Hidden Layers: AI and the People Behind It

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

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

Are AI coding tools actually replacing programmers, or just changing how software gets built? In this episode of Hidden Layers, Ron Green sits down with Dr. ZZ Si and Michael Wharton to unpack what has shifted with modern coding agents, what has not, and where the hype breaks down. They share concrete examples from their own workflows, including how coding tools have moved from autocomplete to handling larger chunks of work, and why the real bottleneck is no longer writing syntax, but defining intent, architecture, and product direction. The conversation also explores how these tools are reshaping team velocity, why senior engineers tend to get more leverage from AI than junior developers, and the risks of weakening the talent pipeline if companies stop investing in early-career engineers. The episode closes with a candid look at what skills will matter most in an AI-assisted world, how abstraction layers are changing the role of programmers, and whether we may already be near peak computer science graduates. 00:00 – The rise of AI coding tools 03:07 – How workflows are changing 06:27 – Team velocity and delivery speed 08:19 – Product thinking vs. engineering execution 09:46 – Is programming actually dying? 11:41 – What “programming” means now 15:23 – Senior vs. junior developer leverage 16:33 – The developer talent pipeline 18:21 – Ego, identity, and automation 19:08 – Before vs. after: building with AI 22:30 – Debugging and fixing issues with AI 24:42 – Spec-writing and product shaping with AI 26:49 – The future of computer science grads 29:20 – Closing reflections

What if the most powerful AI in your organization isn’t the biggest model you can buy, but the one trained on data only you own? In this episode of Hidden Layers, Ron Green is joined by Dr. ZZ Si and Michael Wharton to break down why domain-specific AI models consistently outperform general-purpose systems in real enterprise environments. They explore how narrowly scoped models deliver higher accuracy, lower costs, better reliability, and stronger governance, especially when built on proprietary data. Through real-world examples spanning finance, industrial systems, healthcare, and document understanding, the conversation tackles when to build custom models, when to rely on APIs, and how to identify AI initiatives that actually make it into production. The takeaway is clear: focus beats scale, and specificity is often the fastest path to durable competitive advantage. Chapters 00:00:00 What Is Domain-Specific AI 00:01:15 General Models vs. Focused Systems 00:02:48 Performance, Cost, and Model Size 00:04:13 Proprietary Data as Advantage 00:07:58 Why AI Fails in Production 00:08:42 Real-World Domain-Specific Examples 00:10:54 How to Decide What to Build 00:14:53 Scale, Accuracy, and Uncertainty 00:18:49 The Spectrum of Domain-Specific AI 00:27:01 What We’d Build Differently Today

2025 was another defining year for artificial intelligence. In this special AI Year in Review episode of Hidden Layers, Ron Green is joined by Emma Pirchalski, Michael Wharton, and Dr. ZZ Si to break down what actually mattered in AI this year. The team recaps the biggest developments from 2025, revisits their predictions from 2024 to see what held up (and what didn’t), and shares honest, experience-driven predictions for 2026. Topics include multimodal models, agents, enterprise adoption, governance gaps, workforce impact, ROI pressure, and where AI is truly headed next. This episode cuts past hype to focus on what leaders, builders, and decision-makers should actually be watching as AI moves from experimentation to execution. Chapters 00:00:00 Welcome and Introduction to 2025 AI Year in Review 00:00:56 Emma's Working Models Podcast Announcement 00:01:48 Top AI Developments of 2025 00:16:29 Reviewing 2025 Predictions 00:25:08 2026 Predictions 00:36:49 Closing Thoughts

Artificial intelligence is shifting from prediction to autonomy—and “agentic AI” is leading the charge. In this episode of Hidden Layers, KUNGFU.AI’s Ron Green, Dr. ZZ Si, and Michael Wharton unpack what it really means for machines to act on their own, what’s hype versus real progress, and how far we are from true artificial general intelligence (AGI). They discuss how coding agents are transforming development workflows, why agentic AI is both overhyped and underutilized, the challenges of scaling reliable autonomy, the connection between AGI, biology, and lifelong learning, and whether new architectures or cognitive inspiration will take us the rest of the way. 00:00 – Intro: From prediction to autonomy 01:30 – What is agentic AI? 05:00 – Coding agents and creative workflows 08:00 – Reliability, risk, and real-world use 12:30 – The agentic hype cycle 16:00 – Why businesses underuse (and overuse) AI 19:00 – Narrow AI and domain-specific intelligence 22:00 – The AGI timeline debate 26:00 – Learning from biology and cognition 33:00 – Lifelong learning and what’s missing today

