[HN Gopher] Acquisition of chess knowledge in AlphaZero
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Acquisition of chess knowledge in AlphaZero
Author : Rant423
Score : 80 points
Date : 2022-11-20 15:44 UTC (7 hours ago)
(HTM) web link (www.pnas.org)
(TXT) w3m dump (www.pnas.org)
| osigurdson wrote:
| Here is a thought experiment for beating AlphaZero. Randomly
| select 10K children at a very young age (say 3), have them play
| chess against AlphaZero but simply have them move the exact move
| suggested by AlphaZero (i.e. basically this is AlphaZero playing
| itself). Play 10 games per day for 10 years.
|
| The hypothesis is some children will deeply embed the algorithms
| into their own playing style - leveraging the subconscious to the
| greatest degree possible. Basically, we are training the human
| mind in the same way that we train AI. Would it work? Probably
| not, but our current approach (studying openings, etc.) is
| obviously not working so it makes sense to try something new.
| cognaitiv wrote:
| My 10yo daughter plays a chess app mostly with suggestions
| turned on. It has substantially improved her game when we play
| together. Way more effective for her than my coaching, although
| I'm a total amateur. I think this would work without a doubt.
| macspoofing wrote:
| >Would it work? Probably not
|
| "Probably not" indeed. Human brains need to be stimulated in
| order for them to build their neural net. Blindly following
| instruction is not stimulating.
|
| >but our current approach (studying openings, etc.) is
| obviously not working
|
| That's part of the current approach, not the full approach.
| Kids that show promise in Chess, already train with computers
| today (in addition to personalized coaching, and strategy
| study). I have no doubt that the current generation of chess
| players is best of all time, and that the next generation will
| be even better. Even with that, I don't think any human will be
| able to train enough to beat even Stockfish, much less Alpha
| Zero - just as no human will ever train enough to beat
| computers at arithmetic.
| pvitz wrote:
| Stockfish is stronger than AlphaZero.
| tromp wrote:
| > Summary of Results
|
| > Many Human Concepts Can Be Found in the AlphaZero Network.
|
| > We demonstrate that the AlphaZero network's learned
| representation of the chess board can be used to reconstruct, at
| least in part, many human chess concepts. We adopt the approach
| of using concept activation vectors (6) by training sparse linear
| probes for a wide range of concepts, ranging from components of
| the evaluation function of Stockfish (9), a state-of-the-art
| chess engine, to concepts that describe specific board patterns.
|
| > A Detailed Picture of Knowledge Acquisition during Training.
|
| > We use a simple concept probing methodology to measure the
| emergence of relevant information over the course of training and
| at every layer in the network. This allows us to produce what we
| refer to as what-when-where plots, which detail what concept is
| learned, when in training time it is learned, and where in the
| network it is computed. What-when-where plots are plots of
| concept regression accuracy across training time and network
| depth. We provide a detailed analysis for the special case of
| concepts related to material evaluation, which are central to
| chess play.
|
| > Comparison with Historical Human Play.
|
| > We compare the evolution of AlphaZero play and human play by
| comparing AlphaZero training with human history and across
| multiple training runs, respectively. Our analysis shows that
| despite some similarities, AlphaZero does not precisely
| recapitulate human history. Not only does the machine initially
| try different openings from humans, it plays a greater diversity
| of moves as well. We also present a qualitative assessment of
| differences in play style over the course of training.
| Barrin92 wrote:
| I skimmed the article so sorry in advance if I missed it, but to
| me one fairly trivial way to gauge whether AlphaZero has human-
| like conceptual understanding of chess would be to throw a few
| games of Fischer random at it.
|
| I remember with Deepminds breakout AI one very easy way to see
| the difference to human play was to change the shape of the
| paddle. Even very slight changes completely threw the AI off, so
| it was obvious it hadn't understood the 'breakout ontology' in a
| human way.
|
| I'd expect the same from chess. Humans who understand chess at a
| high level well obviously play worse in non-standard variants but
| the familiar concepts are still in play. If an AI has a human-
| like grasp of high level concepts it ought to be pretty robust to
| some changes to the game rules like changing the dimensionality
| of the board.
| wwarner wrote:
| I think this is great work. Interpretability is the worst problem
| in deep learning, as the lack of insight into what the model has
| learned prevents it from being useful for serious decision
| making.
| jameshart wrote:
| How much insight into what humans have learned is necessary
| before you find them useful for serious decisionmaking?
| hnews_account_1 wrote:
| The explicability of our decision making is like 95% of our
| progress. It is why we can identify biases evolution builds
| into us but we need to fight against. Or try and justify
| something beyond mere intuition. This is like asking how much
| math are humans using anyway.
| civilized wrote:
| If I needed large numbers added together, I would trust a
| human who can explain their general addition algorithm to me,
| and I wouldn't trust an AI that can spit out some usually
| correct answers on small problems.
| Jensson wrote:
| Humans as a group has proven to be able to build everything
| we have today. No AI has proven to do anything similar, there
| just isn't much data there to make us confident in their
| abilities thus far.
