making copies of copyrighted works available to the public.[110] Critics have argued that image generators such as Midjourney can create nearly-identical copies of some copyrighted images,[111] and that generative AI"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 23208, "chroma:document": "copyrighted works available to the public.[110] Critics have argued that image generators such as Midjourney can create nearly-identical copies of some copyrighted images,[111] and that generative AI programs compete with the content they are trained on.[112] As of 2024, several lawsuits related to the use of copyrighted material in training are ongoing. Getty Images has sued Stability AI over the use of its images to train Stable diffusion.[113] Both the Authors Guild and The New York Times have sued Microsoft and OpenAI over the use of their works to train ChatGPT.[114][115] Copyright of AI-generated content[edit] A separate question is whether AI-generated works can qualify for copyright protection. The United States Copyright Office has ruled that works created by artificial intelligence without any human input cannot be copyrighted, because they lack human authorship.[116] However, the office has also begun taking public input to determine if these rules need to be refined for"}


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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 24011, "chroma:document": "without any human input cannot be copyrighted, because they lack human authorship.[116] However, the office has also begun taking public input to determine if these rules need to be refined for generative AI.[117] Concerns[edit] See also: Ethics of artificial intelligence and Existential risk from artificial general intelligence The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments, and actions by multiple governments. In a July 2023 briefing of the United Nations Security Council, Secretary-General Ant\u00f3nio Guterres stated \"Generative AI has enormous potential for good and evil at scale\", that AI may \"turbocharge global development\" and contribute between $10 and $15 trillion to the global economy by 2030, but that its malicious use \"could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale\".[118] Job"}


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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 24810, "chroma:document": "to the global economy by 2030, but that its malicious use \"could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale\".[118] Job losses[edit] A picketer at the 2023 Writers Guild of America strike. While not a top priority, one of the WGA's 2023 requests was \"regulations around the use of (generative) AI\".[119] Main articles: Workplace impact of artificial intelligence and Technological unemployment From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements.[120] In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost.[121][122] In July 2023, developments in"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 25613, "chroma:document": "judgements.[120] In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost.[121][122] In July 2023, developments in generative AI contributed to the 2023 Hollywood labor disputes. Fran Drescher, president of the Screen Actors Guild, declared that \"artificial intelligence poses an existential threat to creative professions\" during the 2023 SAG-AFTRA strike.[123] Voice generation AI has been seen as a potential challenge to the voice acting sector.[124][125] The intersection of AI and employment concerns among underrepresented groups globally remains a critical facet. While AI promises efficiency enhancements and skill acquisition, concerns about job displacement and biased recruiting processes persist among these groups, as outlined in surveys by Fast Company. To leverage AI for a more equitable society, proactive steps encompass mitigating biases, advocating transparency, respecting privacy and consent, and"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 26413, "chroma:document": "groups, as outlined in surveys by Fast Company. To leverage AI for a more equitable society, proactive steps encompass mitigating biases, advocating transparency, respecting privacy and consent, and embracing diverse teams and ethical considerations. Strategies involve redirecting policy emphasis on regulation, inclusive design, and education's potential for personalized teaching to maximize benefits while minimizing harms.[126] Racial and gender bias[edit] Generative AI models can reflect and amplify any cultural bias present in the underlying data. For example, a language model might assume that doctors and judges are male, and that secretaries or nurses are female, if those biases are common in the training data.