Artificial intelligence is not as smart as you (or Elon Musk) think

As appeared in, By  Ron Miller (@ron_miller)
25 July 2017


In March 2016, DeepMind’s AlphaGo beat Lee Sedol, who at the time was the best human Go player in the world. It represented one of those defining technological moments like IBM’s Deep Blue beating chess champion Garry Kasparov, or even IBM Watson beating the world’s greatest Jeopardy! champions in 2011.

Yet these victories, as mind-blowing as they seemed to be, were more about training algorithms and using brute-force computational strength than any real intelligence. Former MIT robotics professor Rodney Brooks, who was one of the founders of iRobot and later Rethink Robotics, reminded us at the TechCrunch Robotics Session at MIT last week that training an algorithm to play a difficult strategy game isn’t intelligence, at least as we think about it with humans.

He explained that as strong as AlphaGo was at its given task, it actually couldn’t do anything else but play Go on a standard 19 x 19 board. He relayed a story that while speaking to the DeepMind team in London recently, he asked them what would have happened if they had changed the size of the board to 29 x 29, and the AlphaGo team admitted to him that had there been even a slight change to the size of the board, “we would have been dead.”

“I think people see how well [an algorithm] performs at one task and they think it can do all the things around that, and it can’t,” Brooks explained.

Brute-force intelligence

As Kasparov pointed out in an interview with Devin Coldewey at TechCrunch Disrupt in May, it’s one thing to design a computer to play chess at Grand Master level, but it’s another to call it intelligence in the pure sense. It’s simply throwing computer power at a problem and letting a machine do what it does best.

“In chess, machines dominate the game because of the brute force of calculation and they [could] crunch chess once the databases got big enough and hardware got fast enough and algorithms got smart enough, but there are still many things that humans understand. Machines don’t have understanding. They don’t recognize strategical patterns. Machines don’t have purpose,” Kasparov explained.

Gil Pratt, CEO at the Toyota Institute, a group inside Toyota working on artificial intelligence projects including household robots and autonomous cars, was interviewed at the TechCrunch Robotics Session, said that the fear we are hearing about from a wide range of people, including Elon Musk, who most recently called AI “an existential threat to humanity,” could stem from science-fiction dystopian descriptions of artificial intelligence run amok.


"I think it’s important to keep in context how good these systems are, and actually how bad they are too, and how long we have to go until these systems actually pose that kind of a threat [that Elon Musk and others talk about]"

- Gil Pratt, CEO, Toyota Institute


“The deep learning systems we have, which is what sort of spurred all this stuff, are remarkable in how well we do given the particular tasks that we give them, but they are actually quite narrow and brittle in their scope. So I think it’s important to keep in context how good these systems are, and actually how bad they are too, and how long we have to go until these systems actually pose that kind of a threat [that Elon Musk and others talk about].”

Brooks said in his TechCrunch Sessions: Robotics talk that there is a tendency for us to assume that if the algorithm can do x, it must be as smart as humans. “Here’s the reason that people — including Elon — make this mistake. When we see a person performing a task very well, we understand the competence [involved]. And I think they apply the same model to machine learning,” he said.

Facebook’s Mark Zuckerberg also criticized Musk’s comments, calling them “pretty irresponsible,” in a Facebook Live broadcast on Sunday. Zuckerberg believes AI will ultimately improve our lives. Musk shot back later that Zuckerberg had a “limited understanding” of AI. (And on and on it goes.)

It’s worth noting, however, that Musk isn’t alone in this thinking. Physicist Stephen Hawking and philosopher Nick Bostrom also have expressed reservations about the potential impact of AI on humankind — but chances are they are talking about a more generalized artificial intelligence being studied in labs at the likes of Facebook AI Research, DeepMind and Maluuba, rather than the more narrow AI we are seeing today.

Brooks pointed out that many of these detractors don’t actually work in AI, and suggested they don’t understand just how difficult it is to solve each problem. “There are quite a few people out there who say that AI is an existential threat — Stephen Hawking, [Martin Rees], the Astronomer Royal of Great Britain…a few other people — and they share a common thread in that they don’t work in AI themselves.” Brooks went onto say, “For those of us who do work in AI, we understand how hard it is to get anything to actually work through product level.”

AI could be a misnomer

Part of the problem stems from the fact that we are calling it “artificial intelligence.” It is not really like human intelligence at all, which Merriam Webster defines as “the ability to learn or understand or to deal with new or trying situations.”


"The analogy that the brain is like a computer is a dangerous one, and blocks the progress of AI."

- Pascal Kaufmann, founder of Starmind


The algorithm would probably be tuned to wait for the pedestrians to cross, but what if they had no intention of crossing because they were waiting for a school bus? A human driver could signal to the pedestrians to go, and they in turn could wave the car on, but a driverless car could potentially be stuck there endlessly waiting for the pair to cross because they have no understanding of these uniquely human signals, he wrote.

Each of these examples show just how far we have to go with artificial intelligence algorithms. Should researchers ever become more successful at developing generalized AI, this could change, but for now there are things that humans can do easily that are much more difficult to teach an algorithm, precisely because we are not limited in our learning to a set of defined tasks.