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This will come across as vituperative and I guess it is a bit but I've interacted with Gwern on this forum and the interaction that has stuck to me is in this t

This will come across as vituperative and I guess it is a bit but I've interacted with Gwern on this forum and the interaction that has stuck to me is in this thread, where Gwern mistakes a^nb^n as a regular (but not context-free) language (and calls my comment "not even wrong"): https://news.ycombinator.com/item?id=21559620 https://news.ycombinator.com/item?id=21559620 Again I'm sorry for the negativity, but already at the time Gwern was held up by a certain, large, section of the community as an important influencer in AI. For me that's just a great example of how basically the vast majority of AI influencers (who vie for influence on social media, rather than research) are basically clueless about AI and CS and only have second-hand knowledge, which I guess they're good at organising and popularising, but not more than that. It's easy to be a cheer leader for the mainstream view on AI. The hard part is finding, and following, unique directions. With apologies again for the negative slant of the comment.

aubanel 2y ago
> For me that's just a great example of how basically the vast majority of AI influencers (who vie for influence on social media, rather than research) are basically clueless about AI and CS This is a bit stark: there are many great knowledgeable engineers and scientists who would not get your point about a^nb^n. It's impossible to know 100% of of such a wide area as "AI and CS".
- nocobot 2y ago
  is it really? this is the most common example for context free languages and something most first year CS students will be familiar with. totally agree that you can be a great engineer and not be familiar with it, but seems weird for an expert in the field to confidently make wrong statements about this.
  - YeGoblynQueenne 2y ago
    Thanks, that's what I meant. a^nb^n is a standard test of learnability. That stuff is still absolutely relevant, btw. Some DL people like to dismiss it as irrelevant but that's just because they lack the background to appreciate why it matters. Also: the arrogance of youth (hey I've already been a postdoc for a year, I'm ancient). Here's a recent paper on Neural Networks and the Chomsky Hierarchy that tests RNNs and Transformers on formal languages (I think it doesn't test on a^nb^n directly but tests similar a-b based CF languages): https://arxiv.org/abs/2207.02098 https://arxiv.org/abs/2207.02098 And btw that's a good paper. Probably one of the most satisfying DL papers I've read in recent years. You know when you read a paper and you get this feeling of satiation, like "aaah, that hit the spot"? That's the kind of paper.
    - GistNoesis 2y ago
      a^nb^n can definitely be expressed and recognized with a transformer. A transformer (with relative invariant positional embedding) has full context so can see the whole sequence. It just has to count and compare. To convince yourself, construct the weights manually. First layer : zeros the character which are equal to the previous character. Second layer : Build a feature to detect and extract the position embedding of the first a. a second feature to detect and extract the position embedding of the last a, a third feature to detect and extract the position embedding of the first b, a fourth feature to detect and extract the position embedding of the last b, Third layer : on top that check whether (second feature - first feature) == (fourth feature - third feature). The paper doesn't distinguish between what is the expressive capability of the model, and the finding the optimum of the model, aka the training procedure. If you train by only showing example with varying n, there probably isn't inductive bias to make it converge naturally towards the optimal solution you can construct by hand. But you can probably train multiple formal languages simultaneously, to make the counting feature emerge from the data. You can't deduce much from negative results in research beside it requiring more work.
      - YeGoblynQueenne 2y ago
        >> The paper doesn't distinguish between what is the expressive capability of the model, and the finding the optimum of the model, aka the training procedure. They do. That's the whole point of the paper: you can set a bunch of weights manually like you suggest, but can you learn them instead; and how? See the Introduction. They make it very clear that they are investigating whether certain concepts can be learned by gradient descent, specifically. They point out that earlier work doesn't do that and that gradient descent is an obvious bit of bias that should affect the ability of different architectures to learn different concepts. Like I say, good work. >> But you can probably train multiple formal languages simultaneously, to make the counting feature emerge from the data. You could always try it out yourself, you know. Like I say that's the beauty of grammars: you can generate tons of synthetic data and go to town. >> You can't deduce much from negative results in research beside it requiring more work. I disagree. I'm a falsificationist. The only time we learn anything useful is when stuff fails.
