Episode 102 | Active Physical Intelligence Unleashed | Tara Javidi & Sam Bigdeli

Written By Greg Toroosian

How do you get AI to seek the right data in the real world instead of drowning in all of it?

In this episode, I sit down with Tara Javidi (UCSD professor and AI researcher) and Sam Bigdeli (repeat founder & former semiconductor supply‑chain exec), co-founders of Kav AI, to talk about “active physical intelligence”—hypothesis‑driven, curiosity‑led AI that hunts for the signals that matter in physical systems.

We cover:

  • Why passive, data-soaks-everything AI hits a wall in the physical world

  • Hypothesis-driven learning: letting models ask “what should I look at next?”

  • From oil & gas spills to structural failures—predicting the next “leak” like a language model predicts the next word

  • Handling massive, messy, multimodal sensor streams in real time (volume of context, not just length)

  • Interpretability when your model is deciding which sensor to query and why

  • What academia gets wrong (and right) about startups—and vice versa

  • The hardest part of moving from novelty-driven research to problem-driven product

  • How (and when) to disagree productively as co-founders

Links mentioned

🎙 Connect with me
LinkedIn – https://www.linkedin.com/in/greg-toroosian

Greg Toroosian
Previous

Episode 103 | Inside The Defense VC Boom | Nick Snoad
Next

Episode 101 | AI Powered Compliance = Faster Certification | Akshay Chalana