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Earlier this year, Microsoft announced that they have started using intelligent virtual agent for Microsoft Support customer care in the US. At Ignite conference this week, Microsoft revealed that their virtual agent is now handling over 650,000 sessions per week, also it is increasing both customer and support agent satisfactions. Microsoft is now opening up this technology to other organisations through Dynamics 365 AI solutions. HP Inc. and Macy's are now trying out this solution for their respective customer care effort through Early Access Program. "HP is reinventing the support experience by empowering our customers to easily solve a broad range of issues on their own," said Jon Flaxman, Chief Operating Officer, HP Inc. "Microsoft's advanced Artificial Intelligence capabilities enables HP Customer Service to deliver and manage these self-service solutions efficiently and with more precision." You can learn more about the new Dynamics 365 AI solutions here.

Certifiable Trust in Autonomous Systems: Making the Intractable Tangible

Artificial intelligence (AI) research in Singapore is focused on accelerating the country's development into a smart nation. Specifically, AI has been employed extensively in either augmenting the intelligence of humans or in developing automated methods and systems to improve quality of life in Singapore. In this column we summarize Singapore's Our focus in this column is primarily limited to the efforts of Singapore to become a smart nation. The key areas of AI research summarized here include mobility, security, manufacturing, and health care. In addition, there are also translational domain has taken a number of interesting directions.

In 2018, a advances in research, education, limited number of complimentary The goal of this program is to provide and application. Submissions are due technical program registrations will be a forum in which students can present November 15. View previous entries available for students who volunteer and discuss their work during its early and award winners at the AI Videos during the conference. Preference will stages, meet some of their peers who Past Competitions page (www.

Recently, a number of noteworthy results have been achieved in various fields of artificial intelligence, and many aspects of the problem solving process have received significant attention by the scientific community. In this context, the extraction of comprehensive knowledge suitable for problem solving and reasoning, from textual and pictorial problem descriptions, has been less investigated, but recognized as essential for autonomous thinking in Artificial Intelligence. In this work we present a challenge where methods and tools for deep understanding are strongly needed for enabling problem solving: we propose to solve mathematical puzzles by means of computers, starting from text and diagrams describing them, without any human intervention. We are aware that the proposed challenge is hard and of difficult solution nowadays (and in the foreseeable future), but even studying and solving only single parts of the proposed challenge would represent an important step forward for artificial intelligence.

Deep learning and machine learning tailored toward a specific Next to convex optimization, contributed were hot topics, and the workshop application. It is now recognized that papers addressed the problems included papers from across the globe formal languages, and their symbolic of symbolic stochastic planning on deep reinforcement learning agents underpinnings, can enable descriptive and shortest path problems.

This article discusses verification and validation (V&V) of autonomous systems, a concept that will prove to be difficult for systems that were designed to execute decision initiative. V&V of such systems should include evaluations of the trustworthiness of the system based on transparency inputs and scenario-based training. Transparency facets should be used to establish shared awareness and shared intent between the designer, tester, and user of the system. The transparency facets will allow the human to understand the goals, social intent, contextual awareness, task limitations, analytical underpinnings, and team-based orientation of the system in an attempt to verify its trustworthiness. Scenario-based training can then be used to validate that programming in a variety of situations that test the behavioral repertoire of the system. This novel method should be used to analyze behavioral adherence to a set of governing principles coded into the system.

Previous work on story planning has shown success in the generation of plans that are both intention-coherent and demonstrate aspects of inter-character conflict. However, the initial models of intention and conflict have been limited, in that they lack methods to generate story plots wherecharacters drop sub-plans to achieve their goals in believably consistent and expressive ways and adopt new sub-plans in the face of plan failure. In current work, we have developed models of failed actions in stories that go hand in hand with erroneous belief models for character. Motivated by characterizations of rational agents' intentions as choice combined with commitment, we provide a framing of the plan generation process that is intended to show how characters form their own plans to achieve their own goals, act upon those plans until they feel that conditions no longer support their plans, and then re-plan in the face of adversity to achieve their goals. We show an example story plan that contains several types of character-based intention dynamics targeted by our approach.

We argue that social simulation can help us understand social justice issues. In particular, modeling certain social dynamics within computational systems can be used to creatively explore and better understand the social and identity dynamics of oppression. Writing theories of oppression in code forces us to explicate everything, and question what we leave out or what we can’t account for. As an early step in this direction, we present an in-progress social simulation of group discussion in activist meetings, developed in the already-existing AI system, Ensemble. Through this minimal, highly constrained social arena, we can explore wide-reaching phenomena like privilege, intersectionality, and power dynamics in nonhierarchical groups, but in a way that’s grounded in concrete, person-to-person interactions. We propose that this kind of social simulation can aid in the process of unlearning hegemonic ways of being, and imagining liberatory alternatives.