[ML News] CVPR bans social media paper promotion | AI restores Rembrandt | GPU prices down
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper mlnews ml news cvpr social media research discussion peer review bias toxic language model stochastic parrots rembrandt painting painting restoration convolutional neural networks nvidia alias-free gan deep learning news science news technology news tech news twitter academic academic twitter twitter academia anonymity free speech what is deep learning
#cvpr #socialmedia #machinelearning

In this week's ML news we look at CVPR's controversial action to ban paper promotions on social media during the review phase, among other things!

OUTLINE:
0:00 - Intro & Overview
0:25 - CVPR bans social media paper discussions
5:10 - WalMart uses AI to suggest substitutions
6:05 - NVIDIA releases Alias-Free GAN
7:30 - Confession Video in Myanmar possibly a DeepFake
8:50 - AI restores Rembrandt painting
10:40 - AI for healthcare not problem-free yet
11:50 - ML interviews book
12:15 - NVIDIA canvas turns sketches into paintings
13:00 - GPU prices down after crypto shock
13:30 - Facebook AI improves shopping experience
14:05 - DeepLab2 released on GitHub
14:35 - Toxic Language Models: Nobody cares
16:55 - Does AI have common sense?

References:
CVPR forbids social media promotion
https://twitter.com/wjscheirer/status/1408507154219384834

WalMart uses AI to substitute out-of-stock products
https://www.supermarketnews.com/technology/walmart-enlists-artificial-intelligence-online-grocery-substitutions

NVIDIA releases Alias-Free GAN
https://nvlabs.github.io/alias-free-gan/

Myanmar Politician's confession could be DeepFake
https://www.wired.com/story/opinion-the-world-needs-deepfake-experts-to-stem-this-chaos/

Rembrandt restored using AI
https://www.smithsonianmag.com/smart-news/lost-edges-rembrandts-night-watch-are-restored-using-artificial-intelligence-180978056/

AI in healthcare still shaky
http://www.greenvillebusinessmag.com/2021/06/22/360303/prisma-health-announces-artificial-intelligence-partnership
https://www.theverge.com/2021/6/22/22545044/algorithm-hospital-sepsis-epic-prediction

ML interviews book
https://huyenchip.com/ml-interviews-book/

NVIDIA Canvas Beta available
https://blogs.nvidia.com/blog/2021/06/23/studio-canvas-app/

GPU prices down as China cracks down on Crypto
https://www.theregister.com/2021/06/22/as_china_shutters_cryptomining_plants/

Facebook AI's big goal of improving shopping
https://ai.facebook.com/blog/advancing-ai-to-make-shopping-easier-for-everyone/

GoogleAI releases DeepLab2
https://github.com/google-research/deeplab2

Toxic Language Model: Nobody cares
https://arxiv.org/pdf/2105.03023.pdf

AI has no common sense
https://www.analyticsinsight.net/incapable-yes-artificial-intelligence-cant-do-these-things/
https://6b.eleuther.ai/

