salamo

Sam Altman having direct access to my chat messages is a close second.

> We have paused some frontier RL training to ensure that we can meet the appropriate alignment, security and monitoring standards for the new level of capabilities in front of us. Model progress is now extremely rapid, and we always said we would take action if we felt that model capabilities were outstripping the pace of safety and alignment.

> We care very deeply about AI safety. We believe the entire field will have to coordinate on shared safety standards, but will act unilaterally in the meantime.

> We expect confidence in safety to increasingly set the pace of AI progress. We are optimistic about the alignment work we are doing, and we remain committed to making frontier capabilities widely available.

My guess is this is related to the HuggingFace incident last month, not Sam Altman announcing the downfall of OAI.

This matches my experience with Sol. Read and thought for a while, and edited files, tested, edited again, then ran out of budget in a relatively short time. But its solution was very good and was done quickly, so all things equal I prefer that over something much more verbose like Deepseek.

As someone who's done something similar (https://blog.lukesalamone.com/posts/creating-tiny-semantic-s...) the expensive part wasn't the training itself but the data curation and evaluation post-training. For this, getting a reasonable distribution of tool calls when the tool call can be anything isn't easy.

Once you have that, the model is small enough batch sizes are probably enormous and training can probably be done on a consumer-grade GPU in a week or less. Or even faster on a bigger GPU.

Possible reasons:

- They might be dynamically adjusting these at inference time [1]. For example, start with a low temperature and generate samples with increasingly high temperatures until one of them passes some quality gate.

- They don't want you to fine-tune on high temperature completions (rejection fine-tuning). You could call this "rejection fine-tuning rejection".

[1] https://rlhfbook.com/c/09-rejection-sampling#related-best-of...

Yeah, the model is small enough that inference is already basically instant for my usecase (only 6 transformer layers for the blog search).

Yeah, I also found that for ultra low footprint models ORT is a big portion of the total payload, because it contains logic for general ONNX graph operations. In my case I found that ORT alone was 3.4MB over the wire, so I swapped it out for a tiny wasm that was 850x smaller and only contained the operations I needed: https://blog.lukesalamone.com/posts/creating-tiny-semantic-s...

On the one hand, compacting at 300k saves OpenAI 40%. That's great.

On the other hand, $10 for 1M tokens still seems really high? It's not too hard to blow through that in an hour or two.

I come at it from a slightly different angle.

I write technical blog posts with visualizations and live demos. That usually means embedding a bit of custom javascript in the page for the demo. Or shipping custom wasm to enable extreme semantic model compression.

I do this by pushing content from my machine to github pages which is wired up to my subdomain.

If github pages stops being a good, free option for this, I will find another. Not sure I would call this "hardcore" really.

$1.7 billion is small potatoes. My bill is over $155 billion and growing. I'm worried if the trend continues I'll have depleted my rainy day fund.

An alternative algorithm which would probably converge faster than 100 questions would be something like Elo or Glicko 2.

A word's "difficulty" would be some function of how rare it is. Once you have a reasonable estimate of the user's "skill" you can infer that a user won't know more difficult words. The benefit of this is you're not spending time asking the user about words they probably know.

Of course it's possible at an individual level, difficulty does not monotonically increase as a function of how rare the word is. A person might be very familiar with a domain-specific subset of English. But the "stratified sampling" approach will also have this problem.

There is a similar problem in chess, where players have ratings which really only change on one dimension. So there can theoretically be a mismatch when puzzles are also scored on a single axis, since a "harder" puzzle that contains a motif a player is familiar with will actually be easier for the player.

I’m working on an iOS app, One Million Checkmates [1]. It scratches an itch I had of chess puzzles for a long plane ride. This app has a functionally unlimited number of puzzles, all offline.

There was a decent amount of work involved in getting the download size reasonable since we need to store all valid moves in a position. There are puzzles with over 40 million valid move sequences, so I had to aggressively prune and compress the move trees.

[1] https://apps.apple.com/us/app/one-million-checkmates/id67625...

> I'd really wouldn't on the machine.

I'll second this. Much better to set up a second machine you can ssh/tailscale into. If a training run takes down your training machine, you don't want it to also take down your home entertainment server.

See, that's why I have to post these things. Someone will inevitably reply with something more clever.

You'll also have some fun pinning down the difference between an "inaccuracy", a "mistake", and a "blunder". These are meaningful delineations for humans but not for a chess algorithm. Objectively, any amount of centipawn loss either changes the best possible outcome for the player or it does not.

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

Really happy to see additional solutions for on-device ML.

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

I mainly blog for myself in the future, but in a slightly different flavor than the author mentions. If there's a complicated ML concept that I'd really like to understand, explaining it to an audience (even if that audience is myself in the future) is a great way to understand it.

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.

Ok, I emailed you.

I live in the South Bay and would be willing to "host". But there's no way to announce a meetup or even reach out to some people.

I believe that observation is borne out in the statistics too, but traditional chess training usually centers around finding the best, hard-to-find move in a position rather than avoiding blunders. I think it would be great if there was more blunder-avoidance training. In other words, a normal position where a blunder looks attractive but the player needs to avoid it.

Sure, go ahead!