mrbungie

Yep. I was looking at the prices of lower tier models a few weeks ago for zero/few shot tasks (pre Jev) and Haiku rates just didn't make sense at all. I ended up using 5.6-luna.

Good to know that is going back to being an actual option from perf/price perspective.

Since LLMs are different between each other (and even version of the same family) I'd expect the differences to be explained by each lab idiosincratic biases in the post-training phase rather than by internet corpora characteristics during the pre-training phase.

Ah, over-the-top larger-than-life LLM-isms, they are really funny when you see them in a company blog, but they are vomitive when it's your coworker copy-pasting it and insisting you on reading it.

It is an extraordinary claim, it is a millenium prize problem after all. We don't even know, even if there was no copying, how much human involvement there was in the result.

> Wall street is less forgiving.

We've seen multiple times how "Wall street" is an exit for insiders, with retail investors left holding the bag.

Extraordinary claims require extraordinary evidence.

An article post that wouldn't even amount to a white paper + the LEAN proof is not evidence of how they got to produce it.

I mean, a disaster could happen even with properly human-aligned AI. In a "AI makes the shots" world with undecipherable and fast feedback loops, which is the one we are seemingly heading towards, a string of short-sighted decisions made by AI agents may be the only thing separating us from extinction.

You are not seriously suggesting that nature and AI-accelerated gain-of-function research are similar in regards to risks, are you?

They represent different AI usage patterns. OpenAI wants everyone to believe that it was done with a practically autonomous network of thousands of agents with little to no human intervention for 88 hours, while Buckmaster/Alpöge were using AI in a more guided way for months. If OpenAI actually used anything from Buckmaster/Alpöge work they would be misleading the public.

Hopefully. We'll need to wait until mathematicians confirm how easy it is to digest whatever GPT did.

For sure, but this was supposedly ~18 million dollars of compute, afaik 100 pages paper / lean proof and only god knows how many bytes of chat interactions + thought traces. Scale matters.

Probably an AI-written Lean proof is very different to how a human would write it, and some may say it's more like mathy neuralese. For sure it works but it is not human-friendly and needs to be transformed into something more readable and digestible to be able to extract insights from it.

Not that different from when trying to read an out-of-control vibe coded codebases, or an sloppy AI long email that someone may send you at 9 AM.

In what world a tweet and a screenshot of a private convo are evidence of good faith? Plain sociopathic behavior.

We are not in an everything-has-an-API world yet, and it'll for sure take some time to get there.

> 1) The article is written in a way that states clearly they threw a lot of compute at the problem. In api cost millions of dollars.

Did I say otherwise?

> 2) Millenium Problems have been the goal every AI company wanted to achieve since their diffusion, all companies have thrown a lot of resource to solve these problems, as they are very famous and scientists spent a lot of time trying to solve them. The first company to solve it will remain in history, despite all of you finding excuses about it.

I know, but I don't know how that relates to my point, which is about the way they are doing it.