Watson Will Soon Be a Bus Driver In Washington D.C.

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IBM has teamed up with Local Motors, a Phoenix-based automotive manufacturer that made the first 3D-printed car, to create a self-driving electric bus. Named "Olli," the bus has room for 12 people and uses IBM Watson's cloud-based cognitive computing system to provide information to passengers. In addition to automatically driving you where you want to go using Phoenix Wings autonomous driving technology, Olli can respond to questions and provide information, similar to Amazon's Echo home assistant. The bus debuts today in the Washington D.C. area for the public to use during select times over the next several months, and the IBM-Local Motors team hopes to introduce Olli to the Miami and Las Vegas areas by the end of the year. By using Watson's speech to text, natural language classifier, entity extraction, and text to speech APIs, the bus can provide several services beyond taking you to your destination.


CA "Platinum Sponsor" of @CloudExpo NY & Silicon Valley @CAinc #DevOps

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SYS-CON Events announced today that CA Technologies has been named "Platinum Sponsor" of SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY, and the 21st International Cloud Expo, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. CA Technologies helps customers succeed in a future where every business - from apparel to energy - is being rewritten by software. From planning to development to management to security, CA creates software that fuels transformation for companies in the application economy. With CA software at the center of their IT strategy, organizations can leverage the technology that changes the way we live - from the data center to the mobile device. CA's software and solutions help customers thrive in the new application economy by delivering the means to deploy, monitor and secure their applications and infrastructure.


A Modern Retrospective on Probabilistic Numerics

arXiv.org Machine Learning

The field of probabilistic numerics (PN), loosely speaking, attempts to provide a statistical treatment of the errors and/or approximations that are made en route to the output of a deterministic numerical method, e.g. the approximation of an integral by quadrature, or the discretised solution of an ordinary or partial differential equation. This decade has seen a surge of activity in this field. In comparison with historical developments that can be traced back over more than a hundred years, the most recent developments are particularly interesting because they have been characterised by simultaneous input from multiple scientific disciplines: mathematics, statistics, machine learning, and computer science. The field has, therefore, advanced on a broad front, with contributions ranging from the building of overarching generaltheory to practical implementations in specific problems of interest. Over the same period of time, and because of increased interaction among researchers coming from different communities, the extent to which these developments were -- or were not -- presaged by twentieth-century researchers has also come to be better appreciated. Thus, the time appears to be ripe for an update of the 2014 Tübingen Manifesto on probabilistic numerics[Hennig, 2014, Osborne, 2014d,c,b,a] and the position paper[Hennig et al., 2015] to take account of the developments between 2014 and 2019, an improved awareness of the history of this field, and a clearer sense of its future directions. In this article, we aim to summarise some of the history of probabilistic perspectives on numerics (Section 2), to place more recent developments into context (Section 3), and to articulate a vision for future research in, and use of, probabilistic numerics (Section 4).


Facebook heads to Canada in search of the next big AI advance

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The first genuinely impressive AI assistant may well have a Canadian accent. Facebook announced today that it is tapping into Canada's impressive supply of artificial-intelligence talent and expertise by creating a major AI research center in Montreal. Several big recent advances in AI can be traced back to Canadian research labs, and Facebook is hoping that the new lab may help it take advantage of whatever comes next. The new center will focus, in particular, on an area of AI known as reinforcement learning (see "10 Breakthrough Technologies 2017: Reinforcement Learning"). The center will seek to apply this and other novel approaches to language, with the aim of producing more coherent and useful virtual assistants, says Yann LeCun, director of AI research at Facebook.


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

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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.