There's no question that AI is redefining processes across a whole spectrum of businesses. There is, however, a question of what that means for the overall economy. Canada is now investing in AI research with the expectation that it will benefit the country in general. DeepMind, the London-based leader in artificial intelligence owned by Google's parent-company Alphabet, is now reaching across the pond to Canada. On July 5, Demis Hassabis, co-founder and CEO, DeepMind announced "the opening of DeepMind's first ever international AI research office in Edmonton, Canada, in close collaboration with the University of Alberta."

Google's DeepMind artificial intelligence team has been based in the UK ever since it was acquired in 2014. However, it's finally ready to branch out -- just not to the US. DeepMind has announced that its first international research lab is coming to the Canadian prairie city of Edmonton, Alberta later in July. A trio of University of Alberta computer science professors (Richard Sutton, Michael Bowling and Patrick Pilarski) will lead the group, which includes seven more AI veterans. As Recode observes, you can chalk it up to a combination of familiarity and political considerations.

Google's high-profile artificial intelligence unit has a new Canadian outpost. DeepMind, which Google bought in 2014 for roughly $650 million, said Wednesday that it would open a research center in Edmonton, Canada. The new research center, which will work closely with the University of Alberta, is the United Kingdom-based DeepMind's first international AI research lab. DeepMind, now a subsidiary of Google parent company Alphabet (goog), recruited three University of Alberta professors from to lead the new research lab. The professors--Rich Sutton, Michael Bowling, and Patrick Pilarski--will maintain their positions at the university while working at the new research office.

Researchers at the Canadian Conference on Artificial Intelligence in Edmonton this week envision a day when robots will be a mainstay in our homes and workplaces -- doing daily tasks like cooking, cleaning and even keeping us company. The AI conference, held at the University of Alberta's Lister Centre, is expected to draw hundreds of academics and industry leaders to Edmonton between May 16 and 19. "We want computers to be able to be smarter and to be able to understand [a] question, that's the first step," explained Greg Kondrak, a computer science professor at the the University of Alberta. Upon hearing a question, the smart computer would scan the internet for accurate results and "formulate the answer in a way that we kind of expect from other people." Kondrak's area of expertise is natural language processing.

"Mass Effect: Andromeda" developer BioWare is now apologizing for its portrayal of a transgender character in the action RPG. The Edmonton, Canada-based video game developer is reportedly saying sorry after it received a lot of criticism over its new NPC Hainly Abrams. In a statement the developer published on Twitter this Wednesday, BioWare admitted that it did not think carefully on how to present the transgender character in the new "Mass Effect" game. The company then apologized and vowed to fix the problem by releasing a new update that will change Abrams' dialogue in the game. "In'Mass Effect: Andromeda,' one of our non-player characters, Hainly Abrams, was not included in a caring or thoughtful way.

Top professional poker players have been been beaten by AI bots at no-limits hold'em. A complex variant of poker is the latest game to be mastered by artificial intelligence (AI). And it has been conquered not once, but twice, by two rival bots developed by separate research teams. Each algorithm -- which plays a'no limits' two-player version of Texas hold'em -- has in recent months hit a crucial AI milestone: they have beaten human professional players. The game first fell in December to DeepStack, developed by computer scientists at the University of Alberta in Edmonton, Canada, with collaborators from Charles University and the Czech Technical University in Prague.

At the end of November, I was detained at the American-Canadian border, and I find myself struggling with feelings of resentment, anger and shame. I had traveled from Los Angeles to Edmonton, Canada, where I delivered an academic lecture at the University of Alberta. Getting off a plane and gaining entry into Alberta, I was treated with respect and dignity, and was sent on my way with a lilting, "Welcome to Canada, professor." Returning home to the United States was a different experience. To set the scene, I should mention that I was traveling with my wife and 11-year old son.

Dr. Richard Sutton presents "The Future of Artificial Intelligence" in the Technology and Future of Medicine course LABMP 590 http://www.singularitycourse.com This video has the greatest potential to save the world, and improve everyone's preparation for the future and improve the actual future itself, of any videos we have produced to date. If you do not want to watch the whole thing from the beginning, watch from 00:27:24 The Enslavement Problem. This is from the September 10th, 2015 lecture in eHub at the University of Alberta in Edmonton, Canada. Dr. Kim Solez gave a poetry reading on subjects related to this lecture at the Strathearn Art Walk on September 12, 2015, with Dr. Sutton in the audience and commenting afterward see https://www.youtube.com/watch?v jTVO7... .

While suboptimal best-first search algorithms like Greedy Best-First Search are frequently used when building automated planning systems, their greedy nature can make them susceptible to being easily misled by flawed heuristics. This weakness has motivated the development of best-first search variants like epsilon-greedy node selection, type-based exploration, and diverse best-first search, which all use random exploration to mitigate the impact of heuristic error. In this paper, we provide a theoretical justification for this increased robustness by formally analyzing how these algorithms behave on infinite graphs. In particular, we show that when using these approaches on any infinite graph, the probability of not finding a solution can be made arbitrarily small given enough time. This result is shown to hold for a class of algorithms that includes the three mentioned above, regardless of how misleading the heuristic is.

Artificial Intelligence (AI) techniques have been widely used in video games to control non-playable characters. More recently, AI has been applied to automated story generation with the objective of managing the player's experience in an interactive narrative. Such AI experience managers can generate and adapt narrative dynamically, often in response to the player's in-game actions. We implement and evaluate a recently proposed AI experience manager, PACE, which predicts the player's emotional response to a narrative event and uses such predictions to shape the narrative to keep the player on an author-supplied target emotional curve.