Creativity: how is AI impacting this human skill?

AI is the product of processing a mixture of data, often aided by other information (labels or domain models, such as Wolfram Language). The big emphasis today is on the former, but there is enormous merit in having a balance of the two.

Creativity is one of the most human qualities. So how is the rapid evolution of AI and machine learning affecting our creativity? Here’s a simple template:

  • Clarify: what do we mean by “creative”?
  • Illustrate: show examples of both products and project into the future based on research today around AI creativity.
  • Act: Where does this leave this human skill? What should we do concretely?

Clarifying: what is creativity?

“Creativity is reorganisation of existing knowledge” — Pierluigi Serraino

“Creativity is the phenomenon of something new and somehow valuable is formed” — Wikipedia

“Creativity is… making a connection between item 1 and item 2 which might not ordinarily seem connected.” — Isaac Asimov

As I’m specifically looking at this human skill from the point of view of automation, I’ve landed with this definition:

Creativity is an assessment of the diversity and complexity of information used to make new things.

Let’s test it out:

  • There is no value statement in creativity: I think this is fine. You can be very creative and produce nothing of value.
  • It’s a reflection of a process rather than definition: yes. You can have a creative process, one which produces things that are creative, which is said to have creativity in it.
  • The assessment criteria is missing: creativity does mean different things in different situations; for the workplace it is usually associated in a positive way with “intuitive leaps” and “good application of imagination”. In creative industries, creativity is more to do with aesthetics.

Illustrate: creativity with AI


At this point I draw out my favourite tech quote: “Any technology sufficiently advanced is indistinguishable from magic” — Arthur C. Clarke

Are there others? Yes indeed. More coherent writing is possible with Narrative Science, Logojoy makes logo creation point-and-click, AlphaGo in its specialised zone was awesomely creative, the list goes on. With sufficient interest I will drill into this area further.


Deep learning and GANs

Source: Andrew Ng, machine learning luminary

“Way back” in 2015, some clever fellow asked the question “what happens if I reversed this? What would it produce?” On a personal note I was still at Google at the time and I remember an internal post something to the effect of “this is what happens when you code until 2am in the morning”. This kind of thing:

As the code was open sourced, innovation was abound. Quickly, Prisma came along:

Prisma has produced some really astonishing artistic renderings of my photos. This uses “Udnie”.

Prisma is an app that takes the style information from one photo and applies it to another. I know I’m on to something when my wife, a trained artist, looks at pictures above and says “I love how it’s done the transparency of the hair”. There are a stack of apps in this space; Oilist is another fun one of note.


Google Autodraw thought my face was either a washing machine, a toilet, or perhaps a donut. I can’t imagine why it got confused.

Create new faces with a single brushstroke

Moving closer to the twilight zone see how this experimental tool uses basic colour choices and pointer gestures to create completely new pictures.

Text to image

Science fiction right? Decide after learning about StackGAN. StackGAN is a research project that takes a phrase and creates a completely new picture that represents it. You’ll notice that a lot of those images look pretty bizarre, but in terms of progress, it doesn’t take much imagination to consider where this is headed:

StackGAN is showcased in a 2-minute paper

Act: developing resilient creativity skills

Strive to gain a wide range of experiences and expertise

On the AI horizon, devices are spreading throughout the world to collect data (in the home and in the workplace). Equally, designing better ways to learn faster is also a very active area for AI. Virtual simulation looks particularly promising: the ability to simulate a good approximation of the real world environment of the scenario in question entirely virtually, which then enables testing many thousands of times faster than in the real world.

Be adaptable

  • Think of adaptability as a habit, something to practice. This means you will probably fail a lot as I have. Accept this, brush yourself off, ask “how can I do this better next time?” and try again. Encourage people around you to help pick you up too.
  • Invest time finding a team or environment that demonstrates an adaptable culture. It’s really hard to be adaptable in an environment that is indifferent, or even outright reticent to it.
    “Immerse yourself in the community of people being like you want to be” — Paramhansa Yogananda, Man’s Eternal Quest

On the AI horizon, adaptability translates to “transfer learning”: each of the examples I provided here are transferring an unarticulated abstraction of the learning concepts to accelerate learning in a new sphere. While this is still largely an aspirational frontier, it is a reality in some specific areas such as Google Translate, that appears to have developed a way of using what it learnt in one language pair (e.g. French < = > English) to other languages.

If you enjoyed this piece and would like to see it fleshed out more, please recommend it and I’ll use this as an indicator.

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Technical Product guy specialising in the impact of machine learning on speech and text (NLP). Ex-Google, but none of this matters if we don't fix the planet.