[127] Similarly, an image model prompted with the text \"a photo of a CEO\" might disproportionately generate images of white male CEOs,[128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 27205, "chroma:document": "a CEO\" might disproportionately generate images of white male CEOs,[128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts[129] and reweighting training data.[130] Deepfakes[edit] Main article: Deepfake Deepfakes (a portmanteau of \"deep learning\" and \"fake\"[131]) are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks.[132] Deepfakes have garnered widespread attention and concerns for their uses in deepfake celebrity pornographic videos, revenge porn, fake news, hoaxes, health disinformation, financial fraud, and covert foreign election interference.[133][134][135][136][137][138][139] This has elicited responses from both industry and government to detect and limit their use.[140][141] In July 2023, the fact-checking company Logically found that the popular generative AI models Midjourney, DALL-E 2 and Stable"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 28009, "chroma:document": "both industry and government to detect and limit their use.[140][141] In July 2023, the fact-checking company Logically found that the popular generative AI models Midjourney, DALL-E 2 and Stable Diffusion would produce plausible disinformation images when prompted to do so, such as images of electoral fraud in the United States and Muslim women supporting India's Hindu nationalist Bharatiya Janata Party.[142][143] In April 2024, a paper proposed to use blockchain (distributed ledger technology) to promote \"transparency, verifiability, and decentralization in AI development and usage\".[144] Audio deepfakes[edit] Main article: Audio deepfake Instances of users abusing software to generate controversial statements in the vocal style of celebrities, public officials, and other famous individuals have raised ethical concerns over voice generation AI.[145][146][147][148][149][150] In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 28813, "chroma:document": "have raised ethical concerns over voice generation AI.[145][146][147][148][149][150] In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse through safeguards and identity verification.[151] Concerns and fandoms have spawned from AI-generated music. The same software used to clone voices has been used on famous musicians' voices to create songs that mimic their voices, gaining both tremendous popularity and criticism.[152][153][154] Similar techniques have also been used to create improved quality or full-length versions of songs that have been leaked or have yet to be released.[155] Generative AI has also been used to create new digital artist personalities, with some of these receiving enough attention to receive record deals at major labels.[156] The developers of these virtual artists have also faced their fair share of criticism for their personified programs, including backlash for \"dehumanizing\" an artform, and also creating"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 29610, "chroma:document": "labels.[156] The developers of these virtual artists have also faced their fair share of criticism for their personified programs, including backlash for \"dehumanizing\" an artform, and also creating artists which create unrealistic or immoral appeals to their audiences.[157] Cybercrime[edit] Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams.[158] Deepfake video and audio have been used to create disinformation and fraud. In 2020, former Google click fraud czar Shuman Ghosemajumder argued that once deepfake videos become perfectly realistic, they would stop appearing remarkable to viewers, potentially leading to uncritical acceptance of false information.[159] Additionally, large language models and other forms of text-generation AI have been used to create fake reviews of e-commerce websites to boost ratings.[160] Cybercriminals have created large language models focused on fraud, including WormGPT and"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 30414, "chroma:document": "of text-generation AI have been used to create fake reviews of e-commerce websites to boost ratings.[160] Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.[161] A 2023 study showed that generative AI can be vulnerable to jailbreaks, reverse psychology and prompt injection attacks, enabling attackers to obtain help with harmful requests, such as for crafting social engineering and phishing attacks.[162] Additionally, other researchers have demonstrated that open-source models can be fine-tuned to remove their safety restrictions at low cost.