Eric Siegel

Speech 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.

• 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.

• 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.

• 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, bestselling 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.

• How Predictive Analytics Fortifies Healthcare

  Predictive analytics addresses today’s pressing challenges in healthcare effectiveness and economics by improving operations across the spectrum of healthcare functions.

• 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.

• Algorithmic Bias in Healthcare Triage

  Predictive models generated by machine learning can inadvertently disenfranchise groups that are already disenfranchised. Often called "machine bias" or "algorithmic bias", this has been demonstrated as it applies to predictive policing and credit scoring (loan approvals). In a nutshell, the false positive rates end up differing between protected groups. In this talk, "Predictive Analytics" author Eric Siegel will describe how this same phenomenon can arise for healthcare triage.

• Machine Learning for Healthcare Heal Thyself: Most Models Don't Deploy, But They Could

  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.

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