It’s extremely likely that you already have some idea of what AI is.
And there’s a good chance that idea has been shaped by popular culture –
films, TV shows and books set in the distant future,
based on far-fetched, science-fiction ideas.
Because of this,
despite what we might hear about real advances in technology around us,
in our minds there exists a disconnect between AI and reality.
So rather than start out by telling you what AI is,
I’d like to tell you what AI isn’t.
It is not science fiction. AI is real.
It’s been real for a while, and it’s about to get an awful lot more real.
AI, of course, stands for Artificial Intelligence.
And simply put, it means the ability of a computer program or a machine
to think, and to learn, for itself.
That’s a pretty fascinating idea,
which explains why it’s featured so much in fiction -
often telling scary, dystopian stories about machines taking over.
But it’s partly because of this ubiquity in science fiction
and the extreme nature of those scary stories that AI isn’t taken seriously,
and it’s important that we do take it seriously, because it’s the future.
And the future, not to be too dramatic, is already here.
AI is already in your life. AI is already in your house.
It’s Siri and Alexa learning your voice to better understand you,
it’s Netflix and Spotify learning your viewing and listening preferences
and making recommendations accordingly.
It’s your smart thermostat, your spam filter,
it’s Facebook advertising and Google translate.
And that’s just in your home -
we haven’t even started talking about AI being used in traffic management
or Smart Cars, or any number of other places.
So why don’t we think of these things as AI?
John McCarthy,
who coined the term “Artificial Intelligence”
way back in 1956,
claimed that “as soon as it works, no one calls it AI anymore”.
This seems to be because we see it as an inherently science-fiction notion,
something which couldn't possibly exist in the present.
That’s probably because what exists in all those sci-fi stories
is actually what’s either referred to as “AGI” - Artificial General Intelligence,
or “ASI” - Artificial Super Intelligence.
AGI is intelligence at a human level
- the ability of a machine to do any task a human could do,
or even to pass as a human.
ASI is intelligence which far surpasses that of a human.
We haven’t achieved either of these so far,
but we’ve become very good indeed at
ANI - Artificial Narrow Intelligence:
Creating a machine or program
which is incredibly good at one or two tasks, but just those tasks.
Think of a self driving car -
it’s going to be quicker
and more efficient at making driving decisions than a human,
because it was designed to just do that -
but it won’t beat you at a game of chess.
Although a specific chess AI will. Sorry.
As it exists in its current form, ANI is largely like any other computer program,
but with the ability to learn through trial and error.
It tests if a particular action achieves the desired result,
notes the outcome,
and uses that information in deciding what action to perform next time.
In the same way that humans
who do nothing but play piano for years on end becomes virtuosos,
computers can learn and hone particular skills by repeating them over and over
and noting the variable results they encounter.
The thing is,
they don't need to spend time on pesky things like eating and sleeping.
This method of learning is only made more effective when you consider
that they can also benefit from learning as a network -
a hive mind where the whole learns from the experiences of the individual.
If a self-driving car learns how to navigate a tricky junction,
or learns a new traffic rule,
every self driving car which is a part of that network
now benefits from that same knowledge.
It would be like you or I
suddenly having the ability to play some incredibly complex Rachmaninov piano piece
just because someone else in the world had practised it for hundreds of hours.
So that’s where we are right now -
ANI is a powerful and dynamic tool that we’re utilising extensively,
but what comes next?
Because we’re still trying to develop AGI
- the one which is as smart as humans -
there are lots of differing opinions on how we’re going to get there.
We’ve tried directly copying a human brain,
and we’ve tried recreating evolution -
some sort of ‘survival of the fittest’ system for computers,
but these are both basically akin
to trying to create a flying machine by mimicking the way birds flap their wings.
It’s probably possible, but it’s trying to build
an incredibly complex system which took billions of years to evolve,
in a tiny fraction of that time. Hardly ideal. So what’s left?
If we’re so good at creating ANI systems which are great at one or two tasks,
what if we create an ANI where the tasks it’s really good at are
1) AI related research, and 2) Self improvement?
We give it the tools to teach itself to be smarter,
maintaining it and updating it along the way
with better storage and processing power
as it becomes available.
The common consensus seems to be that this is most likely to yield results.
And the surprising thing
is just how fast we might see those results.
The advancement of AI is directly linked to the advancement of computing power,
and the advancement of computing power is subject to exponential growth.
Exponential growth is a fascinating topic in its own right,
but when it comes to AI what it essentially boils down to is this:
the more an AI learns about self improvement,
the faster it is going to improve.
That rate of improvement makes for a pretty steep curve.
Imagine this:
Say you have the night sky,
and it’s entirely empty except for one star in the middle.
In an unspecified amount of time, that doubles to 2 stars,
and then that doubles to 4, again to become 8, then 16, then 32.
This constant doubling is exponential growth:
the larger the number, the larger the increase.
So our stars will continue to double for thirty days,
at which point the night sky will completely covered -
a giant blanket of light.
So after day 20, we’re two-thirds of the way through the process,
and we now have hundreds of millions of stars.
But how much of the night sky is covered?
50%? 60%? 80%?
In actual fact, after two thirds of the process, only 0.01% of the sky is covered.
So what this means is that in those last 10 days,
we’ll go from what seemed like 20 days of excruciatingly slow progress
to a sudden blinding flash of brilliant light.
This is where we are -
AGI as a fully functional technology is coming, it’s coming really really fast,
but we won’t notice until it’s suddenly a part of everyday life.
I’ve said it already,
but I’ll say it again because it’s an incredibly important point:
AI is the future.
And not just the distant, Star Trek, “I’ll be long gone by then" future.
It’s the sooner-than-you-think, right-around-the-corner,
“my kids might not need to learn to drive” future.
And when it gets here, the impact of AGI and ASI is going to be huge.
In January 2015, Stephen Hawking, Elon Musk, Bill Gates
and dozens of leading AI experts
co-signed an open letter calling for research on the societal impacts of AI.
In that letter, they state that AI could provide
incalculable benefits to humankind,
that it has the potential to wipe out disease and poverty.
For a technology with those kinds of potential benefits,
it’s not unreasonable to expect AI to touch virtually every element of our lives
and revolutionise the way that industry operates.
There’s no doubt that
the future evolution of AI is an exciting unknown,
and it’s natural that as with many unknowns, it can be a little scary too.
That’s why it’s important that we understand the potential pitfalls
as well as the potential benefits of such a technology,
so that when the time inevitably comes we can use it to our advantage
as fully as possible.