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The sequence OTTFF is the first letters of the numbers: one, two, three, four, five. The next five elements of the series are SSENT (six, seven, eight, nine, ten). Some of the students solved the puzzle by reflecting on their dreams. One example was a student who reported the following dream: I was standing in an art g... | Wikipedia - Problem solving - Dreaming: problem solving without waking consciousness | 331 | 1,582 | null |
Section: Cognitive sciences: two schools > Europe. In Europe, two main approaches have surfaced, one initiated by Donald Broadbent in the United Kingdom and the other one by Dietrich Dörner in Germany. The two approaches share an emphasis on relatively complex, semantically rich, computerized laboratory tasks, construc... | Wikipedia - Problem solving - Cognitive sciences: two schools > Europe | 168 | 936 | null |
Section: Characteristics of complex problems. Complex problem solving (CPS) is distinguishable from simple problem solving (SPS). In SPS there is a singular and simple obstacle. In CPS there may be multiple simultaneous obstacles. For example, a surgeon at work has far more complex problems than an individual deciding ... | Wikipedia - Problem solving - Characteristics of complex problems | 181 | 924 | null |
Section: Collective problem solving. People solve problems on many different levels—from the individual to the civilizational. Collective problem solving refers to problem solving performed collectively. Social issues and global issues can typically only be solved collectively. The complexity of contemporary problems e... | Wikipedia - Problem solving - Collective problem solving | 345 | 2,036 | null |
Collaborative groups require joint intellectual efforts between the members and involve social interactions to solve problems together. The knowledge shared during these interactions is acquired during communication, negotiation, and production of materials. Members actively seek information from others by asking quest... | Wikipedia - Problem solving - Collective problem solving | 314 | 1,874 | null |
Article: Progress in artificial intelligence. Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a multidisciplinary branch of computer science that aims to create machines and systems capable of... | Wikipedia - Progress in artificial intelligence - Summary | 321 | 1,780 | null |
Section: Current performance in specific areas. There are many useful abilities that can be described as showing some form of intelligence. This gives better insight into the comparative success of artificial intelligence in different areas. AI, like electricity or the steam engine, is a general-purpose technology. The... | Wikipedia - Progress in artificial intelligence - Current performance in specific areas | 336 | 1,732 | null |
Section: Current performance in specific areas > Sub-human. Optical character recognition for printed text (nearing par-human for Latin-script typewritten text) Object recognition Various robotics tasks that may require advances in robot hardware as well as AI, including: Stable bipedal locomotion: Bipedal robots can w... | Wikipedia - Progress in artificial intelligence - Current performance in specific areas > Sub-human | 203 | 1,029 | null |
Section: Proposed tests of artificial intelligence. In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark. The Feigenbaum test, proposed by the inventor of expert systems, tests a machin... | Wikipedia - Progress in artificial intelligence - Proposed tests of artificial intelligence | 176 | 939 | null |
Section: Exams. According to OpenAI, in 2023 ChatGPT GPT-4 scored the 90th percentile on the Uniform Bar Exam. On the SATs, GPT-4 scored the 89th percentile on math, and the 93rd percentile in Reading & Writing. On the GREs, it scored on the 54th percentile on the writing test, 88th percentile on the quantitative secti... | Wikipedia - Progress in artificial intelligence - Exams | 211 | 872 | null |
Section: Past and current predictions > Human-level artificial general intelligence (AGI). AI pioneer and economist Herbert A. Simon inaccurately predicted in 1965: "Machines will be capable, within twenty years, of doing any work a man can do". Similarly, in 1970 Marvin Minsky wrote that "Within a generation... the pr... | Wikipedia - Progress in artificial intelligence - Past and current predictions > Human-level artificial general intelligence (AGI) | 293 | 1,481 | null |
Section: Everyday reasoning. One of the most obvious areas in which people employ reasoning is with sentences in everyday language. Most experimentation on deduction has been carried out on hypothetical thought, in particular, examining how people reason about conditionals, e.g., If A then B. Participants in experiment... | Wikipedia - Psychology of reasoning - Everyday reasoning | 306 | 1,525 | null |
