Artificial Creativity – Rise of the Idea Machines

by Nicholas Skillicorn

Artificial Creativity

One of the favorite stories in Science Fiction is of a future where robots are so advanced that they have taken on human characteristics and act as advanced servants. Asimo is currently the most advanced robot displaying this, able to move freely and interact in many ways with people. But even SciFi have difficulty imagining a world where robots can come up with their own ideas. This world is closer than you may think.

In the not too far future machines and robots will not only become more advanced, they will also begin to exhibit aspects of Creativity, and may soon exceed people in the ability to produce simple creative outputs. However, while I believe robots will be able to imitate a human’s ability for crafting creative work, I don’t believe this is the same as true creativity.

Skeptical? Let’s me outline the three technological advances which will lead to the breakthroughs, and then see my predictions of jobs robots will soon steal from creative people:

1. Modelling of the human mind

A lot of advances in robot technology have been about making them more independent (able to move in a new space independently, recognising faces and commands etc). The big upcoming leaps come from research into how machines can emulate the human thought process. The EU is investing €1billion into the modelling of the human brain over the next 10 years, which will likely include experiments into modelling thought processes.

Even before that, IBM created a new type of knowledge supercomputer called Watson, which managed to win the Gameshow ‘Jeopardy’. Unlike previous supercomputers used to search for data faster, Jeopardy questions are often ambiguous and rely on cryptic connotations within them, so Watson needed to analyse queries in a more human-like manner to react, and did so very successfully.

Finally, one of my favorite experiments is called Yossarianlives, which is a metaphorical search engine. Instead of searching for specific data, it lets you search for a concept and returns results of what its databank from internet searches say are related metaphorical concepts. Its close to a digital brainstorming session.

2. Machine Learning

In order to make machines more independent, many researchers are looking into robots building their own awareness of their surroundings over time. The aim is to reduce the requirement of humans to programme all of the information they need in advance. So now there are machines which are learning new information in the same way that toddlers do, and learning about their own body in order to learn how to move. It can even begin to imagine what is going on in the minds of people it is interacting with.

While that is interesting, the real changes will come out of letting learning computers loose on the internet’s data so that they can learn human concepts. Last year, Google created a neural network of 16,000 computers and fed it random image thumbnails from Youtube. Without any previous knowledge, it was able to form a concept of similarity between many images, and to learn what the most common object was. In case you were guessing, it was a cat. Thanks Youtube. Given more processing power and time, these machines will soon look at objects and see not only descriptions which humans have programmed, but the meaning people give to them.

3. Big data, predictions and instant experimentation

‘Big Data’ is one of the biggest trends in analytics from the past few years, already doing everything from predicting what you will search for in Google Autocomplete, which type of Toaster Amazon should recommend to you, and which Movies Netflix thinks you would like to see on a Tuesday evening. By feeding a system enough data it is able to discern the underlying trends more effectively than a person ever could and make predictions of what may work in the future. It is already predicting what music you will listen to.

Pandora’s Music Genome project gets input from music experts on thousands of songs, including how the lyrics work, aspects of the base melody, genre, style, speed, and impact. It also runs thousands of experiments with its millions of users when producing a personal track list, streamed as a radio station, and gets real-time feedback on how successful it was by how the user interacts with the suggested music. This helps it figure out how people react to and enjoy aspects of music in different settings, and so is able to produce a list of new music a customer may like.

But what about the next evolution of big data? Computers are already able to understand voice, language structure and word meanings. If big data analysed the lyrics to every song released in the last 100 years and saw how popular they fared, it is likely it could find the underlying patterns and predict new lyrics. More than that, it could instantly test them with people to see how they fared. Imagine a programme able to take a concept, find metaphors for it, use big data to predict potential lyrics which would be popular, and then produce 100 slightly different versions. It could produce a song by “singing” the lyrics using a computer voice over a synthesised track, and release each version either on Youtube or a radio streaming service. Based on user feedback, it would then amend the content and style, run the experiment again, get more feedback, until it had a song which users loved, and then release it to its iTunes account, without any human every writing a note.

Similarly, big data could be used to analyse previous links between all forms of media and internet chatter and its effect on the success of media released after that. Would there have been a way to predict the success of ‘Vampire’ based media earlier? Could it predict the rise of a music genre representing the attitudes of a demographic like Grunge did in the 90s? How far in advance could you predict what will be popular? Big Data will eventually enable all of this.

So what comes next?

