This is a broad question. On the one hand, we are distinguished by our people (and executives), products, and philosophy. But those answers are found elsewhere on this site, so instead we will focus in this FAQ on how Cyc compares to ML AI solutions.
Symbolic Reasoning versus Machine Learning
Cyc leverages symbolic reasoning rather than machine learning (ML). Symbolic reasoners were dubbed Good Old Fashioned Artificial Intelligence (GOFAI) by John Haugeland. In short, ML approaches were pioneered in the late ’50s and ’60s and allow computers to ‘learn’ by training over large data sets. In contrast, GOFAI systems start with a logical representation of knowledge and then search and perform inference over this knowledge to come to conclusions.
GOFAI and ML have different strengths and weaknesses. ML shines when there are large, representative data sets. This is perfect for tasks like determining what movies Netflix should recommend to users. GOFAI shines when outputs can and must be explained, and when there is value to be gained in re-using the representations. This is why Cycorp has targeted areas with high stakes and regulation: energy, healthcare, and fintech.
While we distinguish our approach from ML-centric AI, we do not want to disparage ML. To the contrary, we firmly believe that solving artificial general intelligence will require a solution with both ML and GOFAI components. Many of our clients utilize Cyc to fill in the gaps left by ML solutions. While ML can harness massive datasets and computing power to find novel solutions, Cyc can serve as a check against those systems. Cyc can apply its understanding of the world to sanity check ML outputs, or to work from first principles in areas with sparse or bad data that defies ML approaches.
For a more in-depth discussion of how Cyc compares to ML approaches, read this white paper.