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Improving AI with Transfer Learning Featuring Frederic Van Haren | Utilizing AI: 2×07

Productive use of AI requires the application of existing models to new applications through a process called transfer learning. In this episode, High-Performance Computing and AI Expert Frederic Van Haren joins Stephen Foskett to discuss the topic of transfer learning and what it means, from voice recognition to autonomous driving and enterprise applications. Transfer learning is analogous to the way teachers impart knowledge and experience to their students, and represents a feedback loop that improves the model over time. This is a valuable concept for applications like language processing but requires a feedback mechanism or it is something of a dead end. One challenge for machine learning is that models do not truly understand the world the way people do, but they can fool us into thinking that they do because of their uncanny ability to match patterns the way we would. Over time, we all must develop a better understanding of this technology even as it is being widely deployed around us.

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About the author

Stephen Foskett

Stephen Foskett is an active participant in the world of enterprise information technology, currently focusing on enterprise storage, server virtualization, networking, and cloud computing. He organizes the popular Tech Field Day event series for Gestalt IT and runs Foskett Services. A long-time voice in the storage industry, Stephen has authored numerous articles for industry publications, and is a popular presenter at industry events. He can be found online at,, and on Twitter at @SFoskett.

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