Automated with Brian Heater

Get a direct line to the biggest names and brightest minds in robotics, Physical AI, and automation. Automated with Brian Heater brings you long-form conversations and unfiltered insights into how we got here, where we’re going, and what’s behind the technologies that are shaping how we live and work.

Automated with Brian Heater is a science podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Automated with Brian Heater, a science show with 0 episodes published.

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  Episode #60

  Dr. Ahmad Bahai on the Semiconductor Race Behind AI and Robotics

  Sep 23, 202650 min

  AI and robotics may look like software revolutions. But every breakthrough depends on the semiconductors underneath them. In this episode of Automated, Brian Heater speaks with Dr. Ahmad Bahai, chief technology officer at Texas Instruments, about the chips, power systems, sensors, and edge-processing technologies enabling the next generation of AI and robotics. Ahmad explains why companies cannot predict the next major market a decade in advance. What they can see are the underlying trends, including the growing demand for power density, bandwidth, timing accuracy, and local processing. Those needs now extend from massive AI data centers to mobile devices and robots that must react without waiting for the cloud. Brian and Ahmad also trace his path from early Wi-Fi research at Bell Labs to Kilby Labs and Texas Instruments. Ahmad reflects on what made Bell Labs unique, why that model is difficult to reproduce today, and how companies can connect academic research with technology that can be manufactured at scale. The conversation also covers the semiconductor supply chain, the search for more efficient AI algorithms, and why technologies like autonomous vehicles and humanoid robots take longer to reach the mainstream than early predictions suggest. Finally, Ahmad explains why supposed physical limits rarely end technological progress. Scientists once argued that chips could not scale below 55 nanometers. Engineers found another way, and today the industry has reached two nanometers. Learn more about Dr. Ahmad Bahai and semiconductor innovation at Texas Instruments: https://www.ti.com/video/6372229233112 Learn more about Texas Instruments: https://www.ti.com/about-ti.html Read TI’s insights on scaling humanoid robots: https://www.ti.com/about-ti/behind-chip/articles/what-will-it-take-to-bring-humanoid-robots-into-the-real-world-read-our-experts-insights.html We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org . You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  Episode #56

  Dhruv Batra on What AI Still Cannot Do in the Physical World

  Sep 15, 202658 min

  AI can speak fluently, reason through problems, and describe the world. Getting it to do something in the world is a much harder problem. In this episode of Automated, Brian Heater speaks with Dhruv Batra, co-founder and chief scientist at Yutori, about the gap between language models that generate answers and intelligent systems that can take action. Dhruv explains why many post-ChatGPT robot demos are still little more than a physical wrapper around a language model. A robot may be able to hold a convincing conversation, but that does not prove it can navigate, manipulate objects, understand physical space, or respond to the movement of people around it. The conversation begins with an even more fundamental question: What does it mean for an AI system to have a belief? Dhruv traces the idea back to Bayesian probability and explains why a rational decision-maker should never assign exactly zero probability to an event. Once a possibility reaches zero, no amount of new evidence can change that belief. Brian and Dhruv also examine the human language used to describe artificial intelligence. Words like “belief” and “thinking” carry rich meanings in everyday life, but AI researchers often use them in much narrower technical ways. Dhruv argues that this trade-off can create confusion, while also giving researchers something precise enough to measure and improve. They trace Dhruv’s path from probabilistic machine learning and computer vision to visual question answering, Grad-CAM, embodied AI at Meta FAIR, and eventually Yutori. Along the way, he explains how his team trained virtual robots to navigate directly from pixels to actions without building a map, using LiDAR, or separating perception from planning. The conversation also explores why simulation has earned a complicated reputation in robotics, where sim-to-real transfer works well for locomotion but remains far more difficult for raw camera images and dexterous manipulation. Dhruv recalls the blunt question roboticists asked whenever he presented work in simulation: “Did you touch a robot?” At Yutori, Dhruv is now applying many of those embodied AI ideas to web agents. These systems perceive a browser through screenshots and take actions such as clicking buttons, entering information, and completing tasks. He calls them “robots of the web.” Unlike traditional automation, they do not depend on a website remaining perfectly structured or unchanged. The result is a wide-ranging conversation about uncertainty, paradigm shifts, robot learning, web agents, and the difference between an AI system that can answer a question and one that can act on your behalf. Connect with Dhruv Batra https://www.linkedin.com/in/dhruv-batra-dbatra/ Learn more about Dhruv Batra https://dhruvbatra.com/ Learn more about Yutori https://yutori.com/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  Episode #55

