Eye On A.I.

What does it actually mean to build a foundation model for robots?

In this episode of Eye on AI, Craig Smith sits down with Sergey Levine, co-founder of Physical Intelligence and professor at UC Berkeley, to explore a fundamentally different approach to building robots, one inspired not by programming a single perfect machine, but by training AI on the broadest and most diverse data possible so robots can learn, adapt, and operate in the unpredictable real world.
Sergey explains why the secret to general-purpose robots isn't perfecting one single machine, but training on massive, diverse data from all kinds of robots and even humans. The more variety the model sees, the better it gets. Just like ChatGPT learned from all the text on the internet, robotic foundation models learn from every robot that has ever moved, grabbed, or interacted with the real world.
We also get into the big humanoid robot debate. Are they the future, or is it mostly hype? Sergey gives an honest and technical take on why the form factor conversation is changing now that foundation models exist, and why that actually opens the door for more creativity, not less.
Finally, Sergey shares what he's most excited about next, building a true data flywheel where robots get smarter the more they are deployed, creating a continuous learning cycle that could change everything.
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(00:00) Introduction: What Are Foundation Models for Robots?
(01:44) Meet Sergey Levine: Physical Intelligence and UC Berkeley
(02:51) Breaking Down Foundation Models for Non-Technical People
(06:46) Why Real World Data Beats Simulation
(15:00) Building a Broad Robotics Foundation From Scratch
(24:00) The Open World Problem in Robotics
(40:00) Generalist vs Specialist Robots: Which Wins?
(47:00) Humanoid Robots: Real Innovation or Just Hype?
(55:10) The Future: Continuous Learning and the Data Flywheel
(56:23) Guilty Pleasure: Sci Fi and Thinking Beyond the Limits

AI has been trained like software.
But what if it should be grown like life?
In this episode of Eye on AI, Craig Smith sits down with Sebastian Risi, professor and leading researcher in neuroevolution and artificial life, to explore a fundamentally different approach to building intelligence, one inspired by how nature evolves, grows, and adapts.
Sebastian explains why traditional AI systems are limited by fixed architectures and one-time training, and how evolutionary algorithms can create systems that continuously learn, self-organize, and even grow their own neural structures over time.
They dive into concepts like plastic neural networks that keep updating during their lifetime, AI systems that can recover from damage, and models that develop from a single "cell" into complex structures, similar to biological organisms.
The conversation also explores how combining large language models with evolutionary search could unlock more creative and open-ended problem solving, from merging specialized models to building AI systems capable of generating and testing scientific ideas.
If you want to understand where AI is headed beyond today's transformer models, and why the future may look more like living systems than software, this episode offers a clear and thought-provoking perspective.
Subscribe for more conversations with the people building the future of AI and emerging technology.
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Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI


(00:00) Why copy nature's evolution for AI
(01:20) What neuroevolution actually means
(05:52) How evolutionary search replaces gradients
(08:03) Plastic neural networks and continuous learning
(11:53) Growing neural networks like living systems
(18:08) Scaling challenges and limits of growth
(23:16) Can evolving systems replace LLM training
(27:28) Continual learning and model merging
(30:27) Artificial life, self-repair, and resilience
(35:10) AI scientists and evolution with LLMs

AI has been trained like software.
But what if it should be grown like life?
In this episode of Eye on AI, Craig Smith sits down with Sebastian Risi, professor and leading researcher in neuroevolution and artificial life, to explore a fundamentally different approach to building intelligence, one inspired by how nature evolves, grows, and adapts.
Sebastian explains why traditional AI systems are limited by fixed architectures and one-time training, and how evolutionary algorithms can create systems that continuously learn, self-organize, and even grow their own neural structures over time.
They dive into concepts like plastic neural networks that keep updating during their lifetime, AI systems that can recover from damage, and models that develop from a single "cell" into complex structures, similar to biological organisms.
The conversation also explores how combining large language models with evolutionary search could unlock more creative and open-ended problem solving, from merging specialized models to building AI systems capable of generating and testing scientific ideas.
If you want to understand where AI is headed beyond today's transformer models, and why the future may look more like living systems than software, this episode offers a clear and thought-provoking perspective.
Subscribe for more conversations with the people building the future of AI and emerging technology.
Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI


