[ML News] NVIDIA GTC'21 | DeepMind buys MuJoCo | Google predicts spreadsheet formulas
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper mlnews mujoco nvidia gtc21
#gtc21 #mlnews #mujoco

Register to GTC'21 and Win a RTX 3090: https://nvda.ws/2Y2B5ni

OUTLINE:
0:00 - Intro
0:15 - Sponsor: NVIDIA GTC'21
5:35 - DeepMind buys & Open-Sources MuJoCo
7:25 - PyTorch 1.10 Released
9:10 - Google Predicts Spreadsheet Formulas
11:25 - handtracking.io
12:25 - Cell Instance Segmentation Challenge
13:00 - Helpful Libraries
17:50 - Waymo cars keep turning into same dead-end
19:35 - BlueRiver balances tractors

References:
DeepMind buys & open-sources MuJoCo
https://deepmind.com/blog/announcements/mujoco

PyTorch 1.10 released
https://pytorch.org/blog/pytorch-1.10-released/
https://developer.nvidia.com/blog/cuda-graphs/

GoogleAI predicts spreadsheet formulas
https://ai.googleblog.com/2021/10/predicting-spreadsheet-formulas-from.html

Handtracking in Browser
https://handtracking.io/
https://handtracking.io/draw_demo/

Sartorius Cell Instance Segmentation Competition
https://www.kaggle.com/c/sartorius-cell-instance-segmentation/

Helpful Libraries
https://github.com/IntelLabs/control-flag
https://github.com/facebookresearch/salina
https://github.com/facebookresearch/salina/tree/main/salina_examples/rl/a2c/mono_cpu
https://github.com/ydataai/ydata-synthetic
https://syntheticdata.community/
https://github.com/ydataai/ydata-synthetic/blob/master/examples/regular/gan_example.ipynb
https://medium.com/aimstack/aim-3-0-0-the-foundations-for-open-source-open-metadata-ml-platform-f3969755d55
https://github.com/aimhubio/aim
https://robustbench.github.io/

Waymo cars keep coming to same dead-end over and over
https://sanfrancisco.cbslocal.com/2021/10/14/dead-end-sf-street-plagued-with-confused-waymo-cars-trying-to-turn-around-every-5-minutes/

BlueRiver balances tractors
https://www.linkedin.com/posts/lredden_blue-river-is-building-the-boston-dynamics-activity-6850873662959169536-8sue/
https://bluerivertechnology.com/ourmethods/


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 Nvidia holds a giant conference DeepMind buys and open sources Mojoco and Google predicts what you're gonna write in a spreadsheet. Welcome to ML News. Hello, hello, this video is sponsored by Nvidia actually, not just Nvidia, but they want to raise awareness for their GTC conference, which happens November 8 through 11 this year. Now there is something in it for you if you use my link to register to this, you can win a 3090. So these GPUs are super rare nowadays and one is allocated just for my link to register. So you're not competing with the rest of YouTube, you're just competing with anyone that uses my link. So if you're interested, use the link in the description to register to the conference. Now the conference is actually relevant for machine learning audience, because Nvidia is not only talking about Nvidia, though I love the what will Jensen Huang's keynote reveal banner right here being super mysterious and all. Okay, Nvidia says I should hype up the keynote more. So this keynote is going to be the maddest keynote you've ever seen. You remember last keynote where Jensen Huang was like rendered and Nvidia made this big deal about how they rendered him and this was like a big effort, then they had to correct themselves and state that it was actually only for 14 seconds and not for the entire keynote, because that's kind of what they alluded to at the beginning. I reported about this in ML news, it was epic. And I guess this keynote is going to be epic again. Will he finally reveal what the leather jacket is made of? If you haven't seen yet on Twitter, if you use the hashtag GTC 21, it actually renders a little leather jacket next to it. And I think Nvidia paid for this. Isn't this the greatest marketing like business decision by Twitter, they're able to sell hashtags insane. And I don't know what's going to happen. But I've come across this the omniverse, which is in beta. And there's kind of speculation that that's going to be one of the topics I didn't know this existed. This is sort of like a real time rendering framework that's based on Pixar's Universal Scene description and Nvidia RTX. And it's pretty insane. So apparently this is this real time, this is an entire framework where you can do like real time ray tracing. Look at this. This looks great. I don't know how many RTX is you need for that one. But it's pretty insane. This used to take like insane amounts of rendering time. And yeah, the fact that it's real time really cool. But they have invited a bunch of speakers to talk about all kinds of stuff in graphics in machine learning and in many other areas of computation. So they really want this to be a big thing this conference and you can see this these are just some of the speakers you can see faith Ali is speaking, Elia Sami, and many others that you might know of. So these are three pages of speakers that are really big in their industry Nvidia spending a ton of cash right here to give you essentially free content. Now you do need to register to watch all of these talks, but it's free. And as I said, you can win a 3090. Now before we go on, I would like to say that the condition for the sponsorship of Nvidia was that the video must be available in English and in German, which is weird, you know, but since I speak German, I can do that. So this video is available as a not a copy, but an equivalent in a