How We Learn
Why Brains Learn Better Than Any Machine . . . for Now
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.