[ML News] AI predicts race from X-Ray | Google kills HealthStreams | Boosting Search with MuZero
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper mlnews schmidhuber kaust saudi arabia ai initiative reading race xray race xray ai race ai bias facebook primates muzero muzero code muzero paper google muzero health streams deepmind health wandb dmca github dmca distill distill gnn graph neural networks ai depression unconstrained scene generation transformers
#mlnews #schmidhuber #muzero

Your regular updates on what's happening in the ML world!

OUTLINE:
0:00 - Intro
0:15 - Sponsor: Weights & Biases
1:45 - Google shuts down health streams
4:25 - AI predicts race from blurry X-Rays
7:35 - Facebook labels black men as primates
11:05 - Distill papers on Graph Neural Networks
11:50 - Jürgen Schmidhuber to lead KAUST AI Initiative
12:35 - GitHub brief on DMCA notices for source code
14:55 - Helpful Reddit Threads
19:40 - Simple Tricks to improve Transformers
20:40 - Apple's Unconstrained Scene Generation
21:40 - Common Objects in 3D dataset
22:20 - WarpDrive Multi-Agent RL framework
23:10 - My new paper: Boosting Search Agents & MuZero
25:15 - Can AI detect depression from speech?

References:
Google shuts down Health Streams
https://techcrunch.com/2021/08/26/google-confirms-its-pulling-the-plug-on-streams-its-uk-clinician-support-app/

AI predicts race from X-Rays
https://www.iflscience.com/technology/ai-makes-strangely-accurate-predictions-from-blurry-medical-scans-alarming-researchers/?fbclid=IwAR2ddIP4w0p6VNbMRoe_9OPXQS6NA365XdB22v7rMlVOcuqnxe1ST7ZuvtA&utm_source=pocket_mylist
https://arxiv.org/ftp/arxiv/papers/2107/2107.10356.pdf

Facebook labels black men as primates
https://www.nytimes.com/2021/09/03/technology/facebook-ai-race-primates.html
https://en.wikipedia.org/wiki/Human

Distill articles on GNNs
https://distill.pub/2021/gnn-intro/
https://distill.pub/2021/understanding-gnns/

Jürgen Schmidhuber leads KAUST AI initiative
https://people.idsia.ch/~juergen/kaust-2021.html

GitHub issues court brief on code DMCAs
https://github.blog/2021-08-31-vague-infringement-allegations-considered-harmful/

Useful Reddit Threads
https://www.reddit.com/r/MachineLearning/comments/phvgzb/r_how_machine_learning_will_revolutionise_physics/
https://www.reddit.com/r/MachineLearning/comments/pe9jyt/d_what_are_the_most_important_problems_in_ml_today/
https://www.reddit.com/r/MachineLearning/comments/phnx8c/d_do_you_reproduce_a_method_for_sota_comparison/
https://www.reddit.com/r/MachineLearning/comments/pev04l/d_what_kind_of_hyperparameter_optimisation_do_you/

Tricks to improve Transformers
https://arxiv.org/pdf/2108.12284.pdf

Unconstrained Scene Generation
https://apple.github.io/ml-gsn/

Common Objects in 3D dataset
https://ai.facebook.com/blog/common-objects-in-3d-dataset-for-3d-reconstruction

WarpDrive Multi-Agent RL framework
https://blog.einstein.ai/warpdrive-fast-rl-on-a-gpu/

Boosting Search Engines / MuZero Code
https://arxiv.org/abs/2109.00527
https://github.com/google-research/google-research/tree/master/muzero
https://github.com/google-research/language/tree/master/language/search_agents

Can AI detect depression?
https://venturebeat.com/2021/08/31/ai-startups-claim-to-detect-depression-from-speech-but-the-jurys-out-on-their-accuracy/?utm_source=pocket_mylist

