Artificial intelligence, machine learning, and advanced analytics are critical to the challenge of producing intelligence from the enormous amounts of data available to the Intelligence Community. This day-long symposium will focus on the challenge of "Big Data" and what the IC needs to do to address it. Panels will examine the IC's AIM Strategy; the importance of the Pentagon's newly created Chief Digital and AI Officer; challenges and success stories with operationalizing AI; and the capabilities needed to make it all work. Media Policy This event is open to the press. Contact [email protected] for more information.

The event chaired by Mark Maybury (Chief Technology Officer, Stanley Black & Decker, mark.maybury@sbdinc.com) From steam power and electrification in the first industrial revolution to assembly line driven mass production of the second industrial revolution to computerization in the third industrial revolution, disruptive innovations have driven key change including urbanization, global travel, and information discovery and sharing. Equally if not more profoundly, the current cyber-physical fourth industrial transformation is driving fundamental changes not only in the way we manufacture but also because of the kinds of products and services created ways in which we live, work, and play. Studies from intelligent manufacturing experts at the World Economic Forum have identified a set of key foundational elements for Industry 4.0. These include the Internet of Things (IOT), big data, cloud computing additive manufacturing, augmented reality, autonomous robots, and modeling and simulation.

Once upon a time, there was the AAAI 2005 Fall Symposium on Machine Ethics. It is the one event perhaps that I wish I could have attended, otherwise I do not entertain desires of being older. Much of the work in machine ethics can be traced back to the discussions at that seminar, but they also seemed to have spurred many questions that we still find interesting. When the AAAI 2021 Spring Symposium on Implementing AI Ethics was announced, it was not difficult to clear the schedule for "a deeper discussion on how intelligence, agency, and ethics may intermingle in organizations and in software implementations". The 2021 symposium has not per definition been the spiritual descendant of the 2005 seminar, but by composition of participants and discussion structure, I expect great ideas will be developed after this one as well.

AAAI is pleased to announce the continued expansion of its awards program in 2000. The first AAAI Effective Expository Writing Award will be presented at the National Conference on Artificial Intelligence, to be held July 31-August 3 in Austin, Texas. This award joins the two special awards established last year, the AAAI Classic Paper Award and the AAAI Distinguished Service Award. The AAAI Effective Expository Writing Award honors the author(s) of a high-quality, effective piece of writing, accessible to the general public or to a broad AI audience (not just a subarea), written within the last two years. The contribution should be based on sound science, interesting ideas, or systematic review, with nontrivial content, but the award is primarily for the exposition--the author need not be the one who has done the research.

The AAAI News Column covers membership and conference statistics, reports on workshops, financial data, Council activities, and other ongoing activities pursued by the society Please send suggestions and feedback by electronic mail to AAAI-Office@Sumex-Aim Stanford.edu Spring Symposium Series In March AAAI sponsored the first Spring Symposium Series, intended to promote technical discussion of specialty topics in small-group meetings. As the following reports testify, the Series was a tremendous success Plans are underway for the next Series, to be held at Stanford University again next March. Suggestions should be sent to Hector Levesque or Claudia Mazzetti. The following are edited reports provided by the five symposium leaders.

We apply artificial intelligence techniques to perform data analysis and activity recognition in smart homes. Sensors embedded in smart home provide primary data for reasoning about observations. The final goal is to provide appropriate assistance for residents to complete their Daily living Activities. Here, we introduce a qualitative approach that considers spatiotemporal specifications of activities in the Activity Recognition Agent to do knowledge representation and reasoning about the observations. We consider different existing uncertainties within sensors observations and Observed Agent’s activities. In the introduced approach, the more details about environment context would cause the less activity recognition process complexity and more precise functionality. To represent the knowledge, we apply the fuzzy logic to represent the world state by the fuzzified received values from sensors. The knowledge would be represented in the fuzzy context frame. To reduce the amount of collected data, meaningful changes in sensors generated values are considered to do Activity Recognition. Applying possibility distributions for event occurrence orders and sequences within different scenarios of activities realization, we are able to generate hypotheses about future possible occur-able events. The possible occur-able events and fuzzy digit parameters of their possible happening moments are represented in matrix format. The hypotheses about possible future observable contexts are generated considering spatial, temporal and other environmental parameters and then they would be ranked. Our final goal is to better explain the observations. If no possible explanation about observation be found, it would be recognized as abnormal behavior. In the case that no expected event be observed, we can reason that maybe event has occurred but not triggered and so next available events in previously learned scenarios would be expected. The system patience for number of possible missed events depends to trade-off between the degrees of resident's forgetfulness and probability of events trigger by applied sensors.

Recent years have seen a resurgence of interest in the use of metacognition in intelligent systems. This article is part of a small section meant to give interested researchers an overview and sampling of the kinds of work currently being pursued in this broad area. The current article offers a review of recent research in two main topic areas: the monitoring and control of reasoning (metareasoning) and the monitoring and control of learning (metalearning).

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Computer Science Department, was pleased to present its 2006 Spring Symposium Series held March 27-29, 2006, at Stanford University, California.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1997 Spring Symposium Series on 24 to 26 March at Stanford University in Stanford, California. This article contains summaries of the seven symposia that were conducted: (1) Artificial Intelligence in Knowledge Management; (2) Computational Models for Mixed-Initiative Interaction; (3) Cross-Language Text and Speech Retrieval; (4) Intelligent Integration and Use of Text, Image, Video, and Audio Corpora; (5) Natural Language Processing for the World Wide Web; (6) Ontological Engineering; and (7) Qualitative Preferences in Deliberation and Practical Reasoning.

We report on the spring 1992 symposium on diagrammatic representations in reasoning and problem solving sponsored by the Association for the Advancement of Artificial Intelligence. The symposium brought together psychologists, computer scientists, and philosophers to discuss a range of issues covering both externally represented diagrams and mental images and both psychology -- and AI-related issues. In this article, we develop a framework for thinking about the issues that were the focus of the symposium as well as report on the discussions that took place. We anticipate that traditional symbolic representations will increasingly be combined with iconic representations in future AI research and technology and that this symposium is simply the first of many that will be devoted to this topic.