Research models use data from Toronto's Don River and Calgary's Bow River TORONTO, November 11, 2019 – Using complex models based on artificial intelligence (AI) and data from the Don River in Toronto and Bow River in Calgary, researchers at the Lassonde School of Engineering can now predict the water levels in rivers days in advance of floods. "We've created methods to predict real-time flood risk," says Usman T. Khan, professor in the Department of Civil Engineering at York's Lassonde School of Engineering. "These results outline an approach that can be used to create models with higher accuracy and lower data requirements, which translates to improved flood early warning systems. Early warning systems are considered the most effective way to mitigate flood induced hazards." The study, led by Khan, was published today in the Journal of Hydrology.

RS Energy Group, Inc. (RSEG) is an advanced analytics and technology firm that delivers industry-leading, comprehensive insights to those operating, investing in or servicing the energy space. RSEG's work environment is positive, supportive, innovative, and dynamic, with interdisciplinary teams focused on leveraging the latest in technology, machine learning, data science and AI. Headquartered in Calgary, RSEG also has offices in Houston, New York and Conshohocken.

Calgary-based Chata Technologies, which has developed a cloud-based conversational application allowing users to access, search, and analyze their business data through natural language, has raised a $4.5 million CAD seed round. "It's been very exciting to see the vast potential and profound implications of Conversational AI-based data interactions." The funding was raised from undisclosed local investors, with the round officially closing in August. The new capital will be put towards the research, development, and implementation of Chata Technologies' new product, Data Messenger, currently under the name Chata.io. Chata Technologies claims that the product is the first conversational interface designed specifically for data query and analysis.

The DX Summit takes place in Chicago at November 4 – 6, 2019. Gain key digital customer experience skills that help you shape your 2020 initiatives Learn first hand how Google, Shutterfly, SunTrust Bank and the University of Calgary run their digital ops. Get up to speed on the aspirations and realities of AI, Machine Learning and Customer Data Platforms. Take a guided tour through the modern digital customer experience ecosystem, learn strategies for building high performance teams, leveraging the latest in customer data platforms and navigating the procurement minefield. See inside world class digital, VoC and Customer Experience teams Get unique, honest insights via practical case studies delivered by your peers.

CALGARY, Alberta--(BUSINESS WIRE)--The Alberta Machine Intelligence Institute (Amii) and Imperial have announced a two-year agreement to collaborate on the development of Imperial's in-house machine learning capabilities, which will enable a range of applied artificial intelligence (AI) projects. Through these projects, Imperial will work to develop more effective ways to recover oil and gas resources, reduce environmental impacts and improve the safety of its workforce. "At Imperial, we are taking action to be a leader in advancing digital and AI technology across the value chain," said John Whelan, Imperial's senior vice-president, upstream. "Amii is not only a leader in the AI space globally, but based locally in Alberta. We believe the institute is a perfect partner to help us showcase Alberta's leadership in technology and digital solutions for responsibly-produced oil and gas."

We've created a conversational user interface (UI) that allows people to intuitively communicate with databases so they can get answers from their data quickly and easily. What does this look like in practice? We're focused on helping small and medium-sized businesses make their data work for them, so we've built our interface to facilitate data access and analysis through natural language processing and dynamic query building. In simpler terms, users can ask questions in their own words and get answers from the business software they use day-to-day, in real time. We believe that being able to understand and access your data can help you make strong decisions about your business.

The purpose of language is to reveal the contents of our minds, says Julie Sedivy. We are social animals and language is what springs us from our isolated selves and connects us with others. Sedivy has taught linguistics and psychology at Brown University and the University of Calgary. She specializes in psycholinguistics, the psychology of language, notably the psychological pressures that give birth to language and comprehension.

Universal mCloud is creating a more efficient future with the use of AI and analytics, curbing energy waste, maximizing energy production, and getting the most out of critical energy infrastructure. Through mCloud's AI-powered AssetCare platform, mCloud offers complete asset management solutions to three distinct segments: smart facilities, power generation, and process industries including oil and gas. IoT sensors bring data from connected assets into the cloud, where AI and analytics are applied to maximize their performance. Headquartered in Vancouver, Canada with offices in locations worldwide including Calgary, San Francisco, and Beijing, the mCloud family includes an ecosystem of operating subsidiaries that deliver high-performance IoT, AI, 3D, and mobile capabilities to customers, all integrated into AssetCare. With over 100 blue-chip customers and more than 35,000 assets connected in thousands of locations worldwide, mCloud is changing the way energy assets are managed.

Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimensionality of data can be much lower than the ambient dimensionality. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to map both the generated and target distributions to the latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space. The resulting method, perceptual generative autoencoder (PGA), is then incorporated with a maximum likelihood or variational autoencoder (VAE) objective to train the generative model. With maximum likelihood, PGAs generalize the idea of reversible generative models to unrestricted neural network architectures and arbitrary latent dimensionalities. When combined with VAEs, PGAs can generate sharper samples than vanilla VAEs. Compared to other autoencoder-based generative models using simple priors, PGAs achieve state-of-the-art FID scores on CIFAR-10 and CelebA.

Much of the information in the guide has been gathered via our work with students, faculty, and universities. In particular, Google would like to express our profound gratitude to our outstanding volunteer faculty advisors: Laleh Behjat, University of Calgary; Judith Gal-Ezer, Open University of Israel; Mia Minnes, University of California San Diego; Sathya Narayanan, California State University Monterey Bay; and S. Monisha Pulimood, The College of New Jersey. They gave substantial input to the design and content, and helped us keep the needs of their faculty peers and students front and center.