[ML News] Microsoft combines Images & Text | Meta makes artificial skin | Russians replicate DALL-E
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper rudalle rudall-e dall-e openai openai clip microsoft turing turing bletchley mlnews yannic kilcher kilcher news machine learning news meta ai reskin meta digit digit sensor reskin sensor artificial skin artificial touch touch sensor arxiv doom arc game neural mmo pytorch lightning zillow zestimate ai culture ai corporate culture facebook algorithm
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The latest and greatest from the Machine Learning world

OUTLINE:
0:00 - Intro
0:15 - Sponsor: Weights & Biases Tables
3:25 - Microsoft Turing Bletchley: Universal Image Language Representation Model
6:35 - Meta AI Tactile Sensing
9:55 - AnimeGANv2
11:35 - General In-Hand Object Re-Orientation
13:05 - Does Facebook score the "Anger" Emoji too high?
17:05 - IsomorphicLabs: New Alphabet Company for Drug Discovery
18:15 - ruDALL-E: Russian DALL-E
20:40 - Image Scaling Attacks
23:25 - Azure OpenAI Service
24:10 - Neural MMO
25:40 - ArxivDOOM
26:50 - ARC Game
29:35 - ResNeXtGuesser
29:55 - Zillow loses money based on AI home price estimation
31:35 - Helpful Things
35:40 - AI will make your company great! Promise, Human!

Sponsor: Weights & Biases
https://wandb.com

References:
Microsoft Turing Bletchley: Universal Image Language Representation Model
https://www.microsoft.com/en-us/research/blog/turing-bletchley-a-universal-image-language-representation-model-by-microsoft/?utm_source=pocket_mylist
https://turing.microsoft.com/bletchley

Meta AI Tactile Sensing
https://ai.facebook.com/blog/teaching-robots-to-perceive-understand-and-interact-through-touch
https://ai.facebook.com/blog/reskin-a-versatile-replaceable-low-cost-skin-for-ai-research-on-tactile-perception
https://twitter.com/AIatMeta/status/1455144066698596357?s=09&t=K70DGbvdZNzfrN6uZzTuvg&utm_source=pocket_mylist

AnimeGANv2
https://huggingface.co/spaces/akhaliq/AnimeGANv2
https://github.com/bryandlee/animegan2-pytorch
https://github.com/TachibanaYoshino/AnimeGANv2
https://tachibanayoshino.github.io/AnimeGANv2/

General In-Hand Object Re-Orientation
https://taochenshh.github.io/projects/in-hand-reorientation
https://arxiv.org/abs/2111.03043

Does Facebook score the "Anger" Emoji too high?
https://www.washingtonpost.com/technology/2021/10/26/facebook-angry-emoji-algorithm/?utm_campaign=The%20Batch&utm_medium=email&_hsmi=178545675&_hsenc=p2ANqtz-81GmHTt04J5kbV0CHD6Oo6qlXZZGmk_36ArvcLn631roKuSUtLS7nZ-4wtWzcla9m9WsWGRJq1Y1rCu6UfaisuE8ur0A&utm_content=178542269&utm_source=hs_email

IsomorphicLabs: New Alphabet Company for Drug Discovery
https://twitter.com/demishassabis/status/1456283985554939907?s=20
https://www.isomorphiclabs.com/blog

ruDALL-E: Russian DALL-E
https://github.com/sberbank-ai/ru-dalle
https://huggingface.co/spaces/anton-l/rudall-e
https://colab.research.google.com/github/tg-bomze/collection-of-notebooks/blob/master/Text2Image_v4.ipynb
https://huggingface.co/sberbank-ai/rudalle-Malevich?text=attention+is+all+you+need
https://rudalle.ru/
https://habr.com/ru/company/sberbank/blog/586926/
https://habr-com.translate.goog/ru/company/sberbank/blog/586926/?_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=en-US&_x_tr_pto=nui

Image Scaling Attacks
https://twitter.com/AlexTamkin/status/1456149826337263621
https://twitter.com/rzhang88/status/1456324822833762304
https://arxiv.org/abs/2104.11222
https://twitter.com/arxiv_org/status/1241847623616618497
https://bifold.berlin/preventing-image-scaling-attacks-on-machine-learning/
https://embracethered.com/blog/posts/2020/husky-ai-image-rescaling-attacks/

Azure OpenAI Service
https://blogs.microsoft.com/ai/new-azure-openai-service/
https://azure.microsoft.com/en-us/services/openai-service/#overview

Neural MMO
https://openai.com/blog/neural-mmo/?utm_source=pocket_mylist
https://github.com/jsuarez5341/neural-mmo-client
https://github.com/jsuarez5341/neural-mmo
https://jsuarez5341.github.io/neural-mmo/build/html/rst/game_wiki.html#icon-combat
https://jsuarez5341.github.io/neural-mmo/build/html/rst/userguide.html#neural-mmo-at-neurips-2021
https://arxiv.org/abs/2110.07594

ArxivDOOM
https://sniklaus.com/arxivdoom?utm_source=pocket_mylist

ARC Game
https://github.com/volotat/ARC-Game
https://volotat.github.io/ARC-Game/?

ResNeXtGuesser
https://twitter.com/resnextguesser/status/1455270938719653890?utm_source=pocket_mylist

Zillow loses money based on AI home price estimation
https://www.reddit.com/r/MachineLearning/comments/qlilnf/n_zillows_nnbased_zestimate_leads_to_massive/
https://www.cbsnews.com/news/zillow-layoffs-closing-zillow-offers-selling-homes/
https://www.businessinsider.com/zillow-offers-ibuyer-sell-phoenix-homes-at-a-loss-2021-10?r=US&IR=T
https://archive.ph/qEITQ

Helpful Things
https://github.com/PyTorchLightning/pytorch-lightning/releases/tag/1.5.0
https://www.reddit.com/r/MachineLearning/comments/qnktqk/p_league_of_legends_patch_1121_game_playing_ai/?utm_source=pocket_mylist
https://devpost.com/software/iris-7s3yna
https://github.com/prabhuomkar/iris
https://araffin.github.io/post/rliable/
https://github.com/google-research/rliable
https://paperswithcode.com/dataset/medmnist-v2

AI will make your company great! Promise, Human!
https://fortune.com/2021/11/05/ai-artificial-intelligence-workplace-culture-collaboration-employee-morale-bcg/
https://sloanreview.mit.edu/projects/the-cultural-benefits-of-artificial-intelligence-in-the-enterprise/

