EPIC AI Failures; Two AI Medical Case studies, MYCIN and WATSON
Abstract: EPIC, describing AI Failures are carefully worded and selected and meant to consider the level of failure when understanding MYCIN, a significant and historical rules-based LISP expert system. MYCIN began in 1974 with a team of Stanford MD's and PhDs led by Bruce G. Buchanan and Edward H. Shortliffe, spanning 10 years of MYCIN experiments and culminating with a publication of the classic MYCIN AI study, "RULE-BASED EXPERT SYSTEMS: THE MYCIN EXPERIMENTS OF THE STANFORD HEURISTIC PROGRAMMING PROJECT" by Buchanan and Shortliffe. For my Masters work in the mid80's, I was given the assignment to read (748 pages!), write an analysis of this work, and to write in LISP a backward-chain inference engine. The book focused on the need for MD's to rapidly identify both the specific bacteria infection agent and corresponding antibiotics.
Twenty two years after MYCIN was released and no longer used by MD’s, my sister, father, and mother were all victims of the lengthy time (~ 3 days) hospitals and labs required to incubate (in a petri dish) and identify both the infectious bacteria and the effective antibiotic. Three members of my family passed after 2 days and just before the answers came in from the lab. Hence my frustration. We are in the year 2024, and the same test was applied in the 80's but the number of effective antibiotics is reduced! In some cases, referenced to the number zero. IBM's WATSON was introduced in 2011 as the AI question-answering computer that beat Ken Jennings in "Jeopardy". IBM focused WATSON on answering significant medical problems and we will discuss why this system failed even after IBM spent over 60 million dollars and created Medical partnerships. To end on a positive note, we will mention embedded ML and prove smart sensors have been measurably successful. You will find this well-referenced speech fascinating!
Bio: The speaker, Joe Jesson, co-founded & was CTO of a General Electric business, Asset Intelligence, a GE business that designed
and sold remote IoT sensors for the logistics and energy sectors. Machine learning and LPWAN sensor communication became an
integral part of the remote monitoring and management of mobile and remote assets. An ongoing research goal is to reduce the smart
energy costs where 100% of the power is generated by ambient energy harvesting. Joe is currently CEO of RF Sigint Group and has
over 25+ years of engineering and management experience with Motorola APD, Oak Technology, BP, and General Electric. Master's
degree from DePaul University & working on a Ph.D.defense. Contact: [email protected]
Note: this is a TCF Event – No need to register, but you need to purchase a TCF all-day admission ticket at https://tcf-nj.org/
Room: ED204, Bldg: Education Building , The College of New Jersey, Metzger Dr, Ewing, New Jersey, United States, 08618
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