  Amanda Prorok on Robot Teams, Physical AI, and Trust

  Sep 14, 202642 min

  The future of robotics may not belong to one machine that can do everything. It may belong to teams of specialized robots that learn how to work together. In this special episode of Automated, recorded at the Davos Tech Summit in Switzerland, Brian Heater speaks with Amanda Prorok, professor of collective intelligence and robotics at the University of Cambridge and founder of the Prorok Lab. Amanda explains why she believes the world is built on collective intelligence. From ants using pheromone trails to find the shortest path to food to robot teams dividing complex work among specialized members, nature repeatedly shows that cooperation can produce capabilities no individual agent possesses alone. That principle produced a surprising result in Amanda’s own research. Her team gave a group of AI agents one simple objective: score a goal. Without being taught human soccer strategy, the agents organized themselves into goalkeepers, defenders, and attackers. They independently discovered the same role specialization humans developed for the game. Brian and Amanda also explore the challenges of coordinating robots with different bodies and capabilities. Amanda explains how drones, wheeled robots, and legged robots can share information even when they move through the world differently, and how a “blind” robot can navigate using cameras distributed throughout its environment. The conversation then turns to one of the biggest unresolved questions in physical AI: trust. Many systems are being deployed based on empirical performance rather than mathematical guarantees. A robot may work well most of the time while still failing in ways researchers cannot fully explain. Amanda shares a personal moment that brought that uncertainty into focus after she stood beside a humanoid robot with her three-week-old baby. She also describes her lab’s work on remotely detectable robot policy watermarks, which could allow someone to record a robot with a phone and verify the origin of the policy controlling its behavior. Connect with Amanda Prorok https://www.linkedin.com/in/aprorok/ Learn more about the Prorok Lab https://www.proroklab.org/ View Amanda’s University of Cambridge profile https://www.cst.cam.ac.uk/people/asp45 Explore the robot policy watermarking research https://www.proroklab.org/publications/iclr2026-watermarking/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  Automated with Brian Heater

  This is Automated

  Sep 11, 20261 min

  Get a direct line to the biggest names and brightest minds in robotics, Physical AI, and automation. Automated with Brian Heater brings you long-form conversations and unfiltered insights into how we got here, where we’re going, and what’s behind the technologies impacting how we live and work. The show features conversations with visionaries from AWS, NVIDIA, Universal Robots, and beyond. From groundbreaking research in humanoid robotics to real-world industrial applications, each weekly episode explores the people and ideas shaping the future of work, industry, and society. Subscribe and be the first to get new episodes on Wednesdays. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  Episode #54

  Jan Liphardt on Building the AI Brain for Humanoid Robots

  Sep 9, 202653 min

  Humanoid robots can walk, gesture, and look astonishingly capable in a polished demo. Then Jan Liphardt bought one for his house, along with three quadruped robots, and discovered how quickly the illusion breaks. After charging the batteries and getting the humanoid to stand, it simply stood there. It could not become the R2-D2-like helper, companion, teacher, or coworker he had imagined. In this episode of Automated, Brian Heater speaks with Jan Liphardt, founder and CEO of OpenMind and associate professor of bioengineering at Stanford University, about the software, intelligence, and trust systems that robots still need before they can become useful parts of everyday life. Jan explains why the final 3% of humanoid deployment can consume nearly all the work. A robot that performs well in a lab still has to handle traffic, animals, wet pavement, moving leaves, different languages, and countless other edge cases before it can operate safely in an unstructured environment. The conversation explores why OpenMind is deliberately not trying to solve every robotics problem. Rather than building hands, arms, or high-speed control systems, the company combines its own models with technology from other teams. Its OM1 runtime and FABRIC coordination layer are designed to help different robots become more capable, understandable, and useful around people. Brian and Jan also discuss why social intelligence requires much more than speech. A compelling humanoid has to track attention, move its head and shoulders, use its hands, understand body language, remember preferences, and respond in ways that feel natural. Jan even argues that a robot’s imperfections can strengthen the bond between the machine and the person helping it. They also dig into one of OpenMind’s most distinctive ideas: connecting sensors and models through natural language so people can inspect how a robot reaches a decision. That approach trades some speed for intelligibility, giving developers a clearer way to debug behavior, add guardrails, and improve governance. Jan also challenges the idea that robotics has one universal data problem. Teaching math, folding a T-shirt, and completing an assembly task require very different data and system designs. In some cases, he argues, a robot may learn more effectively from physical constraints than from watching millions of videos of humans. The conversation also covers Jan’s path from physics and bioengineering into robotics, why elite degrees do not always predict great engineers, what a 17-year-old calling from a robot field trial taught him about hiring, and why combining academia with the real world can produce better research questions. Connect with Jan Liphardt https://www.linkedin.com/in/jan-liphardt Learn more about OpenMind https://openmind.com/ Learn more about Jan’s work at Stanford https://profiles.stanford.edu/jan-liphardt Register for the Automated Happy Hour in Mountain View on September 22 https://luma.com/n688dcl5 Learn more about the Advanced Vision and AI Conference on September 23 and 24 https://www.automate.org/events/advanced-vision-and-ai-conference/register We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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