Patreon: https://www.patreon.com/yannickilcher
 Microsoft trains a universal image language representation model, Facebook gets all touchy touchy and the Russkies release their own Dali model. Welcome to ML News. Hello there, this video is sponsored by weights and biases tables. Yes, the video is sponsored by a feature. That's a new thing. You haven't seen that before. So weights and biases tables is an interactive way to not only explore your experiments like you usually do with weights and biases, but to explore your data as well and the combinations of your data, your models, your predictions, your experiments, anything you want essentially can go into a table, you can see they can include pictures, even little sound files that can include videos, they can include image samples and overlay the models predictions as a mask, as you can see here, and you can compare different models to each other in a single table. This is extremely powerful. And if the user interface is not enough, they have a special syntax with which you can do pretty much anything you want. Really cool for visualizing predictions such as this one. Look, here is the picture and then the overlays of the masks of the model. Now it's probably my browser that doesn't load that fast enough, but the effect is a cool one. Let's see that again. Oh, yeah. So it's also really powerful if you want to compute some metrics on the fly, like counting false positives, counting false negatives, area under curve f1 score, anything like this. Very cool. So they have this example of a data set of Reddit comments. I know red is the most wholesome place on the planet. And this data set is annotated with all kinds of emotions, whether or not they appear in the comment by human raiders. So you can load this data set directly into a weights and biases table and then do all kinds of analysis with it. Honestly, it might just be cool to just load the data set in without even having to do any sort of experiments on it because this is a great viewer. For example, I can filter all the rows which contain both joy equals one and sadness equals one. How's that? So apply the filter. And I can immediately see all the comments that match both joy and sadness. Okay, what are these? Let's see. That made me cry tears of sadness and joy at the same time. Excellent. That's what we're looking for. Another really cool feature is the ability to group by certain columns. So here I group by subreddit. And then we can analyze all kinds of stuff across these different groups. For example, let me add a column here that tracks ratio of sadness inside of each subreddit. Sadness dot sum divided by row dot count should give us that result. And we have a result. And now we can sort by this. And look at that the soccer is in third place, who would have guessed though it only has 12 samples. So maybe we would want some more complicated metric. Luckily, with weights and biases, you can put all kinds of expressions in the cell expression tables. And if that is not enough for you, they have a special syntax with which you can create entire panels and visualizations give weights and biases as a whole a try. It's cool system. And thanks for sponsoring this video. Hey, how's everyone doing on this wonderful Monday, let's dive into our first story on the research blog, Microsoft says they have trained a universal image language representation model called Turing Bletchley. Now Turing is the effort by Microsoft to go into large scale models, large scale language models, for example, and Bletchley is a reference I believe to Bletchley Park where Alan Turing cracked the enigma not entirely sure my concept of these things is based off of Hollywood movies. In any case, this is a model much like clip that combines text and image modalities. And not only that, but it also combines text from different languages. So this is really a model that can understand the relationship between images and text in various languages all in the same embedding space, they achieve this by crawling the internet for images that come alongside text in various languages. And then they have basically two different objectives. One objective is to make the image representation close to the representations of the various texts that go with the image. And the other loss is to have the representations of two pieces of text that go with the same image also be close together. And that means they achieve a representation space where concepts no matter whether they're expressed in images or in any language cluster together if they mean the same thing. So they demonstrate this on various different examples right here. For example, the model understands a Coca Cola ad, irrespective of the languages, it can do a little bit of OCR and recognize words. And it's not only for natural images. But as you can see right here, it also understands things like maps and the multimodality means that you can even mix languages and scripts as you put things into the model and the model will still understand it. For example, on the left here, it says posing for a photo at the Great Wall of China. But the Great Wall of China is spelled in Chinese characters. And as you can see, the nearest neighbors in the embedding space are still models where people pose for a photo at Great Wall of China. Yeah, cat programming. This cat isn't programming. How do you know these cats are programming? This is clearly a gamer cat. They even have a little demo right here. Now here is where you see the smart PR people and lawyers come in all of the queries that you're able to do. There are a lot of them, but they are all pre programmed. So even though you can type here, you can only select one of the things that are already in here. For example, space needle at night, crazy pants. No, I think this isn't so much because they want to present you cherry picked examples. It's probably much more so people can't retrieve things like not safe for work images and even images that might have some copyright associated with it that ended up in this data set. But there is an interface for English queries, universal queries, and even image queries. So you can try out what the model thinks which are images which are sort of close in the space of meaning. Now here's a fatal flaw. If I'm not mistaken, this here is actually song gohan and not song goku as all the others. So that changes everything terrible model. Meta AI Facebook AI meta underscore Facebook AI says today as part of a larger tactile sensing ecosystem, we're announcing two major advances. Digit a commercially available touch sensing hardware produced in partnership with gel site and reskin a replaceable low cost tactile skin. So Facebook is going into the hardware of touch sensors and general tactile data. This isn't just hardware. This is sort of a big conglomeration of new advances in hardware coupled with machine learning advances. So the first one is reskin a versatile replaceable low cost skin for AI research on tactile perception. So this is really a piece of skin a piece of soft material that can sense when it touches something. So you can see right here this patch of skin that the person attached here to the robot hand allows the robot to get tactile feedback as it grabs things which is pretty cool because grabbing something like a blueberry is very hard when you don't want to squish it. And as you saw maybe up here, one robot simply, you know, don't like, no. So there are several advances right here. And they're not all hardware advances. Notably, usually you'd have to recalibrate every single individual one of these skin sensors because this being soft material, you can't really manufacture it in such a consistent way that all the sensors achieve the same accuracy. So you can't just calibrate once you have to recalibrate every individual thing. And the recalibration in this case, as far as I can read is done using a self supervised technique rather than supervised calibration, which makes things a whole lot easier. So there are various applications for this, you can see that not only do you get tactile feedback or whether you're touching something, you actually do also see where you touch something. So there are like enormous amounts of applications for this technology. This goes along with another technology called digits, which is also a touch sensor, but it is a little bit different. Namely, these are the small sensors that you can see right here. So this isn't necessarily deformable skin, but this is a very high precision touch sensor, like you might have it in a fingertip, I guess that's why it's called digit. Also, they say that this is quite low cost and they have open sourced the design. Now, as you can see here, the resolution on sensing on these sensors is quite high, you can see it's able to sense very, very, very detailed things on the things that it grabs. This goes along with a new pytorch library that they've built called pi touch that is able to take in this data and transform it in various ways. And also they are open sourcing tactile, which is a simulator for these types of data. So all in all, meta Facebook is really making an advance into this tactile ecosystem reskin deformable skin digit, the super high precision touch sensor, tactile, the simulator and pi touch the library. And they say soon they'll be out with a bunch of data sets and benchmarks for people. Very cool. I'm quite excited to see the technologies that are going to be possible with the sensors and processing tools. Anime again, is all the rage right now, all timelines of all my social networks are filled with people to define themselves and putting their faces and pictures into anime again, and it does look quite cool. So this is a series of advancements right here, starting from classic anime again, improving this to anime gan v2, which makes various improvements over the classic anime gan. By the way, this is a mixture of a style transfer and generative adversarial network, the code to anime gan was released in tensorflow, but has been ported to pytorch. And that again has been released as