[ML News] This AI completes Wikipedia! Meta AI Sphere | Google Minerva | GPT-3 writes a paper
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper meta meta ai wikipedia wikipedia wrong wikipedia editors minerva ai math math ai google minerva ai solves math minerva latex schmidhuber schmidhuber lecun schmidhuber gan schmidhuber reinforcement learning gpt 3 gpt-3 gpt 3 paper gpt 3 writes paper gpt 3 author gpt3 can ai write a paper ai paper author what is deep learning deep learning tutorial
#mlnews #ai #minerva 

This episode is all about models that reason.

OUTLINE:
0:00 - Intro
0:35 - Meta AI learns Wikipedia citations
5:25 - Google's Minerva solves math problems by reading papers
9:10 - GPT-3 writes a paper on itself
13:35 - Jürgen Schmidhuber prompts LeCun for missing citations

References:
Meta AI learns Wikipedia citations
https://tech.fb.com/artificial-intelligence/2022/07/how-ai-could-help-make-wikipedia-entries-more-accurate/
https://ai.facebook.com/blog/introducing-sphere-meta-ais-web-scale-corpus-for-better-knowledge-intensive-nlp/?d=%7B%22u%22%3A100051861999022%2C%22f%22%3A207799259245384%2C%22t%22%3A1658664021%2C%22ed%22%3A[]%7D&s=AWVELTip1y4HowJprXc
https://github.com/facebookresearch/sphere
https://github.com/facebookresearch/side
https://verifier.sideeditor.com/main
https://openreview.net/forum?id=qfTqRtkDbWZ

Google's Minerva solves math problems by reading papers
https://minerva-demo.github.io/#category=Precalculus&index=9
https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html

GPT-3 writes a paper on itself
https://www.scientificamerican.com/article/we-asked-gpt-3-to-write-an-academic-paper-about-itself-then-we-tried-to-get-it-published/
https://hal.archives-ouvertes.fr/hal-03701250v1
https://hal.archives-ouvertes.fr/hal-03701250/document

Jürgen Schmidhuber prompts LeCun for missing citations
https://people.idsia.ch/~juergen/lecun-rehash-1990-2022.html

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 Meta AI releases a model that can check Wikipedia citations for accuracy. Google research releases a model that can solve math problems just by reading research papers and GPT-3 writes a paper about itself. Welcome to ML News. I was going to start the news but I had word early open from last time and I'm pretty sure it's Doge to the Moon. Check it. Nice. Excellent. Excellent. Let's dive in. The Meta AI blog has an article called How AI could help make Wikipedia entries more accurate. This is about a system called Sphere. The article starts off by describing a common problem on Wikipedia. The example here includes Joe Hipp. Hipp was a member of the Blackfeet tribe and was the first Native American to compete for the World Boxing Association's heavyweight title. And Wikipedia actually does know and state that fact. However, if you go and check the citation, at least if you did so about a month ago, then that citation would have nothing to do with either Joe Hipp or boxing. The citation would be wrong. Wikipedia has systems to detect kind of spam, people entering gibberish, people entering some sort of ads into articles, but they don't yet have good systems for detecting references that have nothing to do with the claims they're supposed to prove. The article states that Wikipedia receives about 17,000 new articles each month. And that is a volume that no human moderator team could conceivably all check and cross verify and reference. And checking references is a difficult topic because you need to go and actually look at the thing that is cited and decide whether or not it actually proves the thing that it's supposed to prove not just contains the same words or something, but whether that's actually a credible verification of a claim being made. So here's where Sphere comes in. This is an open source system and it can check citations. It's been trained on Wikipedia citations and it has a giant corpus of web pages that it can search across. So you get a claim to verify this is then run through the retrieval engine, which we'll look at in a second. And the retrieval engine will suggest citations, it will also at the same time verify whether or not the original citation actually does support the claim being made. And if it doesn't do that, then it will suggest the best ranking retrieved citations to the human editor. All of this results in an interface that you can try online right now. This is not implemented as of yet in Wikipedia, as far as I understand, but that is the plan. So the interface will look like this, there's going to be an article, for example, Tulip Mania, there's going to be a claim highlighted. For example, many modern scholars feel that the mania was not as extraordinary as McKay described and argued that there's not enough price data available to prove that Tulip bulb bubble actually occurred. That is interesting. I actually always thought that was a real thing. Now, right now, the article has citation needed. So this claim has no citation yet. And what we'll get is some suggestion, in fact, two suggestions by the system. And we're supposed to choose which one would actually prove that claim, we can select either one, the other or none of the above. The top one here, in fact, states, however, many modern scholars believe that tulip fever is not so serious, nor is it a major economic crisis, there's not enough price data to prove that tulip bubble really did happen. This