Stanford professor Andrew Ng, former “Google Brain” leader, Coursera founder, and current chief scientist at Chinese web giant Baidu, told Backchannel:
[Caleb Garling] Often people conflate the wiring of our biological brains with that of a computer neural network. Can you explain why that’s not accurate?
[Andrew Ng] A single neuron in the brain is an incredibly complex machine that even today we don’t understand. A single “neuron” in a neural network is an incredibly simple mathematical function that captures a minuscule fraction of the complexity of a biological neuron. So to say neural networks mimic the brain, that is true at the level of loose inspiration, but really artificial neural networks are nothing like what the biological brain does.
Today machines can recognize, say, a dog jumping. But what if someone is holding a piece of meat above the dog. We recognize that that’s a slightly different concept, a dog trick. And the piece of meat isn’t just a piece of meat, it’s a treat—a different linguistic idea. Can we get computers to understand these concepts?
Deep learning algorithms are very good at one thing today: learning input and mapping it to an output. X to Y. Learning concepts is going to be hard.
It may well be impossible. Most concepts involve emotional responses, which stem from experiencing life as a self. Thoughts?
See also: Darwin’s “horrid doubt”: The mind
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