This is part of a three post series spanning two blogs.
- One post (this one) gives a general historical overview of the artificial intelligence business.
- One post 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.
0. The concept of artificial intelligence has been around almost as long as computers — or even before, if you recall that robots were imagined by the 1920s. But for a while it was mainly academic and perhaps military/natural security research. There’s been a robotics industry for over 50 years. But otherwise, when I first became an analyst in 1981, AI commercialization efforts were rather new, and were concentrated in three main areas:
- Expert systems.
- Natural language query.
- General AI underpinnings (especially LISP machines).
1. If I’ve ever gotten too close to a group of companies, it was probably the 1980s AI vendors. I unfortunately earned investment banking fees by encouraging people into money-losing investments in all three areas cited above, in Teknowledge, Artificial Intelligence Corporation and Symbolics respectively. I dated women who worked for Symbolics and Teknowledge. I wrote and performed a satirical song about Inference at an employee party for Intellicorp. Accordingly, when I write about individual companies in the sector, I fear that I may go on at self-indulgent length. So I’ll save all that for another time, and content myself now with a brief and dry survey that does little more than establish some context.
2. The 1980s also saw military-funded research into autonomous vehicles, as well as continued efforts in robotics and machine vision. Frankly, there wasn’t a lot of commercial overlap between these areas and the rest of AI at that time, and the rest of AI is what I tracked more closely.
But in one counterexample, a machine vision company named Machine Intelligence spun off a company that was building a PC DBMS with some natural language query capability. The spin-off company was Symantec. (Obviously, Symantec his pivoted multiple times since.) Machine Intelligence cofounder Earl Sacerdoti also wound up at expert system vendor Teknowledge for a while. So maybe there was more overlap in theory than there was in commercial practice.
Note: The fact that I’ve mentioned companies named “Artificial Intelligence” and “Machine Intelligence” should illustrate just how early in the AI industry’s history I’m discussing.
3. Also in the 1980s mix was Japan’s government-funded Fifth Generation Computer project. Let’s ignore that fiasco and move on.
4. The next three developments of note were, in no particular order:
- Expert systems and LISP both fizzled. Some of their ideas were co-opted by object-oriented programming, which was a huge success.
- Text search became real.
- Speech recognition became real, at least for the purpose of dictation.
Neural networks also arose in academia, which helped pave the way for today’s machine learning.
5. Text mining, later called “text analytics”, also eventually became a thing. I wrote about that extensively on the Text Technologies blog, so I won’t recapitulate here.
And that’s it for now. More later.
- My chief technical contact at Symbolics was the late and much-missed Dan Weinreb. His posts on Symbolics history seem to still be available via the Internet Archive.