19 episodes
The Dr. Data Show with Eric Siegel Eric Siegel
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- Technology
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5.0 • 1 Rating
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Eric Siegel covers why machine learning is the most important, most potent, and most misunderstood technology. And did I 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 and its new sister, Generative AI Applications Summit, 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
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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 Companies
Also see/listen to his Forbes article: Artificial General Intelligence Is Pure Hype -
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
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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 read the original non-narrated article)
What Leaders Should Know About Measuring AI Project Value – why predictive AI needs – but usually doesn't have – business metrics (or read the original non-narrated article in MIT Sloan Management Review).
The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric Siegel -
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.europeanbusinessreview.com/where-fico-gets-its-data-for-screening-two-thirds-of-all-card-transactions/
This article is excerpted from the book, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, with permission from the publisher, MIT Press. It is a product of the author’s work while he held a one-year position as the Bodily Bicentennial Professor in Analytics at the UVA Darden School of Business.
Other links from the article:
What Leaders Should Know About Measuring AI Project Value – why predictive AI needs – but usually doesn't have – business metrics (MIT Sloan Management Review).
Citations and other notes (PDF) -
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 predictive AI:
1) What it takes to capitalize on predictive AI:
Getting Machine Learning Projects from Idea to Execution
Harvard Business Review (print article)
2) Why predictive AI needs – but usually doesn't have – business metrics:
What Leaders Should Know About Measuring AI Project Value
MIT Sloan Management Review (print article)
Both of these articles are adapted from Eric Siegel's new book, The AI Playbook. -
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 Eric's new book, The AI Playbook: http://www.bizML.com
The full details of the article's central example are within a sidebar of the original article (not read through in detail within this podcast episode). You can also access this spreadsheet with the same calculations if you would like to try out different scenarios — such as varying the model lift, the number of transactions held, or the cost of each FP and FN.