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hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | hi there today we're going to look at curl contrastive unsupervised representations for reinforcement learning by Aravind Sreenivas Michel Laskin and Petra Biel so this is a general framework for unsupervised representation learning for our L so let's untangle the title a little bit it is for reinforcement learning whi... | 00:00:00 | 00:00:42 | 0 | 42 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=0s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | going to you know dive into specific or allowed rooms today it is unsupervised which means it doesn't need any sort of labels and it also doesn't need a reward signal forum RL which is pretty cool because usually the entire RL pipelines rely on some sort of a reward or auxiliary reward signal now there is a training ob... | 00:00:42 | 00:01:23 | 42 | 83 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=42s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | it is contrastive and that is the the kind of secret sauce in here the training objective it's what's called contrastive learning and that's what we're going to spend most of our time on today exploring what that means alright so here's the general framework you can see it down here sorry about that so you can see that... | 00:01:23 | 00:02:04 | 83 | 124 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=83s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | algorithm is kind of fundamental now if someone explains RL to you or reinforcement learning usually what they'll say is there is some kind of actor and there is some kind of environment right and the environment will give you an observation right observation Oh which is some sort of let's say here is an image right so... | 00:02:04 | 00:02:48 | 124 | 168 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=124s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | them so there is a little shot here right you need to shoot those meteorites right so this is the observation oh and then as an age as an actor you have to come up with some sort of action and the actions here can be something like moved to the left move to the right press the button that you know does the shooting so ... | 00:02:48 | 00:03:23 | 168 | 203 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=168s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | another action in response to that and the environments going to give you back another reward and the next observation and so on so what you want to do is you want to find a mapping from observation to action such that your reward is going to be as high as possible right this is the fundamental problem of RL and usuall... | 00:03:23 | 00:04:02 | 203 | 242 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=203s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | you're trying to learn given the input observation what output action you need to do and you can think of the same here so you have this input observation up here and down here after the reinforcement learning the output is going to be an action right and so this this function we talked about up here is usually impleme... | 00:04:02 | 00:04:39 | 242 | 279 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=242s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | want to shove the observation in directly right we don't want the observation directly but what we put into the RL framework is this Q thing now the Q is supposed to be a representation of the observation and a useful representation so if we think of this of this game here of this Atari game up here what could be the w... | 00:04:39 | 00:05:22 | 279 | 322 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=279s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | RL algorithm than just the pure pixels of the image right so if I have to craft a representation let's say it's a vector right let's say our our our representations need to be vectors what I would do is I would probably take the x and y coordinates of the little spaceship right x and y and put it in the vector that's p... | 00:05:22 | 00:06:07 | 322 | 367 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=322s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | is pointing to that should be pretty useful because if I shoot I want to know where I shoot right so theta here and then probably maybe the X and y coordinate of the of the shot here of the red shot that I fired if there is one right also going to put that into my representation so x and y and maybe Delta X Delta Y som... | 00:06:07 | 00:06:52 | 367 | 412 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=367s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | guaranteed it would turn out to be a better or L agent that learns faster than if I put in the original observation which is the the pixel image of the game right because of course in order to play the game correctly in order to play the game to win you need to extract this information right you need to get our there's... | 00:06:52 | 00:07:29 | 412 | 449 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=412s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | that is useful it can learn much faster all right so you can see if I handcraft a good representation it's pretty easy for the RL algorithm to improve now we want to come up with a framework that automatically comes up with a good representation right so it alleviates the RL algorithm here that reinforcement it allevia... | 00:07:29 | 00:08:10 | 449 | 490 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=449s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | extract useful information from the from the observation space right so how do we do this this is Q here is supposed to be exactly that it's supposed to be a good representation but not one that we handcrafted but a used with a technique that can be employed pretty much everywhere and the goal sorry that the secret sau... | 00:08:10 | 00:08:55 | 490 | 535 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=490s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | going to explain in this case for this kind of image based for image based reinforcement learning but just for image based neural networks how can we come up with a contrastive loss so you see there's kind of a two pipeline thing going on here there is like this and this and then one of them is going to be the good enc... | 00:08:55 | 00:09:50 | 535 | 590 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=535s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | need to do is we need to produce three different things