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Smarter Than All My Bosses

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Right now, AI can't tell the difference between a cat and a dog. AI needs thousands of pictures in order to correctly identify a dog from a cat, whereas human babies and toddlers only need to see each animal once to know the difference. But AI won't be that way forever, says AI expert and author Max Tegmark, because it hasn't learned how to self-replicate its own intelligence. However, once AI learns how to master AGI—or Artificial General Intelligence—it will be able to upgrade itself, thereby being able to blow right past us. A sobering thought. Max's book Life 3.0: Being Human in the Age of Artificial Intelligence is being heralded as one of the best books on AI, period, and is a must-read if you're interested in the subject.

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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."

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TRANSCRIPT:

Max Tegmark: I define intelligence as how good something is at accomplishing complex goals. So let’s unpack that a little bit. First of all, it’s a spectrum of abilities since there are many different goals you can have, so it makes no sense to quantify something’s intelligence by just one number like an IQ.

To see how ridiculous that would be, just imagine if I told you that athletic ability could be quantified by a single number, the “Athletic Quotient,” and whatever athlete had the highest AQ would win all the gold medals in the Olympics. It’s the same with intelligence.

So if you have a machine that’s pretty good at some tasks, these days it’s usually pretty narrow intelligence, maybe the machine is very good at multiplying numbers fast because it’s your pocket calculator, maybe it’s good at driving cars or playing Go.

Humans, on the other hand, have a remarkably broad intelligence. A human child can learn almost anything given enough time. Even though we now have machines that can learn, sometimes learn to do certain narrow tasks better than humans, machine learning is still very unimpressive compared to human learning. For example, it might take a machine tens of thousands of pictures of cats and dogs until it becomes able to tell a cat from a dog, whereas human children can sometimes learn what a cat is from seeing it once. Another area where we have a long way to go in AI is generalizing.

If a human learns to play one particular kind of game they can very quickly take that knowledge and apply it to some other kind of game or some other life situation altogether.

And this is a fascinating frontier of AI research now: How can we have machines—how can we can make them as good at learning from very limited data as people are?

And I think part of the challenge is that we humans aren’t just learning to recognize some patterns, we also gradually learn to develop a whole model of the world.

So if you ask “Are there machines that are more intelligent than people today,” there are ...

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