In this episode of Hidden Layers, Ron is joined by Michael Wharton and Dr. ZZ Si to explore one of the most pressing and puzzling issues in AI: hallucinations. Large language models can tackle advanced topics like medicine, coding, and physics, yet still generate false information with complete confidence. The discussion unpacks why hallucinations happen, whether they’re truly inevitable, and what cutting-edge research says about detecting and reducing them. From OpenAI’s latest paper on the mathematical inevitability of hallucinations to new techniques for real-time detection, the team explores what this means for AI’s reliability in real-world applications.

In this episode of Hidden Layers, we dive into the most important AI developments of the month. We cover OpenAI’s highly anticipated and controversial GPT-5 release, debate where we really are on the AGI timeline, explore groundbreaking new world models like Google’s Genie 3 and Tencent’s Huanyuan Gamecraft, and unpack Meta’s DINO V3 image encoder breakthrough.

In this episode of Hidden Layers, host Ron talks with Dr. Hannah Lu, assistant professor at the University of Texas at Austin and core faculty at the Odin Institute for Computational Engineering and Sciences. Dr. Lu is pioneering the use of AI-powered surrogate models to make complex scientific simulations—like CO₂ absorption in geological formations—faster, more accurate, and more useful for real-world decision-making.They discuss:How surrogate models work and why they’re so powerfulThe challenges of applying AI to physics-based systemsHow digital twins and uncertainty quantification are shaping the future of environmental modelingThe intersection of generative AI, physics constraints, and climate science

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.

In this episode of Hidden Layers: Decoded, Ron Green teams up with KUNGFU.AI's ZZ Si and Michael Wharton to explore groundbreaking advancements in artificial intelligence. From DeepMind’s AlphaFold 3 revolutionizing computational biology to debates on the limits of scaling AI models, the conversation covers all the latest AI news from the last month. Highlights include robotic surgery advancements powered by Johns Hopkins’ AI, NVIDIA’s LLaMA-Mesh for 3D mesh generation, and the rise of generative AI in gaming with the groundbreaking Oasis AI game.

In this episode of "Hidden Layers," Ron Green dives into the transformative impact of AI on software development with Peter Wang, Chief AI and Innovation Officer at Anaconda. They discuss the rise of AI coding assistants, tools like GitHub Copilot and Cursor, and their potential to change how developers work. From coding support to the future of AI-native languages, they explore whether AI could replace programmers or simply elevate them to a new level. Peter shares insights from his pioneering work in the Python data science community and the broader implications of AI in fields like edge computing, data privacy, and open-source development.

In this episode of Hidden Layers, Ron Green is joined by KUNGFU.AI's Michael Wharton and Dr. Steve Kramer to discuss the latest news in AI. They cover OpenAI’s leadership turnover, the rise of smaller, more efficient AI models, and the growing importance of AI governance. Plus, they explore Meta's Llama 3.2, a new multimodal model, and share insights from recent AI conferences. The conversation concludes with a discussion of AI experts winning Nobel Prizes for their groundbreaking work in physics and chemistry.

In this episode of Hidden Layers, host Ron Green talks with Cole Camplese, the Chief Information Officer at the University of Texas at Austin, about the transformative impact of AI in higher education. With over 25 years of experience in driving digital transformation, Cole discusses how AI is reshaping universities, from personalized learning to addressing the digital divide. They explore the challenges of adopting AI, balancing innovation with governance, and managing rapid advancements in technology. Cole also shares insights on the future of academic integrity in the "golden age of cheating" and how he uses custom AI tools, like GPT, in both his personal and professional life.

