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Description
"In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood, and that we can enhance our learning and memory at any age. We can all 'learn to learn' by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback, and consolidation. The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. The exciting advancements in A.I. of the last twenty years reveal just as much about our remarkable abilities as they do about the potential of machines. How We Learn finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain's learning algorithms, in our schools and universities as well as in everyday life"--… (more)
User reviews
I particularly found the organization of the book helpful. I learned about the neurological function of sleep in memory retention, what is true and false about brain plasticity, the grim realities of trauma and addiction, the affect of music education, the benefit of bilingualism from an early age, and the enormous potential of the small child's mind. In fact, I'll never look at small children or even tiny babies in the same way again after reading this book. When I go to bed, I'm also going to make it a habit to dwell on the positive aspects of the day, or something neat I have learned during the day, just before I go to sleep, in order to maximize the chances that my brain will hold onto it better. Adults can't learn as easily as children, but we can still learn quite a bit with the right techniques.
Machine learning, on the other hand, has a long way to go. As the author points out, some human person must input massive amounts of data in order to get AI to do a few things. We're the opposite: with a few pieces of data, we can do massive numbers of things.
I received an advanced readers copy from the publisher and was encouraged to submit an honest review.
I felt disappointed that
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DDC/MDS
153.15 |