Stanford Professor Chris Manning contrasts Moonlake's engine-based world model with mainstream generative video models when discussing spatial audio.
Opinion
Manning: Vision understanding stalled; language does 90% of work in VLMs
“I mean, I think it's fair to say that, you know, vision understanding sort of stalled out, right? You got to object recognition, and then progress just wasn't being made, right? If you look at any of these vision language models, it's the language that's doing…”
Opinion
Manning: Yann LeCun underestimates language and symbolic representations in intelligence
“Jan LeCun is a dear friend of mine but he has never appreciated the power of language in particular or symbolic representations in general. Yarn is a very visual thinker. He always wants to claim that he thinks visually, and there are no words, symbols, or mat…”
Opinion
Manning: Transformer internal weights can act as joint representations for world models
“I'm not actually convinced that's right, because although the token production is this autoregressive process that's heading, you know, left to right, I guess don't have to be left or right, but anyway, in sequence of tokens, we could have right to left Arabic…”
Insight
Manning: Mainstream vision models fail by operating solely on pixel surfaces
“Believing that there can be a really rich connection between a more symbolic layer of abstracted understanding of visual domains, which aren't in the mainstream vision models, which are still trying to operate on the surface level of pixels.”
Insight
Manning: True World Models Require Action Conditioning and Semantic Abstraction
“You only actually have a world model if you can predict, given some action is taken, what is going to change in the world because of that, and in particular that becomes hard over longer time scales, so if you're simply, you know, trying to predict the next vi…”
Insight
Manning: Semantic abstractions require five orders of magnitude less data than pixels
“If there are ways in which you can work with five orders of magnitude, less data than people working purely from pixels, you're going to be able to make a lot more progress, a lot more quickly, and that's the bet here.”