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Super Data Science: ML & AI Podcast with Jon KrohnAuthor: 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. Language: en Genres: Science, Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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985: The Four Types of Memory Every AI Agent Needs, with Richmond Alake
Tuesday, 21 April, 2026
Oracle’s Director of AI Developer Experience Richmond Alake returns to the show to talk to Jon Krohn about agent memory; the network of systems, models, databases and LLMs that enable AI agents to learn and adapt over time. Listen to the episode to hear about Richmond’s “100 Days of Agent Memory” initiative, retrieval-augmented generation’s (RAG) limitations with AI agents, the layers of the AI agent stack, and what makes the Oracle AI database so useful to developers. Additional materials: www.superdatascience.com/985 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (03:15) What agent memory is and why it’s important (28:28) RAG’s limitations for AI agents (35:19) What matters in the AI agent stack beyond memory (41:34) Why memory was undervalued in the AI agent stack
