Google’s internal review blocked it from publication. Stated reasons were about paper quality. You can speculate whether that was the real reason.

Gebru issued an ultimatum email and said she would resign if some list of conditions weren’t met.

Google said “thanks, we accept your resignation”.

She claims it is retaliation, but it seems more like an own-goal if you ask me. She basically handed Google the solution to their problem.

Practical lesson: don’t tell your employer you might quit before you’re ok with leaving.

The state-of-the-art models aren't at "can fully replace knowledge worker" levels yet and I doubt they'll get there any time soon, so charging $2000 / month for access isn't going to happen. Right now everyone and their dog is being handed subsidized credits to play with AI, but the actual outcome is rarely good enough to be worth the money they'd need to charge for it. It might very well take another order of magnitude or two to get LLMs to be truly good (if it is even possible at all), and considering how much money is already being pumped into it I just don't see that happening.

On the other hand, the dumb models are more than adequate for simple noncritical tasks, like directing a user to the appropriate FAQ entry, or playing phone decision tree. There's a lot of money in making chatbot assistants actually useful, or in augmenting website search. Turning it into a glorified "language-to-API-call" translator doesn't take a lot of smarts, but as long as it's cheap you can make a killing in volume.

I understood it as a proof-of-concept, not a for-mass-production single blueprint - i.e.: "if you need your NN in a CIM form on ASIC, we can do it".

Their next proof-of-concept was said to be meant to be about size: "we showed you we can do it with 8b, now we are working to show you we can do 24b or 32b". Then, "and we plan to go bigger and faster".

> Our second model, still based on Taalas’ first-generation silicon platform (HC1), will be a mid-sized reasoning LLM. It is expected in our labs this spring and will be integrated into our inference service shortly thereafter. // Following this, a frontier LLM will be fabricated using our second-generation silicon platform (HC2). HC2 offers considerably higher density and even faster execution. Deployment is planned for winter (19 Feb 2006)

I'd say virtually all robots you've seen in the real world today rely on classical approaches - you build a rudimentary map, then use classical algorithms to find paths/do area coverage. The robots do no reason or understand what they're looking for, they're more like in-game units. At most there's some bounded, lightweight image classification going on.

LLMs can understand and reason about the world natively. nVidia has a Tesla FSD/Waymo competitor which simply their 7B reasoning LLM but instead of outputting tokens directly, its outputs are fed to a 2B diffusion model that outputs 1.6 second long trajectory for the car, and this is enough for an L2 system. But to make this work, they need the model to run at 10Hz, so they use super high-end hardware to do it (Jetson Thor) and the car is still "blind" for 100ms at a time (they have a parallel classical safety system).

With on-chip LLMs you could run this loop at like 100Hz on a chip that costs a few hundred bucks, rather than 10Hz on a board that costs several thousand.