[163] Reliance on industry giants[edit] Training frontier AI models requires an enormous amount of computing power. Usually only Big Tech companies have the financial resources to make such investments. Smaller start-ups such as Cohere and OpenAI end up buying access to data centers from Google and Microsoft respectively.[164] Energy and environment[edit] Main article: Environmental impacts of"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 31213, "chroma:document": "Smaller start-ups such as Cohere and OpenAI end up buying access to data centers from Google and Microsoft respectively.[164] Energy and environment[edit] Main article: Environmental impacts of artificial intelligence Scientists and journalists have expressed concerns about the environmental impact that the development and deployment of generative models are having: high CO2 emissions,[165][166][167] large amounts of freshwater used for data centers,[168][169] and high amounts of electricity usage.[170][166][171] There is also concern that these impacts may increase as these models are incorporated into widely used search engines such as Google Search and Bing;[170] as chatbots and other applications become more popular;[170][169] and as models need to be retrained.[170] Proposed mitigation strategies include factoring potential environmental costs prior to model development or data collection,[165] increasing efficiency of data centers to reduce electricity/energy"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 31995, "chroma:document": "Proposed mitigation strategies include factoring potential environmental costs prior to model development or data collection,[165] increasing efficiency of data centers to reduce electricity/energy usage,[168][170][166][169][171][167] building more efficient machine learning models,[168][166][169] minimizing the number of times that models need to be retrained,[167] developing a government-directed framework for auditing the environmental impact of these models,[168][167] regulating for transparency of these models,[167] regulating their energy and water usage,[168] encouraging researchers to publish data on their models' carbon footprint,[170][167] and increasing the number of subject matter experts who understand both machine learning and climate science.[167] Content quality[edit] See also: Slop (artificial intelligence) and Dead Internet theory The New York Times defines slop as analogous to spam: \"shoddy or unwanted A.I. content in social media, art, books and ... in search"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 32790, "chroma:document": "See also: Slop (artificial intelligence) and Dead Internet theory The New York Times defines slop as analogous to spam: \"shoddy or unwanted A.I. content in social media, art, books and ... in search results.\"[172] Journalists have expressed concerns about the scale of low-quality generated content with respect to social media content moderation,[173] the monetary incentives from social media companies to spread such content,[173][174] false political messaging,[174] spamming of scientific research paper submissions,[175] increased time and effort to find higher quality or desired content on the Internet,[176] the indexing of generated content by search engines,[177] and on journalism itself.[178] A paper published by researchers at Amazon Web Services AI Labs found that over 57% of sentences from a sample of over 6 billion sentences from Common Crawl, a snapshot of web pages, were machine translated. Many of these automated translations were seen as lower quality, especially for"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 33593, "chroma:document": "from a sample of over 6 billion sentences from Common Crawl, a snapshot of web pages, were machine translated. Many of these automated translations were seen as lower quality, especially for sentences that were translated across at least three languages. Many lower-resource languages (ex. Wolof, Xhosa) were translated across more languages than higher-resource languages (ex. English, French).[179][180] In September 2024, Robyn Speer, the author of wordfreq, an open source database that calculated word frequencies based on text from the Internet, announced that she had stopped updating the data for several reasons: high costs for obtaining data from Reddit and Twitter, excessive focus on generative AI compared to other methods in the natural language processing community, and that \"generative AI has polluted the data\".[181] The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from University College London estimated that in"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 34396, "chroma:document": "AI has polluted the data\".