Generative adversarial networks, reinforcement learning and transfer learning are approaches that have been explored by theoreticians and researchers for years. Today, with recent improvements in technology, these new deep learning techniques are finally becoming practical for enterprise use.
“They are not really new concepts,” said Hermann Ney, professor of computer science at Germany’s RWTH Aachen University and director of science at speech recognition company AppTek. “But now, in the era of deep learning, they have a better chance to be helpful.”
GANs blur line between real and artificial
Last year, researchers from chipmaker Nvidia, based in Santa Clara, Calif., released a video showing computer-generated faces, cars and furniture suites that were amazingly realistic.
The secret? Generative adversarial networks (GANs), in which two different AI systems battle it out. One system tries to create realistic-looking images; the other system tries to tell which ones are fake and which ones are real.
