Brian Christian – The Alignment Problem
The analogies between human and machine learning strategies are skillfully narrated, but rather drawn out.
Bayesian nets help prevent flawed statistical arguments and enable the leap from correlation to causation
Judea Pearl and Dana MacKenzie – The book of Why
The practical and relevant examples (health effect of smoking, impact of humanity on climate change) of causal inference alone make the book worthwhile.
Flawed data collection and interpretation hurt the position and prospects of women in many different ways
Caroline Criado Perez – Invisible women
Great exercise in spotting biases, and understanding how these manifest themselves in how the world around us is shaped.
Never trust data scientists, in particular when they do not have data
The writer never really succeeds in making the Simulmatics story seem important, partly because due to endless digressions about the bad marriages of the men who founded the company and partly because she avoids any substantial assessment of the actual models they used.
It is a miracle that databases actually work, considering all the things that can go wrong
Martin Kleppmann – Designing Data-intensive applications
Surprisingly readable for a text of this sort of technical depth
The US is losing out in AI, due to a lack of long-term vision and direction
The book’s set-up with multiple scenarios for the future works surprisingly well and is especiall concerning for European readers: Europe is almost completely irrelevant in all of Webb’s scenarios.
Following the rise of Jim Simons’ Renaissance, quants have become a major force in US politics
Gregory Zuckerman – the man who solved the market
The book would have been a better read if it had focused on one of its two narratives: the rise of algorithmic trading and the forays of hedge fund executives into US politics.
“Our program is the ‘story’ and the problem you are trying to solve the ‘idea'”
Charles Severance – Python for everybody
A highly recommended introduction to coding for aspiring data scientists, providing a rare mix of fundamentals and well-chosen practical examples.