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 ...
To Deploy Predictive AI, You Must Navigate These Tradeoffs (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
To Deploy Predictive AI, You Must Navigate These Tradeoffs
Before deploying predictive AI, you must strike a balance between competing business factors. Here's how.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/08/27/to-deploy-predictive-ai-you-must-navigate-these-tradeoffs/
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3:53
Predictive AI Meets GenAI: Michael Griebe on How to Balance between ML Models and LLMs in a Data Science Career
Welcome to the very first Dr. Data Show episode with a guest: Michael Griebe, Chief Data Officer at Hahn Agency!
With the great changes brought to data science careers by genAI, Michael addresses pressing open questions that should be top of mind for every data scientist:
One compelling reason to focus on predictive AI more than genAI.
Why it's a safe bet that some consumer-facing LLMs will eventually be given too much "power," resulting in unexpected exposed vulnerabilities and costly lessons – and one way in which we can now work against that.
How Michael's team created a pilot AI assistant in less than a day – and then launched it into wide-scale production in less than a week.
Excuse my furious typing noises (as I absorbed Michael’s compelling input) and I hope you find his insights as exciting as I did!
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53:21
How Generative AI Helps Predictive AI (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
How Generative AI Helps Predictive AI
Large language models can act as predictive models. Here's an example for misinformation detection—and an introduction to savings curves.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/08/21/how-generative-ai-helps-predictive-ai/
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5:26
The Quant's Dilemma: Subjectivity In Predictive AI's Value (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The Quant's Dilemma: Subjectivity In Predictive AI's Value
When machine learning fails to detect misinformation, medical conditions or spam, the cost of each error is subjective. Here’s how to apply predictive AI nonetheless.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/09/30/the-quants-dilemma-subjectivity-in-predictive-ais-value/
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5:51
The Great AI Myth: These 3 Misconceptions Fuel It (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The Great AI Myth: These 3 Misconceptions Fuel It
The impending arrival of artificial general intelligence is a story of wish fulfillment that lacks concrete evidence.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/07/29/the-great-ai-myth-these-3-misconceptions-fuel-it/
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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