100ideas

what a non-answer answer!

You just answered my question:

Is it the case that UnitedHealth and Cigna each own (or control) one of the "big three" PBMs? If so, that is a just crazy - the control insurance premium pricing, benefit decisions, AND the pricing of covered medications?

yadaebo wrote below "Medical Loss Ratio (MLR) is capped at 85% in the US which means 85% of revenue must go to patients". Does controlling a big PBM allow an insurance company a loophole?

Very interesting comments and moderation discussion on this article.

Oops, thanks. I changed it.

Basically, as LLMs scale up, the author (Soatto, VP at AWS) suggests they're beginning to resemble Solomonoff inference: hypothetically optimal but computationally infinite approach that executes all possible programs to match observed data. Repeating this approach for any given question by definition gives the best answer, yet requires no learning, since the entire process can be repeated for any query (thanks to infinite computation).

The article develops a theoretical framework contrasting traditional inductive learning (which emphasizes generalization over memorization) with transductive inference (which embraces memorization and reasoning). Here's a quote:

"What matters is that LLMs are inductively trained transductive-inference engines and can therefore support both forms of inference.[2] They are capable of performing inference by inductive learning, like any trained classifier, akin to Daniel Kahneman’s “system 1” behavior — the fast thinking of his book title Thinking Fast and Slow. But LLMs are also capable of rudimentary forms of transduction, such as in-context-learning and chain of thought, which we may call system 2 — slow-thinking — behavior. The more sophisticated among us have even taught LLMs to do deduction — the ultimate test for their emergent abilities."

Sadly, the opening quote is not elucidated.

Yes, I should have made that clear in my first comment. Thanks for doing so. I used the quote in my title because I found it a fascinating way to start a technical blog post, and it made me want to read the article to understand what the author was planning to write from such a beginning.

I found the opening quote of this article to be intriguing, especially since it was from a 1992 research lab:

“One year of research in neural networks is sufficient to believe in God.” The writing on the wall of John Hopfield’s lab at Caltech made no sense to me in 1992. Three decades later, and after years of building large language models, I see its sense if one replaces sufficiency with necessity: understanding neural networks as we teach them today requires believing in an immanent entity.

Who are the lobbyists pushing this?

So the control was the previous GILEAD drug (presumably a weekly injection?) for the same condition (HIV+)?