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 Google decommissions DeepMinds health app, Juergen Schmidhuber leads an AI initiative in Saudi Arabia, and I have a new paper. Welcome to ML News. Hey, hey, you. Yes, you. Do you run experiments? Machine learning experiments? Yes. How do you track them? What? That's not a good way to track them. Oh, here's what you should do. You should use weights and biases. Coincidentally, this video is sponsored by them. What is it? It's a system to track your experiments, track your artifacts, reproduce all the things you've ever done, see metrics, data sets, models from the inception of your idea to the final deployment and beyond. This is the ultimate tool, you can get started with just one line of code. Yes, one line of code and be amazed at what it gives you hyper parameter tuning, metrics tracking, resource utilization, model and data set versioning on cloud and on premise. Get this and much more when you sign up to weights and biases. Personal accounts are completely free. What are you waiting for? Sign up now. No, actually, watch the video first, then sign up or sign up now and sign up later. Get your mom to sign up, get your pet to sign up. There's absolutely no reason not to go to this URL and get your account now. Cheers. Hello and welcome to ML news on this beautiful glorious Monday. Let's dive into the first story tech crunch writes Google confirms it's pulling the plug on streams, its UK clinician support app. So this app has a bit of a history since 2015. DeepMind started it up originally trying to bring more AI into the health ecosystem. Now the streams health app isn't actually an AI focused app, it's kind of an app to track health data and assist clinicians in making decisions. The goal was always to bring AI into the picture. But this apparently has never succeeded. The article details the history of the app as it went through DeepMind stages, then of course, the big scandal where it was discovered that DeepMind didn't really have the legal basis for dealing with the data that they were dealing with. That was a weird sentence. And finally, DeepMind handing over the app to Google health, even though they said they would never share anything about this with Google. And now finally, Google deciding to turn off the app completely, whether or not this is a result of data privacy issues, or just being a result of the business case not being strong enough, we don't exactly know if you're interested in this, this article on tech crunch dives fairly deeply into the issue. What is special is how often it is mentioned that the data is going to be deleted. So it starts off with at least two paragraphs saying the data is going to be deleted, it mentions it throughout, and then it ends again with a paragraph on how the data is going to be deleted. So rest assured, the data is going to be deleted. I'm winking, you can't see it. I'm winking. Now the article is also a little bit critical of Google starting up projects and then killing them off after a short while, such as Google plus or the many, many, many, many, many, many messaging apps that Google has released things like Google video and so on. But honestly, I think the strategy has worked out so far, we got a couple of very nice products out of Google that started exactly like this that we might have never gotten if every single new product is an eternal commitment to support it. That being said, bring back the free storage for Google Photos. This was actually useful. So finally, Google is turning off this streams app, there's apparently still one group of customers that is using it ongoing, I guess still have to come to some sort of an agreement until the end of their contract. But going further, let's just wait for the next Google inventions, there should be like some sort of a betting market where you can bet whether or not new Google products will make it five years past their inception could be fun. IFL s writes AI makes strangely accurate predictions from blurry medical scans alarming researchers. So this is an article about this paper right here reading race AI recognizes patients racial identity and medical images, that is a study into various data sets and algorithms and whether or not they can detect a patient's race just from radiological images such as these ones. Now there is a common pattern among articles like this one that usually some confounding variable wasn't taken into account like source of data set or things like this. However, this paper specifically pays a lot of attention to eliminate all such confounding variables and really tests multiple hypotheses on how the model makes its assessment. So there are apparently a few distinct markers of race even in these radiological images. But even if they control for those, the models are still able to make out patients self reported races. The really interesting thing is that even if the images are degraded, such as this one right here and really pixelated, the models are still able to make out the patient self reported race with a higher than random accuracy, but the pictures themselves would be completely undiagnosable for any human and certainly humans couldn't make out the race of the patients. So as I said, the paper is a fairly lengthy investigation into these models and data sets, including trying to tease out race from models that have been trained not on predicting race, which essentially means that in order to predict some health outcome, the models in some part make predictions that correlate with race. And it is a fairly lengthy article. But if you're interested in these things, definitely give it a read. It seems like to be a very thorough study of these things. But the article here frames it all in terms of how terrible this is how biased these algorithms are. And while there's certainly truth to that, and many of these algorithms are in fact bias when they shouldn't be and due to various reasons, there also is the apparently rather shocking conclusions that your health outcomes interact with your genetics, I know new concept. So again, while we can certainly all agree that results like this are worrisome, and there are problems with bias in AI, it seems that people would like their ideologies to overrule reality. And I don't think that's a worthwhile goal. So that all being said, these problems are of course incredibly difficult, but we should look at them with the view of what's going to help the most people and what's going to deliver the best outcomes for all individuals. And there are probably no easy solutions for incredibly interconnected problems that are extremely multifactorial and include things like genetics, environment, society, data gathering, and the entire historical context of all of that. And that I guess is my rather boring take on that. In related news, the New York Times writes Facebook apologizes after AI puts primates label on video of black men, Facebook called it an unacceptable error, the company has struggled with other issues related to race. Now the article is about this Daily Mail video about a couple of black men, and the algorithm asks keep seeing videos about primates Yes or dismiss. So the classification algorithm made a mistake here. And this is not a new thing. As the article states in 2015, Google mistakenly labeled pictures of black people as gorillas. And the article also said more than two years later, wired