Lenat's Google Talk has a lot of examples.

Among them would be giving Cyc a large amount of text and/or images to assimilate and then asking it questions like:

• Query: "Government buildings damaged in terrorist events in Beirut between 1990 and 2001." A moment's thought will reveal how complex this query actually is, and how many ways there are to answer it incorrectly, but Cyc gives the right answer.

• Query: "Pictures of strong and adventurous people." Returns a picture of a man climbing a rock face, since it knows that rock climbing requires strength and an adventurous disposition.

• Query: "What major US cities are particularly vulnerable to an anthrax attack?" This is my favorite example, because it needs to assess not only what "major US cities" are but also what the ideal conditions for the spread of anthrax are and then apply that as a filter over those cities with nuanced contextual awareness.

In general Cyc impresses me because it doesn't use of any kind of neural network architecture, it's just knowledge linked in explicit ontologies with a reasoning engine.

It's good at marketing.