Although exploring alternatives is fundamental to creating better interface designs, current processes for creating alternatives are generally manual, limiting the alternatives a designer can explore. We present Scout, a system that helps designers rapidly explore alternatives through mixed-initiative interaction with high-level constraints and design feedback. Prior constraint-based layout systems use low-level spatial constraints and generally produce a single design. Tosupport designer exploration of alternatives, Scout introduces high-level constraints based on design concepts (e.g.,~semantic structure, emphasis, order) and formalizes them into low-level spatial constraints that a solver uses to generate potential layouts. In an evaluation with 18 interface designers, we found that Scout: (1) helps designers create more spatially diverse layouts with similar quality to those created with a baseline tool and (2) can help designers avoid a linear design process and quickly ideate layouts they do not believe they would have thought of on their own.

This paper shows how knowledge representation and reasoning techniques can be used to support organizations in complying with the GDPR, that is, the new European data protection regulation. This work is carried out in a European H2020 project called SPECIAL. Data usage policies, the consent of data subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2 called PL (policy language); compliance checking and policy validation are reduced to subsumption checking and concept consistency checking. This work proposes a satisfactory tradeoff between the expressiveness requirements on PL posed by the GDPR, and the scalability requirements that arise from the use cases provided by SPECIAL's industrial partners. Real-time compliance checking is achieved by means of a specialized reasoner, called PLR, that leverages knowledge compilation and structural subsumption techniques. The performance of a prototype implementation of PLR is analyzed through systematic experiments, and compared with the performance of other important reasoners. Moreover, we show how PL and PLR can be extended to support richer ontologies, by means of import-by-query techniques. PL and its integration with OWL2's profiles constitute new tractable fragments of OWL2. We prove also some negative results, concerning the intractability of unrestricted reasoning in PL, and the limitations posed on ontology import.

We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count in the presence of an unweighted first-order model counting oracle. The algorithm has applications to inference in a variety of first-order probabilistic representations, such as Markov logic networks and probabilistic logic programs. Crucially for many applications, we make no assumptions on the form of the input sentence. Instead, our algorithm makes use of the symmetry inherent in the problem by imposing cardinality constraints on the number of possible true groundings of a sentence's literals. Realising the first-order model counting oracle in practice using the approximate hashing-based model counter ApproxMC3, we show how our algorithm outperforms existing approximate and exact techniques for inference in first-order probabilistic models. We additionally provide PAC guarantees on the generated bounds.

The recent usage of technical systems in human-centric environments leads to the question, how to teach technical systems, e.g., robots, to understand, learn, and perform tasks desired by the human. Therefore, an accurate representation of knowledge is essential for the system to work as expected. This article mainly gives insight into different knowledge representation techniques and their categorization into various problem domains in artificial intelligence. Additionally, applications of presented knowledge representations are introduced in everyday robotics tasks. By means of the provided taxonomy, the search for a proper knowledge representation technique regarding a specific problem should be facilitated.

The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey. With the availability of large amount of data and advancement of machine learning techniques, the rollout decisions of a supervisor can be effectively automated and the mistakes in decisions made by the supervisor learnt. In this paper, we propose a novel learning algorithm SUPAID which under a natural 'one-way efficiency' assumption on the supervisor, uses a rule mining approach to rank the vehicles based on their roll-out feasibility thus helping prevent the supervisor from makingerroneous decisions. Our experimental results on real data from a public transit agency from a city in U.S show that the proposed method SUPAID can result in significant cost savings.

If the adage that the customer is king is still true, it is especially true in government where customers are often citizens as well. Accenture's own research shows that 85 percent of Americans believe that government digital services should match or exceed commercial service levels. The ability to have seamless experiences across channels (even across competitor platforms) defines what we expect to see everywhere – if Facebook can target ads based on your recent Google search history, shouldn't the DMV be able to figure out that you've moved to a new address? Previously, citizens expected a three to five-year lag in CX standards between government and the commercial sectors. Now, that divide is shrinking to a matter of months, which is why the White House released guidance on customer experience standards for Federal agencies.

Definitions of terminology frequently seen and used in discussions of emerging digital technologies. AGI (Artificial General Intelligence): The intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and future studies. AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning.

Next big wave will be Artificial Intelligence. Nowadays, a lot of enterprises or startups are claiming themselves as AI technology companies or AI-driven companies. According to CBINSIGHTS, since 2010, there have been 635 AI acquisitions. Being an executive or part of the management team, how can we prepare for catching this big wave? If you don't want to miss this opportunity, please follow me and understand and learn what you should do.

A few weeks ago, Google's artificial intelligence (AI) used a machine learning model to improve screening for breast cancer,media reported. Now, the company has used convolutional neural networks (CNN) in instant forecasts of precipitation. Google AI researchers mentioned its use of CNN in short-term precipitation forecasts in an article called Machine Learning for Precipitation Now Fromcasting Radar Images. The results look promising, and according to Google itself, the results are better than the traditional method: this precipitation forecast focuses on 0-6 hours of forecasts, which produce a resolution of 1 km and a total delay of only 5-10 minutes (including data collection delays). Even in the early stages of development, it outperforms traditional models.

The White House's Office of Science and Technology Policy (OSTP) has issued a draft memo to government agencies which spells out the principles agencies must abide by when creating regulations for the use of AI. The principles are designed to achieve three goals: Ensure public engagement, limit regulatory overreach and promote trustworthy technology. The memo includes 10 principles that agencies must consider when drafting AI regulations. The memo follows on from President Trump's executive order on AI in February 2019, which set out the administration's strategy for accelerating the US's position of leadership in AI. This includes fostering public trust in AI systems by establishing appropriate governance of, and standards for the technology.