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."

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.

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.

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.

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.

You had this artificial simulated neural network, it didn’t know what a computer game was, it didn’t know what a computer was, it didn’t know what a screen was, you just fed in numbers that represented the different colors on the screen and told it that it could output different numbers corresponding to different key strokes, which also it didn’t know anything about, and then just kept feeding it the score, and all the software knew was to try to do randomly do stuff that would maximize that score.

I remember watching this on the screen once when Demis Hassabis, the CEO of Google DeepMind showed it, and seeing first how this thing really played total BS strategy and lost all the time.

It gradually got better and better, and then it got better than I was, and then after a while it figured out this crazy strategy in Breakout (where you’re supposed to bounce a ball off of a brick wall) where it would keep aiming for the upper left corner until it punched a hole through there and got the ball bouncing around in the back and just racked up crazy many points.

And I was like, “Whoa, that’s intelligent!” And the guys who programmed this didn’t even know about that strategy because they hadn’t played that game very much.

This is a simple example of how machine intelligence can surpass the intelligence of its creator, much in the same way as a human child can end up becoming more intelligent than its parents if educated well.

This is just tiny little computers, the sort of hardware you can have on your desktop. If you now imagine scaling up to the biggest computer facilities we have in the world and you give us a couple of more decades of algorithm development, I think is very plausible that we can make machines that cannot just learn to play computer games better than us, but can view life as a game and to do everything better than us.