A systematic field guide for critically evaluating the differences and similarities between humans and artificial intelligence.
In an era oversaturated with AI hype and apocalyptic predictions, Not Like Us offers a clear-eyed, level-headed field guide for making sense of artificial intelligence. Dirk Hovy approaches AI through a distinctly human lens, rejecting both techno-utopianism and doom-mongering to present a refreshingly pragmatic framework: AI models are instruments designed to solve specific problems, not oracles that will reshape society or doom humanity. By celebrating what makes humans fundamentally different from machines, the book explores how humans and AI can complement, rather than compete with one another.
Not Like Us demystifies the uncertainty around AI by explaining what today’s systems are, and what they can and cannot do. Through stories drawn from science, history, and everyday technology, the book offers practical questions to evaluate AI critically: What are models genuinely good at? Where do they predictably fail? Who stands to benefit, and who might be disadvantaged? Covering global perspectives, including multilingual AI, universal risks, and diverse regulatory frameworks, Not Like Us gives curious readers a shared vocabulary and a durable mental model to cut through hype, calm fears, and make better decisions about AI as it evolves alongside us.
In an era oversaturated with AI hype and apocalyptic predictions, Not Like Us offers a clear-eyed, level-headed field guide for making sense of artificial intelligence. Dirk Hovy approaches AI through a distinctly human lens, rejecting both techno-utopianism and doom-mongering to present a refreshingly pragmatic framework: AI models are instruments designed to solve specific problems, not oracles that will reshape society or doom humanity. By celebrating what makes humans fundamentally different from machines, the book explores how humans and AI can complement, rather than compete with one another.
Not Like Us demystifies the uncertainty around AI by explaining what today’s systems are, and what they can and cannot do. Through stories drawn from science, history, and everyday technology, the book offers practical questions to evaluate AI critically: What are models genuinely good at? Where do they predictably fail? Who stands to benefit, and who might be disadvantaged? Covering global perspectives, including multilingual AI, universal risks, and diverse regulatory frameworks, Not Like Us gives curious readers a shared vocabulary and a durable mental model to cut through hype, calm fears, and make better decisions about AI as it evolves alongside us.
