According to its website, Statistics Canada "produces statistics that help Canadians better understand their country—its population, resources, economy, society and culture.... Objective statistical information is vital to an open and democratic society. It provides a solid foundation for informed decisions by elected representatives, businesses, unions, and nonprofit organizations, as well as individual Canadians."

Unfortunately, as a story written by the Canadian Press reports, the statistics released by StatsCan in late April on GDP growth in the Canadian provinces were embarrassingly far off.

The Canadian Press story says that StatsCan announced on 28 April that Saskatchewan's economic growth in 2010 was 1.4 percent when it was actually 4.4 percent. After the April number was released, a Royal Bank of Canada economist called StatsCan up and said the statistic didn't make sense given what the bank was seeing.

The call spurred StatsCan to take another look at its numbers. In early May, new figures were released for Saskatchewan and the other provinces, most of which also had their GDP numbers underreported.

A StatsCan spokesperson, the Canadian Press story says, blamed the error on a new computer system that got two steps in reverse order. It implied that the error could happen to anyone.

Internal documents that were reviewed by the Canadian Press, however, stated the problem stemmed from a lack of testing of the new computer system that went operational in December 2010, some nine months late.

The story goes on to say, "The documents do not indicate why the crucial test was omitted but refer in general to 'challenges in regards to funding and finding IT expertise for IT conversion, especially given the complexity and scope of the project.' "

Cut testing when funding is scarce and the schedule is tight? What a surprise.

The Canadian Press story also says that "Statistics Canada, under pressure to cut its budget, dropped a series of surveys and statistical reports last year to save about [C]$7 million."

Given the above, StatsCan should not be surprised if its numbers tend to be looked at skeptically in the future.

PHOTO: iStockphoto

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
DarkBlue1

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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