Quick, can you pick all the labradoodles from
the fried chicken?
How many labradoodles can you see?
Generally people are pretty good at knowing
what we’re looking at.
But it’s been reported that artificial intelligence
struggles to tell the difference between these
pictures.
Or between chihuahuas and blueberry muffins.
sheepdogs and mops. puppies and bagels.
Or dalmatians or ice cream!
Is there something particular about dogs that
computers just don’t get?
Well, not really!
Researchers have thankfully shown that dogs
and food can pretty well be distinguished.
In the dogs versus foods case, algorithms
can identify which is which with some 90 percent
accuracy.
This is thanks to artificial neural networks,
algorithms that are structured in a similar
way to the brain.
In the last episode, we explore how artificial
neural networks are really good at finding
patterns in data.
To learn something, the network takes lots
of examples, say, songs with many instruments
and vocals, it works out what makes auditory
patterns that resemble a voice, then it uses
those patterns to isolate a voice among the
other sounds.
With images, after a deep neural network has
seen thousands of sample dog photos, it can
learn what a dog is and identify dogs in new
photos as accurately as you can.
Or almost as accurately.
Remember, there was some error in the dog/chicken
caper.
The trouble starts when the input signals
are just too similar.
If the pattern that says labradoodle is the
same fuzzy curly pattern that makes up this
sheep skin I bought a few years ago, how is
a computer to tell which is which?
It might seem like a trivial problem.
So what if a computer can’t tell some dogs
from some food?
But this is an example of how hard it is to
close the gap between machine and human intelligence.
If we develop incredibly precise algorithms,
it might mean that if an example only changes
a little, the machine changes its mind about
what’s in the photo, and struggles to understand
that a photo of a dog with a hat is still
a dog.
And this is one of the hardest problems in
artificial intelligence: common sense.
How can we build machines that have common
sense?
So they don’t have to be trained on every
instance of all the objects and animals that
exist?
And coding common sense is not the only problem
engineers have to solve.
Another thing that AI does is that it can
hallucinate.
It can be tricked into seeing and hearing
things that don’t exist.
Some researchers tricked a computer to see
this cat as guacamole.
This happens because no matter how accurate
AI systems get in identifying objects in images
they are still vulnerable to what’s called
“adversarial examples.”
Like our cat.
Adversarial examples fool AI because they
carry a special pattern of noise from things
like lighting or texture that leads to the
machine interpreting the image entirely differently.
Here, MIT researchers 3D printed a turtle,
that because of altering the pattern on its
shell, the artificial neural network sees
as… a rifle.
Similarly the texture of this baseball means
it’s seen as… espresso.
Neural networks can struggle with 3D objects
because they’re normally trained with 2D
images.
Still, this cat photo is recognised as...
guacamole.
But when it’s slightly rotated, this pattern
of noise disappears so it’s correctly identified
as a cat.
Us humans can pretty obviously recognise these
images.
But the machine… sees something that’s
not there.
To be fair, computer vision has seen significant
progress in recent years.
And in some cases it’s more accurate than
a human’s.
But adversarial examples are a big concern
for artificial intelligence.
Sure AI is great at seeing at image, but we
need to do a lot more work in training AI
to confidently recognise 3D objects.
When image recognition is applied to things
like driverless cars, a machine hallucinating
can have big implications.
For now, here’s a final question for you:
given that people eventually look like their
dogs, are there people out there who look
like a blueberry muffin?
Do I look like fried chicken?
