Robot reasoning: why data is not enough

Описание: Robots aren’t just software. They’re AI in the physical world. And that changes everything.

In this episode of TechFirst, host John Koetsier sits down with Ali Farhadi, CEO of Allen Institute for AI, to unpack one of the biggest debates in robotics today: Is data enough, or do robots need structured reasoning to truly understand the world?

Ali explains why physical AI demands more than massive datasets, how concepts like reasoning in space and time differ from language-based chain-of-thought, and why transparency is essential for safety, trust, and human–robot collaboration. We dive deep into MOMO Act, an open model designed to make robot decision-making visible, steerable, and auditable, and talk about why open research may be the fastest path to scalable robotics.

This conversation also explores:
• Why reasoning looks different in the physical world
• How robots can project intent before acting
• The limits of “data-only” approaches
• Trust, safety, and transparency in real-world robotics
• Edge vs cloud AI for physical systems
• Why open-source models matter for global AI progress

If you’re interested in robotics, embodied AI, or the future of intelligent machines operating alongside humans, this episode is a must-watch.

👤 Guest

Ali Farhadi
CEO, Allen Institute for AI (AI2)
Professor, University of Washington
Former Apple researcher

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00:00 – Plato vs Aristotle… in robotics?
00:55 – What “reasoning” means in the physical world
02:10 – How humans predict actions before they happen
03:45 – Why physical AI is fundamentally different from text AI
04:50 – The next revolution: AI in the real world
05:30 – What is MOMO Act?
06:20 – Chain-of-thought… for robots
07:45 – Trajectories as reasoning and robot transparency
08:55 – Trust, safety, and correcting robots mid-action
10:15 – Why predictability builds trust in machines
11:40 – What’s broken with data-only AI approaches
13:10 – Why reasoning + data isn’t an “either/or”
14:00 – Open sourcing robotics models: why it matters
15:20 – How closed AI slows innovation
16:45 – Global competition and open research
17:40 – What’s next for robotics reasoning models
18:20 – Can these models work across robot types?
19:30 – Temporal and spatial reasoning in MOMO 2
20:40 – Scaling robotics vs scaling LLMs
21:10 – Edge vs cloud AI for robots
22:20 – Specialized models, latency, and privacy
23:00 – Final thoughts on the future of physical AI