Super Data Science: ML & AI Podcast with Jon Krohn

• 927: Automating Code Review with AI, feat. CodeRabbit’s David Loker

  Earlier this year, David Loker joined CodeRabbit as their Director of AI. As more people come to write code with the help of large language models, David believes CodeRabbit will become a helpful assistant for code reviewing and pull requests. He tells Jon Krohn how CodeRabbit assists developers with real-time feedback, as well as the reality of vibe coding, the optimization challenges of agentic AI, and other pressing questions in AI and tech.  This episode is brought to you by the ⁠Dell⁠, by ⁠Intel⁠, by ⁠Gurobi⁠ and by ⁠ODSC, the Open Data Science Conference⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/927⁠⁠ In this episode you will learn: (01:26) How CodeRabbit helps with coding    (17:30) Context engineering in context     (40:40) How CodeRabbit keeps data secure      (46:10) David’s thoughts on “vibe coding”                              (1:03:04) If machines will ever be truly creative

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• 926: AI is Disrupting the Legal Industry: Are Paralegals Doomed?

  In this Five-Minute Friday, Jon Krohn explores how AI is reshaping the legal industry. He investigates how AI tools are helping lawyers make conclusions faster, how paralegals are being retrained, and the latest in-demand role in law (hint: It concerns AI). Listen to hear how Harvey AI and Thomson Reuters’ CoCounsel are using AI to help lawyers get ahead. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/926⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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• 925: AI, Automation and the Future of Work, with Oxford’s Prof. Carl Benedikt Frey

  Tech innovation’s dependence on economic systems, trust in technology throughout history, and job displacement through AI: The Dieter Schwartz Associate Professor of AI and work at the University of Oxford, Carl Benedikt Frey, talks to Jon Krohn about his latest book, How Progress Ends, as well as how different economic systems deal with innovation and scaling, dealing with the homogeneity of generative AI output, and how to stay afloat in the new wave of job automation. This episode is brought to you by the ⁠Dell⁠, by ⁠Intel⁠, by ⁠ODSC, the Open Data Science Conference⁠ and by ⁠Gurobi⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/925⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (04:00) All about How Progress Ends: Technology, Innovation, and the Fate of Nations (14:26) The role of weak ties in driving technological innovation (18:22) How to keep innovating as a big business (48:05) What we can learn and apply from previous industrial revolutions (54:33) How workers can try to ‘future-proof’ themselves

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• 924: 95% of Enterprise AI Projects Fail (Per MIT Research)

  MIT lab NANDA (“Networked AI Agents in Decentralized Architecture”) reveals less than promising results for the future of AI adoption in businesses. According to “The GenAI Divide: State of AI in Business 2025”, a whopping 95% of enterprise AI projects “are getting zero return” on their $30-40 billion investment. Jon Krohn takes this Five-Minute Friday to look into why this has happened, with help from a critical response to the report written by Futuriom’s R. Scott Raynovich. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/924⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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• 923: Graph Algorithms, GraphRAG and Causal Graphs, with Graph Guru Amy Hodler

  Graphs, but not as you would expect them: Graph analytics guru Amy Hodler speaks to Jon Krohn about the graph data structure and graph applications, graph algorithms, graph RAG, and graphs as memory systems for AI agents. We can use graphs in a surprising number of ways. Money laundering and fraud, as well as supply-chain crime, leave breadcrumbs at multiple “touch-points” over time, behaviors that graphs are better suited to reveal than rows and tables. Amy sees that most interest in graphs has been in the cybersecurity space. But this work isn’t only restricted to fighting crime! Listen to the episode to hear more case examples and how to get into graph work.  This episode is brought to you by the Dell, by the Intel, by ODSC, the Open Data Science Conference and by Gurobi. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/923⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: 01:49) A brief history of graphs (10:08) Uncovering fraud with graphs (28:31) Where graphs are most commonly applied, to date (34:49) Retrieval augmented generation graphs (48:04) The future of graphs

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