from it we need to produce an anchor what's called an anchor so we need to produce a positive sample positive sample and we need to produce negative samples let's just go with one negative sample for now right so the goal is to come up with a task that where we pr... | 00:09:50 | 00:10:31 | 590 | 631 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=590s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | create our own labels to a task but that we construct the task in a way such that the neural network has no choice but learn something meaningful even though we made the task of ourselves all right I hope this was kind of clear so how are we gonna do this our method of choice here is going to be random cropping now ran... | 00:10:31 | 00:11:16 | 631 | 676 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=631s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | I'm gonna draw the same picture here a couple of times this is all supposed to be the same picture and with the negative sample I'm just gonna leave it empty for now there are two meteorites two meteorites shot shot right so for the anchor we're going to actually not random crop but center crop right so we're going to ... | 00:11:16 | 00:11:59 | 676 | 719 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=676s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | the image is somewhat contained in this and that yeah all right so this is going to be my anchor and then the positive sample is going to be a random crop of the same image so I'm just randomly going to select a same size same size section from that image let's say this is up right here all right and the negative sampl... | 00:11:59 | 00:12:45 | 719 | 765 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=719s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | to take a random crop from this let's say I'm going to take a random crop here let's put a meteorite here as well just for fun all right so these are going to be our three samples and now the question is going to be if I give the anchor to the neural network I'm going to say I give you the anchor right but I'm also goi... | 00:12:45 | 00:13:39 | 765 | 819 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=765s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | anchor now which one of these two which one of these two crops comes from the same image right so as human you look at this and if you just see the center crop you see oh okay down here there's this this tip of this thing and then there's the shot right and in relation to the shot there is a meteor here right and then ... | 00:13:39 | 00:14:18 | 819 | 858 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=819s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | must be you know down here somewhere and then I go over here and I try to do the same thing is okay here's the meteor and you know it it might be it might be in the original image it might be over here somewhere so that's possible I don't see it right that's possible but then there should be there should be a shot righ... | 00:14:18 | 00:15:03 | 858 | 903 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=858s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | is the positive sample while this image here is the negative sample right so this is the task that you ask of the neural network give it the anchor and you ask which one of the of these two comes from the same image right this is called contrastive learning now is a bit more complicated in that of course what you do is... | 00:15:03 | 00:15:50 | 903 | 950 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=903s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | become the query and these are becoming the keys so key one or key two and then you're going to feed it always two of them into a bilinear product right the bilinear product is simply you can think of it as an inner product in a perturbed space that you can learn so you're going to have this you have these two here the... | 00:15:50 | 00:16:39 | 950 | 999 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=950s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | be this high and this might only be this high and then you say aha cool this one's higher so this one must be the positive right and you train the W specifically to make this higher to make the positive ones higher and the negative ones a lower so this is a supervised learning task right where these things here are goi... | 00:16:39 | 00:17:23 | 999 | 1043 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=999s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | do the contrastive learning is this one so as you can see it's a soft max like in multi-class classification of the inner product the bilinear product with the positive samples over the bilinear product with the positive samples plus the bilinear product with all of the negative samples so you're going to come up with ... | 00:17:23 | 00:18:09 | 1043 | 1089 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1043s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | whether you're talking on the anchor or on the what what are called the keys the things you compare to and this is out of a kind of a stability criterion you already maybe you don't you know like something like double q-learning or things like this it sometimes when you train with your own thing so in q-learning you're... | 00:18:09 | 00:18:53 | 1089 | 1133 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1089s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | which isn't you know it leads to instability so in our case we took it three times here or multiple times especially for the same objective here we have twice something that was encoded by the same neural networking isn't the two sides of this by linear product so if we were to use the same neural network that tends to... | 00:18:53 | 00:19:36 | 1133 | 1176 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1133s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | there's a bit of a compromise where we say it is the same neural network but but basically this one is the one we learn and then we always every now and then we transfer over the parameters to that one and in fact each step we transfer over the parameters and do an exponential moving average with the parameters of this... | 00:19:36 | 00:20:21 | 1176 | 1221 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1176s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | have to learn a second neural network but your second neural network is not the same as your first neural network but it it kind of lags behind but it is also it is also performing almost as well so that is