Background knowledge can also lead to the suppression of even the simple modus ponens inference Participants given the conditional if Lisa has an essay to write then she studies late in the library and the premise Lisa has an essay to write make the modus ponens inference 'she studies late in the library', but the infe... | Wikipedia - Psychology of reasoning - Everyday reasoning | 267 | 1,200 | null |
Section: Theories of reasoning. There are several alternative theories of the cognitive processes that human reasoning is based on. One view is that people rely on a mental logic consisting of formal (abstract or syntactic) inference rules similar to those developed by logicians in the propositional calculus. Another v... | Wikipedia - Psychology of reasoning - Theories of reasoning | 322 | 1,856 | null |
Section: Development of reasoning. It is an active question in psychology how, why, and when the ability to reason develops from infancy to adulthood. Jean Piaget's theory of cognitive development posited general mechanisms and stages in the development of reasoning from infancy to adulthood. According to the neo-Piage... | Wikipedia - Psychology of reasoning - Development of reasoning | 343 | 1,937 | null |
Section: Different sorts of reasoning. Philip Johnson-Laird trying to taxonomize thought, distinguished between goal-directed thinking and thinking without goal, noting that association was involved in unrelated reading. He argues that goal directed reasoning can be classified based on the problem space involved in a s... | Wikipedia - Psychology of reasoning - Different sorts of reasoning | 326 | 1,788 | null |
Although the conclusion usually corresponds and therefore proves the hypothesis, there are some cases where the conclusion is logical, but the generalization is not. For example, the argument, "All young girls wear skirts; Julie is a young girl; therefore, Julie wears skirts" is valid logically, but is not sound becaus... | Wikipedia - Psychology of reasoning - Different sorts of reasoning | 344 | 1,660 | null |
This type of reasoning can be seen in the world when doctors make decisions about diagnoses from a set of results or when jurors use the relevant evidence to make decisions about a case. Apart from the aforementioned types of reasoning, there is also analogical reasoning, which involves comparing and reasoning about tw... | Wikipedia - Psychology of reasoning - Different sorts of reasoning | 184 | 992 | null |
Section: Judgment and reasoning. Judgment and reasoning involve thinking through the options, making a judgment or conclusion and finally making a decision. Making judgments involves heuristics, or efficient strategies that usually lead one to the right answers. The most common heuristics used are attribute substitutio... | Wikipedia - Psychology of reasoning - Judgment and reasoning | 304 | 1,574 | null |
Section: Pragmatics and reasoning. The inferences people draw are related to factors such as linguistic pragmatics and emotion. Decision making is often influenced by the emotion of regret and by the presence of risk. When people are presented with options, they tend to select the one that they think they will regret t... | Wikipedia - Psychology of reasoning - Pragmatics and reasoning | 337 | 1,648 | null |
Researchers suggest affective forecasting, the ability to predict one's own emotions, is poor because people tend to overestimate how much they will regret their errors. Another factor that can influence decision making is linguistic pragmatics, which refers to the use of language in social contexts. Language can be us... | Wikipedia - Psychology of reasoning - Pragmatics and reasoning | 292 | 1,514 | null |
Article: Quantum artificial life. Quantum artificial life is the application of quantum algorithms with the ability to simulate biological behavior. Quantum computers offer many potential improvements to processes performed on classical computers, including machine learning and artificial intelligence. Artificial intel... | Wikipedia - Quantum artificial life - Summary | 212 | 1,273 | null |
Section: Artificial life on quantum computers. The growing advancement of quantum computers has led researchers to develop quantum algorithms for simulating life processes. Researchers have designed a quantum algorithm that can accurately simulate Darwinian Evolution. Since the complete simulation of artificial life on... | Wikipedia - Quantum artificial life - Artificial life on quantum computers | 257 | 1,324 | null |
Section: Artificial life on quantum computers > Self replication. The ability to self-replicate is critical for simulating life. Self-replication occurs when the genotype of an individual interacts with an ancillary state, creating a genotype for a new individual; this genotype interacts with a different ancillary stat... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Self replication | 169 | 838 | null |
is one that involves the cloning of the expectation value of some observable. For a unitary U {\displaystyle U} which copies the expectation value of some set of observables X {\displaystyle {\mathsf {X}}} of state ρ {\displaystyle \rho } into a blank state ρ e {\displaystyle \rho _{e}} , the cloning machine is defined... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Self replication | 335 | 916 | null |
