![]() 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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