The future won’t be made by either humans or machines alone – but by both, working together.
copyright by theconversation.com
The future won’t be made by either humans or machines alone – but by both, working together. Technologies modeled on how human brains work are already augmenting people’s abilities, and will only get more influential as society gets used to these increasingly capable machines.
Technology optimists have envisioned a world with rising human productivity and quality of life as artificial intelligence systems take over life’s drudgery and administrivia, benefiting everyone . Pessimists, on the other hand, have warned that these advances could come at great cost in lost jobs and disrupted lives . And fearmongers worry that AI might eventually make human beings obsolete .
However, people are not very good at imagining the future. Neither utopia nor doomsday is likely. In my new book, “ The Deep Learning Revolution ,” my goal was to explain the past, present and future of this rapidly growing area of science and technology. My conclusion is that AI will make you smarter, but in ways that will surprise you.
Deep learning is the part of AI that has made the most progress in solving complex problems like identifying objects in images, recognizing speech from multiple speakers and processing text the way people speak or write it. Deep learning has also proven useful for identifying patterns in the increasingly large data sets that are being generated from sensors, medical devices and scientific instruments .
The goal of this approach is to find ways a computer can represent the complexity of the world and generalize from previous experience – even if what’s happening next isn’t exactly the same as what happened before. Just as a person can identify that a specific animal she has never seen before is in fact a cat, deep learning algorithms can identify aspects of what might be called “cat-ness” and extract those attributes from new images of cats.
The methods for deep learning are based on the same principles that power the human brain. For instance, the brain handles lots of data of various kinds in many processing units at the same time. Neurons have many connections to each other, and those links strengthen or weaken depending on how much they’re used, establishing associations between sensory inputs and conceptual outputs. […]
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read more – copyright by theconversation.com
The future won’t be made by either humans or machines alone – but by both, working together.
copyright by theconversation.com
Technology optimists have envisioned a world with rising human productivity and quality of life as artificial intelligence systems take over life’s drudgery and administrivia, benefiting everyone . Pessimists, on the other hand, have warned that these advances could come at great cost in lost jobs and disrupted lives . And fearmongers worry that AI might eventually make human beings obsolete .
However, people are not very good at imagining the future. Neither utopia nor doomsday is likely. In my new book, “ The Deep Learning Revolution ,” my goal was to explain the past, present and future of this rapidly growing area of science and technology. My conclusion is that AI will make you smarter, but in ways that will surprise you.
Deep learning is the part of AI that has made the most progress in solving complex problems like identifying objects in images, recognizing speech from multiple speakers and processing text the way people speak or write it. Deep learning has also proven useful for identifying patterns in the increasingly large data sets that are being generated from sensors, medical devices and scientific instruments .
The goal of this approach is to find ways a computer can represent the complexity of the world and generalize from previous experience – even if what’s happening next isn’t exactly the same as what happened before. Just as a person can identify that a specific animal she has never seen before is in fact a cat, deep learning algorithms can identify aspects of what might be called “cat-ness” and extract those attributes from new images of cats.
The methods for deep learning are based on the same principles that power the human brain. For instance, the brain handles lots of data of various kinds in many processing units at the same time. Neurons have many connections to each other, and those links strengthen or weaken depending on how much they’re used, establishing associations between sensory inputs and conceptual outputs. […]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
read more – copyright by theconversation.com
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