In this episode of Hidden Layers, Ron Green and Michael Wharton dive into the latest advancements in artificial intelligence, with news from DeepMind, OpenAI, Waymo, Apple, and more. They discuss OpenAI's rumored Strawberry initiative, which promises enhanced reasoning capabilities for ChatGPT, the groundbreaking potential of Alpha Proteo for computational biology, and new developments in autonomous driving with Waymo. The discussion also covers Apple’s AI-powered advancements in their new iOS release, and the implications of California's new AI regulation bill. Whether you're a tech enthusiast or AI researcher, this episode offers insights into the rapidly evolving world of AI and its applications.

Join Ron Green and Dr. ZZ Si, Co-founder and Distinguished Machine Learning Engineer at KUNGFU.AI as they explore the fascinating journey of computer vision. They discuss the evolution of computer vision, from the early days of handcrafted features to the revolutionary impact of deep learning and convolutional neural networks. Dr. Si shares insights from his groundbreaking work at Google and Apple, and they delve into the significance of AlexNet and ImageNet in transforming AI research. The conversation also covers the rise of transformers and their role in bridging computer vision and natural language processing, as well as the exciting advancements in diffusion models and flow matching. Discover how these innovations are being applied in robotics, healthcare, and more.

In this episode of "Hidden Layers," we dive into the latest developments in artificial intelligence, with insights from Ron Green, Michael Wharton, and Nathan Mandi from KUNGFU.AI. The discussion covers Meta's groundbreaking release of Llama 3.1, an AI model that's pushing the boundaries of open-source technology, and SAM2, the latest innovation in video segmentation. We also explore the importance of high-quality data, the architecture of AI models, and how companies like Meta are driving forward the AI ecosystem.

In this episode of Hidden Layers, Ron sits down with Dr. Katrina Riehl, Principal Technical Product Manager for CUDA and Python at NVIDIA, to explore the evolving role of open source in the age of AI. They discuss the foundational impact of open source technologies, the challenges of open source licensing in the AI boom, and the distinction between free-to-use models and true open source. Dr. Riehl shares her journey in the open source community, insights on AI's impact on open source, and her work at NVIDIA.

In this episode, Ron Green and VP of Engineering Michael Wharton discuss the latest developments in artificial intelligence. They explore Microsoft's Florence-2 model, Microsoft's GraphRAG, Anthropic's Claude 3.5 Sonnet, and NVIDIA AI Enterprise offerings, and much more. The conversation also touches on Runway's Gen-3 text-to-video model, Salesforce's time series forecasting foundation model, and the recent changes in OpenAI's board structure. The episode wraps up with a discussion on the hype and reality of AI advancements and the future of AI in various industries.

In this episode of Hidden Layers, Ron Green and Steve Meier dive into the intricate world of artificial intelligence, reflecting on their experiences over the past six years at KUNGFU.AI. They discuss the challenges and successes they've encountered, including the importance of machine learning engineers, the unexpected rise of generative AI, and the critical role of business alignment in AI projects. They also touch on the evolving landscape of AI governance and the significance of having a Chief Data and AI Officer (CDAO). Watch and listen as they share valuable insights and lessons learned in the ever-evolving field of AI.

Join Ron and AI experts Michael Wharton and Reed Coke from KUNGFU.AI as they unpack the latest news in artificial intelligence. Together they discussed Mamba-2 and Kolmogorov-Arnold Networks (KANs),c GPT4 passing the bar exam in 2023, the dissolution of the superalignment team at OpenAI, and whether or not expert jobs are at risk of being replaced.

Explore the critical role of high performance computing (HPC) in artificial intelligence with experts Jay Boisseau and Luke Wilson. Host Ron Green delves into how HPC enables advancements in AI, from the complexities of managing large-scale compute resources to the evolution of supercomputing. Discover the behind-the-scenes technology that powers today's AI breakthroughs and the innovative solutions driving the field forward.

In this episode Ron interviews Scott Aaronson, a renowned theoretical computer scientist, about the challenges and advancements in AI alignment. Aaronson, known for his work in quantum computing, discusses his shift to AI safety, the importance of aligning AI with human values, and the complexities involved in interpreting AI models. He shares insights on the rapid progress of AI technologies, their potential future impacts, and the significant hurdles we face.

