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.
