Automated with Brian Heater

Alexa is entering a very different era.For years, voice assistants were built around rules, scripted responses, and carefully designed commands. But with the rise of large language models and generative AI, Amazon had to rethink what Alexa could be and how people might use it.In this episode of Automated, Brian Heater speaks with Daniel Rausch, Amazon’s Vice President of Alexa and Echo, about Alexa+, the company’s AI-powered evolution of its voice assistant. Daniel explains why the shift from traditional voice assistance to foundational AI assistance required a full rearchitecture of the technology behind Alexa.The conversation explores how Alexa moved from a deterministic system to one powered by more than 70 models, why customers do not care which model is working behind the scenes, and how Amazon thinks about choosing the right AI tool for the job.Brian and Daniel also discuss one of the biggest questions around AI assistants: trust. Daniel explains why Alexa is designed to understand that it is AI, why it should help people prioritize human relationships, and why guardrails matter as assistants become more conversational, personal, and ambient in the home.They also get into the smart home, where Daniel says Alexa+ is changing how people interact with connected devices. Instead of needing to know the right command or app, people can speak naturally, whether they are unlocking a door, checking a Ring camera, controlling lights, or asking for help while cooking.The conversation also covers Echo hardware, privacy controls, personality styles, language and dialect differences, AI’s impact on robotics, and why Daniel sees Amazon as an invention machine at a moment when AI is moving faster than ever.Connect with Daniel Rauschhttps://www.linkedin.com/in/danielrauschLearn more about Alexa+https://www.amazon.com/alexaplus/dp/B0CXRRF584Learn more about Amazon Echo deviceshttps://www.amazon.com/b?ie=UTF8&node=210779651011We’d love to hear from you.Have thoughts or guest suggestions?Reach us at podcast@automate.orgYou can find the transcript and more episodes of Automated at automated.fmUnlock full access to Automated and explore everything automation.Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify.You can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/

Physical AI is moving fast.But Daniela Rus says the future of robotics will not be defined by viral humanoid robot demos alone. The real challenge is building robots that can understand the physical world, make safe decisions in real time, and work reliably outside controlled lab environments.In this episode of Automated, Brian Heater speaks with Daniela Rus, Director of MIT CSAIL, about humanoid robots, self-driving cars, embodied AI, on-device AI, robot learning, and why the next wave of artificial intelligence needs to move beyond the cloud and into the physical world.Daniela explains why humanoid robots are exciting, but not ready for prime time. A robot may look impressive in a short demo, but operating safely and consistently around people requires common sense, physical understanding, and real-world adaptability that robots still do not fully have.The conversation also explores why self-driving cars remain one of the hardest problems in robotics. Daniela breaks down the long tail of autonomous driving, from bad weather and unpredictable human behavior to the messy edge cases that make real-world deployment so difficult.Brian and Daniela also discuss why the future of AI robotics may depend on smaller, more efficient AI models that can run directly on devices. If a car is moving at 60 miles an hour, it cannot wait for the cloud to decide what to do next. For robotics, speed, safety, energy use, and reliability all point toward a hybrid future where AI runs both in the cloud and on the machine itself.Daniela also shares why physical AI needs more than video data. Robots interact with the world through forces, torques, motion, contact, and uncertainty. For many tasks, robot learning requires a deeper understanding of physics, not just visual imitation.The episode also moves into some of the most fascinating frontiers of AI and robotics, including Daniela’s work with Project CETI and the effort to better understand sperm whale communication using machine learning, robotics, and large-scale data collection.Finally, Daniela talks about AI systems that could help design robots from natural language prompts, why engineering constraints can drive creativity, what octopus intelligence can teach us about decentralized robots, and why this moment in robotics feels like the future researchers imagined decades ago is finally arriving.Connect with Daniela Rushttps://www.csail.mit.edu/person/daniela-rusLearn more about MIT CSAILhttps://www.csail.mit.edu/Learn more about Liquid AIhttps://www.liquid.ai/team/daniela-l-rusLearn more about Project CETIhttps://www.projectceti.org/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, and Spotify.Subscribe to the Automated Newsletter:https://www.automate.org/automation/automated-newsletterYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/

