plus what we can learn from Gaussian models of data. Take it away. OK, so thank you very much. First of all, I would like to thank the organizers for inviting me to this very interesting workshop. So broadly speaking today, I'm going to talk about the interplay between data structure and machine learning performances. And in particular, I will try to shed some lights on what we can learn from Gaussian models of data. So to start with, as we all know, the great success of deep learning can be mainly ascribed to two distinct factors. On one side, the design of efficient learning algorithm and architectures, while on the other side, increasingly higher amount of data at disposal. However, up to this point, lots of efforts have been devoted in trying to understand the role played by both architectures and algorithm, but very few has been done concerning data. This actually started becoming quite an odd topic within the machine learning community as also this workshop testify. And indeed, quite recently, Andrew and G posted this tweet, where he basically started wondering whether it wouldn't be the case to actually start pushing both researchers and company to work on data while keeping architectures and algorithm fixed rather than doing the opposite. So why is this the case? Well, actually, because data play a central role in machine learning applications. However, if you look at the ill-statistical physics works in the field, like the seminal paper by Gardner and Derrida, you will easily realize that despite these works managed to capture several aspects of learning, they completely ignore the role played by data. So basically, data are considered to be unstructured and sampled identically and independently from a Gaussian distribution. However, we know that in machine learning application, real data sets are instead structured, and it is precisely this structure that the machine learning models tries to grasp in order to learn how to generalize well on previously unseen data. Now, quite recently, there has been a huge activity within the statistical physics community where we basically try to extend the previous statistical physics works in such a way to start including data structure into the game. Up to the point, we now manage to deal with data that are drawn either from a single Gaussian or from mixtures of Gaussian, but with non-trivial means and covariances. Okay, so despite we know that real-world data sets are not Gaussian distributed, given this recent advancement in statistical physics theory and in particular, the replica method, one may still ask himself, can, I mean, are there machine learning settings where basically Gaussian data models can exactly describe the behavior of machine learning models when trained in real-world data set scenarios? And moreover, even if they do not, can we still learn something from Gaussian data models? So to start with the first question, it turns out that there are indeed some learning settings where the machine learning model is completely blind to the fine details of the input data distribution. What he just said, it's about the first moments of the distribution itself. This is, for instance, the case of the example. I'm going to show you on these slides, and then I'm basically going to discuss in the next couple of slides, which basically corresponds to the case of a data set where the input data points are basically drawn from a mixture of K different Gaussians, and the labels are completely random in the sense that they are not correlated at all with the input data distribution. Now, this data set defines a task which is well-known in a machine learning application, and that goes under the name of storage capacity problem. So in this task, we basically take a machine learning model that in the specific example of these slides, I will consider to be a single layer neural network. And then once you have this model, the goal is to train the model in such a way to be able to fit some randomly labeled examples with a given training loss plus L2 regularization. Now, the model, the setting that I'm basically analyzing in these slides can be analytically solved, and analytically solved, I mean that we can actually exactly compute the training loss with the replica method in the high-dimensional regime, and express then this training loss as a function of simple scalar quantities plus some parameters that characterize the input data distribution. Now, if you do that, and you compute actually this quantity with this computation, what we realize is that there are actually some Gaussian universalities that go around, and in particular, a Gaussian universality at finite regularization, which is well summarized by the following theorem. So if the loss is symmetric, which is basically not such a strict requirement because basically almost all loss is employed in machine learning use basically this requirement, and if the Gaussian mixture is homogeneous in the sense that all the different modes of the mixture share precisely the same covariance matrix omega, then the training loss of a mixture of homogeneous Gaussian is equivalent to the training loss that you would get by approximating the homogeneous Gaussian mixture with a single Gaussian, but with matching covariance omega. Is that clear? Are there any questions up to this point? Okay, cool. So the proof of this theorem can actually be found in the paper, but actually this result can be further confirmed by numerical experiments. So to see that, what you could do is to basically take a single layer neural network and train it with a mixture of Gaussians with random labels, and then you can actually measure the training loss as a function of the sides of the training set. So if you do that, you will get precisely these orange dots points that I'm showing you here on these three different plots. The solid black line instead corresponds precisely to the theoretical predictions that we get from the replica theory by approximating the homogeneous Gaussian mixture with a single Gaussian of matching covariances. So as you can see, there is a perfect agreement between what the theory predicts and the numerical simulation, which further basically corroborates the idea that there exists this Gaussian universality and that in the end, what the model is actually observing is not a mixture of homogeneous Gaussian but a single Gaussian with matching covariances, okay? Now, once we have basically observed this behavior, what we wanted to check was whether this sort of universality can actually pops out even when we basically consider real-world data sets. So to do that, what we did was the following experiment. So you can basically take the real data sets of your choice, for instance, Amnist, Fashion Amnist, Cypher 10 or Tiny ImageNet, and you repeat precisely the same experiment. So you take your single layer neural network, you train it on the input data points of one of these real data sets and then you measure the training loss as a function of the size of the training set, okay? If you do that, once again, you will get these colored points that I'm showing you here on these metrics of plots. The solid black line instead, once again corresponds to the theoretical prediction of the replica theory that is basically obtained by approximating the true underlying distribution of all these real data sets with a single Gaussian, whose covariance metrics precisely correspond to the empirical covariance of the specific data set, okay? Is that clear or are there questions? Okay. So if you see, there is already quite a good agreement between the simulations and the theoretical prediction. And indeed, when I actually saw this agreement, I had to rerun the simulations several times just to convince myself that there were no mistakes going around. But despite the agreement is quite good, you can still observe some deviations from the Gaussian universality. So if you see there are some context in which for instance, the agreement is still not perfect. So here the central question is actually, why we are actually observing these deviations? Well, if you remember, one of the main assumption of the theorem was that the Gaussian mixture, the different modes needs to be homogeneous. However, if you actually take a real data set, like for instance, the Cypher 10 in these lights, and you actually plot the covariance matrices of the different modes, for instance here, the three different modes of Cypher 10, you will easily realize that the structure of the covariance matrix is far from being homogeneous. So the modes are pretty different between each other. So this is what is actually causing the deviations from the universality in real-world data set. But actually, what it turns out is that if instead of considering the real data sets as they are, you actually preprocess your data sets with some transformation, for instance, either random feature or wavelet scattering transform, then the covariance matrices becomes more homogeneous than what they were in the very beginning. And if you now rerun the simulations after having applied this preprocessing, then you actually will get now a very perfect match. So what this is basically suggesting you is that if you basically train shallow neural networks with random labels and apply this sort of preprocessing, what the learning model is observing and is trying basically to learn from data is just the covariance matrix. So the second moment, is that clear or are there any questions? Yes, please. Yes. Standardization. Yes. So still the same results obtained? Yeah, exactly. So if you, I mean, what we did here in the simulation was to first preprocess the data set by standardizing, which is, I didn't mention it because of course is a standard practice that you typically do. But if you also apply this further transformation, then you get a better agreement because basically this transformation are making your covariance matrices more homogeneous. And so you are basically, let's say, satisfying the assumption of the theorem. Okay, thank you. Okay. Yes. One clarification. So these are all the training losses still? Yes. Yeah, these are the training losses, for instance, the square logistic or hinge as a function of the size of the training set. And here the different lines corresponds to different regularization strength. Because if you remember at the very beginning, I was inserting in the model an L2 regularization. And so lambda here is the strength of the L2 regularization. But if you saw some other metric like generalization or other