Daniel and Chris sit with Citadel AI’s Rick Kobayashi and Kenny Song and unpack AI safety and security challenges in the generative AI era. They compare Japan’s approach to AI adoption with the US’s, and explore the implications of real-world failures in AI systems, along with strategies for AI monitoring and evaluation.
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Dan and Chris break down Winning the Race: America's AI Action Plan, issued by the White House in July 2025. Structured as three "pillars" — Accelerate AI Innovation, Build American AI Infrastructure, and Lead in International AI Diplomacy and Security — our dynamic duo unpack the plan's policy goals and its associated suggestions — while also exploring the mixed reactions it’s sparked across political lines. They connect the plan to international AI diplomacy and national security interests, discuss its implications for practitioners, and consider how political realities could shape its success in the years ahead.
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Allegra Guinan of Lumiera helps leaders turn uncertainty about AI into confident, strategic leadership. In this conversation, she brings some actionable insights for navigating the hype and complexity of AI. The discussion covers challenges with implementing responsible AI practices, the growing importance of user experience and product thinking, and how leaders can focus on real-world business problems over abstract experimentation.
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Dan sits down with guests Mark Daniel Ward and Katie Sanders from The Data Mine at Purdue University to explore how higher education is evolving to meet the demands of the AI-driven workforce. They share how their program blends interdisciplinary learning, corporate partnerships, and real-world data science projects to better prepare students across 160+ majors. From AI chatbots to agricultural forecasting, they discuss the power of living-learning communities, how the data mine model is spreading to other institutions and what it reveals about the future of education, workforce development, and applied AI training.
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We unpack how AI is reshaping hiring decisions, shifting job roles, and creating new expectations for professionals — from engineers to marketers. They explore the rise of AI-assisted teams, the growing compensation bubble, why continuous learning is now table stakes, and how some service providers are quietly riding the AI wave.
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In this episode, Chris sits down with Igor Nikitin, CEO and co-founder of Nice Technologies, to explore how AI and modern engineering practices are transforming the actuarial field and setting the stage for the future of actuarial modeling. We discuss the introduction of programming into insurance pricing workflows, and how their Python-based calc engine, AI copilots, and DevOps-inspired workflows are enabling actuaries to collaborate more effectively across teams while accelerating innovation.
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In this episode of Practical AI, Chris and Daniel explore the fascinating world of agentic AI for drone and robotic swarms, which is Chris's passion and professional focus. They unpack how autonomous vehicles (UxV), drones (UaV), and other autonomous multi-agent systems can collaborate without centralized control while exhibiting complex emergent behavior with agency and self-governance to accomplish a mission or shared goals. Chris and Dan delve into the role of AI real-time inference and edge computing to enable complex agentic multi-model autonomy, especially in challenging environments like disaster zones and remote industrial operations.
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Chris's definition of Swarming:
Swarming occurs when numerous independent fully-autonomous multi-agentic platforms exhibit highly-coordinated locomotive and emergent behaviors with agency and self-governance in any domain (air, ground, sea, undersea, space), functioning as a single independent logical distributed decentralized decisioning entity for purposes of C3 (command, control, communications) with human operators on-the-loop, to implement actions that achieve strategic, tactical, or operational effects in the furtherance of a mission.
© 2025 Chris Benson
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In the first episode of an "AI in the shadows" theme, Chris and Daniel explore the increasing concerning world of agentic misalignment. Starting out with a reminder about hallucinations and reasoning models, they break down how today’s models only mimic reasoning, which can lead to serious ethical considerations. They unpack a fascinating (and slightly terrifying) new study from Anthropic, where agentic AI models were caught simulating blackmail, deception, and even sabotage — all in the name of goal completion and self-preservation.
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In this episode, we sit down with Joey Conway to explore NVIDIA's open source AI, from the reasoning-focused Nemotron models built on top of Llama, to the blazing-fast Parakeet speech model. We chat about what makes open foundation models so valuable, how enterprises can think about deploying multi-model strategies, and why reasoning is becoming the key differentiator in real-world AI applications.