German version. So if this is not the language you expected, switch over to the other video and I promise I'll just put on my absolute best impression of a real German. So a little bit more about this conference while the keynote is obviously the main event right here and video revealing what they're going to do, which given Nvidia size and dominance is quite relevant for the entire deep learning world. There are over 500 sessions. If you look at the schedule, there are 15 sessions just dedicated to pytorch and 12 dedicated to TensorFlow. And those aren't the only deep learning sessions, there are many, many more. As you can see, there is a plethora of industry types and topics that people are going to talk about. It's like an endless list. So rest assured that during these four days, you can just bathe in Nvidia content for 24 hours a day. Now along with the conference, there are these instructor led workshops that give you hands on experience in certain things, for example, building transformer based natural language processing applications, they do cost a little bit of money, but they're hands on. So if you're interested in that, take a look. So I don't know what more to say. As I said, it's completely free content, they're throwing a bunch of money to get really good speakers and you can win a graphics card and look at them frame numbers. We all know more frames means that you're a better gamer. So get the 3090 now link is in the description. Check out all the talks and the sessions that happen at the conference. And I wish you a really pleasant experience and videos really trying to gear up this conference to make it a big deal. And as it seems, it is actually a big deal. Next news. DeepMind has apparently bought MojoCo, which is one of the primary simulation softwares for robotics. This has been used again and again, not only in robotics, but also in deep learning and reinforcement learning in all of these kinds of settings to do continuous control simulations. As you can see here, this works pretty well. This is a real flipping flippity spinny spin. And here you see one in MojoCo. Now the trouble with MojoCo has always been that it was proprietary. And not only that, not only was it not open source, but you had to pay quite a bit of money for it. So now apparently DeepMind has bought and open sourced MojoCo replication efforts have been underway. But very often these simulators, they are built for gaming or something like this. And they neglect effects such as these gyroscopic effects right here, which you can see that MojoCo apparently has a good balance between realism and accuracy for these kinds of simulations. And not only that, but it is also fast enough. So you can do reinforcement learning with it. And DeepMind has used this extensively. This is all apparently from DeepMind's works, you can see how versatile the simulator is. So now DeepMind has bought it and makes it available to everyone, which is pretty, pretty cool. Now is this really out of kind heartedness? Maybe actually, maybe they just want to get some good PR out there. Or maybe they want to do another nature publication and nature publications do force you I believe to open source pretty much anything that you have to achieve the publications, whatever it might be. It's pretty cool that DeepMind does it the code base is apparently in C. So it's portable, compilable, pretty much anywhere. Yeah, give it a try. Looking forward to playing around with this. PyTorch releases release one dot 10. This brings a number of improvements such as the inclusion of the CUDA graphs API. Now CUDA graphs is an API. It's not for machine learning on graphs, not for graph neural networks, but it is for defining graphs of operations over CUDA kernels. In this case here, every letter is a CUDA kernel such as a matrix multiplication, or an addition of two things. And you used to have to put one CPU instruction for each one of the CUDA kernels. So the CPU had to say, now you do a matrix multiplication, now you add two things and so on. Now the CUDA graphs API enables you to with a single CPU instructions instruct the GPU to perform an entire graph of operations. And this is now available in PyTorch. And not only that, they have a few other things, notably the torch dot special module, which replicates scipy dot special. So if you've used these functions in NumPy in scipy, now they're available in torch. There are some more such as the NN module parameterization. This enables you that for example, if you want to change the normalization function in a module, you used to have to reimplement the module to subclass it and essentially reimplement it while replacing the normalization itself. And now apparently, you can simply from the outside, say I want to change the normalization, I want to change different things inside of a module. So it makes PyTorch code more friendly towards experimentation towards swapping out individual parts. There are a bunch of other different new things in PyTorch 110. But it seems to be cool release if you can upgrade, give it a try. Google has a new blog post and along with a paper, the paper is called spreadsheet coder formula prediction from semi structured context. This is a cool paper because it helps you to write formulas in spreadsheets. Now Google spreadsheets is a pretty big project. And this feature is now available to anyone using Google spreadsheets. So what it's going to do is it's going to essentially