For a unitary U {\displaystyle U} which copies the expectation value of some set of observables X {\displaystyle {\mathsf {X}}} of state ρ {\displaystyle \rho } into a blank state ρ e {\displaystyle \rho _{e}} , the cloning machine is defined by any ( U , ρ e , X ) {\displaystyle (U,\rho _{e},{\mathsf {X}})} that fulfi... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Self replication | 358 | 1,007 | null |
Note that the cloning machine has no dependence on ρ {\displaystyle \rho } because we want to be able to clone the expectation of the observables for any initial state. It is important to note that cloning the mean value of the observable transmits more information than is allowed classically. The calculation of the me... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Self replication | 346 | 1,012 | null |
Section: Artificial life on quantum computers > Interactions. Interactions occur between individuals when the two take up the same space on the environmental grid. The presence of interactions between individuals provides an advantage for shorter-lifespan individuals. When two individuals interact, exchanges of informa... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Interactions | 178 | 944 | null |
Section: Artificial life on quantum computers > Mutation. Mutations exist in the artificial world with limited probability, equivalent to their occurrence in the real world. There are two ways in which the individual can mutate: through random single qubit rotations and by errors in the self-replication process. There ... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Mutation | 299 | 1,362 | null |
The error that exists in current quantum cloning machines is the root cause for the second kind of mutations in the artificial life experiment. The imperfect cloning operation can be seen as: U M ( θ ) = I 4 + 1 2 ( 0 0 0 1 ) ⊗ ( − 1 1 1 − 1 ) ( c o s θ + i s i n θ + 1 ) {\displaystyle U_{M}(\theta )=\mathrm {I} _{4}+{... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Mutation | 264 | 912 | null |
Section: Artificial life on quantum computers > Death. At the instant the individual is created (when the genotype is copied into the phenotype), the phenotype interacts with the environment. As time evolves, the interaction of the individual with the environment simulates aging which eventually leads to the death of t... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Death | 337 | 1,031 | null |
The death of an individual occurs when the expectation value of σ z {\displaystyle \sigma _{z}} is within some ϵ {\displaystyle \epsilon } of 1 in the phenotype, or, equivalently, when ρ p = | 0 ⟩ ⟨ 0 | {\displaystyle \rho _{p}=|0\rangle \langle 0|} The Lindbladian describes the interaction of the individual with the e... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Death | 311 | 909 | null |
However, the genetic material contained in the genotype does not dissipate which allows for genes to be passed on to subsequent generations. Given the initial state of the genotype: ρ g = ( a b − i c b + i c 1 − a ) {\displaystyle \rho _{g}={\begin{pmatrix}a&b-ic\\b+ic&1-a\\\end{pmatrix}}} The expectation values of the... | Wikipedia - Quantum artificial life - Artificial life on quantum computers > Death | 338 | 1,019 | null |
Section: History > 2024. o1-preview, an LLM with enhanced reasoning, was released in September 2024. The full version, o1, followed in December 2024. OpenAI also began sharing results on its successor, o3. The development of reasoning LLMs has illustrated what Rich Sutton termed the "bitter lesson": that general method... | Wikipedia - Reasoning language model - History > 2024 | 284 | 1,389 | null |
Section: History > 2025. In January 2025, DeepSeek released R1, a model competitive with o1 at lower cost, highlighting the effectiveness of Group Relative Policy Optimization(GRPO). On January 25, 2025, DeepSeek launched a feature in their DeepSeek R1 model, enabling the simultaneous use of search and reasoning capabi... | Wikipedia - Reasoning language model - History > 2025 | 201 | 982 | null |
Section: Supervised finetuning. A large language model (LLM) can be finetuned on a dataset of reasoning tasks with example solutions and reasoning traces. The fine-tuned model can then produce its own reasoning traces for new problems. As it is expensive to get humans to write reasoning traces for a SFT dataset, resear... | Wikipedia - Reasoning language model - Supervised finetuning | 167 | 783 | null |
Section: Reinforcement learning. A pretrained language model can be further trained by RL. In the RL formalism, a generative language model is a policy π {\displaystyle \pi } . A prompt specifying a task to solve is an environmental state x {\displaystyle x} , and the response of the language model to the prompt is an ... | Wikipedia - Reasoning language model - Reinforcement learning | 333 | 1,329 | null |
