The hard truth of AI is that methods that exploit deep, hard-won human knowledge about a particular domain are outperformed by methods that cleverly exploit the increasing power of computation. I have a modest proposal to distill this fundamental truth into a set of conditions which, when met, will revolutionise a given field. I do not claim this insight about the fundamental mechanism of progress in AI as original; I have been heavily influenced by Richard Sutton's powerful essay The Bitter Lesson. The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. Sutton is not alone in this observation.

The other night I attended a dinner with a dozen CEOs of AI startups. Once again, I heard a near universal discomfort with the term "artificial intelligence" as they sipped Pinot Noir and fumbled to describe what they do. "We're not really trying to create intelligence that's artificial," said the CEO of a product strategy company. Another, who has built AI-based payment technologies, found the term dystopic. "Too many people think AI means the Terminator," he said.

Nine years ago, renowned futurist and Google Chief Engineer, Ray Kurzweil, and X-Prize Foundation Chair, Peter Diamandis, founded Singularity University in order to explore and explain the potential of exponential technologies to address our great global challenges. Last week I had the privilege to attend their Executive Programme. During our week long bootcamp, we were exposed to future technological trends in robotics, artificial intelligence, neuroscience, digital manufacturing, digital medicine and space exploration, to name but a few. Underpinning spectacular advances across all of these areas is the explosion of computational power, as predicted by Moore's Law. For example, genomes that initially cost $3bn to sequence now cost $1,000 and are on their way to cost $0.01.

Many of the benefits of structured data come about when users can re-use existing vocabularies rather than create new ones, but it is currently difficult for users to find, create, and host new vocabularies. Moreover, the value of any given vocabulary as a foundation for applications depends on the perceived certainty that the vocabulary — both its machine-readable schemas and human-readable specification documents — will remain reliably accessible over time and that its URIs will not be sold, re-purposed, or simply forgotten. This note proposes two approaches for solving these problems: one for multiple Vocabulary Hosting Services and a Vocabulary Preservation System to keep them linked together.