Preface

Jakub Langr

When I first discovered GANs in 2015, I instantly fell in love with the idea. It was the kind of self-criticizing machine learning (ML) system that I always missed in other parts of ML. Even as humans, we constantly generate possible plans and then discriminate that just naively running into a door is not the best idea. GANs really made sense to me—to get to the next level of AI, we should take advantage of automatically learned representations and a machine learning feedback loop. After all, data was expensive, and compute was getting cheap.

The other thing I loved about GANs—though this realization came later—was its growth curve. No other part of ML is so “new.” Most of computer vision was invented before 1998, whereas GANs were not working before 2014. Since that moment, we have had uninterrupted exponential growth until the time of this writing.

To date, we have achieved a great deal, cat meme vectors included. The first GAN paper has more than 2.5 times the number of citations the original TensorFlow paper got. GANs are frequently discussed by, for example, McKinsey & Company and most mainstream media outlets. In other words, GANs have an impact far beyond just tech.

It is a fascinating new world of possibilities, and I am honored and excited to be sharing this world with you. This book was close to two years in the making, and we hope it will be as exciting to you as it is to us. We can’t wait to see what amazing inventions you bring to the community.

Vladimir Bok

In the words of science fiction writer Arthur C. Clarke, “Technology advanced enough is indistinguishable from magic.” These words inspired me in my early years of exploring the impossible in computer science. However, after years of studying and working in machine learning, I found I had become desensitized to the advances in machine intelligence. When, in 2011, IBM’s Watson triumphed over its flesh-and-bone rivals in Jeopardy, I was impressed; yet five years later, in 2016, when Google’s AlphaGo did the same in the board game Go (computationally, an even more impressive achievement), I was hardly moved. The accomplishment felt somewhat underwhelming—even expected. The magic was gone.

Then, GANs came along.

I was first exposed to GANs during a research project at Microsoft Research. It was 2017 and, tired of hearing “Despacito” over and over again, my teammates and I set out to experiment with generative modeling for music using spectrograms (visual encodings of sound data). It quickly became apparent that GANs are vastly superior to other techniques in their ability to synthesize data. Spectrograms produced by other algorithms amounted to little more than white noise; those our GAN outputted were, quite literally, music to our ears. It is one thing to see machines triumph in areas where the objective is clear (as with Jeopardy and Go), and another to witness an algorithm create something novel and authentic independently.

I hope that, as you read our book, you will share my enthusiasm for GANs and rediscover the magic in AI. Jakub and I worked tirelessly to make this cutting-edge field accessible and comprehensive. We hope you will find our book enjoyable and informative—and our humor bearable.