  - aubanel 2y ago
    In my country (France), I think most last-year CS students will not have heard of it (pls anyone correct me if I'm wrong).
- YeGoblynQueenne 2y ago
  >> This is a bit stark: there are many great knowledgeable engineers and scientists who would not get your point about a^nb^n. It's impossible to know 100% of of such a wide area as "AI and CS". I think, engineers, yes, especially those who don't have a background in academic CS. But scientists, no, I don't think so. I don't think it's possible to be a computer scientist without knowing the difference between a regular and a super-regular language. As to knowing that a^nb^n specifically is context-free, as I suggest in the sibling comment, computer scientists who are also AI specialists would recognise a^nb^n immediately, as they would Dyck languages and Reber grammars, because those are standard tests of learnability used to demonstrate various principles, from the good old days of purely symbolic AI, to the brave new world of modern deep learning. For example, I learned about Reber grammars for the first time when I was trying to understand LSTMs, when they were all the hype in Deep Learning, at the time I was doing my MSc in 2014. Online tutorials on coding LSTMs used Reber grammars as the dataset (because, as with other formal grammars it's easy to generate tons of strings from them and that's awfully convenient for big data approaches). Btw that's really the difference between a computer scientist and a computer engineer: the scientist knows the theory. That's what they do to you in CS school, they drill that stuff in your head with extreme prejudice; at least the good schools do. I see this with my partner who is 10 times a better engineer than me and yet hasn't got a clue what all this Chomsky hierarhcy stuff is. But then, my partner is not trying to be an AI influencer.
  - natch 2y ago
    Strong gatekeeping vibes. "Not even wrong" is perfect for this sort of fixation with labels and titles and an odd seemingly resentful take that gwern has being an AI influencer as a specific goal.
    - deleted 2y ago
      [deleted]
    - YeGoblynQueenne 2y ago
      OK, I concede that if I try to separate engineers from scientists it sounds like I'm trying to gatekeep. In truth, I'm organising things in my head because I started out thinking of myself as an engineer, because I like to make stuff, and at some point I started thinking of myself as a scientist, malgré moi, because I also like to know how stuff works and why. I multiclassed, you see, so I am trying to understand exactly what changed, when, and why. I mean obviously it happened when I moved from industry to academia, but it's still the case there's a lot of overlap between the two areas, at least in CS and AI. In CS and AI the best engineers make the best scientists and vv. I think. Btw, "gatekeeping" I think assumes that I somehow think of one category less than the other? Is that right? To be clear, I don't. I was responding to the use of both terms in the OP's comments with a personal reflection on the two categories.
empiricus 2y ago
Minor: if n is finite, then a^nb^n becomes regular?
- YeGoblynQueenne 2y ago
  a^nb^n is regular, but it is also context free. I don't think there's a restriction on the n. Why do you say this? Edit: sorry, I read "finite" as "infinite" :0 But n can be infinite and a^nb^n is still regular, and also context free. To be clear, the Chomskky Hierarchy of formal languages goes like this: Finite ⊆ Regular ⊆ Context-Free ⊆ Context-Sensitive ⊆ Recursively Enumerable That's because formal languages are identified with the automata that accept them and when an automaton accepts e.g. the Recursively Enumerable languages, then it also accepts the context-sensitive languages, and so on all the way down to the finite languages. One way to think of this is that an automaton is "powerful enough" to recognise the set of strings that make up a language.
- nahumfarchi 2y ago
  Yes, all finite languages are regular. Specifically, you can construct a finite automata to represent it.
newmanpo 2y ago
I take the Feynman view here; vain memory tricks are not themselves net new production, so just look known things up in the book. Appreciate the diversity in the effort, but engineering is making things people can use without having to know it all. Far more interesting endeavor than being a human Google search engine.
- YeGoblynQueenne 2y ago
  No, look. If a student (I'm not a professor, just a post-doc) doesn't know this stuff, I'll point them to the book so they can look it up, and move on. But the student will not tell me I'm "not even wrong" with the arrogance of fifty cardinals while at the same time pretending to be an expert [1]. It's OK to not know, it's OK to not know that you don't know, but arrogant ignorance is not a good look on anyone. And there's a limit to what you need to look up in a book. The limit moves further up the more you work with a certain kind of tool or study a certain kind of knowledge. I have to look up trigonometry every single time I need it because I only use it sparingly. I don't need to look up SLD-Resolution, which is my main subject. How much would Feynman need to look up when debating physics? So when someone like Feynman talks about physics, you listen carefully because you know they know their shit and a certain kind of nerd deeply appreciates deep knowledge. When someone elbows themselves in the limelight and demands everyone treats them as an expert, but they don't know the basics, what do you conclude? I conclude that they're pretending to know a bunch of stuff they don't know. ________________ [1] ... some do. But they're students so it's OK, they're just excited to have learned so much and don't yet know how much they don't. You explain the mistake, point them to the book, and move on.
