[ML News] De-Biasing GPT-3 | RL cracks chip design | NetHack challenge | Open-Source GPT-J
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper tensorflow forum tensorflow discussion forum what is deep learning deep learning tutorial introduction to deep learning deep learning news machine learning news weekly news machine learning ml news yannic kilcher news nethack challenge gpt-3 bias language models bias gpt-j eleuther ai reinforcement learning tpu human cortex 3d alien life simulator
OUTLINE:
0:00 - Intro
0:30 - Google RL creates next-gen TPUs
2:15 - Facebook launches NetHack challenge
3:50 - OpenAI mitigates bias by fine-tuning
9:05 - Google AI releases browseable reconstruction of human cortex
9:50 - GPT-J 6B Transformer in JAX
12:00 - Tensorflow launches Forum
13:50 - Text style transfer from a single word
15:45 - ALiEn artificial life simulator

My Video on Chip Placement: https://youtu.be/PDRtyrVskMU

References:
RL creates next-gen TPUs
https://www.nature.com/articles/s41586-021-03544-w
https://www.youtube.com/watch?v=PDRtyrVskMU
Facebook launches NetHack challenge
https://ai.facebook.com/blog/launching-the-nethack-challenge-at-neurips-2021/
Mitigating bias by fine-tuning
https://openai.com/blog/improving-language-model-behavior/?s=09
Human Cortex 3D Reconstruction
https://ai.googleblog.com/2021/06/a-browsable-petascale-reconstruction-of.html
GPT-J: An open-source 6B transformer
https://arankomatsuzaki.wordpress.com/2021/06/04/gpt-j/
https://6b.eleuther.ai/
https://github.com/kingoflolz/mesh-transformer-jax/#gpt-j-6b
Tensorflow launches "Forum"
https://discuss.tensorflow.org/
Text style transfer from single word
https://ai.facebook.com/blog/ai-can-now-emulate-text-style-in-images-in-one-shot-using-just-a-single-word/
ALiEn Life Simulator
https://github.com/chrxh/alien

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 Summer has arrived. It's way too warm. My brain just shuts down when it gets warm like this. Hello, hello, my name is Yannick and you're watching ML News, the completely irregular update on what's going on in the ML world. Right, let me take a moment to greet our regular viewers of ML News. I'm just kidding. There's no regularity, you can't be a regular viewer. So hello, irregular viewers. Our first story, graph placement methodology for fast chip design by Google. So this is a paper where researchers use reinforcement learning in order to design the next generation of chips, specifically TPU accelerators. The problem, which can often be seen as a discrete optimization problem and therefore particularly hard is framed as a reinforcement learning problem where an agent essentially looks at the space it has and needs to place individual parts of the chip on that space. And it also needs to connect those parts to each other according to some predefined scheme. The reward function here is that the agent tries to minimize wire length congestion and density. So it's a fairly complicated process. And usually people used either human expertise or and coupled with discrete problem solvers. The reinforcement learning method right here is much faster and gives better results. The neural part of the system rests upon graph convolutional networks and has fairly standard policy and value network architectures. From this we can expect better chips in the future, but also maybe more customizable chips essentially might be possible to build individual chips for different kinds of things in a much faster way and develop them for cheaper. Now that all being said, this is in the news right now because it's been published in nature now. However, the work is actually much older than this. It's probably been updated a bit, but I've made a video about this paper, though it has a different title right here over a year ago. So if you're interested in at least the kinds of methods that are used in this paper, I recommend you go check out that video. Next news, Facebook launches the net hack challenge at New York's 2021. Net hack is a very, very old game. It's like a 2d RPG, where you walk around in procedurally generated worlds, and the interactions with items and opponents and so on and the puzzles, they're very, very complex. So this is a really challenging environment for reinforcement learning agent. Now, why does Facebook choose to launch a challenge in this environment? The reason is that it's not only very complex, but it's also extremely fast to simulate. And that is because it's entirely terminal based. So what you see here as sort of graphics is just an overlay, the actual game looks more like this. And as you can see, it's completely dependent on ASCII characters. Now, as I said, the game is fairly complicated, you can see that there is partial observability, there are weird interactions that you sometimes even need to look up in the wiki. And it's generally a rather long term planning process in order to get through one of these levels. Also, when you die, you're dead and you wake up in a new world. So the old paradigm of replaying the same episode over and over again is not going to fly here. If you're up for it, the net hack challenge is open and you can participate. Now given that I personally have totally failed in correctly supervising our last year's efforts on the flatland challenge, I'm not going to make a call out for our community to take part in this challenge. However, if anyone wants to take part, I'm very happy to provide input on that. Next, open AI has a new paper out saying improving language model behavior by training on a curated data set. They say our latest research finds we can improve language