While I do believe that machines will soon replace certain aspects of the creative process, I don’t think they will ever be truly creative. This is due to the distinct difference between creativity (the generation of new and valuable ideas) and craft (turning those ideas into something tangible). Machines will overtake humans in craft, and in many cases already have (manufacturing), they can produce the ‘What?’ and ‘How?’, but not the ‘Why?’.  Until there is a machine which has gone beyond using inputs as data, and using data as experiences, then all of its information, no matter how much analysis went into it from however many millions of sources, is still second hand from human.

That being said, here are my predictions of creative jobs that will be at least partially replaced by machines in the next decade:

  • Advertising: Programs will produce try out hundreds or thousands of designs, slogans etc, and try them out in small scale on the internet before a full campaign launch. Based on user reaction they will refine the campaign and iterate until an ideal message is found.
  • Music: The first fully digitally written, sung and produced song will be released. It will likely have very generic lyrics about ‘Love’, ‘Beauty’ and use the word ‘Baby’ a lot. But the second album will show a lot more nuance and variety. And the live performances will have a lot of lighting effects but not much soul.
  • Architecture & Design: By providing the exact functionality required from a building or product, a programme will produce several very different designs which all meet the underlying requirements.
  • Writing, Screenwriting & TV: By finding the underlying trends in public opinion, software will be able to predict what books, films and TV shows will be popular in the 1, 2 & 3 years time. It will then compare this against previous films to suggest story arcs which the book / film / TV show should follow to enhance likelihood of success.

Do you think that machines will ever be able to produce truly creative work? Let us know in the comments below.
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Nick SkillicornNick Skillicorn is an Innovation consultant and Creativity coach in London, and author of 30 Days of Creativity Training. Find out what Improvides can do for your organisation.

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  1. At first I agreed with you and then I second guessed myself. Before I was half way through the article, I started thinking of a way that I could program a robot to be creative and I figured it out. By the time I got to your statement, “This is due to the distinct difference between creativity (the generation of new and valuable ideas) and craft (turning those ideas into something tangible). Machines will overtake humans in craft, and in many cases already have (manufacturing), they can produce the ‘What?’ and ‘How?’, but not the ‘Why?’. Until there is a machine which has gone beyond using inputs as data, and using data as experiences, then all of its information, no matter how much analysis went into it from however many millions of sources, is still second hand from human. ”

    That’s when I realized that you don’t understand how creativity really works and so you don’t see the flaw in your thinking about it.

    Like I said, I used to think like you and dismiss the idea of creative robots. Now, I know they can be created. However, I’ll never do it, nor teach anyone else how…

    • Marshall, thanks for your note. I was hoping you could follow up on your statement about how creativity really works. I always enjoy the debate around the subject, especially with practitioners in the Innovation space, although I will consider myself to understand how humans develop ideas, both individually and in groups. And my argument in this article is that any computer coming up with an idea will, for now at least, be producing something which they estimate is statistically likely to please humans (either individuals or whole societies). But the probability calculations do not mean the software understands the emotional reason ‘why’ a person may react to the new idea in a certain way, whereas humans can use their empathy to understand this.

      Do you (or anyone else) still disagree?

  2. Marshall Barnes

    Dear Nicholas:

    I just saw your response to my response (better late than never…) and your statement “But the probability calculations do not mean the software understands the emotional reason ‘why’ a person may react to the new idea in a certain way, whereas humans can use their empathy to understand this” is easily resolved. You can program the software to go as deep into a psychological basis as you want. Just because no one has done that yet doesn’t mean that they can’t or won’t.

    The reason I believe that machines could become creative is because of what I know about creativity and, because I understand intimately where it comes from and how it works, I understand how that could be programed into a computer. I’m not saying it would be easy, but it is certainly not impossible. I could probably have the guys at IBM have Watson functioning as a full creative computer in less than a year, maybe just a couple of months.

    As for where creativity comes from – I get paid for consulting on that one because I can prove I know more about it than anyone. Here’s an easy way to do – ask anyone that claims to be an expert on creativity how many different fields they applied what they know about creativity and then ask, in how many of those fields have they made a significant breakthrough. Most will say no more the 5 and 1. Sometime you’ll get a crazy lopsided answer like 40 and 1. That means they’re probably making it up. If they say 1 and 40, then it means they concentrated in one area but they really kicked ass in it. Mine is 30 and 40. Everything from art, publishing, video technology and music technology to advanced concept technology in the aerospace area. Here’s a taste – http://about.me/marshallbarnes.

    The bottom line is it was a good article and I don”t want creative machines running around, even though I know how to make them…

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