“Eric Siegel... makes machine learning easy to understand.”
—Charles Duhigg on Siegel’s The AI Playbook
“Eric Siegel literally wrote the book on predictive analytics.”
—Walter Isaacson, Trailblazers
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.
Experience: Eric has been commissioned for 110+ keynotes (full list) at events across industry sectors: ad tech, sales, marketing, market research, e-commerce, manufacturing, financial services, insurance, fraud auditors, high tech, news media, healthcare, pharmaceuticals, government, human resources, human services, restaurants, travel, real estate, construction, and law – plus executive, university, and analytics vendor conferences.
For more info about Eric Siegel, click here.
Eric Siegel, Keynote Speaker
Making AI and machine learning accessible and captivating
Style and Content
Eric's presentations on AI, machine learning, and predictive analytics are understandable to all audience members. He keeps it relevant, engaging, and entertaining.
And yet Eric delves down enough to concretely demonstrate how machine learning works: How it actionably delivers business value – including example case studies – and how it works under the hood. Eric's keynotes are concrete and substantive, diving deep rather than only invoking the often-heard generalities and buzzwords surrounding AI.
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Available Keynote Topics
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.
More Available Keynote Topics
Seven Reasons You Need Machine Learning Today
Machine learning has come of age as a core enterprise practice necessary to sustain competitive advantage. This is the technology that enacts a wholly new phase of enterprise evolution by applying organizational learning, which empowers the business to grow by deploying a unique form of data-driven risk management across multiple fronts. In this keynote, industry leader Eric Siegel reveals seven strategic objectives that can be attained to their full potential only by employing machine learning, namely Compete, Grow, Enforce, Improve, Satisfy, Learn, and Act.
Machine Bias: The Inequity in Predictive Models
In this presentation, author Eric Siegel – who has published a dozen op-eds on responsible machine learning – will show exactly what is often referred to as "machine bias": unequal false-flag rates across groups, affecting consequential decisions such as who's incarcerated and who's approved for a loan. Further, he'll also cover a more fundamental precursor issue, which strangely is rarely discussed and as-yet unresolved: discriminatory models, whereby machines explicitly base decisions on a protected class, treating people differently on that basis. Until there's consensus ruling out that practice, agreement on the more inneundoed topic of machine bias will never be possible.
How Machine Learning Fortifies Healthcare
Machine Learning addresses today’s pressing challenges in healthcare effectiveness and economics by improving operations across the spectrum of healthcare functions, including:
Clinical services and other healthcare management operations such as targeting screening and compliance intervention
Insurance pricing and management
Healthcare product marketing
Applied in these areas, machine learning serves to improve patient care, reduce cost, and bring greater efficiencies. In this keynote address, Eric Siegel will cover today’s rapidly emerging movement to fortify healthcare with data science’s biggest win: the power to predict.
Pitfalls: the Seven Deadly Sins of Machine Learning
It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections. And yet, we still live in a primordial era of errors. Many or most machine learning projects fail to deliver value because, time and again, certain recurring, treacherous pitfalls derail machine learning projects. In this keynote, former professor, "Predictive Analytics" author, and industry leader Eric Siegel will cover seven common machine learning pitfalls – each one like a boobytrap, a common mistake waiting to happen. It's a deadly sin indeed if it leads you to draw a false conclusion, misinterpret results, optimize the wrong thing, or mess up the data in the first place. Circumventing these major pitfalls is the lifeblood of machine learning because to avoid them is to get machine learning to actually work.
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