These Strange New Minds
How AI Learned to Talk and What It Means
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- $16.99
Publisher Description
An insider look at the Large Language Models (LLMs) that are revolutionizing our relationship to technology, exploring their surprising history, what they can and should do for us today, and where they will go in the future—from an AI pioneer and neuroscientist
In this accessible, up-to-date, and authoritative examination of the world’s most radical technology, neuroscientist and AI researcher Christopher Summerfield explores what it really takes to build a brain from scratch. We have entered a world in which disarmingly human-like chatbots, such as ChatGPT, Claude and Bard, appear to be able to talk and reason like us - and are beginning to transform everything we do. But can AI ‘think’, 'know' and ‘understand’? What are its values? Whose biases is it perpetuating? Can it lie and if so, could we tell? Does their arrival threaten our very existence?
These Strange New Minds charts the evolution of intelligent talking machines and provides us with the tools to understand how they work and how we can use them. Ultimately, armed with an understanding of AI’s mysterious inner workings, we can begin to grapple with the existential question of our age: have we written ourselves out of history or is a technological utopia ahead?
PUBLISHERS WEEKLY
This superlative study from Oxford University neuroscientist Summerfield (Natural General Intelligence) explores how large language models work and the thorny questions they raise. He explains that neural networks learn by guessing the relationships between data points and developing "weights" that prioritize the processing pathways most likely to produce correct answers. Wading into debates around whether LLMs possess knowledge or merely proffer predictions, Summerfield makes the provocative argument that human learning is essentially predictive, depending on the same trial-and-error strategy LLMs use. According to the author, this indicates human knowledge is comparable to AI knowledge. Summerfield is remarkably levelheaded in his assessment of AI's capabilities, suggesting that while obstacles to designing AI assistants that can book trips and pay bills may be resolved in the next several years, it's unlikely LLMs will ever become sentient given their inability to experience physical sensation. The lucid analysis also makes clear that technological improvements will never overcome such pitfalls as determining when to provide answers as definitive or up for debate, since such problems depend on subjective judgment. By inquiring into the nature of knowledge and consciousness, Summerfield brings some welcome nuance and clarity to discussions of LLMs. In a crowded field of AI primers, this rises to the top.
