[ML News] EU regulates AI, China trains 1.75T model, Google's oopsie, Everybody cheers for fraud.
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper deep learning news machine learning news academic fraud geoffrey hinton please commit more academic fraud wudao wudao china baai google kannada ugliest language mcdonalds machine learning ai predicts stock market european union ai eu ai regulation ai regulation machine learning regulation this week in machine learning
#mlnews #wudao #academicfraud

OUTLINE:
0:00 - Intro
0:25 - EU seeks to regulate AI
2:45 - AI COVID detection systems are all flawed
5:05 - Chinese lab trains model 10x GPT-3 size
6:55 - Google error identifies "ugliest" language
9:45 - McDonald's learns about AI buzzwords
11:25 - AI predicts cryptocurrency prices
12:00 - Unreal Engine hack for CLIP
12:35 - Please commit more academic fraud

References:
https://www.lawfareblog.com/artificial-intelligence-act-what-european-approach-ai
https://blogs.sciencemag.org/pipeline/archives/2021/06/02/machine-learning-deserves-better-than-this
https://www.nature.com/articles/s42256-021-00307-0
https://en.pingwest.com/a/8693
https://arxiv.org/pdf/2104.12369.pdf
https://www.bbc.com/news/world-asia-india-57355011
https://www.zdnet.com/article/mcdonalds-wants-to-democratise-machine-learning-for-all-users-across-its-operations/
https://www.analyticsinsight.net/ai-is-helping-you-make-profits-by-predicting-cryptocurrency-prices/
https://twitter.com/arankomatsuzaki/status/1399471244760649729
https://jacobbuckman.com/2021-05-29-please-commit-more-blatant-academic-fraud/

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 The European Union seeks to regulate AI. Chinese researchers train a model 10 times as large as GPT-3. Google makes an oopsie and Jacob Buckman appeals to the community to please commit more academic fraud. This and much more in today's ML News. Have fun. So, Lawfare writes, the European Union unveils its proposals for the Artificial Intelligence Act seeking to regulate AI and harmful uses thereof. So what does this actually mean? First of all, how do they even define AI? They say, artificial intelligence systems means software that is developed with one or more of the techniques and approaches listed in Annex 1 and can for a given set of human defined objectives generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with. In Annex 1, these things are described as either machine learning approaches, logic and knowledge based approaches or statistical approaches. So in essence, I think there is an easier name for all of this under one hat. It's called software. If you think that's a bit far reaching, don't be worried. European Union divides different AI applications into different categories of risk, ranging from minimal risk to unacceptable risk and prescribes different things you'll have to do if your application falls into any of those sections. For example, if you're in the high risk category, you have to do a conformity assessment, which either you can do yourself or you'll have to submit to some sort of regulatory body. Now rest assured that these regulatory bodies are of course not going to be staffed by lobbyists from the exact corporations that are going to apply for exceptions to the rules right here. If you're in the unacceptable risk category, which includes things like facial recognition and social scoring, you are prohibited from performing these things. Of course, there are going to be exceptions as well for things like law enforcement and so on. Safe to say in its quest to regulate everything under the sun, and if they could the sun itself, the European Union's regulations have always only brought benefit to humanity. I mean, aren't we all just so much better informed about how our data is used now that every single website has a yes, I accept the cookies banner that certainly helps your helping European Union. Thank you very much. So for now, this is a proposal, but safe to say the European Union will probably go forward with regulating AI in some capacity. In an article in Science Mag, Derek Lowy writes machine learning deserves better than this. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. In which the authors identify over 2000 studies of which they finally select 62 and say a review finds that none of the models identified are of potential clinical use due to methodological flaws and or underlying biases. There are Chloe elaborates on this and goes on a very good rant against how machine learning practice is not living up to the scientific standards of the fields where it is applied to and very often it's just used to get some papers published without actually bringing benefit to the field. In one example, he says one commonly used pneumonia data set turns out to be a pediatric collection of patients between one and five. So comparing that to adults with coronavirus infections is problematic to say the least you're far more likely to train the model to recognize children versus adults. In general, the studies fail in doing things like revealing key details about the training and experimental sets, not performing robustness or sensitivity analysis, not performing external validation work, not showing any confidence intervals, and many more. And being in the machine learning field, obviously, this is the case. So if you are looking to apply machine learning to any fields, that's not core machine learning, please get familiar with the common practices in that field