One of my favorite science fiction tales is a time in the
future when robots have become so sophisticated that they have evolved into intelligent
servants, much like humans. Boston Dynamics presently produces the most
sophisticated robot demonstrating this, capable of moving freely and interacting
with people in various ways. But even science fiction has difficulty picturing
a world where robots can think for themselves. You’re closer than you would
imagine to this world.
Machines and robots will not only improve in the not-too-distant
future, but they will also start to show signs of creativity. It’s possible
that in the near future, they may be able to produce more straightforward
creative outputs than people. But even though I think robots can mimic human
creativity, I don’t believe this is the same as genuine creativity. Sceptical?
Let’s first look at the technological developments that will result in
breakthroughs, and then we’ll see my predictions for the kind of employment
that creative people will soon lose to robots:
1) Making mental models: Many robot technological
advancements have aimed to increase their independence (e.g., by enabling them
to move around autonomously in a new environment and recognize faces and
commands). The major advances in the near future come from studies into how to
make machines mimic human thought. This procedure is becoming increasingly more
efficient in recent years thanks to big data, deep learning algorithms, and the
capacity to distribute processing power over thousands of computers in the
cloud. For instance, Skype can now translate a real-time video chat between two
people if they speak different languages. The European Union has already
started investing €1 billion over the next ten years in research that will
likely involve tests that replicate thinking processes in the human
brain. Before that, IBM developed Watson, a brand-new class of knowledge
supercomputer, which was successful in winning the game show
“Jeopardy.” Jeopardy questions are frequently vague and rely on
cryptic implications inside them. Thus, Watson needed to analyze queries more
humanistically to reply, and he did so extremely well. This is in contrast to
earlier supercomputers used to search for data faster.
Watson has only grown more formidable and impressive over
time. And two examples demonstrate how it is already displaying creative traits,
coming up with concepts that not only no human has encoded into it but which no
human has previously conceived of and which have been judged to be excellent by
human beings: (1a) Chef Watson is developing new dishes. With the help of
a partnership between IBM and the food publication Bon Appetit, Watson was
given access to the magazine’s extensive recipe database. Watson learned from
successful recipe samples and determined which elements work well together.
Watson then investigates this data in more detail, examining elements such as
the molecular structure of distinct substances, their flavor profiles, and how
they respond to cooking. (1b) Writing a TED talk of its own. IBM has started
the Watson AI XPrize. The team that designs an AI that can craft its own
compelling TED talk.
2) Machine Learning: Many scientists are
investigating how to enable robots to gradually develop their knowledge of
their surroundings in order to make them more autonomous. The goal is to lessen
the need for humans to pre-program all of the information they require. In
order to learn how to move, these machines study their bodies and gain new
knowledge in the same way that young children do. It can even imagine what is
happening in the thoughts of those with whom it is engaging. Even if it’s
fascinating, the major innovations will result from letting machines that can
learn concepts learn from the data on the internet. Google built a neural
network using 16,000 processors in 2012 and fed it YouTube thumbnails of random
images. It could construct a sense of resemblance across numerous photos and
determine the most common thing without any prior knowledge. It was a cat, in
case you weren’t able to tell. Regards, YouTube! These machines will soon look
at items and see the descriptions that humans have programmed and the meaning
people attach to them if given greater processing power and time. This was
taken a step further by Google around 2015, which permitted these robots to
begin viewing visuals in a “dreamlike” manner. They did this to
understand how their system was actually “seeing” the images being
sent to it. As a result, they permitted it to merge portions of an image that
it thought were similar to other photos it already knew. It will therefore
alter the patterns on a butterfly with eye-shaped patterns to resemble eyes.
Alternatively, if it notices a cloud that it thinks is somewhat like a dog’s
head, it will make it appear more like a dog’s head. With the use of this
technology, computers, and neural networks are now able to improve at
interpretation, a talent that they had been lousy at. Based on some
initial data and production requirements, an advanced version of this would
soon be able to create works that had not yet been created.