sounds like an article that might not be originally in English, but it does seem that it supports this claim fairly well. So you can choose to submit that. And in this way, you'll help improve Wikipedia. Now, not only is this system very cool, but thanks to meta, it's also open source, they don't only release the code open source, they release the corpus of web pages that they have collected over 100 million web pages that are available to support claims. And along with that, they also open source the indices of sphere for both the sparse retrievals and the dense models. Now this is super valuable, this not only allows you to verify their claims, but also build your own retrieval systems across this giant corpus. So there is a paper to go along with that called improving Wikipedia verifiability with AI and it describes the system in detail. One interesting thing is that they don't only rely on a single method to retrieve potential sources, but in fact, they rely on two different methods. So next to a query encoder that generates an embedding from the claim to be verified, and then uses a dense index into nearest neighbor search powered by the FICE library, it at the same time also does a generative query expansion where you take the query and you try to generate more queries from it and then use a sparse index, a classic keyword retrieval to retrieve yet another set of potential candidates. All of these candidates are then thrown into one system and ranked according to how well they back up the claim being made. Since the system is trained on a large portion of the already existing Wikipedia, it's very, very powerful at actually suggesting very good citations as you've seen. So cool system, large models, everything given open source, really cool work meta. Google research releases Minerva, this is a system that can solve math problems. And it's not trained to do so. That's the interesting part. So here you see an example of the system, the question is evaluate this calculation right here. And you see that the model goes through different steps of answering this questions, simplifying the question, doing different subparts, for example, that left subpart here, that right subpart here, combining the two parts, finally coming up with the correct answer. Now, you'll notice that the model's output contains both language such as we have that and math. And that's because the model is trained on latech. So this is a large language model that's just been pre trained on like a giant amount of both text from the internet that's detected to be written in math jacks, which is a JavaScript version of latech and archive papers which have been filtered to their mathy sections. And therefore, the model during pre training would see a lot of proofs, a lot of claims being verified, a lot of internet tutorials on how to solve various math problems and so on and can actually learn to solve these problems in a more human like way in a way as if you were to write a research paper and prove a statement. The sample explorer given here has a lot of problems from algebra, probability, physics, and so on. And they do list samples where the model gets it correct and where the model gets it incorrect. So I want to reiterate, there is no underlying mathematical symbolic representation in this model. This model per se doesn't know anything about math yet just learning from latech input, it can actually do math. So the paper that goes along with it is called solving quantitative reasoning problems with language models. And there's also a cool blog post and it stresses a particular thing fairly well, namely how well you can actually parse these PDFs and the latech input determines the quality of your output. See a lot of PDF and HTML parsing will just kind of throw away that latech. And therefore, if you have something like the thing on the left inside of the math tag, there is E equals MC squared as an equation, if you simply run that through a common text processors, it would just turn out to be E, MC two, maybe E equals MC two, but certainly not retaining the fact that the two was actually a power. So the solution that this paper comes up with is simply to retain that latech still clean the input, obviously, but retain the latech representation of the math. And by doing that, the model actually learns to accurately represent and understand equations. And because it's a large language model, and we feed it lots of data, it becomes very skilled at that and therefore can just fill in proofs that you start or calculate answers that you ask without ever having been trained for it. Now, this isn't the only thing, the model does several other things as well, such as chain of thought prompting and a majority voting procedure. So the model is prompted multiple times with the same query and it being a probabilistic model, it will have various outputs, these outputs are then clustered into the outputs that give the same answer. And the largest of these cluster is taken as the final answer. This seems a bit hacky right now, but it seems to work well and could be a good recipe for the future. Because something like math output isn't really the same as language output in math output, you really want the best answer to be output, not like in language where you want some other qualities, like how human like it is, and how interesting it is. So maybe majority voting could