That said, Hewitt is far from a neural networking detractor. He notes that neural networking techniques—in which webs of nodes function as information processing units in ways similar to biological neurons—are immensely powerful when it comes to learning patterns from very large datasets. This is their utility in otherwise computationally prohibitive tasks like text and speech recognition. That's one of the brain's superpowers: finding meaning within relentless floods of sensory data. The brain's auditory and visual centers must take vast amounts of input in the form of waves and pixels, turn it all into data, and then capture the meaning, the statistical regularities, in that data."There is even recent evidence that the representations it finds are not too dissimilar from those discovered by a neural network," Hewitt notes. "I contend, deep learning may well provide a fantastic starting point for many problems in perception."But only a starting point:"If they have succeeded in anything superficially similar, it has been because they saw many hundreds of times more examples than any human ever needed to."
It's easy to get swamped by Singularity noise and science fictional grand stands against gun-toting machine intelligence, so well-reasoned AI reality checks like Hewitt's are worth spotlighting. The reality is often so, so far from the hype. Deep learning, whether it's our brains contending with floods of sensory input or algorithms reading handwriting, is necessary for intelligence, but it's not intelligence in itself.The many facets of human thought include planning towards novel goals, inferring others' goals from their actions, learning structured theories to describe the rules of the world, inventing experiments to test those theories, and learning to recognise new object kinds from just one example. Very often they involve principled inference under uncertainty from few observations. For all the accomplishments of neural networks, it must be said that they have only ever proven their worth at tasks fundamentally different from those above. If they have succeeded in anything superficially similar, it has been because they saw many hundreds of times more examples than any human ever needed to.