| gateorade wrote:
| Conversely though, how many people are killed in the world
| every single day simply because of human error? When (in
| the US) a 16 year old gets their license, we don't ask them
| to provide a formal proof of their driving technique that
| shows it's impossible for them to ever get in a crash. We
| say 'you've had the training, you've demonstrated that you
| can safely drive, be careful out there'.
| mannykannot wrote:
| We have a good understanding of this risk and have
| collectively chosen to accept it (while many countries
| have chosen not to accept it in the case of 16-year-
| olds.)
| jojobas wrote:
| You can question a human in an exam and ask to "show your
| work". If you can say solve an equation but can't explain why
| you did this or that transformation you'll be rightly failed.
| With current NNs you get an answer and that's it, there's no
| introspection.
| retrac wrote:
| It's not just a practical problem; it's one of the most
| important philosophical problems in the area, too.
|
| Something like GPT-3 can do multi-digit arithmetic much better
| than chance, giving results for values it was certainly never
| trained on. Similarly, transfer learning, where you start
| training a model on some input less related to the task, and
| then switch to inputs closer to your task at the end, can
| substantially reduce total training time. The task can be
| _radically_ different; to use GPT-3 as an example again,
| compared to starting with a completely randomized model, it
| reduces training by a factor of about 10x to go from PCM audio
| samples encoded as text patterns, or abstract art bitmaps
| encoded as text patterns, to English text. GPT-3 is learning
| something about arithmetic. It 's learning something that is
| common to music, abstract art, and English text. It might be as
| simple as basic patterns from geometry and arithmetic (that's
| my guess). But no one could even begin to point you in the
| direction of what that structure it is teasing out really is.
| Waterluvian wrote:
| Does AI still struggle with "I can't tell you how I derived this
| answer"? Is that improving much?
| Tenoke wrote:
| Yes exploitability is improving but this hasn't been that much
| of a problem with top chess engines at any point.
| ambyra wrote:
| I always wondered if a chess engine would learn better/faster if
| the opening positions and piece movement rules were randomized.
| Has anyone tried this?
| tromp wrote:
| There is some discussion on reddit:
|
| https://www.reddit.com/r/chess/comments/c4dgas/alpha_zero_fi...
| gliptic wrote:
| If the piece movement rules are randomized, it's not going to
| be learning chess, is it...
| awb wrote:
| That would be Fischer Random Chess (aka Chess960). I know
| engines play it, but I don't know if they're trained on it.
| umanwizard wrote:
| That indeed randomizes the starting position (subject to some
| constraints), but it doesn't randomize the abilities of the
| pieces.
| EvgeniyZh wrote:
| I think many chess players will agree that latest chess engines
| (Stockfish NNUE/Leela) are playing better conceptually, so it's
| less useful to use older ones (SF8/A0) to study learned concepts.
| Still cool work tho.
| mtlmtlmtlmtl wrote:
| Haven't read the paper yet but given relevance to what I'm
| doing atm it's high on my list.
|
| I think using pre-NNUE Stockfish is partly because the classic
| Stockfish evaluation function has a lot of human knowledge
| explicitly built in in already interpretable ways, making it a
| good contrast for comparison.
| tiagod wrote:
| You'll find that the abstract makes the explicit point that
| Alpha0 lends itself to more interesting findings as it had no
| exposure to how humans think about chess.
| civilized wrote:
| The goal is to discover and analyze the concepts used by an
| expert AI player, so it isn't vital that the player be the
| strongest so long as it is expert.
|
| AlphaZero is also more interesting when you want to know how a
| general game-playing AI trained from scratch approaches the
| game.
| dsjoerg wrote:
| Anyone know how this differs from a similar-seeming paper that
| was published a year ago?
|
| https://en.chessbase.com/post/acquisition-of-chess-knowledge...
| https://arxiv.org/pdf/2111.09259.pdf
| sega_sai wrote:
| It's the same paper, it was just accepted to pnas/(published on
| the website).
| bnprks wrote:
| > Data, Materials, and Code Availability
|
| > [...] However, sharing the AlphaZero algorithm code, network
| weights, or generated representation data would be technically
| infeasible at present.
|
| Very interesting paper overall. However, the excuse that code
| sharing is "technically infeasible" is wearing thin nearly 5
| years after the initial AlphaZero paper was released.
| mgraczyk wrote:
| My assumption is that the code is not separate from other
| deepmind code. It would be very difficult for them to share the
| AlphaZero code without also sharing everything else deepmind is
| working on.
|
| The representations are probably not manifest in a way that
| would be intelligible if shared.
|
| I don't have an explanation for why they wouldn't share the
| weights.
| espadrine wrote:
| In some frames of the Deepmind documentary film on AlphaGo,
| we can see code for loading SSTs (a common key-value data
| format at Google) from GFS (the Google file system).
|
| It is possible that the entire codebase depends on Google-
| only infrastructure.
| mgraczyk wrote:
| That part is true, but things like that are usually not too
| bad by themselves. For example, you can use open source
| tensorflow to access files on Google's internal filesystem
| with tf.io.gfile.
|
| It's possible other infra is somewhat hairy to decouple,
| for example the code they use to allocate and use GPU
| resources is internal.
|
| (I work on ML at Google and we use some of Deepmind's
| stuff)
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