OpenAI disbands its robotics research team

Makes sense I guess, integrating robot hardware requires an entirely different set of skills to ML research and has a much slower dev cycle.

I think OpenAI has progressively narrowed down its core competency - for a company like 3M it would be something like "applying coatings to substrates", and for OpenAI it's more like "applying transformers to different domains".

It seems like most of their high-impact stuff is basically a big transformer: GPT-x, copilot, image gpt, DALL-E, CLIP, jukebox, musenet

their RL and gan/diffusion stuff bucks the trend, but I'm sure we'll see transformers show up in those domains as well.

And we're further away since nobody bought Schaft from Google, and Schaft was shut down. They had the best humanoid.

But so many of the little problems have been solved. Batteries are much better. Radio data links are totally solved. Cameras are small and cheap. 3-phase brushless motors are small and somewhat. Power electronics for 3-phase brushless motors is cheap. 3D printing for making parts is cheap.

I used to work on this stuff in the 1990s. All those things were problems back then. Way too much time spent on low-level mechanics.

You can now get a good legged dog-type robot for US$12K, and a good robot arm for US$4K. This is progress.

> What area of machine learning do you feel is closer to how natural cognition works?

None. The prevalent ideas in ML are a) "training" a model via supervised learning b) optimizing model parameters via function minimization/backpropagation/delta rule.

There is no evidence for trial & error iterative optimization in natural cognition. If you'd try to map it to cognition research the closest thing would be behaviorist theories by B.F. Skinner from 1930s. These theories of 'reward and punishment' as a primary mechanism of learning have been long discredited in cognitive psychology. It's a black-box, backwards looking view disregarding the complexity of the problem (the most thorough and influential critique of this approach was by Chomsky back in the 50s)

The ANN model that goes back to Mcculloch & Pitts paper is based on neurophysiological evidence available in 1943. The ML community largely ignores fundamental neuroscience findings discovered since (for a good overview see https://www.amazon.com/Brain-Computations-Edmund-T-Rolls/dp/... )

I don't know if it has to do with arrogance or ignorance (or both) but the way "AI" is currently developed is by inventing arbitrary model contraptions with complete disregard for constraints and inner workings of living intelligent systems, basically throwing things at the wall until something sticks, instead of learning from nature, like say physics. Saying "but we don't know much about the brain" is just being lazy.

The best description of biological constraints from computer science perspective is in Leslie Valiant work on "neuroidal model" and his book "circuits of the mind" (He is also the author of PAC learning theory influential in ML theorist circles) https://web.stanford.edu/class/cs379c/archive/2012/suggested... , https://www.amazon.com/Circuits-Mind-Leslie-G-Valiant/dp/019...

If you're really interested in intelligence I'd suggest starting with representation of time and space in the hippocampus via place cells, grid cells and time cells, which form sort of a coordinate system for navigation, in both real and abstract/conceptual spaces. This likely will have the same importance for actual AI as Cartesian coordinate system in other hard sciences. See https://www.biorxiv.org/content/10.1101/2021.02.25.432776v1 and https://www.sciencedirect.com/science/article/abs/pii/S00068...

Also see research on temporal synchronization via "phase precession", as a hint on how lower level computational primitives work in the brain https://www.sciencedirect.com/science/article/abs/pii/S00928...

And generally look into memory research in cogsci and neuro, learning & memory are highly intertwined in natural cognition and you can't really talk about learning before understanding lower level memory organization, formation and representational "data structures". Here are a few good memory labs to seed your firehose

https://twitter.com/MemoryLab

https://twitter.com/WiringTheBrain

https://twitter.com/TexasMemory

https://twitter.com/ptoncompmemlab

https://twitter.com/doellerlab

https://twitter.com/behrenstimb

https://twitter.com/neurojosh

https://twitter.com/MillerLabMIT

I think most people believe that the problem with robots is that we don't have the right software, and if we just knew how to program them then today's robots could be incredibly useful in everyday life. From that perspective, this move from OpenAI seems dumb.

That belief is wrong. Today's robots can't be made useful in everyday life no matter how advanced the software. The hardware is too inflexible, too unreliable, too fragile, too rigid, too heavy, too dangerous, too expensive, too slow.

In the past the software and hardware were equally bad, but today machine learning is advancing like crazy, while the hardware is improving at a snail's pace in comparison. Solving robotics is now a hardware problem, not a software problem. When the hardware is ready, the software will be comparatively easy to develop. Without the right hardware, you can't develop the appropriate software.

OpenAI is right to ignore robotics for now. It's a job for companies with a hardware focus, for at least the next decade.

I'm not an expert, but I'd suggest hardware is trailing software, but there is still great progress happening in materials design, soft-robots, miniaturization, etc. Just as in the computer era, we go through phases where the software is ahead of hardware, then hardware gets ahead of software. It seems that is a pendulum that swings, and the argument many people have to the integrated pipeline Apple operates.