Eye On A.I.

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In this episode of the Eye on AI, Craig Smith speaks with Zuzanna Stamirowska about how Pathway is enabling AI systems to work with live, continuously updating data.
 
Most AI applications rely on static datasets that quickly become outdated. Pathway takes a different approach, allowing developers to build AI systems that process real-time data streams, keeping models, knowledge bases, and AI agents constantly up to date.
 
Craig and Zuzanna explore why real-time data may be critical for the next generation of LLM applications, RAG systems, and enterprise AI infrastructure, and what it takes to build AI that can operate in a constantly changing world.
 
Subscribe for more conversations with the researchers and builders shaping the future of AI.



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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?

AI often looks fully automated. But behind the scenes, a huge amount of human judgment is shaping how these systems actually work.
 
In this episode, Craig Smith speaks with Phelim Bradley, co-founder and CEO of Prolific, a platform that connects millions of real people with researchers and AI labs to evaluate and improve AI systems.
 
They explore the hidden human layer behind modern AI, why traditional benchmarks are becoming less reliable, and why AI companies increasingly rely on real human feedback to measure model performance in the real world.
 
Phelim also explains how demographic differences influence how models are evaluated, why human judgment remains critical even as AI improves, and how the collaboration between humans and AI will shape the next phase of development.
 
This conversation reveals the human backbone behind today's AI systems.



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(00:00) Preview and Intro
(02:45) Founding Prolific And Early Pain Points
(06:30) From Mechanical Turk To Representativeness
(09:55) Academic Research And AI Use Cases Split
(13:40) Vetting Real Participants And Fighting Fraud
(17:45) Scale, Community Growth, And Talent Mix
(22:00) High-Complexity Projects Over Commoditised Labeling
(26:40) Measuring Model Persuasion With Live Conversations
(30:20) Demographic-Aware Model Preference Benchmarks
(34:10) The Rise Of Human Evaluation Over Benchmarks
(38:00) Enterprise Model Choice And Continuous Evaluation
(42:00) Why Humans Won't Disappear From The Loop

AI is not just getting smarter. It is getting faster by learning how to optimize the hardware it runs on.

In this episode, Sharon Zhou, VP of AI at AMD and former Stanford AI researcher, explains how language models are beginning to write and optimize their own GPU kernel code. We explore what self improving AI actually means, how reinforcement learning is used in post training, and why kernel optimization could be one of the most overlooked scaling levers in modern AI.

Sharon breaks down how GPU efficiency impacts the cost of training and inference, why catastrophic forgetting remains a challenge in continual learning, and how verifiable rewards from hardware profiling can help models improve themselves. The conversation also dives into compute economics, synthetic data, RLHF, and why infrastructure may define the next phase of AI progress.

If you want to understand where AI scaling is really happening beyond bigger models and more data, this episode goes under the hood.


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(00:00) Preview and Intro
(00:25) Sharon Zhou's Background and Transition to AMD
(02:00) What Is Self-Improving AI?
(04:16) What Is a GPU Kernel and Why It Matters
(07:01) Using AI Agents and Evolutionary Strategies to Write Kernels
(11:31) Just-In-Time Optimization and Continual Learning
(13:59) Self-Improving AI at the Infrastructure Layer
(16:15) Synthetic Data and Models Generating Their Own Training Data
(20:48) AMD's AI Strategy: Research Meets Product
(23:22) Inside the NeurIPS Tutorial on AI-Generated Kernels
(30:59) Reinforcement Learning Beyond RLHF
(39:09) 10x Faster Kernels vs 10x More Compute
(41:50) Will Efficiency Reduce Chip Demand?
(42:18) Beyond Language Models: Diffusion, JEPA, and Robotics
(45:34) Educating the Next Generation of AI Builders

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Artificial intelligence is reaching a turning point. Instead of building bigger and bigger models, what if the real breakthrough comes from letting AI evolve?

In this episode of Eye on AI, David Ha, Co-Founder and CEO of Sakana AI, explains why evolutionary strategies and collective intelligence could reshape the future of machine learning. We explore model merging, multi-agent systems, Monte Carlo tree search, and the AI Scientist framework designed to generate and evaluate new research ideas. The conversation dives into open-ended discovery, quality and diversity in AI systems, world models, and whether artificial intelligence can push beyond the boundaries of human knowledge.

If you're interested in AGI, evolutionary AI, frontier models, AI research automation, or how AI could start discovering science on its own, this episode offers a clear look at where the field may be heading next.

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(00:00) AI Should Evolve, Not Just Scale
(03:54) David's Journey From Finance to Evolutionary AI
(10:18) Why Gradient Descent Gets Stuck
(18:12) Model Merging and Collective Intelligence
(28:18) Combining Closed Frontier Models
(32:56) Inside the AI Scientist Experiment
(38:11) Parent Selection, Diversity and Innovation
(49:25) Can AI Discover Truly New Knowledge?
(53:05) Why Continual Learning Matter

In this episode of Eye on AI, Craig Smith speaks with Amanda Luther, Senior Partner at Boston Consulting Group and global lead of BCG's AI Transformation practice, about what their latest 1,500-company AI study reveals about the widening gap between AI leaders and laggards.
Only 5% of companies are truly "future-built" with AI embedded across their core business functions. These firms are seeing measurable gains in revenue growth, EBIT margins, and shareholder returns. Meanwhile, 60% of organizations are either experimenting or struggling to extract real value.
Amanda breaks down how BCG measures AI maturity across 41 capabilities, how AI impact flows through the P&L, and why leading companies invest twice as much in AI as their competitors. She explains where AI is actually creating value today, from sales and marketing to procurement and retail operations, and why most of that value comes from core business functions, not back-office automation.
The conversation also explores the rise of agentic systems, why many early agent deployments fail, and what it really takes to redesign workflows around AI. Amanda shares practical advice for companies stuck in experimentation mode, how to prioritize the right use cases, and why training and change management matter more than chasing the perfect vendor.
If you want to understand how AI is reshaping competitive advantage in enterprise organizations, this episode provides a data-backed look at what separates the leaders from everyone else.
 
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(00:00) The AI Value Gap
(01:17) Inside BCG's 1,500-Company AI Study
(04:14) What "Future-Built" Companies Do Differently
(09:30) How AI Impact Is Measured on the P&L
(12:57) Why AI Leaders Invest 2X More
(14:16) Where AI Is Driving Real Cost Reduction
(16:20) Agentic AI: Hype vs Reality
(20:13) Where Agents Actually Create Value
(24:22) Tech vs Talent: Where the Money Goes
(26:58) Will AI Laggards Slowly Disappear?
(31:58) Why Adoption Is Accelerating Now
(40:07) How to Start: Amanda's Advice to AI Laggards