If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The programme aims to produce AI engineers with a highly relevant skillset in AI topics. Students will learn how to use and develop intelligent computer systems that can learn from experience, recognise patterns in vast amounts of data and reason strategically in complex decision-making situations. ...
Not a day goes by without news about advancements in the artificial intelligence (AI) field. Growing adoption of AI technologies like Machine Learning based on Neural networks, Natural Language Processing etc., has created new markets like that of smart assistants from Google and Amazon. However, artificial intelligence applications are more far reaching. There are numerous use-cases, ranging from self driving cars to fraud prediction in finance. But implementation of AI isn't limited only tech giants.
Dinesh Verma is an IBM Fellow, the company's pre-eminent technical distinction granted in recognition of outstanding and sustained technical achievements and leadership in engineering. Dinesh has worked in IBM Research for nearly 25 years, holds more than 150 patents, is a member of the IBM Academy of Technology, and heads a team that is focused on Distributed Artificial Intelligence (AI). The IBM THINK Blog caught up with Dinesh recently to talk about his current work, as well as his career at IBM. The following is an excerpt and is part of our Perspectives series featuring stories by and about IBMers who take the "long view." Whether it's discovering the next breakthrough, starting the next chapter in their career, or examining skills that drive the next wave of innovation, Perspectives captures the story.
A dynamic network is a network that changes with time. Nature, society, and the modern communications landscape abound with examples. Molecular interactions, chemical reactions, social relationships and interactions in human and animal populations, transportation networks, mobile wireless devices, and robot collectives form only a small subset of the systems whose dynamics can be naturally modeled and analyzed by some sort of dynamic network. Though many of these systems have always existed, it was not until recently the need for a formal treatment that would consider time as an integral part of the network has been identified. Computer science is leading this major shift, mainly driven by the advent of low-cost wireless communication devices and the development of efficient wireless communication protocols.
Conjectures are of great importance since they suggest useful lines of research. It is stunning that so many predictions in Turing's 1950 Mind paper were right. In the decades since that paper appeared, with its inspiring challenges, research in computer science, neuroscience, and the behavioral sciences has radically changed thinking about mental processes and communication, and the ways in which people use computers has evolved even more dramatically. Turing, were he writing now, might still replace "Can machines think?" with an operational challenge, but it is likely he would propose a very different test. This paper considers what that might be in light of Turing's paper and advances in the decades since it was written.
These technologies would not exist today without the sustained federal support of fundamental AI research over the past three decades. This article was written for inclusion in the booklet "Computing Research: A National Investment for Leadership in the 21st Century," available from the Computing Research Association, cra.org/research.impact. Early work in AI focused on using cognitive and biological models to simulate and explain human information processing skills, on "logical" systems that perform commonsense and expert reasoning, and on robots that perceive and interact with their environment. This early work was spurred by visionary funding from the Defense Advanced Research Projects Agency (DARPA) and Office of Naval Research (ONR), which began on a large scale in the early 1960s and continues to this day. By the early 1980s an "expert systems" industry had emerged, and Japan and Europe dramatically increased their funding of AI research.
Today, as the world moves into one in which everyone owns at least one mobile device, be it a smartphone, a tablet, or other handheld device, applications on the devices are increasingly more intelligent as well. We will see more and more applications of AI on the mobile devices. This special issue of AI Magazine is devoted to some exemplary works of AI on mobile devices. We include four works that range from mobile activity recognition and air-quality detection to machine translation and image compression. These works were chosen from a variety of sources, including the International Joint Conference on Artificial Intelligence 2011 Special Track on Integrated and Embedded AI Systems, held in Barcelona, Spain, in July 2011. In "User-Centric Indoor Air-Quality Monitoring on Mobile Devices," written by Yifei Jiang, Kun Li, Ricardo Piedrahita, Yun Xiang, Lei Tian, Omkar Mansata, Qin Lv, Robert P. Dick, Michael Hannigan, and Li Shang, the authors develop a novel and important technique for portable indoor air quality (IAQ) detec-
The Twenty-Second National Conference on Artificial Intelligence (AAAI-07) and the Nineteenth Conference on Innovative Applications of Artificial Intelligence (IAAI-07) will be held in Vancouver, British Columbia, Canada at the Hyatt Regency Vancouver, July 22-26, 2007. Please plan to join us for our second conference in Canada! The Twenty-First National Conference on Artificial Intelligence (AAAI-06) and the Eighteenth Conference on Innovative Applications of Artificial Intelligence (IAAI-06) will be held in Boston, Massachusetts at the Seaport Hotel and World Trade Center, July 16-20, 2006. The AAAI-06 keynote address will be given by Tim Berners-Lee, Director of the World Wide Web Consortium, on Tuesday, July 18. Other invited speakers include Pedro Domingos (University of Washington), Ken Koedinger (Carnegie Mellon University), Karen Myers (SRI International), and Dan Roth (University of Illinois at Urbana-Champaign).
The Association for the Advancement of Artificial Intelligence (AAAI) was pleased to present the AAAI 2008 Spring Symposium Series, held Wednesday through Friday, March 26-28, 2008, at Stanford University, California. The eight symposia were titled (1) AI Meets Business Rules and Process Management, (2) Architectures for Intelligent Theory-Based Agents, (3) Creative Intelligent Systems, (4) Emotion, Personality, and Social Behavior, (5) Semantic Scientific Knowledge Integration, (6) Social Information Processing, (7) Symbiotic Relationships between Semantic Web and Knowledge Engineering, (8) Using AI to Motivate Greater Participation in Computer Science. The goal of the AI Meets Business Rules and Process Management AAAI symposium was to investigate the various approaches and standards to represent business rules, business process management, and the semantic web with respect to expressiveness and reasoning capabilities. The focus of the Architectures for Intelligent Theory-Based Agents AAAI symposium was the definition of architectures for intelligent theory-based agents, comprising languages, knowledge representation methodologies, reasoning algorithms, and control loops. The Creative Intelligent Systems symposium included five major discussion sessions and a general poster session (in which all contributing papers were presented).
This editorial to the summer 2015 AI Magazine introduces the specialissue articles on architectures for activity recognition and context-aware computing. Soon people will be carrying devices and working in environments that understand not only our personal declarative and demographic facts (information stored in datebooks, calendars, and social media) but also have a deep understanding of the context and intent of our day-to-day activities. The last 10 years have seen the development of novel architectures and technologies for domainfocused, task-specific systems that know many things, such as who (identities, profile, history) they are with (social context) and in what role (responsibility, security, privacy); when and where (event, time, place); why (goals, shared or personal); how are they doing it (methods, applications); and using what resources (device, services, access, and ownership). Smart spaces and devices will increasingly use such contextual knowledge to help users move seamlessly between devices and applications, without having to explicitly carry, transfer, and exchange activity context. Such systems will qualitatively shift our lives both at work and play and significantly change our interactions both with our physical and virtual worlds.