um I don't know if that makes sense but it is the best I can to explain it so to recap you take your observation ... | 00:20:21 | 00:21:13 | 1221 | 1273 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1221s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | different observations right these become your positive and negative samples then you take you take me push this through your encoders for the query and for the keys respectively you end up with the Q which is the encoded anchor and the case which are the encoded positive and negative samples and then you learn you upd... | 00:21:13 | 00:22:01 | 1273 | 1321 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1273s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | of giving having the observation directly as an input here you now have the Q here as an input right that is it the reinforcement learning works exactly the same but except having the so input Oh you now have the representation input queue and you don't have to worry about anything else in terms of the reinforcement le... | 00:22:01 | 00:22:41 | 1321 | 1361 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1321s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | modular how you how you fit this in it simply comes up with good representations so that is that is basically a deal here right and you hope that the whole procedure of this contrastive learning then gives you good representation of this anchor thing here if you encode that to the queue you hope that this representatio... | 00:22:41 | 00:23:21 | 1361 | 1401 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1361s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | with a stack of observations not just a single observation because so for example in Atari people always concatenate something like for the four last frames right and their their point is okay if we have this stack here if we do this data augmentation you know these crops we kind of need to do them consistently right w... | 00:23:21 | 00:23:59 | 1401 | 1439 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1401s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | so um that that is kind of the additional thing they introduced it with respect to RL that deals with with stacked time frames but it's kind of the same the same diagram as above here right so they explained the the RL algorithms they use and exactly they're they're their thing and here you can see that anchor is a cro... | 00:23:59 | 00:24:42 | 1439 | 1482 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1439s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | image or a different stack of images they have a pseudo code here where that was pretty simple we'll just go through it quickly right you start off with fq + FK these are the encoders for the query and keys you start them off the same then you go through your data loader you do this random augmentation of your query an... | 00:24:42 | 00:25:22 | 1482 | 1522 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1482s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | well so you know just I guess I guess it's a thing you could choose I don't know what exactly is the best thing alright then I forward the query through the FQ and I forward the keys through the FK then important I detach this so I don't train I don't want to train the FK I only want to train the FQ right then I do the... | 00:25:22 | 00:26:13 | 1522 | 1573 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1522s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | moving average for my key encoder and they test on two different things they test on the deepmind control tasks and they always test 100 K time steps so their big point is data efficiency right they they claim they can use learn useful representations with not much data so the task is here how good are you at one 100 c... | 00:26:13 | 00:27:01 | 1573 | 1621 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1573s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | control tasks and it also outperforms a lot of the baselines in the Atari tasks and it actually if you look at the results it doesn't outperform everything but for example here the red is curl and the dashed gray is state as a si now state si si the important thing to note here is it has access to the state whereas cur... | 00:27:01 | 00:27:47 | 1621 | 1667 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1621s | |
hg2Q_O5b9w4 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | or performs equally well to the state si si right so that's pretty impressive especially if you've took at pixel si si sorry which is the same algorithm but does not have access to the state just the pixels it often fails terribly right so um that is pretty interesting to see and even to me it's pretty interesting to s... | 00:27:47 | 00:28:35 | 1667 | 1715 | https://www.youtube.com/watch?v=hg2Q_O5b9w4&t=1667s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | on April 21st jurgen schmidhuber tweeted out stop crediting the wrong people for inventions made by others at least in science the facts will always win at the end as long as the facts have not yet won it is not yet the end no fancy award can ever change that hashtag it self-correcting science hashtag plagiarism and li... | 00:00:00 | 00:00:41 | 0 | 41 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=0s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | name jurgen schmidhuber you again sorry this is this is absolutely great so both actually Schmid over and Hinton are on Twitter you can tweet at them and follow them this article here is a basically a critique of the press release of Honda when they awarded geoff hinton for his achievements and it goes through it step ... | 00:00:41 | 00:01:21 | 41 | 81 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=41s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | AI including the backpropagation algorithm that forms the basis of deep learning approach to AI and schmidhuber just goes off its he basically claims him while Hinton and his co-workers have made certain significant contributions to deep learning he claimed above is plain wrong right he did not invent back propagation ... | 00:01:21 | 00:02:05 | 81 | 125 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=81s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | through a history of this and how it's even earlier I always have a bit of a trouble with claims like who invented what because when it is an algo them really the same thing right and when he when is it a variation on another algorithm and when is it something completely new it's never entirely clear but the the points... | 00:02:05 | 