Thursday, February 17, 2022

Sean Carroll interviews Gary Marcus about AI and common sense

As you may know, Marcus is skeptical about the ability of (deep) learning approaches to go all the way. Here's one bit of the conversation:

0:11:58.7 Sean Carroll: And maybe it’s good to… We’re able to get into details a little bit. The audience likes the details. So let’s try to understand why there has been this progress. And as far as I can tell, the overwhelming majority of recent progress in AI has been driven by neural networks and deep learning algorithms. Is that fair? And what does that mean?

0:12:18.9 Gary Marcus: It’s true, but with some caveats. So, first of all, there are older techniques that everybody takes for granted but are real and are already out there. Second of all, there are things like AlphaGo, they’re actually hybrid models that use classical tree search techniques enhanced with Monte Carlo techniques in order to do what they’re doing. So they’re not just straight multi-layer perception is a kind of stereotype that people have with neural networks. We have some inputs, they feed into a hidden layer that does some summation and activation function goes to an output. They’re not just that. They actually borrow some important ideas about search, for example, and symbols from classical AI. And so they’re actually hybrid systems and people don’t acknowledge that. So this is the second caveat I would give you. The third caveat I would give you… We can come back to the second, but the third caveat I’ll give you is, yeah, most of the progress has been with deep learning lately, but most of the money has been there too, and it was really interesting to see… And I don’t just mean like 60% versus 40%. I mean, like 99.9% of the investment right now literally is in deep learning and classic symbol manipulation AI is really out of favor, and people like Geoff Hinton don’t spend any money on it at all. And so it was really interesting.

0:13:42.7 GM: There was this competition presented at the NeurIPS Conference which is the biggest conference these days in the AI field just a month or so ago, on a game called NetHack, it has various complications in it, and a symbolic system actually won in an upset victory over all this deep learning stuff. And so if you look back at the history of AI, in the history of science more generally, sometimes things get counted out too soon. It is true the deep learning has made a bunch of progress, but the question is, what follows from there?

0:14:13.9 SC: No, I’m not actually trying to make any value judgements. I would like to explain for our audience what the options are. What do you mean by deep learning? What is that and what is that in comparison to symbolic manipulation?

0:14:25.2 GM: So deep learning is fundamentally a way of doing statistical analysis on large quantities of data, at least that’s… It’s Forte. You can actually use it in a bunch of different ways, but most of the progress has come from that. And what’s impressive about the recent work is it allows us to learn from very large quantities of data. The classical AI system really didn’t do a lot of learning at all. They’re mostly hand-coded and sometimes that’s the right thing to do. So we don’t need to learn how to do navigation. We need to learn some details, but we don’t need to learn how to do navigation for the purpose of one of the most useful AI things out there, which is route planning, telling you how to get home from whatever crazy place you wound up in. Right? That’s not a deep learning-driven system. But there are other systems where if you can glom on to all the data that’s out there, you can solve certain problems very effectively, and that’s what deep learning has been good for.

The way forward:

0:20:35.4 GM: Yeah, well let me, before I do that, let me say that I think that we need elements of the symbolic approach, I think we need elements of the deep learning approach or something like it, but the… Neither by itself is sufficient. And so, I’m a big fan of what I call hybrid systems that bring together in ways that we haven’t really even figured out yet, the best of both worlds, but with that preface, ’cause people often in the field like to misrepresent me as that symbolic guy, and I’m more like the guy who said, Don’t forget about the symbolic stuff, we need it to be part of the answer. Okay, so the symbolic stuff is basically the essence of computer programming your algebra or something like that, what’s really about is having functions where you have variables that you bind to particular instances and calculate the values out, so simplest example would be an equation in Algebra, Y equals X plus 2, I tell you what X is, you can figure out what y is… And there it doesn’t matter, which Xs you have seen before, you have this thing that is defined universally is the way a logician might put it, universally for everything in some domain, any physicist would grasp that immediately or any program or any logician.

Note: Marcus remarks here and there that some systems that are presented as deep learning systems, such as AlphaGo (chess playing), Rubik's cube, or protein-folding, are actually hybrid systems, employing aspects of symbolic technology.

There's more at the link.

H/t 3QD.

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