"It is possible for ASICs over time to be successful in the deep-learning world," Mosesmann said. "However, we are of the opinion that at this stage in a multidecade product cycle it is just too early to'fix' the hardware, given that there is a plethora of deep-learning frameworks (Tensorflow, Caffee, MXNet, …

Orange Bank launched artificial intelligence (AI)-based customer service support for its mobile-first bank, a move the company hailed as a first in the French market. The assistant will use Orange's Djingo, a French language AI service unveiled in April 2017 and set to be rolled out across a range of the …

Design students at CMU are exploring ways to enhance our interactions with new technologies and the power of artificial intelligence.

Despite high expectations for the use of artificial intelligence in imaging, use of advanced computing technologies to assist radiologists is still in the early stages. A recent report from KLAS Research finds that "talk about AI in imaging has been more common than actual adoption."

Published on March 6, 2018.

"This is something that can provide us with huge opportunities within our industry development. "Yes, people will be fearful of it, but we should be learning to embrace that change." The group, modelled on the UK version, will examine the economic and social effects of artificial intelligence.

I often have to write in different languages and was shocked to find that overnight Google Translate went from being a joke to "knowing" even my best foreign language better than me. The reason: an AI program called "Deep Learning."

We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings or protein-drug bindings. The canonical representation of such interactions is a matrix (or tensor) with an exchangeability property: the encoding's meaning is not changed by permuting rows or columns. We argue that models should hence be Permutation Equivariant (PE): constrained to make the same predictions across such permutations. We present a parameter-sharing scheme and prove that it could not be made any more expressive without violating PE. This scheme yields three benefits. First, we demonstrate performance competitive with the state of the art on multiple matrix completion benchmarks. Second, our models require a number of parameters independent of the numbers of objects, and thus scale well to large datasets. Third, models can be queried about new objects that were not available at training time, but for which interactions have since been observed. We observed surprisingly good generalization performance on this matrix extrapolation task, both within domains (e.g., new users and new movies drawn from the same distribution used for training) and even across domains (e.g., predicting music ratings after training on movie ratings).

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ThoughtWire, developers of the award-winning Ambiant IIoT platform, today announced the closing of a combined $20 million debt and Series A financing. ThoughtWire will use the financing to further the company's geographic growth using Ambiant to make cities smarter, buildings more automated and energy efficient, and hospitals and workplaces safer, by interconnecting and orchestrating people, workflows, data and things in real-time. The investment round includes a respected syndicate of new and current investors including Yaletown Partners, BDC Capital, Round13 Capital, Epic Capital and Comerica. "We are proud to have new partners who are invested in our vision and mission to orchestrate a healthier, safer and cleaner world, and the enormous market opportunity that lies ahead for ThoughtWire," says Michael Monteith, CEO of ThoughtWire. "We're excited to have the opportunity to apply this latest investment to grow our team and expand the impact of our forward-thinking technology on Smart Cities and buildings, better healthcare and advanced manufacturing." Over the last two years, after attracting worldwide recognition from both Gartner and Frost & Sullivan for Ambiant's use in healthcare, the Ambiant IIoT platform is now being applied across diverse industries to deliver intelligent automation and provide real-time guidance to machines and staff to predict and resolve issues, ensure safety, and achieve energy efficiency.