Epstein Secrets

e first meeting (in Washington, Connecticut) that preceded the crafting of this book, he commented on the change in how “information” is viewed since Wiener’s time: the military-industrial, Cold War era. Back then, Wiener compared information, metaphorically, to entropy, in that it could not be conserved—

and directed the slaves as they mindlessly (mechanically) churned their oars. The Greek had already migrated into modern English via Latin, going 47 Wiener later had to admit the earlier coinage of the word in 1834 by André-Marie Ampére, who had intended it to mean the “science of government,” a concept

ow it an abstract painting and see what it says. But it does a pretty good job. It works using the same neural-network technology that McCulloch and Pitts imagined in 1943 and lots of us worked on in the early eighties. Back in the 1980s, people successfully did OCR—optical character recognition. They t

e first meeting (in Washington, Connecticut) that preceded the crafting of this book, he commented on the change in how “information” is viewed since Wiener’s time: the military-industrial, Cold War era. Back then, Wiener compared information, metaphorically, to entropy, in that it could not be conserved—

and directed the slaves as they mindlessly (mechanically) churned their oars. The Greek had already migrated into modern English via Latin, going 47 Wiener later had to admit the earlier coinage of the word in 1834 by André-Marie Ampére, who had intended it to mean the “science of government,” a concept

ow it an abstract painting and see what it says. But it does a pretty good job. It works using the same neural-network technology that McCulloch and Pitts imagined in 1943 and lots of us worked on in the early eighties. Back in the 1980s, people successfully did OCR—optical character recognition. They t

at our door.” In the wake of advances in unsupervised, self-improving machine learning, a small but influential community of thinkers is considering Wiener’s words again. In Deep Thinking, John Brockman gathers their disparate visions of where AI might be taking us. The fruit of the long history of Broc

nces are intimately bound up in the idea of an impending technological singularity. His work on neuroscience and his initial support of McCulloch and Pitts adumbrated the startlingly effective deep-learning methods of the present day. Over the past decade, and particularly in the last five years, such de

at our door.” In the wake of advances in unsupervised, self-improving machine learning, a small but influential community of thinkers is considering Wiener’s words again. In Deep Thinking, John Brockman gathers their disparate visions of where AI might be taking us. The fruit of the long history of Broc

nces are intimately bound up in the idea of an impending technological singularity. His work on neuroscience and his initial support of McCulloch and Pitts adumbrated the startlingly effective deep-learning methods of the present day. Over the past decade, and particularly in the last five years, such de