  - newmanpo 2y ago
    Ok so you’re arrogantly practicing English writing now and producing little more than a philosophy that just zigs around to maintain your narrative. “Debating physics” in academia is little more than reciting preferred symbolic logic. The map is not the terrain; to borrow from Feynman again, who says in his lectures it’s not good to get hung up on such stuff. So you’re just making this about rhetorical competition, boring gamesmanship. Which like I said, isn’t the point of engineering. Dialectics don’t ship, they keep professors employed though. Whichever way it’s written, physics still seems to work. So the value of STEM is more about the application than the knowing.
dilap 2y ago
Regarding your linked comment, my takeaway is that the very theoretical task of being able to recognize an infinite language isn't very relevent to the non-formal, intuitive idea of "intelligence" Transformers can easily intellectually understand a^nb^n, even though they couldn't recognize whether an arbitrarily long string is a member of the language -- a restriction humans share!, since eventually a human, too, would lose track of the count, for a long enough string.
- YeGoblynQueenne 2y ago
  I don't know what "intellectually understand" means in the context of Transformers. My older comment was about the ability of neural nets to learn automata from examples, a standard measure of the learning ability of a machine learning system. I link to a paper below where Transformers and RNNs are compared on their ability to learn automata along the entire Chomsky hierarchy and as other work has also shown, they don't do that well (although there are some surprising surprises). >> Regarding your linked comment, my takeaway is that the very theoretical task of being able to recognize an infinite language isn't very relevent to the non-formal, intuitive idea of "intelligence" That depends on who you ask. My view is that automata are relevant to computation and that's why we study them in computer science. If we were biologists, we would study beetles. The question is whether computation , as we understand it on the basis of computer science, has anything to do with intelligence. I think it does, but that it's not the whole shebang. There is a long debate on that in AI and the cognitive sciences and the jury is still out, despite what many of the people working on LLMs seem to believe.
  - Vecr 2y ago
    How do you do intelligence without computation though? Brains are semi-distributed analog computers with terrible interconnect speeds and latencies. Unless you think they're magic, any infinite language is still just a limit to them. Edit: and technically you're describing what is more or less backprop learning, neural networks, by themselves, don't learn at all.
    - YeGoblynQueenne 2y ago
bmc7505 2y ago
FWIW, I’ve had a very similar encounter with another famous AI influencer who started lecturing me on fake automata theory that any CS undergrad would have picked up on. 140k+ followers, featured on the all the big podcasts (Lex, MLST). I never corrected him but made a mental note not to trust the guy.
deleted 2y ago
[deleted]
YeGoblynQueenne 2y ago
To the person that commented that five years is an awful long time to remember something like that (and then deleted their comment): you are so right. I am trying to work through this kind of thing :/
n2d4 2y ago
This is such an odd comment. In the thread you linked, Gwern says in response to someone else that NNs excel at many complex real-world tasks even if there are some tasks where they fail but humans (or other models) succeed. You try to counter that by bringing up an example for the latter type of task? And then try to argue that this proves Gwern wrong? Whether they said "regular grammar" or "context-free grammar" doesn't even matter, the meaning of their message is still the exact same.
wyager 2y ago
It seems like his objection is that "parsing formal grammars" isn't the point of LLMs, which is fair. He was wrong about RGs vs CFGs, but I would bet that the majority of programmers are not familiar with the distinction, and learning the classification of a^nb^n is a classic homework problem in automata theory specifically because it's surprising that such a simple grammar is CF.
haccount 2y ago
Being an influencer requires very little actual competence, same goes for AI influencers. The goal of influencers is to influence the segment of a crowd who cares about influencers. Meaning retards and manchildren looking for an external source to form consensus around.

