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Peter Galison’s focus as a science historian is—speaking roughly—on the intersection of theory with experiment.

“For quite a number of years I have been guided in my work by the odd confrontation of abstract ideas and extremely concrete objects,” he once told me, in explaining how he thinks about what he does. At the Washington, Connecticut, meeting he discussed the Cold War tension between engineers (like Wiener) and the administrators of the Manhattan Project (like Oppenheimer: “When [Wiener] warns about the dangers of cybernetics, in part he’s trying to compete against the kind of portentous language that people like Oppenheimer [used]: ‘When I saw the explosion at Trinity, I thought of the Bhagavad Gita—I am death, destroyer of worlds.’ That sense, that physics could stand and speak to the nature of the universe and airforce policy, was repellent and seductive. In a way, you can see that over and over again in the last decades—nanosciences, recombinant DNA, cybernetics: ‘I stand reporting to you on the science that has the promise of salvation and the danger of annihilation—and you should pay attention, because this could kill you.’ It’s a very seductive narrative, and it’s repeated in artificial intelligence and robotics.”

As a twenty-four-year old, when I first encountered Wiener’s ideas and met his colleagues at the MIT meeting I describe in the book’s Introduction, I was hardly interested in Wiener’s warnings or admonitions. What drove my curiosity was the stark, radical nature of his view of life, based on the mathematical theory of communications in which the message was nonlinear: According to Wiener, “new concepts of communication and control involved a new interpretation of man, of man’s knowledge of the universe, and of society.” And that led to my first book, which took information theory—the mathematical theory of communications—as a model for all human experience.

In a recent conversation, Peter told me he was beginning to write a book—about building, crashing, and thinking—that considers the black-box nature of cybernetics and how it represents what he thinks of as “the fundamental transformation of learning, machine learning, cybernetics, and the self.”

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