Moonlake co-founder Fan-yun Sun describes the findings from foundation model pre-training experiments conducted during his time with NVIDIA Research.
Prediction Open · timeframe Apr 2031
Sun: Neural rendering with world priors will replace rasterizers and DLSS
“We actually believe that this is going to be the next paradigm of rendering. So it's going to replace how rasterizers, it's going to replace DLSS today because it not only has these pixel prior that's learned from the world, such that you can literally play an…”
Opinion
Sun: Pixel-coherent world simulators are overrated for causal reasoning and embodied AI
“Having a world simulator that can produce pixel coherency is very, very useful for games and, you know, marketing and all these things, but it's not as useful as people think when it comes to causal reasoning, when it comes to embodied AI.”
Insight
Sun: Embodied General Intelligence Requires Interactive Causal Data
“On our way to, let's call it embodied general intelligence, Models need to learn the consequences behind their actions, which means that they need interactive data.”
Insight
Sun: Using structural abstraction in AI does not contradict Bitter Lesson
“I do feel like sometimes people confuse like, oh, like we're taking an, a method with abstraction. That means they don't believe in bitter lesson. Like that's just false, right? Like we are believers in bitter lesson, but then I feel like the question that we …”
Disclosure
Sun: Moonlake splits world modeling into multimodal reasoning and Reverie rendering
“Within our world modeling framework, we think there are two models that we train, right? Like there's the multimodal reasoning model that we just talked about that essentially handles Mainly the causality, the persistency, and logic, determinism, determinism o…”
Insight
Sun: Code modifies physical rules better than purely data-driven models
“But it's a lot easier to change with code, as opposed to a model that is Learned primarily on data of real world and virtual worlds that are, I guess, like for example, junior, like there's actually trained on a lot of real world data and a lot of virtual gami…”