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Artificial intelligence picks up racial and gender biases when learning language from text, researchers say. Without any supervision, a machine learning algorithm learns to associate female names more with family words than career words, and black names as being more unpleasant than white names.

For a study published today in Science, researchers tested the bias of a common AI model, and then matched the results against a well-known psychological test that measures bias in humans. The team replicated in the algorithm all the psychological biases they tested, according to study co-author Aylin Caliskan, a post-doc at Princeton University. Because machine learning algorithms are so common, influencing everything from translation to scanning names on resumes, this research shows that the biases are pervasive, too.

“Language is a bridge to ideas, and a lot of algorithms are built on language in the real world,” says Megan Garcia, the director of New America’s California branch who has written about this so-called algorithmic bias. “So unless an alg is making a decision based only on numbers, this finding is going to be important.”

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Seti using #AI to find ET.


Join IBM’s Graham Mackintosh, SETI Institute CEO Bill Diamond, and SETI Institute Board of Trustees member Jonathan Knowles as we talk about using machine learning to help better understand the volumes of data collected by the SETI Institute Allen Telescope Array.

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Machine learning algorithms and artificial intelligence systems influence many aspects of people’s lives: news articles, movies to watch, people to spend time with, access to credit, and even the investment of capital. Algorithms have been empowered to make such decisions and take actions for the sake of efficiency and speed. Despite these gains, there are concerns about the rapid automation of jobs (even such jobs as journalism and radiology). A better understanding of attitudes toward and interactions with algorithms is essential precisely because of the aura of objectivity and infallibility cultures tend to ascribe to them. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems. This report highlights the added risks and complexities inherent in the use of algorithmic decisionmaking in public policy. The report ends with a survey of approaches for combating these problems.

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In its new budget, the government of Prime Minister Justin Trudeau pledged $93 million ($125 million Canadian) to support A.I. research centers in Toronto, Montreal and Edmonton, which will be public-private collaborations.


Today’s striking advances in artificial intelligence owe a lot to research in Canada over the years. But the country has so far failed to cash in.

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Despite the popular belief that artificial intelligence is coming to take your jobs away, accountants would love some robotic help to get them through the day. This is according to a new report by Sage, which says 96 percent of accountants are confident about the future of accountancy as well as their role in it.

Despite welcoming change, more than two thirds of respondents (68 percent) expect their roles to change through automation, in the future.

Here’s what accountants are expecting from automation: almost four in ten (38 percent) see number-crunching as their number one frustration. Thirty-two percent still use manual methods for this work. A quarter (25 percent) use Excel while seven percent still use handwritten notes.

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Nowadays, the latest buzzword of attraction is “Artificial Intelligence” and its immediate impact on our advertising sector. As the CEO of Gravity4, I thought it to be only appropriate to help dissect this new evolutionary phase of our industry as we apply it. It is no doubt that ‘Deep Learning’ is our future, and it is on course to have a huge impact on the lives of everyday consumers and business sectors. In the scientific world, deep learning is referred to as “deep neural networks”. These involve a family of artificial intelligence, popularly known as AI, something named way back in 1955, and something which Facebook, Google and Microsoft are all now pushing for with Herculean force. In fact, according to the international data corporation, it is estimated that from a global standpoint, by 2020, the artificial intelligence market could reach close to $50 billion.

Getting to Grips With the Terminology

AI refers to a collection of tools and technologies, some of which are relatively new, and some of which are time-tested. The techniques that are employed allow computers to use these tools and technologies to imitate human intelligence. These include: machine learning such as deep learning, decision trees, if-then rules, and logic.

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The humans never had a chance.

As expected, the latest poker-playing bot powered by an artificial intelligence designed by a duo from Carnegie Mellon University beat a team of some of the best poker players in China.

Lengpudashi, the AI developed by Professor Tuomas Sandholm and Noam Brown, a graduate student at CMU, finished five days of Heads-Up, No-Limit Texas Hold’em with nearly $800,000 in chips and walked away with $290,000.

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