By playing cat-and-mouse games with data, a pair of AI systems
can acquire an imagination.
Two AI systems can spar with each other
to create ultra-realistic original images or
sounds, something machines have never
been able to do before.
WHY IT MATTERS
This gives machines something akin to a
sense of imagination, which may help them
become less reliant on humans—but also
turns them into alarmingly powerful tools
for digital fakery.
Artificial intelligence is getting very
good at identifying things: show it a
million pictures, and it can tell you with
uncanny accuracy which ones depict
a pedestrian crossing a street. But
driving car could use to train itself
without ever going out on the road.
The problem is, creating something entirely new requires imagination—and until now that has
The solution first occurred to Ian
Goodfellow, then a PhD student at
the University of Montreal, during an
academic argument in a bar in 2014.
The approach, known as a generative adversarial network, or GAN,
takes two neural networks— the simplified mathematical models of the
human brain that underpin most modern machine learning—and pits them
against each other in a digital cat-and-mouse game.
Both networks are trained on
the same data set. One, known as
the generator, is tasked with creating variations on images it’s already
seen—perhaps a picture of a pedestrian with an extra arm. The second,
known as the discriminator, is asked
to identify whether the example it
AI is hopeless at generating images of
pedestrians by itself. If it could do that, it
would be able to create gobs of realistic
but synthetic pictures depicting pedestrians in various settings, which a self-
KE Y PLAYERS
Google Brain, DeepMind, Nvidia