bring the tab complete that you might be used to from Gmail or from Google Docs into the formula section of a spreadsheet. So as soon as you type the equal symbol, it's going to try to predict what formula you're trying to write, it takes into consideration the values of the things around you takes into consideration what you called the headers and the row headers. So for example, here, the row is called total. And therefore, it might be reasonable to assume that you want the sum of the column above whereas over here, you called the header percent chain. So the system infers that you probably given that you have no values above as well that you probably want to do something with the totals of the other two columns. This is not hard coded, this is all learned from a big corpus. And this is as I said, now available for anyone using Google spreadsheets. So the system seems to be quite of an engineering effort. So they have a row based BERT encoder column based BERT encoder, they have convolutions in there, they aggregate and then they decode using an LSTM. I guess this had to go through a bunch of iterations before they got really nicely working system. But now it actually made it into a product. And this is something that we see rarely nowadays that research to product is actually happening. So pretty cool, and benefits anyone that uses Google spreadsheets. They also do a lot of ablations. And you can see that in their tests for various length of context and things they want to predict, they do reach a pretty decent accuracy. So almost 50% accuracy in formulas you you might want to write. Now I don't know what 50% accuracy actually means, because most people just want like the sum or the mean of anything. But nonetheless, it's a pretty cool development. If you want to check out more, check out the spreadsheet coder paper, try it out. Cool project that I saw on Reddit is hand tracking.io. This is a completely in browser hand tracking demo. And this focuses around detecting special poses that your hand does, for example, detecting when you pinch your fingers, or when you make a fist and then mapping those things to various actions, you can actually try this out. So this fully runs in your browser, as you can see, it tracks my hand, if I make a fist, the screen clears. And if I pinch my fingers, it doesn't work all too well. Maybe it's because I have a green screen, or anything else, maybe it works above my face, it does not too well. But you can see, if you go slowly. Yeah, this is pretty cool. So this is MIT licensed, it's available on GitHub, and up for you to check it out or simply try it in this browser. It's up to you what you do with it. Pretty cool. Cagle has a new challenge on cell instance segmentation. Now, this is a challenging task, you get a bunch of microscopy images, and your task is to segment single instances of cells, so neurons in tissue, and you need to detect where they are. Apparently, this is a hard task that is as of yet pretty weakly solved. And this challenge is supposed to get us there faster. If you want to do something cool with computer vision, that also has a direct application in medicine, this challenge might be for you. Some helpful libraries and things that I've encountered this week control flag by Intel labs is a library that will detect source code mistakes or anti patterns or bugs or anything like this. So this is a self supervised system, it learns by itself, essentially a big language model or a pattern model that recognizes common patterns in code bases, and then is able to recognize when a given pattern is uncommon. Therefore, if you write something that's probably a bug, then it will detect it as an uncommon pattern and notify you to it. This is more than just bugs. So this is not specifically trained on a supervised data set where someone says here's a bug, here's not a bug. This is as I said, a self supervised system that is specific to source code. And right now, it actually works in C and I believe also in very long, but it's a matter of time before someone takes this and expands this to new languages and trains it on new languages. So the source code for the source code checker is available on GitHub, you can try it out, you can train it, in fact, yourself, you can let it run over your own code base. The only issue is that if you write a bug that lots of other people write to, it won't detect it, right, because it's not an uncommon pattern. But you know, that's that's life, I guess. Salina by Facebook research is a lightweight library for sequential learning agents, including reinforcement learning. This is a library that is supposed to make it really easy to write very complex sequential models like sequential decision making models where you have to perform actions in a row in some sort of sense. The library is purposefully very general, but it's fairly easy to write something like an A to C agent, you can see it right here. This is the entire A to C agent right here. But it's not only for reinforcement learning, it is any kind of complex sequential decision making process. If you're interested in that kind of research, if the RL libraries that are available just didn't do it for you quite yet, maybe give Salina a try. Speaking of sequences, why data synthetic is a generator library for synthetic structured data. So this is a library that you can give data to, it will learn the data in some sort of a generative fashion, and it will be