[ML News] ConvNeXt: Convolutions return | China regulates algorithms | Saliency cropping examined
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper deep learning tutorial deep learning ai deep learning projects mlnews ml news kilcher news salicency cropping twitter cropping image cropping twitter image cropping convnext facebook research meta research meta ai convolutional neural networks cnns vs transformers mt3 yourtts text to speech ai for music china regulation china algorithms china ai
#mlnews #convnext #mt3

Your update on what's new in the Machine Learning world!

OUTLINE:
0:00 - Intro
0:15 - ConvNeXt: Return of the Convolutions
2:50 - Investigating Saliency Cropping Algorithms
9:40 - YourTTS: SOTA zero-shot Text-to-Speech
10:40 - MT3: Multi-Track Music Transcription
11:35 - China regulates addictive algorithms
13:00 - A collection of Deep Learning interview questions & solutions
13:35 - Helpful Things
16:05 - AlphaZero explained blog post
16:45 - Ru-DOLPH: HyperModal Text-to-Image-to-Text model
17:45 - Google AI 2021 Review

References:
ConvNeXt: Return of the Convolutions
https://arxiv.org/abs/2201.03545
https://github.com/facebookresearch/ConvNeXt
https://twitter.com/giffmana/status/1481054929573888005
https://twitter.com/wightmanr/status/1481150080765739009
https://twitter.com/tanmingxing/status/1481362887272636417

Investigating Saliency Cropping Algorithms
https://openaccess.thecvf.com/content/WACV2022/papers/Birhane_Auditing_Saliency_Cropping_Algorithms_WACV_2022_paper.pdf
https://vinayprabhu.github.io/Saliency_Image_Cropping/paper_html/main.html
https://vinayprabhu.medium.com/on-the-twitter-cropping-controversy-critique-clarifications-and-comments-7ac66154f687
https://vinayprabhu.github.io/Saliency_Image_Cropping/

YourTTS: SOTA zero-shot Text-to-Speech
https://github.com/coqui-ai/TTS?utm_source=pocket_mylist
https://arxiv.org/abs/2112.02418?utm_source=pocket_mylist
https://coqui.ai/?utm_source=pocket_mylist
https://coqui.ai/blog/tts/yourtts-zero-shot-text-synthesis-low-resource-languages

MT3: Multi-Track Music Transcription
https://arxiv.org/abs/2111.03017
https://github.com/magenta/mt3
https://huggingface.co/spaces/akhaliq/MT3
https://www.reddit.com/r/MachineLearning/comments/rtlx0r/r_mt3_multitask_multitrack_music_transcription/

China regulates addictive algorithms
https://technode.com/2022/01/05/china-issues-new-rules-to-regulate-algorithms-targeting-addiction-monopolies-and-overspending/
https://qz.com/2109618/china-reveals-new-algorithm-rules-to-weaken-platforms-control-of-users/

A collection of Deep Learning interview questions & solutions
https://arxiv.org/abs/2201.00650?utm_source=pocket_mylist
https://arxiv.org/pdf/2201.00650.pdf

Helpful Things
https://docs.deepchecks.com/en/stable/index.html
https://github.com/deepchecks/deepchecks
https://docs.deepchecks.com/en/stable/examples/guides/quickstart_in_5_minutes.html
https://www.dagshub.com/
https://www.dagshub.com/docs/index.html
https://www.dagshub.com/blog/launching-dagshub-2-0/
https://bayesiancomputationbook.com/welcome.html
https://mlcontests.com/
https://github.com/Yard1/ray-skorch
https://github.com/skorch-dev/skorch
https://www.rumbledb.org/?utm_source=pocket_mylist
https://github.com/DarshanDeshpande/jax-models
https://github.com/s3prl/s3prl

AlphaZero explained blog post
https://joshvarty.github.io/AlphaZero/?utm_source=pocket_mylist

Ru-DOLPH: HyperModal Text-to-Image-to-Text model
https://github.com/sberbank-ai/ru-dolph
https://colab.research.google.com/drive/1gmTDA13u709OXiAeXWGm7sPixRhEJCga?usp=sharing

Google AI 2021 Review
https://ai.googleblog.com/2022/01/google-research-themes-from-2021-and.html