a space on hugging face that you can just try out. So here is a picture of me. It looks kind of weird. Here's a picture of the channel logo. That just looks disturbing. Here's a picture of some industry that looks actually pretty cool as the output. And here's a picture of Captain Picard. And we'll see what happens. Yeah, that looks pretty sweet. So what I want to highlight besides the fact that this is a cool model, it's just the chain of individuals or individual groups that just loosely work together to achieve something like this from the original research to its improvements, its releases, code, the transformation into various frameworks. And then in the end, the deployment as a really user friendly interface that you can use for free. This whole ecosystem is quite, quite cool, and pretty happy it exists. So I'll link everything you can try it out. Researchers from MIT release a paper called a system for general in hand object reorientation. And this pretty cool because it teaches robot hands here in simulation to reorient any sort of object and it can reorient objects that are as you can see, very, very tricky from given their form. And it can even do that in a zero shot fashion. So the trick here is that this is a student teacher model. So the final model, the student only has access to sort of the sensors in the hands like how the joints are oriented right now and to the visual input of a camera. However, it turns out that is quite tricky to learn from you are given the object and you're given a target pose and you need to rotate it somehow to the target pose. Now the task would be a lot easier if you had access to what they call privileged data, such as the velocity of the fingertips and so on and that you do have access if you're in a simulator. So the trick here is that they first train a model that gets access to all that privileged information learns what the model is going to do. So the model is going to learn what to do using that information and then teaches the student model what to do. So the student model doesn't have to learn through reinforcement learning, but it can instead learn from a very, very good teacher exactly what to do in a supervised way. And with this method, they achieve very strong even zero shot performance on new object, whether the hand is upright like this or turned around like this, they can even use the table as as help. Pretty cool and pretty simple. The Washington Post writes five points for anger, one for alike how Facebook's formula fostered rage and misinformation. And by now you should be aware that when you read an article like this that the journalist here wants to tell some sort of a story. So what you usually have to do is you have to go to the very, very bottom and read like the last three paragraphs such that you actually get what's going on. So the whole article is about how Facebook over the years has changed its algorithm to rank different posts on your page, there seems to be a sort of a point system. For example, when someone likes your post, that post gets one point if someone comments on your post, that post gets whatever 10 points or something like this. And these points are then used to score your post among all other posts in your friends and followers newsfeeds. Now the article here is quite long and details how Facebook evolved this algorithm as well over the years, especially after the introduction of additional things. So it used to be just like for a post. And apparently now you can also do love, ha ha, wow, sad and angry. I've actually stopped using Facebook except for posting videos even before this was the case. But you now have various emojis in order to react to content. So the article tries to tell the story specifically about the angry emoji, people reacting to that, and then the algorithm boosting this content. And this sort of ties to this notion that what Facebook's trying to do is to make people as angry as possible such that it maximizes their engagement and so on. And you know, while there is truth to the fact that when something makes you angry, it makes you more engaged, the article's tone and the actual things that happen don't really match up again, this seems to be a recurrent theme in these articles. So when you read the article neutrally, you can see that the problem is actually not that easy. For example, you can see that the title says five points for anger, one for a like, and you would somehow guess that Facebook intentionally the up rated the anger emoji, which is not the case, they simply operated all of the emojis except the like emoji. And the reasoning behind it was that in order to use the other emojis, you actually have to do two clicks. And in order to use the like, you only get to do one click. Therefore, a user doing two clicks is more effort means they engaged more means this should be operated in comparison to when a post only receives a like. In addition to that, Facebook was also trying to push these new features of these new emojis. And that's what platforms often do look at YouTube shorts or YouTube polls or things like this is that they massively upweigh the new features just to get people to use them and then later they'll downweigh them again. So it was technically true at that particular point in time, an angry emoji was five times more worth to the algorithm than a like. But do you think that framing it as the article does here, especially as the title of the article is a fair characterization of what happened? Well, I don't think so. And the rest of the article essentially goes on in this tone where you have difficult problems and you're trying to come up with some sensible solution that weighs a lot of interests against each other, one being profit, but not the only one. And then that solution not being perfect and having to be refined. That is not the same thing as Mark Zuckerberg sitting there going like and the kind of sleazy journalism of the Washington Post right here is just not helping. If you want give the article a read see if you can untie the journalists framing right here from the actual real problems that arise when you program such a recommendation system algorithm. Demis has of his tweets, thrilled to announce the launch of a new alphabet company isomorphic labs. Our mission is to reimagine the drug discovery process from first principles with an AI first approach to accelerate biomedical breakthroughs and find cures for diseases. isomorphic labs appears to be a new company under the umbrella of alphabet, therefore sort of a sister company to Google and deep mind and its goal is to accelerate things like drug discovery and various other things in biology. Demis himself will be the CEO of isomorphic labs, but also remain the CEO of deep mind. Now with deep mind going into things like alpha folder making quite a few advances applying AI to real world things, it's probably makes sense to spin this off into a single direction business effort right here as isomorphic labs, while probably he wants to keep deep mind more on the path of pushing AI research in general and not that deep mind suddenly becomes product implementers for pharma companies or something like this. On the other hand, maybe it's just some scheme to save taxes, you never know. SureBank AI releases a rudali, which is a Russian version of the Dalí model. The original technical report is available in Russian, but Google Translate is fairly good nowadays, they detail how they went about building the model and what they're releasing. So they have two different versions of it, one with 1.3 billion parameters and one with 12, the 1.3 billion parameter model is actually available. This goes along with various helper models such as their own version of clip and a super resolution model to do large images. Now I've heard somewhere that they also want to open source the really large model, but I'm not exactly sure that is super trustworthy. So as I said, both the code and the models they are released on on GitHub, you can go and look at it and the outputs of this model are pretty cool people still figuring out exactly how to prompt them. I think prompting has come a long way given the whole clip and VQ gan combos and we'll probably have to learn how to do the same thing with these Dalí based models. So they have a bunch of examples right here and they all look very cool. There's also a space on hogging face where you can simply type in something now this uses a translation engine to translate from English to Russian because you can only input things in Russian into the model. So if things go wrong, you never really know is it because of the translation is because of the prompt not being appropriate enough or the model fails. So here I input a purple tree on top of a mountain is not exactly what I wanted. But people have gotten quite cool results with it. There are also various notebooks right here that you can try out. And as I said, there is a technical report and a project website if you're interested in how all of it was built is quite detailed and it recounts the engineering challenges that the researchers had when implementing this. It's pretty cool to see that after open AI has already gotten a few challengers in the larger language model space, now more and more challengers also appear in this dali in this image generation from text space, the business model of not releasing your models doesn't seem to hold up for too long. I guess if you wanted to do that, you also shouldn't publish about them. But as soon as you publish other people are bound to reproduce your efforts, which is pretty cool for the rest of us. Excellent. This tweet here has gotten a lot of attention image scaling attacks in the wild. So this is a adversarial attack not on deep learning systems, but on re scaling procedures. Usually this happens when you get an image you want to input into a neural network, the neural networks usually have very defined sizes of images that they take in. So you first resize the image. Now, if you craft an image very smartly, you can craft it such that the resized version looks