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>Do you actually play chess?

Yes. I have a chess set on my kitchen counter from playing blindfold last night. I led my high school to the state championship. I am rated ~2000 in blitz and crazyhouse on Lichess. I watched 10+ analysis videos of AlphaZero when it came out.

If you have a criticism of my claim, just state the criticism. Even if I was a grandmaster, I would not be immune from being wrong.

And to be clear, my claim is that human experts usually (not always) understand the pros and cons of a move made by AlphaZero. My claim is not that human experts usually know why one move, with its set of pros and cons, was selected over another move, with a different set of pros and cons.

And of course, there are exceptions. Super long endgame sequences, for example. Even with a lifetime I doubt any human can understand these sequences beyond the tautological "it's winning because it wins": https://en.chessbase.com/post/just-one-of-17-823-400-766-pos...

Being friends with a bunch of grandmasters....3500 is a long ways you there. You might both be right.

> And to be clear, my claim is that human experts usually (not always) understand the pros and cons of a move made by AlphaZero.

It’s more than human experts apply a posteriori the tools they typically use to analyse chess games to games played by AlphaZero. These tools are specifically crafted to analyse chess so they obviously shed some lights on what’s happening but even then some plays were seen as counterintuitive.

An easy example is how AlphaZero can sacrifice pieces or tempos to cripple the mobility of the opposition pieces. If you carefully analyse the full game, you can see that it’s probably why the move was made but it’s completely counterintuitive when it’s played as the relative value of some winning tradeoffs made by AlphaZero are impossible to evaluate for a human player.

> It’s more than human experts apply a posteriori the tools they typically use to analyse chess games to games play by AlphaZero.

I largely agree with this and I was about to make the same point before reading your comment. However, as GP said upthread:

> This paper builds on that concept and finds that not only are a superhuman AI's moves usually understandable by experts, but even some of the superhuman AI's internal representations are understandable by experts (!).

Internal structures mapping to concepts high level players already use is a good indicator that the AI is "thinking" about chess in the same way as humans do.

Today the world's best players (not just chess) play against bots like AlphaZero. They can learn from their opponent. Today that is what it takes to be the best in the world.

Even if most moves are normal, clearly AlphaZero and other bots are doing something different or they wouldn't be world's best.

> Half the moves made by an AI at 3500+ ELO (e.g. Stockfish 13+) is completely alien to even a human super GM

The analysis in the paper draws the exact opposite conclusion. Alpha zero’s play agrees remarkably with humans despite not seeing a human game (e.g. it found the Berlin defense to the Ruy Lopez).

Also your point is trivially not true for other engines because they use books from human-played games.

Engines don’t win by playing novel moves all the time that humans can’t comprehend. They win by playing the most accurate move every time (never making a mistake).

"Also your point is trivially not true for other engines because they use books from human-played games."

Books are only used in openings, and in (some) endings, in the form endgame tables. Throughout the middle game - where most fireworks happen - an engine is on its own.

And yes, engine's choices are often incomprehensible even for an elite human player (although it isn't literally every second move).

There was this Polish player, Patrycja Waszczuk, accused of cheating (she got banned for 2 years but she appealed, there's an ongoing legal battle).

The case got quite a bit of attention, and parts of her suspicious games ended up analyzed by Nakamura (a world class player, for those unfamiliar with the field).

There've been several moves which left him dumbfounded, as in "no human would even consider that in this position". I don't have a link, but it can be found on YT.

A distinction should be made between coming up with a move and understanding the motivation for the move.

Incomprehensible moves are going to overwhelmingly be from deep searches while fast polynomial time heuristics such as that would come from neural networks are much more likely to be human discernible.

So if we can identify positions where fast NN heuristics make unexplained but very strong moves, we might be able to reverse engineer those heuristics and learn something new about the game?

Yes, I'd say that's a big aspect of what the tools developed in this paper enable.

"A distinction should be made between coming up with a move and understanding the motivation for the move."

Sure, but realizing what the reasoning could be in retrospect (sort of reverse engineering) is by definition easier than coming up with a (paradoxical) move in the first place, so if you can't even do the former...

"polynomial time heuristics such as that would come from neural networks are much more likely to be human discernible."

AlphaZero's playing style definitely feels more human-like, as it has a "speculative" quality to it.

To quote no other than Kasparov:

"I admit that I was pleased to see that AlphaZero had a dynamic, open style like my own. The conventional wisdom was that machines would approach perfection with endless dry maneuvering, usually leading to drawn games. But in my observation, AlphaZero prioritizes piece activity over material, preferring positions that to my eye looked risky and aggressive. Programs usually reflect priorities and prejudices of programmers, but because AlphaZero programs itself, I would say that its style reflects the truth."

It's probably a bit deceptive, because what looks risky to us, probably isn't really perceived as "risky" by the neural network, which can "feel" the deeper correctness of the decision, much unlike traditional engines, whose conservative cautiousness stems from their inherent limitations. (We know that now - before AZ it was assumed this is simply the "right" way to play, just like Kasparov says).

> but realizing what the reasoning could be in retrospect (sort of reverse engineering)

Not in this situation. While the chess engine performed a network guided non-deterministic search and returned a result, its neural network did not. With tools like in the paper, we can now map the network's internal states to something close to human relatable concepts.