Join Ron and AI industry veteran Paco Nathan as they delve into the latest developments and future directions of artificial intelligence. Paco, managing partner at Derwen Inc. and author of "Latent Space," shares his insights on emerging areas within AI, such as advanced mathematical analysis of models, the role of hardware evolution in decision support systems, and the potential of hybrid neurosymbolic approaches bridging deep learning and symbolic AI. Discover the cutting-edge work happening in data governance and its profound impact on AI evolution. Paco explores overlooked aspects like the strategic use of domain-specific languages (DSLs) in AI training and the importance of aligning AI systems with rigorous governance frameworks. Looking ahead, Paco predicts a hardware revolution that will drive AI capabilities to the edge, facilitating low-power inference and boosting the adoption of sophisticated AI technologies. While discussing the concept of AGI (artificial general intelligence), Paco emphasizes the challenges and complexities surrounding its realization, drawing parallels with the gradual evolution of self-driving cars.

In this episode, we change things up a bit. On May 1st, Ron met with Michael Wharton and Dr. ZZ Si of KUNGFU.AI to unpack the latest developments in AI. Together, we'll explore recent groundbreaking advancements, dissect their technological underpinnings, and debate their potential long-term effects on society and industry. They chat through Phi-3, Llama 3, Transformer-XL, InternVL, Infini-Attention, Apple’s MM1, OpenELM, Sam Altman's recent remarks on "steamrolling" the competition and more.

Join Ron as he explores the ins and outs of hiring practices in the fast-paced AI industry with KUNGFU.AI’s Director of People, Ryman Stringer. In this episode, Ron and Ryman discuss best practices for building and nurturing diverse AI teams. Ryman shares valuable insights on identifying top talent that aligns not only with technical skills but also with company culture and values.Learn about essential screening techniques, the importance of upskilling and continual growth, and strategies for fostering ethical and psychologically safe environments within AI teams. Discover how KUNGFU.AI cultivates a unique culture that emphasizes collaboration, growth, and inclusivity.

Join Ron Green in episode 17 as he delves into the world of artificial intelligence with industry veterans Paco Nathan and Dr. Steve Kramer.In this episode, they explore the rise of graph neural networks (GNNs), a game-changer in AI for modeling complex relationships across various domains. From weather forecasting to fraud detection, GNNs are revolutionizing how we leverage connected data.Discover how GNNs reduce computation time, tackle the cold start problem in recommendation engines, and even aid in election integrity. Learn about real-world applications in pharma, healthcare, and cybersecurity, and the promising future of hybrid AI architectures merging symbolic and neural approaches.Resources: www.kungfu.ai/videos/episode-17-understanding-real-world-applications-of-graph-neural-networks

Explore the past, present, and future of artificial intelligence in episode 16 of Hidden Layers, where Ron chats with Dr. Raymond Mooney, a luminary in the AI field. Delve into Dr. Mooney's vast experience and witness his firsthand account of AI’s transformative journey. From the rule-based systems of the '80s to the groundbreaking developments in machine learning and natural language processing, Dr. Mooney offers invaluable insights into the evolution of AI technologies. This episode not only traces significant milestones but also contemplates the ethical implications and future potential of AI. Tune in for an enlightening conversation that will deepen your understanding of AI's impact on society and inspire curiosity about what lies ahead.

In our newest episode, Ron Green has a candid conversation with his Co-founder and Managing Director of KUNGFU.AI, Stephen Straus.In this episode you will:-Get insights into their journey into AI and why they opted for services over products. Learn about the pivotal decisions that shaped our company's trajectory.-Gain a deeper understanding of Generative AI's role in today's transformative landscape. Ron and Stephen delve into its implications across various domains, from computer vision to natural language processing.-Explore the significance of culture, psychological safety, and trust in fostering innovation. Discover how these essential elements contribute to KUNGFU.AI's success.-Understand why ethical considerations are at the forefront of their AI ventures. Hear about their decision to turn down the first potential deal and the ethical principles guiding their company through industry challenges.-Learn how companies can navigate technological advancements while making meaningful contributions to society.Ron and Stephen end the episode by reflecting on whether we're experiencing the "golden age" of AI, drawing parallels to the early days of the internet.