nothing like the original version. So you exploit how the resizing algorithm resizes images in order to achieve this goal. It's pretty unbelievable. But if you do resize the image on the left right here, you downscale it to the size on the right, then if you input it into the tensorflow resizing algorithm, this dark picture will turn out again, there's nothing else you take the image on the left, you put it through the downscaling algorithm, just downscaling. And the picture on the right is the output. That's because the picture on the right is sort of like hidden in the picture on the left in an exact way such that once you downsample all the original picture essentially cancels out and this new picture appears. Now the picture itself is actually from quite old work, or by old, I mean, like one year, which is ancient in the learning world. But these image re scaling attacks have been a thing for a while now. So for example, here's a paper about backdooring and poisoning neural networks with image scaling attacks. There is an interesting take here from Richard Chung, which says that this is essentially not a property of rescaling itself, but of faulty implementations of rescaling in various libraries. And there have actually been papers written about this problem, namely that if you want to calculate things like FID, which is often used in GAN as a quality metric, then it actually matters how you rescale images. And if you're rescaling algorithm doesn't do proper anti aliasing, then the rescaled images will have way too much contributions from certain pixels and way too little contributions from other pixels. So here, for example, if you ask these libraries to re scale the circle on the left, which is 128 by 128 to 16 by 16, only the pill Python image library does a good job at it, whereas all the other libraries you can see right here, they have various under or over contributions of different places in the image. And this is exactly the weak spots that these image rescaling attacks use in order to attack these images. So the solution here would be that the frameworks implement proper rescaling of images, which might cost a little bit of speed. So it's not guaranteed that these will make it to the final product. Microsoft Azure announces the open AI service, which essentially isn't an API that you can query GPT three with here, they have an example where GPT three automatically sort of summarizes sporting events from live feeds. And here is a neat corporate little video about boxes and things that connect things Wow, essentially, you're able to call GPT three in an Azure ecosystem right now. If you're an Azure customer, you don't have to go through open a eyes API, you can go directly to Azure. This is invitation only right now. But I think it'll be changed in the future. And you can simply have this as a service on Azure. Here's something cool neural MMO, I've actually reported about this before, but this has now been published at NURBS 21. And there are continuous updates to the framework. The last commit is 13 days ago. So this is very much a project that is alive. This is a framework for running reinforcement learning agents in big worlds with other reinforcement learning agents and that have to live for quite a while. So think of World of Warcraft, but for RL agents. Now the worlds are still quite simple because RL is a data and compute intensive task. So you don't want to make things too complicated. But this is by far one of the most complicated environments that I've seen so far, especially the introduction of other agents into the world. So you can have different sort of species of agents and they'll find different niches in order to survive and things like this, they do a pretty good job of giving you various tools to analyze the results of your runs. So this could be used both for researching reinforcement learning agents, but also researching various sort of population dynamics, if you're interested in anything like this, I think they do hold competitions, if I'm not mistaken, see there is even combat in the game. So if you're into challenges in reinforcement learning that go beyond just single player Atari games or something like this neural MMO might be very cool to look into. Another game that is not meant to be played by machines, but by humans is archive doom. So Steven Nicklaus made this little piece of web based doom right here. And the trick is wait, let me zoom out a little bit that it's doom, but the opponents are sometimes papers, you see, not only are they papers, but they are as far as I have read recent papers from archive. And once you shoot them, they get rejected, see, so this is way let me show show your face paper show your face. Ah, yes, yes, this is so we can scroll down here to see this is attack agnostic detection of adversarial year rejected. So there are these these other opponents as well. And come on, you can actually die reject, you can switch your weapon as well. So there's this machine gun right here. And there's even this blaster. I've never I've never played doom. I'm sorry. If this is standard, I don't know. Ah, go away. Reject. Yeah, if you want to have a bit of fun, give archive doom a try. It's pretty funny. Next up at the intersection of what machines and humans play is the arc game. This is by Alex a Borsky. And it takes the arc data set and makes it into a little web based game that you as a human can play. So we're going to try just one of these challenge things. If you don't know what the arc challenge is, I've made extensive videos about the measure of intelligence. So you essentially get three different examples right here. So the top left is an example, the top right is an example, the bottom middle here is an example, you're supposed to just figure out the pattern and then complete the pattern at the bottom. So here the pattern is that I guess every one of these bows here spits out a yellow thing. So from no yellow thing to yellow thing here as well here as well. So I'm going to take the yellow thing, we're gonna copy this over if you click this right and then here we can just we can color in actually whatever we want. But obviously, this is Yeah, yeah, we got it. We are touring complete. Stay another one. Okay, so actually, let's do a hard one medium hard tedious. Now I don't want tedious. Let's just do hard. Okay, one of the hard ones. Alright, so look at that. So there is this and then there's this, this. So the blue thing seems to be constant, right? We get four examples right here. Okay. Um, right. Okay. And then here. Okay, so what's the catch right here, I guess it's whatever piece can fill from the bottom the holes in the blue thing, such that it's like filled, but it doesn't matter if it reaches over right there only it only matters whether you can actually fill in the hole up until the blue continuous line, you can see why machines would struggle like this. So let's actually check of whether I'm correct. And then you need to color them red. Like once you figure out the rule, you still need to actually actively color them in red. So let's do this. Okay, this one here fills that first thing, this one actually doesn't fill it. This one fills nothing. This one fills it. See, see, this is I'm terrible. What is it? Why not? Why not? Yeah, yeah. This goes here. This goes here. Yeah, both of these could go there. Yep. Well, come on. This clearly goes here. This goes in. Ah, the bottom thing could technically go here on the right. Geez, I failed the touring test. Yeah, I mean, give it a try. Definitely. Just this is very cute. So this is a Twitter bot that takes memes and puts them through Resnext classifier. This is classified as a skunk, which is super interesting, right. So I'm gonna guess that is a image net classes, which expects there to be a single thing per image, but still skunk. Zillow has to lay off 25% of its workforce, and they stopped their house flipping service. So Zillow is this real estate company, they used AI to assess the prices of houses, and then they went in and bought these houses at what they thought were low prices with the goal to sell them at high prices. But this didn't work out. These stories are from CBS News and also Business Insider writes that very often Zillow has their homes at a loss. So they bought them for more than they want to sell them at. This is I guess first and foremost, a lesson in what AI can and can't do. It's very hard sometimes for an AI to just look at data that's available online and make a judgment about a real life thing such as a house, like two houses might be very different, even though their metadata looks exactly the same and a local realtor would know whereas this sort of worldwide algorithm maybe doesn't as much. However, it is special that there are other companies doing pretty much the same thing which are flourishing. So it might simply be a failure of Zillow itself. And it might be not a lesson in what AI can't do. But in you can't just throw AI at a problem and expect it to perform well, you have to actually go out and look for good data, you have to program your algorithms correctly, you have to validate them and so on. And all of this appears to not really have happened too well with Zillow's algorithm here. So let this be a warning. If you're an ML engineer, do a good job. Don't make your company bankrupt. Okay, welcome to this week's helpful things. The first helpful thing is pytorch lightning release 1.5. This is a major release of pytorch lightning, which if you don't know is a framework around pytorch to make training saving loading etc. of models much easier. So the new things in pytorch lightning are fault tolerant training pytorch lightning can now recognize when a training run abrupts unexpectedly or when one of the machines in a distributed run aborts and it can restart training from where it left off. This allows you to use things like preemptible machines without having to worry about you yourself always making sure that the machine isn't shut down or taken away from you, etc. Also very cool lightning light is for when