found that Google solution was to censor the word gorilla from searches while also blocking chimp, chimpanzee and monkey. The article then goes into some more inter company things inside of Facebook trying to link this to the system or something like this, which I find quite shady, honestly, these systems have a number of issues, there are issues, of course, with data collection, there are issues with all kinds of other stuff. But ultimately, these systems are trained in a way that errors are errors. So if you fail to distinguish a yacht from a sailboat, that is an error to the model in the same way as if you fail to distinguish a human from a primate, the model has no inherent way of knowing that one is a socially acceptable error and one is a totally socially unacceptable error, there are ways to mitigate this, but they usually require efforts on the part of humans that go there and essentially correct for all the potential socially terrible errors that the model can do. And very often that burden is so large, it's combinatorically very, very hard to do this, all you can do is just block entire pieces of the search space in order to mitigate these mistakes. This is displayed as some kind of like a negative system, like, well, the AI is still biased, but now we're just sort of censoring it. Yes, I mean, what can you do? It's very easy to complain about these types of things. Now, of course, many of you might have noticed that technically, the model isn't wrong as human are the most abundant and widespread species of primates. But you know, technicalities aside, I think we can all agree that this isn't an output that you would want from your system. So what's the solution? I don't know, probably the best solution would be an attack from multiple sides where the companies invest more work into mitigating these types of errors, which means essentially collecting more training data on these intersections of very socially critical issues such that the models get more confident about them. And on the other hand, it might also require a little bit of a rethinking in society where we see a mistake like this, not as some terrible thing happening, but more into the category of mislabeling a sailboat as a yacht and vice versa. It'd be nice if we get to a point where we think, ah, cool, the system made a mistake. Let's go on with my life. But of course, it's not always that easy, because we use these types of systems in situations where it actually matters what the system predicts. So ultimately, it comes down to close supervision of your products and continuously evaluating their deployments. Again, it's a hard problem. I'm confident we can make progress on it. Complaining about it is fine. Just complaining and acting like it's the most terrible thing and it means something beyond what it actually means is probably not helpful. And it was previously reported that this still is taking a break due to the high load and the very high quality standards they have leading to kind of volunteer burnout, they released what appears to be some of the last articles that they're going to release in a while and they are on graph neural networks. One is a gentle introduction to graph neural networks. The other one is understanding convolutions on graphs. So the article pretty much contain what their title says, if you're interested in graph neural network, I can absolutely recommend you give these articles a read, they have very good illustrations of what's happening examples. And as you are used to from distill articles, their quality is extremely high can definitely recommend check it out. Schmidt Hoover announces that he'll be starting as a director of the cost AI initiative. cost is the King Abdullah University of Science and Technology in Saudi Arabia and is one of the most well funded universities on the planet. read who will remain in all his other positions and lead the AI initiative there apparently traveling back and forth. And on his blog, he writes, we hope the new AI initiative will contribute to a new golden age for science analogous to the Islamic golden age that started over a millennium ago. So quite likely we'll be hearing a lot more from KAUST in the near future. Not really ml related, but maybe a little bit if you care about codecs and models that produce code, GitHub has submitted a friend of the court brief, which is essentially an advisory letter to the courts on DMCA takedown notices of copyrighted material in the space of programming. Specifically, the brief concerns what they say is claims involving non literal copying of software. And they give an example case right here where the SAS Institute has brought infringement claims against world programming software. And specifically, they claim that it is not specific lines of code that the defendant has copied, but only that other aspects like the codes overall structure and organization were used. The blog post here also says, after examining the first question, the court found SAS Institute simply repeated and repeated that their system was creative, but did not point to any specific examples that would enable the court or the defendant to identify which parts were used in order to ultimately define those parts that were actually protected by copyright. The court ruled for the defendant leading to this appeal. Imagine something like you didn't exactly copy my picture, but you use the same organization of putting paint on the canvas. Now get a life SAS. Now of course, I don't know all the behinds like copyright is such a complicated issue. And there are legitimate cases where people steal from each other. And I can even see that there are some cases where you can say, well, the structure of my code is so unique and creative, and they copied it or something like this. Like, can't you just spend the money on something useful. So GitHub's position on this is that with a DMCA takedown notice, the noticer should specify in as much detail as possible what are the parts of the defendant's work that are infringing on the copyright such that there is even a possibility of responding. Apparently, it's totally possible to issue a DMCA takedown notice simply by saying, well, there's something in there. And I agree, that's not helpful. But ultimately helpfulness and what ultimately results from the legal system and the courts don't always match. So we'll keep an eye open on how this develops. So this week, there wasn't really many questions in the news to be answered. But there were some really nice questions on Reddit, some really good threads, I thought at least going with it. So there was a thread on how machine learning will revolutionize physics simulations in games. This is almost like a blog article in a Reddit post seems a little bit wasted, honestly, but it's pretty cool. It details what kind of models exist for doing physics simulations and what their advantages and disadvantages are. For example, here's one that's specifically good at modeling large deformations and tears and so on. This is a piece of bread tearing apart. And it also details how machine learning is being used in order to speed up the simulations. Essentially, what you want to do is you want to run the simulations, which are very intensive until you have a data set. And then you want to train the model to sort of predict the end of the simulation from the beginning, which seems like it should be impossible. But