CSD Faculty Candidate Teaching Demo

— 1:30pm

Location:
Remote Access - Zoom

Speaker:
Lin Chase

Teaching Demo

  • Candidate Talk — 25 February 2021 → 2:00 pm
  • Teaching Demo — 26 February 2021 → 12:00 pm

Multiple industry reports in recent years have shown than an inordinate number (85%) of artificial intelligence (AI) projects fail to produce their desired results.  Having worked in the heart of the effort to bring the power and benefits of AI to the enterprise world for thirty years, I have witnessed and come to understand the reasons for this failure “up close and personal.”  In this talk I’ll lay out an argument that the problem is not with the AI models and algorithms, but rather with the things that happen upstream, downstream, and all around the AI technology in the organizations attempting to adopt it.  In particular, I’ll focus on the less-than-ideal state of data engineering practices.  We’ll discuss in detail what a successful and comprehensive approach to data engineering for AI requires.  And I’ll share my thoughts on how Carnegie Mellon University can play a leading role in creating much improved real-world outcomes for the AI technologies it has played such a key role in inventing.

—

Lin Chase is an experienced “tech” executive with an extensive track record in the successful development and delivery of artificial intelligence, data, and networking technologies in complex business environments. She has spent thirty years applying emerging software technologies around the world in the US, Europe, the Middle East, India, and East Asia.

In early 2020, the COVID crisis chased Lin back to California from South Korea, where she was working on infusing the world’s first 5G network with AI capabilities. Not long after she returned, her friend Rebecca Bates (chair of the Integrated Engineering department at Minnesota State University, Mankato) lost a senior faculty member to sudden retirement. This combination of events led to Lin teaching undergraduate courses in networking, operating systems, and machine learning as an adjunct. She loved the experience so much that she decided to seek a full-time university position. Lin’s goal is to join a vibrant community in which she can teach, connect students with industry, and continue her industry-oriented writing projects. As an alumna of the School of Computer Science’s Robotics Ph.D. program, Lin is especially delighted to have the opportunity to interview at Carnegie Mellon. 

Faculty Hosts:  David Kosbie (CSD) / Charlie Garrod (ISR)

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