Deep Learning and the Human Brain: Inspiration, Not Imitation
Learn about deep learning and the human brain.
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Artificial intelligence is the future. Structurally, artificial intelligence is perceived almost to be an individual entity influencing every technology. Machine learning is one of the sciences behind this entity, and deep learning is the engine that propels the science.
Deep learning transcends human ability to process a large volume of data. With a rush of data and the advent of faster GPUs and TPUs, deep learning is taking giant strides in the realm of image analysis, facial recognition, autonomous driving, etc.
Enterprises are taking the technique out of the labs and deploying it in real-world scenarios too. NVidia’s Tegra X1 SoC processor can analyze a mind-boggling 258 images per second. Qualcomm’s advanced SoCs in dashcams and bodycams boast analytics including voice activation, face recognition, intelligent motion detection, and object tracking. Through such features, users can move from expensive servers in the cloud to efficient camera-based analytics. Google’s autonomous cars have leveraged the power of deep learning in image analysis and motion detection and has been involved in only 11 accidents so far.
Let’s Dig Deep
Deep learning consists of artificial neural networks that are modeled on similar networks present in the human brain. As data travels through this artificial mesh, each layer processes an aspect of the data, filters outliers, spots familiar entities, and produces the final output.
The perception is that deep learning functions just like the human brain.
Yes, the human brain functions in a similar fashion — but only at a highly advanced level. The human brain is a far more complex web of diverse neurons where each node performs a separate task. Our understanding of things is far more superior. If we are taught that lions are dangerous, we can deduce that bears are too.
Deep Learning Doesn’t Display Deep Deduction
Deep learning also uses deduction, but in a linear, basic, and one-dimensional way. Training the artificial neural networks to classify lions as dangerous might make them sensitive only to lions. A bear can’t get classified as dangerous automatically. Training them to identify a cat will only make them recognize a cat, but not deduce that a leopard belongs to the cat family.
Similarly, through facial recognition, deep learning can tag faces on photos but might stumble when there are faces of Siamese twins. The human brain’s deduction capability can make out even latent differences in this case.
Google’s self-driving cars might have faced 11 instances of accidents but there were 341 instances of “disengagements” by the cars in a year — test drivers had taken over from the computer 341 times. The instances were when the computer signaled for help or when the test drivers sensed imminent danger and forcibly took over. So, technically, the human brain displayed its power of deduction and saved 341 more instances of accidents.
Google cars carry the burden of being too safe. The stupidity-proof and super-safe autonomous cars cannot perceive a jaywalker jumping out of the way or people in crowded streets walking in front all day long. In these instances, a human driver’s ability to maneuver and pilot the car is superior. No wonder Ford’s “Remote Repositioning” project allows a human driver seated thousands of miles away to remotely drive a car.
Artificial Neural Networks Mimic an Infant’s Brain
At this stage, it could be said that deep learning mimics an infant’s brain. An infant’s brain is like a sponge, and it learns through training. It takes some years for the web of neural networks in it to mature and infer or deduce multiple things through one set of training.
The future where deep learning can help artificial intelligence think, interpret, analyze, and deduce on its own is not too far. A C-3PO — or a Terminator if you will — might be in the offing. Cynics predict our jobs will be at stake. But till then, deep learning will be inspired by the human brain, not imitate it.
After all, artificial neural networks still do not know they need electricity to survive.
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