In this episode, your host Ron Green is joined by colleague Alex Olden to dissect the multifaceted world of data science and its intersection with product sense in AI development.Alex, a senior data scientist at KUNGFU.AI, shares insights on anticipating user needs, identifying opportunities, and aligning AI development with customer requirements. With a background in English and applied statistics, Alex brings a unique perspective to the discussion.Episode Highlights:Understanding product sense through real-world analogies.Identifying opportunities through user-centric approaches.Balancing data-driven insights with intuition in AI development.Managing stakeholder relationships for effective collaboration.Navigating challenges unique to data science-driven product features.Link to Alex's blog: www.kungfu.ai/blog-post/product-sense-a-hidden-lynchpin-in-data-science-and-ai

In episode 13, join Ron Green as he explores the intersection of artificial intelligence and marketing with Jasper’s Al Biedrzycki, an expert with over 15 years of experience in scaling up organizations.In this episode:-Explore how generative AI is transforming marketing strategies and budgets, while navigating the crucial ethical considerations in balancing human creativity with automation.-Discover how Jasper serves as an AI co-pilot for enterprise marketing teams, focusing on empowering marketers to scale their efforts efficiently.-Uncover the dominant use cases of Jasper's generative AI tool, from orchestrating campaigns to ensuring brand consistency through tailored content generation.-Delve into the delicate balance between human creativity and AI automation, as Al shares insights on the necessity of human oversight and editing in content creation.-Explore how Jasper ensures responsible AI usage through thought leadership, technology, and governance measures, ensuring transparency and accountability.

In episode 12, join Ron as we delve into the fascinating world of protein engineering and bioinformatics with special guest Daryl Barth, a doctoral candidate at The University of Texas at Austin. Daryl's groundbreaking research in protein engineering is revolutionizing waste plastic management using cutting-edge AI technologies.Discover how Daryl's journey from materials science to biology led her to tackle the global pollution crisis head-on. Learn about the essential role of proteins in living organisms and why protein folding is a crucial puzzle in scientific research.Explore the integration of advanced AI tools like AlphaFold and Evolutionary Scale Modeling in Daryl's workflow, and uncover the transformative impact these innovations have on traditional approaches in protein engineering.Join us as we discuss the potential impacts of Daryl's research on waste management and recycling industries, and gain insights into the future of enzyme identification and design.

In episode 11, Ron is joined by Michael Wharton, Vice President of Engineering at KUNGFU.AI, to unravel the art and science behind building effective AI teams. They discuss optimal hiring strategies, crucial technical skills, and the distinctive challenges AI teams face compared to traditional software development.Michael, an expert in computer vision with a focus on remote sensing, shares his journey from aerospace engineering to deep learning and reflects on the transformative moments that shaped his career. The conversation unfolds into the unique aspects of AI projects, emphasizing the need for a mindset shift towards embracing probabilistic outcomes. Together they explore the organizational dynamics of AI teams, highlighting the importance of flexibility, diversity, and creative collaboration. Michael sheds light on the pitfalls of organizing around disciplines and stresses the value of cross-functional, adaptable teams. As we navigate through the discussion, Michael provides insights into the hiring process at KUNGFU.AI AI, emphasizing the need for a holistic skill set beyond deep learning expertise. The conversation touches on work-life balance, ethical considerations in AI, and the significance of soft skills in a services-oriented industry. Finally, they dive into the challenges faced on AI projects and the strategies employed to overcome setbacks. Michael shares a compelling example where a collaborative team effort led to a breakthrough in solving a complex problem for a client. Don't forget to subscribe for more engaging conversations with industry experts on Hidden Layers! We're also currently hiring so please visit our careers page for more info: https://www.kungfu.ai/careers

In episode 10, Ron, dives into the fascinating world of neuroscience and artificial intelligence with our special guest, Dr. Sanjeev Namjoshi, a Machine Learning Engineer at VERSES.In this episode, we unravel the connection between neuroscience and artificial intelligence, exploring Dr. Namjoshi's upcoming books on active inference and the free energy principle. We get into the unique advantages of active inference over other cognitive frameworks for modeling human behavior and cognition. #Neuroscience #ActiveInference #FreeEnergyPrinciple