to generate valid scientific contribution, though we all know that valid scientific contributions probably isn't the main motivation of most people doing these kinds of things. I love this comment by Derek Jones who says you have completely misunderstood the purpose of machine learning in academia, machine learning provides a means for people who don't know anything about a subject to publish papers in the field, all that's needed is some data some button pressing and the ability to convincingly sprout techno babble and getting lucky with reviewers couldn't agree more. Next news, Ping West writes that a Chinese AI lab challenges Google and open AI with a model of 1.75 trillion parameters, which is 10 times the size of open AI GPT three model, and we don't know too much about this model, it is apparently trained with pytorch, and uses a fast mixture of expert architecture, which allowed without to be trained on both supercomputers and regular GPUs with significantly more parameters, the mixture of experts architecture generally is more of a sparse architecture akin to Google switch transformers. So directly comparing the model size to GPT three is not exactly valid. But this model called Wudao is a multimodal model, and its individual parts can do things like caption generation, generating poetry and even generating images from a description. And in all of these things, they appear to outperform the current models that Google and open AI have right now. All this comes out of the Beijing Academy of Artificial Intelligence. And the researchers not only seek to build models for language and images, they say we are also building tian dao as a model for physics and Tian Yan as the model for life sciences, adding that the end game plan is to fuse all of them together, making AI not only work inside computers, but also cross the universe. Not sure what that means, but sounds exciting. Of course, we were already impressed when a team earlier this year out of Huawei released pangu alpha, which was slightly bigger than GPT three. But this here is of course another level, and we're excited to see what comes out of scaling models larger and larger. Alright, next the BBC writes, Google apologizes for ugliest Indian language search results. So there's this image going around tweet by PC Mohan, Googling ugliest language in India, the Google question answering system triggers and replies with apparently a language that exists there. Now not so long ago, all of us understood that Google is a search engine and gives you things that it finds on the web and that this here might just be a slight but humorous failure of technology, we would all sort of have a laugh about that, whether you spoke this language or not. But apparently in today's time, it is very fashionable to absolutely freak out when something like this happens and point out how valuable this language is that it has a long tradition and that is so harmful to the people who speak this language. And you just kind of have to ask yourself, what's up? Are people actually upset about this? Or are people just pretending to be upset about this and working themselves up because they can get some internet power from this? So I happen to have right here. Now actually, I happen to have here a bucket. And this pocket actually contains all the damage that was done by this search result. So if Oh, it's empty. Oh, so I mean, come on, what is this upset culture? I mean, even if this has upset someone, the ability of Google to quickly serve you this kind of information is, you know, pretty good. We recognize that, you know, sometimes it picks up something from the internet. And we all understand that this is not an authoritative answer. Don't pretend that this is somehow a source of truth. All right, let's try this out. Best machine learning framework. Apache Spark. Oh, wow. I didn't know. Well, my mind just changed. Craziest machine learning researcher. Jeff Hinton. Ha, who knew? Most handsome deep learning, learning researcher. Carpati. Now, of course, I'm not saying we should not criticize Google for doing things like this. Google has apologized and fixed it. But I do think there is a giant overreaction to these things and blowing out of proportion about actually how important this is. And also a real real overstatement of how many people are actually affected by this except for getting outraged on the internet. Next news, zdnet writes McDonald's wants to democratize machine learning for all users across its operations by users, they mean internal teams, so don't get confused. And by democratize, they apparently mean just apply. So in the quotes from the McDonald's execs, you'll find things like we want to enable more end to end automation and machine learning operations in general, and we want to continue to implement governance and also cost control measures in order to make sure that we're doing from the business perspective continues to make sense. And also the way we do is, is we bring all the data into an s3 bucket where data lake is enabled, which helps us to do data versioning and also build scalable and performance feature engineering pipelines in the platform. And further, we've not only identified the tools, the technology, we've done the legal paperwork, which can always be a hassle, but also identified use cases, built the models and deployed them. What are you doing? This is zero information. How can people say so much without saying anything at all in terms of content? So