Collaborating Authors

Large expert-curated database for benchmarking document similarity detection in biomedical literature search


Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.

2020 Summer Software Development Engineer Intern (SEA) - Deep Learning


We are hiring innovative minded Software Development Engineer interns into our technical development teams throughout the US. We are headquartered in Seattle, WA, but also have exciting opportunities at our offices across the US and Canada! In Canada, we are growing rapidly in: Vancouver, BC, Toronto, ON, and Ottawa, ON. Our interns and co-ops write real software and partner with a select group of experienced software development engineers, who both help and challenge them as they work on projects that matter to our customers. You'll be given the opportunity to have direct impact on the evolution of Amazon's technologies and lead mission critical projects early in your career.

Game Theoretic and Decision Theoretic Agents (GTDT '02)

AAAI Conferences

Piotr Gmytrasiewicz (cochair), University of Illinois at Chicago Simon Parsons, University of Liverpool Cristina Biccheri, Carnegie Mellon University Craig Boutilier, University of Toronto Jon Doyle, North Carolina State University Amy Greenwald, Brown University Jeff Kephart, IBM Institute for Advanced Research Sarit Kraus, Bar-Ilan University Ronald Parr, Duke University Richard E. Stearns, University of Albany Wynn Stirling, Brigham Young University Gerald Tesauro, IBM Watson Research Center Leon van der Torre, Vrije Universiteit Amsterdam Russell Vane, Litton PRC Michael Wooldridge, University of Liverpool Shlomo Zilberstein, University of Massachusetts This AAAI-02 Workshop was held July 28, 2002, in Edmonton, Alberta, Canada Contents

Startup CEOs on how to keep the artificial intelligence ball rolling in Canada


The next time you pull out your smartphone and ask Siri or Google for advice, or chat with a bot online, take pride in knowing that some of the theoretical foundation for that technology was brought to life here in Canada. Indeed, as far back as the early 1980s, key organizations such as the Canadian Institute for Advanced Research embarked on groundbreaking work in neural networks and machine learning. Academic pioneers such as Geoffrey Hinton (now a professor emeritus at the University of Toronto and an advisor to Google, among others), the University of Montreal's Yoshua Bengio and the University of Alberta's Rich Sutton produced critical research that helped fuel Canada's rise to prominence as a global leader in artificial intelligence (AI). Stephen Piron, co-CEO of Dessa, praises the federal government's efforts at cutting immigration processing timelines for highly skilled foreign workers. Canada now houses three major AI clusters – in Toronto, Montreal and Edmonton – that form the backbone of the country's machine-learning ecosystem and support homegrown AI startups.