The Dr. Data Show with Eric Siegel

All Episodes (19)

From new to old

From new to old From old to new

1. 

   1. Forbes Article: Meta’s New GenAI Is Theatrical. Here’s How To Make It Valuable

   In this episode, Eric Siegel narrates his article in Forbes, "Meta’s New GenAI Is Theatrical. Here’s How To Make It Valuable."Concern about a generative AI bubble is growing. To defend against disillusionment, measure its concrete value.Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/04/21/metas-new-genai-is-theatrical-heres-how-to-make-it-valuable/See/listen also to Eric Siegel's Harvard Business Review article: HBR Article: The AI Hype Cycle Is Distracting Compan

   Apr 21,2024 07:36
2. 

   2. Forbes Article: Artificial General Intelligence Is Pure Hype

   In this episode, Eric Siegel narrates his article in Forbes, "Artificial General Intelligence Is Pure Hype."The belief that we’re gaining ground on AGI is misguided—reports of the human mind's looming obsolescence have been greatly exaggerated.Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/04/10/artificial-general-intelligence-is-pure-hype/See/listen also to Eric Siegel's Harvard Business Review article: HBR Article: The AI Hype Cycle Is Distracting Companies 

   Apr 10,2024 09:24
3. 

   3. Forbes Article: AI Success Depends On How You Choose This One Number

   In this episode, Eric Siegel narrates his article in Forbes, "AI Success Depends On How You Choose This One Number."AI can drive millions of operational decisions, but first the business must strategically select a single number that differentiates the yeses from the nos.Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/03/25/ai-success-depends-on-how-you-choose-this-one-number/Links from the article:3 Ways Predictive AI Delivers More Value Than Generative AI (or rea

   Mar 26,2024 09:09
4. 

   4. Where FICO Gets Its Data for Screening Two-Thirds of All Card Transactions

   In this episode, Eric Siegel narrates his article in The European Business Review, "Where FICO Gets Its Data for Screening Two-Thirds of All Card Transactions."The detection of fraudulent credit card transactions is an ideal candidate for the application of machine learning technology. However, in order to learn how to spot attempted fraud, such a system needs someone to tell it which historic transactions were OK, and which were not.Access the original article here: https://www.europeanbusiness

   Mar 26,2024 16:50
5. 

   5. Forbes Article: 3 Ways Predictive AI Delivers More Value Than Generative AI

   In this episode, Eric Siegel narrates his article in Forbes, "3 Ways Predictive AI Delivers More Value Than Generative AI."Generative AI attracts headlines, but predictive AI delivers greater value. This article covers three ways predictive AI eclipses generative AI.Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/03/04/3-ways-predictive-ai-delivers-more-value-than-generative-ai/Also listen to narrations of two of Eric Siegel's other recent, related articles on pred

   Mar 11,2024 11:29
6. 

   6. SMR Article: The Rare, Crucial Move to Business Metrics for Establishing ML's Value

   In this episode, Eric Siegel narrates his article in MIT Sloan Management Review, "What Leaders Should Know About Measuring AI Project Value."Most AI/machine learning projects report only on technical metrics that don’t tell leaders how much business value could be delivered. To prevent project failures, press for business metrics instead.Access the original article here: https://sloanreview.mit.edu/article/what-leaders-should-know-about-measuring-ai-project-value/This article is excerpted from

   Feb 26,2024 18:52
7. 7. Five insights from "The AI Playbook"

   This episode covers five insights from the new book, The AI Playbook, which come from a piece originally published by The Next Big Idea Club.On a related note, the book has been included as a Next Big Idea Club Must Read.Also, here is the book's recent Bloomberg Businessweek Radio segment, which was mentioned herein.For more about The AI Playbook, see the details, endorsements, and ordering options at www.bizML.com.

   Feb 13,2024 18:03
8. 

   8. The AI Playbook – book launch, early reviews, FAQ

   I'm excited to announce that today, my new book has published!The AI Playbook: Mastering the Rare Art of Machine Learning DeploymentInfo at: http://www.bizML.comIn my first book, Predictive Analytics, I explained how machine learning works. Now, in The AI Playbook, I show how to capitalize on ML. The book presents a greatly-needed business framework that I call bizML.See all the details, recommendations from the likes of Scott Galloway, Charles Duhigg, Mustafa Suleyman, the CEO of FICO, and DJ P

   Feb 06,2024 23:31
9. 

   9. HBR Article: The AI Hype Cycle Is Distracting Companies

   In this episode, Eric Siegel narrates his article in The Harvard Business Review, "The AI Hype Cycle Is Distracting Companies."Access the original article here: https://hbr.org/2023/06/the-ai-hype-cycle-is-distracting-companiesLearn more about and order Eric's new book, The AI Playbook: http://www.bizML.comMachine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we di

   Jan 31,2024 14:54
10. 

