[gtsop]

Dark times for science when such quotes are thrown as legitimate.

The article is extremely technical and doesn't really explain the quote other than acknowledging that there are stuff we don't understand yet.

And really, a person will never grasp machine learning and AI as long as they keep drawing unbased parallels to humans and machines.

[talldayo]

The article is bullshit shrouded in turboencabulator-speak. He's trying to roll the same Sisyphian boulder that crushed the computational linguists, thinking that AI makes it different this time. Conveniently he also waived any attempts at providing proof for his claims.

>> If the training data subtend latent logical structures, as do sensory data such as visual or acoustic data, models trained as optimal predictors are forced to capture their statistical structure.

There are a lot of red flags to pick from in this article, but this one stood out to me as the most absurd. AI doesn't get magical multimodal powers from reading secondhand accounts describing a sensation. You can say it in as fancy of a phrasing as you want, but the proof is in the pudding. The "statistical structure" of that text doesn't propagate a meaningful understanding of almost anything in the real world.

> And really, a person will never grasp machine learning and AI as long as they keep drawing unbased parallels to humans and machines.

I think you're right on the money with this one.

[100ideas]

I think you both make valid points, but I also get the sense that the article is articulating insights gained from pure math explorations into the theoretical limitations of learning, which in the article can sound "turboencabulator-speak" when compressed into words.

Maybe I should have just linked to the research paper:

[B'MOJO: Hybrid state space realizations of foundation models with eidetic and fading memory](https://www.arxiv.org/abs/2407.06324)