In 1997 IBM’s Deep Blue famously defeated chess
Grand Master Garry Kasparov after a titanic battle. It had actually lost to him
the previous year, though he conceded that it seemed to possess “a weird kind
of intelligence.” To play Kasparov, Deep Blue had been pre-programmed with
intricate software, including an extensive playbook with moves for openings,
middle game and endgame.
Twenty years later, in 2017, Google unleashed AlphaGo Zero
which, unlike Deep Blue, was entirely self-taught. It was given only the basic
rules of the far more difficult game of Go, without any sample games to study,
and worked out all its strategies from scratch by playing millions of times
against itself. This freed it to think in its own way.
These are the two main sorts of AI around at present. Symbolic
machines like Deep Blue are programmed to reason as humans do, working through
a series of logical steps to solve specific problems. An example is a medical
diagnosis system in which a machine deduces a patient’s illness from data by
working through a decision tree of possibilities.
Artificial neural networks like AlphaGo Zero are loosely
inspired by the wiring of the neurons in the human brain and need far less
human input. Their strong point is realizing, which they do by breaking down
enormous measures of information or rules, for example, the standards of chess
or Go.They have had notable success in recognizing faces and patterns in data
and also power driverless cars. The big problem is that scientists don’t know
as yet why they work as they do.
The reason that the two systems create that really points up the
difference between them is nothing but art, literature and music. Symbolic
machines can create highly interesting work, having been fed enormous amounts
of material and programmed to do so. Artificial neural networks are way too
exciting which actually teaches themselves and which can therefore be said to
be more truly creative.
Symbolic AI produces art which that is recognizable to the human
eye as art, but it’s art which that has been pre-programmed. There are no
surprises. The AARON algorithm of Harold
Cohen’s Aaron produces beautiful paintings using templates which that have been
programmed into it. Similarly, Simon Colton at the college of Goldsmith’s
College in the University of London programs The Painting Fool to create a
likeness of a sitter in a particular style. However,
neither of these ever jumps past its program.
Artificial neural networks are far
more experimental and unpredictable. The work springs from the machine itself
without any human intervention. Alexander Mordvintsev set the ball rolling with
his Deep Dream and its nightmare images spawned from convolutional neural
networks (ConvNets) and that seem almost to spring from the machine’s
unconscious. Then there’s Ian Goodfellow’s GAN (Generative Adversarial Network)
with the machine acting as the judge of its own creations, and Ahmed Elgammal’s
CAN (Creative Adversarial Network), which creates styles of art never seen
before. All of them create way more challenging and difficult works—the
machine’s idea of art, not ours. Rather than being a tool, the machine
participates in the creation.
In AI-created music the contrast is
even starker. On the one hand, we have François Pachet’s Flow Machines, loaded
with software to produce sumptuous original melodies, including a well-reviewed
album. On the other, researchers at Google use artificial neural networks to
produce music unaided. But at the moment their music tends to lose momentum
after only a minute or so.
By the two types of machines, AI
created literature illustrates which are best of all the difference in what can
be created. Emblematic machines are stacked with programming and rules for
utilizing it and prepared to produce material of a particular sort, for
example, Reuters' news reports and meteorological forecasts. An emblematic
machine furnished with a database of plays on words and jokes creates business
as usual, giving us, for instance, a corpus of machine-produced thump jokes.
However, similarly as with craftsmanship their artistic items are in accordance
with what we would anticipate.
neural networks have no such restrictions. Ross Goodwin, now at Google, trained
an artificial neural network on a corpus of scripts from science fiction films,
then instructed it to create sequences of words. The result was the fairly
gnomic screenplay for his film Sunspring. With such an absence
of limitations, counterfeit neural systems will in general produce work that
appears to be dark—or would it be a good idea for us to state "test"?
This kind of machine wanders into an area past that of our current
comprehension of language and can open our psyches to a domain regularly
assigned as hogwash. NYU's Allison Parrish, an arranger of PC verse,
investigates the line among sense and hogwash. Hence, fake neural systems can
start human inventiveness. They can acquaint us with new thoughts and lift our
Proponents of symbolic machines argue
that the human brain too is loaded with software, accumulated from the moment
we are born, which means that symbolic machines can also lay claim to emulating
the brain’s structure. Symbolic machines, however, are programmed to reason
from the start.
Conversely, proponents of artificial
neural networks argue that, like children, machines need first to learn before
they can reason. Artificial neural networks learn from the data they’ve been
trained on but are inflexible in that they can only work from the data that
To put it simply, artificial neural
networks are built to learn and symbolic machines to reason but with the proper
software they can each do a little of the other. An artificial neural network
powering a driverless car, for example, needs to have the data for every
possible contingency programmed into it so that when it sees a bright light in
front of it, it can recognize whether it’s a bright sky or a white vehicle, in
order to avoid a fatal accident.
What is needed is to develop a
machine that includes the best features of both symbolic machines and
artificial neural networks. Some computer scientists are currently moving in
that direction, looking for options that offer a broader and more flexible
intelligence than neural networks by combining them with the key features of
At Deep Mind in London, scientists
are developing a new sort of artificial neural network that can learn to form
relationships in raw input data and represent it in logical form as a decision
tree, as in a symbolic machine. In short, they’re trying to build in flexible
reasoning. In a purely symbolic machine all this would have to be programmed in
by hand, whereas the hybrid artificial neural network does it by itself.
By merging the two systems in this
manner could lead to more intelligent solutions and also to forms of art,
literature and music which that are more accessible to human audiences
while also being experimental, challenging, uncertain and fun.
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