    10. HBR Article Narration: Getting Machine Learning Projects from Idea to Execution

    In this episode, Eric narrates his new Harvard Business Review article, "Getting Machine Learning Projects from Idea to Execution," adapted from his new book, The AI Playbook.Access the article: https://hbr.org/2024/01/getting-machine-learning-projects-from-idea-to-executionLearn more about and order The AI Playbook: http://www.bizML.com 

    Jan 23,2024 15:57
11. 

    11. THE AI PLAYBOOK – Announcement and Free Audiobook Offer

    Announcing Eric Siegel's new book:The AI Playbook: Mastering the Rare Art of Machine Learning DeploymentInfo: www.bizML.comThis podcast episode includes a book overview, book sample – the opening of the book's Introduction – and a free audiobook offer.In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it.SPECIAL OFFER: FREE AUDIOBOOKPre-order The AI Playbook as a hardcover or e-book on Amazon – shipping February

    Jan 02,2024 11:56
12. 

    12. Five Reasons to Go to Machine Learning Week, June 19-24 2022 in Vegas

    In this special episode, rather than the usual conceptual coverage of machine learning, Eric Siegel will pitch you on the machine learning conference series he founded in 2009, the leading cross-vendor, cross-industry event covering the commercial deployment of machine learning and predictive analytics.Join him in Las Vegas June 19-24 for Machine Learning Week 2022, with seven tracks of sessions covering the commercial deployment of machine learning. Register to attend one or more of MLW’s five

    Apr 04,2022 19:08
13. 

    13. Four Things the Machine Learning Industry Must Learn from Self-Driving Cars

    When it comes to deploying machine learning, we must learn from the self-driving car movement – both to gain inspiration as to what it takes and as a major cautionary tale as to what mistakes to avoid. This episode covers four things the entire machine learning industry must learn from the self-driving car movement.

    Mar 21,2022 28:24
14. 

    14. Why Deep Learning Could Expedite the Next AI Winter

    Deep learning, the most important advancement in machine learning, could inadvertently expedite the next AI winter. The problem is that, although it increases value and capabilities, it may also be having the effect of increasing hype even more. This episode covers four reasons deep learning increases the hype-to-value ratio of machine learning.

    Feb 28,2022 28:24
15. 

    15. P-Hacking: How to Know Your Predictive Discovery Is Conclusive

    “An orange used car is least likely to be a lemon.” At least that’s what was claimed by The Seattle Times, The Huffington Post, The New York Times, NPR, and The Wall Street Journal. However, this discovery has since been debunked as inconclusive. As data gets bigger, so does a common pitfall in the application of standard stats: Testing many predictors means taking many small risks of being fooled by randomness, adding up to one big risk. The tragic but common mistake is called p-hacking. In thi

    Feb 10,2022 41:09
16. 

    16. Real-Time Machine Learning: Why It’s Vital and How to Do It

    Organizations often miss the greatest opportunities that machine learning has to offer because tapping them requires real-time predictive scoring. In order to optimize the very largest-scale processes – which is a vital endeavor for your business – predictive scoring must take place right at the moment of each and every interaction.The good news is that you probably already have the hardware to handle this endeavor: the same system currently running your high-volume transactions – oftentimes a m

    Jan 31,2022 33:11
17. 

    17. The Accuracy Fallacy and the Media’s Bogus Coverage of AI

    Misleading headlines abound, claiming that machine learning can "accurately" predict criminality, psychosis, sexual orientation, and bestselling books. But, when practitioners claim their model achieves "high accuracy," it's often bogus. Can AI "tell" if you're going to have a heart attack? Contrary to bold, public claims, no it cannot. This episode unpacks the undeniable yet common "accuracy fallacy," which misleads the public into believing that machine learning can distinguish between positiv

    Jan 24,2022 30:41
18. 

    18. Models Are Rarely Deployed – an Industrywide Failure in Machine Learning Leadership

    Our latest industry poll reconfirms today's dire industry buzz: Very few machine learning models actually get deployed. In this episode, I summarize the poll results and argue that this pervasive failure of machine learning projects comes from a lack of prudent leadership. I also argue that MLops is not the fundamental missing ingredient – instead, an effective machine learning leadership practice must be the dog that wags the model-integration tail.Links:https://www.kdnuggets.com/2022/01/models

    Jan 15,2022 26:00
19. 

    19. Trailer for The Dr. Data Show with Eric Siegel

    Eric Siegel covers why machine learning is the most important, most potent, most screwed up, most misunderstood, and most dangerous technology. And did I mention most important?

    Jan 15,2022 03:28