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 CVPR forbids tweeting about papers, AI is used to restore a Rembrandt, and a potential deepfake has big consequences in the country of Myanmar. Welcome to this week's ML News. Hello and welcome to ML News, your absolutely regular every week on Monday update on what's going on in the machine learning world. The first one fresh of the press Walter Shira writes the result of the CVPR 2021 PAMI-TC votes are in all four motions passed this decides over the future of the CVPR conference in the next few years. Now you can see the motions here and particularly interesting is motion number four social media limitation during review overwhelmingly accepted. This motion was proposed by Michael Black and says social media promotion of papers is prohibited during the review period for CVPR except for automatic posting of new preprints by archives. Essentially means during the review period, you're not allowed to go and tweet about your papers, you're only allowed to upload them to archive and there is an exception because archive sometimes automatically tweets new papers anything else, no go. Now there is a bit of an outrage about this. I have to say it's not as big of a rule change as it seems. So the reasoning behind this is there already used to be a press release ban during the review period. And this motion simply extends the press release ban to social media because effectively while you can do a press release, you could still tweet about your papers and get the word out this way. The big concern here is that groups with a lot of following or a lot of press influence will have their papers exposed to more people which could bias the review process. Now in the light of already existing press ban, extending the ban to social media makes sense. However, I feel the bigger issue is why is there a press ban at all? Why aren't you allowed to talk about your papers as they're under review? So the argumentation of the proposal is that this can bias the reviewers judgment if they're exposed to this work. Now as much as I like the idea of peer review, it's really not working currently. They say peer review is the backbone of science process helps detect mistakes or false claims before the work appears in public. Yeah, right. When has this happened the last time I've exposed more false claims on my channel than the entire ZVPR conference in the review process, we have to get away from this notion that peer review is adequately constituted by three dudes sitting on the toilet whilst flicking through your paper on their smartphone and then giving a weak reject. I argue that social media is the actual peer review. What seems weird to me is that they have sort of an FAQ here answering some of the worries about this. So there are questions why won't this slow down scientific progress? And what about archive and their claim here is that no, this won't slow down scientific progress because experts in the field make scientific progress, not the general public. And here again, archive tweets are largely followed by experts in the field and not the general public. Wait, I thought the peer review was supposed to be experts. Aren't the peer reviewers exactly the people who would follow the archive publications? Like if it was just the general public receiving the social media posts, why are we worried? After all, experts make the contributions in the scientific field, not the general public. The truth is that currently social media imperfect unbalanced with different followings as it is constitutes a much more rigorous peer review process than what we have at conferences, the social network that we've built up online effectively highlights interesting papers. And yes, a lot of them come from big companies. But let's face it, they have really good researchers and a lot of resources. But often it happens enough that some no name paper gets surfaced because it is interesting, whereas in the conference proceedings, it would just get lost. This is in the light of other conferences doing things like archive blackouts before submitting and people calling for entirely banning archive uploads before conferences. All of this is highly suspicious. Now who is really profiting from the current system and who's really going to lose from a more open approach to publishing, it's going to be people that take part in the nice little collusion rings that we have. These are people publishing dozens and dozens and dozens of paper each year in some niche field where everyone knows everyone and everyone knows who everyone's paper is from, and they just kind of accept each other. However, when the public encounters these papers, they're generally boring, not interesting, and don't actually contribute anything to the knowledge of humankind. So yeah, if research is more in public, that's not going to fly anymore, which is a good thing. So future CVP or submitters, all the youtubers inboxes are at your disposal, enough of us are bribable, so you still have good outlets if you have money. Well, won't that tilt the balance even more into the direction of big corporations. So in conclusions, conferences are hell bent on making themselves not important even faster than they already are. Next news, supermarket news writes Walmart enlists artificial intelligence for online grocery substitution. So this actually pretty interesting in that Walmart has people going around shopping for you. So you place an online order and these people go and they buy stuff for you. However, sometimes items are out of stock. And when that happens, a substitution needs to happen. So Walmart apparently has built some sort of a recommender system that tells these shoppers which product they can substitute. I originally thought this was a pretty simple problem like, oh, we don't have this milk, have this other milk, but it seems to be that it's not that easy. And they claim since deploying the AI solution, customer acceptance of online grocery substitutions has climbed over 95%. So good for them real world