Every new media technology starts by reproducing content from existing media technologies. Radio began with music performances and operetta programmes, television initially broadcast sound films. They still do today, but the new media of past times soon developed their own media formats. AI will follow the same path.

One of the first steps of any new technology is to make existing applications more efficient and effective. With generative AI, we can produce text, images, videos, or code faster and cheaper, so we do that first. (At least, in theory. In practice, it’s a different story.) The real impact starts as soon as we invent completely new applications, something that wasn’t possible before and is now enabled by the new technology.

Abstractly speaking, it’s about product. To be a little more specific, AI will birth new media formats, which it is already doing. This is the realm of artists and creative technologists: people like Babusi Nyoni, a Zimbabwean tech entrepreneur working between Amsterdam, Nairobi and Bulawayo. For Nike, he created an app that could recognise five different dance styles. Users competed in a dance challenge and a final showdown event.

The technology behind the app goes back to 2018, when he launched a dance app in South Africa to demonstrate users’ readiness for emerging technology. And then he repurposed the dance algorithm to build a prototype for the early diagnosis of Parkinson’s disease.

This prototype measures the relative position of a subject’s limbs as they walk, including the rigidity of the torso, their gait and arm movement and their posture and makes an assessment after 3 evaluations. This is all done on the device and does not require an internet connection to function.

The intersection of community, arts, and AI

Different topic, similar approach: Pau Garcia, founder of Domestic Data Streamers in Barcelona, created Synthetic Memories, a project at the intersection of community, arts, and AI. With the help of generative AI and skilful prompting, the team creates images out of people’s memories. They make artificial, synthetic photos, or “memory-based reconstructions”, of the past.

In Barcelona, the project is now recording people’s memories of the city. As well as that project, Domestic Data Streamers works with researchers to find out if the approach can help to treat dementia.

The team collaborated with researchers in Barcelona on a small pilot with 12 subjects, applying the approach to reminiscence therapy—a treatment for dementia that aims to stimulate cognitive abilities by showing someone images of the past. Developed in the 1960s, reminiscence therapy has many proponents, but researchers disagree on how effective it is and how it should be done.

The pilot allowed the team to refine the process and ensure that participants could give informed consent, says Garcia. The researchers are now planning to run a larger clinical study in the summer with colleagues at the University of Toronto to compare the use of generative image models with other therapeutic approaches.

Babusi Nyoni and Pau Garcia will be on stage at NEXT24 in September, together with Elizabeth Valleau, who leads Accenture Song’s New Realities group. They will discuss how AI is revolutionising art and creativity. These examples show how the creative use of new technology can open up new avenues. This is where the real potential of generative AI lies.

It’s not in the efficient reproduction of things that already exist. That’s the industrial approach. Artists and creative professionals in general rightly fear that their work is being appropriated by algorithms, and that they themselves are being forced out of business.

AI is about to change the media industries

This legitimate concern is often dismissed as Luddism. But, as Pau Garcia points out, the English textile workers of the early 19th century, known as Luddites, were not against technology per se.

Their main grievances were the plummeting wages, the rising profits of the factory owners, and the escalating food prices. They rallied against the inhumane working conditions, the exploitation of child labour, and the production of subpar goods that tarnished the reputation of the entire textile industry. Contrary to popular belief, Luddites did not want just to destroy machinery at random, but targeted those machines owned by employers who underpaid their workers. Their acts of machine-breaking were desperate cries for economic fairness and justice.

Once again, we are engaged in a debate that we have been having again and again since the advent of the web. Technologists tend to think that everything on the web is in the public domain. But it isn’t. Is it fair use to scrape the whole web, feed it as training data into an AI system, and then use that system to create output that puts the original creators out of the media business? Probably not. Or, as Benedict Evans wrote in his newsletter:

There is a growing collision between the philosophical view in many AI circles that training-by-looking is no different to what people do (after all, these systems aren’t Napster – they can’t generally reproduce what’s in the training data) and the legal status of ‘using’ people’s property in an entirely new way but without any new model for permission.

We can learn a few things from the music industry, one of the first industries to be deeply affected by the internet. Digital changed it twice, first by the widespread and easy copying of digital music (Napster, for older generations) and then by the transition to streaming. There have been winners and losers, but overall the industry has survived and thrived. Now, generative AI is about to change the creative industries, again.

We already see fights about the distribution of opportunities and risks, gains and losses from the spread of GenAI. Ultimately, AI will bring forth new media, like the web and social media did. Artists and creative technologists are at the forefront of this process, with their experiments and creative use of new technology. Down the road, we have plenty of work to do. Benedict Evans noted that we probably

need to go though the conventional process of customer discovery towards product-market fit.

We can’t do this overnight, and the business models are another part of the puzzle. New technologies can give birth to new media, but this doesn’t automatically include the business model. Artists and creative people will have to fight for their piece of the pie.

Picture by Jr Korpa from Unsplash.