If you own a Roomba, you can thank Rodney Brooks, because he's the co-founder of iRobot. He launched the company almost three decades ago when he was a professor at MIT. He's trying to disrupt the world of industrial robotics with his new startup, Rethink Robotics. While I was in Boston a few weeks ago, I visited Rod at his home in Cambridge and captured some great stories. His extensive experiences over his career inspired the new company.

Well they are, but not quite as remorselessly or as swiftly as the movies might have conditioned us to imagine. And when the robot age does arrive, the impact on New Zealand -- in jobs and economic disruption -- may not be as apocalyptic as some future scenarios imagine. At least that is the position of two leading robotics researchers, Armin Werner of Lincoln Agritech, and Bruce MacDonald, an Auckland University computer engineering specialist with over 30 years' skin in the robot game. Werner, whose background is in precision agriculture that will have its most advanced developments in robotics, believes the broad use of automation will create more jobs, at least at the skilled end of the labour market. His view is that the embrace of technology will lead to different forms of work, rather than making work more difficult.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

Natural selection has been a cornerstone of evolutionary theory ever since Darwin. Yet mathematical models of natural selection have often been dogged by an awkward problem that seemed to make evolution harder than biologists understood it to be. In a new paper appearing in Communications Biology, a multidisciplinary team of scientists in Austria and the United States identify a possible way out of the conundrum. Their answer still needs to be checked against what happens in nature, but in any case, it could be useful for biotechnology researchers and others who need to promote natural selection under artificial circumstances. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. A central premise of the theory of evolution through natural selection is that when beneficial mutations appear, they should spread throughout a population.

We survey the AI research carried out in Greece recently. A milestone for AI research in Greece came in 1988, when the Hellenic Artificial Intelligence Society (EETN) was founded as a nonprofit scientific organization devoted to organizing and promoting AI research in Greece and abroad. EETN is an affiliated society of the European Association for Artificial Intelligence (EurAI, formerly known as ECCAI). One of the many roles of EETN is the organization of conferences, workshops, summer schools, and other events, such as the Hellenic Conference on Artificial Intelligence (SETN). The first SETN was Science with a team well grounded in KR.

Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.

The 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2017) was held at the Snowbird Ski and Summer Resort in Little Cottonwod Canyon in the Wasatch Range of the Rock Mountains near Salt Lake County, Utah. Along with the main conference presentations, the meeting included two tutorials, three workshops, and invited keynotes. This report summarizes the main conference. It also includes contributions from the organizers of the three workshops.

Every day in Australia, 30 people are rescued from drowning. And saving lives is about to get easier. Later this year, during the Southern Hemisphere summer, drones will be used to help rescue stranded swimmers and to spot sharks that might be getting a little too close for comfort.

Spotting the next star athlete has always been as much art as science, but artificial intelligence of the sort that's transforming everything from business to healthcare is starting to muscle in on professional athletics too. Computer vision, machine learning and other forms of AI use algorithms to analyze player performance statistics, game videos, and data from various sensors to identify talent that coaches and scouts might otherwise miss. And since the algorithms comb through data far faster than humans can, they give teams in-depth information on more players than previously possible. Professional baseball, basketball and hockey are among the sports now using AI to supplement traditional coaching and scouting. Baseball scouts in particular have long used statistics to evaluate players.

Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate -- for example, a 90% credible interval may not contain the true outcome 90% of the time. Here, we propose a simple procedure for calibrating any regression algorithm; when applied to Bayesian and probabilistic models, it is guaranteed to produce calibrated uncertainty estimates given enough data. Our procedure is inspired by Platt scaling and extends previous work on classification. We evaluate this approach on Bayesian linear regression, feedforward, and recurrent neural networks, and find that it consistently outputs well-calibrated credible intervals while improving performance on time series forecasting and model-based reinforcement learning tasks.