in the last paragraph, you'll actually find McDonald's will include carrying out very fine grain SQL level forecasting for its restaurants, automated marketing and personalization related activities beyond what he refers to as good machine learning for marketing. So they want to predict your behavior and want to sell you more stuff and want to use machine learning to give you diabetes faster. Why can't you just say this at the beginning? In any case, I wasn't aware that McDonald's was deep into machine learning, but obviously it makes sense, you know, good for them. Next up analytics insight rights AI is helping you make sense. AI is helping you make profits by predicting cryptocurrency prices, all the buzzwords in one thing artificial intelligence cryptocurrency latest news. Now the article is pretty short. But if I may brag for just a bit on our discord, you'll find a link in the description. We have had forever a community project channel called stock market prediction, I highly recommend you check that out because we've been doing that stuff for ages. If you've seen my AI generated music video or are in the space of generating images using the clip model, you love this trick. Aranko Matsuzaki writes that there is a simple hack. If you just add unreal engine to your text prompt, these systems tend to generate much higher quality images, for example, here looks really cool. So try it out or look at this thread. There are many more examples right here. And general I love how prompt engineering is really becoming something that people pay attention to. I think there's a lot of potential that is as of yet on tap. And in our last news, people are paying a lot of attention to Jacob Buckman's article, please commit more blatant academic fraud. Now of course, this is a bit of a sarcastic take on the recent news about collusion rings in ML, which we've covered in last week's ML news. Now I have to say since last week, I've had my ears a bit more open to these kinds of things. And I can promise you this happens much more often than you think. Now the point of this article claiming please commit more blatant academic fraud is to contrast it with the low level not so blatant academic fraud that the community is already doing day to day, such as cherry picking examples or not doing certain ablations, because you'll know they won't turn out well and all the things we generally do to get our papers accepted. He considers this as sort of a low key fraud indistinguishable from simple mistakes. And that's the reason we usually let it slip. And of course, this whole procedure of being sort of a little bit dishonest in your papers then gets into the broader culture and intensifies as more people need to publish papers in the same conferences. He says worst of all, because everybody is complicit in this subtle fraud, nobody's willing to acknowledge its existence, who would be such a hypocrite as to condemn in others behavior that they can clearly see in themselves. And with large respect, he actually does he calls out his own papers and claims that they are bulls**t. And I have to say I can claim the same thing about my own papers for the most part. And it's often the case that in a paper, you actually have a scientific contribution, there is something that may work in certain situations. But in order to get it published, you have to present it in such a way that is just absolutely unrealistic in how good it is and how absolutely zero criticisms against it you can have and that it works in all situations at all times. So the author finishes with the call to please commit more academic fraud because he argues that because the fraud is so blatant that we can't ignore it, this is the only chance of the community to actually do something against the widespread low key fraud. So once we pay attention to scientific malpractices, we have a chance to weed it out and get to a better place. So I think this is not going to happen. I think people will continue as is this is going on, as I said, more than you think the credibility of the whole field will just slowly fade away because more than half of all papers published at conferences have absolutely zero effect and so on. zero scientific credibility. The author here points out that readers of a paper have to become much more like reviewers questioning the paper analyzing it from a critical perspective instead of simply taking for granted that if it was published in a peer reviewed scientific conference, we can sort of get this as a seal of approval. And I fully agree. In fact, I think we should abolish the peer review at the conference or at least make it transparent. Absolutely. Surprised when people always call for more anonymity, more politics, more in transparency in this process, why not make everything open? Why not have everyone as a collective decide on what's valuable and what's not? If you're worried that the big names will get all the credit, they already do. So I highly invite you to check out the article right here. It's written in a fun way and it makes very good points. All right, this was it for this week's end. This week's ML news and no, this is not a weekly thing. This is not a regular thing. Stop telling me that stop telling me that this can be a regular thing. But I appreciate all the feedback we've got last week. Thanks to all the viewers. I hope this helps tell me if you would like to see more of whatever less of whatever and I'll see you next time. Thank you.