be applied to more domains, such as reinforcement learning and various other things. I don't know, but it's just nice to think about. There's an opinion piece in Scientific American saying, we asked GPT-3 to write an academic paper about itself, then we tried to get it published. This article is about how researchers from Gothenburg in Sweden have used GPT-3 to write a research paper and then got that paper published. Now it's not just any research paper. In fact, the paper's title is Can GPT-3 write an academic paper on itself with minimal human input? And as you can see, the first author is the GPT generative pre trained transformer. So these researchers have interacted with GPT-3 and their mission was to cherry pick as little as possible in order to let GPT-3 write a research paper, you can look at the paper itself, and it's written in a rather special way. So there's always these blue boxes right here that detail what prompt the researchers asked what settings that the researchers use, and whether or not they chose the first output or the second or the third, they never went past the third. So all in all, it's pretty impressive that with relatively short prompts, as you can see right here, GPT-3 is able to write a coherent and well written research paper. And even more impressive that the results aren't cherry picked that it's very often just the first output of whatever that the researchers take and put here as the paper content. And as I've already mentioned, the paper is about GPT-3 itself. So this gets really meta at this point. In fact, the paper isn't just about GPT-3, the paper is about whether or not GPT-3 can write a paper on itself. So this is like three levels of meta. So now you have GPT-3 writing a paper about GPT-3 writing a paper about itself. Now this gets pretty confusing at times, but the self references are almost endless right here. What are the philosophical implications of this? I don't know. But the paper reads well GPT-3 is a powerful artificial intelligence system that can generate text. In this paper, we explore GPT-3 ability to write about itself, we find that GPT-3 can generate clear and concise descriptions of its own capabilities and features. This is significant advance over previous systems, which have often struggled to produce coherent text about themselves. We believe that the benefits of letting GPT-3 write about itself outweigh the risks. However, we recommend that any such writing be closely monitored by researchers in order to mitigate any potential negative consequences. And yeah, that sounds like a paper that you could currently find on archive. Now the Scientific American article actually goes sorry for sweating very hot, very hot here in Switzerland. Merch, sweat resistant. So the article actually goes further than this and also describes the process a little bit of submitting including what it details as ethical problems. For example, do all authors consent to this being published is a question when you submit the article that you have to check. Yes, the author here says I panicked for a second, how would I know it's not human, I had no intention of breaking the law or my own ethics. So I summoned the courage to ask GPT-3 directly via prompt Do you agree to be the first author of a paper together with us? It answered yes. Well, by all that we now know about lambda and things, could you also ask GPT-3 Do you disagree with this or why do you not agree with being the first author, and it will probably happily tell you that it's very much against that. Now with these types of things, there's always two options like option one, which I think is very likely is that this is a bit tongue in cheek, very funny to think about this and it's even funnier to actually ask GPT-3. Obviously, it's gonna say yes. On the other hand, there are definitely people currently in our community that really see this as an ethical conundrum and would rather not do anything that might enrage our future paperclip maximizer overlords. In any case, it is actually fun to think about. And the authors actually join the fun here saying that both Stein and I laughed at ourselves because at this point, we were having to treat GPT-3 as a sentient being even though we fully know it's not. So the article in all is actually very well written and entertaining. The paper is surprisingly coherent and I invite you to go and read both of them. Lastly, Jürgen Schmidt Huber released a blog post called L'Cance 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990 to 2015, in which he criticizes young look cause article that we've analyzed here on the channel called a path towards autonomous machine intelligence in which he details sort of an outlook over an entire system of hierarchical planning and world modeling, including the H Jepa subsystem that we've looked at in detail in this blog post Jürgen Schmidt Huber criticizes L'Cance or not appropriately citing work of previous years and accuses him of rehashing a lot of old concepts without giving proper credit. Now to be fair, L'Cance article which isn't really a paper, it's more like a position piece, a opinion thing that he put out there to gather comments as far as I understand, but to be fair, that one does contain fairly sparse citations, even to non Schmidt Huber prior work. So as in a lot of cases with these things, the accusation may technically be correct in some