aubanel 2y ago

> For me that's just a great example of how basically the vast majority of AI influencers (who vie for influence on social media, rather than research) are basically clueless about AI and CS This is a bit stark: there are many great knowledgeable engineers and scientists who would not get your point about a^nb^n. It's impossible to know 100% of of such a wide area as "AI and CS".

nocobot 2y ago
is it really? this is the most common example for context free languages and something most first year CS students will be familiar with. totally agree that you can be a great engineer and not be familiar with it, but seems weird for an expert in the field to confidently make wrong statements about this.
- YeGoblynQueenne 2y ago
  Thanks, that's what I meant. a^nb^n is a standard test of learnability. That stuff is still absolutely relevant, btw. Some DL people like to dismiss it as irrelevant but that's just because they lack the background to appreciate why it matters. Also: the arrogance of youth (hey I've already been a postdoc for a year, I'm ancient). Here's a recent paper on Neural Networks and the Chomsky Hierarchy that tests RNNs and Transformers on formal languages (I think it doesn't test on a^nb^n directly but tests similar a-b based CF languages): https://arxiv.org/abs/2207.02098 https://arxiv.org/abs/2207.02098 And btw that's a good paper. Probably one of the most satisfying DL papers I've read in recent years. You know when you read a paper and you get this feeling of satiation, like "aaah, that hit the spot"? That's the kind of paper.
  - GistNoesis 2y ago
    a^nb^n can definitely be expressed and recognized with a transformer. A transformer (with relative invariant positional embedding) has full context so can see the whole sequence. It just has to count and compare. To convince yourself, construct the weights manually. First layer : zeros the character which are equal to the previous character. Second layer : Build a feature to detect and extract the position embedding of the first a. a second feature to detect and extract the position embedding of the last a, a third feature to detect and extract the position embedding of the first b, a fourth feature to detect and extract the position embedding of the last b, Third layer : on top that check whether (second feature - first feature) == (fourth feature - third feature). The paper doesn't distinguish between what is the expressive capability of the model, and the finding the optimum of the model, aka the training procedure. If you train by only showing example with varying n, there probably isn't inductive bias to make it converge naturally towards the optimal solution you can construct by hand. But you can probably train multiple formal languages simultaneously, to make the counting feature emerge from the data. You can't deduce much from negative results in research beside it requiring more work.
    - YeGoblynQueenne 2y ago
      >> The paper doesn't distinguish between what is the expressive capability of the model, and the finding the optimum of the model, aka the training procedure. They do. That's the whole point of the paper: you can set a bunch of weights manually like you suggest, but can you learn them instead; and how? See the Introduction. They make it very clear that they are investigating whether certain concepts can be learned by gradient descent, specifically. They point out that earlier work doesn't do that and that gradient descent is an obvious bit of bias that should affect the ability of different architectures to learn different concepts. Like I say, good work. >> But you can probably train multiple formal languages simultaneously, to make the counting feature emerge from the data. You could always try it out yourself, you know. Like I say that's the beauty of grammars: you can generate tons of synthetic data and go to town. >> You can't deduce much from negative results in research beside it requiring more work. I disagree. I'm a falsificationist. The only time we learn anything useful is when stuff fails.
- aubanel 2y ago
  In my country (France), I think most last-year CS students will not have heard of it (pls anyone correct me if I'm wrong).
YeGoblynQueenne 2y ago
>> This is a bit stark: there are many great knowledgeable engineers and scientists who would not get your point about a^nb^n. It's impossible to know 100% of of such a wide area as "AI and CS". I think, engineers, yes, especially those who don't have a background in academic CS. But scientists, no, I don't think so. I don't think it's possible to be a computer scientist without knowing the difference between a regular and a super-regular language. As to knowing that a^nb^n specifically is context-free, as I suggest in the sibling comment, computer scientists who are also AI specialists would recognise a^nb^n immediately, as they would Dyck languages and Reber grammars, because those are standard tests of learnability used to demonstrate various principles, from the good old days of purely symbolic AI, to the brave new world of modern deep learning. For example, I learned about Reber grammars for the first time when I was trying to understand LSTMs, when they were all the hype in Deep Learning, at the time I was doing my MSc in 2014. Online tutorials on coding LSTMs used Reber grammars as the dataset (because, as with other formal grammars it's easy to generate tons of strings from them and that's awfully convenient for big data approaches). Btw that's really the difference between a computer scientist and a computer engineer: the scientist knows the theory. That's what they do to you in CS school, they drill that stuff in your head with extreme prejudice; at least the good schools do. I see this with my partner who is 10 times a better engineer than me and yet hasn't got a clue what all this Chomsky hierarhcy stuff is. But then, my partner is not trying to be an AI influencer.
- natch 2y ago
  Strong gatekeeping vibes. "Not even wrong" is perfect for this sort of fixation with labels and titles and an odd seemingly resentful take that gwern has being an AI influencer as a specific goal.
  - deleted 2y ago
    [deleted]
  - YeGoblynQueenne 2y ago
    OK, I concede that if I try to separate engineers from scientists it sounds like I'm trying to gatekeep. In truth, I'm organising things in my head because I started out thinking of myself as an engineer, because I like to make stuff, and at some point I started thinking of myself as a scientist, malgré moi, because I also like to know how stuff works and why. I multiclassed, you see, so I am trying to understand exactly what changed, when, and why. I mean obviously it happened when I moved from industry to academia, but it's still the case there's a lot of overlap between the two areas, at least in CS and AI. In CS and AI the best engineers make the best scientists and vv. I think. Btw, "gatekeeping" I think assumes that I somehow think of one category less than the other? Is that right? To be clear, I don't. I was responding to the use of both terms in the OP's comments with a personal reflection on the two categories.

