EZRA KLEIN: And that streetlight is deep learning.

GARY MARCUS: The streetlight is deep learning. And what’s being ignored, I think, is the rest of cognitive science. So deep learning is a very interesting mathematical technique for setting some values in some system based on data. It turns out to be tremendously useful for a bunch of things.

But it’s only part of what we need. If you take a step back to cognitive science, which is what I was trained on, and you say, well, what is a human mind? Well, part of it is it does some pattern recognition. And some of what deep learning does is at least part of what we do in pattern recognition. So great.

But you also, if you look at the human mind, there’s a whole bunch of other things going on. So we use language. When we use language, we go from hearing a sentence to building a model in the world of what the other person’s talking to. We use analogies. We plan things and so forth and so on.

The reality is we’ve only made progress on a tiny bit of the things that go into intelligence. Now I’m not saying that A.I. has to be exactly like a human. In fact, I think humans are pretty flawed, and we don’t want to just replicate humans. We have, for example, confirmation bias where we notice things in favor of our own theories and not against them. We have all kinds of flaws that we might not want to have in our A.I. systems.

But there are many things that our A.I. systems can do that aren’t part of what’s fashionable right now and are not under that streetlight that everybody’s looking at. So on a technical level, I think there’s a narrowness to what A.I. has been in the last decade, where there’s this wonderful new tool, but people are a little bit misled about what that tool is actually appropriate for. It’s like if you discovered a power screwdriver and you’d never had one before, that’d be great. But that doesn’t mean that you build the house just with your power screwdriver. And we need other tools here.

Then culturally, and this relates — there’s been a history that’s actually much older than I am, goes back to the 1940s or ’50s depending on how you want to count it — of people who build neural networks in conflict with people who take a more classical approach to A.I., where there’s lots of symbols and trees and databases and reasoning and so forth.

And there’s a pendulum that’s gone back and forth. People in the two areas pretty much hate each other, or at least often do. I think it’s getting a little bit better. But there’s a lot of history of hostility between these two areas. So the people with the bigger streetlight right now are the people building the neural networks. And they feel like they’ve been oppressed by the symbolic people, and now they’re oppressing the symbolic people. There’s an old phrase, the victim becomes the victimizer.

So it’s not a pleasant intellectual area like some fields that I’ve seen. Of course, they’re not always pleasant. But there is definitely a culture right now where the people who are in power and have a lot of money are really trying to shape the decisions around what gets researched and what doesn’t.

EZRA KLEIN: Take a moment and explain what symbol manipulation is because I don’t think that the term for it is very intuitive.

GARY MARCUS: So symbols are just things that stand for other things. And manipulating just means you do stuff with them. So algebra is actually symbol manipulation. So you have an equation like y equals x plus 2. And you have variables in that like y and x. And then you can set those variables to particular instances. So I can say that x is 3. Then I can calculate the value of y from that equation.

So we do it in algebra. We do it in logic. And linguists like Noam Chomsky would say that we do it in the course of understanding language. It’s also essential to computer programming. Almost everything you see in a computer program is going to be a function that takes variables. You do something with those variables. The variables are symbols. The values that you set those variables are symbols.

There’s a whole tradition. Most of the world’s software is, in fact, built using software that manipulates symbols, where you describe things abstractly. You also have things like databases, and those databases are symbols. And neural networks have mostly been used as an alternative to that. And they’ve been very effective in some things like speech recognition. But on the other hand, nobody would build a GPS system or a web browser or a word processor using pure neural network approach. Anybody would use mostly symbols for that.

And there’s this weird argument, weird discourse where people who like the neural network stuff mostly don’t want to use symbols. What I’ve been arguing for 30 years, since I did my dissertation with Steve Pinker at M.I.T. studying children’s language, has been for some kind of hybrid where we use neural networks for the things they’re good at, and use the symbol stuff for the things they’re good at, and try to find ways to bridge these two traditions.
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