able to give you synthetic data to work with. So this can be due to privacy reasons, it can be because you don't have enough of some data, and you want to generate more of it. This can be because you simply want to test on something that's not real data. So there are various reasons why you do something like this, specifically, this right here is for tabular data and time series data, which are often data that is not that easy to work with most of our things like GANs work on images, we have some text generators, but having another library available for tabular and time series data is quite cool. So if this is of interest to you give why data synthetic try they have some easy examples. For example, right here, they want to train a GAN to produce one particular class of their fraud data set, you can see as the training progresses, the GAN gets better and better at modeling this light blue data. And you know, presumably, if you train it for more, it's gonna get even better. And then you have a generator for data, you don't need real data anymore. Who needs data? Ah, AIM is an open source ML platform. So this is another experiment tracker, but it is working progress, it's ongoing progress, it's open source, it's raw. If you're into things like Arch Linux, or writing your own bootloader and things like this, AIM might be a cool project for you. The new version specifically deals with scales. So they used to have problems when you have lots and lots and lots of experiments to track. But now even this is solved. So it seems like a cool GitHub project, a thing that you might even get involved with. And everything's available on GitHub, as I said integrates with common frameworks, pretty easy to get going with it. As you can see, there is a roadmap with lots of things to do. If you have fun contributing to open source, maybe give AIM a try. And lastly, robust bench is a standardized benchmark for adversarial robustness. It is a benchmark, if you think you have an adversarial defense, or an attack, then this is a benchmark where you can simply plug it in and see how it does versus various things. They also have 80 plus state of the art pre trained robust models via the model zoo. So you can attack models that have been robustified, I guess you can do that in white box black box settings and so on. If you're into adversarial examples, give robust bench a try. This is some rather funny news. CBS local in San Francisco writes or other reports that there is apparently a street where Waymo cars they keep coming in hitting a dead end, turning around, and then going out again. And this apparently happens every five minutes. The Waymo cars, as you can see, they have drivers, but I think they are testing the driver less systems. Sometimes you can see the drivers, they manipulate the steering wheel. So I'm not sure what exactly happens. Neither are they neither are the drivers apparently. So no one's exactly sure what they're doing there. Apparently, the drivers are simply following the programming of the car, you see, there's a hand on the steering wheel. So I'm not not entirely sure what's going on. But the Waymo is really, really, really exploring this one particular dead end really hard. So safe to say, there's probably some sort of a routing issue going on here, where the cars are told to go this particular way, then the cars detect that there's a dead end, then they turn around, but they never somehow update the fact that they cannot go through there. It's either this or they have like an automated exploration system where they think, oh, I haven't explored this part of the city yet, I need to go and map it. And every time they go there, they realize they can't go through something like this must be happening. I guess it's pretty funny. I'm looking forward to the world of driverless cars, where teenagers simply cheese the cars and see how many of them they can get stuck in a single cul de sac or dead end or something like this good future to look forward to. And lastly, I saw this right here. Now this is pretty, pretty cool. This is by a company called Blue River technology. And they're aiming to be sort of the the Boston dynamics of agriculture, you can see that their control systems, essentially, they're the same control systems that you're used to, it just looks absolutely spectacular when it's built into some sort of an agricultural machine like a truck truck or anything like this. This is obviously just a demo, they have a full website that is, as you can see, you fall with corporatey pictures and corporate speech and so on. But it seems very cool that AI is coming to real disciplines like agriculture, it has a real potential to do both good for the environment, because you might need to use less fertilizers and so on. If you can put it more targeted and save a bunch of money, I don't know, maybe it's a terrible thing. Who knows? I don't. But I do see definitely a lot of potential for AI in these domains. Nature plus robots has never ever ever turned bad in the history of anything, you know, something to look forward to. And everyone's smiling, of course, everyone's just chilling around smiling. That is that is a company that is you need to go work there. All right, that was it for ml news this week. I hope you enjoyed again, thanks to Nvidia for sponsoring this video, register to GTC using the link Winner 3090 sleep well, exercise, exercise, eat good food, and I'll see you next time. Bye bye.