00:02:42 | 125 | 162 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=125s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | introduced the a fast learning algorithm for restricted Boltzmann machines that allowed them to learn a single layer of distributor representation without requiring any labeled data these methods allow deep learning to work better and they led to the current deep learning revolution and he is no dr. Hinton's interestin... | 00:02:42 | 00:03:16 | 162 | 196 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=162s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | pre training and he basically again says apart from this Hinton's unsupervised pretending was conceptually a rehash of my unsupervised pre training for deep recurrent neural networks so he you know as you know she made Ober has done a lot of work in recurrent neural networks and he basically says it it was just a rehas... | 00:03:16 | 00:03:55 | 196 | 235 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=196s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | fighter you don't have to do that but it's also doubtful that this this was a step even though even if it wasn't on the exact path to the current situation it was a thing that got people excited maybe and so the critique is like half valid and also it doesn't help me to burn that he always compares it to his own things... | 00:03:55 | 00:04:34 | 235 | 274 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=235s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | from from these times people just wrote papers sometimes I haven't read this specific one but sometimes people just wrote papers writing down their ideas like one could do this and this and this never doing any experiments or actually specifying exactly what they mean they just kind of wrote down a bunch of ideas and t... | 00:04:34 | 00:05:11 | 274 | 311 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=274s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | really clear in ideas or just had by everyone I think people people mistake this that think that the ideas are unique it's not ideas that are unique many people have the same ideas but some there's also execution and exact formalization and so on and exact level of specificity this all of this is really hard and then t... | 00:05:11 | 00:05:48 | 311 | 348 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=311s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | this because speech recognition is of course prime LS TM territory so you don't want to go near this and the Honda further says revolutionized computer vision by showing that deep learning worked far better than existing state of the art and again he says the basic ingredients were already there and so on and the our t... | 00:05:48 | 00:06:32 | 348 | 392 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=348s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | it doesn't change the fact that Alex net1 imagenet in 2012 and that was like the start of the deep learning revolution it was like wow you can cut the learn like the error rate by something like 30% simply by doing this deep learning stuff so again even if Dan that he says it blew away the competition it just seems it ... | 00:06:32 | 00:07:22 | 392 | 442 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=392s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | their dramatic results dr. Hinton also invented a widely used new method called dropout which reduces overfitting no like no and like no just no like randomly dropping parts in order to make something more robust that is surely not a new thing and he also says much early it is there's this stochastic Delta rule and so ... | 00:07:22 | 00:08:08 | 442 | 488 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=442s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | already I think they just did it and then because it's a natural idea and then they gave it a name and the name stuck right it's not about the idea itself and then lastly they say of the countless AI based technological services across the world it is no exaggeration to say that few would have been possible without the... | 00:08:08 | 00:08:47 | 488 | 527 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=488s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | bit of a cheap shot right clearly honda if they're not saying it would have been you know physically him possible without his contributions its but certainly Hinton has has if even if he hadn't invented any of those things he certainly has created like a spark and his these things created a splash got people excited pe... | 00:08:47 | 00:09:35 | 527 | 575 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=527s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | through the statements of Schmidt who were most of them are technically correct right and you know that so that was that and then I thought okay cool but then someone posted II didn't read it and then Hinton replies and this is okay don't you love this so Hinton says having a public debate with schmidhuber about academ... | 00:09:35 | 00:10:15 | 575 | 615 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=575s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | like having multiple aliases in Wikipedia to make it look as if other people agree the patient on his website about Alan Turing is a nice example of how he goes on trying to these are like these are shots fired and he says I'm going to respond once and only once I have never claimed that I invented backpropagation Davi... | 00:10:15 | 00:10:56 | 615 | 656 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=615s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | first published about rock crop but he doesn't say he invented it what I've claimed is that I was the person to clearly demonstrate that back prop could learn interesting in turn represent and that that this is what made it popular right so this goes into into the direction schmidhuber is very much on academic contribu... | 00:10:56 | 00:11:35 | 656 | 695 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=656s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | is he says it is true that many people in the press have said I invented back prop and I've spent a lot of time correcting them here's an excerpt from 2018 where this is I guess a quote from this book that quotes Hinton where he says lots of people invented different versions of back prop before day with normal heart t... | 00:11:35 | 00:12:08 | 695 | 728 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=695s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | use it for learning distributed