hey, it's deep learning. So so pretty cool. If you're interested in the intersection of deep learning and physics, give the Reddit post a read and of course, an upvote. So good job, Syed HM for contributing to the ML subreddit. aristocratic octopus asks, What are the most important problems in ML today? And I specifically want to highlight this thread because the answers are both diverse and really good. They range from diverse environment learning, catastrophic forgetting, modular learning, unstructured data, causality, few shot learning, generalization, and so on. Now, these are things that are researched today. Yet I think if you are coming into this field and looking for something to do, you don't really have an idea of what to work on. This thread might be a little bit of inspiration for you. Kam war asks, do you reproduce a method for state of the art comparison? Or do you just take the result from the paper of the method for state of the art comparison? It's an interesting question. I've seen people doing both. But the user says, for example, they try to reproduce a method, yet they couldn't get the exact same score saying they only got a 30% accuracy on a task, but the paper claimed they can obtain a 70% accuracy. They say they just ran the author's code with maybe a little modification. Some authors said that they need to tune the hyper parameters. And they also say they spend almost 90% time just trying to reproduce previous methods. Welcome to ML research, that is. Yeah, I don't know what the answer is here. There are also various opinions in the comments, you can almost guarantee that a lot of these research papers nowadays, you cannot really count on their numbers, they might leave away from the paper a lot of tricks that they have done to reach that number, or the numbers are just fake altogether. Of course, it could also be that the code they have on GitHub is kind of old code, which happens often if you resubmit somewhere, you redo some experiments, something changes in the meantime, so there can be legit and illegitimate reasons why you don't get the numbers you do. What you can do is you can report both the number they have in the paper, you can also report the number that you achieved with their method and simply consider this as two different baselines and explain yourself in the paper, it is a problem that you spend like ginormous amounts of time reproducing baselines. And as the PhD progressed, I more and more moved away from trying to get the exact numbers that baselines have gotten and simply give it my best shot at reproducing them and then reporting that I think it's up to you as long as you detail in the paper what you do, at least you can't be faulted. And lastly, Oli Mac P asks what kind of hyper parameter optimization do you use? And again, if you are looking for good advice, this thread might be something nice for you. There are suggestions such as ray tune up to no hyper opt, and so on. If you want a cheap method, I would start with all the hyper parameters on the default setting, then simply take the one you think is most important and vary it a little bit while keeping the others constant. Then once you found a good setting for that one, keep that one constant and vary one of the other ones while also keeping the other one constant. If you found a good setting for that one, keep going one by one through the parameters until you've tuned all of them once and start from the beginning. And at some point, you'll converge, you might get into a loop, but it's kind of unlikely that usually got me to relatively good places in hyper parameter search. And it takes way less compute than running some kind of big grid search. Usually these hyper parameters aren't that dependent on each other. So tuning them individually is okay. Speaking of tuning and reproducing and performances, there is a new paper from it's CIUSI and subsea called the devil is in the detail simple tricks to improve systematic generalization of transformers, which gives a number of hints to what you might want to tune when you train transformers. So the paper is an in depth investigation into what it takes to train transformers and what matters. And it gives some advice, for example, relative positional embeddings seem to outperform absolute positional embeddings for certain tasks. Also, you should be careful on how you do early stopping and how you scale your embeddings among other things. And lastly, the paper highlights the trouble with only having IID validation splits and not some sort of test that measures generalization capabilities beyond the exact distribution that the model was trained on. If this is of interest to you, give it a read. Also collaboration between Apple and the vector Institute release unconstrained scene generation with locally conditioned radiance fields at ICP 2021, releasing code on GitHub as well. And this is pretty cool. So this is scene generation, but with a freely moving camera. So apparently previous works have sort of focused on small camera movements, which is already impressive. But with this technique, it allows you to generate scenes from a generator. So this is essentially a GAN that first creates a latent floor map. And then based on that floor map generates the 3d environment in which you can then move around the camera freely. So essentially, you can render that scene from wherever you want. It still looks a little bit wonky. But I think the possibilities of these techniques to make it into entertainment into training into simulation into gaming is pretty cool. And probably not that far away. Again, the code is on GitHub. Check it out. Facebook AI research open sources common objects in 3d, a large scale data set for 3d reconstruction. So this is a data set for 3d reconstructing what they call common objects. Apparently, this is a crowdsourced data set of objects that people just apparently happen to come across, which is pretty cool because these are things that actually appear in real life seems like an extremely challenging data set. But often the most challenging data sets spur new types of discoveries. If you work in 3d reconstruction, this might be your next challenge. Salesforce releases warp drive extremely fast reinforcement learning on an Nvidia GPU. We've seen a number of libraries recently, such as Brax and Isaac gym that make reinforcement learning a lot faster by making use of the accelerators warp drive is especially geared to do multi agent reinforcement learning. So multi agent reinforcement learning is where you have many agents in the same world, and they need to interact with each other somehow cooperating or competing. And the difficult part is of course that you need to evaluate strategies for all of them, they depend on each other and things like backpropagation become extremely hard, especially if you're limited in compute power. This library makes optimal use of the power that you have. And I can definitely recommend that you check it out if you are not a giant corporation. Speaking of giant corporations and reinforcement learning, there's a new paper called boosting search engines with interactive agents. And look, it's me. So I've worked on this with this team as part of my internships and consultancy gigs at Google, but I am in no way the main author here. The paper is about developing agents that search in more than one step. So if you go to a search engine, usually you