janalsncm

> There isn't any reason to think AI have more or less trouble with hidden information games.

How about the fact that a child can beat the best rock paper scissors player in the world in a game, but no human can beat the best chess engine? Same thing with poker, a novice could get lucky and win a hand against the best poker player.

Based on my experience, these kinds of cultural generalizations are interesting to talk about but as with many populations there is much more variance between people within the group than between groups. For example young urban Chinese do not put Mao posters up in their apartments. For older rural Chinese it’s almost universal. Therefore, it is much more effective to get to know individuals than to write to no one in particular.

Additionally, the question of how Chinese society looks in general is less important than how the decision makers think. You don’t need to convince average Chinese people. You need to convince the equivalent of a VP at Google.

Based on those two things, and especially because as far as I can tell the Rationalists have framed the entire thing as a sort of prisoner’s dilemma, the only way out is to build trust. And in my opinion the best way to do that is for Americans to meet Chinese people and for Chinese to meet Americans.

Imo, this is the critical piece and what makes the AI work at all.

With hidden information games, the best move depends on information you don’t have. So a move could be good or bad, it just depends on something that’s impossible to know.

You’d like to search ahead, meaning “if I do this they will do that” but that’s impossible since you don’t even know what the opponent can do because you don’t know their hidden state.

If the possible hidden states are randomly distributed, you are screwed. It’s just like rock paper scissors: there’s no best move if your opponent is unpredictable.

However if you can quickly learn to predict their moves, it becomes possible to make informed decisions about what to do.

Also they won’t know who you started dating. It’s most likely not the last person you matched with or messaged. So any training data will probably be incredibly noisy, because romantic compatibility is not something that happens in the app.

The hard part is the data and evaluation. Sure, it’s not that hard to build a fast model with good predictive power. But fast at doing what? You probably don’t care about classifying whether a hotdog is a sandwich (which is the Jev demo).

As far as data brokers I would imagine it’s possible to get info on an individual if you want to. You can even determine someone’s location based on data brokers info. But dating apps will want info on millions of people, which will be messy and hard to do.

Tinder doesn’t know if they were successful or not. They just know you stopped opening the app. In fact they are incentivized not to make matches that lead to marriage, because then you will stop paying.

The government knows whether you got married and stayed married, and given the social cost of divorce, they are pretty incentivized to keep people married.

You can test the whole thing for free on a GPU with Google Colab. Test both the transformer and your new architecture on a larger dataset. Something that maxes out the GPU for an hour each run.

Also, the readme mentions keeping the same optimizer schedule which sounds nice at first but they are completely different architectures. The loss is high on the transformer, did you try raising the learning rate on it?

In general I’m interested in parameter efficient architectures. I don’t think transformers are optimal, and indeed many improvements have been made to vanilla transformers. But if you have an idea for something better you need to show it.

That was just one settlement but precedent is clear. If you do what Anthropic and OpenAI did, expect to be in court. This is one reason why you don’t see labs popping up out of nowhere in the US.

Also if you distill from Anthropic and OpenAI, expect to be in court. Whether you think distillation is fair game or not, the US court system is not cheap. But it turns out that Z.ai, Minimax, Moonshot, Xiaomi, Deepseek, Alibaba etc don’t need to worry about that.

For instance, one regulation which really puts American AI companies at a disadvantage is IP law. It shouldn’t be a surprise that most of the best of the text-to-video models are Chinese.

Similarly, the legal grey area around model distillation gives Chinese labs a major advantage. This one I feel better about relaxing.

https://www.goodreads.com/quotes/7515521-william-roper-so-no...

You should use precision (when your model says “spam” how often is it spam?), recall (how many of the spam emails did it catch), or f1 (balanced between those two).

So I think this post performed well in spite of the annoying parts, not because of it.

> I didn't want to remove the "mostly useless" information since clearly it was useful for a lot of people

The information was not “clearly” useful. It had obviously incorrect information that no one even noticed. In my opinion that is strong evidence for the opposite conclusion, that people ignored it because it was noise.

Why would people ignore this information? Demos are a show, don’t tell thing. For example, you don’t have to tell people that the latency is low. They should be able to see it from the demo.

Therefore, when showing something to humans, the first thing they see should be the first thing you want them to see. And since this is a demo, the first thing they see should be the demo, with sane defaults, above the fold.

Also, it is likely that no one (including the author) has read this “fine print” because it includes incorrect information. GPT-4 isn’t a frontier model anymore.

> If the tail shows up in buying threads only because it posts a lot, random reassignment says it should write about 7.9% of the brand mentions there, and almost always between 6.3% and 10.1%. It wrote 11.3%.

Is it just me or is this just a block of painfully hard to parse text? This is core to the author’s analysis.

Rather than pasting the analysis from Claude, the author should just explain it themselves. Just the core of their analysis. It doesn’t have to be 2000 words. Put an AI;DR at the top and give us the core of your argument. I would take a paragraph from a human any day over an essay from Claude.

When I hear “architecture” I am thinking number of parameters and latency.

When I hear “accuracy” I think training recipe, data, and (later) number of parameters.

So when you say that Jev’s architecture may not be necessary, the evidence I expect to see is comparable quality at comparable latency. Not equal quality at 2x latency and 4x the cost.

People didn’t know they wanted classifiers until OpenAI gave them a taste.

1) The designation has nothing to do with hacking, either from Anthropic or otherwise. The dispute is about Anthropic barring the DoD from using Claude to develop autonomous weapons and conduct domestic surveillance.

2) The DoD does not care about any of the hacking OpenAI does as long as the DoD can do what they want with ChatGPT.

3) Historically, the US government has been interested in hacking other nation states, friendly and not. So OpenAI being able to hack Australian systems is a selling point.

It doesn’t care whether your system is made of LLMs, decision trees, or bananas.

However, the problem is that unlike something like a neural net, you can’t exactly use backprop to improve.

On the flip side if the quality of the prediction doesn’t matter, I might as well have Claude spin up something shiny that does the same thing faster, cheaper, and with 10x the magic sparkles.

1) FTC chairman says AI agents will be treated just like any other tool (“resist anthropomorphizing”)

2) Studying enforcement of rules around personalized (“surveillance”) pricing, specifically rideshare, airplanes and food delivery

3) Public comment period open now on new rule to punish companies like Meta and Google that advertise scams.

The last one seems closest to happening but it’s still just a public comment period so it might die.