Hidden Layers: AI and the People Behind It

In this episode of Hidden Layers: Decoded, Ron Green, Dr. ZZ Si, and Michael Wharton unpack July’s biggest AI developments—from flawed reasoning tests to surprising training breakthroughs.Apple’s “Illusion of Thinking” paper draws sharp critiques—from both humans and language models. Meta revives a forgotten 2019 attention mechanism to reshape scaling laws. Video generation tools from BlackForest Labs and others hit new levels of realism and interactivity. Federal courts weigh in on Anthropic and Meta’s use of copyrighted training data. A one-line tweak in training recurrent models dramatically boosts performance on long sequences. Cloudflare announces it will block AI scrapers by default—though it might be too late.From Transformer alternatives to copyright battles, this episode dives into the fast-moving intersection of AI research, engineering, and regulation.

In this episode of Hidden Layers, Ron Green sits down with Dr. Karl Friston—world-renowned neuroscientist and originator of the Free Energy Principle—and Dan Mapes, founder of Verses AI and the Spatial Web Foundation. Together, they explore how neuroscience is beginning to reshape artificial intelligence.They break down complex but powerful ideas like active inference, biologically plausible AI, and collective intelligence. You'll hear how concepts from brain science are influencing next-gen AI architectures and what the future might hold beyond large language models.From the limitations of backpropagation to the promise of decentralized, embodied, and domain-specific models, this is a deep dive into the future of intelligent systems—and the science behind them.

In this episode of Hidden Layers: Decoded, Ron Green, Dr. ZZ Si, and Michael Wharton explore the latest AI breakthroughs, including Sakana AI’s biologically-inspired “Continuous Thought Machines,” the self-taught coding model Absolute Zero, and Salesforce’s unified vision-language system BLIP3-o. They discuss the growing importance of reinforcement learning in a data-constrained world, Google’s diffusion-based language and video models, and Anthropic’s industry-leading interpretability efforts. The team also covers Apple’s AI missteps and a new study revealing why single, well-structured prompts outperform long chat sessions. Throughout, they reflect on alignment risks, emergent reasoning, and the changing shape of model development and training strategy.

In this episode of Hidden Layers, Ron Green sits down with Dr. Risto Miikkulainen — Vice President of AI Research at Cognizant Advanced AI Labs and Professor of Computer Science at UT Austin — to explore the fascinating world of evolutionary computation. They dive deep into the differences between supervised learning, reinforcement learning, and evolutionary techniques, and why evolutionary approaches offer unique advantages for creativity, scalability, and innovation in AI. Dr. Miikkulainen shares real-world examples of unexpected discoveries, from cyber agriculture breakthroughs to designing new AI architectures. They also discuss the future of multi-agent systems, surrogate modeling, and how evolutionary computation could help us better understand the emergence of intelligence and language. Plus, Dr. Miikkulainen previews his upcoming book Neural Evolution: Harnessing Creativity in AI Model Design.

In this episode of Hidden Layers, Ron Green talks with Dr. ZZ Si, Michael Wharton, and Reed Coke about recent AI developments. They cover Anthropic’s work on Claude 3.5 and model interpretability, OpenAI’s GPT-4 image generation and its underlying architecture, and a new approach to latent reasoning from the Max Planck Institute. They also discuss synthetic data in light of NVIDIA’s acquisition of Gretel AI and reflect on the delayed rollout of Apple Intelligence. The conversation explores what these advances reveal about how AI models reason, behave, and can (or can’t) be controlled.

In this episode of Hidden Layers, host Ron Green sits down with Dr. Anna Ivanova, Assistant Professor of Psychology at Georgia Tech and Director of the Language, Intelligence, and Thought Lab. Dr. Ivanova's research explores the intricate relationship between language, cognition, and artificial intelligence, shedding light on how the brain processes language and how large language models (LLMs) compare to human thought.

In this episode of Hidden Layers: Decoded, Ron Green, Dr. ZZ Si, and Michael Wharton explore the latest AI breakthroughs, including DeepSeek’s R1 model, Meta’s work on intuitive physics, and Stanford’s S1 model. They discuss the rise of cost-effective reinforcement learning, diffusion-based language models, and DeepMind’s advances in geometry-solving AI. The team also dives into AI-driven biology with Evo2 and the emergence of civilizations in a Minecraft simulation. Throughout, they reflect on the future of AI, from domain-specific models to the impact of world models on business and science.

In this episode of Hidden Layers, host Ron Green speaks with Dr. Peter Stone, a leading expert in AI and robotics, about the evolution of autonomous systems. They explore multi-agent AI, RoboCup’s ambitious goal of creating robot soccer players that can beat humans by 2050, and the ongoing hardware vs. software challenge in robotics. Dr. Stone shares insights on the power of large language models, the rise of agentic AI, and the importance of balancing neural networks with traditional planning systems. They also discuss AI ethics, alignment, and what the next decade could bring for intelligent agents and general-purpose service robots.

In this episode of Hidden Layers: Decoded, we dive into cutting-edge AI advancements over the last month. Explore Agentic AI and innovations like DeepMind Genie 2 and Cosmos Text2World, transforming virtual environments. Discover breakthroughs like RStar Math and DeepSeek v3, delivering efficiency and performance in reasoning and problem-solving. We also discuss test-time scaling, coding agents, and the drama behind the NeurIPS Best Paper Award.

In this special 2024 AI Year in Review, Ron is joined by AI experts ZZ Si (Co-Founder & Distinguished Engineer), Emma Pirchalski (AI Strategist), and Michael Wharton (VP of Engineering) to reflect on the most important AI moments of 2024. They come together to discuss the defining stories, key breakthroughs, and major challenges that shaped AI in 2024. Ron leads the conversation, drawing out their perspectives on the year's most impactful developments, unfiltered reflections, bold insights, and forward-looking predictions for the future of AI.