HAL. The Terminator. Gigolo Joe. Machines that can learn from – and interact with – the world around them have long been a science-fiction staple. What was once a storytelling trope, however, is quickly becoming an everyday truth.
My kid loves bowling on the Xbox with Kinect. She takes it for granted that the Kinect can detect where she is standing and the movement of her hands. In the same way, millions of Android users rely on Google’s voice recognition technology for everyday tasks like finding directions or making phone calls.
Behind all these consumer products is a technology called “machine learning” (part of a bigger field called artificial intelligence). Machine learning is about teaching machines or computers to understand data, for example, so they can recognize voices or detect humans standing in front of a camera.
As you may imagine, machine learning is difficult; that’s why it’s only recently that we are reaping its benefits. Thanks to a combination of recent algorithmic breakthroughs and the high performance of GPUs, however, researchers have seen dramatic improvements in accuracy for machine learning problems for services that are more than just lab experiments.
Today, many companies use GPUs to build sophisticated artificial neural networks to deliver services enjoyed every day. A few examples:
- Baidu Visual Search uses GPUs to create precise image search results (Wired article)
- Microsoft Xbox Kinect enables accurate classification of body positions (Microsoft blog)
- Yandex Search uses GPUs to deliver search ranking results (Yandex blog)
- Google has built neural networks for various projects (Wired article)
- Nuance is exploring new algorithms to deliver more accurate voice-recognition products (Nuance blog)
Artificial intelligence is affecting our daily lives, and GPUs are at the heart of this revolution.
“Using GPUs for training deep neural networks has become an absolute requirement. It takes us weeks to train with GPUs, it might take us years on CPUs alone,” says Professor Rob Fergus from the Courant Institute at NYU, a research group in deep neural networks, which can be used for applications such as image recognition.
And it’s only the beginning. Beyond image search and voice recognition, future applications will include self-driving cars affordable enough to motor into the mainstream – rather than just prowl up and down California’s Highway 101 as prototypes – and intelligent recommendation engines on shopping websites. We may be many years away from computers achieving human-scale brain capabilities. But at our current pace of innovation, fueled by GPUs, the future is closer than we think.
If you are working in the field of machine learning, we’d love to hear from you in the comment box below.
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