Theatre Terminology
Pronounced "sike"
Short for cyclorama. There are several kinds:

fly cyc: a u-shaped expanse of light blue or white canvas enclosing the acting area on three sides. It’s raised and lowered by a counter weight system. Used for sky effects.
trip cyc: similar to a fly cyc, but will fold as it is raised requiring less grid height.
linnebach cyc: This one is suspended from a curved track and moves horizontally to store in the wings. It’s named after Adolph Linnebach.
(Disclaimer: everything in this w/u is drawn from my imperfect human memory, from a magazine article I read over ten years ago. YMMV)

CYC (named from enCYClopedia) was an AI knowledge base whose developers were teaching it much in the way one would teach a human child - by teaching it facts and relationships, and how to build them together. The most memorable line from the article was the conclusion drawn by CYC when presented with the fact of Napoleon's death, "Wellington was saddened."

History of Cyc

In 1984, virtually every university with a Computer Science department had one or two Artificial Intelligence projects, some so ambitious that their leaders were positive they would surpass human reasoning in a matter of years. Dr. Douglas B. Lenat was a professor at Stanford's CS department who had his own radical ideas about the exciting new field, ideas based in the current state of the art rather than the creation of something entirely new. Lenat envisioned a twenty year long, twenty-five million dollar program that would produce something unprecedented in the world of AI: a program that had common sense. He named the project Cyc, after encyclopedia.

Since Dr. Lenat knew that no university would be willing to take on such a large investment in time and money, he decided to look for money outside of academia. He found his funding in the Microelectronics and Computer Technology Corp., a Department of Defense research initiative also backed by a few large corporations. They gave Lenat a long-term contract and he begun work on Cyc, eventually hiring a staff of fifteen people to work on the knowledge base. As it turned out, many of the ambitious claims about AI turned out to be false, and the bottom dropped out of the market in the late 80s. Fortunately, Dr. Lenat's contract was for longer than that depression, and he was able to continue work on Cyc all the way through it.

By 1995 Lenat's project was up to thirty employees, from areas as diverse as linguistics, philosophy, and anthropology, all adding facts about human existence to the database daily. He decided to create a company, Cycorp, to market Cyc-based products and help integrate them with customers' systems. Today Cycorp is still working hard on the knowledge base, having entered over one million facts in the past fourteen years. Doug Lenat is also at work finding new frontiers his software can be applied to, and designing the system extensions that will allow them to go there.

How Cyc Works

The Cyc knowledge base is built entirely out of what Cycorp calls constants, which are written in CycL, a proprietary language that's based on first-order predicate calculus. Constants are simultaneously the rules which the knowledge base uses in inferencing, and the subjects about which inferences are made. That is to say, each constant is self-descriptive, but it also has relations to all of the other constants, so the Cyc evaluator knows how it relates to the rest of the constants in the knowledge base. These CycL constants are as diverse as things, collections, attributes, relationships, functions, etc., and when put together they are Cyc's sum knowledge about the real world, and the root of its apparent common sense.

Since CycL constants are written in a form of predicate calculus, they relate to one another with strict logical formality. This allows well-defined relationships to be found between any two constants in the knowledge base. Also, logical inferences about the constants can be synthesized within the knowledge base to create information that wasn't explicitly entered into it. Inferencing is the basis of Cyc's apparent ability to reason, but since it follows the rules of logic (modus ponens, resolvable existential quantification, etc.) strictly, all the new information is tractable and can be understood by a human if necessary.

For a new concept to be introduced to Cyc, it must obviously be stated in terms of a CycL constant. Once entered, that constant will be integrated into the rest of the database, again using the rules of logic. Resolution is done against all of the pre-existing knowledge to make sure the new constant is at least a prima facie valid description of reality. If resolution succeeds, the constant becomes a part of the knowledge base, and thus can itself be inferenced upon.

