The Dallas Cowboys had just won the Vince Lombardi Trophy, handing the Buffalo Bills their third straight loss in the Super Bowl. Bill Clinton was sworn into office as the 42nd president of the United States. And American music icon Prince became The Artist Formerly Known as Prince. It was also the last time a Canadian hockey team won the Stanley Cup. On Saturday night, the Edmonton Oilers hope to take the first step toward breaking that drought when they take on the Florida Panthers in Game 1 of the Stanley Cup Final.

An Edmonton company is leading the way in the artificial intelligence sector by providing funding for research and projects. Mikaela Henschel explains how AI is becoming a part of everyday life.

In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent. Signalling is intimately connected to time and timing. In service of generating and receiving signals, humans and other animals are known to represent time, determine time since past events, predict the time until a future stimulus, and both recognize and generate patterns that unfold in time. We investigate how different temporal processes impact coordination and signalling between learning agents by introducing a partially observable decision-making domain we call the Frost Hollow. In this domain, a prediction learning agent and a reinforcement learning agent are coupled into a two-part decision-making system that works to acquire sparse reward while avoiding time-conditional hazards. We evaluate two domain variations: machine agents interacting in a seven-state linear walk, and human-machine interaction in a virtual-reality environment. Our results showcase the speed of learning for Pavlovian signalling, the impact that different temporal representations do (and do not) have on agent-agent coordination, and how temporal aliasing impacts agent-agent and human-agent interactions differently. As a main contribution, we establish Pavlovian signalling as a natural bridge between fixed signalling paradigms and fully adaptive communication learning between two agents. We further show how to computationally build this adaptive signalling process out of a fixed signalling process, characterized by fast continual prediction learning and minimal constraints on the nature of the agent receiving signals. Our results therefore suggest an actionable, constructivist path towards communication learning between reinforcement learning agents.

Promise Robotics CEO Ramtin Attar with a few Kuka Industrial robots similar to ones fitted with custom tooling developed by Promise Robotics to perform complex construction tasks. Robots constructing homes may sound like science fiction. Yet a Toronto-based startup aims to make this futuristic idea a reality within the next year, leveraging advances in automation, advanced manufacturing, cloud computing and artificial intelligence (AI). Promise Robotics was launched in 2019 by founders Ramtin Attar – a former technology lead at a multinational technology company – and Reza Nasseri, the chief executive officer of Landmark Homes. The technology company, which also has operations in Edmonton, seeks to bring emerging technologies to the home building industry to address the industry's biggest challenge: meeting the rising demand for housing amid a growing shortage of affordable homes.

The number of artificial intelligence (AI) researchers in Canada's private sector is proportionally higher than that of other countries, according to Montreal-based Element AI's 2020 Global AI Talent Report. The report found that Canada has 367 AI researchers, making it second only to the US. The Global AI Talent Report measured the size of the available talent pool in the AI industry through self-reported data on social media and demand via the monthly total job postings for the same role up to August 2020. The goal of the report is to assess the most current global patterns for the worldwide AI talent pool. The report tracked 477,956 people worldwide working in the AI industry, of which 61 percent worked in productization, 38 percent in engineering, and a mere one percent in research.

NexOptic Technology Corp., reports that it has made significant advancements to its cutting-edge artificial intelligence (AI) imaging solution. NexOptic's Advanced Low Light Imaging Solution (ALLIS) provides immediate solutions to problems that have plagued the imaging industry for decades. NexOptic's engineered AI drastically reduces image noise common to all imaging systems while improving performance in low light conditions. This is accomplished with NexOptic's expanding suite of patent-pending, deep learning algorithms. Some of the key benefits of ALLIS include: improved low-light performance; dramatic reduction in image noise; improved downstream applications (computational imaging, facial recognition); enhanced long-range image stabilization; major reduction in file sizes.

StartupBlink, a Swiss interactive platform for startups, has released its 2019 Ecosystem Ranking Report, with Canada holding on to the third position, boasting five cities in the top 100 startup ecosystems globally. "Canada's major strength lies in the distribution of strong startup hubs scattered throughout the country." Along with Canada, the US, UK, and Israel all held on to the top four spots for countries with the strongest startup ecosystems. However, Canada's typical tech hubs fell in the rankings compared to 2017. This year, Toronto ranked 15, after dropping four spots, while Vancouver dropped six places to 24.

Ever since computers were clunky, whirring machines that took up entire floors, humans have marvelled at their potential, envisioning all the ways they could help or even be like us. Tapping into our own dark nature, science fiction tends to reach what creepily feels like the natural conclusion of obscenely smart machines with human dispositions; our demise. There's no robot apocalypse on the horizon, but the revolution is well under way. It's been here, in some form, since the '60s, and it's poised to lead the city, and world, in to the future. On April 1, 1964, U of A built Canada's first Department of Computing Science around five academics, a small support staff and the LGP-30, an 800-pound, deep freeze-shaped digital computer.

Although it was bought by Google in 2014, AI firm DeepMind has always been true to its British roots -- expanding its offices in London, working closely with UK institutions like the NHS, and even teaching in the country's universities. Now, though, the company is opening its "first ever international AI office" -- in Edmonton, Canada. It's a natural fit for DeepMind, which has close links with the AI research community in Edmonton's University of Alberta. The company says nearly a dozen Alberta grads have joined its ranks, and the firm has sponsored the university's machine learning lab for a number of years. Richard Sutton, professor of computing science at Alberta, was also DeepMind's first outside advisor, and will head up the company's new base along with colleagues Michael Bowling and Patrick Pilarski.

Two years ago, it started being possible. Today, it's a certainty: Canada is well on its way to becoming a global hub for artificial intelligence, or "AI." We are steadily attracting some of the brightest students and talented scientists to study and conduct research here. The challenge, however, is expanding the number of AI companies, spaces, and institutes to increase job availability and retain these talented individuals. If we can expand the pool of available jobs for our AI researchers and graduates, we stand a chance of retaining them to pursue their AI careers here, instead of losing them to Silicon Valley and giant companies like Google, Microsoft, Apple, and Amazon.