representations so I'd like to set the record straight on that and then he said maybe Jurgen would like to set the record straight on who invented LST M's boom boom crazy shot shots fired by Hinton here this is I mean this is just great but again look at what Hinton says Hinton basically... | 00:12:08 | 00:12:56 | 728 | 776 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=728s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | schmidhuber of course being Schmidt who replies again down here he has a a response to the reply and I don't expect Hinton to reply again so I waited for a bit but but I I believe him when he says he does it only once so he goes into this summary the facts presented in sections 1 2 3 4 5 are still valid so he goes what... | 00:12:56 | 00:13:40 | 776 | 820 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=776s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | and he just says another ad hominem attack and then he goes into that schmidhuber tries to discredit Alan Turing and then shmita goes into this big long big long basically claim that Alan Turing wasn't as important as people made him out to be and people invented this kind of Turing machine equivalents before that agai... | 00:13:40 | 00:14:28 | 820 | 868 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=820s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | as it may this is correct and then when when Hinton goes that he doesn't stay and invent backdrop and me to persist this is finally response related to my post which is true right however he does not at all contradict what I wrote and it is true that he credited his co-author Rommel Hart with the invention but but neit... | 00:14:28 | 00:15:05 | 868 | 905 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=868s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | exact time when backprop was invented even though it probably wasn't in the current exact current formulation and it probably existed someone like this so but again and he his main claim is dr. Hinton except the Honda Prize although he apparently agrees that Honda's claims are false he should ask Honda to correct their... | 00:15:05 | 00:15:48 | 905 | 948 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=905s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | as a as a PhD advisor but the to summarize dr. Hinton's comments and ad hominem arguments diverged from the contents of my post and do not challenge the facts and so on and i have to say after reading this this this is a this is correct right hinton basically replies to hey i I never claimed I invented back prop and ot... | 00:15:48 | 00:16:30 | 948 | 990 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=948s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | Hinton did and it doesn't hidden basically agrees with him and also schmidhuber says dr. Hinton accepted the Honda Prize although he apparently agrees that the claims are false he should ask Honda to correct their statements and it is true that Hinton accepted this price under this release right now you might be able t... | 00:16:30 | 00:17:02 | 990 | 1022 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=990s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | really publicly stated that you didn't invent these things and you know made it clear and then you get a prize and they write this thing maybe you just don't want to go after every single press statement and correcting that but you know in essence basically Hinton and understood this as an attack on himself that he cla... | 00:17:02 | 00:17:40 | 1022 | 1060 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=1022s | |
hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | can understand so this is my take on this issue it's kind of both or correct and they just kind of talk past each other and schmidhuber is always on the the idea existed before and Hinton is correct when he says it's not always just about the idea progress is also made by people being excited people actually getting so... | 00:17:40 | 00:18:28 | 1060 | 1108 | https://www.youtube.com/watch?v=hDQNCWR3HLQ&t=1060s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | The power of yet. I heard about a high school in Chicago where students had to pass a certain number of courses to graduate, and if they didn't pass a course, they got the grade "Not Yet." And I thought that was fantastic, because if you get a failing grade, you think, I'm nothing, I'm nowhere. But if you get the grade... | 00:00:00 | 00:00:56 | 0 | 56 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=0s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | how children coped with challenge and difficulty, so I gave 10-year-olds problems that were slightly too hard for them. Some of them reacted in a shockingly positive way. They said things like, "I love a challenge," or, "You know, I was hoping this would be informative." They understood that their abilities could be de... | 00:00:56 | 00:01:50 | 56 | 110 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=56s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | Instead of luxuriating in the power of yet, they were gripped in the tyranny of now. So what do they do next? I'll tell you what they do next. In one study, they told us they would probably cheat the next time instead of studying more if they failed a test. In another study, after a failure, they looked for someone who... | 00:01:50 | 00:02:40 | 110 | 160 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=110s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | On the left, you see the fixed-mindset students. There's hardly any activity. They run from the error. They don't engage with it. But on the right, you have the students with the growth mindset, the idea that abilities can be developed. They engage deeply. Their brain is on fire with yet. They engage deeply. They proce... | 00:02:40 | 00:03:32 | 160 | 212 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=160s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | Their biggest goal is getting the next A, or the next test score? And are they carrying this need for constant validation with them into their future lives? Maybe, because employers are coming to me and saying, "We have already raised a generation of young workers who can't get through the day without an award." So wha... | 00:03:32 | 00:04:22 | 212 | 262 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=212s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | But praising the process that kids engage in, their effort, their strategies, their focus, their perseverance, their improvement. This process praise creates kids who are hardy and