Google’s new AI learns nearly as fast as humans
Make no mistake, artificial intelligence (AI) has humans in its perfect, heightened, machine vision sights. Deep learning machines already have superhuman skills when it comes to tasks such as seat of your pants air to air combat, facial recognition, language translation, lip reading, running hedge funds and annihilating troops of hardened online gamers. And as a consequence you’d be forgiven for thinking that in many fields humans are already outgunned.
But it’s not time to pack our bags and head home just yet because there’s one crucial area where we’re still the undisputed masters of the universe – we still learn faster than they do, and by a country mile.
When it comes to mastering classic video games, for example, even the best deep learning machines can still take over 200 hours of gameplay to reach the same skill levels that we can achieve in just two hours. As a consequence, it should come as no surprise that computer scientists would love to speed up how fast their AI pets learn.
Oh how we can still scoff at those stupid AI’s. Ha ha ha.
However, last week Alexander Pritzel and his DeepMind team made a breakthrough, and made me pause mid scoff – and that’s another breakthrough, in what’s almost becoming a daily occurrence for the folks down at DeepMind, which is arguably one of the world’s most advanced AI companies. Last week for example they announced that they’d given their DeepMind AI a human memory, and it’s that breakthrough, which is just the tip of the iceberg, that has now helped them achieve this breakthrough.
Pritzel and his team have just built a deep learning system that’s capable of assimilating new learning experiences and then acting on them – the result is a machine that learns ten times faster than it did previously, and which is now edging towards learning at almost human speed.
As a result it might be time to scoff less, and soon, according to the Law of Accelerating Returns, which is the same law that’s often used to predict the exponential rate of technological progress, we might soon see the day when these systems don’t just catch us up, but they overtake us like a hypersonic jet racing a bumble bee.
First a technology lesson, I know you like those. Deep learning uses layers of neural networks to look for patterns in data, and when it spots one it sends this information on to the next layer, which looks for other patterns in the signal, and so on and so on. For example, in facial recognition, one layer might look for the edges in an image in order to try to identify the outline of the persons face. The next layer then looks for circular patterns, such as the shapes that make up our eyes and mouths, and the next layer might look for a triangulation pattern that’s identifies the two eyes and mouth as a human face.
As always though the devil is in the detail. Did the deep learning system just identify the face of a human, or a Gorilla? And so the pattern matching, and the feedback systems, which learn by adjusting a variety of internal parameters go on and on until they can identify the image correctly.