evaluation. No, we just saw the training loss because with random labels, I mean, generalization cannot be defined. But actually I will have generalization after. But you planted models. Sure. Okay. So, okay. But however, this is not yet the end of the story concerning district Gaussian universality. Because it turns out that there is a second type of Gaussian universality that it is indeed much stronger with respect to the previous one that you basically get in the zero regularization limit. And that is basically well summarized by this second theorem. So this theorem basically states that in the random label settings under the same assumption of the previous three theorem, so symmetric loss and homogeneous covariance matrices. If the optimization problem is convex and the data covariance is full rank, then the training loss of an homogeneous mixture of Gaussian does not even depend on the covariance matrix itself. So if you run once again the simulations, you will easily realize because actually we can run and repeat exactly the same experiment to measure in the training loss as a function of the size of the training set in the limit of zero regularization for different datasets. And what you will actually observe is that all these datasets, so all the learning curves associated to all these different real datasets, they all collapse on the same curve. So not only the interpolation threshold is the same, but also the full learning curve. And the learning curve on which all these data models are actually collapsing corresponds to the one of completely unstructured data. So there is this much stronger type of universality that basically tells you that in the zero regularization limit, not even the covariance matrix matters. Okay. So going back to our central question, can Gaussian data models exactly describe the behavior of real-world datasets? Well, yes and no. I show you that there are some settings in which this actually appears. But I mean, it's not the full story because not for all machine learning settings, this is the case. So going to the second question, even if there are some machine learning settings where you can't actually identify some strict Gaussian universality, can we still learn something from Gaussian data models? Because maybe they are actually well approximating in a qualitative way the behavior of the machine learning model. So to basically give you some intuitions about that, I will basically talk about three different examples of Gaussian models that actually turns out to be useful for understanding some interesting machine learning settings. And the first one is the so-called correlated hidden manifold model that actually turns out to be very powerful for actually start understanding something about transfer learning. The second model is the mean field generalized spots which turned out to be extremely useful for start approaching self-supervised learning tasks and in particular transformers. The third model is instead the teacher mixture model which basically allowed us to start approaching the thorny issue of fairness in machine learning problem. Now this, let's say, third model is extremely simple, is simply a mixture of Gaussian with the labels provided by a teacher vector. But unfortunately, I mean, for time constraint and for the purpose of this talk, I'm not going to talk about this. I will specifically, let's say, focus on the first two models. So they correlated the hidden manifold model and then we filled the generalized spots. But before doing that, let me give you some, let's say, motivation of why these two settings, transfer learning and self-supervised are so important and why we actually devoted so much efforts in the design Gaussian models of data to better investigate this sort of framework, okay? So basically, as we all know, deep learning is intrinsically data-angry in the sense that it requires lots of data to actually generalize well on previously unseen examples. However, if you think about it, there are some settings where actually collecting huge amounts of label data is simply unpracticable. For instance, in healthcare, one should think to design a pool of medical experts which can actually label each single frame of each single patient medical examination. And this, of course, is a cost in terms of both time and money. So a possible solution which can consistently mitigate the need of new label data is transfer learning. This is a deep learning technique that is based on the idea that the generalization performances of a neural network that has to be trained on a data scars target task can be consistently improved by exploiting the knowledge that a second network is previously acquired on a related but data-abundant source task. So the typical transfer learning pipeline occurs in the following way. You first train a network A on the source task. Then all those layers which are actually responsible for feature extraction are transferred to a second network, the network B, that is then trained on the data scars target task while keeping the transfer feature map frozen and letting just the very last layer to re-adapt to the target set. Now, deep learning prediction is typically farther at the stage of fine tuning where they basically unlock the transfer feature map and then retrain the entire network on the target set. Now, despite being actually widely used in deep learning applications, transfer learning still remain poorly understood from a theoretical point of view. And indeed, there are several questions that still remains open. For instance, how related do the source and the target task needs to be? Now, in this work, what we basically did was to propose the correlated hidden manifold model as a model for structured and correlated datasets where basically the correlations between the source and the target set appears explicitly and are directly tunable, okay? This basically allowed us to explore several transfer learning settings and therefore to delineate the boundaries of transfer learning effectiveness. Now, as the name itself suggests, the building block of the correlated hidden manifold model is the hidden manifold model itself. This is a model that has been proposed by Sebastian Gold and collaborators in 2019. And it is basically based on the evidence that real-world datasets do not span uniformly the entire input space, but they are rather confined in a lower dimensional manifold. So according to this model, each input X is constructed as an linear combination of some Gaussian coefficient C times some generative features F. And here, HELL is precisely the size, the dimension of the lower dimensional manifold. So the intrinsic dimension of your dataset, if you want. Then the C can therefore be interpreted as the lower dimensional representation of the X of the input data points in the feature space, okay? The labels are instead provided by a teacher vector theta that directly act on the Gaussian coefficient C, so on the latent space. So you could think to this model as one, I mean as behaving along the lines of modern generative models that basically starting from latent Gaussian variables then can generate high dimensional inputs as much as you want, okay? Are there any questions up to this point? Okay, perfect. So there is a silent rate in the hidden manifold model that is that it basically provides a direct access to the generative features, the teacher vector, and the intrinsic dimension of the dataset. So in the correlated hidden manifold model, what we did was to exploit this rate. And in particular, we constructed the source task as a standard hidden manifold model while the target task is constructed from the source task by applying three different types of manipulation on the generative features on the teacher vector and on the intrinsic dimension of the dataset. So the first one is feature perturbation and substitution, the second one addition or deletion, and the third one teacher perturbation. So keep in mind that all these transformation are actually meant to describe the situations which can concretely occur when you train machine learning models on real-world datasets. For instance, teacher perturbation would correspond to the case where you have basically two datasets and these datasets are sharing a common set of inputs but they are labeled according to different labeling rules, okay? So given this data model, we consider then the following transfer learning setting where we basically took a two-layer neural network that we train numerically on the source task. Then we basically took the first-layer weights and we transferred them to a second-layer neural network that has to be trained on the target set. And it is trained on the target set while keeping the first-layer weights frozen and letting just the second layer to read up to the target set. We call this model the transfer feature model, okay? And the goal that this model has to achieve is basically to reach the lowest possible generalization error on the target set by performing some empirical risk minimization over an L2 regularized logistic loss. Now, once again, this model plus the correlated hidden manifold model can be solved analytically thanks to the recent advancement in statistical physics to include the structure that I was mentioning you at the very beginning. And if you actually solve this model that is computing through the replica theory, the generalization error, you will realize that some Gaussian universality appears also in transfer learning, even if they are less strict than the one that I was showing you in the very first example. And to see that, you can actually consider the following experiment. So you take basically the amnesty letters dataset, which is simply a dataset of unwritten letters and you just consider a subset of these letters. Then the source task is constructed by dividing this subset into two distinct groups, assigning to each one of these groups a different label, just because we wanted to deal with binary classification tasks, okay? And then the target task is constructed from the source task by simply substituting one letter per group, okay? So given this dataset, we have basically trained the transfer feature model on this data type. And what we got, if we measure the test error as a function of the sites of the target training set, it's precisely this light blue curve, okay? The orange curve instead corresponds to the random feature model, the green curve to a two-layer neural network trained completely from scratch, and the dark blue curve to transfer feature plus