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) …
Given the well-known limitations of the Turing Test, there is a need for objective tests to both focus attention on, and measure progress towards, the goals of AI. In this paper we argue that machine performance on standardized tests should be a key component of any new measure of AI, because attaining a high level of performance requires solving significant AI problems involving language understanding and world modeling - critical skills for any machine that lays claim to intelligence. In addition, standardized tests have all the basic requirements of a practical test: they are accessible, easily comprehensible, clearly measurable, and offer a graduated progression from simple tasks to those requiring deep understanding of the world.
Soderland, Stephen (University of Washington) | Roof, Brendan (University of Washington) | Qin, Bo (University of Washington) | Xu, Shi (University of Washington) | Mausam, - (University of Washington) | Etzioni, Oren (University of Washington)
Information extraction (IE) can identify a set of relations from free text to support question answering (QA). Until recently, IE systems were domain-specific and needed a combination of manual engineering and supervised learning to adapt to each target domain. A new paradigm, Open IE operates on large text corpora without any manual tagging of relations, and indeed without any pre-specified relations. We explore the steps needed to adapt Open IE to a domain-specific ontology and demonstrate our approach of mapping domain-independent tuples to an ontology using domains from DARPA's Machine Reading Project.
Barkowsky, Thomas, Bruza, Peter, Dodds, Zachary, Etzioni, Oren, Ferguson, George, Gmytrasiewicz, Piotr, Hommel, Bernhard, Kuipers, Benjamin, Miller, Rob, Morgenstern, Leora, Parsons, Simon, Schultheis, Holger, Tapus, Adriana, Yorke-Smith, Neil
The 2007 Spring Symposium Series was held Monday through Wednesday, March 26-28, 2007, at Stanford University, California. The titles of the nine symposia in this symposium series were (1) Control Mechanisms for Spatial Knowledge Processing in Cognitive/Intelligent Systems, (2) Game Theoretic and Decision Theoretic Agents, (3) Intentions in Intelligent Systems, (4) Interaction Challenges for Artificial Assistants, (5) Logical Formalizations of Commonsense Reasoning, (6) Machine Reading, (7) Multidisciplinary Collaboration for Socially Assistive Robotics, (8) Quantum Interaction, and (9) Robots and Robot Venues: Resources for AI Education.
Woods, William, Uckun, Sendar, Kohane, Isaac, Bates, Joseph, Hulthage, Ingemar, Gasser, Les, Hanks, Steve, Gini, Maria, Ram, Ashwin, desJardins, Marie, Johnson, Peter, Etzioni, Oren, Coombs, David, Whitehead, Steven
The Association for the Advancement of Artificial Intelligence (AAAI) held its 1994 Spring Symposium Series on 19-23 March at Stanford University, Stanford, California. This article contains summaries of 10 of the 11 symposia that were conducted: Applications of Computer Vision in Medical Image Processing; AI in Medicine: Interpreting Clinical Data; Believable Agents; Computational Organization Design; Decision-Theoretic Planning; Detecting and Resolving Errors in Manufacturing Systems; Goal-Driven Learning; Intelligent Multimedia, Multimodal Systems; Software Agents; and Toward Physical Interaction and Manipulation. Papers of most of the symposia are available as technical reports from AAAI.
In his recent papers, entitled Intelligence without Representation and Intelligence without Reason, Brooks argues for mobile robots as the foundation of AI research. The article proposes real-world software environments, such as operating systems or databases, as a complementary substrate for intelligent-agent research and considers the relative advantages of software environments as test beds for AI. Brooks's mobile robots tug AI toward a bottom-up focus in which the mechanics of perception and mobility mingle inextricably with or even supersede core AI research. In contrast, the softbots (software robots) I advocate facilitate the study of classical AI problems in real-world (albeit, software) domains.