So this can deduct algorithms from data? Wow!

1:42 stockfish isn't handcrafted anymore. It also uses a neural network for eval, just a very small one that is optimized for incremental evaluation on a cpu (NNUE)

Now we need an algorithm to analyze why the chicken really crossed the road

What a time to be alive!

saying a chess ai can outperform gpt 4 in chess is like saying a sumo master can knock over a child

Does it mean that this is the start of making full algorithms out of neural networks instead of black boxes which they are currently?

If you thought you couldn't be more surprised… wow!

This is a clear derivation of the word prediction of the large language models. All these pseudo-smart systems are deeply impressive tools for humans, and I can't wait to see what we'll do with them!

Sounds like the AI just learned to play blitz really well. It's just pattern recognition, no calculations are needed and humans can do it too.

I tried this approach a while ago. The network I trained could at least beat me (~1000 elo), which I was satisfied with. I could have trained it longer, but this was 6 years ago and my graphics cards was really bad back then. It was also a smaller network and not a transformer, but an AlphaZero style convolutional network. One fun thing you can do with this is to condition on the rating of the player/chess engine to have the network mimic a certain skill level.

The neural net embeds the lookahead library.

Leela Chess Zero (or just Leela) tried a similar test to this one after the team saw the DeepMind paper. They compared how the most recent Leela weights performed against DeepMind's AI. The cool thing is that Leela performed a little better than the DeepMind one. The results are published on Leela's Blog.

This AI proves that it's possible to reach 2800 using pure intuition (pattern recognition) without calculating (searching)

I’m really curious about the real world benefit of this model beyond the fact that it’s small and fast and can be run efficiently locally on low cost and low power devices. I know this supposedly isn’t about chess (???), but they essentially managed to build a worse version of Alpha Zero 7 years later with a completely different model architecture. And yes, the new model can be applied to many domains beyond chess or even board games in general, so there’s some usefulness in that, but… this just seems like a repackaged form of supervised learning that has been around for ages, and the result / accuracy doesn’t seem that different than what older methods of supervised learning would yield. What am I missing here?

Dear Karoly, thank you for extensive coverage of the most exciting reakthrough in computer science and more. But this one, you did not convinse me this paper has any significant result. They deleted the search part and it preformed worse than tree-search algorithms... duh... In fact it couldn't even make mate in 3 on its own. That whole speach about the algorithms in the end seems to be on the opposite side of what was done in the paper. I don't get it whats new or exciting about this particular paper. And I do not like the general sentiment that now the transformer is a soluion for everything, just throw more compute into it. No, I believe, it is still very important to develop different algorithms and different architecture to do really impressive stuff, and other videos on this channel illustrate this idea perfectly.

I've been waiting for this since several YEARS! I'm doing my own research and it's always interesting to see where others are at. And I feel the urge to solve chess too lol

i tried something similar about 15 years ago for connect 4. i had a very good traditional connect 4 program (like stockfish for chess here), and i let a neural net learn boards and what my player would do. but i never got it working as intended. i always had a feeling that i simply didnt give it enough training examples. i wanted to go to tic tac toe, so i could give it maybe enough examples with the computing power i had available back then, but then i lost interest. nice to see, that it works! even on chess!

even tho i dont completely understand the how, this sounds like the biggest leap I've heard in the last year. sounds like there is room for crazy optimization of way bigger models. ... wow

What about the fact that the input/output parameter count is drastically reduced by iteration ? Better to compute "just a move" rather than a whole board ? Or am I misled ? :)

what kind of computer is used to handle all these operations and parameters, do they just used multiple gpus for deep learning like this?

😌 I need this AI for checkers as AR Sun Glassea, like those Meta + Rey-ben

If you adjusted the amount of moves it could see, this could be a great engine for chess players to see actually human playable lines

Can they use this as a new evaluation function that they would use inside a search algorithm ?

So this is just a distilled student model from Stockfish?

Were can i Download that ai?

2:10 "Lichess elo" of 2895 sounds more like IM (International Master) strength, than that of a GM's strength. Alphazero (AI chess engine of Deepmind) could easily be 3300+. Leela Chess Zero (Lc0) built using the same architecture is 3600 today. 3:24 ChatGPT is not even good. In the sense it was not made for chess. Gothamchess channel has covered a video with GPT playing chess. At times it played moves that are illegal. Regardless, incredible results 👍🏻

Leela Chess Zero surpassed it last year. Hopefully Google gives their vast resources to the Leela team so they can make it even better.

Your enthusiasm is always inspiring!

Next, we do Shogi.

