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Friday, April 14, 2017

Neural networks made easy | TechCrunch

Photo: Ophir Tanz
Photo: Cambron Carter
"If you’ve dug into any articles on artificial intelligence, you’ve almost certainly run into the term “neural network.” Modeled loosely on the human brain, artificial neural networks enable computers to learn from being fed data." notes Ophir Tanz, CEO of GumGum, an artificial intelligence company with particular expertise in computer vision and Cambron Carter, leads the image technology team at GumGum, where he designs computer vision and machine learning. 


The efficacy of this powerful branch of machine learning, more than anything else, has been responsible for ushering in a new era of artificial intelligence, ending a long-lived “AI Winter.” Simply put, the neural network may well be one of the most fundamentally disruptive technologies in existence today.

This guide to neural networks aims to give you a conversational level of understanding of deep learning. To this end, we’ll avoid delving into the math and instead rely as much as possible on analogies and animations.

Thinking by brute force  
One of the early schools of AI taught that if you load up as much information as possible into a powerful computer and give it as many directions as possible to understand that data, it ought to be able to “think.” This was the idea behind chess computers like IBM’s famous Deep Blue: By exhaustively programming every possible chess move into a computer, as well as known strategies, and then giving it sufficient power, IBM programmers created a machine that, in theory, could calculate every possible move and outcome into the future and pick the sequence of subsequent moves to outplay its opponent. This actually works, as chess masters learned in 1997.*

With this sort of computing, the machine relies on fixed rules that have been painstakingly pre-programmed by engineers — if this happens, then that happens; if this happens, do this — and so it isn’t human-style flexible learning as we know it at all. It’s powerful supercomputing, for sure, but not “thinking” per se.

Teaching machines to learn  
Over the past decade, scientists have resurrected an old concept that doesn’t rely on a massive encyclopedic memory bank, but instead on a simple and systematic way of analyzing input data that’s loosely modeled after human thinking. Known as deep learning, or neural networks, this technology has been around since the 1940s, but because of today’s exponential proliferation of data — images, videos, voice searches, browsing habits and more — along with supercharged and affordable processors, it is at last able to begin to fulfill its true potential. 
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Source: TechCrunch


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