you have a pure pytorch model. So not a pytorch lightning model, you can still use some of the features of pytorch light by simply wrapping the model in this lightning light module. And you do get almost all of the basic benefits of pytorch lightning, such as multi device training, multi node training, automatic dispatching to accelerators, and so on. So there are various other improvements right here, which I'm not going to mention, you can check them out for yourself. But I do like pytorch lightning as a framework. And it's cool to see that it's still being improved. There's a new data set of League of Legends game playing data. This is essentially a recording of agents in the game human agents, and you are supposed to learn from them. So this is available for you. The data set contained 72 games initially, but now has been expanded to contain 987 games. They're all filtered to relatively short games such that the individual episodes aren't too long. But this is supposed to be a base data set for doing offline reinforcement learning or imitation learning from teacher demonstrations. If you're into lol, and would like to train agents for it, maybe this is a cool resource for you. Iris is an open source alternative to Google Photos. This is submission to the pytorch annual hackathon 21. And it seeks to provide the functionalities of Google Photos, especially that now Google Photos does actually count your photos towards your quota. This is a welcome addition to the ecosystem, even though I don't think that people are going to self host their photos thing in the future. But maybe this will spur some kind of competition. So this is a framework that essentially ingests your photos index system does vector descriptions of your images, but also face detection and so on. And after that, you're able to search for images using text, for example, here, pizza on the left, or you can recognize what people are in the photos. And you can search by those. I love how the website design is like exactly like Google Photos. But the icon in the browser is just like the default react icon. In any case, very cool, open source, check it out. Our liable is a library by Google Research that is supposed to make evaluation of reinforcement learning agents more reproducible. So this does things like score normalization, stratified bootstrapping and calculates various other metrics that make reinforcement learning algorithms just a bit more comparable than like a single number on the Atari benchmark. Very cool code is on GitHub. Check it out. Medemnist v2 is a data set that seeks to be an MNIST like collection of standardized biomedical images. So these are various data sets 18 to be exact 12 of them are in 2d 828 by 28 pixels and six of them are in 3d 28 by 28 by 28 voxels. They say everything is available in standard formats with corresponding classification labels, no background knowledge is required for users. So if you're looking for an easy entry into biomedical data, this might be for you. I especially love the papers with code usage graph right here, the histogram, number of papers, one. Excellent. And lastly, we have an article from fortune saying AI won't break your company's culture, and it might even boost morale. This goes along with a new report by people associated with the Boston consulting group, as far as I can tell about the cultural benefits of artificial intelligence in the enterprise. So the article is trying to make the point that introducing AI products or AI mechanisms into companies might lead to various benefits, especially benefits that people might not realize initially, but it just sounds like this has been written by an AI to sort of make humans comply more saying things like every CEO worries that culture will make or break their company's AI deployment. But few realize that conversely, AI can also transform organizational culture, specifically using AI results in the following more collective learning, greater collaboration, clearer roles, higher morale, saying things like as many as 79% of the survey respondents reported an increase in morale after deployment of AI in their companies, like what this is definitely written by an AI to make us more compliant. Look at all these benefits if you use AI CEO, but you know, if the carrot isn't working, you also need to get out the stick, which the AI authors of this article definitely understand. So in the last paragraph saying, deploying AI at scale may not be easy, but CEOs would do well to remember that doing so will not only deliver financial benefits, but also create high performance cultures. CEOs would do well to remember. Excellent stuff right here. Totally humans who wrote this totally. Thank you. All right. This was already it for this week's ML news. Thank you so much for being here listening. Let me know what you think in the comments. Stay tuned for next week. Bye bye.
 Microsoft trains a universal image language representation model,
 Facebook gets all touchy touchy and the Russkies release their own Dali model. Welcome to ML News.
 Hello there, this video is sponsored by weights and biases tables. Yes, the video is sponsored by
 a feature. That's a new thing. You haven't seen that before. So weights and biases tables is an
 interactive way to not only explore your experiments like you usually do with weights and biases,
 but to explore your data as well and the combinations of your data, your models,
 your predictions, your experiments, anything you want essentially can go into a table,
 you can see they can include pictures, even little sound files that can include videos,
 they can include image samples and overlay the models predictions as a mask, as you can see here,
 and you can compare different models to each other in a single table. This is extremely powerful. And
 if the user interface is not enough, they have a special syntax with which you can do pretty much
 anything you want. Really cool for visualizing predictions such as this one. Look, here is the
 picture and then the overlays of the masks of the model. Now it's probably my browser that doesn't
 load that fast enough, but the effect is a cool one. Let's see that again. Oh, yeah. So it's also
 really powerful if you want to compute some metrics on the fly, like counting false positives,
 counting false negatives, area under curve f1 score, anything like this. Very cool. So they have
 this example of a data set of Reddit comments. I know red is the most wholesome place on the planet.
 And this data set is annotated with all kinds of emotions, whether or not they appear in the
 comment by human raiders. So you can load this data set directly into a weights and biases table
 and then do all kinds of analysis with it. Honestly, it might just be cool to just load
 the data set in without even having to do any sort of experiments on it because this is a great viewer.
 For example, I can filter all the rows which contain both joy equals one and sadness equals one.
 How's that? So apply the filter. And I can immediately see all the comments that match both
 joy and sadness. Okay, what are these? Let's see. That made me cry tears of sadness and joy at the
 same time. Excellent. That's what we're looking for. Another really cool feature is the ability
 to group by certain columns. So here I group by subreddit. And then we can analyze all kinds of
 stuff across these different groups. For example, let me add a column here that tracks ratio of
 sadness inside of each subreddit. Sadness dot sum divided by row dot count should give us that
 result. And we have a result. And now we can sort by this. And look at that the soccer is in third
 place, who would have guessed though it only has 12 samples. So maybe we would want some more
 complicated metric. Luckily, with weights and biases, you can put all kinds of expressions
 in the cell expression tables. And if that is not enough for you, they have a special syntax with
 which you can create entire panels and visualizations give weights and biases as a whole a try.
 It's cool system. And thanks for sponsoring this video. Hey, how's everyone doing on this wonderful
 Monday, let's dive into our first story on the research blog, Microsoft says they have trained
 a universal image language representation model called Turing Bletchley. Now Turing is the effort
 by Microsoft to go into large scale models, large scale language models, for example, and Bletchley
 is a reference I believe to Bletchley Park where Alan Turing cracked the enigma not entirely sure
 my concept of these things is based off of Hollywood movies. In any case, this is a model
 much like clip that combines text and image modalities. And not only that, but it also
 combines text from different languages. So this is really a model that can understand the relationship
 between images and text in various languages all in the same embedding space, they achieve this by
 crawling the internet for images that come alongside text in various languages. And then
 they have basically two different objectives. One objective is to make the image representation
 close to the representations of the various texts that go with the image. And the other loss is to
 have the representations of two pieces of text that go with the same image also be close together.
 And that means they achieve a representation space where concepts no matter whether they're
 expressed in images or in any language cluster together if they mean the same thing. So they
 demonstrate this on various different examples right here. For example, the model understands
 a Coca Cola ad, irrespective of the languages, it can do a little bit of OCR and recognize words.
 And it's not only for natural images. But as you can see right here, it also understands things
 like maps and the multimodality means that you can even mix languages and scripts as you put things
 into the model and the model will still understand it. For example, on the left here, it says posing
 for a photo at the Great Wall of China. But the Great Wall of China is spelled in Chinese characters.