fine tuning. Now, there will be many things to say about these plots and comparing all these different learning models, but at this stage of the work, we were actually interested to check whether the correlated hidden manifold model can concretely reproduce this scenario that we are actually observed in the experiment of real datasets. And to basically check this, we constructed the source task as a standard hidden manifold model, and the target task is basically constructed from the source task by perturbing the 30% of the feature, okay? If you, with this dataset, you perform exactly the same experiment, so you measure once again, the test error is a function of the sites of the training set, you will get this behavior here. So there is a striking qualitative agreement between what we can observe with real datasets and what instead we are actually observing with Gaussian data. Now, even if there is, in this case, not, I mean, a numerical agreement, this qualitative agreement is still there and actually allows us to basically start investigating and deriving different phase diagrams for basically understanding transfer learning effectiveness. And therefore, we actually learn something from, I mean, this theoretical analysis with the Gaussian model that turns out to be useful for deep learning application. For instance, the first thing that we learned is that transfer feature model is a hexabit delayed interpolation threshold. So they are actually able to fit much more data and this is simply due to the correlations that are encoded within the transfer feature map, okay? The second thing is that transfer learning is highly asymmetric in the sense that transferring from harder to simpler task in terms of generalization performances is way better than doing the opposite. A third thing is that transfer learning can be actually effectively, but it needs to be used wisely because it strongly depends on how much the source and the target task are actually correlated. And this basically allows us to notice, I mean, this strong dependency on the two correlations that basically, contrary to what deep learning prediction is typically does, that is to transfer all the features, all the layers of feature extractor at once. It is better convenient to, depending on the correlations between the two data sets to transfer just small parts of the network. That is why in this very last paper we basically design an algorithm that allows to basically identify the optimal transfer depth and also we propose a strategy to select the optimal source task among all the available candidates. And all of these came from a Gaussian data model. Okay, so are there any questions up to this point? Yes, please. So you could measure similarity in many different ways, I presume. So I'm wondering sort of what you use here or how you measure that, does that change your qualitative? You mean the... The similarity between the source and target. Ah, okay. No, here actually, if I go back in the data model you measure the similarity between these parameters. So for instance, here you see you have the teacher vector of the source task. Then you have the teacher vector of the target task and Q is basically identifying you the overlap between the two teacher. So this will allows you to actually measure the correlations between the two data sets. Then given these two data sets you can actually check how this correlation propagates throughout the networks. And for that we have some more, I mean we use some well-defined similarity measure among neural networks. Like for instance, information imbalance which is a measure that has been developed quite recently in Alessandro Laiu's group. Okay. So okay, so understanding basically how transfer learning works is extremely important because basically deep learning models nowadays are never trained completely from scratch. And transformers do not make an exception. So transformers are actually a special type of artificial neural networks that are currently achieving a state of the art results in various domains. For instance, language modeling or image classification. But here the central question is why are these network type of networks so special? Well, if you think about it, when we either read some text or we look to a given image, we do not just blindly go through all the words or through all the elements of an image, but we are also able to assign them and meaning depending on the surrounding context. So for instance, if I give you this sentence, the animal did not cross the street because it was too tired. Directly from the context, you will basically realize that the pronoun it is referring to the word animal. Now, if I give you instead this other sentence, the animal did not cross the street because it was too wide. Again, from the context, you will easily realize that the pronoun it is referring to the word street. Now, transformers are actually very good in playing this game that is learning context and meaning from sequential type of data. And once again, one of the key things that make their success is transfer learning. Now, in the context of large language models, the source task is a special type of self-supervised learning task, which is called the Masked Language Modeling. This task basically consists in training a neural network to actually predict missing words in a large amounts of row text. And in this way, basically, the transformer is pushed to learn the interactions among the different words in a sentence. Now, this task is of course self-supervised because the labels are directly sampled from the input. And as you can see, it's data abundant by definition because we can always take and collect huge amounts of row text because we do not require any type of annotation. The only requirement that is asked to the user is simply to mask here and then randomly some words. Okay. Now, once the transformer has been pre-trained on this task, it can be actually fine-tuned to some downstream tasks, for instance, text generation as in the famous example of chat GPT. Now, despite actually transformers are widely used in deep learning applications, they still are considered as a sort of black box models. And indeed one may ask, for instance, what transformers actually learned when they are trained on masking language modeling tasks or how many samples are required to achieve good generalization performance is when we train neural network models in self-supervised learning mode. Okay. Once again, to answer this type of question, in this paper, we basically proposed the generalized POTS model as a good data model for, let's say, mimicking the interactions between the different words in a sentence. So how does this model work? Well, we model sentences of length big L as the sequences of one not encoded vectors. So here the dimension C is nothing but the size of the vocabulary. So if you want the total number of different words that you have in your data set. Once you have this, each sequence is then sampled from the Gibbs measure of the generalized POTS model, where the energetic function differ with respect to the standard POTS model because we do not just allow interaction among the positions of the different words through the matrix J, but we also allows interaction with respect to the semantic meaning of the different words. Okay. Are there any questions up to this point? Yes, please. Yes, if you do this one hot encoding, it seems that the dimension C would be very large for a realistic case like English or something. So then would you have enough data to fit these models or something? Well, actually what people typically do in Transformers is that after having done this transformation, they apply some layers of embedding which basically reduce the dimensionality. For instance, from, I don't know, 50,000, it goes to 500. And then actually you also learn these metrics that performs the embedding. So it's really, I mean reduced. So roughly I should be thinking that even though one is doing this one hot encoding, like all words are not actually distinct. There are many words that are actually pretty similar. So if I project it down to a little bit. Yes, exactly, exactly. Because then you have first stage of tokenization, it's called inJergo. So where you basically construct from words token which are basically vectors made of some embedding plus positional encoding. I mean, these are all technicalities of Transformers, but yes, you can actually reduce the dimensionality in the end. Okay, so once we had, I mean, having this data model, then we construct the Masked Language Modeling Task in the framework of the generalized spots by simply sampling big M sequences from the Gibbs distribution. And then to construct the data set, we basically take these sequences of why not encoded vectors and we mask one why not encoded vector randomly per sequence. This defines the input of the data sets associated to the Masked Language Modeling Task, while the label will be the Masked why not encoded vector itself. So given this data set, then the goal is again the usual one that is to basically train at Transformers in order to be able to find a good estimate JET and UET of the true interaction matrices of your model in order to basically achieve the lowest possible generalization loss. Okay, so having defined the data model and the task, what about, I mean, the first question what Transformers learns with Masked Language Modeling? I'm not going to talk about this, but if you are interested in Sebastian Golt is going to talk about it during his talk. What I will hear focus is instead how many samples are required to achieve good generalization performances in self-supervised learning. Well, it turns out that statistical physics as we all know is extremely powerful in answering these type of questions, especially in teacher-student settings where the labels are provided by a teacher vector T and then the goal of the student is to find a good estimate WET of the teacher T by actually minimizing the test loss. Okay, so as I was saying at the very beginning of the talk, once again the extension to include the structure in replica theory turns out to be extremely useful because we know actually how to compute these test loss. I mean, even in context where dataset are structured. And this actually turned out to be useful also for studying Masked Language Modeling task because if we now make an assumption on our data model, so we basically make it Gaussian by relaxing the discrete nature of the one not encoding and substituting with some real variables, the