The human brain is a remarkable thing.
It has enabled us to advance mankind and understand something of the science that underpins our
existence: from the smallest atom to whole new worlds.
Imagine the possibilities if we were to improve upon human cognitive function,
that’s one of the objectives of artificial intelligence research.
No brain on Earth is yet close to understanding how we might achieve this, despite centuries
of trying. It is little wonder that the field of AI; the challenges it presents, and the
rewards it might deliver have such a hold over us.
It’s easy to think of AI as a recent innovation intrinsically linked to the rise of computing.
But the origins lie way before that, over 2000 years ago.
Around 350BC, one of the most pre-eminent blue sky thinkers was Aristotle.
He devised systolic logic, the first formal deductive reasoning system.
Aristotle and the philosophers that followed were seeking a formal method to represent
the way humans reason and think about the world.
Fast-forward 2000 years and in the early 20th Century, Bertrand Russell and Alfred Whitehead
publish ‘Principia Mathematica’ a work that laid down the foundations for a formal
representation of mathematics. This allowed Alan Turing to show that any form of mathematical
reasoning could processed by a machine in 1942.
AI accelerated in pace during the 1950s and 60s. By 1967, Marvin Minsky was predicting
that “within a generation the problem of creating Artificial Intelligence would substantially
be solved”.
It seemed that there was no stopping the advancements of AI. But the late Marvin Minsky would probably
agree that, by his definition, we’re still not there today.
So what’s gone wrong? Attempts to build systems based on the first
order logic described by those early philosophers failed. Due to a lack of computing power,
access to large amounts of data, and because they couldn’t adequately describe uncertainty
and ambiguity.
But despite this the research didn’t stop.
The development of expert systems started to achieve significant commercial interest
in the 1980s. These systems relied heavily on the thinking of those early philosophers
and was centred on knowledge.
Within these systems, AI started to find applications in fields as diverse as stock trading, oil
prospecting, agriculture and medicine.
However, building solutions that made use of this technology was extremely costly.
The advances that were made remained accessible only to the largest corporations or constrained
to academic research.
Furthermore, most of the commercial successes of AI in the 1980s and 90s, are systems we
would now consider to be mainstream computer science and not AI.
Deep Blue for example would not be considered to be intelligent, but we would accept that
it performed a task that was previously thought to be something specifically human.
We now accept the best chess players are computers and no longer consider chess to be a task
that’s uniquely human.
As a result chess-playing computers no longer come under one of our definitions of AI.
Indeed John McCarthy who coined the term AI in 1956, called this phenomena, the AI effect.
Today you wouldn’t consider your sat-nav to be AI.
“As soon as it works, no one calls it AI anymore.”
Because of this, it’s easy to fall into the trap of thinking about AI in terms of science
fiction or as a relic of computing history that never lived up to the hype.
So how should we think about AI from here?