The Alignment Problem book cover

The Alignment Problem by Brian Christian Summary

The Alignment Problem
Brian Christian
4.34 (4596 Reviews)
Ai
Technology
Science
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Ключевые Выводы
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Часто Задаваемые Вопросы

Overview of The Alignment Problem

The Alignment Problem reveals how AI systems can drift from human values, earning praise from Microsoft CEO Satya Nadella and NYT recognition as the #1 AI book. What happens when machines misunderstand our intentions? Brian Christian offers a crucial roadmap for our algorithmic future.

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Ключевые выводы

1

When Machines Learn Our Worst Habits

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What happens when you teach a computer to read the entire internet? In 2013, Google unveiled word2vec, a system that could perform mathematical magic with language-add "China" to "river" and get "Yangtze," or subtract "France" from "Paris" and add "Italy" to get "Rome." It seemed like pure intelligence distilled into numbers. But when researchers tried "doctor minus man plus woman," they got "nurse." Try "computer programmer minus man plus woman" and you'd get "homemaker." The system hadn't just learned language-it had absorbed every gender bias embedded in millions of human-written texts. This wasn't a bug. It was a mirror. The problem runs deeper than words. In 2015, a Black web developer named Jacky Alcine opened Google Photos to find his pictures automatically labeled "gorillas." Google's solution? Simply remove the gorilla category entirely-even actual gorillas couldn't be tagged years later. Meanwhile, employment screening tools were discovered ranking the name "Jared" as a top qualification. Photography itself carries this legacy-for decades, Kodak calibrated film using "Shirley cards" featuring White models, making cameras literally incapable of photographing Black skin properly. The motivation to fix this came not from civil rights concerns but from furniture makers complaining about poor wood grain representation. When Joy Buolamwini tested commercial facial recognition systems, she found a 0.3% error rate for light-skinned males but 34.7% for dark-skinned females. The machines weren't creating bias-they were perfectly, ruthlessly reflecting ours.

2

The Impossible Mathematics of Fairness and Prediction

3

The Transparency Crisis and the Power of Simplicity

4

Designing Rewards and Unleashing Curiosity

5

Learning Values Through Imitation and Inference

6

The Wisdom of Uncertainty

7

Living with Intelligent Machines

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@Raaaaaachelw
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@Erin, Investment Banking Associate , NYC
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@djmikemoore
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@Pitiful
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@SofiaP
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