EMULATING THE LIVING BRAIN
Superintelligent AI? Machines will first have to learn how to emulate the structures of the living brain.
Futurists would have you believe that artificial intelligence could one day transcend our own in an event that you’ve probably heard referred to as the technological singularity. So are our biological intelligences condemned to eventually languish in the shadow of the machine? Not quite. Developments in machine intelligence are actually subject to two almost paradoxical crosscurrents. While the machine could eventually surpass the human, the burgeoning field of deep learning and artificial neural networks is showing that in order for computers to become smarter, they could first take a few lessons from nature.
Running Off GPUs
At the forefront of it all is a handful of GPU-accelerated technologies that use the power of GPUs’ massive parallel architectures to process multiple tasks simultaneously and more efficiently, and also run compute-intensive deep learning algorithms. NVIDIA’s own DIGITS ecosystem provides data scientists and researchers who want to train their own algorithms and neural networks with a way to do so easily, even without any technical knowledge pertaining to GPUs.
Deep learning was also the focus of the Asia South leg of the NVIDIA GPU Technology Conference (GTC) 2015, where Marc Hamilton, VP, Solution Architecture, at NVIDIA spoke of various realworld applications for deep learning algorithms. One particularly exciting use would be in automated driving systems, where selfdriving cars could respond to changing road conditions on-the-fly, instead of being programmed to respond according to a rigid set of parameters.
So if conditions deviate from the norm – perhaps when road markings are obscured by snow or traffic lights are blocked by tree branches – these cars will still know how to respond correctly. The hilarious report about a particular Google autonomous car’s reaction to a cyclist doing a track stand at a traffic intersection also shows just how much self-driving cars could benefit from the ability to react to new and unexpected situations.
However, these neural networks need to be trained and fed huge amounts of data in order to become smarter. But because they learn from matching similar patterns to each other, instead of recognizing
The NVIDIA DIGITS DevBox is powered by four Titan X GPUs, providing developers with ready-made hardware to advance the field of deep learning.