The Atomic Human: Understanding Ourselves in the Age of AI

(Hardback)

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A world expert on AI brings a fresh new perspective to the new era- how artificial intelligence can help us understand what makes us human A vital perspective is missing from the discussions we're having about Artificial Intelligence- what does it mean for our identity Our fascination with AI stems from the perceived uniqueness of human intelligence. We believe it's what differentiates us. Fears of AI not only concern how it invades our digital lives, but also the implied threat of an intelligence that displaces us from our position at the centre of the world. Neil D. Lawrence's visionary book shows why these fears may be misplaced. Atomism, proposed by Democritus, suggested it was impossible to continue dividing matter down into ever smaller components- eventually we reach a point where a cut cannot be made (the Greek for uncuttable is 'atom'). In the same way, by slicing away at the facets of human intelligence that can be replaced by machines, AI uncovers what is left- an indivisible core that is the essence of humanity. By contrasting our own (evolved, locked-in, embodied) intelligence with the capabilities of machine intelligence through history, The Atomic Human reveals the technical origins, capabilities and limitations of AI systems, and how they should be wielded. Not just by the experts, but ordinary people. Either AI is a tool for us, or we become a tool of AI. Understanding this will enable readers to choose the future we want.

Author Bio

Neil Lawrence is the DeepMind Professor of Machine Learning at the University of Cambridge where he leads the university-wide initiative on AI, and a Senior AI Fellow at the Alan Turing Institute. Previously he was Director of Machine Learning at Amazon, deploying solutions for Alexa, Prime Air and the Amazon supply chain. Co-host of the Talking Machines podcast, he's written a series for The Guardian and appeared regularly on other media. Known for his policy and societal work with the UK's AI Council, the Centre for Data Ethics and Innovation, and the OECD's Global Partnership on AI, his research focuses on improving data governance, accelerating scientific discovery, and how humans can take back control of large AI systems.