we continue the creative industry podcast, our guest today is denis dimitrov, a. the person who createddinsky neural network, elena kiper and roman karmanov are still with you, and what successes kandinsky is recognized for, this is what he does better than his analogues, let’s say, analogues are wrong according to his word, well, it looks like neural networks that solve the same problem, generating images from text, for example, what we’re cooler in, although kandinsky’s videos are generated, now uh-huh, now, well, that is , already, as it were, we’ve just recently introduced. and you can imagine how many terabytes of data storage is needed there, well , there is a supercomputer, it’s like it’s all learned on a supercomputer and stored nearby in special storage facilities, they are called c3, but these are these big, big data storages that are really for, say, in order for the model to be able to see the dataset, they need to be put there, and not just put, they need to be removed. all low-quality pairs, that is, filter out all low-quality pairs, well, if you go to the internet, then i

we continue the creative industry podcast, our guest today is denis dimitrov, and the man who creatednd roman karmanov. and what successes are recognized for kandinsky already has this, what he does better than his analogues, let’s say so. well, it looks like neural networks that solve the same problem, generating images from text, for example, where we are cooler, although kandinsky videos are now generated, uh-huh, now, well, that is , it’s already, as it were, we recently presented a model, which generates the video, they called it kandinsky video, although it’s probably not... but these are these big, big data warehouses that really , say, for the dataset model to be able to see, they need to be put there, and not just put it, you need to remove all low-quality pairs, that is, filter, remove everything, well, if you go to the internet, then in general half of the data there will probably not be of very good quality, there is a lot of rubbish there , but that’s exactly the picture does not correspond to the description that is written there below. this also needs to be dealt with,

we continue the creative industry podcast, our guest today is denis dimitrov, the man who created thelena kiper and roman karmanov are still with you. and what successes are recognized for kandinsky?

we continue the creative industry podcast, our guest today is denis dimitrov, and the man who createdk is still with you. elena kiper and roman karmanov. and what successes are recognized by zakandinsky already? that's what he does better than, let's say, analogues that are wrong according to his word? well, it looks like neural networks that solve the same problem, generating images from text, for example, where we are cooler, although kandinsky videos are generated now, yeah, now, well, that is, it’s like, we’ve recently presented the model , which generates the video, was called kandinsky video, although, probably, necessary. from the internet, that is, on open image description pairs, does it seem to capture data at the time of the request? no, naturally, there is a team that deals with data, and that is, the data is already contained in it, it is not searching on the internet at this moment, no, no, no, in no case, it is creating it, this is kind of unique, where are they stored ? this volumetric quantity is a huge amount of data, kandinsky the third studied on one and a half bil

creative industry podcast, our guest today is the man who created the kandinsky neural network, denis dimitrovimagine like a person creates a picture, any, any image, any painting, he has some kind of white canvas, well, not necessarily white, just white paper. some kind of empty, napkin, napkin, yes, there is a tablet screen, it has some kind of idea, necessarily, he starts sketching out some details, with a pen, a stylus, whatever, he adds details, maybe removes something, well, a person also has patterns in his head, that is, he will remember something, he, he he has a pattern, this is after training, step by step, he simply creates a picture, like creating a kandinsky model, in principle you can draw an analogy, he begins to draw a picture with the so-called white noise, what is it? well, this is when there is no signal on the tv, there is interference, just some kind of matrix naturally, this is your request, this is the same idea. the final picture is lysed, so to speak, appears, so to speak, for example, you want to make a pug in space, yeah, so you always have white noise at the beginn

model, simply transfers the face, that is, this is the real you, in principle. yes, dear friends, denis dimitrovest in the creative industry podcast on channel one, the man who created the kandinsky neural network, a wonderful team, and the main conclusion, even if we don’t quite understand everything, in general, in the end, we understand, there’s still nothing to be afraid of , look how nice, smart people are, doing those things that we may be afraid of for some reason, not we must be afraid, because this is the competitiveness of our country, and it is in the reliable hands of people like denis. denis, thank you very much, we are waiting for the next stage. the antennas are on your head, with you there are still analog, analog versions of the presenters, elena kiper, producer and video director, and roman karmanov, general director of the presidential fund for cultural initiatives. it was a creative industry podcast on channel one. see you. this is a podcast of witnesses from einstein and its leading film historians natalya ryabshchikova and stanislav didinsky, we talk about how to watch your

to make your own developments, we continue the creative industry podcast, our guest today is denis dimitrovn from, it learns from data from the internet, that is, from open pairs of description images, it seems to capture the data at the moment, naturally, there is a team that deals with the data, and that is, the data is already contained in it, it does not search in this moment on the internet, no, no, no, in no case, it creates, in this it’s kind of unique where they are stored, this voluminous amount of huge amount of data, this kandinsky the third studied on one and a half billion pairs of pictures and text, and can you imagine how many? there - a terabyte of data and storage is needed, well, on a supercomputer there is , as it were, all this is learned on a supercomputer and stored nearby on special storage facilities, they are called s3, but these are such large, large data storage facilities that are really for, say, a dataset model i had the opportunity to see that they need to be put there, and not just put, all the low-quality pairs need to be removed, that is, what does this mea