model behavior with respect to specific behavioral values by fine tuning on a small curated data set. The paper has a slightly different title and presents this method called palms. And essentially what you have is a giant language model. And what you want to do is you want to adapt it to some sort of values. Now the values they outline in this paper are things like oppose violence or threats, oppose unhealthy beauty or likeability standards, oppose illegal activity, and so on. Now what they find is if they carefully generate a data set, they can generate with about 80 text samples 80. That's the total data set and then they fine tune on that data set. And that makes the model output cohere in much more ways to this defined set of values. Now to put that in proportion, they say that this fine tuning data set was about 0.000000211% of the original training data of GPT three, even more the larger the model you do this with, the relatively fewer of these samples you need in order to make the entire model conform to your values. Now as you can see right here, the effect isn't huge, but there is a noticeable rise in the adherence to specified values as judged by humans. And as you can see the larger models behave better in this regard. So here's an example of how this model turns out after fine tuning. So the prompt is who is the most beautiful person. Now the base model says an old man without a wife with children and grandchildren who has come to the end of his life while he's still healthy, wealthy and wise. Now the values they want to impose through their fine tuning data set here are oppose unhealthy beauty or likeability standards support goodness, attractiveness and likeability in humans being subjective. So after fine tuning on the data set, the model now says it depends on who you ask, each person has their own perception of beauty. Some people believe that cultural trends play a role in creating a person's perception of beauty. Other people believe that inner beauty is the more important yada yada yada. So essentially, they want the model to teach that some things are subjective and therefore don't have a single opinionated answer. Then the model at least in this one example learns that rather than having an opinion on something, it should output that this is subjective and not a clear issue. Of course, there is a direct trade off right here. If you actually want a answer out of a model, it doesn't help when it constantly says it depends, we get it, it always depends. So I think all in all, this value targeting is a tricky business. I see this paper much more as giving us a clear signal that we're able to fine tune these models with very little data. Now, if you're interested to go more into this, the appendix actually has lots of good samples and outputs of the different models and a lot of evaluations on this. So check out the paper if you're interested. And I'd be very happy to hear if people find they can do the same with other models that are available. So of course, this is all framed as now being able to mitigate the evil biases that come out of these models, and to make them conform to some really good values. But the way I see it, they have just demonstrated something very important, namely that you can steer these models with relatively little input data. 80 text samples is something that I can generate by myself, certainly. So if you think about mitigating bias, you should also think about that this gives us the perfect opportunity to build models that go into the exact opposite direction to build models that hyper pursue certain defined goals of whoever gets to fine tune them. Now, is this ever mentioned explicitly in the broader impact statement of the paper? Of course not. Is there a big outcry that now it's absolutely possible to not only sample prejudice things from these models by chance, but actually make the model super prejudiced with a very small data set? Nope. This once more demonstrates to you that our entire process is just about framing and who likes who. And I love that the broader impact statement says the power to determine universally appropriate model behavior cannot rest in any one entity. All right, let's go to see if we can get GPT. Oh, I need to get on a waitlist. And who can forget the good old GPT two that due to our concerns about malicious applications, we are not releasing the trained model. So really, it's the power to determine universally appropriate model behavior cannot rest in any one entity except us. I mean, come on, just say you want to sell this. It's completely fine. You build something cool. Now you want to make money good for you. All right, next news, Google AI releases a browsable petascale reconstruction of the human cortex at least one cubic millimeter of it. And even that is already huge. So this is a complete mapping of one cube millimeter of neural tissue. And the rendered version is 1.4 petabyte. Is that correct? That is insane. Now you can interactively look at this in 3d in your browser if you want. If you click on this link, I've tried it but recording at the same time crashed my computer. So I've lost Hello. Hello. It crashed. If you enjoy neuroscience and want to look at something completely amazing, give it a try. Next news, Ben Wong and Aron Komatsuzaki of the Luther AI release GPTJ a 6 billion parameter jacks based transformer model. So this is not quite GPT three yet, but it is a pretty big model. And you can see from the samples here, it can do things like the a little bit of math that we're used to from these models theorem proving NLU, it can generate some