Masked Sequences will now be sampled from a Gaussian mixture. And how basically these words are basically connected to each other is controlled by the covariance metrics of the Gaussian distribution. Now, if you make this assumption, you can actually apply the whole machinery of the replica theory also in Masked Language Modeling with the only difference that now the labels are not provided by any teacher vector but are instead directly sampled from the input distribution itself. So the goal of the student in this case is not to infer a generic teacher vector but precisely the rows of the covariance metrics. So we can, thanks to this approximation, compute the generalization loss in Masked Language Modeling. And if you do that, what you would get is precisely the solid black line here where basically I'm displaying here the test loss as a function of the number of samples per sequence length. Now, this is what the Gaussian approximation of the generalized spots model is giving us. But we can actually run concretely some simulation on the actual data models on the generalized spots. And what we will get once again is a striking qualitative similarity between what the Gaussian data model can predict and what instead is actually observed with the true data model. Are there any questions up to this point? Yes, please. Yeah, yeah, because actually the appearance of the peak that is something I'm going to discuss in very few minutes later, it's basically due to, it basically appears at the interpolation threshold. And this interpolation we actually discovered that strongly depends on the type of loss that you are using. Now, in the Gaussian data model, I was using a square loss because the values are real. While in the data model, I mean, since it's not encoded, so our discrete variables, it should be interpreted as a classification problem. So actually you are using a cross-entropy loss in this case, so the interpolation threshold change. But despite that, I mean, in both cases, you actually observe the presence of this peak. And this peak actually is directly related to the double descent phenomena. But this time, I mean, the appearance of this peak is not due to label noise, but it comes directly from the noise that is encoded in the input data distribution because it's a self-supervised learning task, okay? And moreover, I mean, as you can see, there is an absence of the initial descent. And this is basically due to the higher level of noise that you have in the input and that you can't control because it's intrinsic of the input itself. So just to basically conclude, I was basically starting with these two questions. Now the first one, we have already seen it. The second one, can we still learn something from Gaussian data model? Well, the answer is yes. There are some machine learning settings where you can still learn something. For instance, we have seen an example in transfer learning, but also in self-supervised learning. And with this, I basically conclude and I would like to thank all the people involved in this project, in particular Bruno and Ludovic that are here in the audience. And thank you very much for your attention. Questions? Please, can you go back to the slide on task similarity? This one? No. Task similarity, the second part of the talk. Where you are designing the correlation between the high-dead manifold model. Ah, sorry, yes. Okay, yes. This one. Yes. On the source task, you have same view. On the target, you also have same view. If, for example, I decide to change, like, not to use the same same view. Is this meaningful? I don't think so, because these are basically some Gaussian random variables. So they do not provide any type of information. The structure in the input is really encoded in the generative features, F, because it's really like if you have an image, for instance, of a cat, these features are basically telling you that a face of a cat is made of some, with some noise, and I mean, the typical shape of the cat. And the shape of the cat is controlled by these features. So this is what you need to basically change if you want to change the structure of your dataset. Then you can also play with intrinsic dimension of the dataset. So how much, let's say, data points are spread in the lower dimensional manifold. And also you can act on the teacher because you could have, for instance, I don't know, you could take MNIST and divide it in even odd digits. The images are the same, but the task is different because you want to classify even odd images. And you could take also MNIST and try to classify numbers that are greater or smaller than five. And then this is another task where again the images are the same, but the labeling rule is different. So these are the three things that are basically important for the data structure. Okay, last question. On the last language task, can you share the intuition behind the semantic interaction? Yes, okay. Because basically when you have, let's say some words in a sentence, they occupy in this sentence a particular position, okay? But the meaning that we assign to this word is not just about the position that they basically take in the sentence, but also the intrinsic meaning that they have. For instance, if I, in a sentence, I have the word apple, I know that this is a fruit independently on the position that it takes. And so when sentences are constructed, what it matters is the position that the words occupy in a given sentence, but also the intrinsic meaning of that word that is the image that is triggered in your brain when you think about that word, okay? Hi, Peter, again, thank you for this wonderful talk. I would like to come back to this collapse of all the curves in the random label setting. I want you to understand whether my intuition is correct. So in a sense, what you're doing is, so okay, when you learn, you can learn two things. If I'm not mistaken, you can learn the rule linking inputs and outputs, what we usually call the teacher in these setups, and something intrinsic to the structure of the inputs, right? So in this setting, there is no rule because you have random labels. And by making the, by homogenizing the inputs by this standardization or this whitening or whatever, you are also kind of destroying the data structure. So is it really surprising that at the end, everything collapses on the same curves because both aspects are kind of... Yeah, actually, I mean, you are not completely destroying it because you still have the Gaussian clouds that are found in a given position. What is the same? It's just the covariance matrix. And actually, as you, I mean, can see even if you have random labels, the covariance matrices needs to be the same because actually you are also, even if you have random labels, you are still learning something about the input. And so this is what it was surprising because at the first glance, one can say, okay, yes, but if you have random labels, you are not learning anything about the input data structure. Instead, this is not the case because if the covariance matrices are not homogeneous, then you start observing something that deviates from discussion universality. But I mean, you still have some structure. Even if I agree with you that it's a little bit less evident in this case. But one thing to say that it's more a further, let's say, branch of the theorem is that if you instead consider square loss functions, then in this case, you observe discussion universality so that they collapse, even if the covariance matrices are no more homogeneous. So for the square loss, there is this farter, stronger universality, even if they are not homogeneous. For the other losses, this is not the case. But of course, we still have to understand so many things about these models and how they work. And this collapses, this universality hold in some specific scalings for the numbers of hidden units and things like this, right? Yeah, I mean, for the moment, we see that it holds when the scaling is linear. So you have a number of samples that scales linearly with the input dimension. I don't know very well if maybe Ludovic and Bruno did some advancement later to include the square regime. But actually we are also start working with, I mean, to extend the statistical physics theory to the square regime. And Manfred did also a beautiful work about that. Yeah, in the 80s, I think, okay. Any other questions? Okay, thank you for your talk. My question is that the Gaussian data model, is it, can we use it for image classification in the sense of breast cancer problem? A problem like in medical, medical feed, for example. Because I can see the example you gave just based on the minister fashion data set. Yeah, that would be cool. And indeed, I have some medical data disposal. So one of the future direction that I typically present when I talk about this transfer learning work is precisely to try to apply what we have basically observed with Gaussian data models to medical imaging. But actually we have already done this in this paper but with the toy medical imaging. So the one that you could download on Kaggle. Sorry, it's here. So in this paper, we have actually applied this algorithm to medical data sets starting from a previous analysis on Gaussian data models. And we actually observe once again the same behavior that is that the optimal transfer depth is finding in a non-trivial position. But I personally also would like to try also with other medical data sets that are not downloaded on Kaggle and which are real one, the one that you actually collected during the experimental, let's say, projects. Okay, the reason why I ask the question is that like in that domain, it involves future extraction, like future selection and things like that. The dimensionality of the data is very wide in terms of I don't know if such Gaussian data model will be able to handle that problem. Just I'm correct in working on that every... Yeah, no, of course I'm not saying that Gaussian data models works for every context but still you can learn something from them. And for instance, in this case, in this list, this is what we learn. Then of course, if you are interested in particular things, then it could be that they do not say anything because maybe the model is too much simple but in this case, it worked. That's okay, I'll show you what I'm trying to say. Sure, thank you. Yeah, in the interest of time, maybe let's move the remaining questions to coffee time and then we reconvene at 10.50. Let's thank Kaderika again for the really great talk. Thank you very much.