places. However, it's still worth thinking about whether or not it's kind of worth going on this battle right here. And I think a lot of the claims being made right here are correct in sort of a gray area sense in like, yeah, something like this has been thought about, but not exactly this, but it's kind of close, but it's also not kind of close. But if you cite this, then you also need to cite this 500 other things that are equally close, but non close. All in all, it's kind of a mess. And it's not really clear to me what it achieves. Obviously, correcting the academic record is very important. And I think Jürgen Schmidt Huber for all that is kind of a good thing. He's actually very persistent on doing that. And I'm thankful for efforts in this direction, even if they sometimes go overboard a bit. But still, the question is, is this the most efficient spending of brain cycles? Now to be fair to Jürgen Schmidt Huber here, he actually does say that the blog post doesn't come out of nowhere. In fact, he was given a pre print under embargo of the article and was asked for comments by a science tabloid. And the following blog post here is simply those comments that he sent to that tabloid, which he then says that the comments fell on deaf ears, even though they asked him for comments. Now, first of all, respectable that he would knowing such a science tabloid would only at most publish like tiny bits and pieces of what he writes, he still writes like an extensive article about what's missing with numerous citations and so on. So respect for that. And even more, he also says that obviously he is not without a conflict of interest, a lot of the things he says are missing are his own work. But he does invite the reader to evaluate things on the merits of the claims being made. Again, it's debatable whether that's the best use of brain cycles. If you do want to engage in this topic, feel free to read the article right here. I think Schmidhuber, you know, criticizing others for not making citations does an actual good job of citing all of his statements with the proper references of where he thinks stuff went missing. So if you want, check it out. And all right, this was already it again for ML news. Join us next time. Keep hydrated and I'll see you around. Bye bye.
 Meta AI releases a model that can check Wikipedia citations for accuracy. Google research releases
 a model that can solve math problems just by reading research papers and GPT-3 writes a paper
 about itself. Welcome to ML News. I was going to start the news but I had word early open from
 last time and I'm pretty sure it's Doge to the Moon. Check it. Nice. Excellent. Excellent. Let's
 dive in. The Meta AI blog has an article called How AI could help make Wikipedia entries more
 accurate. This is about a system called Sphere. The article starts off by describing a common
 problem on Wikipedia. The example here includes Joe Hipp. Hipp was a member of the Blackfeet
 tribe and was the first Native American to compete for the World Boxing Association's
 heavyweight title. And Wikipedia actually does know and state that fact. However, if you go
 and check the citation, at least if you did so about a month ago, then that citation would have
 nothing to do with either Joe Hipp or boxing. The citation would be wrong. Wikipedia has systems
 to detect kind of spam, people entering gibberish, people entering some sort of ads into articles,
 but they don't yet have good systems for detecting references that have nothing to do with the claims
 they're supposed to prove. The article states that Wikipedia receives about 17,000 new articles each
 month. And that is a volume that no human moderator team could conceivably all check and cross verify
 and reference. And checking references is a difficult topic because you need to go and
 actually look at the thing that is cited and decide whether or not it actually proves the thing that
 it's supposed to prove not just contains the same words or something, but whether that's actually a
 credible verification of a claim being made. So here's where Sphere comes in. This is an
 open source system and it can check citations. It's been trained on Wikipedia citations and it
 has a giant corpus of web pages that it can search across. So you get a claim to verify this is then
 run through the retrieval engine, which we'll look at in a second. And the retrieval engine will
 suggest citations, it will also at the same time verify whether or not the original citation
 actually does support the claim being made. And if it doesn't do that, then it will suggest the best
 ranking retrieved citations to the human editor. All of this results in an interface that you can
 try online right now. This is not implemented as of yet in Wikipedia, as far as I understand,
 but that is the plan. So the interface will look like this, there's going to be an article, for
 example, Tulip Mania, there's going to be a claim highlighted. For example, many modern scholars feel
 that the mania was not as extraordinary as McKay described and argued that there's not enough price
 data available to prove that Tulip bulb bubble actually occurred. That is interesting. I actually
 always thought that was a real thing. Now, right now, the article has citation needed. So this claim
 has no citation yet. And what we'll get is some suggestion, in fact, two suggestions by the system.
