These talks introduce IEA to the various communities. Paper/presentation PDFs for selected talks are available by following the links.
July 24, 2018, “A Path To AI - The System View To Apply Machine Learning” presented at NSF/SRC joint workshop on Verification/Test of Machine Learning System, Washington DC.
August 17, 2018, “AI Toward Autonomous Test Engineering” presented at ITC Asia as a keynote speech, Harbin, China
September 7, 2018, “AI Assistant in Test and Diagnosis”, a keynote speech presented at SiP Global Summit in SEMICON Taiwan.
September 25, 2018, “From Applying Machine Learning To AI Assistant”, presented in an e-Workshop hosted by Semiconductor Research Corporation.
September 27, 2018, “Intelligent Assistant - A Journey to AI”, presented at Silicon Valley DFT workshop.
October 28, 2018, “Machine Learning for Test, and Test for Machine Learning – A Journey to AI”, a tutorial presented at ITC 2018 in Phoenix, Arizona.
Tutorial Handout PDF
October 31, 2018, “Concept Recognition in Production Yield Analytics”, technical paper presented at ITC 2018 in Phoenix, Arizona.
October 31, 2018, “An Autonomous System View To Apply Machine Learning”, technical paper presented at ITC 2018 in Phoenix, Arizona.
December 11, 2018, “The Hype and Fear of AI – Introduction to IEA”, Keynote speech at annual MTV workshop, Austin, Texas.
In addition to the keynote paper in the IEA Papers page, the background of the IEA project can also be found in the following tutorial/short course/talk presentations
April 24, 2015, “Big Data Analytics in VLSI Design Automation and Test”, presented a short course.
At this stage, our view for “applying machine learning” was mainly on “knowledge discovery” - slides 18-23 summarize such a view. At this point, we had not yet figured out how to automate the entirety of a knowledge discovery process. Hence, we would not call it as an “Autonomous System” yet.
October, 2016, “Towards Autonomous Analytics in Test Engineering”, presented at EWDTS.
Early on, the idea of building an “autonomous system” to apply machine learning on test data was also presented in a talk at EW D&T Symposium (EWDTS).
September 11, 2017, “Machine Learning for Design and Manufacturing Automation”, a short course presented at an AI seminar.
A short version was presented in other places later. At the end of this tutorial, we conclude that “applying machine learning” would lead to building an “Autonomous System,” which led us to the current IEA project.
In short, it took years for the research to evolve to the current point in 2018, where we believe that building an IEA system makes sense for applying machine learning in an application. In the upcoming talks, we will explain this journey more clearly and show why building an IEA system is really not by choice, but as an implied result naturally occurring in our research evolution process, starting from the original motivation to “apply machine learning” in an application many years ago.
Electrical & Computer Engineering
University of California, Santa Barbara