Huh. Way back in the day, AI researchers asked human experts tons of questions in order to understand their internal "algorithms" - with the problem that often, humans didn't even know how the came to certain conclusions. This algorithm-from-data thing could be groundbreaking for this - and, if I understood correctly - should reduce the 'model' size by a lot, because you could replace millions of neurons with algoithms.

"If we could use the Sharingan, this is how it would have felt."

Did the title change just a few hrs after release? Was this channel always so reliant on clickbait?

“New” from 2017. AI goes by quick

lol "I love the smell of an amazing dusty old paper". xD

Now give it various openings and positions to see where its performance are the maximum and minimum. Then we would have an idea of which openings would lead to play that’s easier and more intuitive

Looks like AI has us in checkmate, but I want a rematch! Great video. Thank you for this information.

this is akin to rote learning with some pattern recognition, you analyse so many board positions from top engine that you literally know the best move in each of those position, for positions which are unseen you do pattern recognition (this part is shady because there is no real way to know if logic was used to make the move as search is not used).

Imma call it now. This is a true breakthrough to the actual start of AGI...provided the paper is true. The ability to derive pattern learning algorithms from the model and duplicate them rapidly and place them in other models would potentially lead to the exponential growth we were actually expecting when AI was announced.

this paper proved that you can have capable neural networks just with datasets that are large enough.

From this papper we can infer that inorder to get human level general language model we we need super human level data.

Give it eyes and it could tell you the laws of physics we still don't understand or know

does the comparison to gpt-4 make sense? i know little about ai but is it really such a surprise that this algorithm is better at chess than a large LANGUAGE model?

6:52 very important disclaimer: if you try to create csam using microsoft AI, they will catch you. Please make sure to disclose that you don't really own the service.

Chess without search 🤯🤯🤯

Great method. The game. Basic, elementary, mathematical... but superb for the search for superiority, elasticity, stubbornness I would say almost perfection. Something that leads us to be proud to see how much we are doing for our future in the end.

If only they could learn from fewer datapoints. That would be an absolutely crazy time.

What happens if it plays tic-tac-toe with itself like in the movie War Games? 😄

Damn, I just read this paper yesterday! That's a coincidence and a half.

Let us know if someone leaks a military tactics AI. I'd be utterly surprised if there weren't several already.

This is a gaint leap forward.

I wonder how far we are from AI being able to examine its own code and make improvements so profound that no human could match them. It’s akin to how no human chess player can defeat today’s best chess engines. There was a time when people doubted that computers could outperform human chess players, but now the situation has completely reversed, and no human can beat the computer. Imagine the possibilities when computers achieve this level of mastery across all aspects of life. Eventually, we may find ourselves with nothing left to teach, as AI begins to enlighten us with its own discoveries. It's scary as hell but also incredibly fascinating, it's like getting a glimpse into the future.

Love Anthropic

top human players don't calculate either for the most part (non classical). It's shocking how shallow their compute is. they have learned a function to make the call without branching. the game with magnus and polgar in the park showed me that

There are a lot of unanswered questions here

Can you imagine specs of machine to run this?

That would be crazy if P=NP turned out to be true.

I thought chess ai is already on the top level that can lead to a draw anytime it can't win

Its pre AGI! its here soon!

I'm all in for those raytracing algorithms 🤤 if this makes better raytracing algos, I'll be a happy scholar

We should have chess tournaments where different types AIs play against each other

For anyone confused, the real breakthrough is that this further proves transformers can reason better at the zero shot level with more scale. By making the neural network 10x bigger than AlphaZero, they were able to get way better zero shot performance.

What a time to be alive!!!! 😄

The cadence of speech in this video differs from earlier ones so much that I can only assume it's AI-generated. It's borderline robotic in it's precise timing.

Wait...wouldn't that be huge for interpretability? Or isn't the algorithm it gives out necessarily the logic it used to make the move?

So it performs at 1000 less elo than stockfish...

I think I'm missing the point. They did a supervised training on pre-generated, powerful chess moves and made a paper out of it? That sounds like something one would do as a simple baseline for further experiments.

algorithm that creates an algorithm that creates an algorithm that creates an algorithm - god

To me, the breakthrough of this paper is kind of limited. Firstly, it seems that this is only supervised learning on a massive dataset of chess games. Secondly, the ground truth of the action is taken from ST, which is already a strong chess engine. ST 16 elo rating is around 3k6, while the learned model achieves a 2k8 rating, which makes sense but is not surprising to me at all. Finally, the whole point of searching and self-play is that we don't know what the right moves are, so the machines have to come up with new moves on their own. In this way, the machines can make up things that can even surprise experts. The hard part of finding good moves in the first place is already done by ST, and given that the labels are provided by ST, the end performance is always upper-bounded by the performance of ST.

