It’s hard to visit a tech site these days without seeing a headline about deep learning for X, and that AI is on the verge of solving all our problems. Gary Marcus remains skeptical.

Marcus, a best-selling author, entrepreneur, and professor of psychology at NYU, has spent decades studying how children learn and believes that throwing more data at problems won’t necessarily lead to progress in areas such as understanding language, not to speak of getting us to AGI – artificial general intelligence.

Marcus is the voice of anti-hype at a time when AI is all the hype, and in 2015 he translated his thinking into a startup, Geometric Intelligence, which uses insights from cognitive psychology to build better performing, less data-hungry machine learning systems. The team was acquired by Uber in December to run Uber’s AI labs, where his cofounder Zoubin Ghahramani has now been appointed chief scientist. So what did the tech giant see that was so important?

In an interview for Flux, I sat down with Marcus, who discussed why deep learning is the hammer that’s making all problems look like a nail and why his alternative sparse data approach is so valuable.

We also got into the challenges of being an AI startup competing with the resources of Google, how corporates aren’t focused on what society actually needs from AI, his proposal to revamp the outdated Turing test with a multi-disciplinary AI triathlon, and why programming a robot to understand “harm” is so difficult.

Gary you are well known as a critic of this technique, you’ve said that it’s over-hyped. That there’s low hanging fruit that deep learning’s good at — specific narrow tasks like perception and categorization, and maybe beating humans at chess, but you felt that this deep learning mania was taking the field of AI in the wrong direction, that we’re not making progress on cognition and strong AI. Or as you’ve put it, “we wanted Rosie the robot, and instead we got the roomba.” So you’ve advocated for bringing psychology back into the mix, because there’s a lot of things that humans do better, and that we should be studying humans to understand why they do things better. Is this still how you feel about the field?

GM: Pretty much. There was probably a little more low hanging fruit than I anticipated. I saw somebody else say it more concisely, which is simply, deep learning does not equal AGI (AGI is “artificial general intelligence.”) There’s all the stuff you can do with deep learning, like it makes your speech recognition better. It makes your object recognition better. But that doesn’t mean it’s intelligence. Intelligence is a multi-dimensional variable. There are lots of things that go into it.

In a talk I gave at TEDx CERN recently, I made this kind of pie chart and I said look, here’s perception that’s a tiny slice of the pie. It’s an important slice of the pie, but there’s lots of other things that go into human intelligence, like our ability to attend to the right things at the same time, to reason about them to build models of what’s going on in order to anticipate what might happen next and so forth. And perception is just a piece of it. And deep learning is really just helping with that piece.

In a New Yorker article that I wrote in 2012, I said look, this is great, but it’s not really helping us solve causal understanding. It’s not really helping with language. Just because you’ve built a better ladder doesn’t mean you’ve gotten to the moon. I still feel that way. I still feel like we’re actually no closer to the moon, where the moonshot is intelligence that’s really as flexible as human beings. We’re no closer to that moonshot than we were four years ago. There’s all this excitement about AI and it’s well deserved. AI is a practical tool for the first time and that’s great. There’s good reason for companies to put in all of this money. But just look for example at a driverless car, that’s a form of intelligence, modest intelligence, the average 16-year-old can do it as long as they’re sober, with a couple of months of training. Yet Google has worked on it for seven years and their car still can only drive —  as far as I can tell since they don’t publish the data — like on sunny days, without too much traffic…

AMLG: And isn’t there the whole black box problem that you don’t know what’s going on. We don’t know the inner workings of deep learning, it’s kind of inscrutable. Isn’t that a massive problem for things like driverless cars?

GM: It is a problem. Whether it’s an insuperable problem is an open empirical question. So it is a fact at least for now that we can’t well interpret what deep learning is doing. So the way to think about it is you have millions of parameters and millions of data points. That means that if I as an engineer look at this thing I have to contend with these millions or billions of numbers that have been set based on all of that data and maybe there is a kind of rhyme or reason to it but it’s not obvious and there’s some good theoretical arguments to think sometimes you’re never really going to find an interpretable answer there.

There’s an argument now in the literature which goes back to some work that I was doing in the 90s about whether deep learning is just memorization. So this was the paper that came out that said it is and another says no it isn’t. Well it isn’t literally exactly memorization but it’s a little bit like that. If you memorize all these examples, there may not be some abstract rule that characterizes all of what’s going on but it might be hard to say what’s there. So if you build your system entirely with deep learning, which is something that Nvidia has played around with, and something goes wrong, it’s hard to know what’s going on and that makes it hard to debug.

AMLG: Which is a problem if your car just runs into a lamppost and you can’t debug why that happened.

GM: You’re lucky if it’s only a lamppost and not too many people are injured. There are serious risks here. Somebody did die, though I think it wasn’t a deep learning system in the Tesla crash, it was a different kind of system. We actually have problems on engineering on both ends. So I don’t want to say that classical AI has fully licked these problems, it hasn’t. I think it’s been abandoned prematurely and people should come back to it. But the fact is we don’t have good ways of engineering really complex systems. And minds are really complex systems.

AMLG: Why do you think these big platforms are reorganizing around AI and specifically deep learning. Is it just that they’ve got data moats, so you might as well train on all of that data if you’ve got it?

GM: Well there’s an interesting thing about Google which is they have enormous amounts of data. So of course they want to leverage it. Google has the power to build new resources that they give away free and they build the resources that are particular to their problem. So Google because they have this massive amount of data has oriented their AI around, how can I leverage that data? Which makes sense from their commercial interests. But it doesn’t necessarily mean, say from a society’s perspective. does society need AI? What does it need it for? Would be the best way to build it?