empiricus 2y ago

Minor: if n is finite, then a^nb^n becomes regular?

YeGoblynQueenne 2y ago
a^nb^n is regular, but it is also context free. I don't think there's a restriction on the n. Why do you say this? Edit: sorry, I read "finite" as "infinite" :0 But n can be infinite and a^nb^n is still regular, and also context free. To be clear, the Chomskky Hierarchy of formal languages goes like this: Finite ⊆ Regular ⊆ Context-Free ⊆ Context-Sensitive ⊆ Recursively Enumerable That's because formal languages are identified with the automata that accept them and when an automaton accepts e.g. the Recursively Enumerable languages, then it also accepts the context-sensitive languages, and so on all the way down to the finite languages. One way to think of this is that an automaton is "powerful enough" to recognise the set of strings that make up a language.
nahumfarchi 2y ago
Yes, all finite languages are regular. Specifically, you can construct a finite automata to represent it.

newmanpo 2y ago

I take the Feynman view here; vain memory tricks are not themselves net new production, so just look known things up in the book. Appreciate the diversity in the effort, but engineering is making things people can use without having to know it all. Far more interesting endeavor than being a human Google search engine.

YeGoblynQueenne 2y ago
No, look. If a student (I'm not a professor, just a post-doc) doesn't know this stuff, I'll point them to the book so they can look it up, and move on. But the student will not tell me I'm "not even wrong" with the arrogance of fifty cardinals while at the same time pretending to be an expert [1]. It's OK to not know, it's OK to not know that you don't know, but arrogant ignorance is not a good look on anyone. And there's a limit to what you need to look up in a book. The limit moves further up the more you work with a certain kind of tool or study a certain kind of knowledge. I have to look up trigonometry every single time I need it because I only use it sparingly. I don't need to look up SLD-Resolution, which is my main subject. How much would Feynman need to look up when debating physics? So when someone like Feynman talks about physics, you listen carefully because you know they know their shit and a certain kind of nerd deeply appreciates deep knowledge. When someone elbows themselves in the limelight and demands everyone treats them as an expert, but they don't know the basics, what do you conclude? I conclude that they're pretending to know a bunch of stuff they don't know. ________________ [1] ... some do. But they're students so it's OK, they're just excited to have learned so much and don't yet know how much they don't. You explain the mistake, point them to the book, and move on.
- newmanpo 2y ago
  Ok so you’re arrogantly practicing English writing now and producing little more than a philosophy that just zigs around to maintain your narrative. “Debating physics” in academia is little more than reciting preferred symbolic logic. The map is not the terrain; to borrow from Feynman again, who says in his lectures it’s not good to get hung up on such stuff. So you’re just making this about rhetorical competition, boring gamesmanship. Which like I said, isn’t the point of engineering. Dialectics don’t ship, they keep professors employed though. Whichever way it’s written, physics still seems to work. So the value of STEM is more about the application than the knowing.

dilap 2y ago