When information must be synthesized for resolution (i.e. the new constant was created from a question) it exists as an existential quantification within the knowledge base. This quantification is automatically skolemized and resolved, and the resolution is returned to the user.

Applications of Cyc

Cyc's ability to use human-like sensibilities to interpret data makes it perfect for the currently popular field of Data Mining. Data Mining involves parsing a database that's much too large to be evaluated by a human, and extracting only the very little bit that is meaningful for the task at hand. Traditionally, this is done by a programmer, who writes SQL queries that look through the database and match one or two features to the rules of what's needed. While this approach works well for simply defined categories of data (e.g. doctors with last name Smith, patients over the age of 60), it doesn't begin to work with more abstract rules.

Cyc already has knowledge about the real world. When it is granted knowledge about the database to be mined (usually as an interface programmed by Cycorp), it can synthesize the two and get database results that make sense in reality. The data flow might look something like this: A user asks a question about the database to the Cyc front-end in plain English. Cyc's Natural Language model converts the query into a statement in CycL, Cyc's proprietary description language. That statement is back-chained with the rest of the knowledge base until it matches information that's actually in the database. All of the necessary fields in the database are acquired using SQL queries built into Cycorp's interface. Finally, the information is reified with the rest of Cyc's knowledge base to make sure the results are consistent with reality, and returned to the user.

Another, related, field that Cyc can be used for is that of classifying opaque information, media such as sound clips, video, pictures, or any other non-text data. In the past this has been done by the extremely old-fashioned method of a card catalog, via captioning. The data was given a small text description (a caption), and the descriptions were cross-referenced based on each word. Thus, a reporter looking for stock pictures of bad used cars for a newspaper article would search for, say, Mazda, and then leaf through the pictures, picking out all of the clunkers on file. The reporter would probably go on to look through all the Fords, Chryslers, and so forth to find all the pictures he or she needed for the article.

If the data was stored in a database with Cyc as a front end, this search would be much easier for the reporter. Instead of doing a handful of searches and leafing through hundreds of photographs, the journalist could simply make a query like "Used cars that you wouldn't want to buy." As with the Data Mining application, Cyc would turn this into a query in its own language, evaluate it against the reality of bad used cars, and return a list of photographs from the database. Since the photographs had already been captioned for the old-fashioned system, relatively little work would have to be done to create the database that Cyc would use.

These two applications are the ones Dr. Lenat is advocating most heavily for Cycorp's business, but there are plenty of other possibilities. Indeed, Lenat sees an eventual use for Cyc in every situation where a human needs to communicate with a computer. Applications would virtually overnight start making much more sense and being more intuitive to use, a copy of Cyc running behind each of them, overseeing their interface with the user.

A Prehistory of Cyc
The Cyc project was actually incepted by Alan Kay at the Atari Research Centre in the 70's. Kay was planning to build a large computerized collaborative encyclopedia of knowledge in an experimental project for Atari.

Sound familiar?

He approached Dr. Douglas B. Lenat for his prowess in the field of artificial intelligence to see if he could make a contribution to the database. It was then that Lenat suggested "automating the white spaces" within the encyclopedia. What he meant was programatically applying common sense rules to the database such as "water cannot flow uphill" and "two people cannot be in the same place at the one time".

As we all know, Atari hit a brick wall in the early 80's and one of the first departments to be struck off was research, so Lenat moved the project to Microelectronics and Computer Technology Corp.

Read Enth's writeup on the history of and application of Cyc.

What stage is Cyc at today?
Cyc has now become so "intelligent" that it scans it's own texts and adds the information to itself, it only requires the grammatical rules of the language to be applied by the researchers.

However, critics of Cyc such as Hubert Dreyfus have described it as the "last bastion of top down, strong artificial intelligence" in that it will fail to produce humanlike intelligence the same way all other top down AI projects are "failing" ie. that the human mind is just a complex symbol processing machine.

However, this is a different debate.

Much information from Paradigms Regained by John L. Casti, a great read.

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