(3) Big data, quick experimentation, and forecasts: One of the major developments
in analytics in recent years is “Big Data,” which is already capable
of predicting everything from your Google Autocomplete searches to the model of
toaster you should get from Amazon to the movies you should watch on a Tuesday
night. A system can determine underlying tendencies more accurately than a
person ever could by being fed enough data, and it can also forecast what might
succeed in the future. You can already guess what kind of songs you’ll like.
The Yossarian lives metaphorical search engine is one of my other favorite
experiments. It allows you to search for an idea rather than a specific piece
of information and delivers results of what its database of internet searches
indicates are relevant metaphorical concepts. Like a virtual brainstorming
session, almost. Experts in the field of music provide feedback to
Pandora’s Music Genome Project on hundreds of songs, including how the lyrics
function, elements of the basic melody, genre, style, tempo, and impact. When
creating a personal track list that is aired as a radio station, it also
conducts hundreds of trials with its millions of users. It receives real-time
feedback on how well it worked based on how the user engages with the recommended
music. In order to create a list of new music a consumer could love, it uses
this information to understand how individuals respond to and enjoy various
components of music in multiple contexts. What about the next stage of the
development of big data? Computers can already comprehend spoken language, word
meanings, and voice. Big data could probably identify the underlying patterns
and forecast new lyrics if it examined the lyrics of every song produced in the
last 100 years to see how popular they were. Additionally, it could immediately
try them out on humans to see how they did. Imagine a program that could take a
notion, come up with metaphors for it, utilize big data to forecast probable
songs that would be popular, and then come up with 100 slightly different
variations of the same concept. It could create music by
“singing” words over a synthesized track using a computer voice and
then publish each version on YouTube or a radio streaming service. It would
then modify the content and style based on user input and popularity, repeat
the experiment, gather more feedback, and iterate until it had a song that
users enjoyed. At that point, it would release it to its iTunes account without
ever receiving a note from a human. Big data can also be used to examine
historical connections between various types of media and online discussion, as
well as how those connections affected the success of later media. Would it
have been possible to foresee the popularity of “Vampire”-based
literature earlier? Could it have predicted the emergence of a musical genre
that, like Grunge in the 1990s, reflected the attitudes of a particular
demographic? How far in advance could you make a popular prediction? If not
improved, all of this will someday be made possible by big data.
So what follows? Machines
will soon replace some parts of the creative process, but I don’t think they’ll
ever fully replace a person’s creativity. This results from the clear
distinction between creativity (developing fresh, worthwhile ideas) and craft (actualizing those
ideas). Machines will eventually surpass humans in craftsmanship, and in many
cases, they already have (manufacturing, image processing); nonetheless, they
can only answer the questions “What?” and “How?” but not
“Why?” No matter how much analysis was done on it from however
many millions of sources, unless a machine has progressed beyond taking inputs
as data and using data as experiences, all of its information is still
second-hand from humans. Therefore, the following are the forecasts for
creative vocations that will be at least largely replaced by robots in the
upcoming ten years:
(A) Advertising: Before a complete campaign
launch, programs will create and test hundreds or thousands of designs,
slogans, etc., online. They will tweak the campaign and iterate until they find
the perfect message based on user feedback.
(B) Music: The first song to be
composed, sung, and recorded entirely digitally will be released. It will
probably include highly cliché lyrics about “Love,”
“Beauty,” and a lot of the term “Baby.” But there will be
much more complexity and variety on the second album. Furthermore, the live
concerts will have numerous lighting effects but no personality.
(C) Architecture & Design: A program will generate numerous
designs that all match the fundamental requirements by giving a building or
product the necessary functionality.
(D) Writing, Screenwriting, & TV: Software will be able to forecast
what books, movies, and TV shows will be popular in the next 1, 2, & 3
years by identifying the underlying trends in public opinion. It will then
compare this to prior movies to identify story arcs that the book, film, or TV
program should follow to increase its chances of success.
Georgios Ardavanis – 15/04/2023