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 Facebook makes ConvNet's return to glory, a new text to speech model lets you speak any language you want, and automated music transcription gets a boost. Welcome to MLNews. Hello and welcome to MLNews, it is so great to have you here. How are you doing? I hope everyone's okay. Let's dive into the first story. Facebook Research publishes a paper called A ConvNet for the 2020s, in which they take on the notion that somehow transformers are to replace ConvNets for computer vision. They make the argument that rather than the attention mechanisms in transformers, it is due to some more kind of subtle improvements that the transformer architectures have over classical ConvNets. Now they show that if they systematically include the best of these changes, then they can make a ConvNet that performs as well or better than vision transformers. This results in the following graphics starting from the original ResNets in the bottom left corner and comparing to various vision transformer architectures on ImageNet 1k and ImageNet 22k that allows also pre trained models. Now this has obviously garnered quite some attention, the code is actually available online if you want to try. But for example, Lucas Byer has pointed out that if you do compare to VIT that is trained, let's say properly with augmentations and so on, then the ConvNext isn't that far ahead. The graphics should look more like this. And Ross Whiteman, maintainer of a popular library of computer vision models also points out that if you take a ResNet and you train it properly, then you will be at the level of like a small ConvNext. And that would mean that the ResNet bubble itself would also be lifted to about the 82 mark right here. And another comment came from Minxin Tan, who augments the graphic by efficient net v2 on ImageNet 1k and 22k, which would result in the following graphic. So safe to say what we can read from this is that the market for models in computer vision isn't decided at all yet. The race is still wide open. And it seems like we can achieve comparable performances with various different architectures. Now maybe it is the case that all you need to do is just take a big model with lots of parameters and it doesn't really matter what you do as long as you do a certain number of things right. On the other hand, it could also be that we haven't yet come across the ultimate architecture yet and there is still an architecture out there somewhere waiting to be discovered to dominate computer vision once and for all. Only time will tell. For now, go and check out the code of ConvNext. It is on GitHub. Interestingly, Meta Research still uses the Facebook Research GitHub handle. There's been a paper making the rounds called Auditing Saliency Cropping Algorithms that investigates popular saliency cropping methods. Saliency cropping is what these platforms, for example Twitter, do to pictures in order to make them fit the predefined format. For example, the picture here on the right is in fact much longer if you click on it, but in order to fit the familiar Twitter timeline, it needs to crop it somewhere. So these platforms, they try to decide what is the most salient, what is the most interesting point in a picture and they try to crop towards that rather than just always cropping to the top or to the bottom or to the middle. Now for a bit more background, people in the past have often criticized the saliency cropping algorithm due to them being said to have certain preferences for certain skin tones and also exhibiting a phenomenon where they would focus on the non face parts, especially of women. There's this famous example of two politicians, one light skinned, one dark skinned, and no matter how you order them, if you make a long picture that has one at the one end and one at the other end, and then a white area in the middle, the different algorithms would choose to focus on different faces repeatedly. This paper systematically investigates the saliency cropping algorithms of Twitter, Google and Apple in both skin tone differences and also with respect to the phenomenon of what they call the male gaze. Now they make a big deal out of this idea of the male gaze, which is a concept that essentially says society will reorder itself, will build products, will make media to represent the male view of the world, specifically how men look at women. Mostly the narrative is around objectification. And when people shared anecdotal evidence of Twitter cropping pictures of women in the following way, this played into this narrative of the male gaze. So the hypothesis would be that through whatever mechanism, mostly how the training data is collected and so on, the algorithm would learn to focus on the non face part of female bodies and therefore reproduce the male gaze that built the data set or built the society where the algorithm was trained in. Obviously that would be a problem and discovering an effect like this would be quite interesting. The paper noticed that the anecdotes posted, the examples posted of this happening were mostly women on runways in red carpet type situations. So they collected a data set of pictures like these and ran them through the saliency algorithm. And surprisingly, they discovered that whenever the algorithm did not focus the face itself, it would actually focus mostly on some sort of corporate logos in the background. Now these corporate logos happen to be very often not on face level, or at least the ones that the algorithm chose to focus on would not be on face