Understanding Geoff Hinton's "forward-forward" algorithm
In the 1980s, Geoffrey Hinton was one of the scientists who invented backpropagation, the algorithm that enables the training of deep neural networks. Backpropagation was key to the success of deep learning and its widespread use today.
But Hinton, who is one of the most celebrated artificial intelligence scientists of our time, thinks it is time that we think beyond backpropagation and look for other, more efficient ways to train neural networks. And like many other scientist, he draws inspiration from the human brain.
In a new paper presented at NeurIPS 2022, Hinton introduced the “forward-forward algorithm,” a new learning algorithm for artificial neural networks inspired by our knowledge about neural activations in the brain. Though still in early experimentation, forward-forward has the potential to replace backprop in the future, Hinton believes. The paper also proposes a new model for “mortal computation,” which brings us closer to the energy-efficient mechanisms of the brain and can support the forward-forward algorithm.
Read the full article on TechTalks.
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