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Interview with gwern

dwarkeshpatel.com

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Re: Interview with gwern

#121

Post by notahacker »

satvikpendem wrote: post #86
notahacker wrote: post #82

Earlier quoted context omitted.

In this case, there isn't much substance to engage with . The original argument made in passing in an interview covering a range of subjects is essentially [answering your question which presupposes that AI takes over all jobs] I think it'll be bottom up because [in my opinion] being a visionary CEO is the hardest thing to automate The fact that similar, often more detailed assertions of the imminent disappearance of…

Again, my comment is not about AI, it is about the faulty construction of the argument in the sentence I quoted. X and Y could be anything, that is not my point.

My point is also not really about AI, my point is that pointing out that the same arguments that X implies Y could (and have been) applied to virtually every V, W, Z (where X and V/W/Z are both in the same category, in this case the category of "disruptive inventions") and yet Y didn't happen as predicted isn't ad hominem tu quoque fallacy or anything to do with hypocrisy, it's an observation that arguments about the category resulting in Y have tended to be consistently wrong so we probably should treat claims about Y happening because of something else in the category with scepticism...

Re: Interview with gwern

#122

Post by Vecr »

YeGoblynQueenne wrote: post #114
dilap wrote: post #112

Earlier quoted context omitted.

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, wou…

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 do…

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.

Re: Interview with gwern

#123

Post by YeGoblynQueenne »

GistNoesis wrote: post #120
YeGoblynQueenne wrote: post #101

Earlier quoted context omitted.

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…

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…

>> 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.

Re: Interview with gwern

#124

Post by [deleted] »

YeGoblynQueenne wrote: post #84

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 Again I'm sorry for the negativity, but already at the time Gwern was held up by a certain, l…

[deleted]

Re: Interview with gwern

#125

Post by natch »

YeGoblynQueenne wrote: post #107
aubanel wrote: post #90

Earlier quoted context omitted.

> 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".

>> 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 regu…

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.

Re: Interview with gwern

#126

Post by YeGoblynQueenne »

YeGoblynQueenne wrote: post #84

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 Again I'm sorry for the negativity, but already at the time Gwern was held up by a certain, l…

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 :/

Re: Interview with gwern

#127

Post by n2d4 »

YeGoblynQueenne wrote: post #84

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 Again I'm sorry for the negativity, but already at the time Gwern was held up by a certain, l…

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.

Re: Interview with gwern

#128

Post by YeGoblynQueenne »

Vecr wrote: post #122
YeGoblynQueenne wrote: post #114

Earlier quoted context omitted.

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 do…

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.

Yes, I'm talking about learning neural nets with gradient descent. See also the nice paper I linked below.

>> How do you do intelligence without computation though?

Beats me! Unlike everyone else in this space, it seems, I haven't got a clue how to do intelligence at all, with or without computation.

Edit: re infinite languages, I liked something Walid Saba (RIP) pointed out on Machine Learning Street Talk, that sure you can't generate infinite strings but if you have an infinite language every string accepted by the language has a uniform probability of one over infinity, so there's no way to learn the entire language by learning the distribution of strings within it. But e.g. the Python compiler must be able to recognise an infinite number of Python programs as valid (or reject those that aren't) because of the same reason, that it's impossible to predict which string is going to come out of a source generating strings in an infinite language. So you have to able to deal with infinite possibilities, with only finite resources.

Now, I think there's a problem with that. Assuming a language L has a finite alphabet, even if L is infinite (i.e. it includes an infinite number of strings) the subset of L where strings only go up to some length n is going to be finite. If that n is large enough that it is just beyond the computational resources of any system that has to recognise strings in L (like a compiler) then any system that can recognise, or generate, all strings in L up to n length, will be, for all intents and purposes, complete with respect to L, up to n etc. In plain English, the Python compiler doesn't need to be able to deal with Python programs of infinite length, so it doesn't need to deal with an infinite number of Python programs.

Same for natural language. The informal proof of the infinity of natural language I know of is based on the observation that we can embed an arbitrary number of sentences in other sentences: "Mary, whom we met in the summer, in Fred's house, when we went there with George... " etc. But, in practice, that ability too will be limited by time and human linguistic resources, so not even the human linguistic ability really-really needs to be able to deal with an infinite number of strings.

That's assuming that natural language has a finite alphabet, or I guess lexicon is the right word. That may or may not be the case: we seem to be able to come up with new rods all the time. Anyway some of this may explain why LLMs can still convincingly reproduce the structure of natural language without having to train on infinite examples.

Re: Interview with gwern

#129

Post by wyager »

throw05678931 wrote: post #22

Lots of the claims about Gwern in the intro are exaggerated. Gwern is an effective altruist and his influence is largely limited to that community. It would be an exaggeration to claim that he influenced the mainstream of AI and ML researchers -- certainly Hinton, LeCun, Ng, Bengio didn't need him to do their work. He influences the AI safety crowd, who have ironically been trying to build AGI to test their AI safety…

> Anthropic has moved so much slower than its competitors

In what sense? Claude is way better than any other LLM available, in the sense of providing useful/correct/intelligent output.

Re: Interview with gwern

#130

Post by dilap »

YeGoblynQueenne wrote: post #114
dilap wrote: post #112

Earlier quoted context omitted.

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, wou…

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 do…

By intellectually understand, I just mean you can ask Claude or ChatGPT or whatever, "how can I recognize if a string is in a^n b^n? what is the language being described?" and it can easily tell you; if you were giving it an exam, it would pass.

(Of course, maybe you could argue that's a famous example in its training set and it's just regurgitating, but then you could try making modifications, asking other questions, etc, and the LLM would continue to respond sensibly. So to me it seems to understand...)

Or going back to the original Hofstadter article, "simple tests show that [machine translation is] a long way from real understanding"; I tried rerunning the first two of these simple tests today w/ Claude 3.5 Sonnet (new), and it absolutely nails them. So it seems to understand the text quite well.

Regarding computation and understanding: I just though it was interesting that you presented a true fact about the computational limitations of NNs, which could easily/naturally/temptingingly -- yet incorrectly (I think!) -- be extended into a statement about the limitations of understanding of NNs (whatever understanding means -- no technical definition that I know of, but still, it does mean something, right?).

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