The main objective of a research in social sciences is primarily concerned with looking into minimizing the risk and managing the risk in social sciences. The benefit of research often lies in its importance of the knowledge to begin and to contribution is to make in science or to the contribution to the society in general. Here it is very important to see that whatever the research we are going to conduct in social sciences is either going to give some benefit to the society or to the mankind or any other person who is involved in the research directly or indirectly. But we also have to see that if there are any kind of the risk involved, the risk should be very minimum. So we have to either minimize the risk or either we have to manage the risk to see that the benefit should be must in a large size which should be delivered to the community. Most researches in social and behavioral sciences poses little or almost no research risk to the society but there are certain cases where scenario could be different. When the primary source of risk is nothing else but the data itself. So you know those situations where itself data is a threat to us, a risk, a source of contention. It is very important to see that how we have to protect the data. When a possible disclosure of participants' responses is a primary source of potential harm, data protection from the unauthorized access can be accompanied in various ways. So these various ways could be managed in different terms. Like probably when we are taking the data of the participants which is very sensitive nature in which there is some kind of information which can lead to their social identity, their psychological identity, their physical identity, any threat which can be a threat, then in that situation data code and then further decode is a very important process because we have to issue some identifiers, some codes so that we can save our data in any software in the computer, either it is data, either it is SPSS, either it could be any other statistical software or package but we have to give certain identification and codes to each participants so that their data is not being shown by their names, rather it is being shown by an IDC and a code. And when we need it, then a mechanism should also be available that if we have to identify that data as a potential threat, it seems to us that there is potential abuse in the data. It seems that there are some kind of symptoms which show the high intensity of psychological behavior. So, we have to remove all the direct identifiers as soon as possible so that we can decode that data. So, this is the process which we have to involve some systems so that we can minimize the risk so that the identity can be saved in computers so that it can not be misused during any data handling. So, we have to remove all the direct identifiers as soon as possible the data has been entered into the computer system, maintain the code list and data files in separate secure equations. Then use and protect computer passwords. First step, we have to take care of the protection of the data sheet and we have to put codes and identifiers on it. But the second step, the computer should have a password and login so that only relevant people can access that system. Then access and store data on computers without internet connections. This is something very important and this is something to be understood on the next level because when internet connectivity is available in a system, it is very easy to hack the data from there. So, by looking into the factors which can manage and minimize the risk, the next thing is to see that in certain circumstances, we can assure that here we can give the certificate of confidentiality and where we can waive the certificates of confidentiality looking at the potential participants who are going to participate in the study. Because it is very important that we do researches in social sciences where we cannot assure confidentiality and we have to see whether the informed consent will reveal our intentions and our objectives to the participants. Will there be a potential threat source for that, whether it is emotionally or sociably disturbed. So, in situations to see, we will talk about certificates of confidentiality further. Certificates of confidentiality are provided to protect identifiable research information from forced disclosure, it may be granted for studies collecting information that if disclosed could damage the financial standing, employability, insurability of the participants. So, if any of these three factors are involved, so we have to see that either the certificate of confidentiality should be given or not. These certificates do not overrule certain requirements and these requirements are to report suspicion of child, elder abuse or any other statement related reporting requirements. Confidentiality is very important but when it comes to a situation where there is an abuse going on, either it is an abuse on a child or a minor, either it is an abuse on a woman, whether it is economical abuse, social abuse, psychological abuse, physical abuse, violence, all of these we have to see that by ensuring a certificate of confidentiality, whether we are bringing security to the threat, if we feel that a researcher at some stage feels that he or she is bound to do that, because these certain situations to individual risk, confidentiality certificate maintained is not so important. Information that can be protected include substance abuse behavior, sexual attitude and preferences, genetic information and psychological well-being. These four are the main corners where we see that if someone is involved in any of the related to the poor paradigms which are enlisted here, they can be protected under the confidentiality certificate.  However, when it comes to abuse, neglect, trauma and any such thing, then the confidentiality certificate must be broken down. Now, managing other risks. Participants could be at risk simply by being involved in a study on stigmatization or illegal activity. One way to diminish their risk to exposure is to consider applying for a waiver of documentation of consent. If consent form is only document that links them to the study. Now, such potential situations where participating in a study itself is risky for you, where there is an illegal activity, where there is a study on such behavior which is not socially appropriate, where there is a stigma involved. If there is no need to document that we write down all the details of the volunteers, then we can waive it off so that it is not present as a proof that other people can manipulate it so that you participate in such research. So, I think one of a very good example for that is that in certain social circles where prostitution is considered to be a very illegal practice, if data is collected there, then consent form is not taken, the confidentiality certificate is insured, but their details, their residential address, the different methods to approach them, or their identity is not asked by them at all because they are only asking about their experiences, their issues, their psychosocial traumas are being asked, but it is not so important that their identity is also noted down. And this goes with all other sensitive cases such as HIV population and things like that. Participants may also be placed at risk by nature of inquiry and trauma. In certain situations, there are issues where there are some injuries or traumas which can be psychological or physical, if we expose them towards the questionnaire and interview items so that they can go back to their trauma. For instance, if someone has been through any rape victim or is suffering from this type of problem, if we are asking such sensitive questions in the interview, if we are taking all the consent from them to ask their identity she or he can go back to that situation where he or she has gained that experience, so that is also very traumatizing. So, it is very important to see this with sensitivity that what kind of the questions we are going to ask and either if the only document is the consent form and it is not that important to get it filled by them, then we can also waive it. In some studies, maybe involved a complete assessment of nature of the impact of trauma with the potential to be distressing to participants. Like if there is a grief trauma, at least we can talk about it. There is a chance of a distress cause. So, investigators need to plan appropriate resources such as supportive counseling, referrals and access to research staff. Why? Because in any situation where maybe the distressful symptoms may trigger, then we will need psychological counseling, assessment, referral because the well-being of the participants is very much important to the researcher and psychologist while conducting any research in social sciences.


It's theCUBE, covering VMworld 2015. Brought to you by VMware and its ecosystem sponsors. And now your host, Dave Vellante. Since the dawn of the cloud. Hi, we're back. This is theCUBE and theCUBE goes out to the events. We extract the signal from the noise, a lot of signal at this event, really around a number of things, but specifically around storage. Storage has been a pain point in the VMware community for a long time. Basically the better part of a decade. And Rich Peterson is here from Sandisk. We're going to talk about some of those problems, what Sandisk is doing. Rich, great to see you again. Thanks for coming on. Thanks for having me.  Some really strategic acquisitions. The market is changing. And we think you're really well positioned. Set it up. Talk about the business where you guys are at. Well, like you say, Sandisk is a well-known leader in the flash industry. Everything from fabrication to the retail presence over 25 years in the business focused exclusively on this technology. But over the past five years plus, we've been expanding our focus to cover the enterprise space. With, as you mentioned, acquisitions, including client technologies, Fusion IO, FlashSoft, where I came from, and others. And we really do see the opportunity to address the storage latency issues in virtualized computing with flash technologies at every level. At the storage, in the server, and in collaboration with software technologies like VirtualSand and software technologies that we bring to market like the FlashSoft software. So what do you have going on here at the event? Well, this is probably the biggest VM world for us yet with respect to FlashSoft because this year we announced that as a result of our collaboration with VMware, we're going to be introducing the next version of our FlashSoft software as a virtual data service that's enabled through a new set of APIs called the vSphere APIs for IO filtering. We were actually a VMware's design partner in the development of the caching capabilities of that framework. So we've been working with them behind the scenes for about a year and a half now to ensure that we can bring this technology to market and it can be delivered and certified VMware ready. So you've been talking about this, David? Yes. You've been telling me it's a big deal. Why is it such a big deal? Why are you so enthusiastic about it? Well, because of the bottleneck of IO within the VMware architecture. And this is the first time they've done a previous job on it. What was it called? The VMware technology? Yes, the VMware. Yeah, the VFlash read cache. That's right. They