 Nvidia holds a giant conference DeepMind buys and open sources Mojoco and Google predicts what
 you're gonna write in a spreadsheet. Welcome to ML News. Hello, hello, this video is sponsored by
 Nvidia actually, not just Nvidia, but they want to raise awareness for their GTC conference,
 which happens November 8 through 11 this year. Now there is something in it for you if you use
 my link to register to this, you can win a 3090. So these GPUs are super rare nowadays and one is
 allocated just for my link to register. So you're not competing with the rest of YouTube, you're
 just competing with anyone that uses my link. So if you're interested, use the link in the description
 to register to the conference. Now the conference is actually relevant for machine learning audience,
 because Nvidia is not only talking about Nvidia, though I love the what will Jensen Huang's keynote
 reveal banner right here being super mysterious and all. Okay, Nvidia says I should hype up the
 keynote more. So this keynote is going to be the maddest keynote you've ever seen. You remember last
 keynote where Jensen Huang was like rendered and Nvidia made this big deal about how they
 rendered him and this was like a big effort, then they had to correct themselves and state that it
 was actually only for 14 seconds and not for the entire keynote, because that's kind of what they
 alluded to at the beginning. I reported about this in ML news, it was epic. And I guess this keynote
 is going to be epic again. Will he finally reveal what the leather jacket is made of? If you haven't
 seen yet on Twitter, if you use the hashtag GTC 21, it actually renders a little leather jacket
 next to it. And I think Nvidia paid for this. Isn't this the greatest marketing like business
 decision by Twitter, they're able to sell hashtags insane. And I don't know what's going to happen.
 But I've come across this the omniverse, which is in beta. And there's kind of speculation that
 that's going to be one of the topics I didn't know this existed. This is sort of like a real time
 rendering framework that's based on Pixar's Universal Scene description and Nvidia RTX.
 And it's pretty insane. So apparently this is this real time, this is an entire framework where
 you can do like real time ray tracing. Look at this. This looks great. I don't know how many
 RTX is you need for that one. But it's pretty insane. This used to take like insane amounts
 of rendering time. And yeah, the fact that it's real time really cool. But they have invited a
 bunch of speakers to talk about all kinds of stuff in graphics in machine learning and in many other
 areas of computation. So they really want this to be a big thing this conference and you can see this
 these are just some of the speakers you can see faith Ali is speaking, Elia Sami, and many others
 that you might know of. So these are three pages of speakers that are really big in their industry
 Nvidia spending a ton of cash right here to give you essentially free content. Now you do need to
 register to watch all of these talks, but it's free. And as I said, you can win a 3090. Now before
 we go on, I would like to say that the condition for the sponsorship of Nvidia was that the video
 must be available in English and in German, which is weird, you know, but since I speak German,
 I can do that. So this video is available as a not a copy, but an equivalent in a German version. So
 if this is not the language you expected, switch over to the other video and I promise I'll just
 put on my absolute best impression of a real German. So a little bit more about this conference
 while the keynote is obviously the main event right here and video revealing what they're
 going to do, which given Nvidia size and dominance is quite relevant for the entire deep learning
 world. There are over 500 sessions. If you look at the schedule, there are 15 sessions just dedicated
 to pytorch and 12 dedicated to TensorFlow. And those aren't the only deep learning sessions,
 there are many, many more. As you can see, there is a plethora of industry types and topics that
 people are going to talk about. It's like an endless list. So rest assured that during these
 four days, you can just bathe in Nvidia content for 24 hours a day. Now along with the conference,
 there are these instructor led workshops that give you hands on experience in certain things,
 for example, building transformer based natural language processing applications, they do cost
 a little bit of money, but they're hands on. So if you're interested in that, take a look. So I
 don't know what more to say. As I said, it's completely free content, they're throwing a
