The technological singularity—or simply the singularity—is a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. According to the most popular version of the singularity hypothesis, I.J. Good's intelligence explosion model, an upgradable intelligent agent will eventually enter a "runaway reaction" of self-improvement cycles, each new and more intelligent generation appearing more and more rapidly, causing an "explosion" in intelligence and resulting in a powerful superintelligence that qualitatively far surpasses all human intelligence.
A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
An artificial general intelligence (AGI) is a type of hypothetical intelligent agent. The AGI concept is that it can learn to accomplish any intellectual task that human beings or other animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in the majority of economically valuable tasks. Creating AGI is a primary goal of some artificial intelligence research and companies such as OpenAI, DeepMind, and Anthropic. AGI is a common topic in science fiction and futures studies.
Bill Hibbard is a scientist at the University of Wisconsin–Madison Space Science and Engineering Center working on visualization and machine intelligence. He is principal author of the Vis5D, Cave5D, and VisAD open-source visualization systems. Vis5D was the first system to produce fully interactive animated 3D displays of time-dynamic volumetric data sets and the first open-source 3D visualization system.
A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference. Markov logic networks generalize first-order logic, in the sense that, in a certain limit, all unsatisfiable statements have a probability of zero, and all tautologies have probability one.
Probabilistic logic involves the use of probability and logic to deal with uncertain situations. Probabilistic logic extends traditional logic truth tables with probabilistic expressions. A difficulty of probabilistic logics is their tendency to multiply the computational complexities of their probabilistic and logical components. Other difficulties include the possibility of counter-intuitive results, such as in case of belief fusion in Dempster–Shafer theory. Source trust and epistemic uncertainty about the probabilities they provide, such as defined in subjective logic, are additional elements to consider. The need to deal with a broad variety of contexts and issues has led to many different proposals.
Ben Goertzel is a cognitive scientist, artificial intelligence researcher, CEO and founder of SingularityNET, leader of the OpenCog Foundation, and the AGI Society, and chair of Humanity+. He helped popularize the term 'artificial general intelligence'.
A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is a piece of software able to infer logical consequences from a set of asserted facts or axioms. The notion of a semantic reasoner generalizes that of an inference engine, by providing a richer set of mechanisms to work with. The inference rules are commonly specified by means of an ontology language, and often a description logic language. Many reasoners use first-order predicate logic to perform reasoning; inference commonly proceeds by forward chaining and backward chaining. There are also examples of probabilistic reasoners, including non-axiomatic reasoning systems, and probabilistic logic networks.
Transcendent Man is a 2009 documentary film by American filmmaker Barry Ptolemy about inventor, futurist and author Ray Kurzweil and his predictions about the future of technology in his 2005 book, The Singularity is Near. In the film, Ptolemy follows Kurzweil around his world as he discusses his thoughts on the technological singularity, a proposed advancement that will occur sometime in the 21st century when progress in artificial intelligence, genetics, nanotechnology, and robotics will result in the creation of a human-machine civilization.
Psi-theory, developed by Dietrich Dörner at the University of Bamberg, is a systemic psychological theory covering human action regulation, intention selection and emotion. It models the human mind as an information processing agent, controlled by a set of basic physiological, social and cognitive drives. Perceptual and cognitive processing are directed and modulated by these drives, which allow the autonomous establishment and pursuit of goals in an open environment.
Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty and complex, relational structure. Note that SRL is sometimes called Relational Machine Learning (RML) in the literature. Typically, the knowledge representation formalisms developed in SRL use first-order logic to describe relational properties of a domain in a general manner and draw upon probabilistic graphical models to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
A probabilistic logic network (PLN) is a conceptual, mathematical, and computational approach to uncertain inference; inspired by logic programming, but using probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values. In order to carry out effective reasoning in real-world circumstances, artificial intelligence software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain logic such ideas as induction, abduction, analogy, fuzziness and speculation, and reasoning about time and causality.
Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. It represents an attempt to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable. It can be used to create systems that help make decisions in the face of uncertainty.
The Conference on Artificial General Intelligence is a meeting of researchers in the field of Artificial General Intelligence organized by the AGI Society and held annually since 2008. The conference was initiated by the 2006 Bethesda Artificial General Intelligence Workshop and has been hosted at the University of Memphis ; Arlington, Virginia ; Lugano, Switzerland ; Google headquarters in Mountain View, California ; the University of Oxford, United Kingdom ; and at Peking University, Beijing, China, Quebec City, Canada. The AGI-15 conference was held in Berlin, Germany.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
Sophia is a social humanoid robot developed by the Hong Kong-based company Hanson Robotics. Sophia was activated on February 14, 2016, and made its first public appearance in mid-March 2016 at South by Southwest (SXSW) in Austin, Texas, United States. Sophia is marketed as a "social robot" that can mimic social behavior and induce feelings of love in humans.
Many scholars believe that advances in artificial intelligence, or AI, will eventually lead to a semi-apocalyptic post-scarcity economy where intelligent machines can outperform humans in nearly, if not every, domain. The questions of what such a world might look like, and whether specific scenarios constitute utopias or dystopias, are the subject of active debate.
ProbLog is a probabilistic logic programming that extends Prolog with probabilities. It minimally extends Prolog by adding the notion of a probabilistic fact, which combines the idea of logic atoms and random variables. Similarly to Prolog, ProbLog can query an atom. While Prolog returns the truth value of the queried atom, ProbLog returns the probability of it being true.
