No, I think the statement's approximately correct. Chess has an average branching factor of 35, Go has an average branching factor of 250, intuition is required to prune candidate moves in Go in a way that it is only extremely minimally required in Chess.

But is this true as humans play it? I'm not good enough at either to really know for sure, but my impression is that while the branching factor makes a big difference for computers, it is essentially impossible for a human to manage the branching in either game (massive numbers of branches vs exceptionally massive numbers of branches). As a result, humans play both games at a high level by relying on intuition.

For instance, I read a while back (approximating and paraphrasing to follow...) that top chess players can think up to 10 moves ahead along a very few branches. So let's say that in chess, there are 30 million possible positions to evaluate, and in go, there are 300 trillion. They're both such an order of magnitude different for humans that it makes really no difference in terms of how we play the game, so intuition takes over. For computers, it's a different story.

I think humans usually have ten or less plausibly-good moves to consider per turn in Chess, and simply consider all of them, compared to tens to hundreds in Go.

The true branching number of chess is much smaller, as most moves are obviously bad.

I know I will offend chess players by saying this but.. I feel like chess is more of a IQ contest, while Go is more Art-ish in how you move and slowly cripple and surround your opponent.

Realistically speaking, there aren't that many moves you can do in chess. Most of them are just blunder that would get you insta-killed by a good player. Contrast that to Go where there are so many good moves. This is why I think the Go AI is more impressive.

Part of me thinks that at some point in the future, we'll have Chess "solved". Not in a "That computer is too good for humans", but more in a mathematical sense where all avenues will have been explored and at any point you mathematically know from a position whether you 100% win. So, the computer will make a move, a second computer will make a move, and then they will agree on a draw. To be able to achieve this, I think it will be some kind of rainbow table for chess (on a much bigger scale obviously), where you can represent one position by a hash and just brute-force all possible solution from the "end-game" to the initial board. So, it's not even about AI, more about bruteforce and hardware. I know it's not possible to do this at the moment but quantum computing would be.

Why wouldn't the same be true for Go? Because the search space of good moves is larger?

Totally could be the same for Go, but yeah the search space seems much bigger. I feel in chess there are a few good moves on every turn whereas for Go it seems there are so many. But then, it may be because I'm less good at Go and I don't see the "obvious" move, not that I necessarily see it in chess but you know what I mean.

The number of legal boardstates in Go is higher than the number of atoms in the universe.

think of it this way: The search space for chess is much smaller, so we can lean very heavily on brute forcing in our A.I. implementations.

The search space for Go is much larger, so while brute force searches are critical in tight fighting, and in endgame play, something more has to happen to play go well in the middle game.

Chess fell to a much earlier generation of A.I. While Go held out until A.I. as a field had advanced as well several generations/decades as well.

Agreed. The part I object to is the unqualified statement that go requires a high degree of intuition, whereas chess doesn't. As humans play the game, I think it's safe to say that this is generally inaccurate. Both games, for humans, rely very heavily a high degree of intuition.

I would tend to agree that there is something interesting and new at work here, though, in that computers didn't get better than humans at go simply by applying the same brute force algorithm, just with more processing power. It does suggest that at least some of what we previously thought required "intuition" can be modeled through a random forest (I think that's what they're using, if not RF, then some other combination of ML).

In the video for the second match, a Google employee mentions that a neural net they call the policy net (trained on a large sample of historical games) provides intuitive moves, while another NN evaluates board strength. They apply the policy net to find multiple interesting moves, then continue to apply the net to anticipate the opponents moves to generate a tree of possible moves. It then just settles on which move to make that gives it the best odds of winning

Starts at 42:00 https://www.youtube.com/watch?v=l-GsfyVCBu0

I can't comment on Go, but with simpler games like Othello it's fairly apparent when your opponent is A.I. driven or human driven, in how they try to move the board position. Even against expert human players.

The AI yesterday played just like a human. You could not tell the human from the AI just based on the moves. That was amazing.

During the second game it made a move that was decidedly not human (which helped lead to the win later):

https://youtu.be/l-GsfyVCBu0?t=1h18m15s

In suppose the defence of the statement is that in chess intuition is used by humans, but not required for a computer. In go the computer needs it too.

But aren't they talking about the games as they apply to computers?