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How We Learn
How We Learn: Why Brains Learn Better Than Any Machine . . . for Now | Stanislas Dehaene
2 posts | 4 read | 1 to read
An illuminating dive into the latest science on our brain's remarkable learning abilities and the potential of the machines we program to imitate them 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. In How We Learn, Stanislas Dehaene decodes the brain's biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but 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 exciting advancements in artificial intelligence 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.
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review
Eva_B
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Mehso-so

What did I learn whilst reading ‘How We Learn‘? That I learn more when I‘m interested in the topic. Hence, I paid attention to probably half of this book and skimmed the rest! 😊I did find his conclusions interesting. They made sense to me. I like the fact he showed some evidence that unguided ‘discovery learning‘ is rubbish and ineffective. Kids need guidance and encouragement to learn. And adults too, judging by my reading of this book! 😂

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jillrhudy
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Cool so far. Early in the book, he makes the argument that machine learning is still light years from human learning. “[M]achines are data hungry, but humans are data efficient.” Geeks pour a staggering amount of data into a neural network to get it to do a few things. We are the opposite: with a few pieces of data we can conclude, and do, a staggering number of things. #neuroscience #education #arc #Netgalley

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