Thanks!

But can it play 5D chess with multiversal time travel

If this is ChatGPT's next brain compartment (after GPT and DALI) we might be getting awfully close to an AI that can tell truth from fiction, which sounds pretty general to me...

AI has been beating grandmasters since the 90s...

Isnt this basically the ReBeL algorithm for poker…

No selfplay? Why would they do that?

lol, i made similiar project for fun with only 10K dataset😂

We need a ai that can learn concepts and strategies, so that it can generalize from much less information.

i saw you changed the title

Very interesting, as usual. One problem with engines like stockfish is that it plays.. well.. like an engine. It would be interesting to train this not on stockfish, but on human/grandmaster games, maybe it would play more "human-like" moves? I think that would be a really powerful learning tool for chess players.

Is this voice AI-generated? Intonations are all kinds of wrong, even more so than usually.

Why did the robot cross the road? Because it was carbon bonded to the chicken.

It's not about winning, it's about sending a message :)

Just to add, stockfish is not one of the best, but is the best😅

Given it creates specific algorithms rather than data piles, does this help with explainability?

I'm always thinking could Dr. TMP be an AI himself from the way he talks

Imitation learning on stockfish? That is probably the most boring approach they could come up with

But if this is trained on board positions generated by algorithms (or people) looking ahead, that information would be encoded in the board position. This is just an artifact of a game like chess that doesn’t depend on psychology or history. This same approach wouldn’t work well in poker. This feels overhyped. Of course it’s better than a large language predicting model.

I see a notable step forward in the alignment problem in this approach. Machines not learning from machines with human set goals, but machines learning approaches and goals from humans. Now we only need a lot of data of desirable human behaviour. Oh ... i see a new problem arise ....

I'm so excited for the future

Interesting video!

This sounds like pattern recognition - across the various states of the game.

Humans aren't very good at chess (compared to top engines), a lot make guesses and aren't completely sure about the position in many positions. It doesn't surprise me that an AI learning from billions of stockfish moves in various positions transfer over to very high playing, even if the positions are a bit more random, it can still compare how similar something is to another position and sometimes extra pawns or something doesn't matter when positionally you have faster tactics or combinations. Maybe it learns combinations in context from so much data which makes it strong at tactics giving it that kind of rating. Heck, it can even learn openings and every part of the game from that many moves...that's a lot. Imagine a human remembering 15 billion random moves (if I remembered the number correctly), even top players only have a few thousand positions/games memorized (except Magnus which has 10k+) if there's on average 50 moves per game, maybe Magnus has 50,000 random positions/moves down and so yeah...actually compared to how little humans train on, that AI thing kinda sucks for having to look at so much data and can only be around a human GM level. Quite inefficient. When you make an AI that you didn't teach to play chess, and it somehow plays at Stockfish level or better, than I'll be extremely impressed. GPT6 or GPT7? Right now it's just doing copy-cat with some RNG at a high level.

So basically, first we solve a problem using a neural net, then we apply this method to learn the underlying algorithm that best resembles the decisions made by the neural net? Yea sounds good. That's probably how humans also do it. First we experience the world, and get some intuition about it, learn to reflexively act. Then, with some conscious thought, we infer rules from these intuitions.

SHALL WE PLAY A GAME? ... WOULDN'T YOU PREFER A NICE GAME OF CHESS?

A step towards a Neural Network based AI that creates a symbolic or neuro-symbolic AI that runs blazingly fast?

Consider upgrading your voice robot.

Great...but...can we play with it?

Ok, so explain the process of algorithms from data?? So, using less parameter, but can do better than more than hundren time of higher GTC.., so it is base on calculcate anything, so that means can can calculcate how everything work so by divide the whole process of whole into fraction of whole, so what that fraction represent, for an examble the whole pie is 1, and you slice 10, so it is part pie, so the fraction is 1/10, so the gap is bigger than other gap, so 1/10 is represent of gap of pie that is one out of ten, so I did algorithms the data that is pie into fraction of pie that I can explain what it means, from whole to part. Algorithms is the part as fraction into whole. Human can be very logical since humans can create infinity than AI since AI has higher need than human, so AI is inferior. So, ya are saying so can calculcate better so will become infinity as adapt to any situation so always right, so never wrong compare to us.

How many did you see? AI: 15 billion How many did i win? AI: 0 so there isn't a chance until it freeze/crashed

How is that different than any old neural network? Nothing new here??

What's about with his voice?