enter some sort of query. And if you don't immediately find what you're looking for, you may look at the top results and then kind of refine your query to find better results. And that's exactly what we try to do with agents here. So here you might start off with who won the US Open, you'll see a bunch of sports appearing and you might rephrase saying that you're specifically interested in tennis, and so on until you achieve the answer that you want. What's specifically cool about this is that there's code to go along with it. So next to the specific code that powers the search agents, there is a implementation of new zero based on a library called seed RL. Now this is also geared at making optimal use of your accelerators in such as a GPU or TPU while massively distributing the inference environments. So the museum algorithm is generic, I have authored part of it. And if you are looking to use new zero, this might be a good implementation for you as the new zero paper as well as the pseudo code they released contain various small subtle errors that nevertheless make the whole thing essentially not work. This implementation right here to the best of my knowledge contains less bugs, and it works pretty much with gym environments. So you plug in a gym environment with a little bit of extra information on how your tensors are shaped and so on. And that's all you have to do to trigger mu zero. So check out paper, check out code, and let us know if something's wrong. And last news, AI startups claim to detect depression from speech, but the jury's out on their accuracy. This is from venture beat. Now time and time again, we see these articles about claims that AI can do something, but it turns out the reality is a little bit more complicated. So there are a lot of examples of systems claiming to detect something to do with COVID. And then it turns out none of them is useful. This here is a little bit less bad because with COVID there was a big academic push to just make use of the hype to get papers published here we're already a little bit into the direction of actual products being implemented, but still the article details numerous problems that startups face some have only collected their data from certain parts of the world to be exact just from one city others focus on only native English speaker and confuse not being able to speak English with showing signs of depression. Still others neglect entire accents even for native speakers, and the list of problems goes on and on and on. Again, I don't think this is a problem where there is any kind of easy solution. I'm strongly of the opinion that we need to make progress in this there is a shortage of mental health professionals, and it's not inconceivable that machines can assist us and can deliver better lives to people even in the mental health area, but exactly what shape that's going to take and exactly how we're going to prevent some sort of dystopian future where some sort of buggy algorithm has way too much power over your life is I guess one of the big challenges of our generation. Again, a good place to start is to continuously monitor and evaluate the systems there are and to allow ourselves to take some risk as we push forward as long as we have it under control. Again, I know not a super strong opinion, but what can I do? I'm boring. Cool. This was it for ml news. Thank you so much for watching, listening and subscribing. If you know someone who's not informed about the world of ml, please tell them about ml news. We're about to reach 100k subscribers. Very exciting. I'll see you next time. Bye bye.
 Google decommissions DeepMinds health app, Juergen Schmidhuber leads an AI initiative in Saudi
 Arabia, and I have a new paper. Welcome to ML News. Hey, hey, you. Yes, you. Do you run experiments?
 Machine learning experiments? Yes. How do you track them? What? That's not a good way to track
 them. Oh, here's what you should do. You should use weights and biases. Coincidentally, this video
 is sponsored by them. What is it? It's a system to track your experiments, track your artifacts,
 reproduce all the things you've ever done, see metrics, data sets, models from the inception of
 your idea to the final deployment and beyond. This is the ultimate tool, you can get started
 with just one line of code. Yes, one line of code and be amazed at what it gives you hyper parameter
 tuning, metrics tracking, resource utilization, model and data set versioning on cloud and on
 premise. Get this and much more when you sign up to weights and biases. Personal accounts are
 completely free. What are you waiting for? Sign up now. No, actually, watch the video first,
 then sign up or sign up now and sign up later. Get your mom to sign up, get your pet to sign up.
 There's absolutely no reason not to go to this URL and get your account now. Cheers.
 Hello and welcome to ML news on this beautiful glorious Monday. Let's dive into the first story
 tech crunch writes Google confirms it's pulling the plug on streams, its UK clinician support app.
 So this app has a bit of a history since 2015. DeepMind started it up originally trying to bring
 more AI into the health ecosystem. Now the streams health app isn't actually an AI focused app,
 it's kind of an app to track health data and assist clinicians in making decisions. The goal
 was always to bring AI into the picture. But this apparently has never succeeded. The article
 details the history of the app as it went through DeepMind stages, then of course, the big scandal
 where it was discovered that DeepMind didn't really have the legal basis for dealing with the data
 that they were dealing with. That was a weird sentence. And finally, DeepMind handing over the
 app to Google health, even though they said they would never share anything about this with Google.
 And now finally, Google deciding to turn off the app completely, whether or not this is a result
 of data privacy issues, or just being a result of the business case not being strong enough,
 we don't exactly know if you're interested in this, this article on tech crunch dives fairly
 deeply into the issue. What is special is how often it is mentioned that the data is going to
 be deleted. So it starts off with at least two paragraphs saying the data is going to be deleted,
 it mentions it throughout, and then it ends again with a paragraph on how the data is going to be
 deleted. So rest assured, the data is going to be deleted. I'm winking, you can't see it. I'm winking.
 Now the article is also a little bit critical of Google starting up projects and then killing them
 off after a short while, such as Google plus or the many, many, many, many, many, many messaging apps
 that Google has released things like Google video and so on. But honestly, I think the strategy has
 worked out so far, we got a couple of very nice products out of Google that started exactly like
 this that we might have never gotten if every single new product is an eternal commitment to
 support it. That being said, bring back the free storage for Google Photos. This was actually useful.
 So finally, Google is turning off this streams app, there's apparently still one group of customers
 that is using it ongoing, I guess still have to come to some sort of an agreement until the end
 of their contract. But going further, let's just wait for the next Google inventions, there should