(00:00) Why copy nature's evolution for AI
(01:20) What neuroevolution actually means
(05:52) How evolutionary search replaces gradients
(08:03) Plastic neural networks and continuous learning
(11:53) Growing neural networks like living systems
(18:08) Scaling challenges and limits of growth
(23:16) Can evolving systems replace LLM training
(27:28) Continual learning and model merging
(30:27) Artificial life, self-repair, and resilience
(35:10) AI scientists and evolution with LLMs

Quantum computing has been "5 years away" for decades.

So what's actually holding it back?

In this episode of Eye on AI, Craig Smith sits down with Izhar Medalsy, Co-founder & CEO of Quantum Elements, to break down the real bottleneck in quantum computing today and why the future of the industry may depend more on classical systems and AI than quantum hardware itself.

Izhar explains how digital twins of quantum systems are being used to simulate real hardware, generate massive amounts of training data, and solve one of the biggest challenges in the field: noise and error correction.

They dive into how his team improved Shor's Algorithm from 80% to 99% accuracy on IBM hardware, without changing the hardware itself, and what that means for the future of quantum performance.

The conversation also explores how AI is being used to optimise quantum systems, why classical computing will continue to play a central role in quantum development, and what milestones to watch as the industry moves closer to real-world applications.

If you want to understand where quantum computing actually stands today and what will unlock its next phase, this episode gives you a clear, grounded perspective.

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Craig Smith on X: https://x.com/craigss

Eye on A.I. on X: https://x.com/EyeOn_AI


(00:00) The 99% Accuracy Breakthrough (Quantum's Turning Point) 
(01:03) Why Quantum Hardware Alone Isn't Enough 
(03:50) Digital Twins Explained (The Missing Layer) 
(08:09) The Real Problem: Noise, Instability & Environment 
(15:43) From 80% to 99% on Shor's Algorithm 
(26:36) How AI Is Fixing Quantum's Biggest Bottleneck 
(33:53) Inside the Platform: From Circuit to Optimization 
(40:51) Logical Qubits & Scaling Quantum Systems 
(43:34) The Limits of Simulation vs Real Quantum Hardware 
(54:29) When Quantum Becomes Useful (Real Timeline)

AI is changing more than just productivity.

It's changing what we can trust.

In this episode, Kevin Tian, Co-founder and CEO of Doppel, breaks down how AI is enabling a new wave of social engineering attacks—from deepfake phone calls to impersonation across LinkedIn, YouTube, and search engines.

The reality is this:
Deepfakes are just one part of a much bigger problem.
Attackers are now operating across multiple channels at once, using AI to manipulate people, not just systems. And as these attacks scale, the real risk isn't just fraud or data loss—it's the erosion of trust in everything we see online.

Kevin explains how Doppel is building an AI-native defense platform to detect, map, and shut down these attacks in real time, and why the future of cybersecurity will be defined by AI vs AI.

If you're thinking about AI, security, or the future of trust online—this conversation is essential.

Stay Updated:
Craig Smith on X: https://x.com/craigss

Eye on A.I. on X: https://x.com/EyeOn_AI


(00:00) AI Deepfakes & The Collapse of Trust
(01:56) Why "Social Engineering" Is Bigger Than Phishing
(05:20) Deepfakes, Misinformation & Multi-Channel Attacks
(09:16) The Rise of Deepfake Phone Calls
(12:43) How Attackers Manipulate AI & Search Results
(14:39) The Origin Story Behind Doppel
(18:55) How Doppel Detects & Stops Attacks in Real Time
(22:55) Can Attackers Misuse AI Defense Tools?
(24:26) How to Tell What's Real vs Fake Online
(28:20) What Is Human Risk Management?
(30:36) AI vs AI: The Future of Cyber Defense
(34:04) What CEOs Must Do About AI Threats
(37:18) Working with Platforms Like YouTube & LinkedIn
(39:52) Can We Ever Fully Stop Deepfakes?
(44:40) How Doppel Works for Enterprises