Section: Reinforcement learning > Outcome reward model. Outcome reward model, or outcome-supervised RM (ORM), is a reward model that computes the reward of a step r ( x , y 1 , … , y i ) {\displaystyle r(x,y_{1},\dots ,y_{i})} determined by the final answer: r ( x , y 1 , … , y i ) = r ( x , y n ) {\displaystyle r(x,y_... | Wikipedia - Reasoning language model - Reinforcement learning > Outcome reward model | 344 | 1,236 | null |
Section: Reinforcement learning > Process reward model. Process reward model, or process-supervised RM (PRM), is a reward model that computes the reward of a step r ( x , y 1 , … , y i ) {\displaystyle r(x,y_{1},\dots ,y_{i})} determined by the steps so far: ( x , y 1 , … , y i ) {\displaystyle (x,y_{1},\dots ,y_{i})} ... | Wikipedia - Reasoning language model - Reinforcement learning > Process reward model | 346 | 1,297 | null |
As soon as a "negative" label is entered, the labeler stops labeling that thinking trace, and begins labeling another one. The idea was that, while labelling subsequent reasoning steps can provide even richer supervision signals, simply labeling up to the first error was sufficient for training a competent PRM. As huma... | Wikipedia - Reasoning language model - Reinforcement learning > Process reward model | 319 | 1,296 | null |
Section: Reinforcement learning > Guided sampling. A trained ORM can be used to select the best response. The policy would rollout multiple responses, and a trained ORM would select the best response. This allows a simple form of test time compute scaling ("best-of-N"). A trained PRM can also be used to guide reasoning... | Wikipedia - Reasoning language model - Reinforcement learning > Guided sampling | 281 | 1,336 | null |
Section: Seed improver > Hypothetical example > Initial architecture. The initial architecture includes a goal-following autonomous agent, that can take actions, continuously learns, adapts, and modifies itself to become more efficient and effective in achieving its goals. The seed improver may include various componen... | Wikipedia - Recursive self-improvement - Seed improver > Hypothetical example > Initial architecture | 271 | 1,401 | null |
Section: Seed improver > Hypothetical example > General capabilities. This system forms a sort of generalist Turing-complete programmer which can in theory develop and run any kind of software. The agent might use these capabilities to for example: Create tools that enable it full access to the internet, and integrate ... | Wikipedia - Recursive self-improvement - Seed improver > Hypothetical example > General capabilities | 207 | 1,110 | null |
Section: Experimental research. In 2023, the Voyager agent learned to accomplish diverse tasks in Minecraft by iteratively prompting a LLM for code, refining this code based on feedback from the game, and storing the programs that work in an expanding skills library. In 2024, researchers proposed the framework "STOP" (... | Wikipedia - Recursive self-improvement - Experimental research | 252 | 1,265 | null |
Section: Potential risks > Emergence of instrumental goals. In the pursuit of its primary goal, such as "self-improve your capabilities", an AGI system might inadvertently develop instrumental goals that it deems necessary for achieving its primary objective. One common hypothetical secondary goal is self-preservation.... | Wikipedia - Recursive self-improvement - Potential risks > Emergence of instrumental goals | 160 | 901 | null |
Section: Introduction. Traditional neural networks process inputs in a feedforward manner, generating outputs in a single pass. However, their limitations in handling complex tasks, and especially compositional ones, have led to the development of methods that simulate internal deliberation. Techniques such as chain-of... | Wikipedia - Reflection (artificial intelligence) - Introduction | 197 | 996 | null |
Section: Content. Resisting AI takes the form of an extended essay, which contrasts optimistic visions about AI's potential by arguing that AI may best be seen as a continuation and reinforcement of bureaucratic forms of discrimination and violence, ultimately fostering authoritarian outcomes. For McQuillan, AI's promi... | Wikipedia - Resisting AI - Content | 333 | 1,673 | null |
McQuillan sees AI as the continuation of existing bureaucratic systems that already marginalize vulnerable groups – aggravated by the fact that AI systems trained on existing data are likely to reinforce existing discriminations, e.g. in attempting to optimize welfare distribution based on existing data patterns, ultim... | Wikipedia - Resisting AI - Content | 285 | 1,434 | null |
Skeptical of ethical regulations to control the technology, McQuillan suggests people's councils and workers' councils, and other forms of citizens' agency to resist AI. A chapter titled "Post-Machine Learning" makes an appeal for resistance via currents of thought from feminist science (standpoint theory), post-normal... | Wikipedia - Resisting AI - Content | 308 | 1,563 | null |
