With every year, machines surpass humans in more and more activities
we once thought only we were capable of.
Today’s computers can beat us in complex board games,
transcribe speech in dozens of languages,
and instantly identify almost any object.
But the robots of tomorrow may go futher
by learning to figure out what we’re feeling.
And why does that matter?
Because if machines and the people who run them
can accurately read our emotional states,
they may be able to assist us or manipulate us
at unprecedented scales.
But before we get there,
how can something so complex as emotion be converted into mere numbers,
the only language machines understand?
Essentially the same way our own brains interpret emotions,
by learning how to spot them.
American psychologist Paul Ekman identified certain universal emotions
whose visual cues are understood the same way across cultures.
For example, an image of a smile signals joy to modern urban dwellers
and aboriginal tribesmen alike.
And according to Ekman,
anger,
disgust,
fear,
joy,
sadness,
and surprise are equally recognizable.
As it turns out, computers are rapidly getting better at image recognition
thanks to machine learning algorithms, such as neural networks.
These consist of artificial nodes that mimic our biological neurons
by forming connections and exchanging information.
To train the network, sample inputs pre-classified into different categories,
such as photos marked happy or sad,
are fed into the system.
The network then learns to classify those samples
by adjusting the relative weights assigned to particular features.
The more training data it’s given,
the better the algorithm becomes at correctly identifying new images.
This is similar to our own brains,
which learn from previous experiences to shape how new stimuli are processed.
Recognition algorithms aren’t just limited to facial expressions.
Our emotions manifest in many ways.
There’s body language and vocal tone,
changes in heart rate, complexion, and skin temperature,
or even word frequency and sentence structure in our writing.
You might think that training neural networks to recognize these
would be a long and complicated task
until you realize just how much data is out there,
and how quickly modern computers can process it.
From social media posts,
uploaded photos and videos,
and phone recordings,
to heat-sensitive security cameras
and wearables that monitor physiological signs,
the big question is not how to collect enough data,
but what we’re going to do with it.
There are plenty of beneficial uses for computerized emotion recognition.
Robots using algorithms to identify facial expressions
can help children learn
or provide lonely people with a sense of companionship.
Social media companies are considering using algorithms
to help prevent suicides by flagging posts that contain specific words or phrases.
And emotion recognition software can help treat mental disorders
or even provide people with low-cost automated psychotherapy.
Despite the potential benefits,
the prospect of a massive network automatically scanning our photos,
communications,
and physiological signs is also quite disturbing.
What are the implications for our privacy when such impersonal systems
are used by corporations to exploit our emotions through advertising?
And what becomes of our rights
if authorities think they can identify the people likely to commit crimes
before they even make a conscious decision to act?
Robots currently have a long way to go
in distinguishing emotional nuances, like irony,
and scales of emotions, just how happy or sad someone is.
Nonetheless, they may eventually be able to accurately read our emotions
and respond to them.
Whether they can empathize with our fear of unwanted intrusion, however,
that’s another story.
