Many of these decisions involve problems that can't be solved just by collecting more training data. (I hope it's a meerkat.) Alas, computer algorithms are already being used to make much more consequential decisions. I'm curious what animal I would get classified as if people who look like me were removed from Google Photos training dataset. I don't know which approach Google went with but "three years later, in 2018, Wired reported that the label 'gorilla' was still manually deactivated in Google Photos." The fast solution is to keep using a biased algorithm and just cover up the most egregious errors. The good solution is to add more Black people to the training dataset. In 2015 Google famously released an image classifier that labelled Black people as "gorillas" because there were so few Black people in its training dataset. At the end it extrapolates on to the choices machine learning creates for our future. Most of the book is dedicated to explaining machine learning problems and their solutions. The book starts with practical real-world problem that are happening right now. Much of it mirrors the actual technical work I'm doing in machine learning. The only possible fault I can imagine with this book is that, since it depends so heavily on cutting-edge research, it might be rendered obsolete in a decade or two. This book would be a useful read both for activists who want to better understand public policy AND for aspiring engineers who want to get up to speed with machine learning. The Alignment Problem addresses advanced technical problems while being readable to non-technical people. Thus, through gritted teeth, I reluctantly acknowledge that The Alignment Problem by Brian Christian is a fantastic book in all respects.ĭespite my best efforts, Brian Christian even taught me lots of cool things about state-of-the-art machine learning.
That's because he dedicated all of Chapter 7: Imitation to the subject. Brian Christian addressed Skinnerian operant conditioning without addressing the real way we manages human groups: leading by example. In the Chapter 5: Shaping I thought I found a major mistake. I spotted (what seemed like) omission after omission only to be frustrated just a few pages later when Brian Christian addressed them. I combed through page after page for factual errors, minor misrepresentations or even just contestable opinions.
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