Awards & notoriety
Selected in MIT Press Top books of 2022.
Named Quant Book of the Year by Rebellion Research, host of the MIT Quant and AI Conference.
Eric Hoffer Award Category Finalist
Analytics India Must read 2022
Reviewers and social media recommendations
Rachid Lassoued, Global Head of Financial Engineering & Risk, Bloomberg
Great initiative and quite innovative framework developed in your platform (and your great book which is certainly one of the most interesting reads I had in 2022)
Oledkssy Lialka, Data Scientist, Kiarna
Microprediction by Peter Cotton is a smart read. The author lays down the foundations of greenfield mass prediction as a service with an economic rationale and a high-level system design vision. The evolution alike we’ve already seen with the cloud infrastructure. The read is dense and packed with statistics, economics, and optimal control, all intertwined into a cohesive and engaging story. Remember to throw it into your weekender bag. Trust me, you will enjoy it.
Joseph Langsam, PhD, Board on Mathematical Sciences and Analytics, National Academies Sciences, Engineering and Medicine; coeditor of Handbook on Systemic Risk
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.”
See MIT Press.
Matthew Kolakowski, Data Scientist
If you want to read a book that will open your mind to new horizons on prediction, buy this one now. Peter takes a fantastic approach and examines the possibilities of microprediction. Great book and a must have on your data science reading list.
Allesandro Chiruzzi, Lead Data Scientist
Peter Cotton might not be ‘the man who solved the market’, but his book sure has a lot of insights and a vision on how to solve many data-related problems companies (of any size) will be facing in the very next future. Microprediction might have a serious and corporate looking front cover, but don’t let that fool you. Inside you’ll find a witty and engaging read, rarely seen in books dealing with such topics.
Scott Treloar, CIO Noviscient
Very interesting book. I bought two because it is so good
Azat Aslanov, PhD Senior Data and Systems Analyst, QBE Insuranc
As for a quant that runs aground on the technical clumsiness of the underlying data pipelines world of today, the brilliant exploration of fascinating novel ideas is a breath of fresh air: from statistical crowd-sourcing to interplay between software-engineered oracles and reinforcement learning to temporal difference.
See full review in Goodreads.
Michael Tselman, AI architect and senior engineer
A fascinating, different, thought provoking view and proposal for predictive Data Science
Chris Hanlon, CFO at DirectBooks
Mark Trinquero. AWS Solutions Architect.
Excellent read (note: You may not agree with every point made) Peter’s deep knowledge of AI/ML/DS domains, combined with expertise in crowdsourcing & belief in the fundamental power of markets provide a very solid argument for building open AI networks that can serve as public utility (creating novel approaches that can even outperform “SOTA” models)
See amazon reviews.
Vacslav Glukov, AI Research, Engineering
You may agree or disagree with some points, it may take some time to get used to the author’s style (for those who never read Peter’s texts before). But - what a joy!
Robert Carver, Writer and Trader.
See full review in Risky Finance
From amazon reviews.
Those expecting bland generalization to sell another “AI book” for the heck of it are in for a shock.
Henry Elgrissy, Defindor
GO BUY THE BOOK. It’s very good
Curtis Raymond, Data Science Manager, Priceline
This is definitley one of my favorite books!!
Graham L. Giller, Phd Author of Adventures in Financial Data Science
Microprediction aims to disrupt what Cotton has coined the ‘artisan data science’ economy and to bring the cost of all prediction to zero.
See MIT Press
Prof William T. Ziemba, University of British Columbia (Emeritus); Distinguished Visiting Research Associate, London School of Economics
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.
See MIT Press.
Important contribution to development of real world applied AI/ML at scale.
From amazon reviews.
George M George
Highly recommend Peter Cotton’s recent book on microprediction! Great read, can’t agree more, and looking forward to the microprediction network becoming more entrenched in reality.