Eric Siegel is a machine learning expert and former Columbia University professor who won awards teaching the graduate-level AI courses. He is the author of The AI Playbook and the bestselling Predictive Analytics – both acclaimed for making machine learning accessible and captivating. He also founded the long-running Machine Learning Week conference series and created a popular online course series.
Session Descriptions:
How Machine Learning Delivers on the Promise of AI
The excitement over machine learning and AI has reached a fever pitch. But what is the value, the function, the purpose? The most actionable win to be gained from data is prediction. This is achieved by analytically learning from data how to render predictions for each individual. Such predictions drive more effectively the millions of operational decisions that organizations make every day. In this keynote, Machine Learning Week founder and bestselling author Eric Siegel reveals how machine learning – aka predictive analytics – works and the ways in which it delivers value to organizations across industry sectors.
The AI Playbook: How to Capitalize on Machine Learning
The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. In this keynote, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. And he illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms.
Most Machine Learning Projects Fail to Deploy – Here’s the Remedy
Industry leader Eric Siegel’s latest research shows most models generated with machine learning to improve business operations in a new way never deploy. It turns out that machine learning operationalization – which changes existing processes in order to improve them – takes a lot more planning, socialization, and change-management efforts than most ever begin to realize. The problem is more in leadership than in technology. In this talk, Eric will outline the required practice needed to run ML projects so that they successfully deploy and deliver a business impact.
How Machine Learning Reduces Risk in Financial Services
The gold standard method for leveraging data to reduce risk – in credit, insurance, and other lines of business – is machine learning. The predictive models this technology generates reduce risk, cut costs, and boost profit. In this keynote address, bestselling author and former Columbia University professor Eric Siegel will clearly demonstrate exactly what is learned from data and how enterprises apply what’s learned to improve the business metrics that matter most in the financial services sector.
The High Cost of AI hype
Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. In this keynote address, author Eric Siegel shows that, for most ML projects, the term “AI” goes entirely too far — it alludes to human-level capabilities. By unpacking the meaning of “AI,” he’ll reveal just how overblown a buzzword it is
Five Ways to Lower Costs with Machine Learning
Question: How does machine learning actively deliver increased returns? Answer: By driving operational decisions with predictive scores – one score assigned to each individual. In this way, an enterprise optimizes on what customers WILL do.
But, in tough times, our attention turns away from increasing returns, and towards decreasing costs. On top of boosting us up the hill, can machine learning pull us out of a hole? Heck, yes. Marketing more optimally means you can market less. Filtering high risk prospects means you will spend less. And, by retaining customers more efficiently, well, a customer saved is a customer earned – and one you need not acquire.
In this keynote, Eric Siegel will demonstrate five ways machine learning can lower costs without decreasing business, thus transforming your enterprise into a Lean, Mean Analytical Machine. You’ll want to run back home and break the news: We can’t afford not to do this.
Uplift Modeling: Optimize for Influence and Persuade by the Numbers
Data driven decisions are meant to maximize impact – right? Well, the only way to optimize influence is to predict it. The analytical method to do this is called uplift modeling (aka, persuasion modeling). This is a completely different animal from standard predictive models, which predict customer behavior. Instead, uplift models predict the influence on an individual’s behavior gained by choosing one treatment over another. In this session, Machine Learning Week founder Eric Siegel provides an introduction to this growing area.
How to Know Your Data Discoveries Are Not BS (Bad Science)
“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 – known as p-hacking: Testing many predictors means taking many small risks of being fooled by randomness, adding up to one big risk. In this keynote, PAW founder Eric Siegel will cover this issue and provide guidance on tapping data’s potential without drawing false conclusions.
Predictive Analytics: Delivering on the Promise of Big Data
The excitement over “big data” has grown dramatically. But what is the value, the function, the purpose? The most actionable win to be gained from data is prediction. This is achieved by analytically learning from data how to render predictions for each individual. Such predictions drive more effectively the millions of operational decisions that organizations make every day. Eric Siegel reveals how predictive analytics works, and the ways in which it delivers value to organizations across industry sectors.
About Eric Siegel:
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, 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 also publishes op-eds on analytics and social justice.
Eric has appeared on Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. Eric and his first book have been featured in Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, The Financial Times, Forbes, Fortune, GQ, Harvard Business Review, The Huffington Post, Luckbox Magazine, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.
