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eagues, including John von Neumann and Claude Shannon, who were interested in the commercialization of the new technologies. Second, computer pioneer John McCarthy disliked Wiener and refused to use Wiener’s term “Cybernetics.” McCarthy, in turn, coined the term “artificial intelligence” and became a founding fa
use, because this method lacks transparency. While recognizing the impressive achievements in deep learning by colleagues such as Michael Jordan and Geoffrey Hinton, he feels uncomfortable with this kind of opacity. He set out to understand the theoretical limitations of deep-learning systems and points out that
achines should not replace Homo in situations requiring respect, dignity, or care, while others (author Pamela McCorduck and computer scientists like John McCarthy and Bill Hibbard) replied that machines can be more impartial, calm, and consistent and less abusive or mischievous than people in such positions. E
euroscience communities need to interact closely, something that is happening already. Some of today’s greatest exponents of machine learning—such as Geoffrey Hinton, Zoubin Ghahramani, and Demis Hassabis—have backgrounds in cognitive neuroscience, and their success has been at least in part due to attempts to mod
eagues, including John von Neumann and Claude Shannon, who were interested in the commercialization of the new technologies. Second, computer pioneer John McCarthy disliked Wiener and refused to use Wiener’s term “Cybernetics.” McCarthy, in turn, coined the term “artificial intelligence” and became a founding fa
use, because this method lacks transparency. While recognizing the impressive achievements in deep learning by colleagues such as Michael Jordan and Geoffrey Hinton, he feels uncomfortable with this kind of opacity. He set out to understand the theoretical limitations of deep-learning systems and points out that
achines should not replace Homo in situations requiring respect, dignity, or care, while others (author Pamela McCorduck and computer scientists like John McCarthy and Bill Hibbard) replied that machines can be more impartial, calm, and consistent and less abusive or mischievous than people in such positions. E
euroscience communities need to interact closely, something that is happening already. Some of today’s greatest exponents of machine learning—such as Geoffrey Hinton, Zoubin Ghahramani, and Demis Hassabis—have backgrounds in cognitive neuroscience, and their success has been at least in part due to attempts to mod
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