These fake images tell a scary story of how far AI has come
In the past five years, machine learning has come a long way. You might have noticed that Siri, Alexa, and Google Assistant are way better than they used to be, or that automatic translation on websites, while still fairly spotty, is hugely improved from where it was a few years ago.
But many still don’t quite grasp how far we’ve come, and how fast. Recently, two images made the rounds that underscore the huge advances machine learning has made — and show why we’re in for a new age of mischief and online fakery.
The first was put together by Ian Goodfellow, the director of machine learning at Apple’s Special Projects Group and a leader in the field. He looked over machine-learning papers published on the online open-access repository arXiv over the past five years, and found examples of machine learning-generated faces from each year. Each of the faces below was generated by an AI. Starting with the faces on the left, from 2014, you can see how dramatically AI capabilities have improved:
In 2014, we’d just started on the task of using modern machine-learning techniques to have AIs generate faces. The faces they generated looked grainy, like something you might see on a low-quality surveillance camera. And they looked generic, like an average of lots of human faces, not like a real person.
In less than five years, all of that changed. Today’s AI-generated faces are full-color, detailed images. They are expressive. They’re not an average of all human faces, they resemble people of specific ages and ethnicities. Looking at the woman above on the far right, I can vividly imagine a conversation with her. It’s surreal to realize she doesn’t exist.
How did we come so far so fast? Machine learning has seen a flood of new researchers and larger research budgets, driving rapid innovations, and a new technique invented in 2014 made a huge difference.
Let’s start with a quick primer on how machine learning can generate images like these. Modern machine learning often uses a technique called a generative adversarial network (GAN). Ian Goodfellow, who compiled the above chart, invented the technique in 2014.
Here’s the idea: Imagine that an AI is trying to generate pictures of people. When it does unusually well, you want to tell it that it did unusually well (so it’ll try similar techniques next time). When it does unusually badly, you want to tell it that it did unusually badly (so it will correct whatever it was doing wrong). Your AI will need lots of practice — it may need to draw millions of pictures — to draw photorealistic humans.
