The Dr. Data Show with Eric Siegel

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?