Creativity and the Machine
AI & Content Creation
For a very long time, people did not think of computers as creative, or original. To automate mechanical and repetitive tasks, sure, computers are fantastic, but for inspiration and creativity, surely this was the sole domain of humans?
With recent, big advances in artificial intelligence, this assumption is rapidly eroding.
Artificial intelligence is already being used in many areas of content creation. For example, Natural Language Generation (NLG) is being used professionally to automatically generate product descriptions and search keywords, create email and direct mail campaigns, and even write entire articles.
Alongside text-based content, an area of artificial intelligence called ‘Computer Vision’ is regularly being used for image, graphics, and video editing. Most professional image and video editing tools now incorporate AI techniques into their software to automate and improve the quality of the editing process.
OpenAI recently released DALL-E, a 12-billion parameter neural network that takes a text caption and generates relevant images, for example, providing the caption “a living room with two white armchairs and a painting of the colosseum. The painting is mounted above a modern fireplace” generates the following images:
When it comes to music, there’s an entire industry being built around AI applications for creating music, such as Amper Music, Google Magenta’s NSynth Super and Sony developed Flow Machines to release a song created by AI called “Daddy’s Car”. AI-based mastering services are also being used for optimizing the listening experience on different devices, and AI is being used to recommend new music in streaming apps.
In the world of digital learning, educational content, such as digital textbooks, lessons and study guides can be generated with the help of AI.
Many types of content that we see in our day-to-day lives are starting to be generated by AI, it’s likely that you have read an article, listened to a piece of music, or watched a film that at least partly involved artificial intelligence in its creation.
How far are we from viable works of literature composed by machine?
Much closer than you would think. Language models, the things that AI researchers use to understand and generate natural language, have recently made huge strides.
One of the historical problems with processing very long passages of text, is that those language models used struggled to remember how different parts of the text relate to each other, partly due to something called the “vanishing (and exploding) gradient problem”. So, generating a fake tweet is easy, while generating a poem is harder, and then generating an entire novel is much harder still.
However, AI researchers have been building bigger language models with better techniques, using huge amounts of data and vastly more computational power. These language models are much better at understanding and generating larger passages of text.
A great example of this is OpenAI’s state-of-the-art GPT-3 model, trained on masses of text, at an estimated cost of $4.6 million. The GPT-3 model has around 175 billion parameters, ten times more than its closest rival. GPT-3 and has been shown to generate surprisingly convincing text, which could fool many readers into thinking it was written by a human.
Right now, I think we’re currently at the stage where AI can generate a convincing poem or article, but not a whole novel. I wouldn’t be surprised if, eventually, language models will be proficient enough to be are writing novels the length of War and Peace.
What parameters are used to teach AI models the definition of quality content? (i.e. there must be a formula for perfect content, differing for each content type)
One person’s definition of quality may differ drastically from another’s, especially where literature is concerned.
How do AI content creation tools account for differences in taste?
Actually, AI models don’t consider “quality” directly. Most deep learning models are trained on huge datasets of text, images, videos, etc. without any consideration for quality, beyond the quality of the data they are trained on.
That being said, “quality” is something that could be implicitly learned. For example, what is called ‘Reinforcement Learning’ could be used to generate many different content variations on a website, and gradually improve that content based on user feedback or behaviour over time.
In what ways could AI content creation tools be misused? What are the dangers?
This is already a big risk, one that I think we are underestimating. One danger is that AI models are being used to generate fake social media posts, that could be used to influence elections or scam consumers.
Another risk is that AI models are being used to generate “deep fakes”. To for example create fake pornography that use people’s likeness without their consent, or to falsify videos of politicians. Hopefully, researchers can will be able to build systems which to identify and take down fake content using these same AI techniques.
Are we at risk of minimizing certain art forms/demographics not properly represented in the models used to train art-producing AI?
Absolutely, since deep learning models are trained on historical data, there is a risk that AI models will contain biases against certain demographic groups that have been learned from the data.
For example, language models trained on articles from the internet may show gender stereotypes found in society. So, we’re seeing that new art or content that has been generated using these biased models may not be truly representative.
On the flip side, AI can also be used to reduce bias if used carefully, and ethical AI is an important and active field of research into how to measure and remove these biases.
Is there still a role for human writers to play in a future in which AI is able to compose technically immaculate content?
Content creators should not see the growth of AI as a threat, but rather a great opportunity, to find new, exciting ways to enhance, inspire and expedite the way they create content. I could see AI tools being used by content creators as a type of “AI muse”, generating different content options for the creator to consider and cherry-pick.
A great book I recently read around AI and creativity, is The Creativity Code by Marcus du Sautoy, I recommend content creators check it out.
Author: Jon Howells is Director of AI & Analytics consultancy, Qualifai
