Abstract datatypes and extensible RDBMS
In my recent Stonebraker-oriented post about database theory and practice over the decades, I wrote
I used to overrate the importance of abstract datatypes, in large part due to Mike’s influence. I got over it. He should too. They’re useful, to the point of being a checklist item, but not a game-changer. A big part of the problem is [that] different parts of a versatile DBMS would prefer to do different things with memory.
and then left an IOU for a survey of abstract datatypes/RDBMS extensibility. Let’s get to it.
Perhaps the most popular term was actually object/relational DBMS, but I’ve never understood the etymolygy on that one.
Although I call RDBMS extensibility a “checklist item”, the list of products that can check it off is actually pretty short.
- PostgreSQL has the granddaddy implementation.
- Its ideas were commercialized as Illustra, which was bought by Informix, which later was bought by IBM.
- Oracle has one of the major implementations.
- IBM has one of the major implementations.
- Sybase has struggled with implementing the technology.
- So did Microsoft SQL Server, which of course started with the Sybase code line.
Surely there are more, but at the moment I can’t really think of which they are.
| Categories: Database management systems, IBM, Informix, Ingres, Microsoft, Oracle, Sybase | 22 Comments |
AI memories — expert systems
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. Read more
| Categories: Artificial intelligence, Fun stuff | 8 Comments |
Historical notes on artificial intelligence
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. Read more
| Categories: Artificial intelligence | 5 Comments |