 The European Union seeks to regulate AI. Chinese researchers train a model 10 times as large as GPT-3.
 Google makes an oopsie and Jacob Buckman appeals to the community to please commit more academic fraud.
 This and much more in today's ML News. Have fun.
 So, Lawfare writes, the European Union unveils its proposals for the Artificial Intelligence Act seeking to regulate AI and harmful uses thereof.
 So what does this actually mean? First of all, how do they even define AI?
 They say, artificial intelligence systems means software that is developed with one or more of the techniques and approaches listed in Annex 1
 and can for a given set of human defined objectives generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with.
 In Annex 1, these things are described as either machine learning approaches, logic and knowledge based approaches or statistical approaches.
 So in essence, I think there is an easier name for all of this under one hat. It's called software.
 If you think that's a bit far reaching, don't be worried. European Union divides different AI applications into different categories of risk,
 ranging from minimal risk to unacceptable risk and prescribes different things you'll have to do if your application falls into any of those sections.
 For example, if you're in the high risk category, you have to do a conformity assessment, which either you can do yourself or you'll have to submit to some sort of regulatory body.
 Now rest assured that these regulatory bodies are of course not going to be staffed by lobbyists from the exact corporations that are going to apply for exceptions to the rules right here.
 If you're in the unacceptable risk category, which includes things like facial recognition and social scoring, you are prohibited from performing these things.
 Of course, there are going to be exceptions as well for things like law enforcement and so on.
 Safe to say in its quest to regulate everything under the sun, and if they could the sun itself, the European Union's regulations have always only brought benefit to humanity.
 I mean, aren't we all just so much better informed about how our data is used now that every single website has a yes, I accept the cookies banner that certainly helps your helping European Union.
 Thank you very much.
 So for now, this is a proposal, but safe to say the European Union will probably go forward with regulating AI in some capacity.
 In an article in Science Mag, Derek Lowy writes machine learning deserves better than this.
 Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans.
 In which the authors identify over 2000 studies of which they finally select 62 and say a review finds that none of the models identified are of potential clinical use due to methodological flaws and or underlying biases.
 There are Chloe elaborates on this and goes on a very good rant against how machine learning practice is not living up to the scientific standards of the fields where it is applied to and very often it's just used to get some papers published without actually bringing benefit to the field.
 In one example, he says one commonly used pneumonia data set turns out to be a pediatric collection of patients between one and five. So comparing that to adults with coronavirus infections is problematic to say the least you're far more likely to train the model to recognize children versus adults.
 In general, the studies fail in doing things like revealing key details about the training and experimental sets, not performing robustness or sensitivity analysis, not performing external validation work, not showing any confidence intervals, and many more.
 And being in the machine learning field, obviously, this is the case. So if you are looking to apply machine learning to any fields, that's not core machine learning, please get familiar with the common practices in that field to generate valid scientific contribution, though we all know that valid scientific contributions probably isn't the main motivation of most people doing these kinds of things.
 I love this comment by Derek Jones who says you have completely misunderstood the purpose of machine learning in academia, machine learning provides a means for people who don't know anything about a subject to publish papers in the field, all that's needed is some data some button pressing and the ability to convincingly sprout techno babble and getting lucky with reviewers couldn't agree more.
