For deeper playbooks and analysis: https://natesnewsletter.substack.com/
What does a world model actually do—and why might a robot learn to fold your laundry before it can make perfect scrambled eggs?
Nate sits down with Ming-Yu Liu, VP of Cosmos Lab at NVIDIA, to explore how world models turn observations into predictions and physical actions. They discuss where the analogy to language models helps, where it breaks down, and why knowing whether an action worked can be harder than generating the action itself.
For builders and operators, this conversation connects the models to the practical questions: what can be tested, what must happen immediately, and where human judgment is still needed.
Subscribe for daily AI strategy and news.
Hosted on Acast. See acast.com/privacy for more information.
","author_name":"Nate B. Jones"}