Roger W. Hoerl

Sep 26, 2024 | sloanreview.mit.edu | Roger W. Hoerl |Thomas C. Redman

Related ReadingR.W. Hoerl and T.C. Redman, “,” MIT Sloan Management Review, Dec. 19, 2023. T.C. Redman and R.W. Hoerl, “,” MIT Sloan Management Review, April 16, 2024. It takes a lot to build and deploy AI models that work well. But when organizations put too much focus on the technology and the algorithms, they often overlook other essential elements, putting their programs at risk. Business leaders can increase the likelihood that their AI programs succeed by assuming a greater role themselves.

Sep 26, 2024 | tribunecontentagency.com | Roger W. Hoerl |Thomas C. Redman |Abbie Lundberg

Fuel AI Success With the Right Data and the Right People MIT Sloan Management Review & Report   September 26, 2024 By Roger W. Hoerl, Thomas C. Redman, and Abbie LundbergRoger W. Hoerl is the Brate-Peschel Professor of Statistics at Union College in Schenectady, N.Y., and coauthor of Leading Holistic Improvement With Lean Six Sigma 2.0. Thomas C. Redman is president of Data Quality Solutions and author of People and Data: Uniting to Transform Your Organization.

Apr 16, 2024 | tribunecontentagency.com | Thomas C. Redman |Roger W. Hoerl

People are often unsure why artificial intelligence and machine learning algorithms work. More importantly, people can’t always anticipate when they won’t work. Ali Rahimi, an AI researcher at Google, received a standing ovation at a 2017 conference when he referred to much of what is done in AI as “alchemy,” meaning that developers don’t have solid grounds for predicting which algorithms will work and which won’t, or for choosing one AI architecture over another.

Apr 16, 2024 | sloanreview.mit.edu | Thomas C. Redman |Roger W. Hoerl

AI and Statistics: Perfect Together Many companies develop AI models without a solid foundation on which to base predictions — leading to mistrust and failures. Here’s how statistics can help improve results. Carolyn Geason-Beissel/MIT SMR | Getty ImagesPeople are often unsure why artificial intelligence and machine learning algorithms work. More importantly, people can’t always anticipate when they won’t work.

Dec 19, 2023 | tribunecontentagency.com | Roger W. Hoerl |Thomas C. Redman

The power of AI and the machine learning models on which it is based continue to reshape the rules of business. However, too many AI projects are failing — often after deployment, which is especially costly and embarrassing. Just ask Amazon about its facial recognition fiascos, or Microsoft about its blunders with its Tay chatbot. Too often, data scientists write off such failures as individual anomalies without looking for patterns that could help prevent future failures.