denis dimitrov is our guest today, managing director for data research at a major bank, i’m very glad is, everyone is already afraid, they are afraid, well, in general, of something, but what did they know, what is it like for me too, we know what it is, right now. remotely, what is artificial intelligence? let 's first at least figure out what kind of thing this is, in fact, artificial intelligence, there is absolutely no need to be afraid of it, since we started with this, it is a tool that helps us do different things, that is, it automates some part of our intellectual work, mostly the routine part, artificial intelligence - this actually usually means some kind of a neural network, what is a neural network, a neural network - you know, you can imagine it... a black box that takes something as an input and gives something as an output, that’s exactly how we imagine it, as black, what’s inside the black box, this is a kind of mathematical function, a function that now, for example, lies behind, for example, generating a picture from text, has, well, there are billions of parameters

transfers the face, that is, this is the real you, in in principle, it is, yes, dear friends, denis, dimitrovrful team, and the main conclusion, even if we are not quite everything, in general in the end we understand, there’s nothing to be afraid of after all, look, what nice, smart people who do those things. first channel. see you. dear graduates, we congratulate you on your graduation from school. now you can behave badly. but the most important thing is that your dreams become goals. and you, never doubting yourself, rushed to achieve your plans. i will come for you tomorrow after seven, i am the blue sun, i appear less often, less often, it hurts me so much to watch how beautifully you lie, you are on the body of the river, you feed on me, because my kiss, like a knife, i cut you, earth, kings of me . snow, love will save world. sleep, tomorrow i will rise for you with the first ray, i want to be weaker, i want to be with you more and more often and closer. i will burn you, but i will already crush you with my hot shoulder. to the white snowy lips and i will never, never see again, hey,

our guest today is denis dimitrov, and the man who created the kandinsky neural network. he does better than his analogues, let’s say, the analogues are wrong according to his word, well, it looks like it means on neural networks that solve the same problem, generating images from text, for example, what we are cooler in, although kandinsky videos are generated now, yeah, now, well, that is, it’s already, as it were, we’ve just recently presented a model that generates video, called kandinsky video. although we probably should have called it something torkovsky, well, they called kandinsky video, we will continue this line, but what does it better, knows the domestic cultural code better, of course, this is what we actually set before the model, which task, and we will certainly develop this further, what is the problem with russian data , knowledge of the russian domain, is that russian data, of course there is less of it, what the model learns from, learns from data from the internet, that is, from open pairs of image descriptions , it seems to capture the data at the t

creative industry podcast, our guests today are the man who created the kandinsky neural network, denis dimitrovh you. i’m wondering if i’ve generated a masterpiece, then when i’ve created his masterpiece, it’s just a picture. impossible, yes, but how it was created, this is how kandinsky creates - pictures, i can also tell you on my fingers, imagine how a person creates a picture, any, any image, any painting, he has some kind of white canvas, well not necessarily white, just white paper, some kind of blank, napkin, napkin, yes, there is a tablet screen, he definitely has some kind of idea, he starts sketching out some details, with a pen, uh, stylus, whatever . adds details, maybe removes something, well, a person also has patterns in his head, that is, he has memorized something, he has, now he has patterns, this is after training, so step by step he simply creates a picture how kandinsky creates a model, in principle an analogy can be drawn, he begins to draw a picture from the so-called white noise, what is it, well, this is when there is no signal on the tv, there is such interference, jus

death on saturday. on the first, we continue the creative industry podcast, our guest today is denis dimitrovgnized for kandinsky already, that’s what he does better, than analogues, let's say, analogues analogues is probably the wrong word, well, it looks like neural networks that solve the same problem. well, that is, it’s as if we recently introduced a model that generates video, we called it kandinsky video, although it probably should have been called something like torkovsky, well, we called it kandinsky video, we will continue this line, but what does it better, the domestic one knows better the cultural code, of course, is what we actually set before the model. what task, and we will certainly develop this further, what is the problem with russian data , knowledge of the russian domain is that data russians, of course there are fewer of them. what does the model learn from, it learns from data from the internet, that is, from open description image pairs, it seems to capture the data at the time of the request, naturally there is a team that deals with the data, and that is, the data

transfers the face, that is, this is the real you, in principle, that’s how it is, yes, dear friends, denis dimitrovdustry podcast on the first channel, the man who created neroset kandinsky, a wonderful team, and the main conclusion, even if we didn’t understand everything in general in the end, there’s still nothing to be afraid of, look how nice, smart people are. deals with those things that you and i may for some reason be afraid of, there is no need to be afraid, because this is the competitiveness of our country, and it is in the reliable hands of people like denis. denis, thank you very much, very much, we are waiting for the next stage, the antennas are on your head, we are still with you analogue, analogue versions of the presenters, elena kiper, producer, video maker, and roman karmanov, general director of the presidential fund for cultural initiatives, it was. creative industry podcast on channel one, see you, well, today our relations with former allies in the second world war, of course, couldn’t be worse, but i must say that at that time they were far from simple. and, of course, the