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” malarky This 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.com http://www.bizML.com http://www.machinelearning.courses http://www.thepredictionbook.com
Episodes
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 ...
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, Phil...
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/pred...
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-ev...
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 al...
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Predictive AI Usually Fails Because It’s Not Usually Valuated
Most predictive AI deployments are scrubbed. Why? They didn't forecast the potential value in business terms like profit or savings.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/11/18/predictive-ai-usually-fails-because-its-not-usually-valuated/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
AI Drives Alphabet’s Moonshot To Save The World’s Electrical Grid
AI is pivotal as global utilities tackle a looming crisis with the electrical grid. Here's how Alphabet uses AI to help the world keep the lights on.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/10/07/why-we-need-ai-alphabets-moonshot-to-save-the-worlds-electr...
In this episode, listen to a narration of Eric Siegel's article in Forbes:
To Deploy Predictive AI, You Must Navigate These Tradeoffs
Before deploying predictive AI, you must strike a balance between competing business factors. Here's how.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/08/27/to-deploy-predictive-ai-you-must-navigate-these-tradeoffs/
- One compelling reason to focus on predictive AI more than genAI.
- Why it's a safe bet that some consumer-facing LLMs will eventually be given too much "po...
In this episode, listen to a narration of Eric Siegel's article in Forbes:
How Generative AI Helps Predictive AI
Large language models can act as predictive models. Here's an example for misinformation detection—and an introduction to savings curves.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/08/21/how-generative-ai-helps-predictive-ai/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The Quant's Dilemma: Subjectivity In Predictive AI's Value
When machine learning fails to detect misinformation, medical conditions or spam, the cost of each error is subjective. Here’s how to apply predictive AI nonetheless.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/09/30/the-quants-dilemma-subjectivity-in-predictive-ais...
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The Great AI Myth: These 3 Misconceptions Fuel It
The impending arrival of artificial general intelligence is a story of wish fulfillment that lacks concrete evidence.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/07/29/the-great-ai-myth-these-3-misconceptions-fuel-it/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The 3 Things You Need To Know About Predictive AI
Stakeholders involved with predictive AI must ramp up on a semi-technical understanding that comes down to 1) what's predicted, 2) how well and 3) what's done about it.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/06/29/the-3-things-you-need-to-know-about-predictive-ai/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Why You Must Twist Your Data Scientist's Arm To Estimate AI's Value
For every machine learning model that you consider deploying, make sure that your data scientists provide you with a full view of its potential business value.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/06/11/why-you-must-twist-your-data-scientists-arm-to-...
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The Rise Of Large Database Models
Even as large language models have been making a splash with ChatGPT and its competitors, another incoming AI wave has been quietly emerging: large database models.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2025/01/13/the-rise-of-large-database-models/
3 Predictions For Predictive AI In 2025 (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
3 Predictions For Predictive AI In 2025
1) GenAI hybrids, 2) ML valuation, 3) bizML—these advances will bring predictive AI back into the spotlight and further amplify its value.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2025/01/06/3-predictions-for-predictive-ai-in-2025/
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