Building an Open AI Network
232 pp., 6 x 9 in, 9 b&w illus.
- Published: November 8, 2022
How a web-scale network of autonomous micromanagers can challenge the AI revolution and combat the high cost of quantitative business optimization.
The artificial intelligence (AI) revolution is leaving behind small businesses and organizations that cannot afford in-house teams of data scientists. In Microprediction, Peter Cotton examines the repeated quantitative tasks that drive business optimization from the perspectives of economics, statistics, decision making under uncertainty, and privacy concerns. He asks what things currently described as AI are not “microprediction,” whether microprediction is an individual or collective activity, and how we can produce and distribute high-quality microprediction at low cost. The world is missing a public utility, he concludes, while companies are missing an important strategic approach that would enable them to benefit—and also give back.
In an engaging, colloquial style, Cotton argues that market-inspired “superminds” are likely to be very effective compared with other orchestration mechanisms in the domain of microprediction. He presents an ambitious yet practical alternative to the expensive “artisan” data science that currently drains money from firms. Challenging the machine learning revolution and exposing a contradiction at its heart, he offers engineers a new liberty: no longer reliant on quantitative experts, they are free to create intelligent applications using general-purpose application programming interfaces (APIs) and libraries. He describes work underway to encourage this approach, one that he says might someday prove to be as valuable to businesses—and society at large—as the internet.
"Microprediction aims to disrupt what Cotton has coined the 'artisan data science' economy and to bring the cost of all prediction to zero."
Graham L. Giller, author of Adventures in Financial Data Science
“Peter Cotton surveys the advantages, costs, and pitfalls involved in the real-time crowdsourcing of data and artificial intelligence. Readers will explore the frontier of this increasingly popular and valuable modeling approach, catching a glimpse of where it might someday lead.”
William T. Ziemba, University of British Columbia (Emeritus); Distinguished Visiting Research Associate, London School of Economics
“Cotton is a brilliant, original, 'out of the box' thinker with command of his subject. Cotton's Microprediction is necessary reading for those whose success depends upon data-driven predictions.”
Joseph Langsam, PhD, Board Member, ACTUS Financial Research Foundation; former member, Board on Mathematical Sciences and Analytics, National Academies Sciences, Engineering and Medicine; coeditor of Handbook on Systemic Risk