• Don't lose the AI predictive forest for the genAI trees with Eric Siegel '98SEAS

  • 2024/06/13
  • 再生時間： 57 分
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Don't lose the AI predictive forest for the genAI trees with Eric Siegel '98SEAS

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  • On Episode 19 of the BRITE Ideas podcast, Matt speaks with Eric Siegel '93SEAS '95GSAS '95SEAS '98GSAS '98SEAS (ah, the multiple degrees that accumulate during a custom-designed PhD effort), former faculty at Columbia University, and author of Predictive Analytics and, his latest, The AI Playbook.

    At the core of the conversation is Eric's reminder that traditional AI/ML efforts -- "predicting who will click, buy, lie or die," etc. -- are highly effective and still underutilized AI tools within organizations. The hype of generative AI is certainly warranted, but Eric is eager to help the data scientists and managers of organizations learn together a business framework and paradigm so more established predictive AI models move beyond testing and into production. Common misunderstandings about the true predictive accuracy of AI are discussed as well as the vital role of ethical reviews of model use. In particular, Eric highlights how important it is to avoid using models that create higher false positive rates and thus deny or penalize groups without just cause.
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On Episode 19 of the BRITE Ideas podcast, Matt speaks with Eric Siegel '93SEAS '95GSAS '95SEAS '98GSAS '98SEAS (ah, the multiple degrees that accumulate during a custom-designed PhD effort), former faculty at Columbia University, and author of Predictive Analytics and, his latest, The AI Playbook.

At the core of the conversation is Eric's reminder that traditional AI/ML efforts -- "predicting who will click, buy, lie or die," etc. -- are highly effective and still underutilized AI tools within organizations. The hype of generative AI is certainly warranted, but Eric is eager to help the data scientists and managers of organizations learn together a business framework and paradigm so more established predictive AI models move beyond testing and into production. Common misunderstandings about the true predictive accuracy of AI are discussed as well as the vital role of ethical reviews of model use. In particular, Eric highlights how important it is to avoid using models that create higher false positive rates and thus deny or penalize groups without just cause.
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