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Jensen Huang, now the chief executive of Nvidia, was one of its founders back in 1993. Then, Nvidia was focused on making graphics better for gaming and other applications.<\/p>\n
In 1999 it developed GPUs to enhance image display for computers.<\/p>\n
GPUs excel at processing many small tasks simultaneously (for example handling millions of pixels on a screen) – a procedure known as parallel processing.<\/p>\n
In 2006, researchers at Stanford University discovered GPUs had another use – they could accelerate maths operations, in a way that regular processing chips could not.<\/p>\n
It was at that moment that Mr Huang took a decision crucial to the development of AI as we know it.<\/p>\n
He invested Nvidia’s resources in creating a tool to make GPUs programmable, thereby opening up their parallel processing capabilities for uses beyond graphics.<\/p>\n
That tool was added to Nvida’s computer chips. For computer games players it was a capability they didn’t need, and probably weren’t even aware of, but for researchers it was a new way of doing high performance computing on consumer hardware.<\/p>\n
It was that capability that helped sparked early breakthroughs in modern AI.<\/p>\n
In 2012 Alexnet was unveiled – an AI that could classify images. Alexnet was trained using just two of Nvidia’s programmable GPUs.<\/p>\n
The training process took only a few days, rather than the months it could have taken on a much larger number of regular processing chips.<\/p>\n
The discovery – that GPUs could massively accelerate neural network processing – began to spread among computer scientists, who started buying them to run this new type of workload.<\/p>\n
“AI found us,” says Mr Buck. <\/p>\n
Nvidia pressed its advantage by investing in developing new kinds of GPUs more suited to AI, as well as more software to make it easy to use the technology.<\/p>\n
A decade, and billions of dollars later, ChatGPT emerged – an AI that can give eerily human responses to questions. <\/p>\n<\/div>\n
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