had some stuff in there but really it wasn't that functional. It wasn't really getting over the basic problem. So it limited your ability to really exploit the... What they've done is introduce you and others into a proper architecture and essentially allow the whole of the stack to be bypassed so that you can go from A to B without going through all the layers. So you're cutting the line in the horrible storage stack? Yeah. Fair way to say it. That's not a bad thing to say. You know, VMware had a number of objectives with this framework. The first objective obviously was to enable write back caching as well as write through caching so that we can accelerate an application's write operations as well as their read operations. This is really important when you're gonna be running a transactional database or a business intelligence application in a virtualized environment because those types of workloads have a large number of random small writes and that's what's really frustrating for storage. But beyond that, they also had the objective to establish a framework that would enable third party data service or software providers to provide caching solutions that would be integrated in an authenticated in a good way with ESX. This means getting third party technologies out of the kernel space and integrating and intercepting IO in the user space. That was a huge objective for VMware and they put some of their top engineers on this framework development. It's been a real privilege for us to work with them. It's really, as you and the other one is, what is it? Primary IO, isn't it? Who's the other partner that you had with you? Well, so in the framework development, EMC was the partner for the replication filter and we were the partner for the development of the caching filter. Okay, so you guys both worked sort of independently with VMware or was it a sort of? The filters actually are sort of at separate layers of this framework and that's for good reason. Simply put, when you have the replication filter at a higher altitude than the caching filter, a write back cache is not gonna interfere with replication. So that's all good. And so there's been a lot of thought about enabling write back caching in the vSphere environment yet making sure that you take care of any problems that could occur such as ensuring the coherence of snapshots or ensuring that write data that's cached is replicated for high availability in the event of any kind of event in the cluster. Or vMotion. vMotion, same thing. Yeah, yeah. So there's a lot of, as the design partner, I like to joke that we have a number of Russian engineers on the team. And if you want somebody that can think of everything that could possibly go wrong, find a Russian engineer. But the truth is that we did have to consider all of these failure scenarios and make sure that we had proper protection of the data. Because again, write back caching as a new, it's a new idea for a lot of administrators. And if you're gonna bring technology to market that is certified VMware ready, that has the assurance that we're gonna take care of you and your data, you've gotta get it right. So how are you going to market with this? I mean, this is a new area for you, isn't it? Because this is not just your technology, it's anybody's technology of support. That's true. The objective of having an API is to create a level playing field for your ecosystem partners. I think we do have an advantage in having been the design partner and working on this problem for a longer period of time and in a greater level of detail. But I think the go-to-market strategy for SanDisk as a whole is a bit of a hybrid strategy. We do have a direct sales effort. We do have a channel operation, but we have a very significant OEM presence as well. And this comes obviously from our hardware business in which we've been supplying solid-state technology, SaaS, SATA, PCIe, even UltraDim through to storage and to server vendors. So we do have a very strong reseller relationships already in place with Dell, with Lenovo, with HP, and with HDS. So our go-to-market strategy is very much in collaboration with VMware as a technology partner, but then also with these major server vendors as business partners. Excellent. So that's a very different way of thinking about software in this particular space, isn't it? Not necessarily for you, but most software vendors are not thinking of it as an OEM player. I think it's a solution-oriented approach. We need to work with the decision maker that's looking at what the architecture for their VMware cluster should be and to understand their options for using Flash Include, not just Flash in storage, but Flash in the server as an intermediate cache that will enable them to really prolong the useful life of the existing storage infrastructure that they've already got. So I got a question from the text. I want to do it on the crowd chat. So the question is, SanDisk strategy, the market's changing, all kinds of startups popping up all over the place, but SanDisk is in a unique place in the value chain, let's call it. So what's the strategy and objective for growing the company specifically in the storage space? Well, clearly, we see the enterprise as an opportunity where we've only seen the tip of the iceberg for Flash. We could talk about the other systems that we've been developing, like the Infiniflash systems and the Ion Accelerator and many of the other technologies that we're bringing to market. But as you mentioned, we are in an interesting position in the industry in that we are a supplier of technology and capability to the vendors that the end users have had long relationships with over decades. And so we want to enable those vendors to be successful with their customers. If we can do that, then I think the future for SanDisk as an enterprise Flash solutions provider is very bright. Because you've got an interesting stack, you've got a lot of pieces. And so essentially the strategy is to build solutions for your partners to take to market. Right, right. And so that's cool, sounds good. The interesting thing is that you can potentially do this for so much less than the traditional storage model, so much less expensive than the traditional storage model. And does that threaten some partners potentially? You know, to some degree, because I'll give you a simple example, people ask me, well, if you have your caching technology which works with existing storage arrays, so you're doing server Flash, but saying keep the storage array and then your colleague over here is telling me about all the virtual San, all Flash virtual San configurations that you're validating and supporting aren't you too competitive. And the simple matter is that, yes, these are alternatives, but the decision fundamentally lies with the storage architect, right? They're going to map their roadmap. They're going to say this is where we want to go. It's really incumbent on us to make sure that whatever choice they make, and in our case with Flashsoft, it's server Flash over your existing storage infrastructure. Now, you may be spending less on that storage infrastructure. You may be enabling storage functionality at the host level now, instead of at the array level, but that's sort of the natural evolution of our industry. It is, and there are a lot of good reasons to do that. There weren't maybe 15 years ago, you know, wouldn't work as well, and we pushed data out for a good reason, but it's coming back for a good reason. And so I guess your point is, we have to drive technology, we have to drive costs down, that's what this industry requires. So, damn the torpedoes, we're doing it. Very much so. I mean, the technology that we're talking about today is one that we began thinking about seven years ago, when we began to imagine what would the world be like when you could buy a terabyte Flash device and put it in, and it sounded crazy at the time, right? But we knew that was coming, and what we have to be thinking about as software developers today is what the world's going to look like five years from now with latencies that are 100 times less and capacities that are 10 times greater. I mean, when you look at the storage industry historically, it's been taking a Seagate or whatever disk drive and adding a lot of functionality to make this actually work as it was a very, very difficult piece of technology to make it work. So, and the markup on it in terms of times the original cost of the disk is 10, 15, 20 times more than the disk. With Flash, that's not nearly the same markup, is there? It appears that there are a lot of problems in terms of the fundamentals of dealing with the Flash and ware-leveling and performance, et cetera, have to be done by the Flash manufacturer themselves because it's unique. I think you're right about that, and certainly our team is extremely strong in that area. Technology's to get optimal utilization and performance from the Flash resource, but at the same time, the software that we provide also has to use Flash resource intelligently. We can't treat, when we're talking about a caching capability, you can't treat solid state storage like spinning disk, nor can you treat it like a DRAM. You know, there are certain capabilities in our software that were built specifically because we knew we would be using non-volatile NAND Flash as the caching resource. And so our software is designed specifically for that, for the physics of that, you know? So following up on what Dave was talking about, is there an opportunity, though, for you to own pretty well a complete stack from, because you'll be able to optimize the software in a whole number of different places. The way that end users are using Flash is so different than the way that they were using disk. Right, right. I can't comment about whether Sandisk has a master plan to own the entire stack, because from my perspective, a lot of our success really does hinge on empowering the users to get the technology that they want the most. A simple example is the fact that our software will work with any vendor's Flash hardware. This is simply because, when you're working in partner relationships with server vendors, the requirement for a tightly coupled hardware software opportunity would lock you out of any long-term relationship with that vendor. Right, right. Now, we obviously hope that their success with our software will lead them to consider our hardware more, but again, we have to win on both fronts. Well, but these are, as the industry evolves, as you evolve, there are opportunities that you're going to assess based on customer demand, and you see opportunities, it's pretty clear you guys aren't afraid to go after them, and you came to Sandisk as a result of one of those opportunities, and it's, to us, it's transformed the company in this space, and it's really exciting to watch. We came to Sandisk because at Sandisk, we can engage in a multi-year, behind the scenes, partner relationship in development of a new technology with a partner like VMware. It'd be very difficult for a startup to do that. Deep, deep partnerships, integration, we see Amazon coming to one end, we see folks like Sandisk having tremendous opportunities, so really appreciate your rich coming back into CUBE, and good to see you again. Thank you very much. All right, keep right there, everybody. We'll be back with our next guest right after this. This is theCUBE, we're live from Moscone, at VMworld 2015.


