Yes this!! Sorry didn’t see it

In vision, the ability for large models to do video segmentation… somewhere in here: https://arxiv.org/abs/2101.01169

If you settle on the Euclidean distance (or weighted Euclidean distance) and you need to do it FAST, can I recommend the amazing MASS algorithm https://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html

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Actually the opposite. Stats is how you design studies, which is what governments, the economy, pharma, the medical field, and most sciences run on.

ML is just used for predictive modeling in low-stakes situations and fun tech demos.

Makes me wonder if pretraining makes the model converge on essentially a more efficient architecture that we could be using instead. I'm hoping this thought has already been explored, it would be interesting to read about.

Stats is tremendously useful, especially when your dataset is small by ML standards. Basically every scientific paper relies on statistics to tell you whether or not their result is meaningful.

ML is great when you have millions of data points, but when you only have a hundred it's not going to help you.

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You want to model:

p(cluster =c | img)

p(c1 == c2 | dist(c1, c2) = d, img1 in c1, img2 in c2)

You could try a couple things:

• Frechet Inception Distance but instead of Inception model you use the medical CNN activations

• distance metric learning

• hdbscan/umap/etc for clustering

• persistent homology based topological data analysis methods for finding clusters

• masked autoencoders for good feature extraction

• JEPA style architecture

We can train with the algebraic notation. Feed it all the games and see how it goes. Or make the LLM self-play. ChatGPT can emit the notations, but all it generates are illegal moves.

Edit: after some corrections, it actually proposed the correct moves. Here are the prompts:

ME: The following is a series of chess moves after opening: 1. Nf3 Nf6 2. c4 g6 3. Nc3 Bg7 4. d4 0-0 5. Bf4 d5 What is the best next move?

ChatGPT: The next move depends on the playing style and strategy of both players. However, here are a few options that are often played in this position:

• e3 - This is a flexible move that allows the player to develop their bishop or castle kingside. It can also be useful to control the center.
• dxc5 - Taking the pawn on c5 can lead to a tactical or strategic advantage for White. This move can open up the c-file and create a strong pawn structure for White.
• Be2 - Developing the bishop to e2 can help to control the center and prepare to castle kingside.
• h3 - This is a prophylactic move to prevent Black from playing Bh4, which can be a nuisance for White.

It's important to note that all these moves have potential drawbacks and should be evaluated based on the player's individual preferences, the position of the other pieces on the board, and Black's response.

ME: Some of the moves are illegal. Look at the board again.

ChatGPT: I apologize for the mistake. Here are the corrected moves:

1. Nf3 Nf6 2. c4 g6 3. Nc3 Bg7 4. d4 d5 5. cxd5 Nxd5

In this position, some of the most common moves are:

• e3 - This is a flexible move that allows the player to develop their bishop or castle kingside. It can also be useful to control the center.
• Bd3 - Developing the bishop to d3 can help to control the center and prepare to castle kingside.
• Qc2 - Developing the queen to c2 can help to control the center and prepare to castle kingside.
• 0-0 - Castling kingside is a common move that helps to get the king to a safer square and connect the rooks.

It's important to keep in mind that these moves should be evaluated based on the player's individual preferences, the position of the other pieces on the board, and Black's response.

ME: No. The opening moves are: 1. Nf3 Nf6 2. c4 g6 3. Nc3 Bg7 4. d4 0-0 5. Bf4 d5

ChatGPT: Thank you for correcting me. Here are the moves as given:

1. Nf3 Nf6 2. c4 g6 3. Nc3 Bg7 4. d4 0-0 5. Bf4 d5

In this position, some of the most common moves for White are:

• e3 - This is a flexible move that allows the player to develop their bishop or castle kingside. It can also be useful to control the center.
• Bg5 - Developing the bishop to g5 can put pressure on the Black Knight on f6.
• Qb3 - Developing the queen to b3 can put pressure on the Black pawn on d5 and prepare to castle queenside.
• cxd5 - Taking the pawn on d5 can open up the center and create a strong pawn structure for White.

It's important to keep in mind that these moves should be evaluated based on the player's individual preferences, the position of the other pieces on the board, and Black's response.

And counting

I’m not clever enough to fully understand what is required, but I’d be curious to see how an LM would behave in a DOTA setting. IIRC the OpenAI bot was RL-based and (1) took a lot of training cycles, (2) didn’t generalize all that well to different styles of adversarial play. Some of the qualities suggested by the papers linked by other commenters would seem advantageous…

Anybody else getting 90s text adventure flashbacks?

Sounds like that story of the guy from 40k who pretty much looked for the underlying connections between all the different kinds of beauty and joy. He found “It” alright…

This is exactly what I would tell others if this worked.

Search for the keyword “deterministic MDP.” This is a relatively well-studied area.

There's been a decent amount of work showing that they should be much more energy efficient. There is some empirical work showing other potential advantages (like robustness) but most of that work is still too nascent to be definitive.

Oh wow, I can't wait to check that out! I was just telling my son about the old days of Telnet gaming of which I dabbled in. I was a member of an old school (post Telnet) early graphical MMORPG called DragonSpires which itself spawned Furcadia, now the longest continuously running MMORPG online last time I checked? Or something like that. Then I went on to help run a Player Worlds based MMORPG called Delrith Online. Seems like soooo long ago now...

I wonder if a LLM model would be able to play chess any better than a RL model. I imagine you'd need to serialize the board into a natural language format.

Hey, seriously, if you are ever interested I can write something up. I need to anyway for future reference, the mechanics of sound are what musicians are all about yet shockingly few actually trace their craft back to the root, the simple physical properties of the medium.

This has been done using Gramian Angular Field as images. See here - https://ieeexplore.ieee.org/document/9080613

You can use Cosine similarity

If you care as you said about both magnitude and direction, try with L2 (Euclidean distance) , not cosine similarity.

I think this is more likely a task for Python. I haven't done anything like this myself, it's just the approach I would start with.

https://arxiv.org/abs/2302.01560

Using language for long term planning in Minecraft.

Agreed, financial markets have built-in protections against this kind of analysis. If it works, everyone else would do it, and the more people do it the less any of them benefit from it.

The only way to beat the market consistently is to have a source of information nobody else has access to, or at least hasn't discovered yet.