[181] The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from University College London estimated that in 2023, more than 60,000 scholarly articles\u2014over 1% of all publications\u2014were likely written with LLM assistance.[182] According to Stanford University's Institute for Human-Centered AI, approximately 17.5% of newly published computer science papers and 16.9% of peer review text now incorporate content generated by LLMs.[183] Visual content follows a similar trend. Since the launch of DALL-E 2 in 2022, it is estimated that an average of 34 million images have been created daily. As of August 2023, more than 15 billion images had been generated using text-to-image algorithms, with 80% of these created by models based on Stable Diffusion.[184] If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur.[185]"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 35198, "chroma:document": "models based on Stable Diffusion.[184] If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur.[185] Training an AI model exclusively on the output of another AI model produces a lower-quality model. Repeating this process, where each new model is trained on the previous model's output, leads to progressive degradation and eventually results in a \"model collapse\" after multiple iterations.[186] Tests have been conducted with pattern recognition of handwritten letters and with pictures of human faces.[187] As a consequence, the value of data collected from genuine human interactions with systems may become increasingly valuable in the presence of LLM-generated content in data crawled from the Internet. On the other side, synthetic data is often used as an alternative to data produced by real-world events. Such data can be deployed to validate mathematical models and to train machine"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 35992, "chroma:document": "the Internet. On the other side, synthetic data is often used as an alternative to data produced by real-world events. Such data can be deployed to validate mathematical models and to train machine learning models while preserving user privacy,[188] including for structured data.[189] The approach is not limited to text generation; image generation has been employed to train computer vision models.[190] Misuse in journalism[edit] This section may contain unverified or indiscriminate information in embedded lists. Please help clean up the lists by removing items or incorporating them into the text of the article. (July 2024) See also: List of fake news websites \u00a7\u00a0Generative AI In January 2023, Futurism.com broke the story that CNET had been using an undisclosed internal AI tool to write at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories.[191] In April 2023, the German tabloid Die Aktuelle published a fake AI-generated interview with former"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 36789, "chroma:document": "at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories.[191] In April 2023, the German tabloid Die Aktuelle published a fake AI-generated interview with former racing driver Michael Schumacher, who had not made any public appearances since 2013 after sustaining a brain injury in a skiing accident. The story included two possible disclosures: the cover included the line \"deceptively real\", and the interview included an acknowledgment at the end that it was AI-generated. The editor-in-chief was fired shortly thereafter amid the controversy.[192] Other outlets that have published articles whose content and/or byline have been confirmed or suspected to be created by generative AI models \u2013 often with false content, errors, and/or non-disclosure of generative AI use - include:  NewsBreak[193][194] outlets owned by Arena Group Sports Illustrated[195] TheStreet[195] Men's Journal[196] B&H Photo[197] outlets owned by Gannett The Columbus"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 37579, "chroma:document": "of generative AI use - include:  NewsBreak[193][194] outlets owned by Arena Group Sports Illustrated[195] TheStreet[195] Men's Journal[196] B&H Photo[197] outlets owned by Gannett The Columbus Dispatch[198][199] Reviewed[200] USA Today[201] MSN[202] News Corp[203] outlets owned by G/O Media[204] Gizmodo[205] Jalopnik[205] A.V. Club[205][206] Quartz[207] The Irish Times[208] outlets owned by Red Ventures Bankrate[209] BuzzFeed[210] Newsweek[211] Hoodline[212][213][214] outlets owned by Outside Inc. Yoga Journal[201] Backpacker[201] Clean Eating[201] Hollywood Life[201] Us Weekly[201] The Los Angeles Times[201] Cody Enterprise[215] Cosmos[216] outlets owned by McClatchy Miami Herald[201] Sacramento Bee[201] Tacoma News Tribune[201] The Rock Hill Herald[201] The Modesto Bee[201] Fort