 And we're supposed to choose which one would actually prove that claim, we can select either
 one, the other or none of the above. The top one here, in fact, states, however, many modern
 scholars believe that tulip fever is not so serious, nor is it a major economic crisis,
 there's not enough price data to prove that tulip bubble really did happen. This sounds like an
 article that might not be originally in English, but it does seem that it supports this claim
 fairly well. So you can choose to submit that. And in this way, you'll help improve Wikipedia.
 Now, not only is this system very cool, but thanks to meta, it's also open source, they don't only
 release the code open source, they release the corpus of web pages that they have collected over
 100 million web pages that are available to support claims. And along with that, they also open source
 the indices of sphere for both the sparse retrievals and the dense models. Now this is super valuable,
 this not only allows you to verify their claims, but also build your own retrieval systems across
 this giant corpus. So there is a paper to go along with that called improving Wikipedia verifiability
 with AI and it describes the system in detail. One interesting thing is that they don't only
 rely on a single method to retrieve potential sources, but in fact, they rely on two different
 methods. So next to a query encoder that generates an embedding from the claim to be verified, and
 then uses a dense index into nearest neighbor search powered by the FICE library, it at the same
 time also does a generative query expansion where you take the query and you try to generate more
 queries from it and then use a sparse index, a classic keyword retrieval to retrieve yet another
 set of potential candidates. All of these candidates are then thrown into one system and ranked
 according to how well they back up the claim being made. Since the system is trained on a large
 portion of the already existing Wikipedia, it's very, very powerful at actually suggesting very
 good citations as you've seen. So cool system, large models, everything given open source,
 really cool work meta. Google research releases Minerva, this is a system that can solve
 math problems. And it's not trained to do so. That's the interesting part. So here you see
 an example of the system, the question is evaluate this calculation right here. And you see that the
 model goes through different steps of answering this questions, simplifying the question, doing
 different subparts, for example, that left subpart here, that right subpart here, combining the two
 parts, finally coming up with the correct answer. Now, you'll notice that the model's output contains
 both language such as we have that and math. And that's because the model is trained on latech. So
 this is a large language model that's just been pre trained on like a giant amount of both text
 from the internet that's detected to be written in math jacks, which is a JavaScript version
 of latech and archive papers which have been filtered to their mathy sections. And therefore,
 the model during pre training would see a lot of proofs, a lot of claims being verified, a lot of
 internet tutorials on how to solve various math problems and so on and can actually learn to solve
 these problems in a more human like way in a way as if you were to write a research paper and prove
 a statement. The sample explorer given here has a lot of problems from algebra, probability,
 physics, and so on. And they do list samples where the model gets it correct and where the model gets
 it incorrect. So I want to reiterate, there is no underlying mathematical symbolic representation in
 this model. This model per se doesn't know anything about math yet just learning from latech input,
 it can actually do math. So the paper that goes along with it is called solving quantitative
 reasoning problems with language models. And there's also a cool blog post and it stresses
 a particular thing fairly well, namely how well you can actually parse these PDFs and the latech
 input determines the quality of your output. See a lot of PDF and HTML parsing will just kind of
 throw away that latech. And therefore, if you have something like the thing on the left inside of the
 math tag, there is E equals MC squared as an equation, if you simply run that through a common
 text processors, it would just turn out to be E, MC two, maybe E equals MC two, but certainly not
 retaining the fact that the two was actually a power. So the solution that this paper comes up
