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? Yup, it’s the most important – yet most 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 bridges the business and tech sides of machine learning. He is the founder of the Predictive Analytics World and Deep Learning World conference series, which have served more than 17,000 attendees since 2009. As the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery”, a winner of teaching awards as a professor, and a popular speaker, Eric has given more than 110 keynote addresses. The executive editor of The Machine Learning Times, he wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been adopted for courses at hundreds of universities. 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 – including op-eds on analytics and social justice. Follow him @predictanalytic. https://www.predictiveanalyticsworld.com http://www.machinelearning.courses http://www.thepredictionbook.com

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Episodes

Monday Apr 04, 2022

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/

Monday Mar 21, 2022

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.

Monday Feb 28, 2022

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.

Thursday Feb 10, 2022

“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

Monday Jan 31, 2022

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

Monday Jan 24, 2022

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  

Friday Jan 14, 2022

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      

Friday Jan 14, 2022

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?

More from Eric Siegel:

 
COURSE:
This end-to-end, three-course series will empower you to launch machine learning. Accessible to business-level learners and yet vital to techies as well, it covers both the state-of-the-art techniques and the business-side best practices.
 
BOOK:
A bestseller | 12 translations | 6 book awards
 
CONFERENCES:
The premier machine learning conference series
 
NEWS SITE:
The machine learning professionals' leading resource

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