Super Data Science: ML & AI Podcast with Jon Krohn
Jon Krohn
The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.
Episodes
Mentioned books
54 snips
Sep 22, 2026 • 1h 10min
1029: How AI Brought a Podcast Back From the Dead, with Linear Digressions’ Katie Malone
Katie Malone, a data scientist, educator, and creator of Linear Digressions, returns to discuss reviving her podcast with AI. She explores why managing AI agents resembles managing people, showcases the agent producing her show, and examines AI slop, process slop, and automation’s threat to expertise. She and Jon Krohn also unpack Simpson’s Paradox and Benford’s Law.
26 snips
Sep 18, 2026 • 30min
1028: The Chip Built for Agentic AI Inference, with SambaNova's Anton McGonnell
Anton McGonnell, SambaNova’s VP of Product, explores why agentic AI is straining traditional inference hardware. He compares GPUs with SambaNova’s spatially mapped reconfigurable dataflow unit, designed for faster token generation and higher concurrency. They discuss the SN50’s economics, six-month payback target, air-cooled deployment, NeoClouds, sovereign infrastructure, and the rising cost of slow inference.
33 snips
Sep 15, 2026 • 1h 3min
1027: Building an Always-On AI Agent for Busy Parents, with Dr. Dilani Kahawala
Dr. Dilani Kahawala, a Harvard-trained physicist and product leader from Meta, Etsy, and Atlassian, co-founded Anna, an always-on AI assistant for busy parents. She discusses turning emails, school apps, WhatsApp, and calendars into family action without relying on an app. Topics include long-running agents, reliability testing, voice interactions, model selection, and the challenging economics of consumer AI.
34 snips
Sep 11, 2026 • 20min
1026: OpenAI’s GPT-6 Astra
A deep dive into GPT-6 Astra’s leap in computer use, coding, abstract reasoning, scientific research, and professional workflows. The discussion explores its ability to handle ambiguity and corrections, plus the safety risks raised by powerful cybersecurity capabilities. It also weighs whether Astra signals AGI or simply marks another major step toward it.
21 snips
Sep 8, 2026 • 1h 11min
1025: Word Gravity: How Transformers Bend Space, with Dr. Luis Serrano
Dr. Luis Serrano, machine-learning educator, author, and founder of Serrano Academy, explores how transformer attention resembles gravity, bending word meanings through layers. He recreates an eclipse experiment inside a model, distinguishes RAG workflows from autonomous agents, and unpacks why agent evaluation is so difficult. He also explains GRPO, updates to Grokking Machine Learning, and recent advances in quantum computing.
20 snips
Sep 4, 2026 • 34min
1024: In Case You Missed It in August 2026
Pete Johnson, MongoDB’s AI field CTO, explores why AI spending often misses ROI. Optimization strategist Jerry Yurchisin explains where mathematical solvers belong in agentic decisions. Author and developer advocate Priyanka Vergadia breaks down better Claude workflows and AI upskilling. dbt Labs founder Tristan Handy discusses semantic layers and the future of analytics engineering.
28 snips
Sep 1, 2026 • 1h 18min
1023: Agentic AI Skills That Matter Now, with Aishwarya Srinivasan
Aishwarya Srinivasan, an AI practitioner and educator who has worked at Google, Microsoft, and IBM, explores what really matters in the age of agentic AI. She breaks down why engineering judgment beats vibe coding, how whole-system evaluations expose real-world risks, and why reinforcement learning is back. She also shares the MIND framework, loop engineering, and the career advantage of relentless experimentation.
126 snips
Aug 28, 2026 • 18min
1022: CLAUDE.md, AGENTS.md, Skills, Hooks and Subagents: A Field Guide to Steering AI Agents
A lively tour of ways to steer AI agents, from where to store instructions to balancing persistence and token cost. Learn about rules, skills, subagents and context isolation. Hear why hooks act as deterministic guardrails and how agents.md is shaping industry standards. Three practical takeaways wrap up how to organize instructions like code.
24 snips
Aug 25, 2026 • 52min
1021: How dbt Won Analytics Engineering, with dbt Lab’s CEO Tristan Handy
Tristan Handy, founder and CEO of dbt Labs who coined analytics engineering and built dbt, talks about why he picked SQL over Spark and the progressive complexity that drove adoption. He explains how dbt transforms raw data into modeled tables, why the semantic layer will matter for agents, Fusion's type-safety approach, and how tiny skill files can speed massive migrations.
66 snips
Aug 21, 2026 • 17min
1020: How to Choose Model Size and Effort Level: The Two Critical Dials
A deep dive into two knobs that shape LLM behavior: which model size handles your request and how much effort the model spends. Explores what each setting actually controls under the hood and when to tweak one versus the other. Compares cost and capability tradeoffs across major providers and offers clear rules for routing work efficiently.