Physical AI is moving fast.But Matthew Johnson-Roberson says robotics is still missing something fundamental. The field has data, models, and momentum, but it still does not have the simple learning objective that helped language models scale so quickly.In this episode of Automated, Brian Heater speaks with Matthew Johnson-Roberson, founding dean of Vanderbilt’s College of Connected Computing, about why physical AI may not follow the same playbook as large language models.Matthew explains why robotics still feels stuck between promise and deployment. We still do not live in a world where you can look out your window and see robots everywhere. That gap is not just about hype. It is about the difficulty of building systems that can learn from physical experience in a way that actually scales.Brian and Matthew also discuss what self-driving taught the broader automation world, why last-mile delivery still has not cracked scale, and what Amazon’s long arc with Kiva robots reveals about how real hardware progress actually happens.The conversation also explores healthcare, where Matthew says AI scribes are already making a real impact, even as outdated infrastructure like fax-based record sharing shows how much friction remains. That experience also helped inspire Patients.app, the startup he co-founded after watching how much clinician time gets lost to documentation.They also get into the tension between startups and academia. Matthew argues that startups are powerful vehicles for scaling known solutions, but much worse fits for decade-long research questions that still do not have clear answers.Finally, Matthew reflects on building Vanderbilt’s new College of Connected Computing, why higher ed can take on 30- and 40-year problems in a way few other institutions can, and how AI agents have changed his own workflow so dramatically that he says he has not directly written a line of code in three months.Connect with Matthew Johnson-Robersonhttps://www.linkedin.com/in/mattkjrLearn more about Vanderbilt’s College of Connected Computinghttps://computing.vanderbilt.edu/bio/matthew-johnson-roberson/Learn more about Patients.apphttps://patients.app/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, and Spotify.Subscribe to the Automated Newsletter:https://www.automate.org/automation/automated-newsletterYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/

Physical AI looks closer than ever.But the hardest part in robotics is not getting a machine to do one impressive task on camera. It is building systems that can improve from real-world experience, handle edge cases, and scale across different robots and environments.In this episode of Automated, Brian Heater speaks with Sergey Levine of Physical Intelligence about why robotics has reached an inflection point, and why progress now requires more than great models in a lab.Sergey explains why the next phase of robotics will depend on something much less flashy than a viral demo: collecting the right real-world data, learning from it efficiently, and building systems that improve through deployment.The conversation explores what makes a robot experience useful in the first place. Sergey describes a concept borrowed from child psychology called the “zone of proximal development,” where the best learning happens when a system is challenged just beyond what it can already do. For robots, that means creating environments where they can succeed, fail, adapt, and improve.Brian and Sergey also discuss how the bottleneck in robotics is changing. Basic motor skills are improving fast. The harder problem now is judgment. A robot may be able to clean dishes, but if it drops a clean plate on the floor, it still has to understand that the plate needs to be washed again. That kind of common sense remains one of the biggest unsolved challenges in physical AI.They also dig into one of the biggest debates in robotics right now: data. Sergey argues that real-world data collection is not the impossible obstacle many researchers once assumed. In fact, he believes the long-term path to better robots is more practical than people think. Deploy systems, collect experience, improve the model, and repeat.The conversation also covers why Physical Intelligence is focused on a general intelligence layer rather than a single-narrow product, why robots should not just be treated as metal versions of people, and what surprised Sergey most about controlling very different robot platforms with the same model.Finally, Sergey reflects on why Physical Intelligence is structured more like a lab than a traditional startup, why experimentation matters so much in modern AI, and how we may one day look back on this era as the moment AI moved beyond internet data and into the physical world.Connect with Sergey Levinehttps://www.linkedin.com/in/sergey-levine-5a31a24Learn more about Physical Intelligencehttps://www.physicalintelligence.company/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.fmUnlock full access to Automated and explore everything automation.Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify.Subscribe to the Automated Newsletter:https://www.automate.org/automation/automated-newsletterYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/

