We all know technology is getting smarter.
But what does ‘smarter’ actually mean?
It’s not raw processing power, or speed - it’s not a technical word at all.
It describes intelligence - which is largely a human trait.
So if we call a machine smart,
we’re really saying that it exhibits a human quality.
And that’s a big deal.
Because our ability to learn, interpret, adapt, evolve -
these are what have always separated us from machines.
So, is that gap now closing? And if so... why?
Over the course of human evolution,
our brains have gotten really, really good at pattern recognition.
It’s central to how we learn to speak, and read.
It’s how we can tell thousands of faces from one another,
or recognise our parents’ voices from only a syllable or two.
And while it might not seem that impressive,
it’s actually an incredibly complex task.
One which is only possible because of how powerful our brains are.
The human brain contains about 86 billion neurons,
interconnected via an incredibly complex system-of-pathways known as synapses.
Neurons function by passing electrical charges to each other via the synapses,
and at any time each neuron is either active or inactive.
When it comes to pattern recognition, we can visualise this like a flow chart,
with each neuron’s two states - active or inactive -
being like yes/no questions.
In the context of facial recognition, think of it like...
a giant game of “Guess Who?”.
At each turn in the game we ask a question about the face,
and each yes or no answer helps us narrow down the possibilities
of who we’re looking for.
Blue eyes? Yes.
Brown hair? No.
Blonde hair? Yes.
Glasses? No.
Until eventually, we narrow it down to who we think it might be.
Now this is an incredibly over-simplistic explanation
but it is basically how the brain works:
through billions of combinations of active and inactive neurons -
trillions of yes/no questions.
And this system of active/inactive, on/off, yes/no,
is pretty comparable to boolean logic,
a form of algebra fundamental to computing,
in which all values are either True (one) or False (zero).
So it follows that a large enough computer system,
using boolean ones and zeros instead of active and inactive neurons,
could do a passable job of imitating the brain.
This kind of system is called a Neural Net.
Right now it’s the closest thing we have to an artificial brain,
and it’s one of the reasons that technology is getting smarter.
Just like a human brain, neural nets operate through pattern recognition,
and also just like a human brain, they need practise to get good at it.
For example, if you show a neural net enough pictures of horses
it will get pretty good at knowing
what a tail looks like, what a mane looks like,
the arrangement of a horse’s eyes, ears, nostrils...
And it will put that all together to become smart enough to recognise a horse.
But then, if you show it a picture of a stuffed toy horse,
it might use all that same understanding and conclude it’s a real horse.
However with enough examples of both, it will learn to tell the difference.
And eventually, it’ll be able to apply this “thinking”
to pictures of real bears and teddy bears
despite never having been taught those specific differences.
Neural nets are proving incredibly useful,
and are becoming central to a lot of different technologies -
some already commonplace, some still developing.
But they’re a long way off the capabilities of the human brain,
so it’s good to be wary of relying on them too much.
In 2018 Amazon scrapped an AI recruitment system it had been using
which relied on a neural net to vet potential candidates.
It had been trained on the resumes of previous successful hires,
the idea being that it would learn to recognise qualifications,
relevant employment experience, that sort of thing.
Unfortunately, it also picked up on another pattern
- a pre-existing bias for employing men over women -
and built this bias into its vetting procedure,
penalising female candidates for being women.
And this isn’t an isolated incident.
In 2019 a Tesla car on ‘autopilot’,
which uses a Neural Net to recognise hazards,
crashed into an overturned truck on a highway,
having only been trained to recognise vehicles like that from behind.
So maybe ‘smart’ isn’t quite the right word to use.
At least for now.
The danger of Neural Nets is that
we’re so accustomed to thinking of computers and machines as dumb mechanisms
that just do as they’re told,
that as soon as they do something that seems beyond that expectation,
something that appears the tiniest bit human,
we’re so amazed that we overestimate what they’re capable of.
There’s no doubt-that as time goes on,
computers are going to be capable of increasingly astounding things.
We just need to maintain a sense of perspective
- in how we use them, and what we expect from them -
keeping in mind that for the time being at least,
the human brain reigns supreme.