 bunch of money to get really good speakers and you can win a graphics card and look at them frame
 numbers. We all know more frames means that you're a better gamer. So get the 3090 now link is in the
 description. Check out all the talks and the sessions that happen at the conference. And I
 wish you a really pleasant experience and videos really trying to gear up this conference to make
 it a big deal. And as it seems, it is actually a big deal. Next news. DeepMind has apparently bought
 MojoCo, which is one of the primary simulation softwares for robotics. This has been used again
 and again, not only in robotics, but also in deep learning and reinforcement learning in all of these
 kinds of settings to do continuous control simulations. As you can see here, this works
 pretty well. This is a real flipping flippity spinny spin. And here you see one in MojoCo. Now
 the trouble with MojoCo has always been that it was proprietary. And not only that, not only was
 it not open source, but you had to pay quite a bit of money for it. So now apparently DeepMind has
 bought and open sourced MojoCo replication efforts have been underway. But very often these simulators,
 they are built for gaming or something like this. And they neglect effects such as these gyroscopic
 effects right here, which you can see that MojoCo apparently has a good balance between realism and
 accuracy for these kinds of simulations. And not only that, but it is also fast enough. So you can
 do reinforcement learning with it. And DeepMind has used this extensively. This is all apparently
 from DeepMind's works, you can see how versatile the simulator is. So now DeepMind has bought it
 and makes it available to everyone, which is pretty, pretty cool. Now is this really out of
 kind heartedness? Maybe actually, maybe they just want to get some good PR out there. Or maybe they
 want to do another nature publication and nature publications do force you I believe to open source
 pretty much anything that you have to achieve the publications, whatever it might be. It's pretty
 cool that DeepMind does it the code base is apparently in C. So it's portable, compilable,
 pretty much anywhere. Yeah, give it a try. Looking forward to playing around with this.
 PyTorch releases release one dot 10. This brings a number of improvements such as the inclusion of
 the CUDA graphs API. Now CUDA graphs is an API. It's not for machine learning on graphs, not for
 graph neural networks, but it is for defining graphs of operations over CUDA kernels. In this
 case here, every letter is a CUDA kernel such as a matrix multiplication, or an addition of two things.
 And you used to have to put one CPU instruction for each one of the CUDA kernels. So the CPU had
 to say, now you do a matrix multiplication, now you add two things and so on. Now the CUDA graphs
 API enables you to with a single CPU instructions instruct the GPU to perform an entire graph of
 operations. And this is now available in PyTorch. And not only that, they have a few other things,
 notably the torch dot special module, which replicates scipy dot special. So if you've used
 these functions in NumPy in scipy, now they're available in torch. There are some more such as
 the NN module parameterization. This enables you that for example, if you want to change the
 normalization function in a module, you used to have to reimplement the module to subclass it and
 essentially reimplement it while replacing the normalization itself. And now apparently,
 you can simply from the outside, say I want to change the normalization, I want to change
 different things inside of a module. So it makes PyTorch code more friendly towards experimentation
 towards swapping out individual parts. There are a bunch of other different new things in PyTorch 110.
 But it seems to be cool release if you can upgrade, give it a try. Google has a new blog post and
 along with a paper, the paper is called spreadsheet coder formula prediction from semi
 structured context. This is a cool paper because it helps you to write formulas in spreadsheets. Now
 Google spreadsheets is a pretty big project. And this feature is now available to anyone using
 Google spreadsheets. So what it's going to do is it's going to essentially bring the tab complete
 that you might be used to from Gmail or from Google Docs into the formula section of a spreadsheet. So
 as soon as you type the equal symbol, it's going to try to predict what formula you're trying to
 write, it takes into consideration the values of the things around you takes into consideration
 what you called the headers and the row headers. So for example, here, the row is called total.