level, resulting in a non face centric crop. Now there's two ways to go from here. One way would be to say, ah, look at this, the algorithm is kind of crap. It misses the face a lot of the times, it focuses on these logos. And that gives the appearance of the algorithm objectifying women or having anything of that effect in there. And therefore we can discard the male gaze hypothesis or whatever we started with. The paper doesn't do this, however, instead it makes a big point of calling these things male gaze like artifacts or male gaze like effects, essentially retaining the opinion or the appearance that this is still problematic in regards to this effect. So instead of saying it's actually not sexist, it's just crap, they do word plays and simply characterize it as whatever they want, dash like. And this I find to be a little bit worrisome. In my opinion, this clearly shows that the authors were out to find this effect, they were out to find something of this nature. And the data just didn't back that up. And honestly, given how many ways you can slice and dice data and do analysis, I'm quite astonished that they didn't find anything that they could show as evidence for that. But then instead of discarding, they choose to keep this hypothesis in there. And they choose to call the artifacts they find male gaze like. Now the paper itself can do a lot of hedging. The paper can say, well, we described what this is, right? We never meant male gaze, we meant male gaze like. They can hedge by saying, well, our paper is mainly about the methods of testing this. It's not really about the results. It's more about the how we collect the data set and so on. So you can construct a paper that no one can essentially criticize you until you can just backtrack into your, I did nothing wrong. And then when you promote the paper, you can be a bit more loose, right? Still not saying anything wrong. You can be a bit more loose. You can just kind of leave away things because you're just promoting it. It's social media or a talk or whatnot. And whenever you get criticized, you can say, well, we clearly defined things in the paper. I'm sorry, Twitter is a short medium and so on. And then maybe other people come and pick it up and they just see kind of the title, maybe a little bit of the abstract, maybe a little bit of the promotion and ta-da-da-da. In the eyes of most people out there, you will have successfully reached the original hypothesis. Now, I'm not saying investigating these things is not good or anything like this. I'm happy that there are people who do these types of investigation. I'm very happy that people publish, look, here is how to collect the data set and here is how to study these things. But if the experiments had turned out the other way, like if they found that the most salient point after the algorithm would always be on women's private parts or something like this, do you think the paper would have sounded the same? Do you think the paper would be of, you know, we just want to get our methodology out there. We don't really, it's not really about the results or so on. Like, nah, nah, no way. As I said, the paper also does a systematic investigation into how the algorithms focus on skin tones. The results there are mixed as well, but I'll leave it at that. I don't want to criticize this paper super particularly, even though I do think it is politically motivated, but it's just difficult to evaluate things when it is quite clear the authors wanted to find a certain thing. There's a new text to speech system called Your TTS towards zero shot multi speaker text to speech and zero shot voice conversion for everyone. Now this system reaches state of the art in zero shot text to speech and it is quite intricately trained, but what you can do is you can have your voice say something in a completely different language. I'm going to try this right here. Hello and welcome. You're listening to ML news. All right, so now I'm going to go to French and I don't actually have to say the same thing in French. Yeah, yeah, no, yeah. Oh, yeah. My baguette. I forgot my baguette. Let's check it out. I forgot my baguette. I forgot my baguette. What's the music playing in the background? I forgot my baguette. All right. Well, in any case, it sounds pretty good. So and it's really fast. The code is available. I'll link to the colab and everything. Give it a try. MT3 is a system for multitask multi track music transcription is part of Google's project magenta that applies machine learning to the arts. This is also available and it's again pretty cool what it can do. There is a hugging face space where you can upload your own audio and have it transcribed and there is this demo on Reddit. Yes it is MIDI like it's not supposed to sound the same but it does transcribe the music into multiple tracks into multiple parallel tracks. It's really hard task and it's really cool that this is sort of possible out of the box. The model is available on GitHub. You can check it out. Quartz writes China's new algorithm rules are at odds with its tech giants business models. This is an article detailing China's new rules for what they call algorithms which are essentially recommender systems. So the new rules mean that algorithm providers need to proactively spread positive energy ensure their algorithms are for good and they curtail algorithms for promoting or causing excessive spending or for the algorithms to lead to developing an addiction to the platforms. This is obviously targeted at many of the newer social media systems that explicitly use recommender