September 8, 2016


Welcome, Robot Overlords. Please Don't Fire Us? : Smart machines probably won't kill us all--but they'll definitely take our jobs, and sooner than you think. (KEVIN DRUMMAY, JUNE 2013, Mother Jones)

 We've moved from computers with a trillionth of the power of a human brain to computers with a billionth of the power. Then a millionth. And now a thousandth. Along the way, computers progressed from ballistics to accounting to word processing to speech recognition, and none of that really seemed like progress toward artificial intelligence. That's because even a thousandth of the power of a human brain is--let's be honest--a bit of a joke. Sure, it's a billion times more than the first computer had, but it's still not much more than the computing power of a hamster.

This is why, even with the IT industry barreling forward relentlessly, it has never seemed like we were making any real progress on the AI front. But there's another reason as well: Every time computers break some new barrier, we decide--or maybe just finally get it through our thick skulls--that we set the bar too low. At one point, for example, we thought that playing chess at a high level would be a mark of human-level intelligence. Then, in 1997, IBM's Deep Blue supercomputer beat world champion Garry Kasparov, and suddenly we decided that playing grandmaster-level chess didn't imply high intelligence after all.

So maybe translating human languages would be a fair test? Google Translate does a passable job of that these days. Recognizing human voices and responding appropriately? Siri mostly does that, and better systems are on the near horizon. Understanding the world well enough to win a round of Jeopardy! against human competition? A few years ago IBM's Watson supercomputer beat the two best human Jeopardy! champions of all time. Driving a car? Google has already logged more than 300,000 miles in its driverless cars, and in another decade they may be commercially available.

The truth is that all this represents more progress toward true AI than most of us realize. We've just been limited by the fact that computers still aren't quite muscular enough to finish the job. That's changing rapidly, though. Computing power is measured in calculations per second--a.k.a. floating-point operations per second, or "flops"--and the best estimates of the human brain suggest that our own processing power is about equivalent to 10 petaflops. ("Peta" comes after giga and tera.) That's a lot of flops, but last year an IBM Blue Gene/Q supercomputer at Lawrence Livermore National Laboratory was clocked at 16.3 petaflops.

Of course, raw speed isn't everything. Livermore's Blue Gene/Q fills a room, requires eight megawatts of power to run, and costs about $250 million. What's more, it achieves its speed not with a single superfast processor, but with 1.6 million ordinary processor cores running simultaneously. While that kind of massive parallel processing is ideally suited for nuclear-weapons testing, we don't know yet if it will be effective for producing AI.

But plenty of people are trying to figure it out. Earlier this year, the European Commission chose two big research endeavors to receive a half billion euros each, and one of them was the Human Brain Project led by Henry Markram, a neuroscientist at the Swiss Federal Institute of Technology in Lausanne. He uses another IBM super­computer in a project aimed at modeling the entire human brain. Markram figures he can do this by 2020.

Posted by at September 8, 2016 3:29 PM