Why artificial intelligence won’t replace human artists
We
appreciate art through extremely human social mechanisms. Humans will continue
to prize originality
This
year’s news about what artificial intelligence can do in the arts has been both
exciting and scary. Neural networks have learnt to paint like masters and
compose sophisticated music. Those of us in creative endeavours might be as
endangered by technological advances as blue-collar workers are often said to
be—though we are protected by certain limitations that technology is never
likely to overcome.
Last
summer, a team of Russian developers released Prisma, a mobile app based on the
work of some German artificial intelligence researchers. The neural network
behind it could redraw an image using techniques it had learnt from studying
the oeuvre of a number of painters, including Vincent van Gogh and Edvard
Munch. The end product was impressive: Prisma could reproduce brushstrokes and
palettes, using only a photo for guidance, almost the way a human painter could
have.
This month, Gaëtan Hadjeres and
François Pachet from the Sony Computer Science Laboratories in Paris published
a paper about an artificial intelligence model called DeepBach, which can
compose polyphonic chorales even professional musicians can mistake for the
work of Johann Sebastian Bach. The chorale is a rather formulaic piece of Lutheran
church music that usually reharmonizes a well-known melody. Bach composed
hundreds, so there’s plenty of material for a neural network to learn.
Musicians who listened to Bach and DeepBach music were more likely to correctly
attribute the great composer’s work than the machine’s, but about 40% of them
misidentified DeepBach chorales as works composed in 18th century
Leipzig—though the machine didn’t plagiarize Bach but produced genuinely new
work.
The researchers wrote:
“Despite
some compositional errors like parallel octaves, the musical analysis reveals
that the DeepBach compositions reproduce typical Bach-like patterns, from
characteristic cadences to the expressive use of nonchord tones.”
The
success of DeepBach follows work by the same team that produced a surprisingly
hummable pop song in the style of The Beatles, and a separate effort by a team
at Google in which an artificial neural network composed jingle-like piano
pieces. Computers have generated music before, but these recent experiments are
different because the machines aren’t programmed to perform specific tasks—they
learn from big datasets to create music without further human input. Models
like DeepBach also allow human intervention, or, rather, collaboration.
Machines also have been getting better at producing literary work. This year,
an AI-written novel passed the first round of a Japanese fiction competition.
Obviously,
these creative efforts are, at this point, somewhat short of stunning—but only
if one considers their origin. Unlike most overhyped human creations, these
only represent the first steps for a technology that most of us only know for
its frustrating and often hilarious implementations in the digital assistants
on our mobile phones: Siri, Google Assistant and Cortana.
Researchers
are working to overcome a number of practical problems: The need for huge
amounts of data to train the algorithms, the narrow specialization of the
neural networks (a chess-playing one can’t write music, for example), the
logical errors the networks make when discerning and interpreting patterns.
Given more time and effort, these will probably be solved, at least to a degree
that makes consumer applications of the algorithms widespread. There is,
however, one boundary that no research team has approached and that, I suspect,
will forever protect creative professions from displacement.
It’s
a problem described in David Hume’s A Treatise Of Human Nature,
published when Bach was still alive: “Even after the observation of the frequent
or constant conjunction of objects, we have no reason to draw any inference
concerning any object beyond those of which we have had experience.”
It’s
possible to teach a machine Van Gogh’s painting technique, but only if it
already exists. An algorithm can write chorales like Bach because it can
“study” Bach. Even when the work produced by AI is less specifically derivative
than it is today—say, when the algorithms learn to combine various techniques
they learn—they will never rise above previous work because the way they work
is based on experience. They are constrained by Hume’s piece of wisdom.
The
one way in which we’re radically different from machines is in our ability to
step into the unknown, to do things that have never been done before with
paint, form, sound and the written word. Most of the rewards to creative
professionals today accrue for that ability, not to skill or the extensive
knowledge of predecessors’ work. Even a derivative work of art needs to be
derivative in groundbreaking ways to be appreciated.
It
works this way because that’s how the infrastructure—critics, publishers,
curators, performers—is set up. One could imagine work produced by machines
getting appreciation, but ultimately, we appreciate art through extremely human
social mechanisms. Humans will take care of their own, and they will continue
to prize originality.
Human
creators will probably use AI for narrow tasks, training it on specific
datasets to write dialogue, orchestrate music or produce variations to make a print
more unique. But they won’t be displaced as long as they have the courage to do
new things. Bloomberg
Source | Mint – The Wall Street Journal | 20 December
2016
Regards
Pralhad
Jadhav
Senior
Manager @ Library
Khaitan & Co
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