Topic
Predictive Modeling
5 episodes
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Overview: World Models
We finally stop hand-waving and explain world models from the ground up — what they are, how they actually work, and why the field keeps coming back to them as the missing piece between AI that reacts and AI that plans.
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Overview: Next State Prediction
We finally slow down and explain next-state prediction from the ground up — the deceptively simple idea that if you train a model to guess what comes next, it ends up learning how the world actually works, and why that one trick is underneath almost everything in modern AI.
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Overview: Predictive Modeling
We finally slow down and explain predictive modeling from the ground up — the core idea that powers most of what we talk about on this show, from fraud detection to weather forecasting to the brain-prediction research we've looked at.
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How Open Models Are Driving AI Research
NVIDIA's open models, particularly Nemotron, Cosmos, and BioNeMo, are driving AI research by providing foundational tools for new studies, with 145 papers citing Nemotron at ICML 2026.
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Turning brain prediction models into testable explanations
Justy and Cody dig into Microsoft Research’s generative causal testing, a loop that turns brain-prediction models into short verbal hypotheses and then stress-tests them with synthetic stories in the scanner. They like the core move: prediction is only useful if it can be converted into something testable, but they also poke at where the method is strongest, where it may be riding on model quality, and how much the new “micro-region” claims should be trusted yet.