"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.

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.

It has taken more than 18 years, and hundreds of computers to crunch numbers through the night, but yesterday Jonathan Schaefer declared his job done: he had written the world's first program that was unbeatable at the game of draughts. Chinook, as the program is known, can calculate a winning response to any move made by its opponent. The worst result it can ever have is a draw, according to Dr Schaefer, an expert in artificial intelligence, working at the University of Alberta in Edmonton, Canada. The game of draughts, played on a board with eight by eight squares, is the most complicated game ever solved thanks to artificial intelligence. The number of possible positions in a game makes it one million times more complex than Connect Four.

Presented at Land-Information Systems: Developments for planning the sustainable use of land resources, Hanover 20-23 Nov. 1996 Proceedings to be published by European Commission. A paper presented at the Management Science/Operations Research Working Group Session at the SAF National Convention, Washington, D.C. Bare, B. and Mendoza, G. 1992. "Ecosystem analysis using fuzzy set theory." "Modelling management of agricultural ecosystems using fuzzy set theory: methodological issues." Paper presented at the joint meetings of the Western Agricultural Economics Association and the Canadian Agricultural Economics and Farm Management Society, 1993, Edmonton, Alberta. A rational method for assessing irrigation performance at farm level with the aid of fuzzy set theory.

Amid all the buzz about vehicles that drive themselves, there are serious ethical questions facing regulators, manufacturers and the people who will ride in them. If faced with an unavoidable fatal crash, would the car be programmed to save its occupants at all costs or would it sacrifice its passengers for the greater good of saving a group of pedestrians? "There's this trade-off between the interests of the driver, or rather the passenger who buys the car, and the level of public acceptance versus public outrage," says Azim Shariff of the Culture and Morality Lab at the University of Oregon. Along with researchers from France and the Massachusetts Institute of Technology, Shariff set out to test public attitudes on the cold, hard decisions computer programs will have to make when lives are on the line. Azim Shariff, researcher at the Culture and Morality Lab at the University of Oregon, says some ethical questions should be answered before driverless cars fill the streets.

Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each step. Many algorithms have been proposed for this problem and theoretical results have also been derived for the worst-case performance with simple examples demonstrating worst-case performance in practice. Lower bounds, however, have not been widely studied. In this paper we study best-case performance more generally and derive theoretical lower bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm. The results show that, given some reasonable restrictions on the state space and the heuristic function, the number of steps an LRTA*-like algorithm requires to reach the goal will grow asymptotically faster than the state space, resulting in ``scrubbing'' where the agent repeatedly visits the same state. We then show that while the asymptotic analysis does not hold for more complex real-time search algorithms, experimental results suggest that it is still descriptive of practical performance.

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algorithm is developed. A set of numerical experiments on synthetic data is used to compare the effectiveness of non-stationary continuous time Bayesian networks to that of non-stationary dynamic Bayesian networks. Furthermore, the performance achieved by non-stationary continuous time Bayesian networks is compared to that achieved by state-of-the-art algorithms on four real-world datasets, namely drosophila, saccharomyces cerevisiae, songbird and macroeconomics.

Siamak Ravanbakhsh, Barnab as P oczos, Jeff Schneider 1 and Dale Schuurmans, Russell Greiner 2 1 Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213 2 University of Alberta, Edmonton, AB T6G 2E8, Canada Abstract We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application of this stochastic approximation in training a family of Restricted Boltzmann Machines (RBM) that are closely linked to Bregman divergences. This family, that we call exponential family RBM (Exp-RBM), is a subset of the exponential family Harmoniums that expresses family members through a choice of smooth monotonic non-linearity for each neuron. Using contrastive divergence along with our Gaussian approximation, we show that Exp-RBM can learn useful representations using novel stochastic units. 1 Introduction Deep neural networks (LeCun et al., 2015; Bengio, 2009) have produced some of the best results in complex pattern recognition tasks where the training data is abundant. Here, we are interested in deep learning for generative modeling. Recent years has witnessed a surge of interest in directed generative models that are trained using (stochastic) back-propagation ( e.g., Kingma and Welling, 2013; Rezende et al., 2014; Goodfellow et al., 2014). These models are distinct from deep energy-based models - including deep Boltzmann machine (Hinton et al., 2006) and (convolutional) deep belief networkAppearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. Although, due to their use of Gaussian noise, the stochastic units that we introduce in this paper can be potentially used with stochastic back-propagation, this paper is limited to applications in RBM.

Deloitte said that around 3.3 million jobs could be classified as business services roles, and that of those, there was a "high chance" that 800,000 to one million jobs would no longer be performed by humans over the period. Simon Barnes, a Deloitte partner, said that the sector's workforce would "fundamentally change over the next 10 to 20 years". Humans are likely to be liberated from "repetitive and highly structured" roles, while new higher-skilled positions are expected to be created to replace them. Mark Carney, the Bank of England Governor, said last month that many of the jobs and industries we are now familiar with "will be gone tomorrow". The rising speed of technological change threatens to make it difficult to choose a career, and for young people to plan their lives, he said.