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) …
One approach to achieving artificial general intelligence (AGI) is through the emergence of complex structures and dynamic properties arising from decentralized networks of interacting artificial intelligence (AI) agents. Understanding the principles of consensus in societies and finding ways to make consensus more reliable becomes critically important as connectivity and interaction speed increase in modern distributed systems of hybrid collective intelligences, which include both humans and computer systems. We propose a new form of reputation-based consensus with greater resistance to reputation gaming than current systems have. We discuss options for its implementation, and provide initial practical results.
It is argued that any real-world, limited-resources general intelligence is going to manifest a mixture of general principles such as Solomonoff induction and complex self-organizing adaptation, with specific structures and dynamics that reflect corresponding structures and dynamics in the tasks and environments in whose context it was created. This interplay between the general and the specific will play out differently in each type of intelligent system. A number of ideas drawn from previous publications are reviewed here — e.g. cognitive synergy, PGMC and the Mind-World Correspondence Principle — which formalize aspects of this perspective, and provide guidance on how to use it to analyze and create general intelligences.
Adams, Sam (IBM) | Arel, Itmar (University of Tennessee) | Bach, Joscha (Humboldt University of Berlin) | Coop, Robert (University of Tennessee) | Furlan, Rod (Quaternix Research, Inc.) | Goertzel, Ben (Independent Researcher and Author) | Hall, J. Storrs (George Mason University) | Samsonovich, Alexei (Tufts University) | Scheutz, Matthias (Southern Illinois University, Carbondale) | Schlesinger, Matthew (University of Buffalo, State University of New York) | Shapiro, Stuart C. (VivoMind Research, LLC) | Sowa, John
We present the broad outlines of a roadmap toward human-level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological and information processing perspectives. The challenge of identifying appropriate tasks and environments for measuring AGI will be addressed, and seven scenarios will be presented as milestones suggesting a roadmap across the AGI landscape along with directions for future research and collaboration.
Goertzel, Ben (Novamente LLC and Xiamen University)
Of all the aspects differentiating lifelong learning from shorter-term, more specialized learning, perhaps none is more central than forgetting — or, to frame the issue more generally and technically, "memory access speed deprioritization." This extended abstract reviews some of the ideas involved in forgetting for lifelong learning systems, and briefly discusses the forgetting mechanisms used in the OpenCog integrative cognitive architecture.
Goertzel, Ben (Novamente LLC) | Pitt, Joel (Hong Kong Polytechnic University) | Wigmore, Jared (Hong Kong Polytechnic University) | Geisweiller, Nil (Novamente LLC) | Cai, Zhenhua (Xiamen University) | Lian, Ruiting (Xiamen University) | Huang, Deheng (Xiamen University) | Yu, Gino (Hong Kong Polytechnic University)
The hypothesis is presented that "cognitive synergy" -- proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory -- may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.
A deeply-interactive hybrid neural-symbolic cognitive architecture is defined as one in which the neural-net and symbolic components interact frequently and dynamically, so that each intervenes significantly in the other's internal operations, and the two form a combined dynamical system at the time-scale of each component's individual cognitive operations. An example architecture of this nature that is currently under development is described: OpenCog NS, based on integration of the OpenCog cognitive architecture (which incorporates symbolic, evolutionary and connectionist aspects) with a hierarchical attractor neural network (HANN). In this integrated architecture, the neural and non-neural aspects each play major roles, and the depth of the interconnection is revealed for example in the facts that symbolic reasoning intervenes in the process of attractor formation within the HANN, whereas the HANN plays a major role in guiding the individual steps of logical inference and evolutionary program learning processes.