The analogies between human and machine learning strategies are skillfully narrated, but rather drawn out.
The practical and relevant examples (health effect of smoking, impact of humanity on climate change) of causal inference alone make the book worthwhile.
Great exercise in spotting biases, and understanding how these manifest themselves in how the world around us is shaped.
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.
Surprisingly readable for a text of this sort of technical depth
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.
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.
A highly recommended introduction to coding for aspiring data scientists, providing a rare mix of fundamentals and well-chosen practical examples.