AI Evolution: Survival of the Smartest
Posted on : 21-05-2018 | By : richard.gale | In : Innovation, Predictions
Tags: AI, Collaboration, Complexity, GAN, Innovation, Machine learning
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Artificial intelligence is getting very good at identifying things: Let it analyse a million pictures, and it can tell with amazing accuracy which show a child crossing the road. But AI is hopeless at generating images of people or whatever by itself. If it could do that, it would be able to create visions of realistic but synthetic pictures depicting people in various settings, which a self-driving car could use to train itself without ever going out on the road.
The problem is, creating something entirely new requires imagination—and until now that has been a step to far for machine learning.
There is an emerging solution first conceived by Ian Goodfellow during an academic argument in a bar in 2014… The approach, known as a generative adversarial network, or “GAN”, takes two neural networks—the simplified mathematical models of the human brain that underpin most modern machine learning—and pits them against each other to identify flaws and gaps in the others thought model.
Both networks are trained on the same data set. One, known as the generator, is tasked with creating variations on images it’s already seen—perhaps a picture of a pedestrian with an extra arm. The second, known as the discriminator, is asked to identify whether the example it sees is like the images it has been trained on or a fake produced by the generator—basically, is that three-armed person likely to be real?
Over time, the generator can become so good at producing images that the discriminator can’t spot fakes. Essentially, the generator has been taught to recognize, and then create, realistic-looking images of pedestrians.
The technology has become one of the most promising advances in AI in the past decade, able to help machines produce results that fool even humans.
GANs have been put to use creating realistic-sounding speech and photo realistic fake imagery. In one compelling example, researchers from chipmaker Nvidia primed a GAN with celebrity photographs to create hundreds of credible faces of people who don’t exist. Another research group made not-unconvincing fake paintings that look like the works of van Gogh. Pushed further, GANs can reimagine images in different ways—making a sunny road appear snowy, or turning horses into zebras.
The results aren’t always perfect: GANs can conjure up bicycles with two sets of handlebars, say, or faces with eyebrows in the wrong place. But because the images and sounds are often startlingly realistic, some experts believe there’s a sense in which GANs are beginning to understand the underlying structure of the world they see and hear. And that means AI may gain, along with a sense of imagination, a more independent ability to make sense of what it sees in the world.
This approach is starting to provide programmed machines with something along the lines of imagination. This, in turn, will make them less reliant on human help to differentiate. It will also help blur the lines between what is real and what is fake… And in an age where we are already plagued with ‘fake news’ and doctored pictures are we ready for seemingly real but constructed images and voices….