 Next news, Ping West writes that a Chinese AI lab challenges Google and open AI with a model of 1.75 trillion parameters, which is 10 times the size of open AI GPT three model, and we don't know too much about this model, it is apparently trained with pytorch, and uses a fast mixture of expert architecture, which allowed
 without to be trained on both supercomputers and regular GPUs with significantly more parameters, the mixture of experts architecture generally is more of a sparse architecture akin to Google switch transformers. So directly comparing the model size to GPT three is not exactly valid. But this model called
 Wudao is a multimodal model, and its individual parts can do things like caption generation, generating poetry and even generating images from a description. And in all of these things, they appear to outperform the current models that Google and open AI have right now. All this comes out of the
 Beijing Academy of Artificial Intelligence. And the researchers not only seek to build models for language and images, they say we are also building tian dao as a model for physics and Tian Yan as the model for life sciences, adding that the end game plan is to fuse all of them together, making AI not only work inside computers, but also
 cross the universe. Not sure what that means, but sounds exciting. Of course, we were already impressed when a team earlier this year out of Huawei released pangu alpha, which was slightly bigger than GPT three. But this here is of course another level, and we're excited to see what comes out of scaling models larger
 and larger. Alright, next the BBC writes, Google apologizes for ugliest Indian language search results. So there's this image going around tweet by PC Mohan, Googling ugliest language in India, the Google question answering system triggers and replies with apparently a language
 that exists there. Now not so long ago, all of us understood that Google is a search engine and gives you things that it finds on the web and that this here might just be a slight but humorous failure of technology, we would all sort of have a laugh about that, whether you spoke this
 language or not. But apparently in today's time, it is very fashionable to absolutely freak out when something like this happens and point out how valuable this language is that it has a long tradition and that is so harmful to the people who speak this language. And you just kind of
 have to ask yourself, what's up? Are people actually upset about this? Or are people just pretending to be upset about this and working themselves up because they can get some internet power from this? So I happen to have right here. Now actually, I happen to have here a
 bucket. And this pocket actually contains all the damage that was done by this search result. So if Oh, it's empty. Oh, so I mean, come on, what is this upset culture? I mean, even if this has upset someone, the ability of Google to quickly serve you this kind of
 information is, you know, pretty good. We recognize that, you know, sometimes it picks up something from the internet. And we all understand that this is not an authoritative answer. Don't pretend that this is somehow a source of truth. All right, let's try this out.
 Best machine learning framework. Apache Spark. Oh, wow. I didn't know. Well, my mind just changed. Craziest machine learning researcher. Jeff Hinton. Ha, who knew? Most handsome deep learning, learning researcher.
 Carpati. Now, of course, I'm not saying we should not criticize Google for doing things like this. Google has apologized and fixed it. But I do think there is a giant overreaction to these things and blowing out of proportion about actually how important this is. And also a real
 real overstatement of how many people are actually affected by this except for getting outraged on the internet. Next news, zdnet writes McDonald's wants to democratize machine learning for all users across its operations by users, they mean internal teams, so don't get
 confused. And by democratize, they apparently mean just apply. So in the quotes from the McDonald's execs, you'll find things like we want to enable more end to end automation and machine learning operations in general, and we want to continue to implement
 governance and also cost control measures in order to make sure that we're doing from the business perspective continues to make sense. And also the way we do is, is we bring all the data into an s3 bucket where data lake is enabled, which helps us to do data
 versioning and also build scalable and performance feature engineering pipelines in the platform. And further, we've not only identified the tools, the technology, we've done the legal paperwork, which can always be a hassle, but also identified use cases, built
 the models and deployed them. What are you doing? This is zero information. How can people say so much without saying anything at all in terms of content? So in the last paragraph, you'll actually find McDonald's will include carrying out very fine grain
 SQL level forecasting for its restaurants, automated marketing and personalization related activities beyond what he refers to as good machine learning for marketing. So they want to predict your behavior and want to sell you more stuff and want to use machine learning to give you diabetes faster. Why can't you just say this at the beginning? In any case, I wasn't aware that McDonald's was deep into machine learning, but obviously it makes sense, you know, good for them. Next up analytics insight rights AI is helping you make sense.