 be like some sort of a betting market where you can bet whether or not new Google products will
 make it five years past their inception could be fun. IFL s writes AI makes strangely accurate
 predictions from blurry medical scans alarming researchers. So this is an article about this
 paper right here reading race AI recognizes patients racial identity and medical images,
 that is a study into various data sets and algorithms and whether or not they can detect
 a patient's race just from radiological images such as these ones. Now there is a common pattern
 among articles like this one that usually some confounding variable wasn't taken into account
 like source of data set or things like this. However, this paper specifically pays a lot
 of attention to eliminate all such confounding variables and really tests multiple hypotheses
 on how the model makes its assessment. So there are apparently a few distinct markers of race even
 in these radiological images. But even if they control for those, the models are still able to
 make out patients self reported races. The really interesting thing is that even if the images are
 degraded, such as this one right here and really pixelated, the models are still able to make out
 the patient self reported race with a higher than random accuracy, but the pictures themselves would
 be completely undiagnosable for any human and certainly humans couldn't make out the race of
 the patients. So as I said, the paper is a fairly lengthy investigation into these models and data
 sets, including trying to tease out race from models that have been trained not on predicting
 race, which essentially means that in order to predict some health outcome, the models in some
 part make predictions that correlate with race. And it is a fairly lengthy article. But if you're
 interested in these things, definitely give it a read. It seems like to be a very thorough study
 of these things. But the article here frames it all in terms of how terrible this is how biased
 these algorithms are. And while there's certainly truth to that, and many of these algorithms are
 in fact bias when they shouldn't be and due to various reasons, there also is the apparently
 rather shocking conclusions that your health outcomes interact with your genetics, I know
 new concept. So again, while we can certainly all agree that results like this are worrisome,
 and there are problems with bias in AI, it seems that people would like their ideologies to overrule
 reality. And I don't think that's a worthwhile goal. So that all being said, these problems are
 of course incredibly difficult, but we should look at them with the view of what's going to help the
 most people and what's going to deliver the best outcomes for all individuals. And there are
 probably no easy solutions for incredibly interconnected problems that are extremely
 multifactorial and include things like genetics, environment, society, data gathering, and the
 entire historical context of all of that. And that I guess is my rather boring take on that.
 In related news, the New York Times writes Facebook apologizes after AI puts primates label on video
 of black men, Facebook called it an unacceptable error, the company has struggled with other issues
 related to race. Now the article is about this Daily Mail video about a couple of black men,
 and the algorithm asks keep seeing videos about primates Yes or dismiss. So the classification
 algorithm made a mistake here. And this is not a new thing. As the article states in 2015, Google
 mistakenly labeled pictures of black people as gorillas. And the article also said more than two
 years later, wired found that Google solution was to censor the word gorilla from searches while
 also blocking chimp, chimpanzee and monkey. The article then goes into some more inter company
 things inside of Facebook trying to link this to the system or something like this, which I find
 quite shady, honestly, these systems have a number of issues, there are issues, of course, with data
 collection, there are issues with all kinds of other stuff. But ultimately, these systems are
 trained in a way that errors are errors. So if you fail to distinguish a yacht from a sailboat,
 that is an error to the model in the same way as if you fail to distinguish a human from a primate,
 the model has no inherent way of knowing that one is a socially acceptable error and one is a totally
 socially unacceptable error, there are ways to mitigate this, but they usually require efforts
 on the part of humans that go there and essentially correct for all the potential socially
 terrible errors that the model can do. And very often that burden is so large, it's combinatorically
 very, very hard to do this, all you can do is just block entire pieces of the search space in order
 to mitigate these mistakes. This is displayed as some kind of like a negative system, like,
 well, the AI is still biased, but now we're just sort of censoring it. Yes, I mean, what can you
 do? It's very easy to complain about these types of things. Now, of course, many of you might have
 noticed that technically, the model isn't wrong as human are the most abundant and widespread species
 of primates. But you know, technicalities aside, I think we can all agree that this isn't an output
 that you would want from your system. So what's the solution? I don't know, probably the best
 solution would be an attack from multiple sides where the companies invest more work into mitigating
 these types of errors, which means essentially collecting more training data on these intersections
 of very socially critical issues such that the models get more confident about them. And on the
 other hand, it might also require a little bit of a rethinking in society where we see a mistake
 like this, not as some terrible thing happening, but more into the category of mislabeling a sailboat
 as a yacht and vice versa. It'd be nice if we get to a point where we think, ah, cool, the system
 made a mistake. Let's go on with my life. But of course, it's not always that easy, because we use
 these types of systems in situations where it actually matters what the system predicts. So
 ultimately, it comes down to close supervision of your products and continuously evaluating their
 deployments. Again, it's a hard problem. I'm confident we can make progress on it. Complaining
 about it is fine. Just complaining and acting like it's the most terrible thing and it means
 something beyond what it actually means is probably not helpful. And it was previously reported that
 this still is taking a break due to the high load and the very high quality standards they have
 leading to kind of volunteer burnout, they released what appears to be some of the last articles that
 they're going to release in a while and they are on graph neural networks. One is a gentle
 introduction to graph neural networks. The other one is understanding convolutions on graphs. So
 the article pretty much contain what their title says, if you're interested in graph neural network,
 I can absolutely recommend you give these articles a read, they have very good illustrations of
 what's happening examples. And as you are used to from distill articles, their quality is extremely
 high can definitely recommend check it out. Schmidt Hoover announces that he'll be starting as
 a director of the cost AI initiative. cost is the King Abdullah University of Science and Technology
