Unlikely Stories, Mostly

• As artificial intelligence (AI) reaches the peak of its popularity, researchers have warned the industry might be running out of training data – the fuel that runs powerful AI systems.
• This could slow down the growth of AI models, especially large language models, and may even alter the trajectory of the AI revolution.

Why high-quality data are important for AI

• For instance, ChatGPT was trained on 570 gigabytes of text data, or about 300 billion words.
• If an algorithm is trained on an insufficient amount of data, it will produce inaccurate or low-quality outputs.
• Low-quality data such as social media posts or blurry photographs are easy to source, but aren’t sufficient to train high-performing AI models.

Do we have enough data?

• At the same time, research shows online data stocks are growing much slower than datasets used to train AI.
• They also estimated low-quality language data will be exhausted sometime between 2030 and 2050, and low-quality image data between 2030 and 2060.
• AI could contribute up to US$15.7 trillion (A$24.1 trillion) to the world economy by 2030, according to accounting and consulting group PwC.

Should we be worried?

• One opportunity is for AI developers to improve algorithms so they use the data they already have more efficiently.
• It’s likely in the coming years they will be able to train high-performing AI systems using less data, and possibly less computational power.
• Being remunerated for their work may help restore some of the power imbalance that exists between creatives and AI companies.
• Read more:
  No, the Lensa AI app technically isn’t stealing artists' work – but it will majorly shake up the art world

Rita Matulionyte is a member of Standards Australia, IT-043 working group.