Worth Star-Telegram[201] Merced Sun-Star[201] Ledger-Enquirer[201] The Kansas City Star[201] Raleigh News & Observer[217] outlets owned by Ziff Davis PC Magazine[201] Mashable[201] AskMen[201] outlets owned"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 38396, "chroma:document": "Merced Sun-Star[201] Ledger-Enquirer[201] The Kansas City Star[201] Raleigh News & Observer[217] outlets owned by Ziff Davis PC Magazine[201] Mashable[201] AskMen[201] outlets owned by Hearst Good Housekeeping[201] outlets owned by IAC Inc. People[201] Parents[201] Food & Wine[201] InStyle[201] Real Simple[201] Travel + Leisure[201] Better Homes & Gardens[201] Southern Living[201] outlets owned by Street Media LA Weekly[218] The Village Voice[218] Riverfront Times[218] Apple Intelligence[219] In May 2024, Futurism noted that a content management system video by AdVon Commerce, who had used generative AI to produce articles for many of the aforementioned outlets, appeared to show that they \"had produced tens of thousands of articles for more than 150 publishers.\"[201] News broadcasters in Kuwait, Greece, South Korea, India, China and Taiwan have presented news with anchors based on Generative AI models, prompting concerns about job losses for human anchors and audience trust in news"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 39195, "chroma:document": "Kuwait, Greece, South Korea, India, China and Taiwan have presented news with anchors based on Generative AI models, prompting concerns about job losses for human anchors and audience trust in news that has historically been influenced by parasocial relationships with broadcasters, content creators or social media influencers.[220][221][222] Algorithmically generated anchors have also been used by allies of ISIS for their broadcasts.[223] In 2023, Google reportedly pitched a tool to news outlets that claimed to \"produce news stories\" based on input data provided, such as \"details of current events\". Some news company executives who viewed the pitch described it as \"[taking] for granted the effort that went into producing accurate and artful news stories.\"[224] In February 2024, Google launched a program to pay small publishers to write three articles per day using a beta generative AI model. The program does not require the knowledge or consent of the websites that the publishers are"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 40000, "chroma:document": "a program to pay small publishers to write three articles per day using a beta generative AI model. The program does not require the knowledge or consent of the websites that the publishers are using as sources, nor does it require the published articles to be labeled as being created or assisted by these models.[225] Many defunct news sites (The Hairpin, The Frisky, Apple Daily, Ashland Daily Tidings, Clayton County Register, Southwest Journal) and blogs (The Unofficial Apple Weblog, iLounge) have undergone cybersquatting, with articles created by generative AI.[226][227][228][229][230][231][232][233] United States Senators Richard Blumenthal and Amy Klobuchar have expressed concern that generative AI could have a harmful impact on local news.[234] In July 2023, OpenAI partnered with the American Journalism Project to fund local news outlets for experimenting with generative AI, with Axios noting the possibility of generative AI companies creating a dependency for these news"}


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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 54389, "chroma:document": "shows\". Reuters. Retrieved July 14, 2024. ^ \"A History of Generative AI: From GAN to GPT-4\". March 21, 2023. Archived from the original on June 10, 2023. Retrieved April 28, 2023. ^ \"Explainer: What is Generative AI, the technology behind OpenAI's ChatGPT?\". Reuters. March 17, 2023. Archived from the original on March 30, 2023. Retrieved March 17, 2023. ^ Roose, Kevin (February 16, 2023). \"Bing's A.I. Chat: 'I Want to Be Alive.'\". The New York Times. Archived from the original on April 15, 2023. Retrieved January 30, 2024. ^ Bommasani, R.; Hudson, D. A.; Adeli, E.; Altman, R.; Arora, S.; von Arx, S.; Bernstein, M. S.; Bohg, J.; Bosselut, A; Brunskill, E.; Brynjolfsson, E. (August 16, 2021). \"On the opportunities and risks of foundation models\". arXiv:2108.07258 [cs.LG]. ^ Chen, Ming; Tworek, Jakub; Jun, Hongyu; Yuan, Qinyuan; Pinto, Hanyu Philippe De Oliveira; Kaplan, Jerry; Edwards, Haley; Burda, Yannick; Joseph, Nicholas; Brockman, Greg; Ray, Alvin (July 6, 2021). \"Evaluating Large"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 55192, "chroma:document": "Jakub; Jun, Hongyu; Yuan, Qinyuan; Pinto, Hanyu Philippe De Oliveira; Kaplan, Jerry; Edwards, Haley; Burda, Yannick; Joseph, Nicholas; Brockman, Greg; Ray, Alvin (July 6, 2021). \"Evaluating Large Language Models Trained on Code\". arXiv:2107.03374 [cs.LG]. ^ \"Investing in Cursor\". Andreesen Horowitz. ^ Epstein, Ziv; Hertzmann, Aaron; Akten, Memo; Farid, Hany; Fjeld, Jessica; Frank, Morgan R.; Groh, Matthew; Herman, Laura; Leach, Neil; Mahari, Robert; Pentland, Alex \u201cSandy\u201d; Russakovsky, Olga; Schroeder, Hope; Smith, Amy (2023). \"Art and the science of generative AI\". Science. 