Contact plays: Partial yes: I seem remember thinking after Alphago vs Lee Sedol that the bot made some contact plays when a human wouldn't necessarily. I only remember a few now in joseki - game 1 and game 2 to quickly
settle a weak stone. And there is the famous 4-4 attach to the 3-4 stone in the Chinese opening that it played in a self-play game.
Go seigen is also famous for shoulder hit small knight enclosure and tenuki like Master vs Mi Yuting
but is there not the possibility that the go equivalent is the Monte Carlo playout.
I don't really think this is significant. I have experimented with Leelabot 5d with and without the neural net, and the skill level is horribly bad without it, only depending on Monte Carlo. Also, I do think the human early game is a weak point compared to perfect play, while the analysable endgame we are much better at. (e.g. Conway's surreal numbers). I find that bots have a much more accurate sense of logic to guide where to play in the opening, and as I have said elsewhere, it is the opening where we can learn a lot from them. (Pros normally say the opening is irrelevant but I think it is more because no-one has a clue about what value judgement is best) I also think their use of middlegame probes is particularly educational, or being prepared to make a large exchange, or their value judgment when the opponent makes a probe/overplay.
that AlphaGo is seeing opening lines 40 move ahead and evaluating them
I don't know if Uberdude is making an implication about general reading. It looks at some simple variations very far ahead to help it judge the position but it can hardly say it is the best variation for both sides. However, given any one particular variation (e.g. crawling on first line, Lee Sedol game 5), I think the probability it was considered among the myriad of variations is drastically low, especially when the neural net is unlikely to recommend crawling so far on the first line. The key point is in the quality of the neural net.
It seems more likely that it has stumbled upon a previously unsuspected basic go truth such as two eyes early on being even more important than we have ever suspected. Where the human neglects early eye shape, the long-term power of the Monte Carlo method, say, manages to exploit that over a long period.
I agree with the basic meaning expressed here, though it is clearly very complicated. You are describing the "settle fast and simplify" feature of bots. While this may often be a good idea that humans can learn from, I suspect that much of it is due to the nature of the programming of the bot and the neural net:
- It likes to play simply because it is bad at reading, and also may be bad at life and death (not sure how difficult alphago finds this, but other bots certainly have issues). And it fears uncertainty. Its skill will always be limited if it is has such fear.
- Its reading is guided by its own neural net, so it fears what it thinks are good moves, but may miss good moves from the opponent that it wouldn't play. It is used to playing good shape moves, and can miss severe moves from the other side. Though this seems to argue in the opposite direction. But in any case, it can miss local things easily, and this inevitably affects its style. After playing with leelabot a lot for 3 months, though it is a lot stronger than me, I am starting to see the subtle weaknesses in its neural net beyond the basic semeai, reading issues.
Things like that shoulder hit into Mi Yuting's top right shimari make we wonder if most pros are reluctant to do so even if they consider it because they fear it is aji keshi and lack the confidence in their judgement to play it,
for sure
Many pros believe in the value of the centre, but it is so uncertain, few dare to use it, so it becomes impractical for humans to study it. At least my own attempts at the London open to imitate a bot style didn't go so well. And a mutual friend mentioned how he actually gets worse from playing Alphago