systems to drive most of their business. Now while this seems like a pretty unprecedented move especially for China the article also says that some argue that the impact might not be so large because the rules essentially only require that users have the ability to opt out and a lot of users simply are not going to do that but it's pretty cool that at least you have the option to do so and honestly in my opinion I'd much rather have an opt out feature that is like buried somewhere in three layers of setting than every single website asking me whether and what cookies I want. That's just annoying. Not saying I don't see the reasoning behind the rules existences I'm just saying it's freaking annoying. Shlomo Kashani and Amir Ivory release deep learning interviews hundreds of fully solved job interview questions from a wide range of key topics in AI. This is version two and it includes it is a giant PDF that includes questions and solutions. You can see it's over three hundred and sixty pages from all disciplines of ML. So if you're looking to prepare for job interviews or simply up your skill a little bit in a different area of ML this might be a neat resource for you. Alright we'll come to some helpful material helpful libraries helpful things that I found. Deep checks is a tool for validating machine learning models and data. It essentially acts a little bit like a unit test framework for machine learning code. DAG's hub is a platform to version data models experiments and code. They claim to have a GitHub like experience for machine learning. Now while I enjoy the presence of yet another ML Ops system and the launch of release two which also integrates data labeling into their system. The coolest thing about this is their background on the website. See follows your mouse and this is just cool and I think every time you enter you get like a new color. Look at that. Wow. It's completely dark when you start so you don't you never expect it and then what's a Bayesian modeling and computation in Python is a free book that is available online about Bayesian modeling and computation in Python. It is on Amazon if you want the hardcover but you can just read it online if you want to ML contests dot com is a website that just keeps track of machine learning contests. For example on Kaggle AI crowd and more. Ray Scorch is a wrapper around Scorch to use Ray for distributed training. Now what is Scorch you ask? Good question. Scorch is a wrapper around PyTorch in order to make it compatible with SK learn. Rumble is a database that is built on top of Apache Spark and HDFS and it allows you to feed in JSON and process a lot of data very efficiently with a JSON like query language. So you can query heterogeneous data you can query nested data and it will scale from your laptop all the way up to data centers. It's open source you can check it out. Jaxx models is a GitHub repository that says it's an unofficial repository of Jaxx implementations of deep learning models. It is a young project but it does have some models inside and it is growing. If you're into Jaxx and you're looking for a model maybe you'll find it here. S3PRL is a library to process speech specifically a self-supervised speech pre-training and representation learning toolkit. Alright that was it for the helpful stuff. I hope some of you have been helped by the helpful stuff. I've come across this blog post right here explaining AlphaZero and I found it to be very understandable and instructive. So if you want to get into AlphaZero or any of the related algorithms maybe give this blog post a read. It explains everything pretty well and understandably and it's a good first contact with these kinds of algorithms if you don't know yet exactly what they do. The blog post is by Josh Varty and I'll link it in the description. SureBank AI have been making some progresses into large models recently. They release Rudolph after Rudali. Rudolph is what they call a hypermodal transformer. They call it hypermodal because it has multiple multimodal components. The first component is a text to image part and the second component is an image back to text part. With this they can do various tasks such as visual question answering, they can do abstract like visual reasoning and many more things. Finally they can also do whatever the individual parts can do such as image generation from text like dali or image compatibility tasks such as clip. The model tokenizes images into latent tokens using a VQGAN and from there on it essentially treats it as a sequence of token models. The outputs of this models are pretty impressive and the code as well as the small models are available online and there's even a colab for you to try it out. The colab itself is also a little bit of a write up of how the model works so if you're interested in that give it a try. Lastly Jeff Dean has a rather long blog post on a 2021 summary of Google research's advances. It's divided into five trends for example more capable general purpose models, more efficient models and so on. Now a lot of it is not only geared towards Google research but also Google products and I won't go into the blog post itself here but if you're interested this is a good overview over at least a slice of the ML research landscape in 2021. And that was already it for ML news. Thank you so much for tuning in for being here. Everything I've mentioned is in the description. I wish you all the best. See you next time. Bye bye.
 Facebook makes ConvNet's return to glory, a new text to speech model lets you speak