problem AI solves it all good. Is this a marketing piece? Absolutely. But still kind of cool. Okay, Nvidia releases alias free GAN. And this fixes the supposed problem of the strong dependence of GANs on the exact coordinates of the pixels. Now I won't go through the paper here, but you should look at these visualizations. They're pretty, pretty cool. So on the left, you see the old style GAN. And it's so freaky. Look at the hair, it kind of stays in place while the face goes around. Well, of course, their method fixes this particular problem. Here's the same, it just kind of looks like a head that's kind of sliding under a foreground layer of hair. What's also praised about the new model is the sort of better interpolations that you can see right here. And again, you can see the less dependence on the actual pixel coordinates, particularly impressive, I find to be this beach interpolation where you can see style GAN just kind of keeps everything at the same place ish, while as the alias free GAN tends to move around a lot. Now whether these are cherry picked or not, and whether in the final analysis, the alias free GAN is really better than the style GAN, who knows? Safe to say when it comes to GANs, we are pushing the limits of what's doable. And we are really getting into the territories of fine tuning these things. Hard to believe that like five years ago, we could barely make a face. Yeah. Speaking of GANs, apparently in the country of Myanmar, there is a confession video going around of a politician confessing to transferring some money. And due to artifacts in the video, people claim it's a deep fake. Now this article here explores this claim and comes to the conclusion that probably the artifacts are more a compression artifact because the video is very low quality. But it does raise important questions as if we get better and better and better at producing realistic looking images, sound and video in the future, we'll have to develop new expectations of what counts as real evidence of something happening. A video of you saying something or doing something might no longer be enough as you could just always claim that is a deep fake. Now I wouldn't be so overly worried about this because we have the same situation right now with writing, if I simply claim to you that a certain person who recently passed away and once founded an antivirus company has sent me an email briefly before his death, and the email said certain things, I could even present you the email on a sheet of paper yet you wouldn't necessarily believe me. So what we'll have to change is just our expectations of which mediums are valid forms of evidence and not easily tampered with. I don't know what's going to be the solution in the future, but I'm sure we'll come up with something. Smithsonian magazine writes lost edges of Rembrandt's nightwatch are restored using artificial intelligence. Apparently this painting had been cut at some point to hang it on some wall and the cuts have been lost. Now artificial intelligence has been used to restore this painting. How nice. So apparently this is a multi million dollar restoration project. And at the same time, it seems like a really, really concerted effort. But also from what they tell it, it also seems like you could do it in five minutes. On one hand, the input data seems to be really rich, so there is x ray scanners, 528 digital exposures, and so on. On the other hand, they write things like though many museums employ painters to reconstruct masterworks, the senior scientist Robert Erdman was able to use a computer to recreate the missing panels computer. So they apparently they use this new technology called convolutional neural networks, a type of artificial intelligence algorithm that helps computers figure out what images may have once looked like. Okay, the crux of the thing now comes when they say apparently there is a copy of the original painting that sort of shows what it should look like. So essentially what these researchers did appears to be something like a sophisticated style transfer where they use the copy of the image as a base and then transfer the style of Rembrandt on top of it. Now this is both pretty cool in that we now have technology that can do these things. But we also have to be honest about what this is. This is a believable way this could have looked like there is no way of knowing if Rembrandt actually drew this particular thing or something else that resulted in the same copy of this other painter. In any case, the picture is now complete thanks to computer thanks computer. Okay, Greenville Business Magazine writes Prisma Health announces artificial intelligence partnership to make doctors more efficient to inform them with their decisions and so on. And at the same time, the verge writes a hospital algorithm designed to predict a deadly condition misses most cases, and it also had many false alarms. So the algorithm was tasked with detecting sepsis, a complicated condition that can bring patients into critical state. Now the way this was trained was with data labeled not whether the patient has sepsis or not, but whether the doctor would submit a bill for treatment of sepsis. So essentially, it's trying to replicate what the doctors do and not actually predict the patient's state, I get that this is easier labels than actually figuring out what happened. But also don't be surprised if then it doesn't work better than the doctors say it's essentially trying to predict what physicians are already doing. Suffice to say, while AI is a powerful tool that can definitely help with many things, we still have to be careful when we deploy it in the real world and actually measure its performance. And given that this article exists, performance has been measured. And we're going to go back to the drawing board. Chip Yuen and others release a book called Introduction to Machine Learning Interviews. The book is mostly for interviewees, but also for interviewers to prepare for machine learning interviews. So if you have an interview soon, or