resilient. There are other ways to reward yet. We recently teamed up with game scientists from the University of Washington to create a new... | 00:04:22 | 00:05:10 | 262 | 310 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=262s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | And we got more effort, more strategies, more engagement over longer periods of time, and more perseverance when they hit really, really hard problems. Just the words "yet" or "not yet," we're finding, give kids greater confidence, give them a path into the future that creates greater persistence. And we can actually c... | 00:05:10 | 00:06:00 | 310 | 360 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=310s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | and over time, they can get smarter. Look what happened: In this study, students who were not taught this growth mindset continued to show declining grades over this difficult school transition, but those who were taught this lesson showed a sharp rebound in their grades. We have shown this now, this kind of improvemen... | 00:06:00 | 00:06:50 | 360 | 410 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=360s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | or children on Native American reservations. And they've done so poorly for so long that many people think it's inevitable. But when educators create growth mindset classrooms steeped in yet, equality happens. And here are just a few examples. In one year, a kindergarten class in Harlem, New York scored in the 95th per... | 00:06:50 | 00:07:51 | 410 | 471 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=410s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | on the state math test. In a year, to a year and a half, Native American students in a school on a reservation went from the bottom of their district to the top, and that district included affluent sections of Seattle. So the Native kids outdid the Microsoft kids. This happened because the meaning of effort and difficu... | 00:07:51 | 00:08:50 | 471 | 530 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=471s | |
_X0mgOOSpLU | The power of believing that you can improve | Carol Dweck | That's when they're getting smarter. I received a letter recently from a 13-year-old boy. He said, "Dear Professor Dweck, I appreciate that your writing is based on solid scientific research, and that's why I decided to put it into practice. I put more effort into my schoolwork, into my relationship with my family, and... | 00:08:50 | 00:09:50 | 530 | 590 | https://www.youtube.com/watch?v=_X0mgOOSpLU&t=530s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | so then let's get started for today welcome to lecture 10 of cs2 9458 deep unsupervised learning now this lecture will be on compression before we dive into that a couple of logistical things there are main logistical things that are ahead of you are your project milestone which is a three-page Goldbach intermediate re... | 00:00:00 | 00:00:40 | 0 | 40 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=0s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | other thing that's coming up in two weeks we'll have our midterm which will figure out how to do it remote under the current circumstances but the main thing we'll do later this week is release a set of study materials for you that capture the core of the things covered in the class their very core compressive a little... | 00:00:40 | 00:01:15 | 40 | 75 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=40s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | answers and so you'll know exactly what the questions can be and what the answers are that we expect you to get so that will come out later today or tomorrow for you to study link pause here and see if there's any questions about logistics oh and by the way this lecture is recorded so for some reason you you know don't... | 00:01:15 | 00:01:55 | 75 | 115 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=75s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | it and why would we care in general and why would we there in this class so what is it it's an data you might want to reduce the number of bits for encoding a message a message could be an image you want to send or part of speech or maybe some music you want to send across on communication line and it's an original in ... | 00:01:55 | 00:02:34 | 115 | 154 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=115s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | look like you have some bit stream B on the left here so that's where you start out with then what happens next is you want to compress it and end up with a compressed version of that bit stream and the hope that that compressed version has lost its anatomy original so when you send a compressed extreme over a channel ... | 00:02:34 | 00:03:08 | 154 | 188 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=154s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | compression into the original alright so why do we care well you could save time you could save bandwidth over communications channel you could save space when you're storing it so many reasons you might care about this from the AI point of view and part of why it's interesting for this class is that often the ability ... | 00:03:08 | 00:03:44 | 188 | 224 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=188s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | the data so now there's two types of compression lossy versus lossless compression in this lecture we'll be fully focused on lossless compression where the original bits can be completely reconstructed on the output now sometimes in practice you might care about lossy compression you say well I don't need all the detai... | 00:03:44 | 00:04:15 | 224 | 255 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=224s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | you know it exists now one of the very interesting things with compression there are some prizes associated it so recently hutter I should increase the price used to be a 50,000 euro prize for compressing human knowledge and recently it went up to factor 10 it's now a five hundred thousand euro prize if you can compres... | 00:04:15 | 00:04:57 | 255 | 297 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=255s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | won this thing you cracked it the reason how to read out the surprise is not so much because it specifically wants that one gigabyte compressed into one sixteen megabytes because he believes now we go by it has interesting information that any system that can represent it as compactly as one sixteen megabytes must have... | 00:04:57 | 00:05:29 | 297 | 329 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=297s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | st it's not that he asked you to send in a compressor and has a secret test set is gonna test your compressor on to see how it works no it's literally there's a 1 gigabyte file and if you can make it smaller small enough you win the prize what's gonna be able to decompress it so you gotta be able to effectively send hi... | 00:05:29 | 00:06:05 | 329 | 365 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=329s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | reconstruct the original 1 gigabyte file so very very specific problem there's no test said just that one training example but nobody's got them close to actually making this work so interesting challenge maybe something you want to think about at some point and see if it can make some progress then there's another com... | 00:06:05 | 00:06:38 | 365 | 398 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=365s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | in compressor of course and they have a secret asset on which they test how well you can compress and decompress the test examples so two very different challenges but both very much at the core what we're going to be thinking about today in lecture all right so why in this course it turns out that we've studied a lot ... | 00:06:38 | 00:07:19 | 398 | 439 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=398s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | half of this lecture has made several breakthroughs in this PhD research showing how some of the state-of-the-art generative models can be converted into compression algorithms with the CIO narrative models under the hood such that you can get better compression now you might go get otherwise and we'll cover that later... | 00:07:19 | 00:07:55 | 439 | 475 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=439s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | covers the background on essentially information theory slack impression that we'll be covering in this lecture at least the first half in the second half will dive a lot more in the deep learning aspects and how and tied it into this so some applications you might have seen jarick file compression gz p z 7z a zip file... | 00:07:55 | 00:08:29 | 475 | 509 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=475s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | past fax modem Skype and so forth and all of these are examples of where here original information might have been represented with many many bits too large for you to a store on file in that format and because you can reduce them or they can get back out the original you can now store it more efficiently or send it mo... | 00:08:29 | 00:09:02 | 509 | 542 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=509s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | delay assuming you can decode quickly on the other side now maybe you might have followed this TV show called Silicon Valley it's uh well pretty finish I would say with many things that are maybe a little too close to home and too close to true but still pretty funny and if you watch that show on HBO you have noticed t... | 00:09:02 | 00:09:41 | 542 | 581 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=542s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | and that's the secret sauce of their company turns out that some people really do this for their actual company so there's various startups out there that you invent don't disclose exactly what's under the hood but invent new compression algorithms using machine learning under the hood most likely to improve upon past ... | 00:09:41 | 00:10:14 | 581 | 614 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=581s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | a real thing they presented at TechCrunch in 2015 now first question you might ask is can we have universal data compression so their fundamental question you'll see in this lecture a lot of questions we ask them to be very fundamental where we can give actually very very strong theoretical answers sometimes negative a... | 00:10:14 | 00:10:51 | 614 | 651 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=614s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | back out to the original well the things that's possible well okay let's see imagine you want to compress every possible to compress every possible possible bitstream they ever encounter okay so that's not possible no longer we can do this what's the intuition that should be simple we'll do a proof by contradiction sup... | 00:10:51 | 00:11:33 | 651 | 693 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=651s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | bit string B 0 you can compress it to get a smaller bit string B 1 if it strictly last bits otherwise it's not a universal compressor now B you want you can feed into it again it'll turn that into B 2 which is yet smaller you keep doing this you do this especially many times at some point you'll have a big string of si... | 00:11:33 | 00:12:09 | 693 | 729 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=693s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | somebody tells you I have a universal data compression already compress everything no problem here's a prove that this is actually not possible to prove it another way also another way to prove it is to do it by Counting you can say okay suppose your algorithm can compress all thousand histories ok how many thousand bi... | 00:12:09 | 00:12:47 | 729 | 767 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=729s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | original back out but if we look at what's possible with all possible shorter bit strings actually you cannot encode all two to the 1000 possible thousand bit strings so since we can't include all possible to the 1000 bit strings it means we cannot compress all of them so we have two different proves here to show that ... | 00:12:47 | 00:13:26 | 767 | 806 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=767s | |
pPyOlGvWoXA | L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning | example here's a piece of text and I'll give you all a minute to read this text so as you're reading this text you'll you'll notice well likely you'll notice that there's something fun about the text and that the words are mostly misspelled but despite these was being misspelled it's how she's still very feasible to re... | 00:13:26 | 00:14:12 | 806 | 852 | https://www.youtube.com/watch?v=pPyOlGvWoXA&t=806s |
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