This is part of a four post series spanning two blogs.
- One post gives a general historical overview of the artificial intelligence business.
- One post (this one) specifically covers the history of expert systems.
- One post gives a general present-day overview of the artificial intelligence business.
- One post explores the close connection between machine learning and (the rest of) AI.
As I mentioned in my quick AI history overview, I was pretty involved with AI vendors in the 1980s. Here on some notes on what was going on then, specifically in what seemed to be the hottest area at the time — expert systems. Summing up:
- The expert systems business never grew to be very large, but it garnered undue attention (including from me). In particular, the companies offering the technology didn’t prosper much.
- What commercial investment there was in expert system projects, successful or otherwise, foreshadowed some of what would be tried using other analytic technologies. Application areas included, among others, credit granting, financial trading, airline flight pricing and equipment maintenance.
- Technological reasons the industry failed included:
- The difficulties of debugging and maintaining a collection of rules.
- Lack of ability to crunch data, or to benefit from data crunching. (This is surely why few expert systems use cases were in the marketing area.)
- A paradigm that assumed the required rules pre-existed inside expert humans’ heads.
- There were some successful projects even so.
First, some basics.
- The core expert system metaphor was:
- Facts in.
- A question (often implicit) in.
- A recommended decision out.
- The essential technology of expert systems was what we’d now call a “rules engine”, but then was often called an “expert systems shell” or “inference engine”.
- The development process consisted of “knowledge engineers” talking to human experts and coming up with rules.
- Any product could handle 10s of rules. A good one could straightforwardly handle 100s of rules. 1000s of rules was a real test of performance, and only a few expert systems were that complex.
- The canonical research expert system was MYCIN, which advised as to which antibiotic to use for an infectious disease.
- The canonical research expert system shell was MYCIN’s successor EMYCIN.
- The other major expert system shell research project was from DEC (Digital Equipment Corporation). It was called R1, leading to the oft-repeated line “I wanted to be a knowledge engineer, and now I are one.”
- The expert systems business featured the “four horsemen” startups Teknowledge, Intellicorp, Inference Corporation and Carnegie Group. A few more companies came along later, including Aion and Neuron Data.
All combined, the expert system vendors didn’t accomplish much. However, there were a few successes in financial services, famously including credit-decision support at American Express. Airlines adopted the technology fairly vigorously, in areas such as scheduling and aircraft maintenance. There were tries in manufacturing too, including in materials selection (I forget the use case — something to do with composites) and, again, equipment maintenance. In general, a number of application categories — and this fits with the EMYCIN antecedent — could be characterized as having something to do with diagnosis.
The most remarkable expert system story I recall, however, was of something entirely built in-house. At a small conference in 1984 organized by John Clippinger, a guy from United Airlines said that they had built a system for flight pricing, and were gaining over $100 million/year from it. I just assumed he was misspeaking, but other people thought he was serious. Either way, it was a long time before United allowed the subject to be aired in public again.
In contrast, Teknowledge’s standard demo was stunningly trivial — a Wine Advisor, based on about 40 rules (if I recall correctly), selecting a wine to go with your hypothetical meal. When I suggested they develop a more serious demo, they pled resource constraints. This rang alarm bells for me about the difficulty of using the technology; I should have paid more attention to them.
Teknowledge was basically the company that commercialized EMYCIN. In general it was the most hyped-up of the expert system technology companies, with support from the relevant big-name Stanford professors and so on, especially Ed Feigenbaum. They raised a bunch of money (I got my biggest-ever investment banking bonus for helping) and got some visibility, but didn’t do much to overcome the technical problems I highlighted at the start of this post. Jerry Kaplan also got his first commercial experience there.
Intellicorp’s product KEE (Knowledge Engineering Environment, plus the obvious pun that Knowledge is Key) was more in the vein of STEAMER. The canonical KEE demo was what we’d now call a simple real-time BI dashboard — with dials and so on, so the dashboard metaphor could be taken pretty literally.* Intellicorp later pivoted from expert systems to object-oriented programming, and that was frankly a better architectural fit. Ed Feigenbaum’s name is also associated with them, but frankly I remember them more as being folks out of Texas Instruments (which had some AI efforts in the 1970s).
*Even so, KEE wasn’t used for much in the way of database query. I’ve now forgotten why.
Intellicorp also knew how to have fun. COO Tom Kehler led conference after-party sing-a-longs with his guitar. Workstations were named after famous disasters — Tacoma Narrows Bridge, Crash of ’29, Apollo 13 and so on. (The latter was said to have confused their Apollo salesman.) Managers put their desks in hallways, defying anybody who still had an office to complain about cramped quarters.
Inference Corporation marketed its rules engine ART on the strength of allegedly superior performance, because it was written in C and because it relied on the forward-chaining RETE algorithm rather than EMYCIN’s back-chaining. Sometime after they started telling the performance story, it actually became true. Even Inference didn’t get much out of the inference engine market, however, and eventually the product pivoted (unsuccessfully) to general object-oriented app development, while the company also pursued an effort in case-based reasoning.
The glossary to ART’s documentation is the first place I saw the entry
Recursion. See recursion.
I later stole that joke for my 1990s book on application development tools.
I had little contact with Carnegie Group — I don’t get very often to Pittsburgh — but I think it wound up focusing on the manufacturing sector.
Two other expert system companies are perhaps worth a mention:
- Aion came along slightly later than the four horsemen, and outlasted them, perhaps because its product was targeted at the IBM mainframe/enterprise market. Aion later merged with natural language query pioneer Artificial Intelligence Corporation to form Trinzic Corporation, which later was absorbed into Platinum Technology. Ironically, Bob Goldman ran Trinzic, while Platinum was of course founded and run by Andrew “Flip” Filipowski, Bob’s predecessor as the #2 guy at Cullinet.
- By the time Neuron Data came along, I’d stopped caring about the sector. But I do know that it eventually wound up owned by Fair Isaac, and rules engine zealot James Taylor — who wrote a book about same with Neil Raden — comes out of that effort.
And I think with that I’ll finish this post. If there’s enough interest, I can write up more information later.