Eye On A.I.

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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(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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(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.

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(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

This episode is sponsored by Modulate.
Meet Velma, voice AI that detects tone, intent, and stress:http://preview.modulate.ai
 
Baris Gultekin, Head of AI at Snowflake, breaks down how enterprise AI is actually being built, deployed, and scaled today. From running AI directly inside governed data environments to enabling natural language access across entire organizations, this conversation explores the shift from experimentation to real-world impact.
 
You'll learn why Snowflake's core philosophy centers around bringing AI to the data, how data agents are transforming decision-making across teams, and what it takes to build trustworthy AI systems with governance, guardrails, and high-quality retrieval at the core.
 
Baris also shares how leading companies are already saving thousands of hours through AI-driven automation, why culture and leadership determine AI success, and what the future looks like as agents move from pilots to full-scale production.
 
If you want to understand where enterprise AI is actually headed and what separates hype from real execution, this episode breaks it down.
 
(00:00) The Evolution of Snowflake AI
(01:40) Baris Gultekin: Background & AI Mission
(02:59) Why AI Must Run Next to Data
(04:29) Inside Snowflake's AI Infrastructure
(09:08) Model Choice vs Product Layer Strategy
(12:16) Building Trust: Governance, Guardrails & Quality
(16:01) How Enterprise Agents Are Built & Orchestrated
(20:10) AI Adoption Across the Entire Organization
(24:39) Reasoning vs Retrieval: What Matters More
(27:43) Real Use Case: Faster Decision-Making with AI
(31:44) AI as a Co-Pilot for Leaders
(36:52) Preparing Data for AI at Scale
(38:46) What the AI Data Cloud Really Means

This episode is sponsored by tastytrade.
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This episode dives into why Pathway's Baby Dragon Hatchling (BDH) might mark the beginning of the post-transformer era in AI.

Zuzanna Stamirowska, Pathway's CEO and co‑author of BDH, explains why today's transformer-based LLMs hit a wall on long-horizon reasoning, how memory and synaptic plasticity are built directly into BDH's architecture, and what that means for continual learning, hallucinations, and "generalization over time."

The conversation ranges from complexity science and brain-inspired computation to practical implications for real-world, small-data, and safety‑critical applications.
 
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(00:00) The Core Problem: Why Today's AI Lacks Memory
(03:16) Pathway's Mission to Bring Memory Into AI
(04:53) Zuzanna's Background in Complexity Science
(10:30) Why Transformers Reset Like "Groundhog Day"
(14:34) The Brain-Inspired Dragon Hatchling Architecture
(23:59) How the Network Learns and Builds Connections
(37:38) Performance vs Transformers on Language Tasks
(49:37) Productizing the Technology With NVIDIA and AWS
(54:23) Can Memory Solve AI Hallucinations?