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.
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.
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.
Well, maybe it does. I don't know if that can be avoided. I think most people don't take criticism well. I've learned for example that there are some conversations I can't have with certain members of my extended family because they're not used to being challenged about things they don't know and they react angrily. I'm specifically remember a conversation where I was trying to explain the concept of latent hypoxia and ascent blackout [1] (I free dive recreationally) to an older family member who is an experienced scuba diver, and they not only didn't believe me, they called me an ignoramus. Because I told them something they didn't know about. Eh well.
_____________
[1] It can happen that while you're diving deep, the pressure of the water keeps the pressure of oxygen in your blood sufficient that you don't pass out, but then when you start coming up, the pressure drops and the oxygen in your blood thins out so much that you pass out. In my lay terms. My relative didn't believe that the water pressure affects the pressure of the air in your vessels. I absolutely can feel that when I'm diving- the deeper I go the easier it gets to hold my breath and it's so noticeable because it's so counter-intuitive. My relative wouldn't have experienced that during scuba diving (since they breathe pressurised air, I think) and maybe it helps he's a smoker. Anyway I never managed to convince him.
As I never managed to convince him that we eat urchins' genitals, not their eggs. After a certain point I stopped trying to convince him of anything. I mean I felt like a know-it-all anyway, even if I knew what I was talking about.
So thanks for that, even though it's entirely unrelated to AI.
[0]: though I've seen videos of the exact same effect on a plastic water bottle, but my brain didn't make the connection
My formative experience as a PhD student was when a senior colleague attacked my work. That was after I asked for his feedback for a paper I was writing where I showed that my system beat his system. He didn't deal with it well, sent me a furiously critical response (with obvious misunderstandings of my work) and then proceeded to tell my PhD advisor and everyone else in a conference we were attending that my work is premature and shouldn't be submitted. My advisor, trusting his ex-student (him) more than his brand new one (me), agreed and suggested I should sit on the paper a bit longer.
Later on the same colleague attacked my system again, but this time he gave me a concrete reason why: he gave me an example of a task that my system could not complete (learn a recursive logic program to return the last element in a list from a single example that is not an example of the base-case of the recursion; it's a lot harder than it may sound).
Now, I had been able to dismiss the earlier criticism as sour grapes, but this one I couldn't get over because my system really couldn't deal with it. So I tried to figure out why- where was the error I was making in my theories? Because my theoretical results said that my system should be able to learn that. Long story short, I did figure it out and I got that example to work, plus a bunch of other hard tests that people had thrown at me in the meanwhile. So I improved.
I still think my colleague's behaviour was immature and not becoming of a senior academic- attacking a PhD student because she did what you 've always done, beat your own system, is childish. In my current post-doc I just published a paper with one of our PhD students where we report his system trouncing mine (in speed; still some meat on those old bones otherwise). I think criticism is a good thing overall, if you can learn to use it to improve your work. It doesn't mean that you'll learn to like it, or that you'll be best friends with the person criticising you, it doesn't even mean that they're not out to get you; they probably are... but if the criticism is pointing out a real weakness you have, you can still use it to your advantage no matter what.
But above I'm only discussing my experience of criticism as an aside, unrelated to Gwern. To be clear, my original comment was not meant as constructive criticism. Like I think my colleague was at the time, I am out to get Gwern because I think, like I say, that he is a clueless AI influencer, a cheer-leader of deep learning who is piggy-backing on the excitement about AI that he had nothing to do with creating. I wouldn't find it so annoying if he, like many others who engage in the same parasitism, did not sound so cock-sure that he knows what he's talking about.
I do not under any circumstances claim that my original comment is meant to be nice.
Btw, I remember now that Gwern has in other times accused me , here on HN, of being confidently wrong about things I don't know as well as I think I do (deep learning stuff). I think it was in a comment about Mu Zero (the DeepMind system). I don't think Gwern likes me much, either. But, then, he's a famous influencer and I'm not and I bet he finds solace in that so my criticism is not going to hurt him in the end.
The physical principles remain regardless of how humans write them down.
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.
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
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.
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.
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.
The current wisdom is that by optimizing for multiple tasks simultaneously, it makes the energy landscape smoother. One task allow to discover features which can be used to solve other tasks.
Useful features that are used by many tasks can more easily emerge from the sea of useless features. If you don't have sufficiently many distinct tasks the signal doesn't get above the noise and is much harder to observe.
That the whole point of "Generalist" intelligence in the scaling hypothesis.
For problems where you can write a solution manually you can also help the training procedure by regularising your problem by adding the auxiliary task of predicting some custom feature. Alternatively you can "Generatively Pretrain" to obtain useful feature, replacing custom loss function by custom data.
The paper is a useful characterisation of the energy landscape of various formal tasks in isolation, but doesn't investigate the more general simpler problem that occur in practice.