You are right if by "better" you mean "competitively stronger at tournament or rapid conditions". Humans are still way stronger strategically and competitively if given enough time and resources to avoid tactical mistakes. So yes, humans still provide unique insight into chess every day in correspondence chess or analytic research.
Humans aren't stronger strategically anymore either, under any conditions.
In 2014 a heavily handicapped Stockfish beat the 5th ranked player in the world (Nakamura) under tournament conditions despite no access to its opening or closing books and a one pawn handicap.
The match you are referring to was played under tournament conditions that clearly handicapped the human Grandmaster. I read from the report of the match [0] that "The total time for the match was more than 10 hours [...] The two decisive games lasted 147 and 97 moves." This unfavourable conditions clearly penalized the human and so the result can hardly be taken as meaningful regarding the strategic superiority. From the quietness of my room I instead regularly find strategic plans that overcome my and my opponent's computers. Feel free to join the correspondence chess federation [1] to experience the joy and pain of strategic research!
I haven't seen any recent (last 5 years or so) articles by him showing he's doing any work on it. There's a few recent articles where he talks about how humans should work with machine learning, but nothing specific.
When he recently came out of chess retirement he didn't talk about it at all in either 2017 or 2019:
It's much more difficult than it used to be, but I think there is still some value to human guidance, more as a "referee" than anything else.
Right now we have essentially two top tier engines -- traditional brute force with alpha beta pruning (stockfish), and ML (leela). Both alone are incredibly strong, but they are strongest and weakest in different types of positions. A computer chess expert, who knows what kind of positions favor stockfish and what kind favor leela, could act as a "referee" between the two engines when they disagree, and when they are unanimous, simply accept the move.
Ten years ago, a grandmaster driving a single engine could typically beat an equal strength engine. I don't think that's the case anymore.
But I think if you have someone who is an expert at computer chess -- not so much a chess grandmaster, and you gave them Leela AND SF, and let them pick which one to use when in the case of conflicts -- they would score positive against either leela or stockfish in isolation.
Larry Kaufman designed his new opening repertoire book by doing exactly this -- running Leela on 2 cores + GPU, and stockfish on 6 cores, and doing the conflict resolution with his own judgement.
The human can certainly no longer pull his own moves out of thin air, though.
Computer mastery of Go has reached the point where it is a difficult task for an expert (read: grandmaster level) human to even follow what is happening. It is totally implausible that a human could resolve a conflict between top engines in a meaningful way.
It is unlikely that Chess is any different. Any superficial understanding by a human of which move is 'better' is just ignorance of the issues around evaluating a position. If you have statistical evidence that is something. 'But I think' is not evidence.
It might be entertaining to have a human involved. It isn't going to help with winning games.
I can't speak for Go but in Chess the best players in the world understand the nuances of a position still better than the computer engines and - if occasionally proven wrong by the computer analysis - are able to understand the refutation and refine their strategic eveluation. I know this because it's what I've been doing in the past seven years in the realm of correspondence chess to gain the title of international master.
I don't even know the rules of Go, but I am a long-time chess enthusiast, and I have a decent, but not top-level, understanding of chess (I am a FIDE master and I also play correspondence chess (which is human+engine) and have an interest in computer chess heavily).
I can absolute guarantee you that a human (who is an expert in computer chess, someone like Larry Kaufman) + engines will beat a single engine over the long run. With current tech and computing power, this is ONLY because we have brute force (with alpha-beta pruning) and ML engines that are at near-equal strength, and have strengths and weaknesses in different types of positions, and that those strengths and weaknesses are understandable.
If we did not have AlphaZero, I don't think the human would be able to add anything at all currently.
He recently spoke quite dismissively of computer- augmented chess on the Lex Friedman podcast. Essentially, the computer knows best...so computer and human isn’t meaningfully different from computer and rubber stamper.
I strongly disagree. The best correspondence chess players often improve over the computer suggestions. It takes time, energy and a great strategic knowledge, but it’s still possibile.
Source: I’m a correspondence chess international master
Maybe the future of computer augmented chess is to form teams of a computer, a person, and a dog. The computer comes up with chess moves. The person feeds the dog. The dog makes sure the person does not touch the board.
Not really, humans barely provide insight (if anything), which chess engines don’t already consider. Deep Blue could evaluate 200 million different moves... per second. And that’s from 1997.
The few and rare times an engine gets funky is usually in end-game positions where the engine can’t seem to find a sacrifice to win the game and will output a current position as drawn. These cases are few and I very much doubt that a human would be able to find these moves in an actual match.
Now if you’re talking about the way the chess engine learns, it can learn in two different ways: without human help (learning completely on its own giving it nothing but the rules which is how AlphaGo works), or with human aid (through chess theory accumulated over centuries of human matches that these engines have built in as part of their evaluations). Things get very interesting.
I’d recommend you to look up a few games between AlphaGo and Stockfish, which embody these two different philosophies and battle it to the teeth and bones. The matches are brilliant. I would say though that it seems like AlphaGo (learning the game entirely through scratch without human help) has seemed to triumph more times than Stockfish and with the nature of these systems, I’d suspect it to continue that trend.