 any language you want, and automated music transcription gets a boost.
 Welcome to MLNews.
 Hello and welcome to MLNews, it is so great to have you here.
 How are you doing?
 I hope everyone's okay.
 Let's dive into the first story.
 Facebook Research publishes a paper called A ConvNet for the 2020s, in which they take
 on the notion that somehow transformers are to replace ConvNets for computer vision.
 They make the argument that rather than the attention mechanisms in transformers, it is
 due to some more kind of subtle improvements that the transformer architectures have over
 classical ConvNets.
 Now they show that if they systematically include the best of these changes, then they
 can make a ConvNet that performs as well or better than vision transformers.
 This results in the following graphics starting from the original ResNets in the bottom left
 corner and comparing to various vision transformer architectures on ImageNet 1k and ImageNet
 22k that allows also pre trained models.
 Now this has obviously garnered quite some attention, the code is actually available
 online if you want to try.
 But for example, Lucas Byer has pointed out that if you do compare to VIT that is trained,
 let's say properly with augmentations and so on, then the ConvNext isn't that far ahead.
 The graphics should look more like this.
 And Ross Whiteman, maintainer of a popular library of computer vision models also points
 out that if you take a ResNet and you train it properly, then you will be at the level
 of like a small ConvNext.
 And that would mean that the ResNet bubble itself would also be lifted to about the 82
 mark right here.
 And another comment came from Minxin Tan, who augments the graphic by efficient net
 v2 on ImageNet 1k and 22k, which would result in the following graphic.
 So safe to say what we can read from this is that the market for models in computer
 vision isn't decided at all yet.
 The race is still wide open.
 And it seems like we can achieve comparable performances with various different architectures.
 Now maybe it is the case that all you need to do is just take a big model with lots of
 parameters and it doesn't really matter what you do as long as you do a certain number
 of things right.
 On the other hand, it could also be that we haven't yet come across the ultimate architecture
 yet and there is still an architecture out there somewhere waiting to be discovered to
 dominate computer vision once and for all.
 Only time will tell.
 For now, go and check out the code of ConvNext.
 It is on GitHub.
 Interestingly, Meta Research still uses the Facebook Research GitHub handle.
 There's been a paper making the rounds called Auditing Saliency Cropping Algorithms that
 investigates popular saliency cropping methods.
 Saliency cropping is what these platforms, for example Twitter, do to pictures in order
 to make them fit the predefined format.
 For example, the picture here on the right is in fact much longer if you click on it,
 but in order to fit the familiar Twitter timeline, it needs to crop it somewhere.
 So these platforms, they try to decide what is the most salient, what is the most interesting
 point in a picture and they try to crop towards that rather than just always cropping to the
 top or to the bottom or to the middle.
 Now for a bit more background, people in the past have often criticized the saliency cropping
 algorithm due to them being said to have certain preferences for certain skin tones and also
 exhibiting a phenomenon where they would focus on the non face parts, especially of women.
 There's this famous example of two politicians, one light skinned, one dark skinned, and no
 matter how you order them, if you make a long picture that has one at the one end and one
 at the other end, and then a white area in the middle, the different algorithms would
 choose to focus on different faces repeatedly.
 This paper systematically investigates the saliency cropping algorithms of Twitter, Google
 and Apple in both skin tone differences and also with respect to the phenomenon of what
 they call the male gaze.
 Now they make a big deal out of this idea of the male gaze, which is a concept that
 essentially says society will reorder itself, will build products, will make media to represent
 the male view of the world, specifically how men look at women.
 Mostly the narrative is around objectification.
 And when people shared anecdotal evidence of Twitter cropping pictures of women in the
 following way, this played into this narrative of the male gaze.
 So the hypothesis would be that through whatever mechanism, mostly how the training data is
 collected and so on, the algorithm would learn to focus on the non face part of female bodies
 and therefore reproduce the male gaze that built the data set or built the society where
 the algorithm was trained in.
 Obviously that would be a problem and discovering an effect like this would be quite interesting.
 The paper noticed that the anecdotes posted, the examples posted of this happening were
 mostly women on runways in red carpet type situations.
 So they collected a data set of pictures like these and ran them through the saliency algorithm.
 And surprisingly, they discovered that whenever the algorithm did not focus the face itself,
 it would actually focus mostly on some sort of corporate logos in the background.
 Now these corporate logos happen to be very often not on face level, or at least the ones
 that the algorithm chose to focus on would not be on face level, resulting in a non face
 centric crop.
 Now there's two ways to go from here.
