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Comment by bradley_long | Hacker News Reader

Human can become tired, emotional and nervous. However, a computer/ software would not have these problems.

Especially for Lee, the whole world is looking at him. An "ordinary" human like me won't be able to make the right decisions under this pressure.

A great respect to Lee and the Developers of AlphaGo. Good Game!

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To address this, has anyone considered pitting AlphaGo against a team of 9p players consulting with each other, and perhaps taking charge of different conflicts on the board?

I can't find the source now, but I remember reading a while ago that a team of go players is, perhaps contrary to intuition, not significantly better than their strongest individual alone.

There's just no useful way to "pool" human thinking power and redistribute it to where it's needed the most – all the players will simultaneously consider the same branches. At best you reduce the risk of making a silly mistake, but sitting pros are already pretty good at that.

> all the players will simultaneously consider the same branches

A computer-assisted pool of humans might work. Feed each human a board state advanced by 1/2/3/N moves down the decision tree in some direction, and have them evaluate that particular sub-tree. It's a map-reduce problem!

For some reason this made me think of the Focused in A Deepness in the Sky - where real general AI isn't possible [at least where we are] so human minds are harnessed to solve problems in a deeply unpleasant way.

Amazon's Mechanical Turk!

AFAIK Mechanical Turk is just getting random people to do stuff for you - the concept of Focus is something else entirely and genuinely quite terrifying.

The book is on my reading list. I guess I'll have to bump it closer to the front, then.

If you haven't read A Fire Upon the Deep I'd recommend reading that first. It's not vital but there are some subtle links between the two that are rather cool (not the shared character, more the shared "physics" and tech).

Is there a non-dystopian way we can do this? The immediate problems seem to be bandwidth of communication and ability to quickly generate shared culture and jargon.

Are Bridgewater Capital's employees Ray Dalio's focused?

Possibly something like the "priming mods" used by police while on duty in Greg Egan's Quarantine:

https://en.wikipedia.org/wiki/Quarantine_%28Greg_Egan_novel%...

That's actually a really interesting idea. At this point you're basically replacing the "policy" neural network in AlphaGo with biological human neural networks.

> have them evaluate that particular sub-tree

Is there a meaningful response that each one could give that would be optimal? I figure this would only stand a chance of working if the pool of humans were cloned from a 9 dan :-)

Each one needn't give a 9 dan response. Each person gives one response to the best of their ability, and scores the situation on a scale of like 1–100. Collect all the responses and distribute the new board situations over the same – or a different – group of people. The score is mostly used to value the quality of previous moves. If a branch leads from a situation most people rated as 60 to a situation where most people rate 10 it's not a good branch.

Random human beings simply voting fared pretty well in chess though: https://en.wikipedia.org/wiki/Kasparov_versus_the_World

Of course, Go has way too many eligible possible moves for random people to vote on, but a large enough group of top pros might be able to do well just by voting.

I think the "Random" part is a bit disingenuous.

Four to five expert chess players suggested moves for the world team. I feel that for any reasonable non-expert it comes down to "choose between these suggested moves" rather than "pick a move". It's also not really random human beings, since the selection of participants is self-selected and therefore much more likely to contain very good chess players.

This is mostly semantics, but anyone who doesn't read your link might get the wrong idea, so I felt like clarifying it a bit.

Fair points. Thanks for pointing this out. I used "random" to really mean "not-top-pros." Moves suggested by experts being voted on by a large sum of people are kind of similar to how democracy and capitalism work.

Part of the reason go is a difficult computational problem is that local conflicts can have global relevance. If it was the kind of thing that could be decomposed easily to different humans then it would be an easier game for a computer to crack.

Yes, particularly because of the sente/gote dynamic. However, it takes time to read individual situations through, and this might be chunked and distributed. The problem, as usual, is efficiently communicating analysis in a useful way. There would probably need to be a hierarchy where "specialists" report to the captain about the battle, and then the captain has to prioritize and decide on the order of action for each region. But the minute detailed analysis for each region would be entirely delegated. Note that leiutenents would be responsible for understanding their region's relationship to the whole, but only the captain is truly responsible for understanding it all.

At the same time, only now are people seeing how its hard for people to play Go against computers. Until AlphaGo I didn't even think computers were close.

I know how hard it is. I still cannot beat my 15 year-old PDA when playing chess with it. The intelligence of Lee is far higher than I can interpret.

Go is much harder than chess. The tree of available moves is massive and difficult to prune, as it's hard to get a reasonable heuristic about the "strength" of a given Go position.

Actually computers were already in the top 10 percentile or so. I mean top go playing bots ranked something like 5dan amateur which is very hard to achieve, so it could already defeat most human players.

From being in the top 10% to beating the top player is a gigantic difference. Until AlphaGo computers were indeed "not close" to besting humans at Go.

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