Sunday, May 1, 2016

The long tail of artificial narrow superintelligence

As readers of the blog have probably guessed, there is a distinct strain of futurism in the lab, mostly led by Paul, Ally, Ian and I (everyone else just mostly rolls their eyes, but what do they know?). So it was against this backdrop that we had a heated discussion recently about the implications of AlphaGo.

It started with a discussion I had with someone who is an expert on machine learning and knows a bit of Go, and he said that AlphaGo was a huge PR stunt. He said this based on the fact that the way AlphaGo wins is basically by using deep learning to evaluate board positions really well, while doing a huge number of calculations to determine what play to make to evaluate that position. Is that really “thinking”? Here, opinions were split. Ally was strongly in the camp of this being thinking, and I think her argument was pretty valid. After all, how different is that necessarily from how humans play? They probably think up possible places to go and then evaluate the board position. I was of the opinion that this is a different type of thinking than human thinking entirely.

Thinking about it some more, I think perhaps we’re both right. Using neural networks to read the board is indeed amazing, and a feat that most thought would not be possible for a while. It’s also clear that AlphaGo is doing a huge number of more “traditional” brute force computations of potential moves than Lee Sedol was. The question then becomes how close the neural network part of AlphaGo is compared to Lee Sedol’s intuition, given that the brute force logic parts are probably tipped far in AlphaGo’s favor. This is sort of a hard question to answer, because it’s unclear how closely matched they were. I was, perhaps like many, sort of shocked that Lee Sedol managed to win game 4. Was that a sign that they were not so far apart from each other? Or just a weird flukey sucker punch from Sedol? Hard to say. I think the fact that AlphaGo was probably no match for Sedol a few months prior is probably a strong indication that AlphaGo is not radically stronger than Sedol. So my feeling is that Sedol’s intuition is still perhaps greater than AlphaGo’s, which allowed him to keep up despite such a huge disadvantage is traditional computation power.

Either way, given the trajectory, I’m guessing that within a few months, AlphaGo will be so far superior that no human will ever, ever be able to beat it. Maybe this is through improvements to the neural network or to traditional computation, but whatever the case, it will not be thinking the same way as humans. The point is that it doesn’t matter, as far as playing Go is concerned. We will have (already have?) created the strongest Go player ever.

And I think this is just the beginning. A lot of the discourse around artificial intelligence revolves around the potential for artificial general super-intelligence (like us, but smarter), like a paper-clip making app that will turn the universe into a gigantic stack of paper-clips. I think we will get there, but well before then, I wonder if we’ll be surrounded by so much narrow-sense artificial super-intelligence (like us, but smarter at one particular thing) that life as we know it will be completely altered.

Imagine a world in which there is super-human level performance at various “brain” tasks. What will be the remaining motivation to do those things? Will everything just be a sport or leisure activity (like running for fun)? Right now, we distinguish (perhaps artificially) between what’s deemed “important” and what’s just a game. But what if we had a computer for doing proving math theorems or coming up with algorithms, one vastly better than any human? Could you still have a career as a mathematician? Or would it just be one big math olympiad that we do for fun? I’m now thinking that it’s possible for virtually everything humans think is important and do for "work" could be overtaken by “dumb” artificial narrow super-intelligence, well before the arrival of a conscious general super-intelligence. Hmm.

Anyway, for now, back in our neck of the woods, we've still got a ways to go in getting image segmentation to perform as well as humans. But we’re getting closer! After that, I guess we'll just do segmentation for fun, right? :)


  1. Doesn't someone need to do it first, and many times over, for a dumb machine to learn it well enough from the data to replace us? AlphaGo had many games to look at. There will always be things never tried, combinations of circumstances never encountered, for which extrapolation is needed. The direction in which to extrapolate is a matter of taste, so as long as we trust the direction from our tastes more than a random one from the machine's prior, there will be jobs for humans - to make decisions.

    1. While AlphaGO did have a lot of training data from human matches to learn from, it actually generated a large amount more training data by playing it self, and then using that self-play as the learning set in the next iteration.

      Proof that this is actually valuable and not just circular was that AlphaGo played moves that human players wouldn't think of, even with hindsight.

  2. As long as all we have is narrow AI, there will be a job in deciding what narrow AI needs to be created next. I think once an AI can do that, we must be talking about general intelligence.

    1. That's an interesting definition! And one that admits a more graded, nuanced view of general intelligence.