Predictive AI vs. GenAI: A Crucial, Unavoidable Comparison

In this episode, Eric Siegel narrates his article: Jev Fever: Predictive AI Is Superhot Again A new breed of models has rejuvenated predictive AI. But successful deployment demands a new business-value playbook. ... and then Eric summarizes his follow-on article: Jev AI Will Make You Money – But Exactly How Much? AI users are pouncing on the new predictive capabilities of Jev and its competitors. But to make money with Jev, you must also "valuate" it for your project.

Listen in full to the new preface to Eric Siegel's The AI Playbook: Predictive AI Thrives, Despite Generative AI Stealing the Spotlight This is the new preface to the paperback edition of The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Special offer: Pre-order the paperback of The AI Playbook now and receive free, immediate access to the audiobook. This was also published as an adapted article in: CDO Magazine: GenAI May Own the Hype, but Predictive AI Also Delivers Value The Machine Learning Times: GenAI Owns the Hype, but Predictive AI Is Thriving

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

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  

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?

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

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.

In this episode, listen to a narration of Eric Siegel's article in Forbes: Predictive AI Thrives, Despite GenAI Stealing The Spotlight GenAI and predictive AI battle for resources, but even as the overwhelming attention focuses on genAI, enterprises are still adopting predictive AI just as much. Access the original article here: https://www.forbes.com/sites/ericsiegel/2026/02/11/predictive-ai-thrives-despite-genai-stealing-the-spotlight/ You can access an overview of HYBRID AI 2026 and a description of each enterprise presentation here: https://machinelearningweek.com/

In this episode, listen to a narration of Eric Siegel's article in Forbes: Hybrid AI: Industry Event Signals Emerging Hot Trend AI is not yet the success that it should be, so two dozen enterprises will disclose their move toward a crucial new paradigm – hybrid AI – at a 2026 conference. Access the original article here: https://www.forbes.com/sites/ericsiegel/2026/02/09/hybrid-ai-industry-event-signals-emerging-hot-trend/

The biggest hurdle for data science teams isn't building the model; it's proving its dollar value. This presentation shows how a dental group could translate a no-show prediction model into a clear business case worth $$$ It's about shifting the conversation from abstract metrics to tangible ROI.  Henry Castellanos is a data scientist extraordinaire. He goes beyond establishing a strong technical performance for his ML models to also maximizing their business value. Let this sink in: Most data scientists don't do that – most ML projects don't plan and sell predictive AI deployment according to the the explicit business value. Interestingly, Henry points out that using Gooder AI (www.gooder.ai) to do this even bucks up his own confidence in his models and their business value. Listen to Henry's presentation to see exactly how to bridge the gap from ML to real-world value. To view this presentation as a video, go to: https://youtu.be/BT-GnnuN3jA