if you're looking to interview someone, this might be a nice resource for you. The book is free and available, give it a try, it might just get you a job. As fast as one can go turn sketches into stunning landscapes with Nvidia canvas written by Nvidia. So Nvidia has released this new application called canvas in which you're able to sort of draw a doodle and it will transform it into really nice looking pictures. This is part of the Nvidia sort of artists suite that helps people be more creative, I guess, or less or differently. I'm not sure how to characterize this. The canvas app is available as a beta you can download it if you do have an Nvidia graphics card, I believe I haven't tried it out myself because all the graphics card I have access to don't actually have a monitor on them. So what do I do? Speaking of GPUs, good news for deep learners as the register writes now that China has all but banned cryptocurrencies GPU prices are falling like Bitcoin. So China hasn't fully banned cryptocurrencies but is cracking down majorly on them. And that means that some of the mining power is going away and with it, the GPU demand is lower than it used to be. So if you wanted to buy yourself a data center now might be the time. Facebook is looking to make your shopping experience easier using AI. They have a selection of software called product match that helps identify products from pictures among other things. So this allows sellers to tag their products easily, but it also allows you to find products that you see somewhere or on someone. So artificial intelligence might help you with shopping in the future. And I can't wait to see all the adversarial attacks on these systems. Yes, for sure. I'm going to sell you a Rolex. It's right here. The AI system even says it's one 3000 bucks. Thank you. Google AI releases deep lab two for TensorFlow, which is a library to do pixel based segmentation, or any sort of pixel based labeling tasks. So this is on GitHub, you can go check it out if you are in that space. It seems like it's a good code base if you're in the research directions or tasks of pixel based labeling, such as semantic segmentation, or textual labeling, or explainable AI, give it a look. All right, besides all the news, I feel we should also cover some non news. So I've seen this paper, D experts decoding time control text generation with experts and anti experts. Now this seems to be a good paper, as far as I can tell, it takes on the tasks of mitigating toxicity in language generation. So as you can see right here, you have some sort of a base language model that has some output and then you have what they call the experts and some of them are non toxic and some of them are deliberately toxic and by contrasting non toxic experts and the toxic experts, you can then make sure that you reweigh the outputs towards a non toxic behavior. Now I got nothing against this paper. However, what I want to say is that this is like a 100% recipe of making a super toxic language model. All I have to do is flip this one sign right here, I can just take whatever this is, I can flip one bit in the algorithm and I make the most toxic language model ever. To the big credits of the authors, this is even acknowledged in the broader impact statement, they say, we acknowledge that any controllable detoxification method runs the risk of dual use specifically this technology could be used to automatically generate hateful texts for a broader discussion of such risks and the risks of large pre trained language models in general, please see the stochastic parrots paper. Now there are enough people that with every face up sampling method cry that we shouldn't develop these things and all of this is dangerous, it should be measured by the harm it causes and so on. And here I have a method that flipping one single bit will make it super duper toxic and harmful. Is there anyone complaining about this paper? No, zero. Where are these people? Are you really telling me that a little paragraph in the broader impact statement is gonna not cause the harm? Now I think I know how this works because we gave the proper citation, we have the proper friends, we frame it in the proper way, and the narrative uphold. So in my personal opinion, we should not give too much power to these ethics people unless papers like this one are met with at least as much scrutiny as the papers they're usually criticizing. Again, I'm totally fine with this paper, then again, I'm also totally fine with pretty much all the other papers. I'm just calling for a bit of consistency here. Okay, last news, a dealing Beatrice in analytics inside writes, yes, artificial intelligence can't do these things. It's an article about what artificial intelligence isn't able to do and also a bit of an argument of why it won't be able to do it in the near future. Among these things is the classic use common sense to make decisions argument. And I love the example that they give right here. For example, if we say a woman went shopping, she bought a beautiful dress, she left the place with a big smile. If asked what the woman shopped, a human would instantly say a beautiful dress. But answering these simple questions is very difficult for artificial intelligence. All right, hold on. Here's GPTJ of illuthorai. A woman went shopping, she bought a beautiful dress, she left the place with a big smile. Now she wants to return her purchase of and the model says the dress, she wants her money back. Totally lacking common sense. I get it is just one example. But I think there are much more effective ways to criticize artificial intelligence than it doesn't have common sense. Like if common sense is sort of your intuitive gut feeling of things like it has common sense. All right, this was it for this week's ML news. How did you do today? Did you win? Did you lose? Did you even know there was a game involved? Who knows? We'll be here next week at Monday, nine o'clock. No questions asked. Take care.
 CVPR forbids tweeting about papers, AI is used to restore a Rembrandt, and a potential deepfake
