『Hidden Layers: AI and the People Behind It』のカバーアート

Hidden Layers: AI and the People Behind It

Hidden Layers: AI and the People Behind It

著者： KUNGFU.AI
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概要

Hidden Layers: AI and the People Behind It, is a series focused on all things artificial intelligence. Hosted by our Co-Founder and CTO, Ron Green, who uses his 20+ years of AI experience to break down complex topics into digestible, engaging conversations. ‍

If you’re a tech professional, or just looking to better understand the world of AI, you’re in the right place. Each episode will explore cutting-edge technical advances, discuss the art of the possible, and review some of the incredible work being done in the field.

Kung Fu Solutions Inc 2024
エピソード
  • AI Is Designing the Next Cancer Fighter | EP.53

    AI Is Designing the Next Cancer Fighter | EP.53
    2026/05/14
    What if AI could design proteins to help your immune system find and kill cancer cells? That's not a hypothetical — it's what 28 teams across 40 countries attempted in the Bits-to-Binders Challenge, an open-science competition organized by PhD students at the University of Texas at Austin. In this episode, Ron sits down with three of the organizers — Clay Kosonocky, Daryl Barth, and Aaron Feller — to unpack how they pulled off one of the most ambitious student-led experiments at the intersection of AI and biology. Together, they submitted 12,000 AI-designed protein sequences to bind to a cancer target called CD20, then validated the results in real biological assays. The conversation covers the 100-year history of protein folding, how AlphaFold changed everything, why AI biology can't just rely on benchmarks, what a CAR-T cell actually does, and what a 7% hit rate tells us about where the field really stands. Plus: open source science, the verification gap between digital predictions and wet lab reality, and why a global team of strangers working together might be the most hopeful signal of all. 00:00 Intro & Why AI Protein Design Matters 02:38 Why Protein Folding Is So Important 04:47 What AlphaFold Changed 07:59 From Predicting Proteins to Designing Them 11:10 The Rise of AI Protein Design 13:27 AI Skepticism in Biology 15:34 Why Wet Lab Validation Still Matters 20:36 Inside the Bits-to-Binders Challenge 22:05 Designing CAR-T Cell Proteins 26:57 Why Most Designs Failed 31:12 Open Source Biology & Global Collaboration 33:37 Competition Winners & Best Results 35:39 Final Takeaways on AI + Biology
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    42 分
  • Anthropic Code Leak: A Rare Look Inside Frontier AI | EP.52

    Anthropic Code Leak: A Rare Look Inside Frontier AI | EP.52
    2026/04/23
    What can we actually learn from the recent Anthropic code leak? In this episode of Hidden Layers, Ron Green, Michael Wharton, and Dr. ZZ Si unpack what the leak reveals about how a frontier AI company may be building agentic systems in practice. They explore Anthropic’s apparent approach to memory, skills, and context compaction, and why the biggest takeaway is not model weights, but the harness around the model. The conversation also gets into why simple, human-readable systems may be outperforming more complex architectures, and what these design choices could mean for the next generation of domain-specific AI agents. 00:00 Intro and why the leak matters 00:43 What leaked and what it reveals 03:50 Memory systems and context management 07:20 Skills, extensibility, and simple design 11:39 Compaction and the limits of context windows 17:23 Why the harness matters so much 18:36 A blueprint for building agentic systems
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    28 分
  • The "AI Bubble" Bubble | EP.51

    The "AI Bubble" Bubble | EP.51
    2026/03/12
    Is the AI bubble narrative itself a bubble? Billions of dollars are flowing into chips, data centers, and frontier models. From the outside, it can look speculative. But from inside the industry, the signal looks very different. In this episode of Hidden Layers, Ron Green is joined by Michael Wharton and Dr. ZZ Si to discuss what it actually feels like to build with AI today. They explore rapid advances in model capabilities, the growing power of coding agents, and why many organizations are still struggling to absorb the productivity gains AI already enables. They also examine the massive capital investment in AI infrastructure and debate what signals would actually indicate the industry has hit a plateau. 00:00 – Is the AI Bubble Narrative Itself a Bubble? 03:00 – Rapid Advances in AI Model Capabilities 05:35 – Coding Agents and the Changing Development Workflow 09:30 – Benchmarks Showing AI Capability Acceleration 16:20 – Verifying AI Outputs and the Limits of Evaluation 18:20 – CAPEX, Chips, and the Dot-Com Bubble Comparison 21:50 – What Would Actually Signal an AI Bubble 26:30 – Why AI May Become a Utility
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    32 分
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