Computing with Knowledge

 

Back in the day, there were two distinct paths for making computers "think." The first was a family of inventions that includes multi-layer perceptrons, neural nets, genetic programming and machine learning. These are forms of curve-fitting. If you have ever performed a linear regression then you are familiar with curve-fitting: one graphs a set of points and derives a linear equation that models their behavior. Just a few years ago, higher order curve-fitting appeared as the science behind deciding what kind of music you'd like to hear next, or what else you might want to buy. It's gotten a lot more sophisticated, and is now called "AI."

The other old-school approach to making computers "think" is the product of linguistics and epistemology. Language loosely captures knowledge. Use of a computer to model language and interact with language was called Natural Language Processing (NLP). In the 1970s, classical NLP was a popular pursuit, and it seemed like an academic avenue that would bear intelligent computing more quickly than curve-fitting. But, NLP was difficult, brittle and not scalable. Excitement waned and other forms of artificial intelligence, such a Expert Systems, took the limelight until their prospects dimmed as well. NLP has since come to mean other things like text summarization, translation, data mining and command-and-control, like Siri.

Brainhat is not AI in the current sense

Brainhat is a lonely adult orphan of this second approach to teaching computers to "think," thirty years in the making. It treats human language like a programming language. The problems of brittleness and scale are addressed with massive coarse-grained parallelism. A Brainhat instance can include an unlimited number of computers, each with their own knowledge domains. And, that same swarm of machines can serve many users, all at once, across the globe. Where toy implementations with old-school NLP systems fail, Brainhat approaches the problem with Gestalt.