 And as you can see, the nearest neighbors in the embedding space are still models where people pose
 for a photo at Great Wall of China. Yeah, cat programming. This cat isn't programming. How do
 you know these cats are programming? This is clearly a gamer cat. They even have a little demo
 right here. Now here is where you see the smart PR people and lawyers come in all of the queries
 that you're able to do. There are a lot of them, but they are all pre programmed. So even though
 you can type here, you can only select one of the things that are already in here. For example,
 space needle at night, crazy pants. No, I think this isn't so much because they want to present
 you cherry picked examples. It's probably much more so people can't retrieve things like not
 safe for work images and even images that might have some copyright associated with it that ended
 up in this data set. But there is an interface for English queries, universal queries, and even image
 queries. So you can try out what the model thinks which are images which are sort of close in the
 space of meaning. Now here's a fatal flaw. If I'm not mistaken, this here is actually song gohan
 and not song goku as all the others. So that changes everything terrible model.
 Meta AI Facebook AI meta underscore Facebook AI says today as part of a larger tactile sensing
 ecosystem, we're announcing two major advances. Digit a commercially available touch sensing
 hardware produced in partnership with gel site and reskin a replaceable low cost tactile skin. So
 Facebook is going into the hardware of touch sensors and general tactile data. This isn't
 just hardware. This is sort of a big conglomeration of new advances in hardware coupled with machine
 learning advances. So the first one is reskin a versatile replaceable low cost skin for AI research
 on tactile perception. So this is really a piece of skin a piece of soft material that can sense
 when it touches something. So you can see right here this patch of skin that the person attached
 here to the robot hand allows the robot to get tactile feedback as it grabs things which is
 pretty cool because grabbing something like a blueberry is very hard when you don't want to
 squish it. And as you saw maybe up here, one robot simply, you know, don't like, no. So
 there are several advances right here. And they're not all hardware advances. Notably,
 usually you'd have to recalibrate every single individual one of these skin sensors because
 this being soft material, you can't really manufacture it in such a consistent way that
 all the sensors achieve the same accuracy. So you can't just calibrate once you have to recalibrate
 every individual thing. And the recalibration in this case, as far as I can read is done using a
 self supervised technique rather than supervised calibration, which makes things a whole lot easier.
 So there are various applications for this, you can see that not only do you get tactile feedback
 or whether you're touching something, you actually do also see where you touch something. So there
 are like enormous amounts of applications for this technology. This goes along with another
 technology called digits, which is also a touch sensor, but it is a little bit different. Namely,
 these are the small sensors that you can see right here. So this isn't necessarily deformable skin,
 but this is a very high precision touch sensor, like you might have it in a fingertip, I guess
 that's why it's called digit. Also, they say that this is quite low cost and they have open sourced
 the design. Now, as you can see here, the resolution on sensing on these sensors is quite high,
 you can see it's able to sense very, very, very detailed things on the things that it grabs. This
 goes along with a new pytorch library that they've built called pi touch that is able to take in this
 data and transform it in various ways. And also they are open sourcing tactile, which is a simulator
 for these types of data. So all in all, meta Facebook is really making an advance into this
 tactile ecosystem reskin deformable skin digit, the super high precision touch sensor, tactile,
 the simulator and pi touch the library. And they say soon they'll be out with a bunch of data sets
 and benchmarks for people. Very cool. I'm quite excited to see the technologies that are going
 to be possible with the sensors and processing tools. Anime again, is all the rage right now,
 all timelines of all my social networks are filled with people to define themselves and
 putting their faces and pictures into anime again, and it does look quite cool. So this is a series
 of advancements right here, starting from classic anime again, improving this to anime gan v2,
 which makes various improvements over the classic anime gan. By the way, this is a mixture of a
 style transfer and generative adversarial network, the code to anime gan was released in tensorflow,
 but has been ported to pytorch. And that again has been released as a space on hugging face that
 you can just try out. So here is a picture of me. It looks kind of weird. Here's a picture of the
 channel logo. That just looks disturbing. Here's a picture of some industry that looks actually
 pretty cool as the output. And here's a picture of Captain Picard. And we'll see what happens.
 Yeah, that looks pretty sweet. So what I want to highlight besides the fact that this is a cool
 model, it's just the chain of individuals or individual groups that just loosely work together
 to achieve something like this from the original research to its improvements, its releases, code,
 the transformation into various frameworks. And then in the end, the deployment as a really user
 friendly interface that you can use for free. This whole ecosystem is quite, quite cool, and
 pretty happy it exists. So I'll link everything you can try it out. Researchers from MIT release
 a paper called a system for general in hand object reorientation. And this pretty cool because it
 teaches robot hands here in simulation to reorient any sort of object and it can reorient objects
 that are as you can see, very, very tricky from given their form. And it can even do that in a
 zero shot fashion. So the trick here is that this is a student teacher model. So the final model,
 the student only has access to sort of the sensors in the hands like how the joints are oriented
 right now and to the visual input of a camera. However, it turns out that is quite tricky to
 learn from you are given the object and you're given a target pose and you need to rotate it
 somehow to the target pose. Now the task would be a lot easier if you had access to what they call
 privileged data, such as the velocity of the fingertips and so on and that you do have access
 if you're in a simulator. So the trick here is that they first train a model that gets access to
 all that privileged information learns what the model is going to do. So the model is going to
 learn what to do using that information and then teaches the student model what to do. So the
 student model doesn't have to learn through reinforcement learning, but it can instead
 learn from a very, very good teacher exactly what to do in a supervised way. And with this method,
 they achieve very strong even zero shot performance on new object, whether the hand is upright like
 this or turned around like this, they can even use the table as as help. Pretty cool and pretty
 simple. The Washington Post writes five points for anger, one for alike how Facebook's formula
 fostered rage and misinformation. And by now you should be aware that when you read an article
 like this that the journalist here wants to tell some sort of a story. So what you usually have
 to do is you have to go to the very, very bottom and read like the last three paragraphs such that
 you actually get what's going on. So the whole article is about how Facebook over the years
 has changed its algorithm to rank different posts on your page, there seems to be a sort of a point
 system. For example, when someone likes your post, that post gets one point if someone comments on
 your post, that post gets whatever 10 points or something like this. And these points are then
 used to score your post among all other posts in your friends and followers newsfeeds. Now the
 article here is quite long and details how Facebook evolved this algorithm as well over the years,
 especially after the introduction of additional things. So it used to be just like for a post.
 And apparently now you can also do love, ha ha, wow, sad and angry. I've actually stopped using
 Facebook except for posting videos even before this was the case. But you now have various emojis
 in order to react to content. So the article tries to tell the story specifically about the angry
 emoji, people reacting to that, and then the algorithm boosting this content. And this sort
 of ties to this notion that what Facebook's trying to do is to make people as angry as possible such
 that it maximizes their engagement and so on. And you know, while there is truth to the fact that
 when something makes you angry, it makes you more engaged, the article's tone and the actual things
 that happen don't really match up again, this seems to be a recurrent theme in these articles.
 So when you read the article neutrally, you can see that the problem is actually not that easy.
 For example, you can see that the title says five points for anger, one for a like, and you would
 somehow guess that Facebook intentionally the up rated the anger emoji, which is not the case,
 they simply operated all of the emojis except the like emoji. And the reasoning behind it was that
 in order to use the other emojis, you actually have to do two clicks. And in order to use the like,
 you only get to do one click. Therefore, a user doing two clicks is more effort means they engaged
 more means this should be operated in comparison to when a post only receives a like. In addition
 to that, Facebook was also trying to push these new features of these new emojis. And that's what
 platforms often do look at YouTube shorts or YouTube polls or things like this is that they