🔍 In Episode 9, dive into the fascinating world of self-supervised learning with Dr. Suyog Jain in this insightful interview! 🧠 Discover how this innovative technique is revolutionizing artificial intelligence, especially in complex and sensitive fields like healthcare and pathology.👨‍💼 Dr. Jain, a Research Scientist at Facebook AI Research (FAIR), shares his expertise in multimodal learning and his journey from precision medicine at PathAI to geospatial data consulting with KUNGFU.AI. 🤔 Curious about self-supervised learning and its growing importance? Learn why it eliminates the need for labor-intensive data labeling and how it addresses challenges in acquiring diverse datasets, particularly in healthcare.🌐 Gain valuable insights into the challenges and strategies for handling large, unlabeled datasets in healthcare. Explore the nuances of annotating data in specialized domains and discover lessons applicable across different fields.🚀 Uncover the advantages and limitations of self-supervised learning compared to traditional supervised methods. Dr. Jain provides a deep dive into his work on multimodal learning, emphasizing video datasets and the integration of various data types for enhanced machine learning models.👁️‍🗨 Get a glimpse into the future as Dr. Jain discusses spatial and temporal grounding challenges in vision foundation models and envisions overcoming these obstacles to improve AI's understanding of complex visual scenes.🌟 Finally, explore the most exciting and promising areas of research and development in artificial intelligence and machine learning according to Dr. Suyog Jain.

In episode 8, Ron sits down with Jeff Perry, a prominent engineering scientist in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. Together Ron and Jeff talk about how AI is used in mapping coastlines, using data from the ICESat-2 (Ice, Cloud, and Land Elevation Satellite 2) satellite to map the ocean’s floor and much more. Resources: 3D Geospatial Laboratory website: https://magruder3dgl.com/UT Austin's Center for Space Research: https://www.csr.utexas.edu/SlideRule Earth: https://www.slideruleearth.io/web/SlideRule Earth's GitHub repositories: https://github.com/ICESat2-SlideRule

In episode 7 of Hidden Layers, Ron chats with KUNGFU.AI's Director of Engineering, Dr. Larry Gray. Dr. Gray introduces us to the topic of computational thinking, walks us through his journey to AI, talks about how AI will disproportionately impact minors, and delves in on Python and what it means to him. Learn about how to prepare for an AI driven future, how to apply computational thinking in your own life, and how critical it will be to manage bias in AI going forward.Resources: Get notified when Dr. Gray's book is ready for pre-order and get a free copy of Chapter 1: lawrencegray.com/mastering-python-bookThe impact of generative AI on Black communities: https://www.mckinsey.com/bem/our-insights/the-impact-of-generative-ai-on-black-communitiesDr. Gray's Udemy Course - Python Programming for Absolute Beginners: https://www.udemy.com/course/easy-python-programming-for-absolute-beginners-r/

In episode 6 of Hidden Layers, Ron interviews DeUmbra's Principal Scientist, Dr. Jonathan Mugan. Dr. Mugan sheds light on how robots are learning situational awareness, common sense, and the ability to adapt to never-before-seen situations. Learn about the latest advancements in humanoid robotics and the potential future where robots seamlessly integrate into our daily lives. This episode is a must-listen for anyone fascinated by the convergence of AI, robotics, and our future society.

In episode 5 of Hidden Layers, Ron sits down with Reed Coke, a Director of Engineering and Principal Machine Learning Engineer at KUNGFU.AI. Reed and Ron discuss what a Large Language Model (LLM) is, why and how they are so powerful and why it's critical you pick the right one for your company. They dive into how to measure performance of LLMs, customizing them to fit your specific needs and much more.

In episode 4 of Hidden Layers, Ron Green, sits down with Jean Pierre Trou, CEO and Co-founder of mbue, a company building artificial intelligence systems to instantly review architectural drawings. Jean Pierre is an Architect by training and talks about pain points for all architects, the future of AI in architecture, his vision for mbue, and much more. Visit mbue.ai for more information.

In episode 3 of Hidden Layers Ron sits down with KUNGFU.AI's Chief Strategy Officer, Dr. Benjamin Herndon and Senior AI Strategist Daniel Bruce, to discuss artificial intelligence strategy. They explore the differences between corporate and AI strategy, how to determine when to jump in, and how to stay up to date in a rapidly moving environment.