Practical AI

Chris and Daniel break down what really mattered in AI in 2025 and what to expect in 2026, exploring the rise of agents, multimodal AI, and reasoning models.

Jason Beutler of RoboSource discusses how companies can rethink workflows and integrate AI in accessible ways to handle routine tasks.

Returning guest Ramin Mohammadi discusses evolving expectations for AI engineers and data scientists, and how the roles are converging.

Waymo's VP of Research discusses advances in autonomy, vision models, and large-scale testing for driverless vehicles.

Chris and Daniel identify the patterns that distinguish successful enterprise AI deployments from failed ones, based on conversations with dozens of implementation teams.

Daniel and Chris discuss how AI agents are moving from simple chatbots to complex agentic workflows — and the security risks this introduces. Prompt injection, data exfiltration, and tool misuse in agentic systems.

Bridging the gap between responsible AI principles and practical implementation. Most organizations have AI ethics principles but lack the tools and processes to implement them.

Daniel and Chris discuss the current state of RAG (Retrieval-Augmented Generation) and reasoning models. Why many enterprise AI initiatives stumble and what architectures are actually working in production.

Igor Nikitin of Nice Technologies explores how AI and modern engineering practices are transforming actuarial work and insurance pricing.

How AI is changing hiring: from resume screening to interview analysis to skill assessment. The implications for fairness, efficiency, and the candidate experience.

Chris and Daniel examine which multimodal AI capabilities are ready for production use and which remain research curiosities.

How AI is being deployed in agriculture: crop disease detection, yield prediction, autonomous tractors, and precision farming.

Moving beyond benchmark scores to evaluate LLMs for real-world use. Custom evaluation suites, human judgment, and domain-specific testing.

Deploying AI models on edge devices (phones, IoT, embedded systems) requires navigating hardware constraints, latency requirements, and connectivity limitations.

Purdue University's Data Mine program combines interdisciplinary learning with corporate partnerships to train students for real-world AI work.

Rick Kobayashi and Kenny Song from Citadel AI discuss safety challenges in generative AI, Japan's advanced GenAI adoption, and their LLM-as-a-judge evaluation approach.

Separating AI healthcare hype from reality. Which AI healthcare applications are actually deployed in clinical settings versus which remain research demonstrations.