 has big consequences in the country of Myanmar. Welcome to this week's ML News.
 Hello and welcome to ML News, your absolutely regular every week on Monday update on what's
 going on in the machine learning world. The first one fresh of the press Walter Shira writes the
 result of the CVPR 2021 PAMI-TC votes are in all four motions passed this decides over the future
 of the CVPR conference in the next few years. Now you can see the motions here and particularly
 interesting is motion number four social media limitation during review overwhelmingly accepted.
 This motion was proposed by Michael Black and says social media promotion of papers is prohibited
 during the review period for CVPR except for automatic posting of new preprints by archives.
 Essentially means during the review period, you're not allowed to go and tweet about your papers,
 you're only allowed to upload them to archive and there is an exception because archive sometimes
 automatically tweets new papers anything else, no go. Now there is a bit of an outrage about this.
 I have to say it's not as big of a rule change as it seems. So the reasoning behind this is there
 already used to be a press release ban during the review period. And this motion simply extends the
 press release ban to social media because effectively while you can do a press release,
 you could still tweet about your papers and get the word out this way. The big concern here is
 that groups with a lot of following or a lot of press influence will have their papers exposed to
 more people which could bias the review process. Now in the light of already existing press ban,
 extending the ban to social media makes sense. However, I feel the bigger issue is why is there
 a press ban at all? Why aren't you allowed to talk about your papers as they're under review?
 So the argumentation of the proposal is that this can bias the reviewers judgment if they're exposed
 to this work. Now as much as I like the idea of peer review, it's really not working currently.
 They say peer review is the backbone of science process helps detect mistakes or false claims
 before the work appears in public. Yeah, right. When has this happened the last time I've exposed
 more false claims on my channel than the entire ZVPR conference in the review process, we have
 to get away from this notion that peer review is adequately constituted by three dudes sitting on
 the toilet whilst flicking through your paper on their smartphone and then giving a weak reject.
 I argue that social media is the actual peer review. What seems weird to me is that they have
 sort of an FAQ here answering some of the worries about this. So there are questions why won't this
 slow down scientific progress? And what about archive and their claim here is that no, this
 won't slow down scientific progress because experts in the field make scientific progress,
 not the general public. And here again, archive tweets are largely followed by experts in the
 field and not the general public. Wait, I thought the peer review was supposed to be experts. Aren't
 the peer reviewers exactly the people who would follow the archive publications? Like if it was
 just the general public receiving the social media posts, why are we worried? After all, experts make
 the contributions in the scientific field, not the general public. The truth is that currently
 social media imperfect unbalanced with different followings as it is constitutes a much more
 rigorous peer review process than what we have at conferences, the social network that we've built
 up online effectively highlights interesting papers. And yes, a lot of them come from big
 companies. But let's face it, they have really good researchers and a lot of resources. But often it
 happens enough that some no name paper gets surfaced because it is interesting, whereas in
 the conference proceedings, it would just get lost. This is in the light of other conferences
 doing things like archive blackouts before submitting and people calling for entirely
 banning archive uploads before conferences. All of this is highly suspicious. Now who is really
 profiting from the current system and who's really going to lose from a more open approach to
 publishing, it's going to be people that take part in the nice little collusion rings that we have.
 These are people publishing dozens and dozens and dozens of paper each year in some niche field
 where everyone knows everyone and everyone knows who everyone's paper is from, and they just kind
 of accept each other. However, when the public encounters these papers, they're generally boring,
 not interesting, and don't actually contribute anything to the knowledge of humankind. So yeah,
 if research is more in public, that's not going to fly anymore, which is a good thing. So future CVP
 or submitters, all the youtubers inboxes are at your disposal, enough of us are bribable,
 so you still have good outlets if you have money. Well, won't that tilt the balance even more into
 the direction of big corporations. So in conclusions, conferences are hell bent on making themselves
 not important even faster than they already are. Next news, supermarket news writes Walmart enlists
 artificial intelligence for online grocery substitution. So this actually pretty interesting
 in that Walmart has people going around shopping for you. So you place an online order and these
 people go and they buy stuff for you. However, sometimes items are out of stock. And when that
 happens, a substitution needs to happen. So Walmart apparently has built some sort of a
 recommender system that tells these shoppers which product they can substitute. I originally
 thought this was a pretty simple problem like, oh, we don't have this milk, have this other milk,
 but it seems to be that it's not that easy. And they claim since deploying the AI solution,
 customer acceptance of online grocery substitutions has climbed over 95%. So good for them real world
 problem AI solves it all good. Is this a marketing piece? Absolutely. But still kind of cool.
