
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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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.
The Alignment Problem의 핵심 아이디어를 이해하기 쉬운 포인트로 분해하여 혁신적인 팀이 어떻게 창조하고, 협력하고, 성장하는지 이해합니다.
The Alignment Problem을 빠른 기억 단서로 압축하여 솔직함, 팀워크, 창의적 회복력의 핵심 원칙을 강조합니다.

생생한 스토리텔링을 통해 The Alignment Problem을 경험하고, 혁신 교훈을 기억에 남고 적용할 수 있는 순간으로 바꿉니다.
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