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Section: History > The second AI summer: knowledge is power, 1978–1987 > Success with expert systems. This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first commercially successful form of AI software. Key expert systems were: DENDRAL, which foun... | Wikipedia - Symbolic artificial intelligence - History > The second AI summer: knowledge is power, 1978–1987 > Success with expert systems | 316 | 1,556 | null |
It is described below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum: One of the people at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I told him I wanted an induction "sandbox", he said, "I have ju... | Wikipedia - Symbolic artificial intelligence - History > The second AI summer: knowledge is power, 1978–1987 > Success with expert systems | 349 | 1,501 | null |
The other expert systems mentioned above came after DENDRAL. MYCIN exemplifies the classic expert system architecture of a knowledge-base of rules coupled to a symbolic reasoning mechanism, including the use of certainty factors to handle uncertainty. GUIDON shows how an explicit knowledge base can be repurposed for a ... | Wikipedia - Symbolic artificial intelligence - History > The second AI summer: knowledge is power, 1978–1987 > Success with expert systems | 267 | 1,299 | null |
Section: History > The second AI summer: knowledge is power, 1978–1987 > Success with expert systems > Architecture of knowledge-based and expert systems. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving. The simplest approach for ... | Wikipedia - Symbolic artificial intelligence - History > The second AI summer: knowledge is power, 1978–1987 > Success with expert systems > Architecture of knowledge-based and expert systems | 330 | 1,737 | null |
Section: History > The second AI winter, 1988–1993. At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research. In addition, several artificial intelligence companies, such as Teknow... | Wikipedia - Symbolic artificial intelligence - History > The second AI winter, 1988–1993 | 308 | 1,615 | null |
Section: History > Adding in more rigorous foundations, 1993–2011 > Uncertain reasoning. Both statistical approaches and extensions to logic were tried. One statistical approach, hidden Markov models, had already been popularized in the 1980s for speech recognition work. Subsequently, in 1988, Judea Pearl popularized t... | Wikipedia - Symbolic artificial intelligence - History > Adding in more rigorous foundations, 1993–2011 > Uncertain reasoning | 338 | 1,762 | null |
Section: History > Adding in more rigorous foundations, 1993–2011 > Machine learning. Symbolic machine learning approaches were investigated to address the knowledge acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test technique to generate plausible rule hypotheses to test... | Wikipedia - Symbolic artificial intelligence - History > Adding in more rigorous foundations, 1993–2011 > Machine learning | 345 | 1,637 | null |
In contrast to the knowledge-intensive approach of Meta-DENDRAL, Ross Quinlan invented a domain-independent approach to statistical classification, decision tree learning, starting first with ID3 and then later extending its capabilities to C4.5. The decision trees created are glass box, interpretable classifiers, with... | Wikipedia - Symbolic artificial intelligence - History > Adding in more rigorous foundations, 1993–2011 > Machine learning | 343 | 1,812 | null |
Inductive logic programming was another approach to learning that allowed logic programs to be synthesized from input-output examples. E.g., Ehud Shapiro's MIS (Model Inference System) could synthesize Prolog programs from examples. John R. Koza applied genetic algorithms to program synthesis to create genetic programm... | Wikipedia - Symbolic artificial intelligence - History > Adding in more rigorous foundations, 1993–2011 > Machine learning | 307 | 1,538 | null |
Approaches, other than those above, include: Learning from instruction or advice—i.e., taking human instruction, posed as advice, and determining how to operationalize it in specific situations. For example, in a game of Hearts, learning exactly how to play a hand to "avoid taking points." Learning from exemplars—impro... | Wikipedia - Symbolic artificial intelligence - History > Adding in more rigorous foundations, 1993–2011 > Machine learning | 333 | 1,608 | null |
Section: History > Deep learning and neuro-symbolic AI 2011–now > Neuro-symbolic AI: integrating neural and symbolic approaches. Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust A... | Wikipedia - Symbolic artificial intelligence - History > Deep learning and neuro-symbolic AI 2011–now > Neuro-symbolic AI: integrating neural and symbolic approaches | 339 | 1,729 | null |
System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, deduction, and deliberative thinking. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed. Garcez and Lamb describe research in this... | Wikipedia - Symbolic artificial intelligence - History > Deep learning and neuro-symbolic AI 2011–now > Neuro-symbolic AI: integrating neural and symbolic approaches | 344 | 1,595 | null |
Approaches for integration are varied. Henry Kautz's taxonomy of neuro-symbolic architectures, along with some examples, follows: Symbolic Neural symbolic—is the current approach of many neural models in natural language processing, where words or subword tokens are both the ultimate input and output of large language ... | Wikipedia - Symbolic artificial intelligence - History > Deep learning and neuro-symbolic AI 2011–now > Neuro-symbolic AI: integrating neural and symbolic approaches | 336 | 1,707 | null |
