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Researchers warn we could run out of data to train AI by 2026. What then?

theconversation.com · 845 words · saved by 1 readers

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. But why is a potential lack of data an issue, considering how much there are on the web? And is there a way to address the risk? Read more: AI to Z: all the terms you need to know to keep up in the AI hype age We need a lot of data to train powerful, accurate and high-quality AI algorithms. For instance, ChatGPT was trained on 570 gigabytes of text data, or about 300 billion words. Similarly, the stable diffusion algorithm (which is behind many AI image-generating apps such as DALL-E, Lensa and Midjourney) was trained on the LIAON-5B dataset comprising of 5.8 billion image-text pairs. If an algorithm is trained on an insufficient amount of data, it will produce inaccurat

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