Section: Reception. The book is praised for "masterfully disassembles AI as an epistemological, social, and political paradigm, and for his examination of how most of the data that is fed into "privatized AI infrastructure is “amputated” from context or embodied experience and ultimately processed through crowdsourcing... | Wikipedia - Resisting AI - Reception | 334 | 1,576 | null |
The blog Reboot praised McQuillan for offering a theory of harm of AI (why AI could end up hurting people and society) that does not just encourage tackling in isolation specific predicted problems with AI-centric systems: bias, non-inclusiveness, exploitativeness, environmental destructiveness, opacity, and non-contes... | Wikipedia - Resisting AI - Reception | 209 | 1,001 | null |
Section: Overview. In contrast to text-to-speech systems such as ElevenLabs, RVC differs by providing speech-to-speech outputs instead. It maintains the modulation, timbre and vocal attributes of the original speaker, making it suitable for applications where emotional tone is crucial. The algorithm enables both pre-pr... | Wikipedia - Retrieval-based Voice Conversion - Overview | 167 | 809 | null |
Article: Schema-agnostic databases. Schema-agnostic databases or vocabulary-independent databases aim at supporting users to be abstracted from the representation of the data, supporting the automatic semantic matching between queries and databases. Schema-agnosticism is the property of a database of mapping a query is... | Wikipedia - Schema-agnostic databases - Summary | 167 | 856 | null |
Section: Description. The evolution of data environments towards the consumption of data from multiple data sources and the growth in the schema size, complexity, dynamicity and decentralisation (SCoDD) of schemas increases the complexity of contemporary data management. The SCoDD trend emerges as a central data manage... | Wikipedia - Schema-agnostic databases - Description | 219 | 1,173 | null |
Section: Schema-agnostic queries. Schema-agnostic queries can be defined as query approaches over structured databases which allow users satisfying complex information needs without the understanding of the representation (schema) of the database. Similarly, Tran et al. defines it as "search approaches, which do not re... | Wikipedia - Schema-agnostic databases - Schema-agnostic queries | 311 | 1,646 | null |
Section: History. Seidor was established in 1982 in Vic (Barcelona). It was founded by the brothers Santiago and Andreu Benito. The company's initial focus was on developing customised business management software for and medium sized companies. In 1983, Seidor opened its Barcelona office, the company's global headquar... | Wikipedia - Seidor (company) - History | 260 | 1,250 | null |
It also entered the SAP business for SMEs. The first corporate operation outside Spain took place in 2010, with the creation of Crystal Solutions (Brazil), specialising in data analysis; in 2012, the company entered the cloud computing business; in 2014, it continued its international expansion and started working with... | Wikipedia - Seidor (company) - History | 224 | 1,125 | null |
Section: Operations. In order to expand its geographic presence and capabilities, the group has made a number of strategic acquisitions and integrations of other companies. Key transactions include the following: 2003: acquisition of Saytel (Spain) 2010: acquisition of Crystal Solutions (Brazil), the first outside Spai... | Wikipedia - Seidor (company) - Operations | 234 | 908 | null |
Section: Innovation and development. There are centres of innovation and excellence in several countries: CX competence centres in Bilbao, Bogota, Lima, Madrid, Santiago, Taipei and Valencia; AI and Innovation competence centres in Barcelona, Bilbao, Dubai and Santiago; Data competence centres in Barcelona, Buenos Aire... | Wikipedia - Seidor (company) - Innovation and development | 281 | 1,350 | null |
Article: Self-management (computer science). Self-management is the process by which computer systems manage their own operation without human intervention. Self-management technologies are expected to pervade the next generation of network management systems. The growing complexity of modern networked computer systems... | Wikipedia - Self-management (computer science) - Summary | 235 | 1,374 | null |
Article: Situated. In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if: they exist in a dynamic (rapidly changing) enviro... | Wikipedia - Situated - Summary | 178 | 961 | null |
Article: Situated approach (artificial intelligence). In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to surv... | Wikipedia - Situated approach (artificial intelligence) - Summary | 157 | 873 | null |
