Cyc is a controversial project. It was a bold attempt to come forward and try to put whatever the field has to offer into a single direction. Breakthroughs can be achieved only by such concentrated efforts.
The problem that motivated Cyc was the brittleness of programs. Many problems in expert systems, knowledge sharing, natural language understanding and human interfaces were due to the shallow of support behind the programs' primitive symbols. Cyc proposed to fix this problem by providing a deeper framework, in which each symbol would be situated within a rich ontology from which it can drive meaning.
Cyc was a child of symbolic AI. Its approach to knowledge representation uses a purely explicit symbolic framework. Its inference engines are rooted in the formalist tradition. It pushed these frameworks to their limits. It uncovered many potential problems that arise from the increasing magnitude of knowledge.
As it pushed certain methodologies to their limits, it opened up a need for new approaches. The approach I advocated in this paper is based on removing certain binding constraints that come from the symbolic AI tradition. I will conclude by giving a list of principles I would use to build the next Cyc.
Symbols are not the only kind of data structures that should flow in a system. There is room for other modalities.Deduction is not the only way to reach new facts. Using the appropriate modality to imagine situations and testing them by the efficient engines of the perceptual machinery can be the main inference engine.
Complementary representational systems can express what each one by itself cannot express.
Complementary representational systems can infer things that each one by itself could not infer efficiently.
Finally, higher level domains can be built as analogical layers on primitive domains, and inherit their inference efficiency.