 One way would be to say, ah, look at this, the algorithm is kind of crap.
 It misses the face a lot of the times, it focuses on these logos.
 And that gives the appearance of the algorithm objectifying women or having anything of that
 effect in there.
 And therefore we can discard the male gaze hypothesis or whatever we started with.
 The paper doesn't do this, however, instead it makes a big point of calling these things
 male gaze like artifacts or male gaze like effects, essentially retaining the opinion
 or the appearance that this is still problematic in regards to this effect.
 So instead of saying it's actually not sexist, it's just crap, they do word plays and simply
 characterize it as whatever they want, dash like.
 And this I find to be a little bit worrisome.
 In my opinion, this clearly shows that the authors were out to find this effect, they
 were out to find something of this nature.
 And the data just didn't back that up.
 And honestly, given how many ways you can slice and dice data and do analysis, I'm quite
 astonished that they didn't find anything that they could show as evidence for that.
 But then instead of discarding, they choose to keep this hypothesis in there.
 And they choose to call the artifacts they find male gaze like.
 Now the paper itself can do a lot of hedging.
 The paper can say, well, we described what this is, right?
 We never meant male gaze, we meant male gaze like.
 They can hedge by saying, well, our paper is mainly about the methods of testing this.
 It's not really about the results.
 It's more about the how we collect the data set and so on.
 So you can construct a paper that no one can essentially criticize you until you can just
 backtrack into your, I did nothing wrong.
 And then when you promote the paper, you can be a bit more loose, right?
 Still not saying anything wrong.
 You can be a bit more loose.
 You can just kind of leave away things because you're just promoting it.
 It's social media or a talk or whatnot.
 And whenever you get criticized, you can say, well, we clearly defined things in the paper.
 I'm sorry, Twitter is a short medium and so on.
 And then maybe other people come and pick it up and they just see kind of the title,
 maybe a little bit of the abstract, maybe a little bit of the promotion and ta-da-da-da.
 In the eyes of most people out there, you will have successfully reached the original
 hypothesis.
 Now, I'm not saying investigating these things is not good or anything like this.
 I'm happy that there are people who do these types of investigation.
 I'm very happy that people publish, look, here is how to collect the data set and here
 is how to study these things.
 But if the experiments had turned out the other way, like if they found that the most
 salient point after the algorithm would always be on women's private parts or something like
 this, do you think the paper would have sounded the same?
 Do you think the paper would be of, you know, we just want to get our methodology out there.
 We don't really, it's not really about the results or so on.
 Like, nah, nah, no way.
 As I said, the paper also does a systematic investigation into how the algorithms focus
 on skin tones.
 The results there are mixed as well, but I'll leave it at that.
 I don't want to criticize this paper super particularly, even though I do think it is
 politically motivated, but it's just difficult to evaluate things when it is quite clear
 the authors wanted to find a certain thing.
 There's a new text to speech system called Your TTS towards zero shot multi speaker text
 to speech and zero shot voice conversion for everyone.
 Now this system reaches state of the art in zero shot text to speech and it is quite intricately
 trained, but what you can do is you can have your voice say something in a completely different
 language.
 I'm going to try this right here.
 Hello and welcome.
 You're listening to ML news.
 All right, so now I'm going to go to French and I don't actually have to say the same
 thing in French.
 Yeah, yeah, no, yeah.
 Oh, yeah.
 My baguette.
 I forgot my baguette.
 Let's check it out.
 I forgot my baguette.
 I forgot my baguette.
 What's the music playing in the background?
 I forgot my baguette.
 All right.
 Well, in any case, it sounds pretty good.
 So and it's really fast.
 The code is available.
 I'll link to the colab and everything.
 Give it a try.
 MT3 is a system for multitask multi track music transcription is part of Google's project
 magenta that applies machine learning to the arts.
 This is also available and it's again pretty cool what it can do.
 There is a hugging face space where you can upload your own audio and have it transcribed
 and there is this demo on Reddit.
 Yes it is MIDI like it's not supposed to sound the same but it does transcribe the music
 into multiple tracks into multiple parallel tracks.
 It's really hard task and it's really cool that this is sort of possible out of the box.
 The model is available on GitHub.
 You can check it out.
 Quartz writes China's new algorithm rules are at odds with its tech giants business
 models.
 This is an article detailing China's new rules for what they call algorithms which are essentially
 recommender systems.
 So the new rules mean that algorithm providers need to proactively spread positive energy
 ensure their algorithms are for good and they curtail algorithms for promoting or causing
 excessive spending or for the algorithms to lead to developing an addiction to the platforms.
 This is obviously targeted at many of the newer social media systems that explicitly