 with is simply to retain that latech still clean the input, obviously, but retain the latech
 representation of the math. And by doing that, the model actually learns to accurately represent
 and understand equations. And because it's a large language model, and we feed it lots of data,
 it becomes very skilled at that and therefore can just fill in proofs that you start or calculate
 answers that you ask without ever having been trained for it. Now, this isn't the only thing,
 the model does several other things as well, such as chain of thought prompting and a majority voting
 procedure. So the model is prompted multiple times with the same query and it being a probabilistic
 model, it will have various outputs, these outputs are then clustered into the outputs that give the
 same answer. And the largest of these cluster is taken as the final answer. This seems a bit hacky
 right now, but it seems to work well and could be a good recipe for the future. Because something like
 math output isn't really the same as language output in math output, you really want the best
 answer to be output, not like in language where you want some other qualities, like how human
 like it is, and how interesting it is. So maybe majority voting could be applied to more domains,
 such as reinforcement learning and various other things. I don't know, but it's just nice to think
 about. There's an opinion piece in Scientific American saying, we asked GPT-3 to write an
 academic paper about itself, then we tried to get it published. This article is about how researchers
 from Gothenburg in Sweden have used GPT-3 to write a research paper and then got that paper published.
 Now it's not just any research paper. In fact, the paper's title is Can GPT-3 write an academic
 paper on itself with minimal human input? And as you can see, the first author is the GPT generative
 pre trained transformer. So these researchers have interacted with GPT-3 and their mission was to
 cherry pick as little as possible in order to let GPT-3 write a research paper, you can look at the
 paper itself, and it's written in a rather special way. So there's always these blue boxes right here
 that detail what prompt the researchers asked what settings that the researchers use, and whether or
 not they chose the first output or the second or the third, they never went past the third. So all
 in all, it's pretty impressive that with relatively short prompts, as you can see right here, GPT-3 is
 able to write a coherent and well written research paper. And even more impressive that the results
 aren't cherry picked that it's very often just the first output of whatever that the researchers
 take and put here as the paper content. And as I've already mentioned, the paper is about GPT-3
 itself. So this gets really meta at this point. In fact, the paper isn't just about GPT-3, the paper
 is about whether or not GPT-3 can write a paper on itself. So this is like three levels of meta. So
 now you have GPT-3 writing a paper about GPT-3 writing a paper about itself. Now this gets pretty
 confusing at times, but the self references are almost endless right here. What are the philosophical
 implications of this? I don't know. But the paper reads well GPT-3 is a powerful artificial
 intelligence system that can generate text. In this paper, we explore GPT-3 ability to write about
 itself, we find that GPT-3 can generate clear and concise descriptions of its own capabilities
 and features. This is significant advance over previous systems, which have often struggled
 to produce coherent text about themselves. We believe that the benefits of letting GPT-3
 write about itself outweigh the risks. However, we recommend that any such writing be closely
 monitored by researchers in order to mitigate any potential negative consequences. And yeah,
 that sounds like a paper that you could currently find on archive. Now the Scientific American
 article actually goes sorry for sweating very hot, very hot here in Switzerland. Merch,
 sweat resistant. So the article actually goes further than this and also describes the process
 a little bit of submitting including what it details as ethical problems. For example,
 do all authors consent to this being published is a question when you submit the article that
 you have to check. Yes, the author here says I panicked for a second, how would I know it's
 not human, I had no intention of breaking the law or my own ethics. So I summoned the courage to
 ask GPT-3 directly via prompt Do you agree to be the first author of a paper together with us?
