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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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
The biggest hurdle for data science teams isn't building the model; it's proving its dollar value. This presentation shows how a dental group could translate a no-show prediction model into a clear business case worth $$$
It's about shifting the conversation from abstract metrics to tangible ROI.
Henry Castellanos is a data scientist extraordinaire. He goes beyond establishing a strong technical performance for his ML models to also maximizing their business value. Let this sink in: Most data scientists don't do that – most ML projects don't plan and sell predictive AI deployment according to the the explicit business value.
Interestingly, Henry points out that using Gooder AI (www.gooder.ai) to do this even bucks up his own confidence in his models and their business value.
Listen to Henry's presentation to see exactly how to bridge the gap from ML to real-world value.
To view this presentation as a video, go to: https://youtu.be/BT-GnnuN3jA
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Predictive AI Usually Fails Because It’s Not Usually Valuated
Most predictive AI deployments are scrubbed. Why? They didn't forecast the potential value in business terms like profit or savings.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/11/18/predictive-ai-usually-fails-because-its-not-usually-valuated/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Predictive AI Only Works If Stakeholders Tune This Dial
Machine learning models can drive business operations to great benefit. But, to get there, stakeholders must determine how model probabilities trigger actions.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/11/25/predictive-ai-only-works-if-stakeholders-tune-this-dial/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
AI Drives Alphabet’s Moonshot To Save The World’s Electrical Grid
AI is pivotal as global utilities tackle a looming crisis with the electrical grid. Here's how Alphabet uses AI to help the world keep the lights on.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/10/07/why-we-need-ai-alphabets-moonshot-to-save-the-worlds-electrical-grid/
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/
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!
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/
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/
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/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The 3 Things You Need To Know About Predictive AI
Stakeholders involved with predictive AI must ramp up on a semi-technical understanding that comes down to 1) what's predicted, 2) how well and 3) what's done about it.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/06/29/the-3-things-you-need-to-know-about-predictive-ai/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Why You Must Twist Your Data Scientist's Arm To Estimate AI's Value
For every machine learning model that you consider deploying, make sure that your data scientists provide you with a full view of its potential business value.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/06/11/why-you-must-twist-your-data-scientists-arm-to-estimate-ais-value/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
The Rise Of Large Database Models
Even as large language models have been making a splash with ChatGPT and its competitors, another incoming AI wave has been quietly emerging: large database models.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2025/01/13/the-rise-of-large-database-models/
3 Predictions For Predictive AI In 2025 (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
3 Predictions For Predictive AI In 2025
1) GenAI hybrids, 2) ML valuation, 3) bizML—these advances will bring predictive AI back into the spotlight and further amplify its value.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2025/01/06/3-predictions-for-predictive-ai-in-2025/
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Alphabet Uses AI To Rush First Responders To Wildfires — Takeaways For Businesses
An initiative from Google’s parent company that rushes the National Guard to climate disasters stands ready to review aerial images from the LA wildfires.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2025/01/13/alphabet-uses-ai-to-rush-first-responders-to-disasters-takeaways-for-businesses/
Following up on my previous pod episode covering The AI Playbook, this time I ran my other book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, through NotebookLM and it spat out this artificial dialogue podcast episode.
Listen to this uncanny chat to hear an overview of the book and some specific highlights...
... or listen to it to see if you can identify any tricks behind NotebookLM that make this such an uncanny, real-sounding chat.
I did no work or customization here -- I just uploaded the book manuscript, pushed "go", and waited a few minutes (yes, fewer than the actually length of this artificial podcast episode).
I ran my book, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, through NotebookLM and it spat out this artificial dialogue podcast episode.
Listen to this uncanny chat to hear an overview of the book and some specific highlights...
... or listen to it to see if you can identify any tricks behind NotebookLM that make this such an uncanny, real-sounding chat.
I did no work or customization here -- I just uploaded the book manuscript, pushed "go", and waited a few minutes (yes, fewer than the actually length of this artificial podcast episode).
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
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