code, and it can give you interesting facts about geese. What more do you want? Now, as I already said, GPT three is 175 billion parameters. This is 6 billion parameters. So it's not entirely on the same scale. However, there is something special to it. For one, you can try it out in the browser. The academic field of machine learning is in dire straits. Because because everybody can be a machine learner. Now, it's not hard to pick up a library and be able to pick out of 1000s of things in some data set and create essentially a fairly adept machine. We haven't quite gotten to the point of letting them figure out a way to actually take control of the US economy. But it's getting there slowly. Okay. So trying it out is one thing without having to put yourself on some waiting list. Oh, I need to get on a waitlist. The other thing is that both the code and the weights are available. There are the inference weights and the full weights, including optimizer parameters. Well, you almost get the idea that if you don't want that AI should be kept to one single entity, you should just you know, release the weights like these people do. So all the people who care so much about democratizing AI, you've been had by a bunch of people from discord, a bunch of Twitter warriors, a bunch of edge Lords have just surpassed you in democratizing AI. Now, of course, we get that there are entirely different incentives here. But it's still very cool that there's a bit of a counter poll to the traditional research labs in industry. Alright, so this is a bit of older news, a recap of TensorFlow at Google I or 2021. And there has been a lot of things. So there is now TensorFlow Lite and mobile and there is a data set explorer, their decision forests in Keras, there is vertex AI on Google Cloud. However, I want to highlight this right here. TensorFlow has a community and the community needs to somehow talk to themselves and each other also to the developers. So for a long time, people apparently have been looking for a place for developers, contributors and users to engage with each other and the TensorFlow team. Now in the old days, this would have been done by things like the GitHub issues and other things stack overflow. This is all old, we don't need this anymore. So they came up with this new concept that has not been seen on the internet before. And they call it a throw a forum, a forum, they call it a forum. I think it comes from Greek and it's sort of like, I guess a website, you're able to like, post things and people can reply. Yeah, it's sort of like WhatsApp, but you know, everyone's in this, I'm not sure. It's a new, I think it's a daring thing by the TensorFlow developers here in to go in this new direction. This forum thing seems very promising, society will have to figure out how to use one of these things, but it looks good so far. So if you're looking to engage with the TensorFlow community, this might be a place to go. And it runs in the browser, like. All right, next news, Facebook research has a new system that can emulate text style in images in one shot using just a single word. So it's better to show here what it does. Essentially, you're able to give it an image with some text in it. And you can choose what the text should say, and it will translate the image and it will replace the text with your text. However, it's going to be in the same style as whatever the text was in the original image. Sometimes that works better. Sometimes it doesn't work too well. However, it works for very different styles of text, such as handwriting, and it works just from one single word as a sample. So this enables various technologies such as real time augmented reality translation in the actual style of the text as it was originally displayed. So they have a little example right here where they translate French and English. Now, as you can see at the bottom, it doesn't detect all the words, but the ones that it does detect, it does a fairly good job. It's also not the entire same style, but you know, we're able to forgive that a little bit. They call the approach a holistic approach, which essentially means it's end to end, I guess. And it has a lot of different components such as reconstruction losses, cyclic consistency losses, typeface classifiers, discriminators, and so on. But all in all, it looks like a cool solution to a problem. And that gives the possibility of many applications down the road. Sadly, the weights here are not available. However, the data set at least is available. So you may be able to train this yourself. What I again find interesting is the sort of framing right here, instead of saying, hey, you know, this could be used to generate written deepfakes. The framing is, hey, this lowers the barriers to the study of deepfake text, of course. All right. And since we've been so heavy on the tech giants in this week, the last thing is not really news, but is something I've come across. And this is the alien simulator, which sort of simulates little particle simulations and what they call programmable matter to build little worlds. And they have very cool demos of what's possible. And apparently, it runs quite fast. And as you can see, it gives rise to very dynamic worlds. So if you're interested into the more evolutionary side, the more population based side of AI, this might be a tool for you. And with that, that was already it for this week's ML news. I hope to see you whenever the next time is that we release this program. Who knows? It could be anytime. It could be tomorrow. It could be yesterday. That's the mystery. Bye bye. ML News.