 massively upweigh the new features just to get people to use them and then later they'll downweigh
 them again. So it was technically true at that particular point in time, an angry emoji was five
 times more worth to the algorithm than a like. But do you think that framing it as the article
 does here, especially as the title of the article is a fair characterization of what happened? Well,
 I don't think so. And the rest of the article essentially goes on in this tone where you have
 difficult problems and you're trying to come up with some sensible solution that weighs a lot of
 interests against each other, one being profit, but not the only one. And then that solution not
 being perfect and having to be refined. That is not the same thing as Mark Zuckerberg sitting
 there going like and the kind of sleazy journalism of the Washington Post right here is just not
 helping. If you want give the article a read see if you can untie the journalists framing right here
 from the actual real problems that arise when you program such a recommendation system algorithm.
 Demis has of his tweets, thrilled to announce the launch of a new alphabet company isomorphic
 labs. Our mission is to reimagine the drug discovery process from first principles with an AI first
 approach to accelerate biomedical breakthroughs and find cures for diseases. isomorphic labs
 appears to be a new company under the umbrella of alphabet, therefore sort of a sister company to
 Google and deep mind and its goal is to accelerate things like drug discovery and various other things
 in biology. Demis himself will be the CEO of isomorphic labs, but also remain the CEO of
 deep mind. Now with deep mind going into things like alpha folder making quite a few advances
 applying AI to real world things, it's probably makes sense to spin this off into a single
 direction business effort right here as isomorphic labs, while probably he wants to keep deep mind
 more on the path of pushing AI research in general and not that deep mind suddenly becomes product
 implementers for pharma companies or something like this. On the other hand, maybe it's just
 some scheme to save taxes, you never know. SureBank AI releases a rudali, which is a Russian version of
 the Dalí model. The original technical report is available in Russian, but Google Translate is
 fairly good nowadays, they detail how they went about building the model and what they're releasing.
 So they have two different versions of it, one with 1.3 billion parameters and one with 12,
 the 1.3 billion parameter model is actually available. This goes along with various helper
 models such as their own version of clip and a super resolution model to do large images. Now
 I've heard somewhere that they also want to open source the really large model, but I'm not exactly
 sure that is super trustworthy. So as I said, both the code and the models they are released on on
 GitHub, you can go and look at it and the outputs of this model are pretty cool people still figuring
 out exactly how to prompt them. I think prompting has come a long way given the whole clip and VQ
 gan combos and we'll probably have to learn how to do the same thing with these Dalí based models.
 So they have a bunch of examples right here and they all look very cool. There's also a space on
 hogging face where you can simply type in something now this uses a translation engine to translate
 from English to Russian because you can only input things in Russian into the model. So if things go
 wrong, you never really know is it because of the translation is because of the prompt not being
 appropriate enough or the model fails. So here I input a purple tree on top of a mountain is not
 exactly what I wanted. But people have gotten quite cool results with it. There are also various
 notebooks right here that you can try out. And as I said, there is a technical report and a project
 website if you're interested in how all of it was built is quite detailed and it recounts the
 engineering challenges that the researchers had when implementing this. It's pretty cool to see
 that after open AI has already gotten a few challengers in the larger language model space,
 now more and more challengers also appear in this dali in this image generation from text space,
 the business model of not releasing your models doesn't seem to hold up for too long. I guess if
 you wanted to do that, you also shouldn't publish about them. But as soon as you publish other
 people are bound to reproduce your efforts, which is pretty cool for the rest of us. Excellent.
 This tweet here has gotten a lot of attention image scaling attacks in the wild. So this is
 a adversarial attack not on deep learning systems, but on re scaling procedures. Usually this happens
 when you get an image you want to input into a neural network, the neural networks usually have
 very defined sizes of images that they take in. So you first resize the image. Now, if you craft
 an image very smartly, you can craft it such that the resized version looks nothing like the
 original version. So you exploit how the resizing algorithm resizes images in order to achieve this
 goal. It's pretty unbelievable. But if you do resize the image on the left right here, you
 downscale it to the size on the right, then if you input it into the tensorflow resizing algorithm,
 this dark picture will turn out again, there's nothing else you take the image on the left,
 you put it through the downscaling algorithm, just downscaling. And the picture on the right
 is the output. That's because the picture on the right is sort of like hidden in the picture on
 the left in an exact way such that once you downsample all the original picture essentially
 cancels out and this new picture appears. Now the picture itself is actually from quite old work,
 or by old, I mean, like one year, which is ancient in the learning world. But these image re scaling
 attacks have been a thing for a while now. So for example, here's a paper about backdooring
 and poisoning neural networks with image scaling attacks. There is an interesting take here from
 Richard Chung, which says that this is essentially not a property of rescaling itself, but of faulty
 implementations of rescaling in various libraries. And there have actually been papers written about
 this problem, namely that if you want to calculate things like FID, which is often used in GAN as a
 quality metric, then it actually matters how you rescale images. And if you're rescaling algorithm
 doesn't do proper anti aliasing, then the rescaled images will have way too much contributions from
 certain pixels and way too little contributions from other pixels. So here, for example, if you
 ask these libraries to re scale the circle on the left, which is 128 by 128 to 16 by 16, only the
 pill Python image library does a good job at it, whereas all the other libraries you can see right
 here, they have various under or over contributions of different places in the image. And this is
 exactly the weak spots that these image rescaling attacks use in order to attack these images. So
 the solution here would be that the frameworks implement proper rescaling of images, which might
 cost a little bit of speed. So it's not guaranteed that these will make it to the final product.
 Microsoft Azure announces the open AI service, which essentially isn't an API that you can query
 GPT three with here, they have an example where GPT three automatically sort of summarizes sporting
 events from live feeds. And here is a neat corporate little video about boxes and things that
 connect things Wow, essentially, you're able to call GPT three in an Azure ecosystem right now.
 If you're an Azure customer, you don't have to go through open a eyes API, you can go directly to
 Azure. This is invitation only right now. But I think it'll be changed in the future. And you
 can simply have this as a service on Azure. Here's something cool neural MMO, I've actually reported
 about this before, but this has now been published at NURBS 21. And there are continuous updates to
 the framework. The last commit is 13 days ago. So this is very much a project that is alive. This
 is a framework for running reinforcement learning agents in big worlds with other reinforcement
 learning agents and that have to live for quite a while. So think of World of Warcraft, but for
 RL agents. Now the worlds are still quite simple because RL is a data and compute intensive task.
 So you don't want to make things too complicated. But this is by far one of the most complicated
 environments that I've seen so far, especially the introduction of other agents into the world. So
 you can have different sort of species of agents and they'll find different niches in order to
 survive and things like this, they do a pretty good job of giving you various tools to analyze
 the results of your runs. So this could be used both for researching reinforcement learning agents,
 but also researching various sort of population dynamics, if you're interested in anything like
 this, I think they do hold competitions, if I'm not mistaken, see there is even combat in the game.
 So if you're into challenges in reinforcement learning that go beyond just single player Atari
 games or something like this neural MMO might be very cool to look into. Another game that is not
 meant to be played by machines, but by humans is archive doom. So Steven Nicklaus made this little
 piece of web based doom right here. And the trick is wait, let me zoom out a little bit that it's
 doom, but the opponents are sometimes papers, you see, not only are they papers, but they are as far
 as I have read recent papers from archive. And once you shoot them, they get rejected, see, so this
 is way let me show show your face paper show your face. Ah, yes, yes, this is so we can scroll down
 here to see this is attack agnostic detection of adversarial year rejected. So there are these these
 other opponents as well. And come on, you can actually die reject, you can switch your weapon
