Why go long on artificial intelligence?

Another way of looking at this hype wave is to track the share price of NVIDIA, the leading graphics processing unit (GPUs) designer. In 2012, University of Toronto researchers developed a then state of the art convolutional neural network (CNN) that achieved a record breaking performance on a large scale image classification task. This feat was made possible, in no small part, because the authors optimised their network (henceforth known as'AlexNet') for parallel training and inference on two NVIDIA GPUs. Since then, NVIDIA GPUs along with their parallel computing platform and programming model (CUDA) have veritably become the shovels for the AI gold rush. The dramatic increase in parallelizable computing power has enabled developers to train deep, data-hungry architectures faster than ever before, whether they are neural network or reinforcement learning models. We've achieved incredible breakthroughs in environment perception, autonomy, robotics, machine translation, speech recognition and dialogue, search, image and video super-resolution, and many more to come.