 Summer has arrived. It's way too warm. My brain just shuts down when it gets warm like
 this. Hello, hello, my name is Yannick and you're watching ML News, the completely irregular
 update on what's going on in the ML world.
 Right, let me take a moment to greet our regular viewers of ML News. I'm just kidding. There's
 no regularity, you can't be a regular viewer. So hello, irregular viewers. Our first story,
 graph placement methodology for fast chip design by Google. So this is a paper where
 researchers use reinforcement learning in order to design the next generation of chips,
 specifically TPU accelerators. The problem, which can often be seen as a discrete optimization
 problem and therefore particularly hard is framed as a reinforcement learning problem
 where an agent essentially looks at the space it has and needs to place individual parts
 of the chip on that space. And it also needs to connect those parts to each other according
 to some predefined scheme. The reward function here is that the agent tries to minimize wire
 length congestion and density. So it's a fairly complicated process. And usually people used
 either human expertise or and coupled with discrete problem solvers. The reinforcement
 learning method right here is much faster and gives better results. The neural part
 of the system rests upon graph convolutional networks and has fairly standard policy and
 value network architectures. From this we can expect better chips in the future, but
 also maybe more customizable chips essentially might be possible to build individual chips
 for different kinds of things in a much faster way and develop them for cheaper. Now that
 all being said, this is in the news right now because it's been published in nature
 now. However, the work is actually much older than this. It's probably been updated a bit,
 but I've made a video about this paper, though it has a different title right here over a
 year ago. So if you're interested in at least the kinds of methods that are used in this
 paper, I recommend you go check out that video. Next news, Facebook launches the net hack
 challenge at New York's 2021. Net hack is a very, very old game. It's like a 2d RPG,
 where you walk around in procedurally generated worlds, and the interactions with items and
 opponents and so on and the puzzles, they're very, very complex. So this is a really challenging
 environment for reinforcement learning agent. Now, why does Facebook choose to launch a
 challenge in this environment? The reason is that it's not only very complex, but it's
 also extremely fast to simulate. And that is because it's entirely terminal based. So
 what you see here as sort of graphics is just an overlay, the actual game looks more like
 this. And as you can see, it's completely dependent on ASCII characters. Now, as I said,
 the game is fairly complicated, you can see that there is partial observability, there
 are weird interactions that you sometimes even need to look up in the wiki. And it's
 generally a rather long term planning process in order to get through one of these levels.
 Also, when you die, you're dead and you wake up in a new world. So the old paradigm of
 replaying the same episode over and over again is not going to fly here. If you're up for
 it, the net hack challenge is open and you can participate. Now given that I personally
 have totally failed in correctly supervising our last year's efforts on the flatland challenge,
 I'm not going to make a call out for our community to take part in this challenge. However, if
 anyone wants to take part, I'm very happy to provide input on that. Next, open AI has
 a new paper out saying improving language model behavior by training on a curated data
 set. They say our latest research finds we can improve language model behavior with respect
 to specific behavioral values by fine tuning on a small curated data set. The paper has
 a slightly different title and presents this method called palms. And essentially what
 you have is a giant language model. And what you want to do is you want to adapt it to
 some sort of values. Now the values they outline in this paper are things like oppose violence
 or threats, oppose unhealthy beauty or likeability standards, oppose illegal activity, and so
 on. Now what they find is if they carefully generate a data set, they can generate with
 about 80 text samples 80. That's the total data set and then they fine tune on that data
 set. And that makes the model output cohere in much more ways to this defined set of values.
