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In Fierce AI Race, Everybody Wants This New Human-Like Chip

Unlike the number-crunching alternatives, British startup Graphcore has developed a brain for computers that excels at guesswork.

By Austin Carr, 5 June 2019

Simon Knowles, chief technology officer of Graphcore Ltd., is smiling at a whiteboard as he maps out his vision for the future of machine learning. He uses a black marker to dot and diagram the nodes of the human brain: the parts that are “ruminative, that think deeply, that ponder.” His startup is trying to approximate these neurons and synapses in its next-generation computer processors, which the company is betting can “mechanize intelligence.”

Artificial intelligence is often thought of as complex software that mines vast datasets, but Knowles and his co-founder, Chief Executive Officer Nigel Toon, argue that more important obstacles still exist in the computers that run the software. The problem, they say, sitting in their airy offices in the British port city of Bristol, is that chips—known, depending on their function, as CPUs (central processing units) or GPUs (graphics processing units)—weren’t designed to “ponder” in any recognizably human way. Whereas human brains use intuition to simplify problems such as identifying an approaching friend, a computer might try to analyze every pixel of that person’s face, comparing it to a database of billions of images before attempting to say hello. That precision, which made sense when computers were primarily calculators, is massively inefficient for AI, burning huge quantities of energy to process all the relevant data.

When Knowles and the more business-minded Toon founded Graphcore in 2016, they put “less precise” computing at the heart of their chips, which they call intelligence processing units, or IPUs. “The concepts in your brain are quite vague. It’s really the aggregation of very approximate data points that causes you to have precise thoughts,” says Knowles, whose English accent and frequent chuckle invite comparisons to a Hogwarts headmaster. (Given his constant whiteboard pontificating, Toon jokingly addresses him as “Professor Knowles.”) There are various theories on why human intelligence forms this way, but for machine learning systems, which need to process huge and amorphous information structures known as “graphs,” building a chip that specializes in connecting nodelike data points may prove key in the evolution of AI. “We wanted to build a very high-performance computer that manipulates numbers very imprecisely,” Knowles says.

Put another way, Graphcore is developing a brain for computers that, if its co-founders are right, will be able to process information more like a human instead of faking it through massive feats of number crunching. “For decades, we’ve been telling machines what to do, step by step, but we’re not doing that anymore,” Toon says, describing how Graphcore’s chips instead teach machines how to learn. “This is like going back to the 1970s—we need to break out our wide lapels—when microprocessors were first coming out. We’re reinventing Intel.”

Investor Hermann Hauser, co-founder of Arm Holdings Plc, which controls the most widely used chip designs, is betting that Knowles and Toon’s IPUs will unleash the next wave of computing. “This has only happened three times in the history of computers,” Hauser says—CPUs in the 1970s, GPUs in the 1990s. “Graphcore is the third. Their chip is one of the great new architectures of the world.”

Graphcore’s origins lie in a series of symposiums Hauser organized in 2011 and 2012 at the University of Cambridge for the Royal Society, the scientific fellowship that counts Isaac Newton and Charles Darwin as alums. Around a posh dining room at King’s College, AI experts, neuroscientists, statisticians, and zoologists debated the impact advanced computing would have on society.

 

 

 

 

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