Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Please read this text: 

People consume information in lots of different ways: visually, audibly, through text, and sometimes all three at once. Search engine algorithms like Google’s are advancing to reflect this experience for users. No longer will the algorithms just look at web pages as a single piece of text content. Google will look at all the ways real people consume information: text, images, videos, infographics, tables, charts, and interactive elements that make up a piece of content.

In recent years, we saw the rise of visual search – particularly in image-heavy industries like travel and tourism or ecommerce. Moving into 2023, Google will expand its foothold in multisearch and multisearch near me. 

What does it say about multi-format content in a brief summary?
Response: The text says that people prefer content in different forms like audio, video, or text format. As a result, Google plans to advance the multi-search function and page search algorithms accordingly. 

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.

Please summarize the following text about self-supervised learning, an approach in machine learning that learns from unlabeled data:

Self-supervised learning is a branch of machine learning that enables models to learn from unlabeled data by exploiting inherent patterns and structures within the data itself. Unlike supervised learning, which requires labeled data for training, self-supervised learning algorithms learn by solving pretext tasks, such as predicting missing parts of an image or reconstructing corrupted input data. These pretext tasks are designed to capture meaningful representations and features from the data, which can then be transferred or fine-tuned for downstream tasks, like image classification or natural language processing. Self-supervised learning has gained significant attention due to the abundance of unlabeled data and the costly and time-consuming process of manual data labeling. Several self-supervised learning techniques have been developed, including contrastive learning, autoencoders, and masked language modeling. Contrastive learning aims to learn representations that maximize the similarity between augmented views of the same data sample while pushing apart representations of different samples. Autoencoders learn to reconstruct the input data by encoding it into a compressed representation and then decoding it back. Masked language modeling, used in models like BERT, involves predicting masked tokens in a sentence based on their context. Self-supervised learning has shown promising results in various domains, such as computer vision, natural language processing, and speech recognition, and has the potential to unlock the value of large unlabeled datasets.
user
The given text discusses self-supervised learning, a machine learning approach that enables models to learn from unlabeled data by exploiting inherent patterns and structures within the data itself. The key points are: 1) Self-supervised learning does not require labeled data for training, unlike supervised learning. 2) It learns by solving pretext tasks, such as predicting missing parts of data or reconstructing corrupted inputs, designed to capture meaningful representations. 3) The learned representations can be transferred or fine-tuned for downstream tasks like image classification or natural language processing. 4) Self-supervised learning techniques include contrastive learning, autoencoders, and masked language modeling. 5) It has gained attention due to the abundance of unlabeled data and the costly process of manual labeling. 6) Self-supervised learning has shown promising results in various domains like computer vision, NLP, and speech recognition, unlocking the value of large unlabeled datasets.
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