That sounds like a next token generator to me, which I think is more accurate. The word "predict" is doing a lot of heavy lifting in your formulation. What exactly is being predicted?
2,775 karma · joined June 15, 2016
A model is _sort of_ designed to predict the next token in a string of text. But it's equally accurate to say it's designed to edit code until it passes a suite of unit tests.
Yes, they generate sequentially, so "next token predictor" isn't technically incorrect (which is why I chose the word "meaningless"). But it's pretty clear there's a mechanism inside them that allows them to "plan" what they generate. I might as well call you a next word predictor.
With no humans able to understand the proofs the impersonator has produced, there's no way of knowing.
I think the consensus here is that most of the papers are effectively correct. But it would take years of study just to understand most of the problem statement - so we have to wait for the mathematical community to digest the borderline-nonsense that OpenAI spewed out.
And going from zero proofs to one proof (even a sloppy one) is a big deal regardless of whether it was written by AI or a human.
Some up with an idea and leave the system to check it 15 different ways, and see whether you can simplify an existing body of theory. It'd be like having an army of lightning-fast grad students.
The problems aren’t always that interesting in their own right. But the quest to solve them is often what drives the invention of new techniques.
We ultimately got the Langlands program and a large amount of algebraic number theory out of attempts to solve fermat’s last theorem. A “clever” approach using techniques from the 1800s wouldn’t have been anywhere near as fruitful for mathematics as a discipline.
Use of a transformer is not necessary or sufficient to qualify as an LLM.
The only difference here is that Anthropic is actively trying to make the watermark undetectable.
Otherwise the most common vocab size would be equal to one.
For god's sake, don't commit crimes while you're committing crimes.
The US may have this dual incentive structure since it wants to build its tech giants while limiting their control, but the EU doesn't. The arrival of a foreign tech social media giant might make the legislation a bit more palatable to pass.
It will undoubtedly be complex to regulate all dark patterns away. But there are a few obvious, easy wins. It'd be a shame to make perfect the enemy of good.
That's why it's interesting to try to pick apart where the moat is.