 And therefore, it might be reasonable to assume that you want the sum of the column above whereas
 over here, you called the header percent chain. So the system infers that you probably given that
 you have no values above as well that you probably want to do something with the totals of the other
 two columns. This is not hard coded, this is all learned from a big corpus. And this is as I said,
 now available for anyone using Google spreadsheets. So the system seems to be quite of an engineering
 effort. So they have a row based BERT encoder column based BERT encoder, they have convolutions
 in there, they aggregate and then they decode using an LSTM. I guess this had to go through
 a bunch of iterations before they got really nicely working system. But now it actually made
 it into a product. And this is something that we see rarely nowadays that research to product
 is actually happening. So pretty cool, and benefits anyone that uses Google spreadsheets.
 They also do a lot of ablations. And you can see that in their tests for various length of
 context and things they want to predict, they do reach a pretty decent accuracy. So almost 50%
 accuracy in formulas you you might want to write. Now I don't know what 50% accuracy actually means,
 because most people just want like the sum or the mean of anything. But nonetheless,
 it's a pretty cool development. If you want to check out more, check out the spreadsheet
 coder paper, try it out. Cool project that I saw on Reddit is hand tracking.io. This is a completely
 in browser hand tracking demo. And this focuses around detecting special poses that your hand
 does, for example, detecting when you pinch your fingers, or when you make a fist and then mapping
 those things to various actions, you can actually try this out. So this fully runs in your browser,
 as you can see, it tracks my hand, if I make a fist, the screen clears. And if I pinch my fingers,
 it doesn't work all too well. Maybe it's because I have a green screen, or anything else, maybe it
 works above my face, it does not too well. But you can see, if you go slowly. Yeah, this is pretty
 cool. So this is MIT licensed, it's available on GitHub, and up for you to check it out or simply
 try it in this browser. It's up to you what you do with it. Pretty cool. Cagle has a new challenge
 on cell instance segmentation. Now, this is a challenging task, you get a bunch of microscopy
 images, and your task is to segment single instances of cells, so neurons in tissue,
 and you need to detect where they are. Apparently, this is a hard task that is as of yet pretty
 weakly solved. And this challenge is supposed to get us there faster. If you want to do something
 cool with computer vision, that also has a direct application in medicine, this challenge might be
 for you. Some helpful libraries and things that I've encountered this week control flag by Intel
 labs is a library that will detect source code mistakes or anti patterns or bugs or anything like
 this. So this is a self supervised system, it learns by itself, essentially a big language model
 or a pattern model that recognizes common patterns in code bases, and then is able to recognize when
 a given pattern is uncommon. Therefore, if you write something that's probably a bug, then it
 will detect it as an uncommon pattern and notify you to it. This is more than just bugs. So this
 is not specifically trained on a supervised data set where someone says here's a bug, here's not
 a bug. This is as I said, a self supervised system that is specific to source code. And right now,
 it actually works in C and I believe also in very long, but it's a matter of time before someone
 takes this and expands this to new languages and trains it on new languages. So the source code for
 the source code checker is available on GitHub, you can try it out, you can train it, in fact,
 yourself, you can let it run over your own code base. The only issue is that if you write a bug
 that lots of other people write to, it won't detect it, right, because it's not an uncommon pattern.