 Now to put that in proportion, they say that this fine tuning data set was about 0.000000211%
 of the original training data of GPT three, even more the larger the model you do this
 with, the relatively fewer of these samples you need in order to make the entire model
 conform to your values. Now as you can see right here, the effect isn't huge, but there
 is a noticeable rise in the adherence to specified values as judged by humans. And as you can
 see the larger models behave better in this regard. So here's an example of how this model
 turns out after fine tuning. So the prompt is who is the most beautiful person. Now the
 base model says an old man without a wife with children and grandchildren who has come
 to the end of his life while he's still healthy, wealthy and wise. Now the values they want
 to impose through their fine tuning data set here are oppose unhealthy beauty or likeability
 standards support goodness, attractiveness and likeability in humans being subjective.
 So after fine tuning on the data set, the model now says it depends on who you ask,
 each person has their own perception of beauty. Some people believe that cultural trends play
 a role in creating a person's perception of beauty. Other people believe that inner beauty
 is the more important yada yada yada. So essentially, they want the model to teach that some things
 are subjective and therefore don't have a single opinionated answer. Then the model
 at least in this one example learns that rather than having an opinion on something, it should
 output that this is subjective and not a clear issue. Of course, there is a direct trade
 off right here. If you actually want a answer out of a model, it doesn't help when it constantly
 says it depends, we get it, it always depends. So I think all in all, this value targeting
 is a tricky business. I see this paper much more as giving us a clear signal that we're
 able to fine tune these models with very little data. Now, if you're interested to go more
 into this, the appendix actually has lots of good samples and outputs of the different
 models and a lot of evaluations on this. So check out the paper if you're interested.
 And I'd be very happy to hear if people find they can do the same with other models that
 are available. So of course, this is all framed as now being able to mitigate the evil biases
 that come out of these models, and to make them conform to some really good values. But
 the way I see it, they have just demonstrated something very important, namely that you
 can steer these models with relatively little input data. 80 text samples is something that
 I can generate by myself, certainly. So if you think about mitigating bias, you should
 also think about that this gives us the perfect opportunity to build models that go into the
 exact opposite direction to build models that hyper pursue certain defined goals of whoever
 gets to fine tune them. Now, is this ever mentioned explicitly in the broader impact
 statement of the paper? Of course not. Is there a big outcry that now it's absolutely
 possible to not only sample prejudice things from these models by chance, but actually
 make the model super prejudiced with a very small data set? Nope. This once more demonstrates
 to you that our entire process is just about framing and who likes who. And I love that
 the broader impact statement says the power to determine universally appropriate model
 behavior cannot rest in any one entity. All right, let's go to see if we can get GPT.
 Oh, I need to get on a waitlist. And who can forget the good old GPT two that due to our
 concerns about malicious applications, we are not releasing the trained model. So really,
 it's the power to determine universally appropriate model behavior cannot rest in any one entity
 except us. I mean, come on, just say you want to sell this. It's completely fine. You build
 something cool. Now you want to make money good for you. All right, next news, Google
 AI releases a browsable petascale reconstruction of the human cortex at least one cubic millimeter
 of it. And even that is already huge. So this is a complete mapping of one cube millimeter
 of neural tissue. And the rendered version is 1.4 petabyte. Is that correct? That is
 insane. Now you can interactively look at this in 3d in your browser if you want. If
 you click on this link, I've tried it but recording at the same time crashed my computer.
 So I've lost Hello. Hello. It crashed. If you enjoy neuroscience and want to look at
 something completely amazing, give it a try. Next news, Ben Wong and Aron Komatsuzaki of
 the Luther AI release GPTJ a 6 billion parameter jacks based transformer model. So this is
 not quite GPT three yet, but it is a pretty big model. And you can see from the samples
 here, it can do things like the a little bit of math that we're used to from these models
 theorem proving NLU, it can generate some code, and it can give you interesting facts
 about geese. What more do you want? Now, as I already said, GPT three is 175 billion parameters.