Hello and welcome to This Week in the City, where we bring you highlights from the Public and Media Relations Department. I'm your host, Alicia Utsi. This week in the City on Tuesday, June 19th, Columbia City Council will hold both a work session and evening meeting. The work session begins at 2 p.m. and the evening meeting begins at 6 p.m. Both meetings will be held at City Hall and both meetings will be live streamed on our website at columbiasc.net. You can also check that same website for agenda items. Also this week, a City delegation from Columbia will be traveling to the All America City finalist competition, which is a part of the National Civic League's annual conference. Columbia is one of 20 finalists nationally, and hopefully we can bring home a winning All America City designation. On Friday, Pronto and the Parks will be held at Greenview Park. For more details about this free event for teens, call Parks and Recreation at 545-3100. Also this weekend at Greenview, it's Family Tennis Nights on Saturday. Again for details, call Parks and Recreation at 545-3100. And for a full schedule of weekend events, check out Destination Hometown on our website at columbiasc.net. Also check our social media platforms for Destination Hometown information. It's going to be a very busy week in the city and we hope it's a great one for you.


So that that number in the spy that was it. I mean that was the number So when you know what the numbers are whether something that moves higher or lower, it makes it so much more easier to trade And again, it's the number in the gap So it's always in the gap. It it really has to be in the gap It's not if it moves through it if it's if it gaps to it And that is what happened yesterday and it was real So Let's just take a look here. I I liked Twitter this morning. Now. This is pushing back This was the only short really that I was interested in This is not earnings Obviously And I was considering whether or not we should go long I think we should watch the diamonds today as along and Twitter as a short And I don't think we're gonna be aggressive in anything today. Let's watch Twitter first We really can't be aggressive today We just can and the options trades are gonna look really good here this morning when we open So just leave those being Not sure if anybody held through the target beautiful beautiful Beautiful move in that yesterday, and I did find Netflix late yesterday. I saw it Again, I didn't run the room yesterday, but You could have done that as a day trade and if I had seen it Which I didn't until like I don't know two o'clock three o'clock I might have called to put in it. It's too late now, but that's down again. I think this morning So some things are up Some things are down Let's see if we can get a nice Short in Twitter today or a long in the diamonds know what you're doing because we're watching two different things in two Different directions, which is unusual. We don't have any earnings washes today and nothing else really stands out that much I'll look at that later Jackie my allergies are bothering me so much. I apologize for coughing and hacking There's nothing I could do about it. It's hard to believe that my allergies are bothering when I'm Not leaving my apartment How is that possible? How's it coming in? Let's watch the diamonds here Again, we're watching two things Ty the dust is also really a problem. It could be I could be allergic to dust. I think it could be that I literally cleaned on Thursday or I couldn't Friday last week. There's an inch of dust on my table today. It's Wednesday. I cleaned Friday That's I don't know. I don't know where it comes from. All right. We're watching this and We are also watching This is too late Nobody mentioned this yesterday. Did you guys not see this yesterday? I wish I'd seen this. I wish I'd seen this yesterday It's too late. I was too busy being on TV, but I wish that I'd seen it All right, Twitter We're gonna wait but watch This we're watching this So let's do Twitter Stop and Twitter is gonna be 34 10. Let's do 40 by 10 in Twitter 40 by 10 in Twitter short 40 by 10 First target though is 33 Remember, we're really focused this week Go back to the diamonds in a minute. I don't want to miss this if it goes Here you can do it do it right now and get the stop in this is a short in Twitter You got to know what you're doing here because again the diamonds was a long We'll go back and look at that here. You could do it right now here. There goes there goes 33 though 33 we have to watch 33 Twitter And again, we're gonna we're gonna take just quick profits here and stuff until we get some bigger movements Which I think it's going to be towards the end of the week because Of the earnings we're watching at the end of the week here yesterday today all of this exactly what I said It's just like we were talking about early in the year remember early in the year We were talking about stop drop and roll. I don't remember what what things we were talking about about that I don't I don't remember when that was. I think it was early 2020 though We were talking about stop drop and rolls It's exactly kind of what the market did yesterday Because we rolled right on up this morning gaping up this morning. Here we go twitter You got to be in and if you didn't do it you could do it right here And then we'll check out what's tonight. We'll check out what's tomorrow morning if this doesn't break 33 We got to be out Although this could go to 32 50 it could go to 32 this could drop this could have a big move today But I wouldn't know piggy targets and anything Unless it's super duper duper duper duper duper duper fabulous, which we might get this week and all to our cosco I don't know they don't report till Thursday night But those are the big ones this week Stop dropping roll. You thought that this was going to be easy after yesterday but If you thought that you were kidding yourself But again, if you're up this morning, you can get out right here if you can't stand it So again, you're up in this you can get out.  I I just there's only so many things I can do in the morning I'm not going to touch this didn't call any trades in this this week. I don't know why that's filing If anyone has any articles, send me this we're never going to probably be able to do Until the next earnings, but I'm going to look that up.  All right, 33 23 In fact, I'm going to look it up right now. Anybody has anything else for me to look at let me know I'm going to I just want to I want to know when the Boeing earnings are really do What is today? We're still in May Okay, here we go. Fauci talking and CNN a second wave of COVID-19 could happen But it's not inevitable There's a good chance that a vaccine will be able to deploy it. We've deployed it by the end of the year July 22nd is Boeing Dan it's a little late for that. It's a little late for that here if you're in it. It's 3410. It's the stop in Twitter I remember this is an earnings. Don't expect this to go crazy birds And I'm being very realistic with the 33 target, but you know Theoretically could this keep going? Sure. I mean in fact, here's 33 Here's 32 50. I said the next one I mean, this is I you know, I'm not saying this can't do this But I think this would need the market to get down here Stop dropping rule The diamonds are higher Well, do we want to do this too? Do we want to do along in a short at the same time? Would that be crazy? Probably would be It probably would 252 80 We're gonna do if you want to go along the diamonds put an order out 80 by 80 You're gonna do 80 by 80. This is a long here. This could hit right now 80 by 80 80 by 80 Diamonds long Here's soon enough to watch it. Just put an order out to fill you It's 253 80 if that hits and we'll go back and we'll we'll put the stop in 253 80 you can do this long if you want just put an order in to fill you if you want in that I Think it's worth doing that too So we're in the twitter waiting for it to break the low one more time and it'll probably go down to the 33 number And if this goes over 80, then this is going to pop over the high So you can go along this 253 80 by 252 80. It's buck um targets 254 255 255 and then the twitter we're waiting So this is strange that we're doing along in a short, but I think that's okay today So the diamonds didn't hit yet twitter we're in waiting stops 34 10 Or something else I looked at this morning. What was the other one I looked at this morning? I forget This was that This had earnings as no volume This is no volume. It's kind of a shame Ralph the ren had earnings, but no volume and I don't I get I don't know why Okay, let's wait on this If this breaks the low the stop may change but the entry is still valid if this hits over 80 And we'll wait and see what happens with this. Let's look and see what's out for tonight This is probably it for today So again, none of these things are fast because they're not earnings So, you know, none of these trades are quick or fast. You just have to be a little bit patient We're definitely not giving twitter more room The diamonds didn't hit and let me see what's out for tonight We might get an add in twitter HP Q is tonight and tap is tonight total is tonight, which means the builders are going to move tomorrow morning And tap will be a good one to watch Again, if you didn't do the twitter, you can do it at 40 You can even do it at 50 And a and f is a big one for tomorrow morning. Today's Wednesday tomorrow's thursday Gosh, this week