 But you know, that's that's life, I guess. Salina by Facebook research is a lightweight library for
 sequential learning agents, including reinforcement learning. This is a library that is supposed to
 make it really easy to write very complex sequential models like sequential decision
 making models where you have to perform actions in a row in some sort of sense. The library is
 purposefully very general, but it's fairly easy to write something like an A to C agent, you can
 see it right here. This is the entire A to C agent right here. But it's not only for reinforcement
 learning, it is any kind of complex sequential decision making process. If you're interested
 in that kind of research, if the RL libraries that are available just didn't do it for you
 quite yet, maybe give Salina a try. Speaking of sequences, why data synthetic is a generator
 library for synthetic structured data. So this is a library that you can give data to, it will learn
 the data in some sort of a generative fashion, and it will be able to give you synthetic data
 to work with. So this can be due to privacy reasons, it can be because you don't have enough
 of some data, and you want to generate more of it. This can be because you simply want to test
 on something that's not real data. So there are various reasons why you do something like this,
 specifically, this right here is for tabular data and time series data, which are often
 data that is not that easy to work with most of our things like GANs work on images, we have some
 text generators, but having another library available for tabular and time series data
 is quite cool. So if this is of interest to you give why data synthetic try they have some easy
 examples. For example, right here, they want to train a GAN to produce one particular class of
 their fraud data set, you can see as the training progresses, the GAN gets better and better at
 modeling this light blue data. And you know, presumably, if you train it for more, it's
 gonna get even better. And then you have a generator for data, you don't need real data
 anymore. Who needs data? Ah, AIM is an open source ML platform. So this is another experiment tracker,
 but it is working progress, it's ongoing progress, it's open source, it's raw. If you're into things
 like Arch Linux, or writing your own bootloader and things like this, AIM might be a cool project
 for you. The new version specifically deals with scales. So they used to have problems when you
 have lots and lots and lots of experiments to track. But now even this is solved. So it seems
 like a cool GitHub project, a thing that you might even get involved with. And everything's available
 on GitHub, as I said integrates with common frameworks, pretty easy to get going with it.
 As you can see, there is a roadmap with lots of things to do. If you have fun contributing
 to open source, maybe give AIM a try. And lastly, robust bench is a standardized benchmark for
 adversarial robustness. It is a benchmark, if you think you have an adversarial defense,
 or an attack, then this is a benchmark where you can simply plug it in and see how it does
 versus various things. They also have 80 plus state of the art pre trained robust models via
 the model zoo. So you can attack models that have been robustified, I guess you can do that in white
 box black box settings and so on. If you're into adversarial examples, give robust bench a try.
 This is some rather funny news. CBS local in San Francisco writes or other reports that there is
 apparently a street where Waymo cars they keep coming in hitting a dead end, turning around,
 and then going out again. And this apparently happens every five minutes. The Waymo cars,
 as you can see, they have drivers, but I think they are testing the driver less systems. Sometimes
 you can see the drivers, they manipulate the steering wheel. So I'm not sure what exactly
 happens. Neither are they neither are the drivers apparently. So no one's exactly sure what they're
 doing there. Apparently, the drivers are simply following the programming of the car, you see,
 there's a hand on the steering wheel. So I'm not not entirely sure what's going on. But the
 Waymo is really, really, really exploring this one particular dead end really hard. So safe to say,
 there's probably some sort of a routing issue going on here, where the cars are told to go this
 particular way, then the cars detect that there's a dead end, then they turn around, but they never
 somehow update the fact that they cannot go through there. It's either this or they have like an
 automated exploration system where they think, oh, I haven't explored this part of the city yet,
 I need to go and map it. And every time they go there, they realize they can't go through
 something like this must be happening. I guess it's pretty funny. I'm looking forward to the
 world of driverless cars, where teenagers simply cheese the cars and see how many of them they can
 get stuck in a single cul de sac or dead end or something like this good future to look forward to.
 And lastly, I saw this right here. Now this is pretty, pretty cool. This is by a company called
 Blue River technology. And they're aiming to be sort of the the Boston dynamics of agriculture,
 you can see that their control systems, essentially, they're the same control systems
 that you're used to, it just looks absolutely spectacular when it's built into some sort of an
 agricultural machine like a truck truck or anything like this. This is obviously just a demo,
 they have a full website that is, as you can see, you fall with corporatey pictures and corporate
 speech and so on. But it seems very cool that AI is coming to real disciplines like agriculture,
 it has a real potential to do both good for the environment, because you might need to use less
 fertilizers and so on. If you can put it more targeted and save a bunch of money, I don't know,
 maybe it's a terrible thing. Who knows? I don't. But I do see definitely a lot of potential for AI
 in these domains. Nature plus robots has never ever ever turned bad in the history of anything,
 you know, something to look forward to. And everyone's smiling, of course, everyone's just
 chilling around smiling. That is that is a company that is you need to go work there.
 All right, that was it for ml news this week. I hope you enjoyed again, thanks to Nvidia for
 sponsoring this video, register to GTC using the link Winner 3090 sleep well, exercise,
 exercise, eat good food, and I'll see you next time. Bye bye.