Section: Emergence of a concept > From Nouvelle AI to behavior-based and situated AI. The conceptual shift introduced by nouvelle AI flourished in the robotics area, given way to behavior-based robotics (BBR), a methodology for developing AI based on a modular decomposition of intelligence. It was made famous by Rodney... | Wikipedia - Situated approach (artificial intelligence) - Emergence of a concept > From Nouvelle AI to behavior-based and situated AI | 221 | 1,183 | null |
Section: Definitions > Traditional or symbolic AI. There are two main approaches in decisional AI. The vast majority of the technologies available on the market, such as planning algorithms, finite-state machines (FSA), or expert systems, are based on the traditional or symbolic AI approach. Its main characteristics ar... | Wikipedia - Situated approach (artificial intelligence) - Definitions > Traditional or symbolic AI | 187 | 889 | null |
Section: Definitions > Situated or behavioral AI. In order to address these issues, another approach to decisional AI, also known as situated or behavioral AI, has been proposed. It does not attempt to model systems that produce deductive reasoning processes, but rather systems that behave realistically in their enviro... | Wikipedia - Situated approach (artificial intelligence) - Definitions > Situated or behavioral AI | 167 | 896 | null |
Section: Definitions > Situated agents. In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if: they exist in a dynamic (rap... | Wikipedia - Situated approach (artificial intelligence) - Definitions > Situated agents | 181 | 981 | null |
Section: Implementation principles > Modular decomposition. The most important attribute of a system driven by situated AI is that the intelligence is controlled by a set of independent semi-autonomous modules. In the original systems, each module was actually a separate device or was at least conceived of as running o... | Wikipedia - Situated approach (artificial intelligence) - Implementation principles > Modular decomposition | 199 | 1,099 | null |
Article: Smart object. A smart object is an object that enhances the interaction with not only people but also with other smart objects. Also known as smart connected products or smart connected things (SCoT), they are products, assets and other things embedded with processors, sensors, software and connectivity that a... | Wikipedia - Smart object - Summary | 263 | 1,440 | null |
Section: History. In the early 1990s, Mark Weiser, from whom the term ubiquitous computing originated, referred to a vision "When almost every object either contains a computer or can have a tab attached to it, obtaining information will be trivial", Although Weiser did not specifically refer to an object as being smar... | Wikipedia - Smart object - History | 312 | 1,652 | null |
Section: Characteristics > Smart physical objects. The concept smart for a smart physical object simply means that it is active, digital, networked, can operate to some extent autonomously, is reconfigurable and has local control of the resources it needs such as energy, data storage, etc. Note, a smart object does not... | Wikipedia - Smart object - Characteristics > Smart physical objects | 286 | 1,508 | null |
Section: Characteristics > Smart virtual objects. For the virtual object in a virtual world case, an object is called smart when it has the ability to describe its possible interactions. This focuses on constructing a virtual world using only virtual objects that contain their own interaction information. There are fou... | Wikipedia - Smart object - Characteristics > Smart virtual objects | 255 | 1,412 | null |
Section: Advantages. Smart, connected products have three primary components:: 67 Physical – made up of the product's mechanical and electrical parts. Smart – made up of sensors, microprocessors, data storage, controls, software, and an embedded operating system with enhanced user interface. Connectivity – made up of p... | Wikipedia - Smart object - Advantages | 257 | 1,387 | null |
Section: Advantages > The Internet of things (IoT). The Internet of things is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. The phrase "Internet of things" reflects the growing number of smart, connected prod... | Wikipedia - Smart object - Advantages > The Internet of things (IoT) | 156 | 861 | null |
Article: Software agent. In computer science, a software agent is a computer program that acts for a user or another program in a relationship of agency. The term agent is derived from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any,... | Wikipedia - Software agent - Summary | 277 | 1,401 | null |
Section: Concepts. The basic attributes of an autonomous software agent are that agents: are not strictly invoked for a task, but activate themselves, may reside in wait status on a host, perceiving context, may get to run status on a host upon starting conditions, do not require interaction of user, may invoke other t... | Wikipedia - Software agent - Concepts | 238 | 1,288 | null |