Self-driving cars were supposed to be everywhere by now.They are not.And the reason is not what most people think.In this episode of Automated, Brian Heater speaks with Martial Hebert, Dean of Carnegie Mellon University’s School of Computer Science, about the reality behind decades of robotics and AI development.Martial has spent more than 40 years at the Robotics Institute and worked on some of the earliest autonomous vehicle systems. From that perspective, the story is not about technology failing.It is about expectations being wrong.The core technology for self-driving cars has existed for years. What slowed everything down is something far less visible: validation, safety, and the challenge of proving these systems can operate reliably in the real world.That gap between “it works” and “it can be trusted” is where most timelines break.The conversation also explores why physical AI is fundamentally different from the AI most people are familiar with. Unlike software, robots have to operate in unpredictable environments, interact with people, and handle edge cases that cannot be fully simulated.Martial explains why simulation alone is not enough, and why real-world experimentation is still essential, even when it is slow, expensive, and difficult to scale.They also discuss the robotics data problem. While large language models benefit from massive amounts of internet data, robotics systems struggle to collect the kind of real-world data they actually need.Brian and Martial also dig into a deeper idea that often gets overlooked: progress in robotics is not just about better algorithms. It is about building long-term ecosystems of talent, culture, and expertise.That is part of what turned places like Carnegie Mellon into leaders in autonomy, and why many of today’s breakthroughs are the result of decades of accumulated work.They also explore the role of DARPA and long-term research funding, not as a way to build products quickly, but as a way to push the limits of what is possible and force entirely new breakthroughs.This conversation offers a grounded perspective on why progress in AI takes longer than expected and what it actually takes to move from impressive demos to systems that work in the real world.Connect with Martial Heberthttps://www.linkedin.com/in/martial-hebert-76448756/Learn more about Carnegie Mellon Roboticshttps://www.ri.cmu.edu/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, and Spotify.Subscribe to the Automated Newsletter:https://www.automate.org/automation/newsletter-automation-roundupYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/

Humanoid robots are everywhere right now. From viral demos to bold promises about home automation, it often feels like the future has already arrived.But behind the scenes, the reality is far more complex.In this episode of Automated, Brian Heater speaks with Bren Pierce, founder of Kinisi Robotics and co-founder of Bear Robotics, about what it actually takes to build and deploy robots in the real world.Bren explains why many humanoid robot demonstrations are misleading. While the technology has made major advances in movement and control, real-world deployment is still limited by manipulation, reliability, and the complexity of unstructured environments.The conversation explores why household robotics may be further away than most people think. Despite impressive demos, creating a robot that can operate independently in a dynamic home environment remains an unsolved challenge that could take years to fully unlock.They also discuss the gap between robotics innovation and practical business applications. Many companies are still experimenting, often driven by internal pressure to adopt AI and automation, even when the return on investment is unclear.Bren shares lessons from building multiple robotics companies, including why focusing on real problems matters more than chasing hype. Instead of targeting futuristic home use cases, Kinisi is focused on warehouse and industrial environments where the technology can deliver value today.The episode also dives into the challenges of scaling robotics systems. From deployment complexity to training and usability, the biggest barrier is not just building the technology, but making it reliable and usable without requiring expert engineers.Brian and Bren also explore the parallels between robotics and autonomous vehicles, highlighting how long it can take for breakthrough technologies to transition from demos to real-world impact.This conversation offers a grounded perspective on where robotics actually stands today and what it will take to move from impressive demos to real deployment.Connect with Bren Piercehttps://www.linkedin.com/in/brenpierce/Learn more about Kinisi Roboticshttps://www.kinisirobotics.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, and Spotify.Subscribe to the Automated Newsletter:https://www.automate.org/automation/newsletter-automation-roundupYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/

