The Semantic Folding Theory (SFT) is the attempt to develop an alternative computational theory for the processing of language data. While nearly all current methods of processing natural language use word statistics, Semantic Folding uses a neuroscience based mechanism of distributional semantics. After capturing a given semantic universe of a reference set of documents by means of a fully unsupervised mechanism, the resulting semantic space is folded into each and every word-representation vector. These vectors are large, sparsely filled binary vectors. Every feature bit in this vector not only corresponds but also equals a specific semantic feature of the folded-in semantic space and is therefore semantically grounded.

Did you miss a session from the Future of Work Summit? This article was contributed by Walter Bender, CTO and cofounder of Sorcero. Language AI is now ubiquitous. It helps us filter information everywhere, from search engines to chatbots. But when it comes to helping humans process information, AI has untapped potential.