Section: Techniques and contributions > AI programming languages. The key AI programming language in the US during the last symbolic AI boom period was LISP. LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rap... | Wikipedia - Symbolic artificial intelligence - Techniques and contributions > AI programming languages | 332 | 1,675 | null |
Backtracking and unification are built-in to Prolog. Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog. Prolog is a form of logic programming, which was invented by Robert Kowalski. Its history was also influenced by Carl Hewitt's PLANNER, an assertional database with pattern-directed invoca... | Wikipedia - Symbolic artificial intelligence - Techniques and contributions > AI programming languages | 322 | 1,589 | null |
Section: Techniques and contributions > Knowledge representation and reasoning > Knowledge representation. Semantic networks, conceptual graphs, frames, and logic are all approaches to modeling knowledge such as domain knowledge, problem-solving knowledge, and the semantic meaning of language. Ontologies model key conc... | Wikipedia - Symbolic artificial intelligence - Techniques and contributions > Knowledge representation and reasoning > Knowledge representation | 308 | 1,589 | null |
Section: Techniques and contributions > Knowledge representation and reasoning > Reasoning in knowledge-based systems. Knowledge-based systems have an explicit knowledge base, typically of rules, to enhance reusability across domains by separating procedural code and domain knowledge. A separate inference engine proces... | Wikipedia - Symbolic artificial intelligence - Techniques and contributions > Knowledge representation and reasoning > Reasoning in knowledge-based systems | 197 | 1,028 | null |
Section: Techniques and contributions > Knowledge representation and reasoning > Commonsense reasoning. Marvin Minsky first proposed frames as a way of interpreting common visual situations, such as an office, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has attempted to c... | Wikipedia - Symbolic artificial intelligence - Techniques and contributions > Knowledge representation and reasoning > Commonsense reasoning | 196 | 994 | null |
Section: Techniques and contributions > Natural language processing. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and u... | Wikipedia - Symbolic artificial intelligence - Techniques and contributions > Natural language processing | 231 | 1,348 | null |
Section: Techniques and contributions > Agents and multi-agent systems. Agents are autonomous systems embedded in an environment they perceive and act upon in some sense. Russell and Norvig's standard textbook on artificial intelligence is organized to reflect agent architectures of increasing sophistication. The sophi... | Wikipedia - Symbolic artificial intelligence - Techniques and contributions > Agents and multi-agent systems | 238 | 1,307 | null |
Section: Controversies > The Frame Problem: knowledge representation challenges for first-order logic. Limitations were discovered in using simple first-order logic to reason about dynamic domains. Problems were discovered both with regards to enumerating the preconditions for an action to succeed and in providing axio... | Wikipedia - Symbolic artificial intelligence - Controversies > The Frame Problem: knowledge representation challenges for first-order logic | 320 | 1,645 | null |
Other ways of handling more open-ended domains included probabilistic reasoning systems and machine learning to learn new concepts and rules. McCarthy's Advice Taker can be viewed as an inspiration here, as it could incorporate new knowledge provided by a human in the form of assertions or rules. For example, experimen... | Wikipedia - Symbolic artificial intelligence - Controversies > The Frame Problem: knowledge representation challenges for first-order logic | 289 | 1,465 | null |
Section: Controversies > Connectionist AI: philosophical challenges and sociological conflicts. Connectionist approaches include earlier work on neural networks, such as perceptrons; work in the mid to late 80s, such as Danny Hillis's Connection Machine and Yann LeCun's advances in convolutional neural networks; to tod... | Wikipedia - Symbolic artificial intelligence - Controversies > Connectionist AI: philosophical challenges and sociological conflicts | 330 | 1,806 | null |
On the one hand, for some information-processing tasks (such as pattern recognition) connectionism has advantages over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive reasoning, and generative symbol manipulation processes) the symbolic paradigm offers adequate models, a... | Wikipedia - Symbolic artificial intelligence - Controversies > Connectionist AI: philosophical challenges and sociological conflicts | 346 | 1,773 | null |
... Since then, his anti-symbolic campaign has only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep learning in one of science's most important journals, Nature. It closed with a direct attack on symbol manipulation, calling not for reconciliation but for outright replacement.... | Wikipedia - Symbolic artificial intelligence - Controversies > Connectionist AI: philosophical challenges and sociological conflicts | 329 | 1,651 | null |