 in Saudi Arabia and is one of the most well funded universities on the planet. read who will remain
 in all his other positions and lead the AI initiative there apparently traveling back and
 forth. And on his blog, he writes, we hope the new AI initiative will contribute to a new golden
 age for science analogous to the Islamic golden age that started over a millennium ago. So quite
 likely we'll be hearing a lot more from KAUST in the near future. Not really ml related, but maybe
 a little bit if you care about codecs and models that produce code, GitHub has submitted a friend
 of the court brief, which is essentially an advisory letter to the courts on DMCA takedown
 notices of copyrighted material in the space of programming. Specifically, the brief concerns
 what they say is claims involving non literal copying of software. And they give an example
 case right here where the SAS Institute has brought infringement claims against world programming
 software. And specifically, they claim that it is not specific lines of code that the defendant has
 copied, but only that other aspects like the codes overall structure and organization were used. The
 blog post here also says, after examining the first question, the court found SAS Institute
 simply repeated and repeated that their system was creative, but did not point to any specific
 examples that would enable the court or the defendant to identify which parts were used
 in order to ultimately define those parts that were actually protected by copyright. The court
 ruled for the defendant leading to this appeal. Imagine something like you didn't exactly copy
 my picture, but you use the same organization of putting paint on the canvas. Now get a life SAS.
 Now of course, I don't know all the behinds like copyright is such a complicated issue. And there
 are legitimate cases where people steal from each other. And I can even see that there are some cases
 where you can say, well, the structure of my code is so unique and creative, and they copied it or
 something like this. Like, can't you just spend the money on something useful. So GitHub's position
 on this is that with a DMCA takedown notice, the noticer should specify in as much detail as
 possible what are the parts of the defendant's work that are infringing on the copyright such
 that there is even a possibility of responding. Apparently, it's totally possible to issue a DMCA
 takedown notice simply by saying, well, there's something in there. And I agree, that's not
 helpful. But ultimately helpfulness and what ultimately results from the legal system and
 the courts don't always match. So we'll keep an eye open on how this develops. So this week,
 there wasn't really many questions in the news to be answered. But there were some really nice
 questions on Reddit, some really good threads, I thought at least going with it. So there was a
 thread on how machine learning will revolutionize physics simulations in games. This is almost like
 a blog article in a Reddit post seems a little bit wasted, honestly, but it's pretty cool. It
 details what kind of models exist for doing physics simulations and what their advantages
 and disadvantages are. For example, here's one that's specifically good at modeling large
 deformations and tears and so on. This is a piece of bread tearing apart. And it also details how
 machine learning is being used in order to speed up the simulations. Essentially, what you want to
 do is you want to run the simulations, which are very intensive until you have a data set. And then
 you want to train the model to sort of predict the end of the simulation from the beginning,
 which seems like it should be impossible. But hey, it's deep learning. So so pretty cool. If you're
 interested in the intersection of deep learning and physics, give the Reddit post a read and of
 course, an upvote. So good job, Syed HM for contributing to the ML subreddit. aristocratic
 octopus asks, What are the most important problems in ML today? And I specifically want to highlight
 this thread because the answers are both diverse and really good. They range from diverse environment
 learning, catastrophic forgetting, modular learning, unstructured data, causality, few shot learning,
 generalization, and so on. Now, these are things that are researched today. Yet I think if you are
 coming into this field and looking for something to do, you don't really have an idea of what to
 work on. This thread might be a little bit of inspiration for you. Kam war asks, do you
 reproduce a method for state of the art comparison? Or do you just take the result from the paper of
 the method for state of the art comparison? It's an interesting question. I've seen people doing both.
 But the user says, for example, they try to reproduce a method, yet they couldn't get the
 exact same score saying they only got a 30% accuracy on a task, but the paper claimed they
 can obtain a 70% accuracy. They say they just ran the author's code with maybe a little modification.
 Some authors said that they need to tune the hyper parameters. And they also say they spend almost 90%
 time just trying to reproduce previous methods. Welcome to ML research, that is. Yeah, I don't
 know what the answer is here. There are also various opinions in the comments, you can almost
 guarantee that a lot of these research papers nowadays, you cannot really count on their numbers,
 they might leave away from the paper a lot of tricks that they have done to reach that number,
 or the numbers are just fake altogether. Of course, it could also be that the code they have on GitHub
 is kind of old code, which happens often if you resubmit somewhere, you redo some experiments,
 something changes in the meantime, so there can be legit and illegitimate reasons why you don't get
 the numbers you do. What you can do is you can report both the number they have in the paper,
 you can also report the number that you achieved with their method and simply consider this as two
 different baselines and explain yourself in the paper, it is a problem that you spend like
 ginormous amounts of time reproducing baselines. And as the PhD progressed, I more and more moved
 away from trying to get the exact numbers that baselines have gotten and simply give it my best
 shot at reproducing them and then reporting that I think it's up to you as long as you detail in
 the paper what you do, at least you can't be faulted. And lastly, Oli Mac P asks what kind
 of hyper parameter optimization do you use? And again, if you are looking for good advice,
 this thread might be something nice for you. There are suggestions such as ray tune up to no hyper
 opt, and so on. If you want a cheap method, I would start with all the hyper parameters on the
 default setting, then simply take the one you think is most important and vary it a little bit
 while keeping the others constant. Then once you found a good setting for that one, keep that one
 constant and vary one of the other ones while also keeping the other one constant. If you found a
 good setting for that one, keep going one by one through the parameters until you've tuned all of
 them once and start from the beginning. And at some point, you'll converge, you might get into a
 loop, but it's kind of unlikely that usually got me to relatively good places in hyper parameter
 search. And it takes way less compute than running some kind of big grid search. Usually these hyper