 use recommender systems to drive most of their business.
 Now while this seems like a pretty unprecedented move especially for China the article also
 says that some argue that the impact might not be so large because the rules essentially
 only require that users have the ability to opt out and a lot of users simply are not
 going to do that but it's pretty cool that at least you have the option to do so and
 honestly in my opinion I'd much rather have an opt out feature that is like buried somewhere
 in three layers of setting than every single website asking me whether and what cookies
 I want.
 That's just annoying.
 Not saying I don't see the reasoning behind the rules existences I'm just saying it's
 freaking annoying.
 Shlomo Kashani and Amir Ivory release deep learning interviews hundreds of fully solved
 job interview questions from a wide range of key topics in AI.
 This is version two and it includes it is a giant PDF that includes questions and solutions.
 You can see it's over three hundred and sixty pages from all disciplines of ML.
 So if you're looking to prepare for job interviews or simply up your skill a little bit in a
 different area of ML this might be a neat resource for you.
 Alright we'll come to some helpful material helpful libraries helpful things that I found.
 Deep checks is a tool for validating machine learning models and data.
 It essentially acts a little bit like a unit test framework for machine learning code.
 DAG's hub is a platform to version data models experiments and code.
 They claim to have a GitHub like experience for machine learning.
 Now while I enjoy the presence of yet another ML Ops system and the launch of release two
 which also integrates data labeling into their system.
 The coolest thing about this is their background on the website.
 See follows your mouse and this is just cool and I think every time you enter you get like
 a new color.
 Look at that.
 Wow.
 It's completely dark when you start so you don't you never expect it and then what's
 a Bayesian modeling and computation in Python is a free book that is available online about
 Bayesian modeling and computation in Python.
 It is on Amazon if you want the hardcover but you can just read it online if you want
 to ML contests dot com is a website that just keeps track of machine learning contests.
 For example on Kaggle AI crowd and more.
 Ray Scorch is a wrapper around Scorch to use Ray for distributed training.
 Now what is Scorch you ask?
 Good question.
 Scorch is a wrapper around PyTorch in order to make it compatible with SK learn.
 Rumble is a database that is built on top of Apache Spark and HDFS and it allows you
 to feed in JSON and process a lot of data very efficiently with a JSON like query language.
 So you can query heterogeneous data you can query nested data and it will scale from your
 laptop all the way up to data centers.
 It's open source you can check it out.
 Jaxx models is a GitHub repository that says it's an unofficial repository of Jaxx implementations
 of deep learning models.
 It is a young project but it does have some models inside and it is growing.
 If you're into Jaxx and you're looking for a model maybe you'll find it here.
 S3PRL is a library to process speech specifically a self-supervised speech pre-training and
 representation learning toolkit.
 Alright that was it for the helpful stuff.
 I hope some of you have been helped by the helpful stuff.
 I've come across this blog post right here explaining AlphaZero and I found it to be
 very understandable and instructive.
 So if you want to get into AlphaZero or any of the related algorithms maybe give this
 blog post a read.
 It explains everything pretty well and understandably and it's a good first contact with these kinds
 of algorithms if you don't know yet exactly what they do.
 The blog post is by Josh Varty and I'll link it in the description.
 SureBank AI have been making some progresses into large models recently.
 They release Rudolph after Rudali.
 Rudolph is what they call a hypermodal transformer.
 They call it hypermodal because it has multiple multimodal components.
 The first component is a text to image part and the second component is an image back
 to text part.
 With this they can do various tasks such as visual question answering, they can do abstract
 like visual reasoning and many more things.
 Finally they can also do whatever the individual parts can do such as image generation from
 text like dali or image compatibility tasks such as clip.
 The model tokenizes images into latent tokens using a VQGAN and from there on it essentially
 treats it as a sequence of token models.
 The outputs of this models are pretty impressive and the code as well as the small models are
 available online and there's even a colab for you to try it out.
 The colab itself is also a little bit of a write up of how the model works so if you're
 interested in that give it a try.
 Lastly Jeff Dean has a rather long blog post on a 2021 summary of Google research's advances.
 It's divided into five trends for example more capable general purpose models, more
 efficient models and so on.
 Now a lot of it is not only geared towards Google research but also Google products and
 I won't go into the blog post itself here but if you're interested this is a good overview
 over at least a slice of the ML research landscape in 2021.
 And that was already it for ML news.
 Thank you so much for tuning in for being here.
 Everything I've mentioned is in the description.
 I wish you all the best.
 See you next time.
 Bye bye.