The Doomer's Error: Why AGI Is An Incoherent Concept
25-03-2026 | 48 Min.
What's the strongest anti-AGI case, the argument that reveals the fallacies underlying the belief that AGI is a viable goal – as well as the AI doomerism that believing AGI will soon arrive often spawns? Princeton professor Arvind Narayanan recently made a statement that we feel deserves amplification: For real-world problems, machines face some of the same key fundamental limits and challenges that humans face.
Listen to Luba and Eric unpack, explore, and expound. #noAGI
Predictive AI vs. GenAI: A Crucial, Unavoidable Comparison
17-03-2026 | 48 Min.
In this episode we cover:
- Why predictive AI and generative AI are destined to remain inherently distinct
- Why comparing them is unavoidable, even though they solve different problems
- How they compare
- How companies should balance investments between the two
Pushing the ultimate limits: helping genAI realize its promise of autonomy
11-03-2026 | 53 Min.
In this episode, we talk about real, truly deployed LLM-based systems that push the limits of autonomy. How can we "tame" LLMs to create feasible, practical solutions that are viable for deployment? What are their ultimate limitations?
Superhuman Adaptable Intelligence: LeCun's New Buzzword Challenges AGI
05-03-2026 | 55 Min.
In this episode, Luba Gloukhova and Eric Siegel unpack the new paper, "AI Must Embrace Specialization via Superhuman Adaptable Intelligence," by Yann LeCun and others.
The paper endeavors to "address what’s wrong with our conception of AGI, and why, even in its most coherent formulation, it is a flawed concept to describe the future of AI."
That aligns so well with our episode just two days ago that one of the paper's authors, Philippe Wyder, tweeted us about the paper, bringing it to our attention!
The paper presents the new term "Superhuman Adaptable Intelligence," which is defined as "intelligence that can learn to exceed humans at anything important that we can do, and that can fill in the skill gaps where humans are incapable."
Listen to our break-down and take, and access the full paper here: https://arxiv.org/abs/2602.23643
The Whole Problem with AGI and Its Ridiculous Definitions
03-03-2026 | 40 Min.
In this very special episode, the first with a co-host (Luba Gloukhova), Dr. Data and Miss Information explore why people are messing with the definition of artificial general intelligence, the problem with the concept, how it feeds AI hype, and how we can feasibly realize a good portion of genAI's overzealous promise of autonomy.
Over The Dr. Data Show with Eric Siegel and Luba Gloukhova
Eric Siegel and Luba Gloukhova cover why machine learning is the most important, most potent, and most misunderstood technology. And did we mention most important?Yup, it’s the most important – yet most new ML projects fail to deliver value. This podcast will help you:- Make sure machine learning is effective and valuable- Catch common machine learning oversights- Understand ethical pitfalls – concretely- Sniff out all the ”artificial intelligence” malarkyThis podcast is for both data scientists and business leaders of all kinds – such as executives, directors, line of business managers, and consultants – who are involved in or affected by the deployment of machine learning.To get machine learning to work, both the tech and business sides must make an effort to reach across wide chasm.About the host:Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling ”Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die,” which has been used in courses at hundreds of universities, as well as ”The AI Playbook: Mastering the Rare Art of Machine Learning Deployment.” Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate *computer science* courses in ML and AI. Later, he served as a *business school* professor at UVA Darden. Eric has appeared on numerous media channels, including Bloomberg, National Geographic, and NPR, and has published in Newsweek, HBR, SciAm blog, WaPo, WSJ, and more.https://www.machinelearningweek.comhttp://www.bizML.comhttp://www.machinelearning.courseshttp://www.thepredictionbook.com
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