"As my father lost the ability to do things for himself, my mother would give him gentle prompts to keep him on track," recalled Kautz, associate professor in the University of Washington's Department of Computer Science & Engineering. "So at a stage of the disease where, according to the clinical scales, it would seem he couldn't do anything for himself, he could still perform many of the functions of life. He could shower, get dressed, and so forth because my mother would monitor him and give a prompt when needed." It's a recollection that has guided Kautz in initiating a research effort at the UW to explore ways in which computer science can compensate for diminished mental capacity. The Assisted Cognition Project is a collaborative effort by the UW, Intel Computers and Elite Care, a private company developing a state-of-the-art retirement community in the Portland area that utilizes so-called ubiquitous computing to keep tabs on residents' needs.

Royal Bank of Canada is hiring a pioneer in artificial intelligence (AI) as an adviser to a new research lab the bank is establishing as part of its investment in AI and machine learning, Canada's largest bank said on Wednesday. The bank declined to say how much is being invested, but estimated that its ongoing investments in AI will total in the tens of millions of dollars over the coming years. RBC Research said it is looking into how AI can be applied in banking and it will be working with Richard Sutton, a professor of computing science at the University of Alberta. Sutton has made substantial contributions in the field of "reinforcement learning," a type of machine learning that uses reward and punishment. It is a subset of the science of getting a computer to do something without programming it to do so, and is the technology behind self-driving cars and underpins Google's AlphaGo, the AI program that last year beat the world champion at the ancient board game Go.

A very interesting contest is taking place at the Rivers Casino in Pittsburgh, where four of the world's best poker players are playing against a machine. And as of now anyway, the machine is winning. In this case the machine, named Libratus, is using artificial intelligence (AI) technology developed at nearby Carnegie Mellon University, a hotbed of AI and robotics research. The tournament kicked off Wednesday with odds makers favoring the human players 4 or 5 to 1 over Libratus. "But we ended up ahead," Tuomas Sandholm, CMU professor of computer science told Fortune over the phone, sounding delighted.

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.

Hex is a two-player game invented by Piet Hein in 1942 while a student at Niels Bohr's Institute for Theoretical Physics, and subsequently and independently by John Nash in 1948 while a mathematics graduate student at Princeton. The game was originally called Nash or John, with the latter name at the same time crediting its inventor and referring to the fact that it was frequently played on the tiled floors of bathrooms (Gardner 1959, pp. The name Hex was invented in 1952, when a commercial version was issued by the game company Parker Brothers. Hex is played on a diamond-shaped board made up of hexagons. The game is usually played on a boards of size 11 on a side, for a total of 121 hexagons, as illustrated above.

A very interesting contest is taking place at the Rivers Casino in Pittsburgh where four of the world's best poker players are playing against a machine. And as of now anyway, the machine is winning. In this case the machine, named Libratus, is using artificial intelligence (AI) technology developed at nearby Carnegie Mellon University, a hot bed of AI and robotics research. The tournament kicked off Wednesday with odds makers favoring the human players 4 or 5 to 1 over Libratus. "But we ended up ahead," Tuomas Sandholm, CMU professor of computer science told Fortune over the phone, sounding delighted.

Chess is less complex than Go, but it still has too many possible configurations to solve by brute force alone. Instead, programs cut down their searches by looking a few turns ahead and judging which player would have the upper hand. In Go, recognizing winning and losing positions is much harder: stones have equal values and can have subtle impacts far across the board. To interpret Go boards and to learn the best possible moves, the AlphaGo program applied deep learning in neural networks -- brain-inspired programs in which connections between layers of simulated neurons are strengthened through examples and experience. It first studied 30 million positions from expert games, gleaning abstract information on the state of play from board data, much as other programmes categorize images from pixels.

The Jeopardy Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researcherss, Watson is performing at human expert levels in terms of precision, confidence, and speed at the Jeopardy quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating, and advancing a wide range of algorithmic techniques to rapidly advance the field of QA. The architecture and methodology developed as part of this project has highlighted the need to take a systems-level approach to research in QA, and we believe this applies to research in the broader field of AI. We have developed many different algorithms for addressing different kinds of problems in QA and plan to publish many of them in more detail in the future.