Introduction to Generative Adversarial Networks (GANs) and their potential

Teaching Computers Creativity

It started in a pub. Ian Goodfellow and his fellow doctoral students at the University of Montreal met to celebrate the completion of the 2014 academic year. Their conversation focused on the frontier of AI research: synthetic data generation; specifically, approaches that would allow a computer to produce realistic-looking images. Compared to everything machines have mastered, this problem may not seem worthy of the attention of some of the finest minds in AI research. However, they recognized the profound implications of computer’s shortcomings that lay hidden beneath the mountain of all it could accomplish.

Machine learning algorithms are effective at recognizing patterns in existing data and using that insight for tasks such as classification and prediction. When asked to generate new data, computers tend to struggle. An algorithm can defeat a chess grandmaster or classify whether a credit card transaction is likely to be fraudulent; in contrast, any attempt at making small talk with Amazon’s Alexa or Apple’s Siri is doomed. Indeed, humanity’s most basic and essential capacities — e.g., a convivial conversation or the crafting of an original creation — can leave even the most sophisticated supercomputers in digital spasms. …