[visarga]

Does AlphaGo 'understand' go?

I think the key ingredient is 'being in the game', that means, having a body, being in an environment with a purpose. Humans are by default playing this game called 'life', we have to understand otherwise we perish, or our genes perish.

It's not about symbolic vs connectionist, or qualia, or self consciousness. It's about being in the world, acting and observing the effects of actions, and having something to win or lose as a consequence of acting. This doesn't happen when training a neural net to recognise objects in images or doing translation. It's just a static dataset, a 'dead' world.

AI until now has had a hard time simulating agents or creating real robotic bodies - it's expensive, and the system learns slowly, and it's unstable. But progress happens. Until our AI agents get real hands and feet and a purpose they can't be in the world and develop true understanding, they are more like subsystems of the brain than the whole brain. We need to close the loop with the environment for true understanding.

[goatlover]

It certainly doesn’t understand Go as a board game humans invented as a stimulating mental exercise that became competitive enough to see whether human programmers could come up with a program that could beat any human. And whatever cultural history went along with playing Go. Certainly chess playing has been used as an analogy in the west for many activities involving strategy. This is something no computer currently understands.

[visarga]

It might not understand the socio-cultural context of the game, but it understands strategy better than us.

[goatlover]

Right, so computers are better at us for many tasks, but if we're thinking in terms of general intelligence, the context is pretty important.

[julvo]

If the Agent would 'understand' Go, we'd expect it to adapt to a round board easily. Humans probably would. (argument from Gary Marcus)

[visarga]

Even a simple scaling down of the board from 19x19 to 9x9 has a huge effect on strategy. A circular board would probably produce something that doesn't look like Go and would confuse trained humans as well.

[harperlee]

So we not only need to encode/compress information, we also need to extract meaning that is (at least partially) reusable on new contexts.