 Okay, Nvidia releases alias free GAN. And this fixes the supposed problem of the strong dependence
 of GANs on the exact coordinates of the pixels. Now I won't go through the paper here, but you
 should look at these visualizations. They're pretty, pretty cool. So on the left, you see the
 old style GAN. And it's so freaky. Look at the hair, it kind of stays in place while the face
 goes around. Well, of course, their method fixes this particular problem. Here's the same, it just
 kind of looks like a head that's kind of sliding under a foreground layer of hair. What's also
 praised about the new model is the sort of better interpolations that you can see right here. And
 again, you can see the less dependence on the actual pixel coordinates, particularly impressive,
 I find to be this beach interpolation where you can see style GAN just kind of keeps everything
 at the same place ish, while as the alias free GAN tends to move around a lot. Now whether these
 are cherry picked or not, and whether in the final analysis, the alias free GAN is really better than
 the style GAN, who knows? Safe to say when it comes to GANs, we are pushing the limits of what's
 doable. And we are really getting into the territories of fine tuning these things. Hard
 to believe that like five years ago, we could barely make a face. Yeah. Speaking of GANs,
 apparently in the country of Myanmar, there is a confession video going around of a politician
 confessing to transferring some money. And due to artifacts in the video, people claim it's a deep
 fake. Now this article here explores this claim and comes to the conclusion that probably the
 artifacts are more a compression artifact because the video is very low quality. But it does raise
 important questions as if we get better and better and better at producing realistic looking images,
 sound and video in the future, we'll have to develop new expectations of what counts as real
 evidence of something happening. A video of you saying something or doing something might no
 longer be enough as you could just always claim that is a deep fake. Now I wouldn't be so overly
 worried about this because we have the same situation right now with writing, if I simply
 claim to you that a certain person who recently passed away and once founded an antivirus company
 has sent me an email briefly before his death, and the email said certain things, I could even
 present you the email on a sheet of paper yet you wouldn't necessarily believe me. So what we'll
 have to change is just our expectations of which mediums are valid forms of evidence and not easily
 tampered with. I don't know what's going to be the solution in the future, but I'm sure we'll
 come up with something. Smithsonian magazine writes lost edges of Rembrandt's nightwatch are restored
 using artificial intelligence. Apparently this painting had been cut at some point to hang it
 on some wall and the cuts have been lost. Now artificial intelligence has been used to restore
 this painting. How nice. So apparently this is a multi million dollar restoration project. And at
 the same time, it seems like a really, really concerted effort. But also from what they tell
 it, it also seems like you could do it in five minutes. On one hand, the input data seems to be
 really rich, so there is x ray scanners, 528 digital exposures, and so on. On the other hand,
 they write things like though many museums employ painters to reconstruct masterworks,
 the senior scientist Robert Erdman was able to use a computer to recreate the missing panels
 computer. So they apparently they use this new technology called convolutional neural networks,
 a type of artificial intelligence algorithm that helps computers figure out what images may have
 once looked like. Okay, the crux of the thing now comes when they say apparently there is a copy of
 the original painting that sort of shows what it should look like. So essentially what these
 researchers did appears to be something like a sophisticated style transfer where they use the
 copy of the image as a base and then transfer the style of Rembrandt on top of it. Now this is both
 pretty cool in that we now have technology that can do these things. But we also have to be honest
 about what this is. This is a believable way this could have looked like there is no way of knowing
 if Rembrandt actually drew this particular thing or something else that resulted in the same copy
 of this other painter. In any case, the picture is now complete thanks to computer thanks computer.
 Okay, Greenville Business Magazine writes Prisma Health announces artificial intelligence
 partnership to make doctors more efficient to inform them with their decisions and so on.
 And at the same time, the verge writes a hospital algorithm designed to predict a deadly condition
 misses most cases, and it also had many false alarms. So the algorithm was tasked with detecting
 sepsis, a complicated condition that can bring patients into critical state. Now the way this
 was trained was with data labeled not whether the patient has sepsis or not, but whether the doctor
 would submit a bill for treatment of sepsis. So essentially, it's trying to replicate what the
 doctors do and not actually predict the patient's state, I get that this is easier labels than
 actually figuring out what happened. But also don't be surprised if then it doesn't work better than
 the doctors say it's essentially trying to predict what physicians are already doing. Suffice to say,
 while AI is a powerful tool that can definitely help with many things, we still have to be careful
 when we deploy it in the real world and actually measure its performance. And given that this
 article exists, performance has been measured. And we're going to go back to the drawing board.
 Chip Yuen and others release a book called Introduction to Machine Learning Interviews.
 The book is mostly for interviewees, but also for interviewers to prepare for machine learning
