I don't know how much we learn by surveying working "AI researchers". By their own admission most of them have a religious belief that they will bring about a singularity in the next few years. It's like asking a billionaire if he earned his wealth; regardless of what you think the answer is, you pretty much know how they will answer ahead of time.

What it shows are two unrelated results mixed together for no apparent reason besides marketing. The raw-weight results are impressive, but haven't been demonstrated as ready to scale up to a real language model. The Qwen result is a mildly interesting application of functionality that's been on the market for years.

If you think that the definition of "RSI" includes systems with a coding agent in the critical path, something humans can already do albeit faster, then your definition includes all of human history. If your definition includes AI systems that learn in a similar way to biological intelligence, then only the raw-weight results from this paper are pertinent. Those results are indeed interesting! This article is merely disentangling them from the frontier-lab-fueled hype that overran yesterday's thread.

It obviously is an important distinction to the paper's authors, which is why they used the modifier "native" to mark it as a distinct form of communication than what's already been on the market for years (coding agents). But it's not native, or rather some of the results were native but they mixed toy-scale native results with non-toy-scale non-native results for marketing purposes.

This is like someone telling you they made a "native" brain-to-brain interface but it's just a tin-can phone.

The people who wrote the paper are MIT grad students, not IPOing.

AI research publishing cycles unfortunately have a similar incentive structure.

Eg. This is not uncommon advice: https://maxwellforbes.com/posts/your-paper-is-an-ad/

Your resume is an ad as well. Doesn't mean that it is incorrect.

It saved the stock market. Try telling one of the thousands who was made homeless that it saved the economy.

You're now the second person doing this. I won't try to keep up with the conspiracies, and multiple levels of unstated implications based on the assumptions about those conspiracies being facts. Israel has never been, nor will likely ever be a superpower or anything akin to a near-peer adversary of the United States, so I'm not too sure what there is to talk about. Still waiting for anyone to propose a topic.

The USSR is the superpower I was referring to. You are illiterate.

Well it's important to you. It's evidently not important to the people you vote for, the people who fundraise off of this hostage situation, the people who have had trifectas multiple times during this era and have not even used them to permanently secure the "democracy", let alone the overwhelmingly popular policies with their base that would guarantee they win ever election but piss off their donors.

> half

They own the careers of all but a tiny handful of US politicians

It's hard for me to understand Google's redefinition of "world models" (basically: a txt2videogame modality for LLMs) as anything other than an attempt to bury Yann Lecun's definition of "world models", which includes an implicit bear case against LLMs and thus Google's stock price.

> … hard for me to understand Google's redefinition of "world models"

I’ve also wondered what they mean by “world model”, whereas I interpret them as being about predicting outcomes - something an LLM cannot reliably do.

Is this new model an LLM or a predictive outcome model (as you say a la Yann Lecun), or some hybrid?

From the perspective of your career, the best thing to do is not have objections, or really any principles at all, in the first place.