Neural networks are universal approximators. Choose a function and get enough data from it and you can approximate it very closely and maybe exactly. If initially creating function F required algorithm Y ("search" or whatever), you can do your approximation to F and then say "Look F without Y" and for all we know, the approximation might be doing things internally that are actually nearly identical to the initial F.
What is interesting is performing well under reasonable computational constraints i.e. doing it faster/with fewer flops than stockfish.
Also found in the paper: "While our largest model achieves very good performance, it does not completely close the gap to Stockfish 16". It's actually inferior but they still think it's an interesting exercise. But that's the thing, it's primarily an exercise like calculating pi to a billion decimal points or overclocking a gaming laptop.
So you're estimating from data a function which is itself not necessarily optimal. Moreover, the point is more like how far can we get using a really generic transformer architecture that is not tuned to domain-specific details of our problem, which Stockfish is.
So the network still needs to do some impressive generalization in order to „interpolate“ between those samples.
I think so, anyway (didn‘t read the paper but worked on alphazero-like algorithms for a few years)
But if someone grew an actual dragon in a lab by splicing together DNA fragments, that would still be a major feat. Similarly, training a neural net to play grandmaster level chess is simple in theory but extremely difficult in practice.
However, in reality all positions are actually wins (for black or white) or draws. One reason they gave for why stockfish is needed to finish the game is because their evaluation function is imperfect, which is also an notable result.
I do suspect that this pathological behavior could be trained out with additional fine tuning, but likely not without slightly diminishing the model's overall ability.
It's true that this is a relatively large deficiency in practice: how strong would a player be if he played the middlegame at grandmaster strength but couldn't reliably mate with king and rook?
The authors overcame the practical problem by just punting to Stockfish in these few cases. However, I think it's clearly solvable with LLM methods too. Their model performs poorly because of an artifact in the training process where mate-in-one is valued as highly as mate-in- fifteen. Train another instance of the model purely on checkmate patterns - it can probably be done with many fewer parameters - and punt to that instead.
When we have a won position we want to progress and convert it to an actual win.
I think the operational definition I would use for progress is a prediction of how many more moves the game will last. A neural network can be used for that.
That's like saying you can have eggs without chickens, because when you make an omelette you don't add chickens. It's completely meaningless and a big fat lie to boot.
The truth is that the system created by DeepMind consists of two components: a search-based system used to annotate a dataset of moves and a neural-net based system that generates moves similar to the ones in the dataset. DeepMind arbitrarily draw the boundary of the system around the neural net component and pretend that because the search is external to the neural net, the neural net doesn't need the search.
And yet, without the search there is no dataset, and without the dataset there is no model. They didn't train their system by self-play and they certainly didn't hire an army of low-paid workers to annotate moves for them. They generated training moves with a search-based system and learned to reproduce them. They used chickens to make eggs.
Their approach depends entirely on there being a powerful chess search engine and they wouldn't be able to create their system without it as a main component. Their "without search" claim is just a marketing term.
It could have luckily coalesced from gas (a Boltzmann egg), or perhaps even more radically, been laid by a duck.
you say
>They didn't train their system by self-play and they certainly didn't hire an army of low-paid workers to annotate moves for them.
So you are certainly aware that there are avenues to creating the data set. Given that, it is quite reasonable to say that search is unnecessary.
They should do one of those instead of using search before they claim it’s possible to not use search.
Or to borrow your analogy, you’ll need to show me a duck egg to prove you can make omelettes without chickens. Making an omelette from chicken eggs and claiming hypothetically some mystery other animal could have done it is nonsense.
How is it unnecessary? They used none of those methods, so they had to use search. That is search being necessary, not the opposite.
The whole thing is just political: DeepMind use neural nets, GOFAI is dead and that's the way to AI. That's their story and they're sticking with it.
This has two possible applications: 1. There's far less need to invent techniques like MCTS in the first place. 2. A single AI might be able to play grandmaster level chess by accident.
The catch is you need high quality data in large amounts.
I just took it in the same way as saying that being a vegetarian is generally better for animal welfare, as you're not harming chickens as directly by eating an omelette, as you would by eating their wings.
It's like saying ChatGPT isn't a human brain.
It was trained with human brains. But it isn't a human brain.
The "no-search" chess engine uses search (Stockfish) in in two ways:
1. To score positions in the training data. This is only training data, no search is performed when actually playing.
2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is not "meaningful" in the objective of grandmaster-level play.
Thus, "without search" is a valid description.