380 (6650): 1110\u20131111. arXiv:2306.04141. Bibcode:2023Sci...380.1110E. doi:10.1126/science.adh4451. PMID\u00a037319193. S2CID\u00a0259095707. ^ Ramesh, Aditya; Pavlov, Mikhail; Goh, Gabriel; Gray, Scott; Voss, Chelsea; Radford, Alec; Chen, Mark; Sutskever, Ilya (2021). \"Zero-shot text-to-image generation\". International Conference on Machine Learning. PMLR. pp.\u00a08821\u20138831. ^ Chandraseta, Rionaldi (January 21, 2021). \"Generate"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 55997, "chroma:document": "Chen, Mark; Sutskever, Ilya (2021). \"Zero-shot text-to-image generation\". International Conference on Machine Learning. PMLR. pp.\u00a08821\u20138831. ^ Chandraseta, Rionaldi (January 21, 2021). \"Generate Your Favourite Characters' Voice Lines using Machine Learning\". Towards Data Science. Archived from the original on January 21, 2021. Retrieved December 18, 2024. ^ Temitope, Yusuf (December 10, 2024). \"15.ai Creator reveals journey from MIT Project to internet phenomenon\". The Guardian. Archived from the original on December 28, 2024. Retrieved December 25, 2024. ^ Ruppert, Liana (January 18, 2021). \"Make Portal's GLaDOS And Other Beloved Characters Say The Weirdest Things With This App\". Game Informer. Archived from the original on January 18, 2021. Retrieved December 18, 2024. ^ Kurosawa, Yuki (January 19, 2021). \"\u30b2\u30fc\u30e0\u30ad\u30e3\u30e9\u97f3\u58f0\u8aad\u307f\u4e0a\u3052\u30bd\u30d5\u30c8\u300c15.ai\u300d\u516c\u958b\u4e2d\u3002\u300eUndertale\u300f\u3084\u300ePortal\u300f\u306e\u30ad\u30e3\u30e9\u306b\u597d\u304d\u306a\u30bb\u30ea\u30d5\u3092\u8a00\u3063\u3066\u3082\u3089\u3048\u308b\" [Game Character Voice Reading Software \"15.ai\" Now Available. Get Characters from Undertale and Portal to Say"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 56796, "chroma:document": "(January 19, 2021). \"\u30b2\u30fc\u30e0\u30ad\u30e3\u30e9\u97f3\u58f0\u8aad\u307f\u4e0a\u3052\u30bd\u30d5\u30c8\u300c15.ai\u300d\u516c\u958b\u4e2d\u3002\u300eUndertale\u300f\u3084\u300ePortal\u300f\u306e\u30ad\u30e3\u30e9\u306b\u597d\u304d\u306a\u30bb\u30ea\u30d5\u3092\u8a00\u3063\u3066\u3082\u3089\u3048\u308b\" [Game Character Voice Reading Software \"15.ai\" Now Available. Get Characters from Undertale and Portal to Say Your Desired Lines]. AUTOMATON (in Japanese). Archived from the original on January 19, 2021. Retrieved December 18, 2024. \u82f1\u8a9e\u7248\u30dc\u30a4\u30b9\u306e\u307f\u306a\u306e\u3067\u6ce8\u610f\u3002;\u3082\u3046\u3072\u3068\u306415.ai\u306e\u5927\u304d\u306a\u7279\u5fb4\u3068\u3057\u3066\u6319\u3052\u3089\u308c\u308b\u306e\u304c\u3001\u8c4a\u304b\u306a\u611f\u60c5\u8868\u73fe\u3060\u3002 [Please note that only English voices are available.;Another major feature of 15.ai is its rich emotional expression.] ^ Desai, Saahil (July 17, 2023). \"A Voicebot Just Left Me Speechless\". The Atlantic. Archived from the original on December 8, 2023. Retrieved November 28, 2023. ^ Agostinelli, Andrea; Denk, Timo I.; Borsos, Zal\u00e1n; Engel, Jesse; Verzetti, Mauro; Caillon, Antoine; Huang, Qingqing; Jansen, Aren; Roberts, Adam; Tagliasacchi, Marco; Sharifi, Matt; Zeghidour, Neil; Frank, Christian (January 26, 2023). \"MusicLM: Generating Music From Text\". arXiv:2301.11325 [cs.SD]. ^ Dalugdug, Mandy (August 3, 2023). \"Meta"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 57598, "chroma:document": "Tagliasacchi, Marco; Sharifi, Matt; Zeghidour, Neil; Frank, Christian (January 26, 2023). \"MusicLM: Generating Music From Text\". arXiv:2301.11325 [cs.SD]. ^ Dalugdug, Mandy (August 3, 2023). \"Meta in June said that it used 20,000 hours of licensed music to train MusicGen, which included 10,000 \"high-quality\" licensed music tracks. At the time, Meta's researchers outlined in a paper the ethical challenges that they encountered around the development of generative AI models like MusicGen\". Archived from the original on August 15, 2023. ^ \"Jay-Z's Delaware producer sparks debate over AI rights\". Archived from the original on February 27, 2024. Retrieved February 27, 2024. ^ \"10 \"Best\" AI Music Generators (April 2024) - Unite.AI\". October 19, 2022. Archived from the original on January 29, 2024. Retrieved February 27, 2024. ^ Metz, Cade (April 4, 2023). \"Instant Videos Could Represent the Next Leap in A.I. Technology\". The New York Times. Archived from the original on April 5, 2023."