Interview with gwern

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, 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.

> 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 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.

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.

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.

I sure hope nobody ever remembers you being confidently wrong about something. But if they do, hopefully that person will have the grace and self-restraint not to broadcast it any time you might make a public appearance, because they're apparently bitter that you still have any credibility.

Point taken and I warned my comment would sound vituperative. Again, the difference is that I'm not an AI influencer, and I'm not trying to make a living by claiming an expertise I don't have. I don't make "public appearances" except in conferences where I present the results of my research.

And you should see the criticism I get by other academics when I try to publish my papers and they decide I'm not even wrong. And that kind of criticism has teeth: my papers don't get published.

Please be aware that your criticism has teeth too, you just don't feel the bite of them. You say I "should see" that criticism you receive on your papers, but I don't; it's delivered in private. Unlike the review comments you get from your peers, you are writing in public. I'm sure you wouldn't appreciate it if your peer reviewer stood up after your conference keynote and told the audience that they'd rejected your paper five years ago, described your errors, and went on to say that nobody at this conference should be listening to you.