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Can AI-driven autonomy reduce harm, or does it risk dehumanizing decision-making? In this “AI Hot Takes & Debates” series episode, Daniel and Chris dive deep into the ethical crossroads of AI, autonomy, and military applications. They trade perspectives on ethics, precision, responsibility, and whether machines should ever be trusted with life-or-death decisions. It’s a spirited back-and-forth that tackles the big questions behind real-world AI.
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It seems like we are bombarded by news about millions of dollars pouring into AI startups, which have crazy valuations. In this episode, Chris and Dan dive deep into the highs, lows, and hard choices behind funding an AI startup. They explore early bootstrapping, the transition to venture capital, and what it’s like to trade in code commits for investor decks.
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An recent article in Variety was titled: "Sylvester Stallone-Backed Largo.ai Teams With Brilliant Pictures for ‘World’s First Fully AI-Automated Film Company’". Obviously this caught our attention! We sit down with Sami Arpa, CEO of Largo.ai, to unpack how films are developed, funded, and brought to life using AI. We discover how tools like script analysis, financial forecasting, and digital twins are helping creators and studios make smarter decisions.
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Chong Shen from Flower Labs joins us to discuss what it really takes to build production-ready federated learning systems that work across data silos. We talk about the Flower framework and it's architecture (supernodes, superlinks, etc.), and what makes it both "friendly" and ready for real enterprise environments. We also explore how the generative Generative AI boom is reshaping Flower’s roadmap.
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In this first of a two part series of episodes on federated learning, we dive into the evolving world of federated learning and distributed AI frameworks with Patrick Foley from Intel. We explore how frameworks like OpenFL and Flower are enabling secure, collaborative model training across silos, especially in sensitive fields like healthcare. The conversation touches on real-world use cases, the challenges of distributed ML/AI experiments, and why privacy-preserving techniques may become essential for deploying AI to production.
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Loïc Houssier, Head of Engineering at Superhuman, joins us to discuss how AI and LLMs are reshaping the email experience. He highlights challenges related to the variability of user prompts and infrastructure optimization. Loïc emphasizes that a deep focus on user experience and real human workflows is key to building AI tools people actually love to use.
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In this episode, Daniel and Chris unpack the Model Context Protocol (MCP), a rising standard for enabling agentic AI interactions with external systems, APIs, and data sources. They explore how MCP supports interoperability, community contributions, and a rapidly developing ecosystem of AI integrations. The conversation also highlights some real-world tooling such as FastAPI-MCP.
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In this episode, we explore the intersection of AI, machine learning, and healthcare through the lens of neuroimaging and epilepsy diagnosis. Dr. Gavin Winston shares insights from his work using MRI data and machine learning to uncover subtle abnormalities in brain function. We discuss the cultural and ethical barriers to AI adoption in medicine, how predictive data analysis could transform the diagnostic workflow, and what the future holds for medical imaging in a world increasingly shaped by intelligent systems.
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Vibe coding, agentic workflows, and AI-assisted pull requests? In this episode, Daniel and Chris chat with Robert Brennan and Graham Neubig of All Hands AI about how AI is transforming software development—from senior engineer productivity to open source agents that address GitHub issues. They dive into trust, tooling, collaboration, and what it means to build software in the era of AI agents. Whether you're coding from your laptop or your phone on a morning walk, the future is hands-free (and All Hands).
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In this episode, Daniel sits down with Pavel Veller, EPAM’s Chief Technologist, to explore the practical challenges of orchestrating many AI agents and managing connections to disparate systems/tools. Pavel shares insights from his hands-on work with agentic architectures and internal tools like "DIAL". Pavel also helps us understand things like MCP servers and why connecting assistants via APIs is easy—but making them useful is hard.
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★ Support this podcast ★How do you enable AI acceleration (at both the hardware and software layers) that stays ahead of rapid industry shifts? In this episode, Dhananjay Singh from Groq dives into the evolving landscape of AI inference and acceleration. We explore how Groq optimizes the serving layer, adapts to industry shifts, and supports emerging model architectures.
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