went fast all the retailers dollar general tomorrow morning dollar tree tomorrow morning And after tomorrow morning ll tomorrow morning Plenty of things tomorrow morning. And then the big night is thursday night If you did not do this twitter and you want to do it, you could probably do it at 50 I think i'm just going to let that be and this broke the law so so The the numbers I gave for this In fact cancel the order in this to fill you at 80 because the stop's going to be different So whatever you'd size yourself may not be right Just wait on this if this hits over 80 This is still along as a day trade today, but the stop isn't going to be the same So your position sizing has to be off and that's a lecture for another day a day. We have nothing to do We should talk about sizing sizing is extremely important. If you don't size yourself right You're not going to do well. You can actually have more winners and losers And and lose if your sizing is off And this goes for day trades or options. This is going to hold somewhere I don't know where though yet All right, so we're in the twitter. We're waiting cancel the diamonds because the stop's going to be different Let's look at those weird ones like the marina. There's just no way to play these things. We're so insane. Let's look at that guild again All right, let's watch Is there anything else I missed gyro? Did you have any watches today or anybody else? It really wasn't RL had no volume. I was the only earnings Stopping twitter's 3410 And this never hit and it could still hit And if it hits I guess we could put the stop. No, I think it would have to be 70 250 270 so 80 by 70 is valid I'm just going to watch that for now So we're going to have some snails here until we get some decent earnings moves So happy the market's making sense again this week though and even more so this morning So plan of action here is to stay with the twitter and I I'm not going to move the stop If we get stopped out of this, I'll look for something else, but I'm not overly anxious here today I'm sure we'll find something else to do if we stop in this but this So far is holy I wish we had done that in Netflix Did anybody hold the target through yesterday? That was a really nice move with the option And I'm not going to add to this because it's just too snaily But again, if you want to add in this You could add in 60 if you want. It's too snaily for me now The only thing I might consider like I said is along in the diamonds twitter's holding And I said 33 but to be honest with you if this comes down here and doesn't break the low 20 ish We're going to have to just could take it It's just like really slow L m n x let me look at it. Uh, this has no volume though.  When was that we talked about it? What was the stock we were just was it target? Was it it was something some was a bynd? What where did we where did I coined that phrase? I don't remember now It was was it tesla Was it tesla? All right twitter is a snail, but it's holding I just gotta wait and again, I you know, I said 33 trying to be realistic But low is 33 23 Here, I'll write the targets in the room because if this doesn't break it I mean that's gotta be it like what can I say? It's just very snaily But I'm gonna say dream target would be 32 50 So this is probably gonna hit so 250 380 by 250 270 Do we want to do two things today? Maybe we just do this be done All right, any questions? How's everyone doing tesla was tesla? Yeah Was it january tesla? Was it february? When was it? It was a long here if you did not did the twitter you can do it If you want to get aggressive you can add I don't think I don't think today's a day to be aggressive I just don't think it makes any sense Makes no sense Previous high in the queues was 237 47 I'm just like again Just very shocked that we got so close to the highs But the banks gapped up yesterday and ran and I didn't call any longs in these But you could have day traded these yesterday. I looked at them later in the day Look look at this today. This is I mean look at this In fact, let me look at why I mean we're not we're not doing anything with this but look at that It's seriously I mean, this is a big move for this stock. This is friday It was at 89 and change this is today at 102 and change I mortgages. This is a big move. This is a bigger move that it had it than earnings Look at that. That is a big move and again They could turn around the spy has a ready turn around the banks could turn around they haven't yet But man, are they trying in fact? Let me let me just look at this a little bit harder here They're trying that. Let me look up when the earnings are in this Because that's really what we need probably to get up over the high To be honest with you Okay, this is looking better and better here And here we go. Let's not miss our exit in this no piggy targets. I don't know if it gets to 33 It's money. It's a trade. There was not much today You're up in this if you did it or you could have added if you wanted to be aggressive at hell This is gonna go. I don't know if it breaks the low though. I think 33 is realistic You know 948.  I mean, you know, you have to size yourself, right july 14th That's not that far away That's not that far away at all july 14th is jpm earnings That's the start of the next earnings season that beginning of july That's really not that far away and here's twitter at the low Nice call here today. We're we're gonna have a good week We're gonna have a good week all around here. We go twitter's trying to break the low 23 We're gonna have a good week when options go to weekend day trades. We're gonna have a very good week And here we go through the low twitter twitter twitter twitter Come on. You can get out of 33. That would be a million dollars right now because everything's so slow See if it breaks 20 Yeah, 714. Thank you gyro Any babies yet Still no baby A grand baby. I should say It's not your baby. It's another baby 20 Here we go 19 Here we go Can you imagine delivering a baby during cova 19? I mean going into any hospital. All right, 33 14 Of course, what are you gonna do have a home birth?   It really looks like it's going to get to 33 now, but 950 We'll go over the market here when we're done. I think we'll have time you could be out of this If you don't have the patience If you don't Have the patience Here we go Here we go. Come on. Come on. Come on. Come on. Come on. Here we go twitter. Nice call today We're gonna have a good week. I'm very focused I need to start getting up earlier though. I'm sleeping in way too late 3307 come on I just bounced off the 3307 too quick to even notice it Oh gosh Did anyone just get out there? You stinker You stinker Jackie got out All right, let's talk about the market shall we? Let's talk about the market. Let me see if there's anything else you could have been out of that You definitely definitely definitely could have been out of that. Uh, let's talk about the overall


Hello everyone, my name is Daniela Di Gianantogno and I am the team leader for digital agriculture at the FAO regional office for Europe and Central Asia. The team is on a very exciting mission, which is to accelerate the uptake of digital technologies in the agriculture sector and in rural areas. I am originally Italian, but I lived in several countries across the globe. I am now living in Budapest, Hungary, where the regional office is located. Let me bring the example of our recent day. It started with a meeting with Turkmenistan's Ministry of Agriculture to discuss their priorities and possible entry points for digitalization. It continued with Uzbekistan as we are currently supporting the government with the development of a national digital agriculture strategy, and then with Armenia as we are supporting the introduction of a wine registry system. The day finished with a meeting with Albania, which is currently piloting the DVI readiness assessment tool to identify villages that have the potential to enter the FAO digital village initiative programme, which aims at revitalizing villages and rural areas, thanks to the opportunities brought by technologies. The beginning of my journey within ICT was completely unintentional. I was a master's student in economics in Denmark and I was looking for a job just to support my studies while writing my thesis. And I came across this job post from a software as a service company, something I hadn't even heard before, but I applied. They hired me and I remember walking to the office on my first day thinking, okay, this is just going to be for four months just while I write my thesis. This is not a job for me. This is about technology. What do I have to do with this? It ends up that I stayed with the company for four years and definitely not because I couldn't find another job, but because I found actually purpose in what I was doing. The company was developing technology solutions for people with disabilities and I found a mission and I set that mission to be to empower all people thanks to technology to achieve more. And so here I am, ten years later, still working in ICT. Actually, I would like to pass a message to all girls and especially the ones that are not interested in ICTs. Technology is not just for software developers. It is not just about writing algorithms. It needs a whole bunch of profiles from people good at communication, to people good at project management, to good people managers. So give it a chance. Technology might empower your career as it did with mine.