 parameters aren't that dependent on each other. So tuning them individually is okay. Speaking of
 tuning and reproducing and performances, there is a new paper from it's CIUSI and subsea called the
 devil is in the detail simple tricks to improve systematic generalization of transformers,
 which gives a number of hints to what you might want to tune when you train transformers. So the
 paper is an in depth investigation into what it takes to train transformers and what matters. And
 it gives some advice, for example, relative positional embeddings seem to outperform absolute
 positional embeddings for certain tasks. Also, you should be careful on how you do early stopping
 and how you scale your embeddings among other things. And lastly, the paper highlights the
 trouble with only having IID validation splits and not some sort of test that measures generalization
 capabilities beyond the exact distribution that the model was trained on. If this is of interest
 to you, give it a read. Also collaboration between Apple and the vector Institute release
 unconstrained scene generation with locally conditioned radiance fields at ICP 2021, releasing
 code on GitHub as well. And this is pretty cool. So this is scene generation, but with a freely
 moving camera. So apparently previous works have sort of focused on small camera movements, which
 is already impressive. But with this technique, it allows you to generate scenes from a generator.
 So this is essentially a GAN that first creates a latent floor map. And then based on that floor
 map generates the 3d environment in which you can then move around the camera freely. So essentially,
 you can render that scene from wherever you want. It still looks a little bit wonky. But I think the
 possibilities of these techniques to make it into entertainment into training into simulation into
 gaming is pretty cool. And probably not that far away. Again, the code is on GitHub. Check it out.
 Facebook AI research open sources common objects in 3d, a large scale data set for 3d reconstruction.
 So this is a data set for 3d reconstructing what they call common objects. Apparently,
 this is a crowdsourced data set of objects that people just apparently happen to come across,
 which is pretty cool because these are things that actually appear in real life seems like an
 extremely challenging data set. But often the most challenging data sets spur new types of
 discoveries. If you work in 3d reconstruction, this might be your next challenge. Salesforce
 releases warp drive extremely fast reinforcement learning on an Nvidia GPU. We've seen a number
 of libraries recently, such as Brax and Isaac gym that make reinforcement learning a lot faster by
 making use of the accelerators warp drive is especially geared to do multi agent reinforcement
 learning. So multi agent reinforcement learning is where you have many agents in the same world,
 and they need to interact with each other somehow cooperating or competing. And the difficult part
 is of course that you need to evaluate strategies for all of them, they depend on each other and
 things like backpropagation become extremely hard, especially if you're limited in compute power.
 This library makes optimal use of the power that you have. And I can definitely recommend
 that you check it out if you are not a giant corporation. Speaking of giant corporations
 and reinforcement learning, there's a new paper called boosting search engines with interactive
 agents. And look, it's me. So I've worked on this with this team as part of my internships
 and consultancy gigs at Google, but I am in no way the main author here. The paper is about
 developing agents that search in more than one step. So if you go to a search engine, usually
 you enter some sort of query. And if you don't immediately find what you're looking for, you
 may look at the top results and then kind of refine your query to find better results. And
 that's exactly what we try to do with agents here. So here you might start off with who won the US
 Open, you'll see a bunch of sports appearing and you might rephrase saying that you're specifically
 interested in tennis, and so on until you achieve the answer that you want. What's specifically cool
 about this is that there's code to go along with it. So next to the specific code that powers the
 search agents, there is a implementation of new zero based on a library called seed RL. Now this
 is also geared at making optimal use of your accelerators in such as a GPU or TPU while
 massively distributing the inference environments. So the museum algorithm is generic, I have authored
 part of it. And if you are looking to use new zero, this might be a good implementation for you
 as the new zero paper as well as the pseudo code they released contain various small subtle errors
 that nevertheless make the whole thing essentially not work. This implementation right here to the
 best of my knowledge contains less bugs, and it works pretty much with gym environments. So you
 plug in a gym environment with a little bit of extra information on how your tensors are shaped
 and so on. And that's all you have to do to trigger mu zero. So check out paper, check out code,
 and let us know if something's wrong. And last news, AI startups claim to detect depression from
 speech, but the jury's out on their accuracy. This is from venture beat. Now time and time again,
 we see these articles about claims that AI can do something, but it turns out the reality is a little
 bit more complicated. So there are a lot of examples of systems claiming to detect something
 to do with COVID. And then it turns out none of them is useful. This here is a little bit less bad
 because with COVID there was a big academic push to just make use of the hype to get papers published
 here we're already a little bit into the direction of actual products being implemented, but still
 the article details numerous problems that startups face some have only collected their data from
 certain parts of the world to be exact just from one city others focus on only native English
 speaker and confuse not being able to speak English with showing signs of depression. Still
 others neglect entire accents even for native speakers, and the list of problems goes on and
 on and on. Again, I don't think this is a problem where there is any kind of easy solution. I'm
 strongly of the opinion that we need to make progress in this there is a shortage of mental
 health professionals, and it's not inconceivable that machines can assist us and can deliver
 better lives to people even in the mental health area, but exactly what shape that's going to take
 and exactly how we're going to prevent some sort of dystopian future where some sort of buggy
 algorithm has way too much power over your life is I guess one of the big challenges of our
 generation. Again, a good place to start is to continuously monitor and evaluate the systems
 there are and to allow ourselves to take some risk as we push forward as long as we have it under
 control. Again, I know not a super strong opinion, but what can I do? I'm boring. Cool. This was it
 for ml news. Thank you so much for watching, listening and subscribing. If you know someone
 who's not informed about the world of ml, please tell them about ml news. We're about to reach 100k
 subscribers. Very exciting. I'll see you next time. Bye bye.