 AI is helping you make profits by predicting cryptocurrency prices, all the buzzwords in one thing artificial intelligence cryptocurrency latest news. Now the article is pretty short. But if I may brag for just a bit on our discord, you'll find a link in the description. We have had forever a community project channel called stock
 market prediction, I highly recommend you check that out because we've been doing that stuff for ages.
 If you've seen my AI generated music video or are in the space of generating images using the clip model, you love this trick. Aranko Matsuzaki writes that there is a simple hack. If you just add unreal engine to your text prompt, these systems tend to generate much higher quality images, for example, here looks really cool. So try it out or look at this thread. There are many more examples right here. And general I love how prompt engineering is really becoming something that people
 pay attention to. I think there's a lot of potential that is as of yet on tap. And in our last news, people are paying a lot of attention to Jacob Buckman's article, please commit more blatant academic fraud. Now of course, this is a bit of a sarcastic take on the recent news about collusion rings in ML, which we've covered in last week's ML news. Now I have to say since last week, I've had my ears a bit more open to these kinds of
 things. And I can promise you this happens much more often than you think. Now the point of this article claiming please commit more blatant academic fraud is to contrast it with the low level not so blatant academic fraud that the community is already doing day to day, such as cherry picking
 examples or not doing certain ablations, because you'll know they won't turn out well and all the things we generally do to get our papers accepted. He considers this as sort of a low key fraud indistinguishable from simple mistakes. And that's the reason we usually let it slip. And of course, this whole procedure of being
 sort of a little bit dishonest in your papers then gets into the broader culture and intensifies as more people need to publish papers in the same conferences. He says worst of all, because everybody is complicit in this subtle fraud, nobody's willing to acknowledge its
 existence, who would be such a hypocrite as to condemn in others behavior that they can clearly see in themselves. And with large respect, he actually does he calls out his own papers and claims that they are bulls**t. And I have to say I can claim the same thing about my own papers for the most part. And it's often the case that in a paper, you actually have a scientific contribution, there is something that may work in certain situations. But in order to get it published,
 you have to present it in such a way that is just absolutely unrealistic in how good it is and how absolutely zero criticisms against it you can have and that it works in all situations at all times. So the author finishes with the call to please commit more academic fraud because he argues that because the fraud is so blatant that we can't ignore it, this is the only chance of the community to actually do something against the
 widespread low key fraud. So once we pay attention to scientific malpractices, we have a chance to weed it out and get to a better place. So I think this is not going to happen. I think people will continue as is this is going on, as I said, more than you think the credibility of the whole field will just slowly fade away because more than half of all papers published at conferences have absolutely zero effect and so on.
 zero scientific credibility. The author here points out that readers of a paper have to become much more like reviewers questioning the paper analyzing it from a critical perspective instead of simply taking for granted that if it was published in a peer reviewed scientific conference, we can sort of get this as a seal of approval. And I fully agree. In fact, I think we should abolish the peer review at the conference or at least make it transparent. Absolutely.
 Surprised when people always call for more anonymity, more politics, more in transparency in this process, why not make everything open? Why not have everyone as a collective decide on what's valuable and what's not? If you're worried that the big names will get all the credit, they already do. So I highly invite you to check out the article right here. It's written in a fun way and it makes very good points. All right, this was it for this week's end.
 This week's ML news and no, this is not a weekly thing. This is not a regular thing. Stop telling me that stop telling me that this can be a regular thing. But I appreciate all the feedback we've got last week. Thanks to all the viewers. I hope this helps tell me if you would like to see more of whatever less of whatever and I'll see you next time.
 Thank you.