> Alpha Zero has proved this by smashing Stockfish every time it plays it.

Google played Alpha Zero vs Stockfish under their own conditions. I think this was a mistake to stay in the labs by themselves.

I think Google would have benefited from participating in WCCC 2019 for example, where Johnny (1200x core cluster) won the day.

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Its a completely different field when you're actually competing against someone else. Google did some impressive things in the lab for sure, but they have NOT actually stepped into the competitive ring yet.

I'm sure AlphaZero is good, and probably would make a good showing at one of these contests. But you're putting the cart before the horse here.

EDIT: Maybe MCTS + Neural Nets truly is the superior way of preparing board game knowledge? If so, the next step is building out the opening-database and to start looking for holes where AlphaZero loses. It wasn't a complete blowout: AlphaZero lost some games to Stockfish. Why did AlphaZero lose in those games? Is there a set of opening moves that will lead AlphaZero down that losing path in a true competitive setting?

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EDIT: Case in point: Google did NOT incorporate randomness into AlphaZero's algorithm. It always chose the best move it believed in, this leads it prone to opening database attacks. See how the competitive mindset already messed AlphaZero's careful laboratory preparation up already?

Sure, AlphaZero 2.0 might have programmed randomness added to it, if this were a true competitive environment. But that's how cyborg chess evolves: I point out a weakness in the program, exploit it in a game, and then Google goes back to their labs to make something better next year.

LeelaZero is getting stronger than Stockfish though, so the neural net approach certainly seems to be beating brute force.

AlphaZero does not "smash" Stockfish. In official games they draw 90+% of the time, and stockfish won a non-negligible number of games. AZ is still the "winner" but honestly it didnt seem like a massive leap forward to me.

Though I do still agree in thinking there is likely a deeper theory yet uncovered.

>So there was a window between the 90's and today where a human player could have discarded the traditional measuring sticks of "trade-value" and beaten DeepBlue.

I have to disagree: even after the discoveries by Alpha Zero, the best chess players are not able to beat Stockfish. Stockfish is just too good at Brute forcing long tactical advantages. Playing really well positionally doesn't matter a lot if your opponent can look 30 moves into the future.

They said Deep Blue, not Stockfish. Stockfish is vastly stronger.

I'm not sure how big the window was, if it existed, but it seems like it might have. Kasparov and Deep Blue themselves were fairly equally matched, it doesn't seem impossible that Kasparov + AI-aided theories would have a window of advantage over the Deep Blue -> Stockfish evolution.

That's a really oversimplified view. The truth is beyond a certain point no amount of "theorizing" is enough to overcome the sheer computational power of programs. AlphaZero just took advantage of recent advances in neural networks and GPU hardware to achieve a better tradeoff between hard rules and heuristics than Stockfish. Both of them are probably Pareto efficient in some sense, and barring some insane undiscovered loophole in the rules of chess it's unlikely that any human can come close to beating them.

Question: if we discovered a way for white to always force a win, would humans be able to use that to beat black AIs consistently?

This has been already found for checkers, but players around the world still play it. If the way is too complicated then we cannot apply it unaided.

It feels kind of surprising that this doesn't exist (yet). There are only a handful of reasonable moves per turn, and given the unbelievable amounts of moves that offline chess engines can process given time and server farms, I'm just surprised that doesn't overwhelm the reasonable branches at some point into forcing a win. Maybe someday.

For what it's worth, endgame table databases exist that give perfect play for when there are a limited number of pieces left on the board.

As of now, we have databases for perfect play when 7 pieces remain on the board (including the two kings). The 8-piece tablebase is computationally possible, but I don't believe a comprehensive release has come out yet. Even the current 7-piece tables are incomplete because situations like lone king vs. six opposing pieces haven't been explicitly calculated due to their obviousness.

The size of chess is approximately 10^50

Computers cost about $0.01 / GFlop (10^12 operaions); $1e47 for 10^50 operations.

The world economy is $1e14/yr.

That's 1e33 years to check every chess position, using all of Earth resources.

You'd need to find symmetries to collapse the search spaceby a factor of 10^33

Yes; further reading: https://en.wikipedia.org/wiki/Solving_chess