Comments URL: https://news.ycombinator.com/item?id=47212819

Points: 2

# Comments: 0

So in practice, a drop in win probability greater than 14% is considered a blunder on Lichess.

For reference, lichess uses the following function to map centipawn advantage to the probability bar, derived from observed outcomes: https://github.com/lichess-org/lila/pull/11148

From an ML perspective, this is basically logistic regression with a single feature. However, once we leave the realm of theoretical centipawn value and begin to optimize predictive power, we could imagine adding in other things like the players' ELOs or time remaining per player, etc.

I think there are some interesting theoretical differences between predicted win probability derived from Stockfish CP and actual outcomes. As in, you could even imagine predicting positions where certain players struggle and steering them towards those positions. [0]

[0] https://www.youtube.com/watch?v=KgOC1D8wkyE

Comments URL: https://news.ycombinator.com/item?id=47105038

Points: 47

# Comments: 4

That said, I probably wouldn't use this unless mine was one of the specific use cases supported[0]. I have no idea how hard it would be to add a new model supporting arbitrary inputs and outputs.

For running inference cross-device I have used Onnx, which is low-level enough to support whatever weights I need. For a good number of tasks you can also use transformers.js which wraps onnx and handles things like decoding (unless you really enjoy implementing beam search on your own). I believe an equivalent link to the above would be [1] which is just much more comprehensive.

[0] https://ai.google.dev/edge/mediapipe/solutions/guide

[1] https://github.com/huggingface/transformers.js-examples

That goes double for visuals, which is another reason I use a custom static site. Can't run JS on Medium. I even built out a client-side search and learned a good amount from that too.

Comments URL: https://news.ycombinator.com/item?id=42908500

Points: 2

# Comments: 0

Since the whole thing is executed in the browser (including the model) there aren't a ton of secrets for me to keep. Essentially it is expectation maximization: the bot tries to find the move with the highest value. What is "value"? Essentially, it is the dot product between the probability distribution coming out of the model and the centipawn evaluations from Stockfish.

In other words if the model thinks you will blunder with high probability, it will try to steer you towards making that mistake.

Comments URL: https://news.ycombinator.com/item?id=41875962

Points: 1

# Comments: 0

If you want a computer that plays like a human, you will probably need to imitate the way that a human thinks about the game. This means for example thinking about the interactions between pieces and the flow of the game rather than stateless evaluations.

It uses a similar approach to Maia but with a different neural network, so it had a bit better move matching performance. And on top of that it has an expectation maximization algorithm so that the bot will try to exploit your mistakes.

The exception for me would be situations where I can't use my hands, like driving. I don't want to have to look at a screen. If a voice agent could replicate the functionality of CarPlay, that would be really useful.

I get pretty good recommendations, and they're different from Spotify which is nice.

[0] https://repositori.upf.edu/bitstream/handle/10230/54158/Alon...

Comments URL: https://news.ycombinator.com/item?id=41112820

Points: 4

# Comments: 1

The stove linked in the article is also bonkers. It would have been more impressive with a thermometer readout before they turned it on though. I checked other videos [0] and it seems to be real.

[0] https://www.youtube.com/watch?v=YdawGen0QPc