Can a Computer Devise a Theory of Everything?

Can a Computer Devise a Theory of Everything?

Can a Computer Devise a Theory of Everything?

Can a Computer Devise a Theory of Everything?

“By the times that A.I. comes back and tells you that, then we have reached artificial general intelligence, and you should be very scared or very excited, depending on your point of view,” Dr. Tegmark said. “The reason I’m working on this, honestly, is because what I find most menacing is, if we build super-powerful A.I. and have no clue how it works — right?”

Dr. Thaler, who directs the new institute at M.I.T., said he was once a skeptic about artificial intelligence but now was an evangelist. He realized that as a physicist he could encode some of his knowledge into the machine, which would then give answers that he could interpret more easily.

“That becomes a dialogue between human and machine in a way that becomes more exciting,” he said, “rather than just having a black box you don’t understand making decisions for you.”

He added, “I don’t particularly like calling these techniques ‘artificial intelligence,’ since that language masks the fact that many A.I. techniques have rigorous underpinnings in mathematics, statistics and computer science.”

Yes, he noted, the machine can find much better solutions than he can despite all of his training: “But ultimately I still get to decide what concrete goals are worth accomplishing, and I can aim at ever more ambitious targets knowing that, if I can rigorously define my goals in a language the computer understands, then A.I. can deliver powerful solutions.”

Recently, Dr. Thaler and his colleagues fed their neural network a trove of data from the Large Hadron Collider, which smashes together protons in search of new particles and forces. Protons, the building blocks of atomic matter, are themselves bags of smaller entities called quarks and gluons. When protons collide, these smaller particles squirt out in jets, along with whatever other exotic particles have coalesced out of the energy of the collision. To better understand this process, he and his team asked the system to distinguish between the quarks and the gluons in the collider data.

“We said, ‘I’m not going to tell you anything about quantum field theory; I’m not going to tell you what a quark or gluon is at a fundamental level,’” he said. “I’m just going to say, ‘Here’s a mess of data, please separate it into basically two categories.’ And it can do it.”


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