Regarding your linked comment, my takeaway is that the very theoretical task of being able to recognize an infinite language isn't very relevent to the non-formal, intuitive idea of "intelligence" Transformers can easily intellectually understand a^nb^n, even though they couldn't recognize whether an arbitrarily long string is a member of the language -- a restriction humans share!, since eventually a human, too, would lose track of the count, for a long enough string.

YeGoblynQueenne 2y ago
I don't know what "intellectually understand" means in the context of Transformers. My older comment was about the ability of neural nets to learn automata from examples, a standard measure of the learning ability of a machine learning system. I link to a paper below where Transformers and RNNs are compared on their ability to learn automata along the entire Chomsky hierarchy and as other work has also shown, they don't do that well (although there are some surprising surprises). >> Regarding your linked comment, my takeaway is that the very theoretical task of being able to recognize an infinite language isn't very relevent to the non-formal, intuitive idea of "intelligence" That depends on who you ask. My view is that automata are relevant to computation and that's why we study them in computer science. If we were biologists, we would study beetles. The question is whether computation , as we understand it on the basis of computer science, has anything to do with intelligence. I think it does, but that it's not the whole shebang. There is a long debate on that in AI and the cognitive sciences and the jury is still out, despite what many of the people working on LLMs seem to believe.
- Vecr 2y ago
  How do you do intelligence without computation though? Brains are semi-distributed analog computers with terrible interconnect speeds and latencies. Unless you think they're magic, any infinite language is still just a limit to them. Edit: and technically you're describing what is more or less backprop learning, neural networks, by themselves, don't learn at all.
  - YeGoblynQueenne 2y ago

bmc7505 2y ago

FWIW, I’ve had a very similar encounter with another famous AI influencer who started lecturing me on fake automata theory that any CS undergrad would have picked up on. 140k+ followers, featured on the all the big podcasts (Lex, MLST). I never corrected him but made a mental note not to trust the guy.

deleted 2y ago

[deleted]

YeGoblynQueenne 2y ago

To the person that commented that five years is an awful long time to remember something like that (and then deleted their comment): you are so right. I am trying to work through this kind of thing :/

n2d4 2y ago

This is such an odd comment. In the thread you linked, Gwern says in response to someone else that NNs excel at many complex real-world tasks even if there are some tasks where they fail but humans (or other models) succeed. You try to counter that by bringing up an example for the latter type of task? And then try to argue that this proves Gwern wrong? Whether they said "regular grammar" or "context-free grammar" doesn't even matter, the meaning of their message is still the exact same.

wyager 2y ago

It seems like his objection is that "parsing formal grammars" isn't the point of LLMs, which is fair. He was wrong about RGs vs CFGs, but I would bet that the majority of programmers are not familiar with the distinction, and learning the classification of a^nb^n is a classic homework problem in automata theory specifically because it's surprising that such a simple grammar is CF.

haccount 2y ago

Being an influencer requires very little actual competence, same goes for AI influencers. The goal of influencers is to influence the segment of a crowd who cares about influencers. Meaning retards and manchildren looking for an external source to form consensus around.