Originally published in Forbes, June 29, 2024. Some problems are better solved with predictive AI than with generative AI. To run more effectively, many business operations need prediction more than they need the generation of new content. That’s why predictive AI is the kind of AI companies turn to for improving the effectiveness of large-scale processes. Predictive models decide whom
Originally published in Forbes, July 7, 2024. The National Guard has its work cut out. As first responders to wildfires, floods and other increasingly common catastrophes, Guardsmen must rapidly deploy precisely where needed. This presents one of the...
“Nearly half of S&P 500 companies have talked about AI during earnings calls since May , an NBC News analysis of S&P 500 earnings calls found. In fact, AI was mentioned about as often as the Federal...
Originally published in Built In, May 22, 2024. Your biggest operations are made up of many small decisions. Predictive modeling can help you make them. Predictive AI is the technology businesses turn to for boosting the performance...
Originally published in Forbes, June 11, 2024. If you’ve ever had a data scientist make a machine learning model for you, you probably first experienced excitement, followed by bewilderment. The potential power is awesome. In its enterprise...
Originally published in Forbes, March 4, 2024. Which kind of AI should companies focus on—generative AI, which produces writing, computer code, images, video and other content, or predictive AI, which targets ads, marketing, fraud detection, risk management,...
Originally published in Forbes, March 25, 2024. To do its job, AI needs your help. It has the potential to drive millions of operational decisions—such as whom to contact, approve, investigate, incarcerate, set up on a date...
Originally published in Forbes, April 10, 2024 When OpenAI’s board momentarily ousted Sam Altman from his post as CEO last November, the media obsession was… intense. Why so much fuss about a corporate drama? The public mania—and...
Originally published in KDnuggets. Eric Siegel highlights the chronic under-deployment of ML projects, with only 22% of data scientists saying their revolutionary initiatives usually deploy, and a lack of stakeholder visibility and detailed planning as key issues,...
This article from Morgan Vawter, Global Vice President of Data & Analytics at Unilever, serves as the foreword to The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, by Eric Siegel. Morgan will...
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