The next generation of artificial intelligence is here; we’re
teaching robots how to think like humans more and more each day. You might not
call them robots, but instead artificial intelligence programs tasked with simple
operations like visual recognition or speech recognition. They are different
from conventional AI programs in that they learn how to do things instead of
being told what to do. So, in short, these programs are a lot more like humans
in that they have a new way of “learning,” and it happens to be a lot like the
way our nose-brain makes sense of the world.
This new AI approach is called, in shorthand, ‘neural
networks’ or ‘deep learning.’ Our sense of smell works a lot like a neural
network, putting together various layers of recognition, until entire episodes
of experience are encoded or released.
These neural networks have been coming up in the news quite
often as of late, so I thought I’d re-post some of the good explanations and
examples, all of which come by way of Google Labs and Wired magazine. If you’re
interested in these things, check out some of
Hidden Scents, as it
comes around to the concept of neural networks quite often.
Wired writer Margaret Rhodes writes a piece for those
wondering "what the f*@k
neural
networks are and how they work," but she also links us to Daniel
Smilkov, a member of Google’s Big Picture Research Group, and Shan Carter, who
creates interactive graphics for The New York Times, who both wanted to teach
people what these things really are. Play with their amazing interactive here:
http://playground.tensorflow.org
Later on, another article by the same Wired writer,
Margaret Rhodes, shows us how Google’s deep learning AI plays such a mean game
of Pictionary. And again, notice the trend towards managing ambiguity that
these new methods are so good at.
“But the game’s accuracy,
while impressive, isn’t what makes it a powerful learning tool. It’s how, by
observing the way Google responds to your doodling... The lesson: To understand
the whole, neural networks need pieces of data that not only connect but build on
one another, piece by piece.”
Notes:
WIRED, Apr 2016
WIRED, Nov 2016
Play with Neural Networks
