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Deep learning is as close to being a "newtonian theory" of the brain as it gets -- deep learning abstracts away a lot of the complexity of neural systems (e.g., a simple artificial neuron vs a highly complex biological one) while maintaining a number of essential characteristics: massively parallel computation, error tolerance, graceful degradation, distributed representations, information is stored in slowly-changing synapses, and, most importantly: a simple, local, and powerful biologically-plausible-if-you-squint-hard-enough learning rule. The important question to ask is: is the deep learning abstraction any good? There's a very strong case to be made that the answer is yes: deep learning systems can perform many (of course, not all, at least not yet) tasks that involve perception (computer vision/speech recognition), motor control (the recent openai robot), language understanding (machine translation/BERT/GPT), planning (alphago/dota/the deepmind protein folding), and even some symbolic reasoning (the recent work from facebook on symbolic integration https://ai.facebook.com/blog/using-neural-networks-to-solve-...). Some of these tasks are performed at such a high level that they become commercially useful, and in some cases, surpass "human level". So here we have a "model family" -- deep learning -- with a set of principles so simple that it can be studied with intense mathematical rigor (for example, https://arxiv.org/pdf/1904.11955.pdf or https://papers.nips.cc/paper/9030-which-algorithmic-choices-...), and that produces many of the behaviors we want out of brains (and not just behavioral: see, e.g., https://arxiv.org/abs/1805.10734: " Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial." -- this is just one of many papers that show that even under the hood, trained deep learning systems exhibit many properties of biological neural networks). These reasons strongly suggest (imho) that deep learning is in fact the newtonian theory of neuroscience. More strongly, no other theory comes remotely close in its simplicity and explanatory power. |
Self driving cars can't leave an enclosed environment and might never do so safely.
Richard Dawkins spoke very highly of the brains ability to do some kind of natural calculus for the sake of tracking a ball in flight, but most animals run on simple tricks and reference points.
Deep learning might be the "good think" for the next ten years, some of us are not going to let go of the transcendent truth that the brain is not defined by what we think it is. I see limited reason to see deep learning as more likely than some emergent behaviour from a vast number of simple rules. Like animals flocking together in a boid sim.