Lately,
artificial intelligence has been very much the hot topic in Silicon
Valley and the broader tech scene. To those of us involved in that scene
it feels like an incredible momentum is building around the topic, with
all kinds of companies building A.I. into the core of their business.
There has also been a rise in A.I.-related university courses which is
seeing a wave of extremely bright new talent rolling into the employment
market. But this is not a simple case of confirmation bias - interest
in the topic has been on the rise since mid-2014.
The noise around
the subject is only going to increase, and for the layman it is all
very confusing. Depending on what you read, it's easy to believe that
we're headed for an apocalyptic Skynet-style obliteration at the hands
of cold, calculating supercomputers, or that we're all going to live
forever as purely digital entities in some kind of cloud-based
artificial world. In other words, either The Terminator or The Matrix
are imminently about to become disturbingly prophetic.
Should we be worried or excited? And what does it all mean?
Will robots take over the world?
When
I jumped onto the A.I. bandwagon in late 2014, I knew very little about
it. Although I have been involved with web technologies for over 20
years, I hold an English Literature degree and am more engaged with the
business and creative possibilities of technology than the science
behind it. I was drawn to A.I. because of its positive potential, but
when I read warnings from the likes of Stephen Hawking about the
apocalyptic dangers lurking in our future, I naturally became as
concerned as anybody else would.
So I did what I normally do when
something worries me: I started learning about it so that I could
understand it. More than a year's worth of constant reading, talking,
listening, watching, tinkering and studying has led me to a pretty solid
understanding of what it all means, and I want to spend the next few
paragraphs sharing that knowledge in the hopes of enlightening anybody
else who is curious but naively afraid of this amazing new world.
Oh, if you just want the answer to the headline above, the answer is: yes, they will. Sorry.
How the machines have learned to learn
The
first thing I discovered was that artificial intelligence, as an
industry term, has actually been going since 1956, and has had multiple
booms and busts in that period. In the 1960s the A.I. industry was
bathing in a golden era of research with Western governments,
universities and big businesses throwing enormous amounts of money at
the sector in the hopes of building a brave new world. But in the mid
seventies, when it became apparent that A.I. was not delivering on its
promise, the industry bubble burst and the funding dried up. In the
1980s, as computers became more popular, another A.I. boom emerged with
similar levels of mind-boggling investment being poured into various
enterprises. But, again, the sector failed to deliver and the inevitable
bust followed.
To understand why these booms failed to stick, you
first need to understand what artificial intelligence actually is. The
short answer to that (and believe me, there are very very long answers
out there) is that A.I. is a number of different overlapping
technologies which broadly deal with the challenge of how to use data to
make a decision about something. It incorporates a lot of different
disciplines and technologies (Big Data or Internet of Things, anyone?)
but the most important one is a concept called machine learning.
Machine
learning basically involves feeding computers large amounts of data and
letting them analyse that data to extract patterns from which they can
draw conclusions. You have probably seen this in action with face
recognition technology (such as on Facebook or modern digital cameras
and smartphones), where the computer can identify and frame human faces
in photographs. In order to do this, the computers are referencing an
enormous library of photos of people's faces and have learned to spot
the characteristics of a human face from shapes and colours averaged out
over a dataset of hundreds of millions of different examples. This
process is basically the same for any application of machine learning,
from fraud detection (analysing purchasing patterns from credit card
purchase histories) to generative art (analysing patterns in paintings
and randomly generating pictures using those learned patterns).
As
you might imagine, crunching through enormous datasets to extract
patterns requires a LOT of computer processing power. In the 1960s they
simply didn't have machines powerful enough to do it, which is why that
boom failed. In the 1980s the computers were powerful enough, but they
discovered that machines only learn effectively when the volume of data
being fed to them is large enough, and they were unable to source large
enough amounts of data to feed the machines.
Then came the
internet. Not only did it solve the computing problem once and for all
through the innovations of cloud computing - which essentially allow us
to access as many processors as we need at the touch of a button - but
people on the internet have been generating more data every day than has
ever been produced in the entire history of planet earth. The amount of
data being produced on a constant basis is absolutely mind-boggling.
What
this means for machine learning is significant: we now have more than
enough data to truly start training our machines. Think of the number of
photos on Facebook and you start to understand why their facial
recognition technology is so accurate.
There is now no major
barrier (that we currently know of) preventing A.I. from achieving its
potential. We are only just starting to work out what we can do with it.
When the computers will think for themselves
There
is a famous scene from the movie 2001: A Space Odyssey where Dave, the
main character, is slowly disabling the artificial intelligence
mainframe (called "Hal") after the latter has malfunctioned and decided
to try and kill all the humans on the space station it was meant to be
running. Hal, the A.I., protests Dave's actions and eerily proclaims
that it is afraid of dying.
This movie illustrates one of the big
fears surrounding A.I. in general, namely what will happen once the
computers start to think for themselves instead of being controlled by
humans. The fear is valid: we are already working with machine learning
constructs called neural networks whose structures are based on the
neurons in the human brain. With neural nets, the data is fed in and
then processed through a vastly complex network of interconnected points
that build connections between concepts in much the same way as
associative human memory does. This means that computers are slowly
starting to build up a library of not just patterns, but also concepts
which ultimately lead to the basic foundations of understanding instead
of just recognition.
Imagine you are looking at a photograph of
somebody's face. When you first see the photo, a lot of things happen in
your brain: first, you recognise that it is a human face. Next, you
might recognise that it is male or female, young or old, black or white,
etc. You will also have a quick decision from your brain about whether
you recognise the face, though sometimes the recognition requires deeper
thinking depending on how often you have been exposed to this
particular face (the experience of recognising a person but not knowing
straight away from where). All of this happens pretty much instantly,
and computers are already capable of doing all of this too, at almost
the same speed. For example, Facebook can not only identify faces, but
can also tell you who the face belongs to, if said person is also on
Facebook. Google has technology that can identify the race, age and
other characteristics of a person based just on a photo of their face.
We have come a long way since the 1950s.
But true artificial
intelligence - which is referred to as Artificial General Intelligence
(AGI), where the machine is as advanced as a human brain - is a long way
off. Machines can recognise faces, but they still don't really know
what a face is. For example, you might look at a human face and infer a
lot of things that are drawn from a hugely complicated mesh of different
memories, learnings and feelings. You might look at a photo of a woman
and guess that she is a mother, which in turn might make you assume that
she is selfless, or indeed the opposite depending on your own
experiences of mothers and motherhood. A man might look at the same
photo and find the woman attractive which will lead him to make positive
assumptions about her personality (confirmation bias again), or
conversely find that she resembles a crazy ex girlfriend which will
irrationally make him feel negatively towards the woman. These richly
varied but often illogical thoughts and experiences are what drive
humans to the various behaviours - good and bad - that characterise our
race. Desperation often leads to innovation, fear leads to aggression,
and so on.
For computers to truly be dangerous, they need some of
these emotional compulsions, but this is a very rich, complex and
multi-layered tapestry of different concepts that is very difficult to
train a computer on, no matter how advanced neural networks may be. We
will get there one day, but there is plenty of time to make sure that
when computers do achieve AGI, we will still be able to switch them off
if needed.
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