[trention]

>evidence of concrete applications that are in progress at some of the most well-resourced companies in Silicon Valley

Neither of the 2 companies you posted marketing materials about is among "some of the most well-resourced companies in Silicon Valley".

[rebelos]

> Neither of the 2 companies you posted marketing materials about is among "some of the most well-resourced companies in Silicon Valley".

Two of the founders of Adept AI are authors on the paper 'Attention Is All You Need'. If you don't understand the significance of that, then you're speaking well outside of what you're qualified to comment on. The company has also raised capital from top tier SV investors.

Runway ML has raised money from Lux Capital.

These companies are not just well-resourced, they are positioned in the upper echelon of the innovation business.

[trention]

I mean I can easily dig out data about their funding that will not put them even inside the top 20% of the companies in the valley but at this point, given that you lack the competence to distinguish between founders' achievements prior to founding a company and the companies in question being "some of the most well-resourced companies", it's "why bother" with typical AI bros, incompetent at anything they touch.

[civilized]

I enjoyed the Roon blog post but I found this bit amusing:

> It is easy to bet against new paradigms in their beginning stages: the Copernican heliocentric model of cosmology was originally less predictive of observed orbits than the intricate looping geocentric competitor. It is simple to play around with a large language model for a bit, watch it make some very discouraging errors, and throw in the towel on the LLM paradigm. But the inexorable scaling laws of deep learning models work in its favor. Language models become more intelligent like clockwork due to the tireless work of the brilliant AI researchers and engineers concentrated in a few Silicon Valley companies to make both the model and the dataset larger.

I don't know about you, but if I feed a program with hundreds of billions of "parameters" a huge chunk of the internet and it can then kinda-sorta do a bunch of things, sometimes semi-intelligently, but for the most part couldn't compete with a 4-year-old child... I'd say that's more on the Ptolemaic side of things than the Copernican side. Certainly "it gets better as you feed it more data" is equally true of both paradigms, so I'm not sure what Roon's point is here.

The appeal to the Copernican revolution itself has a bit of a hype-y, cranky odor. Virtually every crank appeals to Copernicus as a role model and vindicator. Real scientists usually don't, because they are busy with the hard, humbling business of actually figuring out how the world works.

Now don't get me wrong, I am thrilled by the research advances of the last couple decades, the foundation models, AlphaGo and AlphaFold, etc. The action model from Adept is great and Adept may become a very successful company. It's all very cool. But every paradigm shift in AI has been heralded as the thing that will Change Everything, and they usually don't. Big, exciting shifts in research don't necessarily mean as much in practice right away. I tend to think that getting AI "right enough" to have a huge, pervasively transformative impact on human life is going to take quite a few decades at least, if not centuries or more.