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Dr. Vijoy Pandey returns to the show to talk to Jon Krohn about Cisco’s work to advance medicine and mitigate the impact of climate change with distributed artificial super-intelligence. Dr. Vijoy Pandey believes in a future where humans and AI agents work together to tackle our biggest challenges. For this to happen, we will need to have multi-agent systems and open-source platforms that let agents work together, avoiding the phenomenon of AI agents being “isolated geniuses” unable to collaborate. He elaborates on what Cisco is doing to close this gap. This episode is brought to you by the⁠ ⁠⁠Dell⁠⁠⁠, by⁠ ⁠⁠Intel⁠⁠⁠, by ⁠Fabi⁠ and by ⁠⁠⁠⁠Scaylor⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/961⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (03:55) A definition of artificial super-intelligence (14:03) Distributed learning through Cisco’s Outshift (21:29) The semantic protocols for sharing intent in a distributed artificial super-intelligence framework (37:44) The cognitive memory fabric of the distributed artificial super-intelligence framework (46:24) Using cognitive engines as part of the distributed artificial super-intelligence framework

In this episode, we explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Professor at Cornell University's Center for Data Science for Enterprise and Society. David shares insights from his research on how machine learning models can inadvertently create unfairness, particularly for minority and niche user groups, even without any malicious intent. We dive deep into his groundbreaking work on Principal Component Analysis (PCA) and collaborative filtering, examining why these fundamental techniques sometimes fail to serve all users equally. David introduces the concept of "power niche users" - highly active users with specialized interests who generate valuable data that can benefit the entire platform. We discuss his paper "When Collaborative Filtering Is Not Collaborative," which reveals how PCA can over-specialize on popular content while neglecting both niche items and even failing to properly recommend popular artists to new potential fans. David presents solutions through item-weighted PCA and thoughtful data upweighting strategies that can improve both fairness and performance simultaneously, challenging the common assumption that these goals must be in tension. The conversation spans from theoretical insights to practical applications at companies like Meta, offering a comprehensive look at the future of personalized recommendations.

Data and AI teams are drowning in tools, but the big trend is consolidation and speed. AI-driven building is making dashboards, internal apps, and even data workflows feel more like products than reports. Custom interfaces, interactive presentations, and ad hoc apps are becoming easier to create than traditional BI artifacts.For working professionals, this raises practical questions: should you build a bespoke reporting site instead of another spreadsheet? Can you connect secure data views and prevent leaks by design? What does quality control look like when an agent writes the code—separate chats, clear plans, and tests? And what’s the real cost of going from idea to deployed app: a few dollars, or hundreds?Matt Palmer works at the intersection of developer experience, product marketing, and AI education. Leading Developer Relations at Replit, he helped grow Replit's revenue from $5M to $100M+. He creates content on vibe-coding, data transformation, AI, and more—blending technical depth with accessibility to empower developers and make complex tools approachable.In the episode, Richie and Matt explore the power of vibe coding, how non-developers are building impactful tools, the potential of AI in app development, the role of Replit in simplifying coding, and the future of personalized applications in data teams, and much more.Links Mentioned in the Show:ReplitCourse: Vibe Coding with ReplitYour Guide to ReplitConnect with MattAI-Native Course: Intro to AI for WorkRelated Episode: Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpotExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

There's concern about the future of AI and how it may affect jobs and employment for the masses. I see plenty of people on both sides of the issue. Some are sure AI technologies won't replace people; some are concerned their jobs will be eliminated, and some are hoping that we will eliminate some jobs and create many more. Sometimes that's the same person. Read the rest of Deep Learning and Craftsmanship Matter

The second Trump administration has launched a full-scale effort to achieve “unchallenged global technological dominance.” It is accelerating the construction of AI infrastructure, from opening up federal lands to ramping up energy production. It has invoked AI-enabled “efficiency” in order to replace federal workers, removed agency guidance on algorithmic discrimination,  and supercharged the use of AI in areas including defense and immigration enforcement. The administration has also pursued novel public ownership efforts, such as taking equity  in Intel and critical minerals firms. To what end? Officials say they are now maximizing the “export of the American AI technology stack.” This is not the deregulatory tech agenda predicted by both supporters and critics of President Trump. So what is it?How should we understand the administration’s actions when it comes to AI? What dynamics are driving these changes in AI policymaking? What might be the downstream consequences for Americans? And how should we respond?