How Neural Networks Are Turning Human Brains Into AI
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Making Computers Smarter
Plenty of things that humans find difficult can be done in a snap by a computer. Want to solve a partial differential equation? No problem. How about creating accurate weather forecasts or scouring the internet for a single web page? Piece of cake. But ask a computer to tell you the differences between porn and renaissance art? Or whether you just said "night" or "knight"? Good luck with that.
Computers just can't reason in the same way humans do. They struggle to interpret the context of real-world situations or make the nuanced decisions that are vital to truly understanding the human world. That's why neural networks were first developed way back in the 1950s as potential solution to that problem.
Taking inspiration from the human brain, neural networks are software systems that can train themselves to make sense of the human world. They use different layers of mathematical processing to make ever more sense of the information they're fed, from human speech to a digital image. Essentially, they learn and change over time. That's why they provide computers with a more intelligent and nuanced understanding of what confronts them. But it's taken a long time to make that happen.
The Winter of Neural Networks
Back in the 1950s, researchers didn't know how the human brain was intelligent-we still don't, not exactly-but they did know that it was smart. So, they asked themselves how the human brain works, in the physical sense, and whether it could be mimicked to create an artificial version of that intelligence.
The brain is made up of billions of neurons, long thin cells that link to each other in a network, transmitting information using low-powered electrical charges. Somehow, out of that seemingly straightforward biological system, emerges something much more profound: the kind of mind that can recognize faces, develop philosophical treatises, puzzle through particle physics, and so much more. If engineers could recreate this biological system electronically, engineers figured, an artificial intelligence might emerge too.
There were some successful early examples of artificial neural networks, such as Frank Rosenblatt'sPerceptron which used analog electrical components to create a binary classifier. That's fancy talk for a system that can take an input-say, a picture of a shape-and classify it into one of two categories like "square" or "not-square." But researchers soon ran into barriers. First, computers at the time didn't have enough processing power to effectively handle lots of these kinds of decisions. Second, the limited number of synthetic neurons also limited the complexity of the operations that a network could achieve.
In the case of the Rosenblatt's Perceptron, for instance, a single set of artificial neurons was able to discern a square from non-squares. But if you wanted to introduce the ability to perceive something else about the squares-whether they were red or not red for example-you'd need a whole extra set.
While the biology of the brain may be straightforward at the microscopic level, taken as a whole it is incredibly complex. And that macro-level complexity was too much for 1950s computers to handle. As a result, over the following decades neural networks fell from favor. It became the "winter of neural networks," as Google's Jason Freidenfelds put it to me.
Neuroscience Advances
But one person's winter is another's summer. From the 1960s onwards, our understanding of the human brain progressed by leaps and bounds.
In those early days of neuroscience, much of the focus was on our visual systems. Professor Charles Cadieu from MIT explains:
It's probably the best understood sensory modality, and probably the best understood part of the brain.We've known for decades now that neurons fire differently as you pass up the visual stream. In the retina, neurons are receptive to points of light and darkness; in the primary visual cortex there's excitement of neurons by edge-like shapes; and in the higher areas of the visual cortex neurons respond to faces, hands... all sort of complex objects, both natural and man-made. In fact, up there, the neurons don't respond to light and dark patches or edge-like features at all.
