How_It_Works_Issue_99_2017

DID YOU KNOW? During the Cold War, the US government funded research into AI in an effort to gain an advantage
For most of us, the words ‘artiicial
intelligence’ (AI) instantly bring an image
of doom to our minds. After all, we’ve all
seen humankind extending its reach beyond its
grasp before on the silver screen, and the result
is always the same. All it takes is for one of us to
create a machine that can truly think – one that
can achieve sentience and ‘wake up’ – and then
it’s all over for humanity. What if this clever
machine doesn’t like the way we do things?
What if it has other ideas?
It’s thoughts like this that have given birth to
many fantastic pieces of science iction over the
years, but in spite of what The Terminator may
depict, AI could have more potential to help us
than to harm us. But considering that respected
scientists and technology experts such as
Stephen Hawking and Elon Musk are warning of
the potential dangers AI could pose, it’s
understandable if you’re still sceptical.
To fully understand the amazing potential of
AI, we irst need to clear up the many
misconceptions surrounding this exciting ield
of technology. To begin with, we should consider
how we’re able to make machines intelligent,
and how it is that they think. The term ‘artiicial
intelligence’ was coined in 1956 and has come to
represent quite a broad spectrum of computer
capability. The phrase is thrown around often
by technology companies showcasing their
latest products, but these ‘intelligences’ are
incredibly varied in what they’re able to achieve.
For the most part, artiicial intelligence has
become an enticing way to describe a fancy
computer programme, but some truly are
learning computers. The most sophisticated of
these are currently conined to the stock market,
the world of scientiic research, or battling ever
more complex games. You may think that
predicting the net worth of a company, building
models using genetic code and becoming a
champion gamer would each require a
completely diferent AI, but all three can be
achieved using the same basic architecture.
True AI works on the principle of machine
learning; the various types of which we’ll
explore more in this feature. Computer
programmes that operate using machine
learning are markedly diferent to most other
programmes, because you don’t need to tell it
how to do something – instead, you show it.
Imagine you want a computer programme that
can ind abnormalities from brain scans. With a
conventional programme, you’d have to write a
very strict and detailed set of rules that it can
use. But with a machine learning programme,
you’d just show it a few thousand normal brain
scans and a few thousand abnormal brain scans
and then let the programme teach itself how to
recognise anomalies.
This machine learning method certainly has
its advantages over conventional programming,
as the computer may well become even better
than the programmer at performing its assigned
task. And the most exciting part of all of this is
scientists are working on programmes like these
right now.
But the kinds of intelligence able to help us in
our everyday lives aren’t just for the world of
“The term ‘artificial intelligence’ has
come to represent quite a broad
spectrum of computer capability”
Meet the artificial brain
Humans learn by using the power of neural
networks, and machines can do the same...
Sensory
information
Our senses are
constantly
collecting huge
amounts of data,
which require
processing by
the brain.
Chemical communication
Neurons communicate with each
other via chemical signals, which they
send to each other across synapses.
A chain reaction
If the chemical input is
strong enough the neuron
will ‘fire’, continuing the
signal along the chain.
Counting the inputs
Like the firing neuron, a
node will compute the
input data and only ‘fire’ if
the value is high enough.
Processing power
Both humans and computers
have powerful processing
equipment for neural networks.
Low-level functionality
Artificial neural networks form
the basis for many sophisticated
types of machine learning.
Computer
vision
Machines
typically map
visual information
onto a grid, which
makes data
processing easier.
WWW.HOWITWORKSDAILY.COM
Artificial synapses
Artificial neural networks
communicate by sending
signals in the form of
numerical values.
How It Works | 013
© Thinkstock; Shutterstock