Translator: Carol Wang Reviewer: Peter van de Ven
I want you to think back
to your favorite scene from a movie or a novel.
What makes that so great?
from the first prehistoric man to paint on a cave
to Andy Warhol,
What defines the geniuses among us
are those whose creations grab your attention,
the kind of wonderment and captivation
that keeps a child awake with a book and a flashlight under the sheets.
We can feel and relate to the lonely solitude of Edgar Allan Poe,
and even the comedic frustrations of everyday life in Seinfeld.
In that sense,
creative works fundamentally foster an unspoken dialogue
between the creator and his or her audience.
So I couldn't believe it
when I read that The Washington Post had published over 850 articles
in the pilot year of their robot writer Heliograph.
I couldn't fathom
that an unthinking, unfeeling machine
could create something to truly engage real human readers,
So it turns out the majority of the articles written by Heliograph
concerned the 2016 election cycle,
and the rest covered sports and the Olympics.
But perhaps what's more interesting
is that the post is not alone in its automation endeavors.
Giants like Associated Press and Forbes
have invested in methods known as natural language processing,
which generate reports and stories automatically
on data-heavy topics like sports and finance.
Long story short,
we are unknowingly reading thousands of stories written by bots.
This begs the question:
Have we reached a point where machines can entirely replace human creativity?
Before we answer that,
let's first define what creativity means.
There's a four-step process
that unifies a life cycle of almost every creative concept:
first, learning - the process of gathering information and knowledge;
then phase two, ideation - the origin of a new creative concept;
followed by three, production - going from concept to reality;
and finally four, bringing creativity to the masses - distribution.
Historically, technology has accelerated the production and distribution phases
of the creative lifecycle.
In the 14th century, during the Renaissance,
perhaps the most notable creative technology of all time
came in the form of the printing press,
which enabled the first widely distributed newspapers and books.
The birth of modern fashion
came with the 18th-and-19th-century industrial revolutions,
which created the textile loom, cotton gin and the first synthetic dyes.
And most recently,
the digital era ushered in new mediums for communication
replacing the telephone and radio with screens - our first televisions -
and computer technologies
such as the digital camera, software, the Internet and social media.
New technologies have always preceded periods of unprecedented creative novelty
in fields ranging from design and fashion to literature and marketing,
and almost universally,
innovation has increased access for creativity
for both consumers and creators.
However, the next revolution, which I call the age of automation,
threatens to extinguish the last flame of human creativity.
Mechanization has already started to sweep across creative endeavors.
Ingenious advertising campaigns,
once brought to life by shrewd creative directors in Madison Avenue agencies,
which make microsecond decisions choosing from hundreds of bidders
based on data on each user's likes and interests.
Big-data, automated decision-making has lobotomized the industry.
Brands now focus on competing how well they can target users
while the creative art of memorable storytelling has been all but lost.
In news media, writers aren't the only ones suffering.
Editors have lost their clout to social feeds,
where content is personalized and curated at scale.
We're seeing the effects in the industry:
5-10% haircuts in staff across the board.
that included prominent on-air personalities,
when 2100 reporters, journalists and editors were laid off
from Huffington Post, BuzzFeed,
Vice and the newspaper conglomerate Gannett.
Machines have even made a foray into fine art.
AI trained on the works of 14th-20th-century French painters
produced a portrait
which went on to sell at Christie's for half a million dollars.
All of this marks a reversal in a trend that has spanned millennia,
and we have to consider two implications in the wake of this dramatic change:
and second, the challenge of human engagement.
A society dominated by consumers of creative content
with few or no original creators
means the death of diversity of opinion and originality of thought.
We used to just have to end up in a waiting room or an airport gate
to find a broadcast or magazine with views that defer from our own,
but today, social feats insulate us in bubbles,
echo chambers of our own thoughts and ideas.
This has dramatic effects on the political and ideological climate,
widening gaps and re-entrenching dichotomies of opinion.
Moreover, the immediate assumption
would be that moving humans from the equation
would decrease bias in media.
But in reality, third-party datasets being pulled by robot writers
This is how social media virtually manufactured the fake-news scandal
by bringing factually incorrect but socially controversial headlines
However, the drawbacks of automation
aren't merely ethical or philosophical dilemmas;
there's a real often overlooked risk reward in business.
attempt to use AI in identifying or creating hit music.
Similarly, publishers motivated by the breakout growth of Facebook and Reddit
have sought to use automation strategies on sites like Huffington Post and Yahoo.
Automation sounds like a promising approach
to reduce decision-making costs and increase creative throughput,
and in the short term it can be.
But as publishers have learned the hard way,
the risk is losing human engagement and revenue in the long term.
Treating AI as a panacea
for all operational inefficiencies and decision-making costs
has long-term consequences,
and continuing to overinvest in it in such approaches
can easily become tomorrow's dotcom or Bitcoin bubble.
The limitations of AI can be understood
by exploring the dominant form of deep learning today,
known as supervised learning.
In this model,
a machine learns a mapping function from an input X to an output Y
by examining a wealth of input data.
do not create truly novel original concepts;
rather they work very well
within the variables and model they're trained for
but cannot consider what Rumsfeld called unknown unknowns.
In the words of Scientific American:
"For AI to get creative, first it must learn the rules, then how to break them."
This shortcoming has been evident
since a 1996 study of a joke bot called Jape,
which generates puns and riddles,
uses the same technology as Forbes and Associated Press do
to create their stories,
and all of its results were taxonomically correct,
but it's comedic success was significantly improved
by pairing with the human creator.
For instance, here's an example of something it came up with on its own,
left to its own devices:
"What do you call a lenient shelter?
A lax deduction."
It's a play on words but it isn't particularly funny.
here's something that a human combined with the bot came up with:
"How's a nice girl like a chocolate birdie?
They're both sweet chicks."
Cute and silly. Definitely funnier.
At this point, it should be abundantly clear
that human decision-making complemented with computer-generated suggestions
In the words of Doug Engelbart, creator of the computer mouse:
"Technology should not aim to replace humans,
rather to amplify human capabilities
and to end automation risks
either having no engagement at all or have the wrong impact altogether."
But at the same time,
I'm not saying that augmentation entails no automation -
merely implementing it in the right aspects of the creative process.
Building human experiences requires human discretion.
And there are examples
of how creative distribution and production have been accelerated
Newsrooms have used automation for image captioning, sourcing,
and aggregating data from various libraries.
Adobe has put out a variety of AI projects
that accelerate time-consuming design tests,
such as stitching scenes and masking skies,
or removing discrepancies from video.
IBM Watson's news Explorer
uses natural language processing and sentiment analysis
to extract meaning and connotation from the news -
aggregating global trends -
and Google's Tensorflow library
accelerates tedious video processing activities,
recognizing objects and even human poses
with applications ranging from video-game design to self-driving cars.
The burden of technologists
is to guide the forward progress of humanity with innovation,
just take, for example,
the incredible potential of atomic fission,
the same concept behind the world's deadliest weapon
as well as what is arguably one of our most renewable energy sources.
Similarly, AI has incredible, unimaginable potential
to change creative possibilities,
but finding its correct implementations will be the difference
in preserving the incredible potential that only humans have for creativity.
But for those of us who aren't creating the latest AI technology,
It will take actionable, self-inspired action
to identify what creative content we consume,
rather than allowing technology to dominate the entire process,
in order to give back power to original creators
in an increasingly mechanized market.
The challenge to pioneers of this latest technological revolution
will be to find the right place for automation in the creative process,
and doing so will mean preserving the incredible and infinite potential