Nvidia’s got a cunning plan to keep powering the AI revolution

#News(General) [ via IoTForIndiaGroup ]

From a bet about recognising cats, Nvidia’s AI business has exploded. The company doesn’t disclose exactly how much it is currently investing into the field, but filings reveal it spent a total of $1.8 billion (£1.4b) on all research in the last financial year. Key areas of investment were gaming, AI and automotive. The effectiveness of its GPUs for artificial intelligence projects has created a scramble amongst Nvidia’s competitors, with Intel, Google and even Facebook investing huge sums of money to try and catch up.

In July 2018, Google announced it was developing its own AI chips for on-device machine learning. Google’s bet is on a new kind of chip: a tensor processing unit designed specifically for neural network machine learning. And a separate, equally important tussle is also unfolding. As well as hardware, Nvidia’s code library is also going up against rival frameworks such as Google Tensorflow or Facebook PyTorch. Think of it as deep learning as a service. And, against all the odds, Nvidia is still holding its own.

But that could soon change. And that’s where Nvidia’s research labs come into play.

“There’s a slew of new AI hardware startups,” says Stephen Merity, an AI research consultant and the founder of a startup in stealth mode. “They may well be a threat but it’s a huge open question. I can’t imagine any of those startups hitting mass scale early on.”

One such startup is UK-based Graphcore, which is now valued at over $1 billion (£760,000) thanks to funding from the likes of BMW, Sequoia, and the founder of Google-owned DeepMind. Graphcore claims its intelligence processing units, which are yet to go into mass production, are 100 times faster than any existing systems. For Nvidia, that could create a financial headache bigger than the cryptocrash.

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