We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production.
In a similar vein, the hype around machine learning in the early 2010s led to lots of hype around the technology, but a lot of the value did not pan out. Four years ago, VentureBeat showed that 87% of data science projects did not make it into production. And in a lot of ways, things haven’t gotten much better. And if we don't learn why that is the case, generative AI could be destined to a similar fate.
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 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, 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 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.
In the episode, Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, the skill gap between business stakeholders and data practitioners, use cases of organizations have leveraged machine learning for operational improvements, what the previous machine learning hype cycle can teach us about generative AI and a lot more.
Links Mentioned in the Show:
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#163 Upgrading Company Culture Using The Geek Way with Andrew McAfee, Principal Research Scientist at the MIT Sloan School of Management
#162 Scaling Data Engineering in Retail with Mohammad Sabah, SVP of Engineering & Data at Thrive Market
#161 Fighting for Algorithmic Justice with Dr. Joy Buolamwini, Artist-in-Chief and President of The Algorithmic Justice League
#160 Adapting to the AI Era with Jason Feifer, Editor in Chief of Entrepreneur Magazine
#159 Building Trustworthy AI with Beena Ammanath, Global Head of the Deloitte AI Institute
#158 Building Human-Centered AI Experiences with Haris Butt, Head of Product Design at ClickUp
#157 Is AI an Existential Risk? With Trond Arne Undheim, Research Scholar in Global Systemic Risk at Stanford University
#156 Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
#155 Building Diverse Data Teams with Tracy Daniels, Chief Data Officer at Truist
#154 Building Ethical Machines with Reid Blackman, Founder & CEO at Virtue Consultants
#153 From Data Literacy to AI Literacy with Cindi Howson, Chief Data Strategy Officer at ThoughtSpot
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#152 How Data can Enable Effective Leadership with Dr. Constance Dierickx, The Decision Doctor
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#150 Unlocking the Power of Data Science in the Cloud
#149 Expanding the Scope of Generative AI in the Enterprise with Bal Heroor, CEO and Principal at Mactores
#148 Why AI is Eating the World with Daniel Jeffries, Managing Director at AI Infrastructure Alliance
#147 The Past, Present & Future of Generative AI—With Joanne Chen, General Partner at Foundation Capital
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