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Learning local and compositional representations for zero-shot learning - Microsoft Research

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In computer vision, one key property we expect of an intelligent artificial model, agent, or algorithm is that it should be able to correctly recognize the type, or class, of objects it encounters. This is critical in numerous important real-world scenarios--from biomedicine, where an intelligent system might be tasked with distinguishing between cancerous cells and healthy ones, to self-driving cars, where being able to discriminate between pedestrians, other vehicles, and road signs is crucial to successfully and safely navigating roads. Deep learning is one of the most significant tools for state-of-the-art systems in computer vision, and its use has resulted in models that have reached or can even exceed human-level performance in important and challenging real-world image classification tasks. Despite their successes, these models still have difficulty generalizing, or adapting to tasks in testing or deployment scenarios that don't closely resemble the tasks they were trained on. For example, a visual system trained under typical weather conditions in Northern California may fail to properly recognize pedestrians in Quebec because of differences in weather, clothes, demographics, and other features.


12 Days of AI: RE•WORK 2017 Highlights

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In the spirit of Christmas, we're going to count down to the new year with the 12 Days of AI, bringing you a new, festive AI post every day! What better way to kick off than to look back at the RE•WORK highlights of 2017 and celebrate some of our successes of the past 12 months. This year saw RE•WORK hosting more events and bringing our globally renowned Summits to new locations. Our first ever Canadian Summit this year took place in Montreal, the'Silicon Valley of AI', and was one of our biggest events to date with over 600 attendees over the two days. We were fortunate enough to be joined by the'Godfathers of AI', Yoshua Bengio, Yann LeCun and Geoffrey Hinton who appeared on a panel together for the first time ever.


Data Collective, Other Top AI VCs, Pour $102M Into Element AI Series A Xconomy

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Canada's Element AI, publicly launched in October, announced today it has raised US$102 million in an outsized Series A financing round seen by experts as a sign that artificial intelligence is ready to solve real-world business problems. The young Montreal-based company, whose staff of AI engineers collaborates with academic AI researchers, offers consulting services to businesses and also plans to co-found AI startups in the future. With founders that include Yoshua Bengio, co-founder and director of the prominent Montreal Institute for Learning Algorithms (MILA), Element AI's big fundraising debut reinforces Canada's claims as a competitive hub in the global development of AI technology. The startup's early trove of capital came from a roster of some of the most active investors in AI. The round was led by Data Collective, joined by Intel Capital, Nvidia, and Microsoft Ventures.