 interviews. So if you have an interview soon, or if you're looking to interview someone,
 this might be a nice resource for you. The book is free and available, give it a try,
 it might just get you a job. As fast as one can go turn sketches into stunning landscapes with
 Nvidia canvas written by Nvidia. So Nvidia has released this new application called canvas in
 which you're able to sort of draw a doodle and it will transform it into really nice looking pictures.
 This is part of the Nvidia sort of artists suite that helps people be more creative, I guess,
 or less or differently. I'm not sure how to characterize this. The canvas app is available
 as a beta you can download it if you do have an Nvidia graphics card, I believe I haven't tried
 it out myself because all the graphics card I have access to don't actually have a monitor on them.
 So what do I do? Speaking of GPUs, good news for deep learners as the register writes now that
 China has all but banned cryptocurrencies GPU prices are falling like Bitcoin. So China hasn't
 fully banned cryptocurrencies but is cracking down majorly on them. And that means that some of the
 mining power is going away and with it, the GPU demand is lower than it used to be. So if you
 wanted to buy yourself a data center now might be the time. Facebook is looking to make your shopping
 experience easier using AI. They have a selection of software called product match that helps
 identify products from pictures among other things. So this allows sellers to tag their products
 easily, but it also allows you to find products that you see somewhere or on someone. So artificial
 intelligence might help you with shopping in the future. And I can't wait to see all the adversarial
 attacks on these systems. Yes, for sure. I'm going to sell you a Rolex. It's right here. The AI system
 even says it's one 3000 bucks. Thank you. Google AI releases deep lab two for TensorFlow, which is
 a library to do pixel based segmentation, or any sort of pixel based labeling tasks. So this is on
 GitHub, you can go check it out if you are in that space. It seems like it's a good code base if you're
 in the research directions or tasks of pixel based labeling, such as semantic segmentation,
 or textual labeling, or explainable AI, give it a look. All right, besides all the news, I feel we
 should also cover some non news. So I've seen this paper, D experts decoding time control text
 generation with experts and anti experts. Now this seems to be a good paper, as far as I can tell,
 it takes on the tasks of mitigating toxicity in language generation. So as you can see right here,
 you have some sort of a base language model that has some output and then you have what they call
 the experts and some of them are non toxic and some of them are deliberately toxic and by
 contrasting non toxic experts and the toxic experts, you can then make sure that you
 reweigh the outputs towards a non toxic behavior. Now I got nothing against this paper. However,
 what I want to say is that this is like a 100% recipe of making a super toxic language model.
 All I have to do is flip this one sign right here, I can just take whatever this is, I can flip
 one bit in the algorithm and I make the most toxic language model ever. To the big credits of the
 authors, this is even acknowledged in the broader impact statement, they say, we acknowledge that
 any controllable detoxification method runs the risk of dual use specifically this technology
 could be used to automatically generate hateful texts for a broader discussion of such risks and
 the risks of large pre trained language models in general, please see the stochastic parrots paper.
 Now there are enough people that with every face up sampling method cry that we shouldn't develop
 these things and all of this is dangerous, it should be measured by the harm it causes and so
 on. And here I have a method that flipping one single bit will make it super duper toxic and
 harmful. Is there anyone complaining about this paper? No, zero. Where are these people? Are you
 really telling me that a little paragraph in the broader impact statement is gonna not cause the
 harm? Now I think I know how this works because we gave the proper citation, we have the proper
 friends, we frame it in the proper way, and the narrative uphold. So in my personal opinion,
 we should not give too much power to these ethics people unless papers like this one are met with
 at least as much scrutiny as the papers they're usually criticizing. Again, I'm totally fine with
 this paper, then again, I'm also totally fine with pretty much all the other papers. I'm just calling
 for a bit of consistency here. Okay, last news, a dealing Beatrice in analytics inside writes,
 yes, artificial intelligence can't do these things. It's an article about what artificial
 intelligence isn't able to do and also a bit of an argument of why it won't be able to do it in the
 near future. Among these things is the classic use common sense to make decisions argument. And I love
 the example that they give right here. For example, if we say a woman went shopping, she bought a
 beautiful dress, she left the place with a big smile. If asked what the woman shopped, a human
 would instantly say a beautiful dress. But answering these simple questions is very difficult for
 artificial intelligence. All right, hold on. Here's GPTJ of illuthorai. A woman went shopping, she
 bought a beautiful dress, she left the place with a big smile. Now she wants to return her purchase
 of and the model says the dress, she wants her money back. Totally lacking common sense. I get
 it is just one example. But I think there are much more effective ways to criticize artificial
 intelligence than it doesn't have common sense. Like if common sense is sort of your intuitive gut
 feeling of things like it has common sense. All right, this was it for this week's ML news. How
 did you do today? Did you win? Did you lose? Did you even know there was a game involved? Who knows?
 We'll be here next week at Monday, nine o'clock. No questions asked. Take care.