Spatial intelligence is AI’s next frontier

Post by byearthithatius » Mon, Nov 10, 2025, 10:34 PM UTC

yzydserd wrote: post #26

dauertewigkeit wrote: post #12

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?

> Fei-Fei: ??? 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.

Post by stirfish » Mon, Nov 10, 2025, 10:36 PM UTC

programjames wrote: post #16

l9o wrote: post #10

Earlier quoted context omitted.

What do you consider "General Intelligence" to be?

A good start would be: 1. Robust to adversarial attacks (e.g. in classification models or LLM steering). 2. Solving ARC-AGI. Current models are optimized to solve the current problem they're presented, not really find the most general problem-solving techniques.

Post by pharrington » Mon, Nov 10, 2025, 10:42 PM UTC

gradus_ad wrote: post #20

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…

Post by [deleted] » Mon, Nov 10, 2025, 10:45 PM UTC

byearthithatius wrote: post #27

toisanji wrote: post #13

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…

This is super cool and I want to read up more on this as I think you are right insofar as it is the basis for reasoning. However it does seem more complex than just that. So how do we go from coordinate system transformations to abstract reasoning with symbolic representations?

Post by dauertewigkeit » Mon, Nov 10, 2025, 10:48 PM UTC

byearthithatius wrote: post #31

yzydserd wrote: post #26

Earlier quoted context omitted.

> Fei-Fei: ??? 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.

Curating a dataset is vastly different than introducing a new architectural approach. ImageNet is a database. Its not like inventing the convolutions for CNNs or the LSTM or a Transformer.