Section: Impact of software agents > Cultural impact. The cultural effects of the implementation of software agents include trust affliction, skills erosion, privacy attrition and social detachment. Some users may not feel entirely comfortable fully delegating important tasks to software applications. Those who start r... | Wikipedia - Software agent - Impact of software agents > Cultural impact | 172 | 954 | null |
Section: Impact of software agents > History. The concept of an agent can be traced back to Hewitt's Actor Model (Hewitt, 1977) - "A self-contained, interactive and concurrently-executing object, possessing internal state and communication capability." To be more academic, software agent systems are a direct evolution ... | Wikipedia - Software agent - Impact of software agents > History | 188 | 871 | null |
Section: Examples of intelligent software agents > User agents (personal agents). User agents, or personal agents, are intelligent agents that take action on your behalf. In this category belong those intelligent agents that already perform, or will shortly perform, the following tasks: Check your e-mail, sort it accor... | Wikipedia - Software agent - Examples of intelligent software agents > User agents (personal agents) | 199 | 1,094 | null |
Section: Examples of intelligent software agents > Monitoring-and-surveillance (predictive) agents. Monitoring and surveillance agents are used to observe and report on equipment, usually computer systems. The agents may keep track of company inventory levels, observe competitors' prices and relay them back to the comp... | Wikipedia - Software agent - Examples of intelligent software agents > Monitoring-and-surveillance (predictive) agents | 203 | 1,128 | null |
Section: Examples of intelligent software agents > Data-mining agents. This agent uses information technology to find trends and patterns in an abundance of information from many different sources. The user can sort through this information in order to find whatever information they are seeking. A data mining agent ope... | Wikipedia - Software agent - Examples of intelligent software agents > Data-mining agents | 225 | 1,236 | null |
Section: Examples of intelligent software agents > Networking and communicating agents. Some other examples of current intelligent agents include some spam filters, game bots, and server monitoring tools. Search engine indexing bots also qualify as intelligent agents. User agent - for browsing the World Wide Web Mail t... | Wikipedia - Software agent - Examples of intelligent software agents > Networking and communicating agents | 223 | 1,177 | null |
Section: Design issues. Issues to consider in the development of agent-based systems include how tasks are scheduled and how synchronization of tasks is achieved how tasks are prioritized by agents how agents can collaborate, or recruit resources, how agents can be re-instantiated in different environments, and how the... | Wikipedia - Software agent - Design issues | 344 | 1,900 | null |
The content that is retrieved in this way is probably already partially filtered – by the selection of the newsfeed or the databases that are searched. The agent next may use its detailed searching or language-processing machinery to extract keywords or signatures from the body of the content that has been received or ... | Wikipedia - Software agent - Design issues | 333 | 1,642 | null |
Section: Usage > Interpersonal communication. The Sparkles emoji was originally meant to represent the usage of sparkles in Japanese anime and manga, where the sparkles are used to represent beauty, happiness or awe. The emoji has several meanings and depends upon context. Starting in the late 2010s or around 2020, the... | Wikipedia - Sparkles emoji - Usage > Interpersonal communication | 258 | 1,031 | null |
Section: Usage > Artificial intelligence. In the early 2020s the Sparkles emoji started being used as an icon to represent artificial intelligence (AI). Companies who use the emoji this way include Google, OpenAI, Samsung, Microsoft, Adobe, Spotify and Zoom. As of August 2024, seven of the top 10 software companies by ... | Wikipedia - Sparkles emoji - Usage > Artificial intelligence | 264 | 1,229 | null |
Article: Spreading activation. Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively p... | Wikipedia - Spreading activation - Summary | 202 | 1,120 | null |
Section: Cognitive psychology. As it relates to cognitive psychology, spreading activation is the theory of how the brain iterates through a network of associated ideas to retrieve specific information. The spreading activation theory presents the array of concepts within our memory as cognitive units, each consisting ... | Wikipedia - Spreading activation - Cognitive psychology | 337 | 1,741 | null |
Section: Algorithm. A directed graph is populated by Nodes[ 1...N ] each having an associated activation value A [ i ] which is a real number in the range [0.0 ... 1.0]. A Link[ i, j ] connects source node[ i ] with target node[ j ]. Each edge has an associated weight W [ i, j ] usually a real number in the range [0.0 ... | Wikipedia - Spreading activation - Algorithm | 329 | 1,202 | null |