 as well. So there's this machine gun right here. And there's even this blaster. I've never I've
 never played doom. I'm sorry. If this is standard, I don't know. Ah, go away. Reject. Yeah, if you
 want to have a bit of fun, give archive doom a try. It's pretty funny. Next up at the intersection
 of what machines and humans play is the arc game. This is by Alex a Borsky. And it takes the arc
 data set and makes it into a little web based game that you as a human can play. So we're going to
 try just one of these challenge things. If you don't know what the arc challenge is, I've made
 extensive videos about the measure of intelligence. So you essentially get three different examples
 right here. So the top left is an example, the top right is an example, the bottom middle here is an
 example, you're supposed to just figure out the pattern and then complete the pattern at the bottom.
 So here the pattern is that I guess every one of these bows here spits out a yellow thing. So from
 no yellow thing to yellow thing here as well here as well. So I'm going to take the yellow thing,
 we're gonna copy this over if you click this right and then here we can just we can color in actually
 whatever we want. But obviously, this is Yeah, yeah, we got it. We are touring complete. Stay another
 one. Okay, so actually, let's do a hard one medium hard tedious. Now I don't want tedious. Let's just
 do hard. Okay, one of the hard ones. Alright, so look at that. So there is this and then there's
 this, this. So the blue thing seems to be constant, right? We get four examples right here. Okay.
 Um, right. Okay. And then here. Okay, so what's the catch right here, I guess it's whatever piece
 can fill from the bottom the holes in the blue thing, such that it's like filled, but it doesn't
 matter if it reaches over right there only it only matters whether you can actually fill in the hole
 up until the blue continuous line, you can see why machines would struggle like this. So let's
 actually check of whether I'm correct. And then you need to color them red. Like once you figure
 out the rule, you still need to actually actively color them in red. So let's do this. Okay, this
 one here fills that first thing, this one actually doesn't fill it. This one fills nothing. This one
 fills it. See, see, this is I'm terrible. What is it? Why not? Why not? Yeah, yeah. This goes here.
 This goes here. Yeah, both of these could go there. Yep. Well, come on. This clearly goes here. This
 goes in. Ah, the bottom thing could technically go here on the right.
 Geez, I failed the touring test. Yeah, I mean, give it a try. Definitely.
 Just this is very cute. So this is a Twitter bot that takes memes and puts them through Resnext
 classifier. This is classified as a skunk, which is super interesting, right. So I'm gonna guess
 that is a image net classes, which expects there to be a single thing per image, but still skunk.
 Zillow has to lay off 25% of its workforce, and they stopped their house flipping service. So
 Zillow is this real estate company, they used AI to assess the prices of houses, and then they
 went in and bought these houses at what they thought were low prices with the goal to sell
 them at high prices. But this didn't work out. These stories are from CBS News and also Business
 Insider writes that very often Zillow has their homes at a loss. So they bought them for more
 than they want to sell them at. This is I guess first and foremost, a lesson in what AI can and
 can't do. It's very hard sometimes for an AI to just look at data that's available online and make
 a judgment about a real life thing such as a house, like two houses might be very different,
 even though their metadata looks exactly the same and a local realtor would know whereas this sort
 of worldwide algorithm maybe doesn't as much. However, it is special that there are other
 companies doing pretty much the same thing which are flourishing. So it might simply be a failure
 of Zillow itself. And it might be not a lesson in what AI can't do. But in you can't just throw AI
 at a problem and expect it to perform well, you have to actually go out and look for good data,
 you have to program your algorithms correctly, you have to validate them and so on. And all of
 this appears to not really have happened too well with Zillow's algorithm here. So let this be a
 warning. If you're an ML engineer, do a good job. Don't make your company bankrupt. Okay, welcome
 to this week's helpful things. The first helpful thing is pytorch lightning release 1.5. This is
 a major release of pytorch lightning, which if you don't know is a framework around pytorch to
 make training saving loading etc. of models much easier. So the new things in pytorch lightning are
 fault tolerant training pytorch lightning can now recognize when a training run abrupts unexpectedly
 or when one of the machines in a distributed run aborts and it can restart training from where it
 left off. This allows you to use things like preemptible machines without having to worry
 about you yourself always making sure that the machine isn't shut down or taken away from you,
 etc. Also very cool lightning light is for when you have a pure pytorch model. So not a pytorch
 lightning model, you can still use some of the features of pytorch light by simply wrapping the
 model in this lightning light module. And you do get almost all of the basic benefits of pytorch
 lightning, such as multi device training, multi node training, automatic dispatching to accelerators,
 and so on. So there are various other improvements right here, which I'm not going to mention,
 you can check them out for yourself. But I do like pytorch lightning as a framework. And it's cool
 to see that it's still being improved. There's a new data set of League of Legends game playing
 data. This is essentially a recording of agents in the game human agents, and you are supposed to
 learn from them. So this is available for you. The data set contained 72 games initially, but now has
 been expanded to contain 987 games. They're all filtered to relatively short games such that the
 individual episodes aren't too long. But this is supposed to be a base data set for doing offline
 reinforcement learning or imitation learning from teacher demonstrations. If you're into lol, and
 would like to train agents for it, maybe this is a cool resource for you. Iris is an open source
 alternative to Google Photos. This is submission to the pytorch annual hackathon 21. And it seeks
 to provide the functionalities of Google Photos, especially that now Google Photos does actually
 count your photos towards your quota. This is a welcome addition to the ecosystem, even though I
 don't think that people are going to self host their photos thing in the future. But maybe this
 will spur some kind of competition. So this is a framework that essentially ingests your photos
 index system does vector descriptions of your images, but also face detection and so on. And
 after that, you're able to search for images using text, for example, here, pizza on the left, or
 you can recognize what people are in the photos. And you can search by those. I love how the website
 design is like exactly like Google Photos. But the icon in the browser is just like the default react
 icon. In any case, very cool, open source, check it out. Our liable is a library by Google Research
 that is supposed to make evaluation of reinforcement learning agents more reproducible. So this does
 things like score normalization, stratified bootstrapping and calculates various other
 metrics that make reinforcement learning algorithms just a bit more comparable than like a single
 number on the Atari benchmark. Very cool code is on GitHub. Check it out. Medemnist v2 is a data set
 that seeks to be an MNIST like collection of standardized biomedical images. So these are
 various data sets 18 to be exact 12 of them are in 2d 828 by 28 pixels and six of them are in 3d
 28 by 28 by 28 voxels. They say everything is available in standard formats with corresponding
 classification labels, no background knowledge is required for users. So if you're looking for an
 easy entry into biomedical data, this might be for you. I especially love the papers with code
 usage graph right here, the histogram, number of papers, one. Excellent. And lastly, we have an
 article from fortune saying AI won't break your company's culture, and it might even boost morale.
 This goes along with a new report by people associated with the Boston consulting group,
 as far as I can tell about the cultural benefits of artificial intelligence in the enterprise. So
 the article is trying to make the point that introducing AI products or AI mechanisms into
 companies might lead to various benefits, especially benefits that people might not realize
 initially, but it just sounds like this has been written by an AI to sort of make humans comply
 more saying things like every CEO worries that culture will make or break their company's AI
 deployment. But few realize that conversely, AI can also transform organizational culture,
 specifically using AI results in the following more collective learning, greater collaboration,
 clearer roles, higher morale, saying things like as many as 79% of the survey respondents
 reported an increase in morale after deployment of AI in their companies, like what this is
 definitely written by an AI to make us more compliant. Look at all these benefits if you
 use AI CEO, but you know, if the carrot isn't working, you also need to get out the stick,
 which the AI authors of this article definitely understand. So in the last paragraph saying,
 deploying AI at scale may not be easy, but CEOs would do well to remember that doing so will not
 only deliver financial benefits, but also create high performance cultures. CEOs would do well to
 remember. Excellent stuff right here. Totally humans who wrote this totally. Thank you. All
 right. This was already it for this week's ML news. Thank you so much for being here listening.
 Let me know what you think in the comments. Stay tuned for next week. Bye bye.