profchemai

Not the author, as a scientist in a scientific field which often gets used as argument for AGI existence, it's great to know these kinds of efforts. Can't agree more withe their message: "Fundamentally, superintelligence does not equal super-solutions. Grand societal challenges are rarely intelligence-limited problems but systems-limited ones."

Sorry for the caution but this is becoming very common. Lots of red flags for AI-Slop: Zenodo, single author work, lots of jargon, author has not had prior work in the field.

Out of 100 of evals, ARC is a very distinct and unique eval, most frontier models are also visual now, don't see the harm in having this instead of another text eval.

Agree, if anything it's Applied Linear Algebra...but that sounds less exotic.

Once I read "This has been enough to get us to AGI.", credibility took a nose dive.

In general it's a nice idea, but the blogpost is very fluffy, especially once it connects it to reasoning, there is serious technical work in this area (i.g. https://arxiv.org/abs/1402.1869) that has expanded this idea and made it more concrete.

I've read this book, I don't think this was the case. I think the name was made in good-faith. It's "transformer" because it involves transformations of molecules involved in life (reactions, enzymes, metabolism). The author has used this term before 2022.

The same argument could be made for the transformer paper: hijacking a nostalgia pop-culture name to name a deep learning bi-linear operator. Many papers are guilty of this, some just become very influencial.

Could be a good idea, but without any evidence (benchmark/comparisons) it's just a flashy name and graphic. Sounds like another "state" that gets contexualized via a gating mechanism wrt previous vectors.

I love this analogy.

Awesome, I am a fan of their work, just wish they did not use the word biology (which is rooted in living) to describe LLMs, we have enough anthropomorphizing of AI tech.

Criticism feels harsh. Of course models don't know what they don't know. Reporters can have the same biases. They could have worded it better "lowers the probability of hallucinating", but it is correct it helps to guard against it. It's just that it's not a binary thing.

Two big points I see is 1) that normal people are not models, 2) peoples looks will vary widely based on their country of origin.

So I could see this working better with normal people and more fields that are not focused on country (ethnicity, skin color, body type, etc.)