Even without such tools we could observe what lines were productive by analyzing a few ply deep and comparing with other moves we could have made. It's closer to something becoming obvious with hindsight, where seeing what works compared to what you thought of helps to highlight the flaw in your plan or something you missed, while reinforcing your understanding of the engine's moves. That scenario is a common enough one.

I meant "reasoning" as in finding out the objective reason, within the realm of chess itself. Not in how the neural network conducted this reasoning under the hood.

To add to Galanwe's point:

Wikipedia says (about AlphaGo):

"AlphaGo's playing style strongly favours greater probability of winning by fewer points over lesser probability of winning by more points. Its strategy of maximizing its probability of winning is distinct from what human players tend to do which is to maximize territorial gains, and explains some of its odd-looking moves."

In other words optimizing the likelihood of winning looks strange to experts who use other heuristics to assess positions.

I'm not a chess player, but I was into competitive Pokémon for a time.

Any decent player will prioritise win probability over loss minimisation, even to the point of sacrificing multiple Pokémon to get a single turn of setup with your main sweeper.

While it seems confusing to newbies, experienced players know exactly why they're making the choices that they are.

The same is true of Magic: The Gathering where an oft repeated lesson is "life is a resource". Meaning you shouldn't be afraid to spend it to win.

My guess would be that for chess it's more difficult to gauge the difference in real time.

Right. I’m saying expert human players of deep and complex games still have a heuristic tied to human concepts: piece count, piece value, safety, mobility, etc. So a professional considering a line of play might be turned off that line if one of those measures would suffer.

There are some amazing games of Alpha Zero playing Stockfish (the other best chess program) where it seems Alpha Zero just doesn’t give a fuck. It’s just sacrificing it’s own pieces and not clear (to a human… or Stockfish) what the plan is. Later it achieves a zugzwang against Stockfish, which is the most humiliating thing it could do (if either program had emotions).

Especially given the rise of speed chess over the last few years. Focus on solid heuristics and speed, not finding strictly optimal moves.

Stockfish prior to adopting a neural network evaluation engine made a lot of questionable-looking moves. But it makes many more natural looking moves after being upgraded to use the NN evaluation engine. Broadly speaking, NN based engines make more human looking moves compared to rule-based evaluations[1], with the human-expert accuracy going up as the human rating increases.

[1]https://www.cs.toronto.edu/~ashton/pubs/maia-kdd2020.pdf (scroll down for the graphs)

I am sorry but you're completely wrong. Modern engine's moves are usually easy to understand if you can play around with the engine running. The engine is just way more precise. Usually the variations needed to understand the moves aren't particularly long either. For me (2300 ELO) it's usually 3-6 ply deep when I go "ahhhh obviously this is better".

The method you're referring to is based on detecting how often a human picks the top move out of 2-3 similarly rated ones and most of the time understanding which one of the 56.5%, 57.1% and 57.3% moves is the best is difficult but understanding why 55% move is better than 51% one is usually pretty easy. I am using AlphaZero/Lc0 evals here btw as I find them more intuitive than traditional engines ones.

I think your opinion might have been formed many years ago. There was indeed a time where engines like traditional Stockfish (before NNUE) were already much stronger than humans but their strategic play made little sense. It turned out that they were in fact weak strategically and modern crop of engines showed it. Try running Lc0 with modern network vs say Stockfish 7 and you will see games which could be described as humiliating defeats one after another.

It turned out that we are actually very good at figuring chess out. Not very precise, slow and inconsistent but we've got most of the high level concepts right.

It doesn't mean engines don't come up with occasional move that is just too deep to get without long analysis and too counterintuitive to consider during play. It's just rare. Most of the moves are intuitive, and make a lot of sense to a human.

Some moves are hard to understand, but it's more like one or two per game. The AI is after all a lot better than Nakamura and Co.

The usual way to detect cheating is detecting incredible accuracy over a whole game, not individual engine like moves.

You can find out why an engine is making a move though. Hikaru and Magnus won't immediately understand why a move was played but they eventually will as they analyze the game.

> Do you actually play chess? Half the moves made by an AI at 3500+ ELO (e.g. Stockfish 13+) is completely alien to even a human super GM like Nakamura or Carlsen

No they're not. GMs get thrown out on certain moves, because we tend to play chess strategically, that is, we make a plan and try to find ways to make it work. AIs look like they have a plan and then immediately change course when they compute it's beneficial to do so.

As GP says, it takes a minute or two, but those same GMs will (Nakamura does this constantly in some of his streams) reevaluate the strategy, play some lines to see what is tripping the AI and then figure it out. It's that rapid change of strategy from, as an example, extremely defensive to wildly aggressive, which makes them suspicious that they're playing a computer.

> And here I think we see the same thing: although a superhuman AI 'understands' chess better than human experts, its understanding is still leverages the same concepts.

In that I'd agree with you that GP is wrong. For the AI there's no concepts, it's evaluating the chance of success, the perceived "weights" are irrelevant, you could map any number of concepts and get a correlation between them.

Sure, but when I watch Carlsen or Nakamura okay, their moves often look alien to me.