Inside Google's DeepMind Project: How AI Is Learning on Its Own
Watch the newest video from Big Think: https://bigth.ink/NewVideo
Join Big Think Edge for exclusive videos: https://bigth.ink/Edge
----------------------------------------------------------------------------------
Chances are, unless you happen to be in the Big Think office in Manhattan, that you're watching this on a computer or phone. Chances also are that the piece of machinery that you're looking at right now has the capability to outsmart you many times over in ways that you can barely comprehend. That's the beauty and the danger of AI — it's becoming smarter and smarter at a rate that we can't keep up with. Max Tegmark relays a great story about playing a game of Breakout with a computer (i.e. the game where you break bricks with a ball and bounce the ball off a paddle you move at the bottom of the screen). At first, the computer lost every game. But quickly it had figured out a way to bounce the ball off of a certain point in the screen to rack up a crazy amount of points. Change Breakout for, let's say, nuclear warheads or solving world hunger, and we've got a world changer on our hands. Or in the case of our computers and smartphones, in our hands. Max's latest book is Life 3.0: Being Human in the Age of Artificial Intelligence
----------------------------------------------------------------------------------
MAX TEGMARK:

Max Tegmark left his native Sweden in 1990 after receiving his B.Sc. in Physics from the Royal Institute of Technology (he’d earned a B.A. in Economics the previous year at the Stockholm School of Economics). His first academic venture beyond Scandinavia brought him to California, where he studied physics at the University of California, Berkeley, earning his M.A. in 1992, and Ph.D. in 1994.

After four years of west coast living, Tegmark returned to Europe and accepted an appointment as a research associate with the Max-Planck-Institut für Physik in Munich. In 1996 he headed back to the U.S. as a Hubble Fellow and member of the Institute for Advanced Study, Princeton. Tegmark remained in New Jersey for a few years until an opportunity arrived to experience the urban northeast with an Assistant Professorship at the University of Pennsylvania, where he received tenure in 2003.

He extended the east coast experiment and moved north of Philly to the shores of the Charles River (Cambridge-side), arriving at MIT in September 2004. He is married to Meia-Chita Tegmark and has two sons, Philip and Alexander.

Tegmark is an author on more than two hundred technical papers, and has featured in dozens of science documentaries. He has received numerous awards for his research, including a Packard Fellowship (2001-06), Cottrell Scholar Award (2002-07), and an NSF Career grant (2002-07), and is a Fellow of the American Physical Society. His work with the SDSS collaboration on galaxy clustering shared the first prize in Science magazine’s "Breakthrough of the Year: 2003."
----------------------------------------------------------------------------------
TRANSCRIPT:

Max Tegmark: I define intelligence simply as how good something is at accomplishing complex goals.

Human intelligence today is very different from machine intelligence today in multiple ways. First of all, machine intelligence in the past used to be just an always inferior to human intelligence.

Gradually machine intelligence got better than human intelligence in certain very, very narrow areas, like multiplying numbers fast like pocket calculators or remembering large amounts of data really fast.

What we’re seeing now is that machine intelligence is spreading out a little bit from those narrow peaks and getting a bit broader. We still have nothing that is as broad as human intelligence, where a human child can learn to get pretty good at almost any goal, but you have systems now, for example, that can learn to play a whole swath of different kinds of computer games or to learn to drive a car in pretty varied environments. And uh...

Where things are obviously going in AI is increased breadth, and the Holy Grail of AI research is to build a machine that is as broad as human intelligence, it can get good at anything. And once that’s happened it’s very likely it’s not only going to be as broad as humans but also better than humans at all the tasks, as opposed to just some right now.
I have to confess that I’m quite the computer nerd myself. I wrote some computer games back in high school and college, and more recently I’ve been doing a lot of deep learning research with my lab at MIT.
So something that really blew me away like “whoa” was when I first saw this Google DeepMind system that learned to play computer games from scratch.

Read the full transcript at https://bigthink.com/videos/max-tegma...