Rodney Brooks Talks about AI

Comments here a little late, but good points.

Just Calm Down About GPT-4 Already And stop confusing performance with competence, says Rodney Brooks      By Glenn  Zorpette in Spectrum IEEE

Rapid and pivotal advances in technology have a way of unsettling people, because they can reverberate mercilessly, sometimes, through business, employment, and cultural spheres. And so it is with the current shock and awe over large language models, such as GPT-4 from OpenAI.

It’s a textbook example of the mixture of amazement and, especially, anxiety that often accompanies a tech triumph. And we’ve been here many times, says Rodney Brooks. Best known as a robotics researcher, academic, and entrepreneur, Brooks is also an authority on AI: he directed the Computer Science and Artificial Intelligence Laboratory at MIT until 2007, and held faculty positions at Carnegie Mellon and Stanford before that. Brooks, who is now working on his third robotics startup, Robust.AI, has written hundreds of articles and half a dozen books and was featured in the motion picture Fast, Cheap & Out of Control. He is a rare technical leader who has had a stellar career in business and in academia and has still found time to engage with the popular culture through books, popular articles, TED Talks, and other venues.

“It gives an answer with complete confidence, and I sort of believe it. And half the time, it’s completely wrong.”

—Rodney Brooks, Robust.AI

IEEE Spectrum caught up with Brooks at the recent Vision, Innovation, and Challenges Summit, where he was being honored with the 2023 IEEE Founders Medal. He spoke about this moment in AI, which he doesn’t regard with as much apprehension as some of his peers, and about his latest startup, which is working on robots for medium-size warehouses.

Rodney Brooks on…

Will GPT-4 and other large language models lead to an artificial general intelligence in the foreseeable future?

Will companies marketing large language models ever justify the enormous valuations some of these companies are now enjoying?

When are we going to have full (level-5) self-driving cars?

What are the most attractive opportunities now in warehouse robotics?

You wrote a famous article in 2017, “The Seven Deadly Sins of AI Prediction.“ You said then that you wanted an artificial general intelligence to exist—in fact, you said it had always been your personal motivation for working in robotics and AI. But you also said that AGI research wasn’t doing very well at that time at solving the basic problems that had remained intractable for 50 years. My impression now is that you do not think the emergence of GPT-4 and other large language models means that an AGI will be possible within a decade or so.

Rodney Brooks: You’re exactly right. And by the way, GPT-3.5 guessed right—I asked it about me, and it said I was a skeptic about it. But that doesn’t make it an AGI.

The large language models are a little surprising. I’ll give you that. And I think what they say, interestingly, is how much of our language is very much rote, R-O-T-E, rather than generated directly, because it can be collapsed down to this set of parameters. But in that “Seven Deadly Sins” article, I said that one of the deadly sins was how we humans mistake performance for competence.

If I can just expand on that a little. When we see a person with some level performance at some intellectual thing, like describing what’s in a picture, for instance, from that performance, we can generalize about their competence in the area they’re talking about. And we’re really good at that. Evolutionarily, it’s something that we ought to be able to do. We see a person do something, and we know what else they can do, and we can make a judgement quickly. But our models for generalizing from a performance to a competence don’t apply to AI systems.

The example I used at the time was, I think it was a Google program labeling an image of people playing Frisbee in the park. And if a person says, “Oh, that’s a person playing Frisbee in the park,” you would assume you could ask him a question, like, “Can you eat a Frisbee?” And they would know, of course not; it’s made of plastic. You’d just expect they’d have that competence. That they would know the answer to the question, “Can you play Frisbee in a snowstorm? Or, how far can a person throw a Frisbee? Can they throw it 10 miles? Can they only throw it 10 centimeters?” You’d expect all that competence from that one piece of performance: a person saying, “That’s a picture of people playing Frisbee in the park.” .... '