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Lädt ... God, Human, Animal, Machine: Technology, Metaphor, and the Search for Meaningvon Meghan O'Gieblyn
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Melde dich bei LibraryThing an um herauszufinden, ob du dieses Buch mögen würdest. Keine aktuelle Diskussion zu diesem Buch. Very engaging. The writer is a former evangelical now religion technology writer. The breaking down of tech talk into religious recycled metaphors is fascinating. Really thought provoking discussions of the hard problems of physics and unknowing, searching for meaning in an “unenchanted” world, the way in which humans can not help attempts at “re-enchantment” and anthropomorphisms. Just an excellent read. Zeige 2 von 2 keine Rezensionen | Rezension hinzufügen
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"A meditation on what it might mean to be human in an age of ever-accelerating technology"-- Keine Bibliotheksbeschreibungen gefunden. |
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Google Books — Lädt ... GenresMelvil Decimal System (DDC)814.6Literature English (North America) American essays 21st CenturyKlassifikation der Library of Congress [LCC] (USA)BewertungDurchschnitt:
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In this new era, what is called “deep learning” presupposes a model of reality that may or may not accurately reflect the state of the universe but we are captives of it nonetheless.
Meghan O’Gieblyn believes that the purveyors of the new enchantment are not to be trusted because it’s all been done before, and is almost too close to Calvin’s theory of divine judgment for comfort.
In this new era we lose ourselves in the metaphors.
How realistic is “the Singularity” — where machines supersede human intelligence?
How likely is it that the universe resembles the rationale engine we perceive humans to be.
Whereas people once thought our rationalism separated us from the animals, or that machines do logic better than humans, where does that leave us? How important or even wise is it that our machines think like humans?
Will people mind take their marching orders from machines? In many scenarios the machines make life and decision making much easier for us. And the less we meddle with the deep learning algorithms the better off we are.
The deep learning era is incomprehensible to most of us, tu then again we’ve always lived in a world we partially understood.
And if we all live in a simulation how could we ever disprove it. ( )