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.

The ritualized procreation in the novel--effectively, state-sanctioned rape--is extrapolated from the Bible. " 'Behold my maid Bilhah, go in unto her; and she shall bear upon my knees, that I may also have children by her,' " Atwood recited. "Obviously, they stuck the two together and out came the baby, and it was given to Rachel.

"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. We apologize to anyone who interacted with or was hurt by this conversation," BioWare stated.

Top professional poker players have 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. Both algorithms plays a'no limits' two-player version of Texas Hold'Em. And each 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.

Machines are finally getting the best of humans at poker. Two artificial intelligence (AI) programs have finally proven they "know when to hold'em, and when to fold'em," recently beating human professional card players for the first time at the popular poker game of Texas Hold'em. And this week the team behind one of those AIs, known as DeepStack, has divulged some of the secrets to its success--a triumph that could one day lead to AIs that perform tasks ranging from from beefing up airline security to simplifying business negotiations. AIs have long dominated games such as chess, and last year one conquered Go, but they have made relatively lousy poker players. In DeepStack researchers have broken their poker losing streak by combining new algorithms and deep machine learning, a form of computer science that in some ways mimics the human brain, allowing machines to teach themselves.

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. A month later, Libratus, developed by a team at Carnegie Mellon University (CMU) in Pittsburgh, Pennsylvania, achieved the feat.

A fresh Texas Hold'em-playing AI terror has emerged barely a month after a supercomputer-powered bot claimed victory over four professional poker players. But instead of relying on a supercomputer's hardware, the DeepStack AI has shown how it too can decisively defeat human poker pros while running on a GPU chip equivalent to those found in gaming laptops. The success of any poker-playing computer algorithm in heads-up, no-limit Texas Hold'em is no small feat. This version of two-player poker with unrestricted bet sizes has 10160 possible plays at different stages of the game--more than the number of atoms in the entire universe. But the Canadian and Czech reseachers who developed the new DeepStack algorithm leveraged deep learning technology to create the computer equivalent of intuition and reduce the possible future plays that needed to be calculated at any point in the game to just 107.

As consumer banking becomes increasingly virtual, banks are setting the bar high for their non-human ambassadors. Machines will soon be able to carry out many autonomous thinking tasks once performed by humans, including figuring out how to respond to questions on the fly, and analyzing customer data in ways that would have been unheard of just 10 years ago. This is all the result of machine learning, an application of artificial intelligence that lets a computer think and learn on its own. In January, Canada's largest bank, the Royal Bank of Canada, announced that it would be working with Richard Sutton, a noted artificial intelligence expert, to explore how it can better integrate machine learning into its work. RBC isn't alone among major world banks that are relying more heavily on this technology.

A clustering agreement index quantifies the similarity between two given clusterings. It is most commonly used to compare the results obtained from different clustering algorithms against the ground-truth clustering in the benchmark datasets. In this paper, we present a general Clustering Agreement Index (CAI) for comparing disjoint and overlapping clusterings. CAI is generic and introduces a family of clustering agreement indexes. In particular, the two widely used indexes of Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI), are special cases of the CAI. Our index, therefore, provides overlapping extensions for both these commonly used indexes, whereas their original formulations are only defined for disjoint cases. Lastly, unlike previous indexes, CAI is flexible and can be adapted to incorporate the structure of the data, which is important when comparing clusters in networks, a.k.a communities.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.