The rise of artificial intelligence (AI), is raising questions at the ethics watchdog for the world's biggest wealth fund. In particular, the threat posed by weapons systems guided by AI brings ethical "challenges," according to Johan H. Andresen, the chairman of the Norwegian Council on Ethics. "It won't necessarily only have to do with weapons, but many other applications," he said in an interview in Oslo. "It's hard to program empathy– we can't demand that– but everything we're looking at is under human control and humans are making the decisions, so both people and companies can be held responsible." Norway's $1 trillion wealth fund has been stepping up the scrutiny of its portfolio and has banned a swathe of companies, including nuclear weapons and cluster bomb producers as well as tobacco and coal companies.

The world is being battered by technological disruption, as innovations such as big data analytics, artificial intelligence (AI), robotics, the Internet of Things, blockchain, 3D printing, and virtual reality change how societies and economies work. Individually, each of these technologies has the potential to transform established products, services, and associated support networks. Taken together, they will upend old business models and institutions, heralding a new era of economic, social, and political history. Major economic transformations typically produce far-reaching change. During the first Industrial Revolution, in the 18th and 19th centuries, new manufacturing processes eventually led to huge improvements in human well-being.

Google has just started to fund computer software which will write local news. A short story written by Japanese computer software made it to second rounds of national literary prize. And an artificial intelligence company called deep mind has created software that can generate music by listening to music. All these foregoing flashy news stories are evident of the benefit and popularization of Artificial Intelligence in the modern world. Earlier, the computer generated works relied heavily upon the input provided by the programmer, the software was very much like a tool or a mechanism like brush or canvas.

Espoo, Finland - Nokia has unveiled the latest version of its Cognitive Analytics for Customer Insight software, providing powerful new capabilities so service provider business, IT and engineering organizations can consistently deliver a superior real-time and personalized customer experience. Nokia Cognitive Analytics for Customer Insight provides a holistic, real-time view of the customer experience to help service providers quickly identify issues and prioritize improvements based on their customer and business impact. It features Nokia's Customer Experience Index (CEI), which correlates information from the network, devices, customer care, billing and other sources with satisfaction surveys like the Net Promoter Score to produce a customer-specific score that tracks service performance and subscriber satisfaction. In this latest release, Nokia CEI now taps advanced machine learning and deep learning algorithms co-developed with Nokia Bell Labs to provide new levels of prediction and automation capabilities to improve the subscriber experience. The algorithms optimize themselves over time, decreasing the time required for the initial tuning of the index from months to days, and delivering a far more accurate view of subscriber satisfaction.

In George Orwell's dystopian novel, 1984, the hero Winston Smith muses privately about the danger of letting his thoughts wander within range of a "telescreen" through which Big Brother monitors the citizens of Airstrip One. "The smallest thing could give you away," Smith thinks to himself as he works silently in front of one of the screens. "A nervous tic, an unconscious look of anxiety, a habit of muttering to yourself – anything that carried with it the suggestion of abnormality, of having something to hide." Orwell even invented a word for the offence that would be committed by Smith if he was to be caught in the act of wearing an improper expression. While the Facebook and Cambridge Analytica privacy scandal has revealed what is claimed to have been a deeply shocking bid to penetrate the thoughts and expressions of real people, the story still perhaps feels strangely remote to the majority of the UK's social media users.

Abstract-- We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imitation learning to simultaneously learn a control policy as well as a threshold over the predictive uncertainty of the learned model, with no hand-tuning required. Corrective action, such as a return of control to the model predictive controller or human expert, is taken when the uncertainty threshold is exceeded. We demonstrate that our method is robust to uncertainty resulting from varying system dynamics as well as from partial state observability. As the deployment of deep neural networks as controllers for physical robotic systems becomes more prevalent, the issue of safety within artificial intelligence becomes an increasingly important concern. Recently the use of end-to-end imitation learning to develop neural network control policies has surged in popularity, due in large part to the ease with which deep models can learn complex dynamics and infer global state from local data while bypassing the need for significant parameter tuning. In contrast, traditional approaches to vision-based control rely on methods such image segmentation and object detection, classification, labeling, and filtering; often, these methods require significant engineering and tuning.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. The APA Technology, Mind, and Research Society Conference. Mind & Society will be held April 5-7, 2018 in Melbourne, Florida, USA. Other Applications of Applied Intelligent be held June 24-29, 2018 in Delft, The on Web and Social Media.

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery

This article presents the results of the first edition of the microRTS (μRTS) AI competition, which was hosted by the IEEE Computational Intelligence in Games (CIG) 2017 conference. The goal of the competition is to spur research on AI techniques for real-time strategy (RTS) games. In this first edition, the competition received three submissions, focusing on address- ing problems such as balancing long-term and short-term search, the use of machine learning to learn how to play against certain opponents, and finally, dealing with partial observability in RTS games.

Recently, AAAI coordinated and The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) cosigned a statement with CRA, and the Thirty-First Conference on Innovative Applications of Artificial expressing concern about the proposed Intelligence (IAAI-19), will be held in Honolulu, Hawaii, USA, January tax bill and its ramifications for graduate 27 - February 1, 2019. The technical conference will continue its student stipends. Other organizational 3.5-day schedule, preceded by the workshop and tutorial programs.