1. It did not happen

2. The two papers are not similar in their approaches. And the OpenAI one is more innovative and tackles a much harder problem (Buckmaster had only solved Euler).

3. Buckmaster distorted what happened. For example, the claim that he asked for Levent to be removed from his own paper the paper because he worked for OpenAI? The context is that one option that OpenAI proposed to Buckmaster was that Tristan could be the lead author on a rewrite of OpenAI’s Navier-Stokes proof. It was in the context of having Tristan rewrite OpenAI's proof to take claim for it that Bubeck said “it would be simpler if Levent was not an Anthropic employee” because he felt it would be inappropriate to invite an Anthropic employee to rewrite an OpenAI paper.

News flash: paper author angry about being beaten to the post by a rival team; libels them. Details at 11.

Yeah, I used to do that too. Decided to stop bothering with the quotation marks a couple months ago.

We're not going to spend the rest of our lives putting quotations around words when talking about models. "Think" and "reason" the words we have in English for what is going on. No need to tiptoe around it. Again: models are not humans. They are not the same as us. But those are the words we have in English for what they're doing.

They are, by definition, Markov chains.

Even in your attempt to be pedantic here (in which the universe and everything within it is a Markov chain), no, it's not. The hardware state is Markovian but the linguistic processing is a Nth order autoregressive process; it depends on the N previous states. Also, your argument is akin to saying "a Boeing 747 is just an arrangement of quarks, so don't get hung up on aerodynamics." it entirely ignores the relevant architectural details, and instead substitutes a model that blows up exponentially explodes in size within a small number of states.

If you tried to build a Markov model to do what LLMs do, and could store one probability in every unit of Planck space across every unit of Planck time, it couldn't handle a prompt longer than about 2/3rds of the first sentence to A Tale of Two Cities.

Is this actually a substantial amount of energy? I'm in Utah (Rocky Mountain Power) and RMP also pays me to use my batteries... but the amount of energy they draw is tiny. 8-10 times per week they draw on my batteries, but it's like 4 kW peak draw (my batteries can sustain 20 kW), and generally for less than 30 seconds, so in any given week I'm only contributing like 0.2 kWh. As I understand it, this is because RMP uses my batteries not really to "serve loads", per se, but just as grid stabilization; brief backfill to keep the voltage from sagging.

Obviously my batteries do serve *my* loads, which reduces the load on the grid, but that's not what RMP pays me for.

Does CA make heavier use of residential batteries to feed the grid?

It would be good if it could locate lava tubes as well. Unlike lava tubes on Earth those on the Moon and Mars are sometimes kilometers wide and can run for hundreds of kilometers. if the interior of a section could be sealed it would create a huge habitat for minimal investment.

I kind of wonder if the best anti-distillation strategy is, if you detect suspicious traffic from someone (which happens a lot, they monitor for anything that looks like distillation), instead of blocking them, feed them say the output from Llama 3.1 8B or whatnot ;) Maybe finetune it a bit so it talks Claude-ish. But basically, subtly poison their dataset with hallucinations and crappy reasoning without it being immediately visibly obvious.

As for copyvio, sorry, this is something for the courts, and so far, the courts have not largely found against the trainers, and have instead found, by and large, that they're being compliant. The most notable setback against Anthropic for example was a finding that they couldn't just download books in training dataset off the internet, but that they could perfectly legally just buy surplus books for pennies on the dollar by the palletfull, scan them in, and train on that. That this is perfectly complaint with US copyright law.

I think a lot of you wish that copyright law was a lot more restrictive than it actually is. Which is a REALLY bizarre thing to see on Slashdot of all places, which back in the day was the beating heart of "Data Wants To Be Free!" philosophy.

To be clear, though... I would welcome a compromise modification to copyright law, which is, if you want to train on the public commons, you absolutely may, indeed, train on whatever you want, zero liability, but then you have to give back to the public commons. So maybe your top frontier models are closed, but you have to simultaneously release smaller distilled equivalent versions of it into the public domain (how to define "smaller distilled equivalent versions" is of course something that would require discussion), and release said frontier models to the public domain within e.g. 1 year or whatnot.

* They remain incentivized to keep pushing the frontier, since some people will always pay for the best
* They get permanently out of the worry of any copyvio liability (beyond basic requirements about not verbatim reproducing copyrighted materials in outputs)
* The public gets a constant stream of ever-better models, at no cost.

Sounds like a balance to me.

It doesnt think

Yeah, it does.

it doesnt rationalize

Yeah, it does.

These are not Markov chains. They're neural nets. They work via extremely complex chained fuzzy logic on superpositions of conceptual states.

And the less slop it has to deal with

This is literally a thread about distillation, aka, training on the outputs of other models. Synthetic data is the cornerstone of modern training. "Model collapse" is not something that actually happens in the real world, only in contrived settings, the model equivalent of if you could lock a person alone in a dark room with only their thoughts for ten thousand years.

And JD Vance called him Hitler. Trump accepts changes of heart as long as they include sufficient groveling and self-humiliation. SBF's problem is he doesn't currently have any money, and doesn't have a lot of friends to put up cash for him either. If SCOTUS will overturn his fine on a technicality and he can get some or all of that money back, then he'll have the cash to buy a pardon. Obviously he needs to get it done in the next 2 years, four months.

Maybe simplistic humans. Well, to be fair, that covers most of them.

As LLMs are based on a massive theft of information, everybody should now steal from the LLMs as well. It is just fair.

Yes. Also, I have seen estimates that they probably burned $20M in tokens for that attack. That makes them like 1000x (or more) more expensive than a competent pen-tester.

Yes. At least for sites easy to hack and there are a lot of those. But the lack of effective isolation is surprising enough to ask whether this was intentionally done. At the very least it is gross negligence.

What is "it" in this sentence?

Are you talking about the minuscule Google RAG model whose weights are probably measured in megabytes to lower-gigabytes and which you could run on a cell phone with capacity to spare, and comparing it to the multi-terabyte frontier models in question?

at least the person downloading such a component is responsible for "looking over it" before using it.

I'm sorry, but since when? You absolutely should not be tasking every single user of major projects with the responsibility of doing a code review on the whole thing before installing it. There needs to be a clear distinction between "fly-by-night thing that some rando uploaded" and "library that a million people depend on". In the traditional approach, the latter was something tat you installed with a package manner, while the former was something you went and fetch off e.g. Github or whatnot - and if you did the latter, then you were accepting that it was untrusted software. But now we have both (including the potential for "soundalikes") installed by the same means with no distinctions made by the install method. That is not good.

The funny thing about all this is that their only goal appears to have been to download publicly-available data off of UK government websites. I'm betting that they were (A) being tested on a knowledge-related task, and (B) not given direct access to the internet, but their tool capabilities involved access to the "gem" command (so that they could install Ruby packages for their work), and so decided to abuse (B) to cheat on (A).

Once again: training a model with the reward being "does it solve the task?" without looking at how it solves the task is very, very dangerous. This is basically the plot of Universal Paperclips.