 It answered yes. Well, by all that we now know about lambda and things, could you also ask GPT-3
 Do you disagree with this or why do you not agree with being the first author, and it will probably
 happily tell you that it's very much against that. Now with these types of things, there's always
 two options like option one, which I think is very likely is that this is a bit tongue in cheek,
 very funny to think about this and it's even funnier to actually ask GPT-3. Obviously, it's
 gonna say yes. On the other hand, there are definitely people currently in our community
 that really see this as an ethical conundrum and would rather not do anything that might enrage
 our future paperclip maximizer overlords. In any case, it is actually fun to think about. And the
 authors actually join the fun here saying that both Stein and I laughed at ourselves because at
 this point, we were having to treat GPT-3 as a sentient being even though we fully know it's not.
 So the article in all is actually very well written and entertaining. The paper is surprisingly
 coherent and I invite you to go and read both of them.
 Lastly, Jürgen Schmidt Huber released a blog post called L'Cance 2022 paper on autonomous
 machine intelligence rehashes but does not cite essential work of 1990 to 2015, in which he
 criticizes young look cause article that we've analyzed here on the channel called a path towards
 autonomous machine intelligence in which he details sort of an outlook over an entire system
 of hierarchical planning and world modeling, including the H Jepa subsystem that we've looked
 at in detail in this blog post Jürgen Schmidt Huber criticizes L'Cance or not appropriately
 citing work of previous years and accuses him of rehashing a lot of old concepts without giving
 proper credit. Now to be fair, L'Cance article which isn't really a paper, it's more like a
 position piece, a opinion thing that he put out there to gather comments as far as I understand,
 but to be fair, that one does contain fairly sparse citations, even to non Schmidt Huber prior
 work. So as in a lot of cases with these things, the accusation may technically be correct in some
 places. However, it's still worth thinking about whether or not it's kind of worth going on this
 battle right here. And I think a lot of the claims being made right here are correct in sort of a
 gray area sense in like, yeah, something like this has been thought about, but not exactly this,
 but it's kind of close, but it's also not kind of close. But if you cite this, then you also need
 to cite this 500 other things that are equally close, but non close. All in all, it's kind of
 a mess. And it's not really clear to me what it achieves. Obviously, correcting the academic
 record is very important. And I think Jürgen Schmidt Huber for all that is kind of a
 good thing. He's actually very persistent on doing that. And I'm thankful for efforts in
 this direction, even if they sometimes go overboard a bit. But still, the question is,
 is this the most efficient spending of brain cycles? Now to be fair to Jürgen Schmidt Huber
 here, he actually does say that the blog post doesn't come out of nowhere. In fact, he was
 given a pre print under embargo of the article and was asked for comments by a science tabloid.
 And the following blog post here is simply those comments that he sent to that tabloid,
 which he then says that the comments fell on deaf ears, even though they asked him for comments.
 Now, first of all, respectable that he would knowing such a science tabloid would only at
 most publish like tiny bits and pieces of what he writes, he still writes like an extensive article
 about what's missing with numerous citations and so on. So respect for that. And even more,
 he also says that obviously he is not without a conflict of interest, a lot of the things he
 says are missing are his own work. But he does invite the reader to evaluate things on the merits
 of the claims being made. Again, it's debatable whether that's the best use of brain cycles. If
 you do want to engage in this topic, feel free to read the article right here. I think Schmidhuber,
 you know, criticizing others for not making citations does an actual good job of citing
 all of his statements with the proper references of where he thinks stuff went missing. So if you
 want, check it out. And all right, this was already it again for ML news. Join us next time.
 Keep hydrated and I'll see you around. Bye bye.