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Far too much marketing speech, far too little math or theory, and completely misses the mark on the 'next frontier'. Maybe four years ago, spatial reasoning was the problem to solve, but by 2022 it was solved. All that remained was scaling up. The actual three next problems to solve (in order of when they will be solved) are: - Reinforcement Learning (2026) - General Intelligence (2027) - Continual Learning (2028) ED…
In my thinking what AI lacks is a memory system
Far too much marketing speech, far too little math or theory, and completely misses the mark on the 'next frontier'. Maybe four years ago, spatial reasoning was the problem to solve, but by 2022 it was solved. All that remained was scaling up. The actual three next problems to solve (in order of when they will be solved) are: - Reinforcement Learning (2026) - General Intelligence (2027) - Continual Learning (2028) ED…
Combinatorial search is also a solved problem. We just need a couple of Universes to scale it up.
I'd imagine Tesla's and Waymo's AI are at the forefront of spatial cognition... this is what has made me hesitant to dismiss the AI hype as a bubble. Once spatial cognition is solved to the extent that language has been solved, a range of applications currently unavailable will drive a tidal wave of compute demand. Beyond self driving, think fully autonomous drone swarms... Militaries around the world certainly are a…
While virtual world systems and physical world systems look similar based on description, a bit like chemistry and chemical engineering, they are largely unrelated problems with limited theory overlap. A virtual world model is essentially a special trivial case that becomes tractable because it defines away most of the hard computer science problems in physical world models.
A good argument could be made that spatial intelligence is a critical frontier for AI, many open problems are reducible to this. I don't see any evidence that this company is positioned to make material progress on it.
Sutton: Reinforcement Learning LeCun: Energy Based Self-Supervised Learning Chollet: Program Synthesis Fei-Fei: ??? Are there any others with hot takes on the future architectures and techniques needed for of A-not-quite-G-I?
Underrated and unsung. Fei Fei Li first launched ImageNet way back in 2007, a hugely influential move sparking much of the computer vision deep learning that followed since. I remember in a lecture about 7 years ago jph00 saying "text is just waiting for its imagenet moment" -> then came the gpt explosion. Fei Fei was massively instrumental in where we are today.
From reading that, I'm not quite sure if they have anything figured out. I actually agree, but her notes are mostly fluff with no real info in there and I do wonder if they have anything figured out besides "collect spatial data" like imagenet. There are actually a lot of people trying to figure out spatial intelligence, but those groups are usually in neuroscience or computational neuroscience. Here is a summary pap…
her company world labs is at the forefront of building spatial intelligence models
Earlier quoted context omitted.
Hasn't RLHF and with LLM feedback been around for years now
Large latent flow models are unbiased. On the other hand, if you purely use policy optimization, RLHF will be biased towards short horizons. If you add in a value network, the value has some bias (e.g. MSE loss on the value --> Gaussian bias). Also, most RL has some adversarial loss (how do you train your preference network?), which makes the loss landscape fractal which SGD smooths incorrectly. So, basically, there'…