Dr. Eric Siegel, Founder of Machine Learning Week, on 6 steps to usher in successful ML projects
Feb 12, 2024
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Dr. Eric Siegel, leading consultant and former Columbia University professor, shares a 6-step process for successful machine learning projects. Topics covered include the distinction between generative AI and predictive machine learning, the lesser known aspect of FICO's business, and controversial predictions and the future of AI.
The term 'AI' lacks a concrete definition, hindering progress in the field of engineering and leading to AI winters.
Dr. Eric Siegel's book 'The AI Playbook' provides a framework for successful machine learning projects, emphasizing the importance of business value and specific steps for project success.
Deep dives
The Problem with the Term 'AI'
The speaker argues that the term 'AI' is problematic in an engineering context because it lacks a concrete definition. Calling things 'AI' leads to AI winters and hinders progress. Concrete definitions are necessary for building and advancing AI technologies.
The AI Playbook for Successful Machine Learning Projects
Dr. Eric Siegel, a leading consultant and former professor, discusses his book 'The AI Playbook' which provides a standard business practice framework for running machine learning projects. The book aims to help business stakeholders and data scientists successfully deploy machine learning initiatives by focusing on the business value and the specific steps required for end-to-end project success.
Generative AI vs Predictive Machine Learning
The speaker highlights the difference between generative AI and predictive machine learning. Generative AI focuses on creating first drafts, while predictive machine learning improves existing operations by making predictions on the behavior or outcome of individuals. The speaker argues that generative AI has captured public imagination due to its resemblance to human-like abilities, but it may not have as much real-world value as predictive machine learning.
Ethical Concerns in AI and Machine Learning
The speaker explores the ethical implications of AI, particularly in relation to bias, discrimination, and privacy. They emphasize the responsibility of data scientists and algorithm developers to be aware of and mitigate human biases that can be replicated by AI systems. They advocate for a proactive approach to ethics, considering it a form of social justice and an opportunity to address historical injustices.
Dr. Eric Siegel 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 World, 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.
Listen and learn
How he’s progressed in the field of machine learning over 30 years
6-step process to usher in machine learning programs from conception to deployment
What 3 things non-technical people in business should know about how machine learning works & delivers value
How to know when to use classical machine learning vs generative AI to solve a data problem
How to mitigate the impact of human bias in shaping AI