Fan-yun Sun
On 1 show, 1 appearance. The Shows tab opens the full record on each.
1shows
1appearances
13statements
1resolved
1supported
0contradicted
100%fully supported
Everything Fan-yun Sun said on any show that made the record, most notable first. Each card names its show and opens the statement there.
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…”
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.”
Sun: Synthetic data matches real-world data for multimodal model pre-training
“We were actually generating a lot of synthetic data and showing that, hey, you can actually, these synthetic data are actually as useful as real-world data when it comes to multimodal pre-training.”
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.”
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 …”
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…”
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…”
Sun: AI world models should treat physics engines as modular cognitive tools
“The way we think about it is like physics engine or tools or code are cognitive tools, like borrowing Chris's term, right? Like tools that the model can employ as means to an end.”
Sun: Moonlake can generate multiplayer environments and persistence databases via prompting
“So if you just actually just like prompt our Model to say, hey, like configure the multiplayer, then it'll do like this. You'll be able to configure multiplayer. Persistency database for you.”
Sun: World model evaluation metrics depend on gaming vs. robotics deployment goals
“Depending on your end goal and purpose, the values should differ. So in the context of games, then the most direct way of measuring is how much time are people actually spending in this world that you create? And if your goal is to say, for example, in the con…”
Sun: Moonlake will automatically generate training environments for embodied AI by 2029
“I'll maybe start with where we see the platform in three years, which is like, okay, the users would tell us what they want to achieve. The end goal could be, hey, I just, I want to make something to teach my kids the value of humility. Or it could be, hey, I …”
Sun: NVIDIA pays heavily to purchase interactive simulation worlds for robotics
“In industry, like folks at NVIDIA are actually paying a lot of dollars to purchase these types of interactive worlds, whether it's for the sake of evaluation or training the robots or policies or models.”
Sun: Moonlake is training a unified multimodal latent representation
“We do want to basically like, we, our model model, like the one we're training is basically Towards the goal of having a combined latent representation across all these different modalities, right? Such that you can like reason across these different modalitie…”
One line per show, most statements first. The link opens Fan-yun's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 LATENT SPACELEDGER | 1 | 13 | 100% 1/1 | full record on Latent Space → |