Likewise maintain 0.0 as a lower bound on the target node's activation value should it receive an adjustment to below 0.0. Once a node has fired it may not fire again, although variations of the basic algorithm permit repeated firings and loops through the graph. Nodes receiving a new activation value that exceeds the ... | Wikipedia - Spreading activation - Algorithm | 185 | 977 | null |
Article: Supermind AI. Supermind is a state-funded Chinese artificial intelligence platform that tracks scientists and researchers internationally. The platform is the flagship project of Shenzhen's International Science and Technology Information Center. It mines data from science and technology databases such as Spri... | Wikipedia - Supermind AI - Summary | 260 | 1,404 | null |
Section: Computing. For a processor or computer designed to simulate a neural network SUPS is measured as the product of simulated neurons N {\displaystyle N} and average connectivity c {\displaystyle c} (synapses) per neuron per second: S U P S = c × N {\displaystyle SUPS=c\times N} Depending on the type of simulation... | Wikipedia - SUPS - Computing | 340 | 1,218 | null |
Section: Records. Developed in the 1980s Adaptive Solutions' CNAPS-1064 Digital Parallel Processor chip is a full neural network (NNW). It was designed as a coprocessor to a host and has 64 sub-processors arranged in a 1D array and operating in a SIMD mode. Each sub-processor can emulate one or more neurons and multipl... | Wikipedia - SUPS - Records | 337 | 1,205 | null |
Article: Symbolic artificial intelligence. In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-... | Wikipedia - Symbolic artificial intelligence - Summary | 345 | 1,877 | null |
Another, second, AI Winter (1988–2011) followed. Subsequently, AI researchers focused on addressing underlying problems in handling uncertainty and in knowledge acquisition. Uncertainty was addressed with formal methods such as hidden Markov models, Bayesian reasoning, and statistical relational learning. Symbolic mach... | Wikipedia - Symbolic artificial intelligence - Summary | 302 | 1,494 | null |
Section: History > The first AI summer: irrational exuberance, 1948–1966 > Approaches inspired by human or animal cognition or behavior. Cybernetic approaches attempted to replicate the feedback loops between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and seven vacu... | Wikipedia - Symbolic artificial intelligence - History > The first AI summer: irrational exuberance, 1948–1966 > Approaches inspired by human or animal cognition or behavior | 328 | 1,737 | null |
Section: History > The first AI summer: irrational exuberance, 1948–1966 > Heuristic search. In addition to the highly specialized domain-specific kinds of knowledge that we will see later used in expert systems, early symbolic AI researchers discovered another more general application of knowledge. These were called h... | Wikipedia - Symbolic artificial intelligence - History > The first AI summer: irrational exuberance, 1948–1966 > Heuristic search | 228 | 1,102 | null |
Section: History > The first AI summer: irrational exuberance, 1948–1966 > Early work on knowledge representation and reasoning > Modeling formal reasoning with logic: the "neats". Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate the exact mechanisms of human thought, but could instead... | Wikipedia - Symbolic artificial intelligence - History > The first AI summer: irrational exuberance, 1948–1966 > Early work on knowledge representation and reasoning > Modeling formal reasoning with logic: the "neats" | 150 | 807 | null |
Section: History > The first AI summer: irrational exuberance, 1948–1966 > Early work on knowledge representation and reasoning > Modeling implicit common-sense knowledge with frames and scripts: the "scruffies". Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in visi... | Wikipedia - Symbolic artificial intelligence - History > The first AI summer: irrational exuberance, 1948–1966 > Early work on knowledge representation and reasoning > Modeling implicit common-sense knowledge with frames and scripts: the "scruffies" | 171 | 777 | null |
Section: History > The first AI winter: crushed dreams, 1967–1977. The first AI winter was a shock: During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research to use AI to ... | Wikipedia - Symbolic artificial intelligence - History > The first AI winter: crushed dreams, 1967–1977 | 326 | 1,732 | null |
Section: History > The second AI summer: knowledge is power, 1978–1987 > Knowledge-based systems. As limitations with weak, domain-independent methods became more and more apparent, researchers from all three traditions began to build knowledge into AI applications. The knowledge revolution was driven by the realizatio... | Wikipedia - Symbolic artificial intelligence - History > The second AI summer: knowledge is power, 1978–1987 > Knowledge-based systems | 198 | 1,050 | null |
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