Mel Silverman walked over to a whiteboard and picked up a marker, listing all the academic disciplines that the band of renegade scientists asking him for money represented.
Assembled there 12 years ago at Vancouver’s Metropolitan Hotel was a group of about 15 people, ranging from computer scientists to biologists to experimental engineers. What united them was their interest in a concept that was, at the time, generally perceived as the domain of the lunatic fringe.
They believed it was possible to teach a machine to learn the same way a child does, through artificial neural networks that mimic the function of the human brain. In the process of teaching a machine to learn like a human, they figured there was likely a lot to discover about how humans learn as well.
The consensus among most computer scientists at the time was that this was nuts. The way to get a computer to do something was to program it to do it, not ask it to learn the task itself. If he had been a computer scientist, Silverman probably would have thanked them for their time and moved on.
But Silverman, who was in charge of recommending what programs to green light at the not-for-profit, mostly publicly funded Canadian Institute for Advanced Research (CIFAR), was a physician by training — a profession he says has a tendency to question authority. He had noticed how the group went quiet and listened reverently when Geoffrey Hinton, a University of Toronto researcher lured to Canada by CIFAR decades earlier, spoke. He liked the ambitious scope of the problem they were trying to tackle and the persistence of the group willing to risk professional ostracism to tackle it.
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“He said, ‘Well, we’re kind of weird,’” Silverman recalled. “If CIFAR is looking for a high-risk, adventurous kind of group that’s willing to step out of its usual areas of comfort, this is the group.’ I said, ‘OK. That sounds cool.’”
Silverman convinced CIFAR to give that band of self-identified weirdoes about $10 million over 10 years, making it pretty much the only organization at the time to back the research of artificial neural networks.
Today, it’s clear they were anything but nuts.
The world’s biggest tech companies are currently spending billions of dollars exploring the technology, which researchers have used to train computers to recognize handwritten characters, understand speech and even identify cats in YouTube videos.
Neural networks are being used to help doctors interpret medical images and give better treatment advice. They’re making machines employers can teach to do factory work. Just about any industry that wants to make the best possible use of vast amounts of data could potentially benefit from artificial intelligence.
As a research project, CIFAR’s mostly taxpayer-funder 2004 program has clearly been a winner.
- More Here
Assembled there 12 years ago at Vancouver’s Metropolitan Hotel was a group of about 15 people, ranging from computer scientists to biologists to experimental engineers. What united them was their interest in a concept that was, at the time, generally perceived as the domain of the lunatic fringe.
They believed it was possible to teach a machine to learn the same way a child does, through artificial neural networks that mimic the function of the human brain. In the process of teaching a machine to learn like a human, they figured there was likely a lot to discover about how humans learn as well.
The consensus among most computer scientists at the time was that this was nuts. The way to get a computer to do something was to program it to do it, not ask it to learn the task itself. If he had been a computer scientist, Silverman probably would have thanked them for their time and moved on.
But Silverman, who was in charge of recommending what programs to green light at the not-for-profit, mostly publicly funded Canadian Institute for Advanced Research (CIFAR), was a physician by training — a profession he says has a tendency to question authority. He had noticed how the group went quiet and listened reverently when Geoffrey Hinton, a University of Toronto researcher lured to Canada by CIFAR decades earlier, spoke. He liked the ambitious scope of the problem they were trying to tackle and the persistence of the group willing to risk professional ostracism to tackle it.
[---]
“He said, ‘Well, we’re kind of weird,’” Silverman recalled. “If CIFAR is looking for a high-risk, adventurous kind of group that’s willing to step out of its usual areas of comfort, this is the group.’ I said, ‘OK. That sounds cool.’”
Silverman convinced CIFAR to give that band of self-identified weirdoes about $10 million over 10 years, making it pretty much the only organization at the time to back the research of artificial neural networks.
Today, it’s clear they were anything but nuts.
The world’s biggest tech companies are currently spending billions of dollars exploring the technology, which researchers have used to train computers to recognize handwritten characters, understand speech and even identify cats in YouTube videos.
Neural networks are being used to help doctors interpret medical images and give better treatment advice. They’re making machines employers can teach to do factory work. Just about any industry that wants to make the best possible use of vast amounts of data could potentially benefit from artificial intelligence.
As a research project, CIFAR’s mostly taxpayer-funder 2004 program has clearly been a winner.
- More Here
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