}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 58395, "chroma:document": "2024. Retrieved February 27, 2024. ^ Metz, Cade (April 4, 2023). \"Instant Videos Could Represent the Next Leap in A.I. Technology\". The New York Times. Archived from the original on April 5, 2023. Retrieved April 5, 2023. ^ Wong, Queenie (September 29, 2022). \"Facebook Parent Meta's AI Tool Can Create Artsy Videos From Text\". cnet.com. Archived from the original on April 5, 2023. Retrieved April 4, 2023. ^ Yang, Sherry; Du, Yilun (April 12, 2023). \"UniPi: Learning universal policies via text-guided video generation\". Google Research, Brain Team. Google AI Blog. Archived from the original on May 24, 2023. ^ Brohan, Anthony (2023). \"RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\". arXiv:2307.15818 [cs.RO]. ^ Abdullahi, Aminu (November 17, 2023). \"10 Best Artificial Intelligence (AI) 3D Generators\". eWEEK. Archived from the original on May 7, 2024. Retrieved February 6, 2024. ^ \"Slash CAD model build times with new AI-driven part creation methodology |"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 59200, "chroma:document": "Intelligence (AI) 3D Generators\". eWEEK. Archived from the original on May 7, 2024. Retrieved February 6, 2024. ^ \"Slash CAD model build times with new AI-driven part creation methodology | GlobalSpec\". Archived from the original on January 23, 2024. Retrieved February 6, 2024. ^ \"The Role of Artificial Intelligence (AI) in the CAD Industry\". March 22, 2023. Archived from the original on February 9, 2024. Retrieved February 6, 2024. ^ Sabin, Sam (June 30, 2023). \"GitHub has a vision to make code more secure by design\". Axios Codebook. Archived from the original on August 15, 2023. Retrieved August 15, 2023. ^ Vincent, James (March 20, 2023). \"Text-to-video AI inches closer as startup Runway announces new model\". The Verge. Archived from the original on September 27, 2023. Retrieved August 15, 2023. Text-to-video is the next frontier for generative AI, though current output is rudimentary. Runway says it'll be making its new generative video model, Gen-2, available to users in 'the"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 60000, "chroma:document": "15, 2023. Text-to-video is the next frontier for generative AI, though current output is rudimentary. Runway says it'll be making its new generative video model, Gen-2, available to users in 'the coming weeks.' ^ Vanian, Jonathan (March 16, 2023). \"Microsoft adds OpenAI technology to Word and Excel\". CNBC. Archived from the original on August 15, 2023. Retrieved August 15, 2023. Microsoft is bringing generative artificial intelligence technologies such as the popular ChatGPT chatting app to its Microsoft 365 suite of business software....the new A.I. features, dubbed Copilot, will be available in some of the company's most popular business apps, including Word, PowerPoint and Excel. ^ Wilson, Mark (August 15, 2023). \"The app's Memories feature just got a big upgrade\". TechRadar. Archived from the original on August 15, 2023. The Google Photos app is getting a redesigned, AI-powered Memories feature...you'll be able to use generative AI to come up with some suggested names like \"a"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 60799, "chroma:document": "from the original on August 15, 2023. The Google Photos app is getting a redesigned, AI-powered Memories feature...you'll be able to use generative AI to come up with some suggested names like \"a desert adventure\". ^ Sullivan, Laurie (May 23, 2023). \"Adobe Adds Generative AI To Photoshop\". MediaPost. Archived from the original on August 15, 2023. Retrieved August 15, 2023. Generative artificial intelligence (AI) will become one of the most important features for creative designers and marketers. Adobe on Tuesday unveiled a Generative Fill feature in Photoshop to bring Firefly's AI capabilities into design. ^ Michael Nu\u00f1ez (July 19, 2023). \"LLaMA 2: How to access and use Meta's versatile open-source chatbot right now\". VentureBeat. Archived from the original on November 3, 2023. Retrieved August 15, 2023. If you want to run LLaMA 2 on your own machine or modify the code, you can download it directly from Hugging Face, a leading platform for sharing AI models. ^ Pounder, Les (March 25,"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 61598, "chroma:document": "August 15, 2023. If you want to run LLaMA 2 on your own machine or modify the code, you can download it directly from Hugging Face, a leading platform for sharing AI models. ^ Pounder, Les (March 25, 2023). \"How To Create Your Own AI Chatbot Server With Raspberry Pi 4\". Archived from the original on August 15, 2023. Retrieved August 15, 2023. Using a Pi 4 with 8GB of RAM, you can create a ChatGPT-like server based on LLaMA. ^ Kemper, Jonathan (November 10, 2022). \"\"Draw Things\" App brings Stable Diffusion to the iPhone\". The Decoder. Archived from the original on August 15, 2023. Retrieved August 15, 2023. Draw Things is an app that brings Stable Diffusion to the iPhone. The AI images are generated locally, so you don't need an Internet connection. ^ Witt, Allan (July 7, 2023). \"Best Computer to Run LLaMA AI Model at Home (GPU, CPU, RAM, SSD)\". Archived from the original on August 15, 2023. Retrieved August 15, 2023. To run LLaMA model at home, you will need a computer build with a"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 62402, "chroma:document": "to Run LLaMA AI Model at Home (GPU, CPU, RAM, SSD)\". Archived from the original on August 15, 2023. Retrieved August 15, 2023. To run LLaMA model at home, you will need a computer build with a powerful GPU that can handle the large amount of data and computation required for inferencing. ^ Westover, Brian (September 28, 2023). \"Who Needs ChatGPT? How to Run Your Own Free and Private AI Chatbot\". Ziff Davis. Archived from the original on January 7, 2024. Retrieved January 7, 2024. ^ @karpathy (December 20, 2023). \"I pretty much only trust two LLM evals right now\" (Tweet) \u2013 via Twitter. ^ @ylecun (January 5, 2024). \"Nabla's shift from ChatGPT to open source LLMs...\" (Tweet) \u2013 via Twitter. ^ @ylecun (November 1, 2023). \"Open source platforms *increase* safety and security\" (Tweet) \u2013 via Twitter. ^ Nellis, Stephen; Lee, Jane (September 1, 2022). \"U.S. officials order Nvidia to halt sales of top AI chips to China\". Reuters. Archived from the original on August 15, 2023. Retrieved August"}


created_at: 2025-02-04 15:04:55
topic: persistent://default/default/d96bc087-722f-46b1-86af-cbd39f5d55a2
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 64806, "chroma:document": "from the original on July 27, 2023. ^ \"FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence\". The White House. October 30, 2023. Archived from the original on January 30, 2024. Retrieved January 30, 2024. ^ Burt, Andrew (October 31, 2023). \"3 Obstacles to Regulating Generative AI\". Harvard Business Review. ISSN\u00a00017-8012. Archived from the original on February 17, 2024. Retrieved February 17, 2024. ^ \"EU AI Act: first regulation on artificial intelligence\". European Parliament. August 6, 2023. Retrieved September 13, 2024. ^ Chee, Foo Yun; Mukherjee, Supantha (June 14, 2023). \"EU lawmakers vote for tougher AI rules as draft moves to final stage\". Reuters. Archived from the original on July 27, 2023. Retrieved July 26, 2023. ^ Ye, Josh (July 13, 2023). \"China says generative AI rules to apply only to products for the public\". Reuters. Archived from the original on July 27, 2023. Retrieved July 13, 2023. ^ \"\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\u670d\u52a1\u7ba1\u7406\u6682\u884c\u529e\u6cd5\". July"}


created_at: 2025-02-04 15:04:55
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{"source": "http://en.wikipedia.org/wiki/Generative_artificial_intelligence", "doc_id": "232f3bfa-45f1-0087-6903-d9d3d42a7d0d", "source_type": "web", "start_index": 65606, "chroma:document": "(July 13, 2023). \"China says generative AI rules to apply only to products for the public\". Reuters. Archived from the original on July 27, 2023. Retrieved July 13, 2023. ^ \"\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\u670d\u52a1\u7ba1\u7406\u6682\u884c\u529e\u6cd5\". July 13, 2023. Archived from the original on July 27, 2023. Retrieved July 27, 2023. ^ a b \"Generative Artificial Intelligence and Copyright Law\". Congressional Research Service. LSB10922. September 29, 2023. Archived from the original on March 22, 2024. Retrieved January 30, 2024. ^ Thompson, Stuart (January 25, 2024). \"We Asked A.I. to Create the Joker. It Generated a Copyrighted Image\". The New York Times. Archived from the original on January 25, 2024. Retrieved January 26, 2024. ^ Hadero, Haleluya; Bauder, David (December 27, 2023). \"The New York Times sues OpenAI and Microsoft for using its stories to train chatbots\". Associated Press News. AP News. Archived from the original on December 27, 2023. Retrieved April 13, 2023. ^ O\u2019Brien, Matt (September 25, 2023). \"Photo giant Getty took a"}


created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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created_at: 2025-02-04 15:04:55
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