Last-mile delivery is one of the most expensive and inefficient parts of the global supply chain. While goods can travel across oceans for just a few dollars, getting them from a local hub to a customer’s door remains disproportionately costly.In this episode of Automated, Brian Heater speaks with Ali Kashani, CEO of Serve Robotics, about the realities of deploying delivery robots in the real world and what it takes to scale autonomous systems beyond early pilots.Ali explains how Serve Robotics evolved from an internal Postmates project into an independent company operating thousands of robots in live environments. This transition reflects a broader shift in robotics from controlled experimentation to real-world deployment at scale.The conversation explores why building in the real world is essential for robotics. Lab environments often miss critical edge cases, while public deployment reveals the unpredictable human behavior, operational challenges, and environmental complexity that define real performance.They also discuss the economic implications of reducing last-mile delivery costs. Lowering delivery from $10 to closer to $1 could unlock new demand, expand local economies, and create new categories of jobs that support and operate these systems.The episode also examines safety, public perception, and the long-term impact of autonomous delivery on cities. From reducing reliance on cars to improving walkability and safety, these systems may reshape how urban environments function.Brian and Ali also explore scaling challenges, lessons from acquisitions, and the operational realities of running thousands of robots in public. From unexpected real-world incidents to long-term infrastructure shifts, this conversation offers a grounded look at what it takes to bring robotics into everyday life.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, and Spotify.Subscribe to the Automated Newsletter:https://www.automate.org/automation/newsletter-automation-roundupYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/

Robotics is advancing quickly, but building systems that can operate reliably in the real world remains one of the most complex challenges in technology.In this episode of Automated, Brian Heater speaks with Zachary Jackowski of Boston Dynamics about the shift from research to commercialization and why generalization is emerging as the defining problem in modern robotics.Zachary explains how Boston Dynamics approaches robot design, from early research platforms like Atlas R1 to more refined production systems. Early versions prioritize exploration and performance, while newer iterations focus on reliability, repairability, and deployment in real environments. This evolution reflects a broader shift across the industry toward building systems that can move beyond controlled demos and operate consistently in the field.The conversation explores why generalization is critical for robotics. Training robots on a single task does not prepare them for real-world variability. Instead, diverse data, multiple environments, and exposure to different behaviors are required to build systems that can adapt and perform across use cases.They also discuss the challenge of data collection and deployment, including the chicken-and-egg problem of needing real-world data to improve systems that are not yet ready for large-scale deployment. Incremental rollout, focused applications, and controlled environments are key steps in bridging that gap.The episode also examines why industrial environments are the starting point for humanoid robots. Factories provide structure, repeatability, and trained operators, while home environments introduce unpredictability that current systems are not yet equipped to handle at scale.Brian and Zachary also explore how different robot platforms, including humanoids, quadrupeds, and wheeled systems, each serve distinct roles. Rather than a single dominant design, the future of robotics will likely involve multiple systems working together and benefiting from shared data and learning.From actuator design and system simplification to deployment strategy and data diversity, this conversation offers a grounded look at what it takes to bring robotics into real-world applications.Key Moments:(00:00) Boston Dynamics and the shift to commercialization(02:11) Zachary’s path into robotics and Boston Dynamics(04:16) From research to product development(07:19) Research versus commercialization in robotics(08:53) Why early robots are built differently(11:16) Designing better systems through iteration(13:22) Advances in actuator performance(14:36) Safety and robot design decisions(16:11) Why humanoid robots are just the starting point(17:21) Why generalization is the real breakthrough(20:10) The data collection challenge in robotics(21:31) Why data diversity matters more than volume(23:24) Why robots are going to factories first25:52 Why robots are not ready for homes31:34 Why complexity increases in real-world roboticsSponsors: maxon designs and manufactures precision drive systems that enable reliable, high‑duty‑cycle performance in industrial automation, robotics, and smart manufacturing. https://www.maxongroup.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, and Spotify.Subscribe to the Automated Newsletter:https://www.automate.org/automation/newsletter-automation-roundupYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/