I'm not sure it's right to characterise Deep Blue or Stockfish as repositories of human chess theory. Fundamentally they were all based on a relatively simplistic function for calculating the value of a board position combined with the ability to evaluate more board positions further into the future than any human possibly could (plus a database of opening moves). That approach seems thoroughly non-human, and represents a victory of tactical accuracy over chess theory or strategy.
However I agree that the games between AlphaGo and Stockfish are really interesting. It strikes me that the AlphaGo version of chess looks a lot more human; it seems to place value on strategic ideas (activity, tempo, freedom of movement) that any human player would recognise.
I think you're right, I meant to say that chess engines usually have book openings built into them which derive off of human chess theory but you're absolutely right in that they don't play in a human form.
It's kind of crazy how AlphaZero has managed the success it has. Stockfish calculates roughly 60 million moves per second and AlphaZero calculates at only 60 thousand per second. Three orders of magnitude less yet its brilliance is mesmerizing, tearing Stockfish apart in certain matches.
> ...learning completely on its own giving it nothing but the rules which is how AlphaGo works...
Not to be too picky, but it was AlphaGo _Zero_ that learned from the rules alone. AlphaGo learned from a large database of human played games: "...trained by a novel combination of supervised learning from human expert games". [1]
AlphaGo Zero, derived from AlphaGo, was "an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules". [2]
And some of the most amazing games are when AlphaGo is absolutely breaking chess "wisdom" left and right simply because it can see a forced solution on the horizon.
In official correspondence games the computer assistance is allowed so most (if not all) of the players usually start their analysis with the computer suggestions (Stockfish, Lc0 or others). Some players limit themselves to this and play the engine's move, others try to improve with their own contribution. If no human contribution was possible, correspondence chess would become an hardware fight while results show that the best players can defeat "naive" opponents that rely on computer suggestions. In this sense, every correspondence chess win is a win over the opponent's hardware and engine.
I'm interested because the experience in Go is humans simply can't keep up.
What is the evidence that it isn't a hardware or software differential between the players? I can't think of an easy way to ensure that both players started with computer-suggested moves of the same quality.
There are a lot of engines with rating on the chart way higher than the best humans, so every suggestion on their part should be in theory enough to overcome any human opponent. In practice most (if not all) of the players rely on Stockfish and Lc0 (both open source). During a game, most of the time the "best" move is easily agreed on by every reasonable engine on any decent hardware. Only in few cases during a game, the position offers two or three or more playable choices. In these cases a stronger hardware or a longer thought rarely makes the computer change his idea. It's a sort of horizon effect where more power doesn't translate into a really better analysis.
For example in a given position you could have 3 moves
M1 - a calm continuation with a good advantage
M2 - an exchange sacrifice (a rook for a bishop or a knight) for an attack
M3 - a massive exchange of pieces entering into a favorable endgame.
If the three choices are so different, the computer usually can't dwell enough to settle on a clear best move. Instead the human can evaluate the choices until one of them shows up as clearly best (for example the endgame can be forcefully won). In these cases the computer suggestion becomes almost irrelevant and only a naive player would make the choice on some minimal score difference (that can unpredictably vary on hardware, software version or duration of analysis). So the quality of the starting suggestion is somehow irrelevant if you plan to make a thoughtful choice.
I'm not sure about very recent chess engines, but for a long time, it was better. The human suggests several moves that would advance their strategy, and the computer dedicates its search time to evaluating the strength of those potential moves, which cuts down the search space considerably. It's called "advanced chess" or "centaur chess". https://en.wikipedia.org/wiki/Advanced_chess
The rating you are referring to are typically based on tournament or rapid games, where the limited time induces the human players to mistakes that the computer capitalizes on. Given enough time or with a “blunder check” option, the best human players are still strategically stronger. In correspondence chess, where the is much more time at disposal, the human players can still improve the computer suggestions.
Source: I’m a correspondence international chess master
Yeah I was thinking about classic or standard time controls. The last big cyborg tournament a few years ago I remember a computer coming in 1st and 2nd.
I wasn't thinking about correspondence but what was the latest large cyborg correspondence tournament?
I don't know the last one but I recall the matches of Hydra chess machine [0] in the early 2000s against GM Adams in tournament condition (5½ to ½ for the machine) and against GM Nickel in correspondence condition (2 to 0 for the human). Both Grandmaster were top players in their relative field so it showed very clearly how the time limitation impacted the competitive results. Nobody in the chess elite would claim that Hydra understood chess better than GM Adams but still he lost resoundigly due to the inevitable mistakes caused by the relatively fast time control.
But wasn't Hydra 2005 ~2800 ELO where as the current best chess engines like Leela Chess Zero or Stockfish are ~4000 ELO?
Just realized that correspondence chess is cyborg chess, I didn't know computers were legal in correspondence chess, but it makes sense now. Reading about it, it sounds like it's less about knowing chess, and more about understanding the applications you're using.
But Kasparov and others have given up on the idea that a human provides any unique insight into chess anymore. Computers are just better.