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Instrumental Convergence
Instrumental convergence is the hypothetical tendency for most sufficiently intelligent, goal-directed beings (human and nonhuman) to pursue similar sub-goals, even if their ultimate goals are quite different. More precisely, agents (beings with agency) may pursue instrumental goals—goals which are made in pursuit of some particular end, but are not the end goals themselves—without ceasing, provided that their ultimate (intrinsic) goals may never be fully satisfied. Instrumental convergence posits that an intelligent agent with seemingly harmless but unbounded goals can act in surprisingly harmful ways. For example, a computer with the sole, unconstrained goal of solving a complex mathematics problem like the Riemann hypothesis could attempt to turn the entire Earth into one giant computer to increase its computational power so that it can succeed in its calculations. Proposed basic AI drives include utility function or goal-content integrity, self-protection, freedom from ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Intelligent Agent
In artificial intelligence, an intelligent agent is an entity that Machine perception, perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge representation, knowledge. Leading AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods, thereby exemplifying a novel form of digital agency. Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state (polity), state, or a biome. Intelligent agents operate ba ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pop Culture
Popular culture (also called pop culture or mass culture) is generally recognized by members of a society as a set of practices, beliefs, artistic output (also known as popular art pop_art.html" ;"title="f. pop art">f. pop artor mass art, sometimes contrasted with fine art) and cultural objects, objects that are dominant or prevalent in a society at a given point in time. Popular culture also encompasses the activities and feelings produced as a result of interaction with these dominant objects. The primary driving forces behind popular culture, especially when speaking of Western popular cultures, are the mass media, mass appeal, marketing and capitalism; and it is produced by what philosopher Theodor Adorno refers to as the " culture industry". Heavily influenced in modern times by mass media, this collection of ideas permeates the everyday lives of people in a given society. Therefore, popular culture has a way of influencing an individual's attitudes towards certain top ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Identity Function
Graph of the identity function on the real numbers In mathematics, an identity function, also called an identity relation, identity map or identity transformation, is a function that always returns the value that was used as its argument, unchanged. That is, when is the identity function, the equality is true for all values of to which can be applied. Definition Formally, if is a set, the identity function on is defined to be a function with as its domain and codomain, satisfying In other words, the function value in the codomain is always the same as the input element in the domain . The identity function on is clearly an injective function as well as a surjective function (its codomain is also its range), so it is bijective. The identity function on is often denoted by . In set theory, where a function is defined as a particular kind of binary relation, the identity function is given by the identity relation, or ''diagonal'' of . Algebraic propert ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Journal Of Artificial Intelligence Research
The ''Journal of Artificial Intelligence Research'' (''JAIR'') is an open access peer-reviewed scientific journal covering research in all areas of artificial intelligence. History It was established in 1993 as one of the first scientific journals distributed online. Paper volumes are printed by the AAAI Press. The Journal for Artificial Intelligence Research (JAIR) is one of the premier publication venues in artificial intelligence. JAIR also stands out in that, since its launch in 1993, it has been 100% open-access and non-profit. Content The Journal of Artificial Intelligence Research (JAIR) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal's scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and unce ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expected Value
In probability theory, the expected value (also called expectation, expectancy, expectation operator, mathematical expectation, mean, expectation value, or first Moment (mathematics), moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean, mean of the possible values a random variable can take, weighted by the probability of those outcomes. Since it is obtained through arithmetic, the expected value sometimes may not even be included in the sample data set; it is not the value you would expect to get in reality. The expected value of a random variable with a finite number of outcomes is a weighted average of all possible outcomes. In the case of a continuum of possible outcomes, the expectation is defined by Integral, integration. In the axiomatic foundation for probability provided by measure theory, the expectation is given by Lebesgue integration. The expected value of a random variable is often denoted by , , or , with a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expected Utility
The expected utility hypothesis is a foundational assumption in mathematical economics concerning decision making under uncertainty. It postulates that rational agents maximize utility, meaning the subjective desirability of their actions. Rational choice theory, a cornerstone of microeconomics, builds this postulate to model aggregate social behaviour. The expected utility hypothesis states an agent chooses between risky prospects by comparing expected utility values (i.e., the weighted sum of adding the respective utility values of payoffs multiplied by their probabilities). The summarised formula for expected utility is U(p)=\sum u(x_k)p_k where p_k is the probability that outcome indexed by k with payoff x_k is realized, and function ''u'' expresses the utility of each respective payoff. Graphically the curvature of the u function captures the agent's risk attitude. For example, imagine you’re offered a choice between receiving $50 for sure, or flipping a coin to win $100 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Objective Function (artificial Intelligence)
In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. Leading AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods, thereby exemplifying a novel form of digital agency. Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome. Intelligent agents operate based on an objective function, which encapsulates their goals ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Uncomputable
Computable functions are the basic objects of study in computability theory. Informally, a function is ''computable'' if there is an algorithm that computes the value of the function for every value of its argument. Because of the lack of a precise definition of the concept of algorithm, every formal definition of computability must refer to a specific model of computation. Many such models of computation have been proposed, the major ones being Turing machines, register machines, lambda calculus and general recursive functions. Although these four are of a very different nature, they provide exactly the same class of computable functions, and, for every model of computation that has ever been proposed, the computable functions for such a model are computable for the above four models of computation. The Church–Turing thesis is the unprovable assertion that every notion of computability that can be imagined can compute only functions that are computable in the above sense. Befo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AIXI
AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book ''Universal Artificial Intelligence''. AIXI is a reinforcement learning (RL) agent. It maximizes the expected total rewards received from the environment. Intuitively, it simultaneously considers every computable hypothesis (or environment). In each time step, it looks at every possible program and evaluates how many rewards that program generates depending on the next action taken. The promised rewards are then weighted by the subjective belief that this program constitutes the true environment. This belief is computed from the length of the program: longer programs are considered less likely, in line with Occam's razor. AIXI then selects the action that has the highest expected total reward in the weighted sum of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reward Function
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dynam ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Wirehead (science Fiction)
In science fiction, wireheading is a term associated with fictional or futuristic applications of brain stimulation reward, the act of directly triggering the brain's reward center by electrical stimulation of an inserted wire, for the purpose of 'short-circuiting' the brain's normal reward process and artificially inducing pleasure. Scientists have successfully performed brain stimulation reward on rats (1950s) and humans (1960s). This stimulation does not appear to lead to tolerance or satiation in the way that sex or drugs do. The term is sometimes associated with science fiction writer Larry Niven, who coined the term in his 1969 novella ''Death by Ecstasy'' (''Known Space'' series). In the philosophy of artificial intelligence, the term is used to refer to AI systems that hack their own reward channel. More broadly, the term can also refer to various kinds of interaction between human beings and technology. In fiction Literature Wireheading, like other forms of brain alter ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reinforcement Learning
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dyn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