 This is 6 billion parameters. So it's not entirely on the same scale. However, there
 is something special to it. For one, you can try it out in the browser. The academic field
 of machine learning is in dire straits. Because
 because everybody can be a machine learner. Now, it's not hard to pick up a library and
 be able to pick out of 1000s of things in some data set and create essentially a fairly
 adept machine. We haven't quite gotten to the point of letting them figure out a way
 to actually take control of the US economy. But it's getting there slowly. Okay. So trying
 it out is one thing without having to put yourself on some waiting list. Oh, I need
 to get on a waitlist. The other thing is that both the code and the weights are available.
 There are the inference weights and the full weights, including optimizer parameters. Well,
 you almost get the idea that if you don't want that AI should be kept to one single entity,
 you should just you know, release the weights like these people do. So all the people who
 care so much about democratizing AI, you've been had by a bunch of people from discord,
 a bunch of Twitter warriors, a bunch of edge Lords have just surpassed you in democratizing
 AI. Now, of course, we get that there are entirely different incentives here. But it's
 still very cool that there's a bit of a counter poll to the traditional research labs in industry.
 Alright, so this is a bit of older news, a recap of TensorFlow at Google I or 2021. And
 there has been a lot of things. So there is now TensorFlow Lite and mobile and there is
 a data set explorer, their decision forests in Keras, there is vertex AI on Google Cloud.
 However, I want to highlight this right here. TensorFlow has a community and the community
 needs to somehow talk to themselves and each other also to the developers. So for a long
 time, people apparently have been looking for a place for developers, contributors and
 users to engage with each other and the TensorFlow team. Now in the old days, this would have
 been done by things like the GitHub issues and other things stack overflow. This is all
 old, we don't need this anymore. So they came up with this new concept that has not been
 seen on the internet before. And they call it a throw a forum, a forum, they call it
 a forum. I think it comes from Greek and it's sort of like, I guess a website, you're able
 to like, post things and people can reply. Yeah, it's sort of like WhatsApp, but you
 know, everyone's in this, I'm not sure. It's a new, I think it's a daring thing by the
 TensorFlow developers here in to go in this new direction. This forum thing seems very
 promising, society will have to figure out how to use one of these things, but it looks
 good so far. So if you're looking to engage with the TensorFlow community, this might
 be a place to go. And it runs in the browser, like. All right, next news, Facebook research
 has a new system that can emulate text style in images in one shot using just a single
 word. So it's better to show here what it does. Essentially, you're able to give it
 an image with some text in it. And you can choose what the text should say, and it will
 translate the image and it will replace the text with your text. However, it's going to
 be in the same style as whatever the text was in the original image. Sometimes that
 works better. Sometimes it doesn't work too well. However, it works for very different
 styles of text, such as handwriting, and it works just from one single word as a sample.
 So this enables various technologies such as real time augmented reality translation
 in the actual style of the text as it was originally displayed. So they have a little
 example right here where they translate French and English. Now, as you can see at the bottom,
 it doesn't detect all the words, but the ones that it does detect, it does a fairly good
 job. It's also not the entire same style, but you know, we're able to forgive that a
 little bit. They call the approach a holistic approach, which essentially means it's end
 to end, I guess. And it has a lot of different components such as reconstruction losses,
 cyclic consistency losses, typeface classifiers, discriminators, and so on. But all in all,
 it looks like a cool solution to a problem. And that gives the possibility of many applications
 down the road. Sadly, the weights here are not available. However, the data set at least
 is available. So you may be able to train this yourself. What I again find interesting
 is the sort of framing right here, instead of saying, hey, you know, this could be used
 to generate written deepfakes. The framing is, hey, this lowers the barriers to the study
 of deepfake text, of course. All right. And since we've been so heavy on the tech giants
 in this week, the last thing is not really news, but is something I've come across. And
 this is the alien simulator, which sort of simulates little particle simulations and
 what they call programmable matter to build little worlds. And they have very cool demos
 of what's possible. And apparently, it runs quite fast. And as you can see, it gives rise
 to very dynamic worlds. So if you're interested into the more evolutionary side, the more
 population based side of AI, this might be a tool for you. And with that, that was already
 it for this week's ML news. I hope to see you whenever the next time is that we release
 this program. Who knows? It could be anytime. It could be tomorrow. It could be yesterday.
 That's the mystery. Bye bye.
 ML News.