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The spectacular rise in the capabilities of artificial intelligence (AI) is directly attributable to the scaling of the computing hardware used to train AI models.
Nigel Toon
“People discovered early on that if you increase the size of those models and the amount of data to train those models, you get a big step-up in accuracy and performance,” says Nigel Toon, CEO and chairman of AI processor firm Graphcore. “The results have been stunning.”
Toon cites research that shows that for large language models the size of the model and the data must be scaled equally.
However, AI developers have started to see a slowdown in the gains achieved solely by such scaling. This is leading to new thinking in how engineers build an AI model and how it generates its output when prompted. The result is a new wave of AI, says Toon.
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