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Comment by conanbatt | Hacker News Reader

I have to say that as a once professionally aspiring Go player, the advances of AI have been incredible.

When I started playing Go, it took me about 4 months to beat the stronger available computer AI. Today, the strongest computers would be a challenge to play against evenly.

However, even after all the patterns and montecarlo solutions, they still fumble the initial stage of the game. As a Go player, I look forward to AI's that beat humans in that stage of the game, because at that point, we will be able to learn from them.

Until then, their victories are purely computational, and they are not even interesting to play with.

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I believe the Chess/Go engines are not really AI. They are operating on a set of rules written in code. It can compute and search the moves faster than a human. It can also remember a longer chain of moves better than a human can. But all those rules have been fed via code and it is just exploring that state space.

Only recently researchers have started to expose chess to reinforcement learning (i.e. a true AI engine that learns chess on its own and beats humans). But the existing commercial engines that beat humans (like stockfish) are anything but AI.

What happens if you try to reframe the argument in terms of chess not requiring a great deal of intelligence?

Right. This whole topic revolves about human identity as well. In Go, you can tell who you are playing with, and what emotions he is going through (particularly easy to do when you are stronger than your opponent).

Historically, when you play with a bot you sense relentlessness(instead of calmness), and sometimes desperation(computers sometimes go wacky when they see they are losing).

What will we sense when they are even or stronger in the more artistic and strategic phases of the game?

I think this is true of all AI today. Personally I've always been a fan of the distinction between "Virtual Intelligence" and "Artificial Intelligence" (Thanks to Mass Effect!). Currently all "AI" is really "VI" in that it's exploiting a closed system of rules that it can implement faster, better, and longer than a human because it can do the equivalent of rote memorization and state tree traversal. However as Zuckerberg is saying, nobody is close to implementing something that actually approximates human/animal intelligence other than in single-dimensional ways.

What you are doing is just moving the goalpost a bit further. Once the computers can learn the rules by themselves, someone will come up and write that it's not true intelligence because of xxx reason. How do you know your own intelligence isn't computational?

Actually there is some recent work on using deep NN's to improve go ai's. This is the same sort of AI used in state of the art image/voice recognition and does try to go beyond exhaustive analysis to extract some deeper latent structure.

It isn't reinforcement learning though. That isn't state of the art (at the moment), instead modern deep NN's are learned by gradient descent. This can be made to work really well but isn't regarded as similar to how biological neural networks work.

So, a "real AI" needs to learn? Why does this distinction matter?

A lot of people mistake Machine Learning and General Artificial Intelligence. From what I understand (and I'm no expert), ML is a focused task which it does one thing really really well.

The idea of General Artificial Intelligence is something that can learn anything you throw by adapting and reinforcing base assumptions.

There's a podcast called Talking Machines that really dives into a lot of this stuff. He's freaking great and pretty darn entertaining.

Actually, today has been a good news for AI. Looks like agent beats the human player at go.

http://www.wired.com/2016/01/in-a-huge-breakthrough-googles-...

Actually this just appeared on HN as well:

http://www.bbc.com/news/technology-35420579

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