Claim OwnershipThe Dr. Data Show with Eric Siegel
Subscribed: 4Played: 14
Subscribe
© Copyright 2022 All rights reserved.
Description
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
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
19 Episodes
Reverse
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
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
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
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)
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.
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.
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.
I'm excited to announce that today, my new book has published!
The AI Playbook: Mastering the Rare Art of Machine Learning Deployment
Info at: http://www.bizML.com
In 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 Patil, the first Chief Data Scientist of the US – and order the book at: www.bizML.com
It's available there as a hardcover, ebook, and audiobook.
This podcast episode includes the book's "FAQ: What This Book Is about and Who It’s For," which can also be read here: https://www.predictiveanalyticsworld.com/machinelearningtimes/faq-for-eric-siegels-new-book-the-ai-playbook/13205/
Here is some early media coverage:
Fast Company called the book, "An antidote to overheated rhetoric of all-powerful AI." https://www.fastcompany.com/91005340/eric-siegel-ai-playbook-interview
It is a Next Big Idea Club Must Read: https://nextbigideaclub.com/magazine/next-big-idea-clubs-february-2024-must-read-books/46393/
Also read or listen to my Next Big Idea Club five-insights overview: https://nextbigideaclub.com/magazine/5-guidelines-successfully-launching-machine-learning-business-bookbite/47774/
Harvard Business Review: https://hbr.org/2024/01/getting-machine-learning-projects-from-idea-to-execution
Book review from Barbara Oakley – co-instructor of the world's most popular course, Coursera’s Learning How to Learn. She writes, “Siegel is a master story-teller... We LOVED this book and cannot recommend it more highly!” https://barbaraoakley.com/recommendation/the-ai-playbook/
Even before its release, the book hit the #1 slot on Amazon’s top 100 Hot New Releases in Technology.
Here’s to accelerating progress as the world improves business with science — thanks and happy reading!
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-companies
Learn more about and order Eric's new book, The AI Playbook: http://www.bizML.com
Machine 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 differentiate most of today’s practical ML projects from those research advances. This begins by correctly naming such projects: Call them “ML,” not “AI.” Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. For most ML projects, the term “AI” goes entirely too far — it alludes to human-level capabilities. In fact, when you unpack the meaning of “AI,” you discover just how overblown a buzzword it is: If it doesn’t mean artificial general intelligence, a grandiose goal for technology, then it just doesn’t mean anything at all.
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-execution
Learn more about and order The AI Playbook: http://www.bizML.com
Announcing Eric Siegel's new book:
The AI Playbook: Mastering the Rare Art of Machine Learning Deployment
Info: www.bizML.com
This 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 AUDIOBOOK
Pre-order The AI Playbook as a hardcover or e-book on Amazon – shipping February 6, 2024 – and receive a free advanced copy of the audiobook version now.
You'll also receive a copy of the audiobook for Eric Siegel's other book, Predictive Analytics, and free access to the first three modules of his online course, "Machine Learning Leadership and Practice: End-to-End Mastery" (a total of 39 instructional videos).
This offer ends January 12, 2024. Click here for details: https://www.machinelearningkeynote.com/the-ai-playbook-free-audiobook-offer
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 co-located conferences: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, and Deep Learning World.
MLW Vegas 2022: http://www.machinelearningweek.com
Predictive Analytics World for Climate Tech:
https://predictiveanalyticsworldclimate.com/
Predictive Analytics World for Industry 4.0 Munich: https://predictiveanalyticsworldindustry40.eu/
Predictive Analytics World for Healthcare Munich: https://predictiveanalyticsworldhealthcare.eu/
Deep Learning World Munich: https://deeplearningworld.de/
The history of these conferences -- from spawning the Target-predicting-pregnancy publicity debacle to getting dinged by the Hollywood action movie star Chuck Norris: https://www.predictiveanalyticsworld.com/machinelearningtimes/a-brief-history-of-paw-on-its-10-year-anniversary/9936/
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.
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.
“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 this episode, we cover this issue and provide guidance on tapping data’s potential without drawing false conclusions.
"Are Orange Cars Really not Lemons?" by John Elder and Ben Bullard, Elder Research, Inc.: www.elderresearch.com/orange-car
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 mainframe. But getting this done requires a specialized leadership practice and strong-willed change management.
For further details, see the article: https://www.predictiveanalyticsworld.com/machinelearningtimes/real-time-machine-learning-why-its-vital-and-how-to-do-it/12166/
See also this webinar on real-time machine learning: https://event.on24.com/wcc/r/3285703/7B596BEFB9D70F8AFF812858C322E5C0?partnerref=ESLI
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 positive and negative cases and usually be right about it.
See my Scientific American blog article to dig in further and access many links: https://blogs.scientificamerican.com/observations/the-medias-coverage-of-ai-is-bogus/
Watch my two-part video coverage of the accuracy fallacy:
https://www.youtube.com/watch?v=81Vv0J2Vw-Y
https://www.youtube.com/watch?v=ui3VkecTX3Y
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-rarely-deployed-industrywide-failure-machine-learning-leadership.html
https://www.kdnuggets.com/2020/10/machine-learning-omission-business-leadership.html
http://www.machinelearning.courses
http://www.theAIparadox.com
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?
Comments
Download from Google Play
Download from App Store
United States