The Dr. Data Show with Eric Siegel
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
Episodes

Monday Jan 24, 2022
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
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
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?