“Isn’t it funny how day by day nothing changes, but when you look back, everything is different?” – C.S. Lewis
For decades, the ancient Chinese game of Go has been the so-called “unsolvable” benchmark to push artificially intelligent (AI) machines to their limits. With the standard 19×19 tournament board containing 361 intersections and allowing for more possible board configurations than there are atoms in the known universe, it’s understandable that a computer might have a bit of difficulty planning its next move. The magnitude and flexibility of this game make all traditional AI methods essentially useless, and enumerating and evaluating each possible move is prohibitively inefficient for even the most powerful supercomputers. But in March of 2016, Google solved it. Well, maybe that’s a bit of an overstatement. Regardless, their “AlphaGo” algorithm had defeated world-champion 9-Dan Go player Lee Sedol 4-1, a task that was completely unheard of just a few years earlier, and even by the most optimistic estimations wasn’t expected to happen for at least 10 more years. In that same year, we saw similarly intelligent algorithms make a variety of break-throughs: WaveNet generated almost indistinguishably human-like synthetic speech from text, one model learned to play Atari games solely from visual input, another became a super-human lip reader, and the Google Translate service improved by an order of magnitude seemingly over night.
All of these incredible feats marked tremendous leaps forward in the field of artificial intelligence, but each and every one of them have one thing in common: they aren’t really intelligent at all. At least by what you might call a “standard” definition. AlphaGo doesn’t know that it’s playing a game, what a game is, or even what a Go board looks like. Similarly, the lip-reading algorithm doesn’t know anything about words, what lips are, or how to speak itself. Yet somehow, these models are performing at superhuman levels with absolutely zero domain-relevant knowledge. This is thanks to a relatively recent (and incredibly successful) paradigm shift in the field of artificial intelligence. For years, researchers and programmers have attempted to create intelligent machines by teaching them immensely complex sets of rules. For a good number of applications, this is a perfectly fine approach. If you want an AI to play tic-tac-toe without losing, you simply program it to block its opponent whenever they have two pieces in a line. If you want a robot to scoot around without running into anything, simply have it turn whenever it detects something in its path. But what if we want to, say, recognize a handwritten digit in an image?
Suddenly, everything becomes much more complicated. Our model’s rules would need to account for an incredible amount of variance to be able to handle the types of noisy, real-world data that humans deal with on a daily basis. That makes for one long “else if” chain. Instead, we can craft our models from the bottom up, letting them learn these rules for themselves by viewing a massive number of examples. As it turns out, this simple idea is responsible for almost all of the unbelievable achievements in AI that we’ve seen in the last decade. Self-driving cars, face-recognition, and recommender systems all rely on an incredibly powerful realization of the “bottom-up” approach: the artificial neural network (ANN). By using techniques from calculus, linear algebra, and statistics, ANNs can be “trained” to approximate incredibly high-dimensional functions that simulate abstract understanding and can be applied to complex problems like computer vision and natural language processing. They’ve revolutionized the field of machine learning and led us into an AI renaissance that will likely continue for decades.
So how did we get here? Just a decade ago this would have sounded like the stuff of science fiction, and now even top researchers have a hard time keeping up with the rate of progress (but they’re working on it). In this blog, we’ll dive into the history of artificial intelligence and explore what exactly led us to this incredible explosion of progress. We’ll analyze multiple types of neural networks and their uses, introduce the work of big names in the industry, and discuss applications of this amazing algorithm. Next week, we start off with an introduction to the deep neural network (DNN)!
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I love this game! It’s fun and addictive. Drift Hunters I played it for hours.