A proper definition of AI concerns knowledge representation and reasoning, autonomous multi-agent systems, planning and argumentation, as well as learning.It is worth noting that, from a theoretical perspective, the boundary of advantages between connectionist AI and symbolic AI may not be as clear-cut as it appears. F... | Wikipedia - Symbolic artificial intelligence - Controversies > Connectionist AI: philosophical challenges and sociological conflicts | 188 | 1,064 | null |
Section: Controversies > Situated robotics: the world as a model. Another critique of symbolic AI is the embodied cognition approach: The embodied cognition approach claims that it makes no sense to consider the brain separately: cognition takes place within a body, which is embedded in an environment. We need to study... | Wikipedia - Symbolic artificial intelligence - Controversies > Situated robotics: the world as a model | 262 | 1,427 | null |
Each layer can temporarily inhibit or suppress a lower-level layer. He criticized AI researchers for defining AI problems for their systems, when: "There is no clean division between perception (abstraction) and reasoning in the real world." He called his robots "Creatures" and each layer was "composed of a fixed-topol... | Wikipedia - Symbolic artificial intelligence - Controversies > Situated robotics: the world as a model | 199 | 951 | null |
Section: Controversies > Current views. Each approach—symbolic, connectionist, and behavior-based—has advantages, but has been criticized by the other approaches. Symbolic AI has been criticized as disembodied, liable to the qualification problem, and poor in handling the perceptual problems where deep learning excels.... | Wikipedia - Symbolic artificial intelligence - Controversies > Current views | 194 | 1,006 | null |
Article: Tech–industrial complex. The expression "tech–industrial complex" describes the relationship between a country's tech industry and its influence on the concentration of wealth, censorship or manipulation of algorithms to push an agenda, spread of misinformation and disinformation via social media and artificia... | Wikipedia - Tech–industrial complex - Summary | 154 | 786 | null |
Section: Etymology. U.S. President Joe Biden used the term in his Farewell Address to the Nation on January 15, 2025: Today, an oligarchy is taking shape in America of extreme wealth, power, and influence that literally threatens our entire democracy, our basic rights and freedoms, and a fair shot for everyone to get a... | Wikipedia - Tech–industrial complex - Etymology | 338 | 1,608 | null |
Section: Analysis. The term was first used in U.S. President Joe Biden's farewell address, and alluded to Dwight D. Eisenhower's warning of the military–industrial complex and what Politico described as "echoing Roosevelt's language in calling out the "robber barons" of a new dystopian Gilded Age". Since Elon Musk purc... | Wikipedia - Tech–industrial complex - Analysis | 265 | 1,169 | null |
Article: Toy problem. In scientific disciplines, a toy problem or a puzzlelike problem is a problem that is not of immediate scientific interest, yet is used as an expository device to illustrate a trait that may be shared by other, more complicated, instances of the problem, or as a way to explain a particular, more g... | Wikipedia - Toy problem - Summary | 234 | 1,203 | null |
Article: Universal psychometrics. Universal psychometrics encompasses psychometrics instruments that could measure the psychological properties of any intelligent agent. Up until the early 21st century, psychometrics relied heavily on psychological tests that require the subject to corporate and answer questions, the m... | Wikipedia - Universal psychometrics - Summary | 244 | 1,331 | null |
Article: Virtual intelligence. Virtual intelligence (VI) is the term given to artificial intelligence that exists within a virtual world. Many virtual worlds have options for persistent avatars that provide information, training, role-playing, and social interactions. The immersion of virtual worlds provides a platform... | Wikipedia - Virtual intelligence - Summary | 333 | 1,907 | null |
Section: Examples of use. Cutlass Bomb Disposal Robot: Northrop Grumman developed a virtual training opportunity because of the prohibitive real-world cost and dangers associated with bomb disposal. By replicating a complicated system without having to learn advanced code, the virtual robot has no risk of damage, train... | Wikipedia - Virtual intelligence - Examples of use | 338 | 1,884 | null |
Section: Collaboration. With assistance from Google, the Ministry of Agriculture and Farmers' Welfare and the Wadhwani AI developed Krishi 24/7, the first AI-powered automated agricultural news monitoring and analysis tool. Through better decision-making, Krishi 24/7 will support the identification of valuable news, pr... | Wikipedia - Wadhwani Institute for Artificial Intelligence - Collaboration | 172 | 960 | null |
Section: History. Anthony Levandowskii founded Way of the Future in 2017 in California. Levandowski established WOTF as a non-profit religious corporation and the organization had tax-exempt status. He serves as the church leader and its unpaid CEO. The primary mission of WOTF was to "develop and promote the realizatio... | Wikipedia - Way of the Future - History | 159 | 726 | null |
Article: Weak artificial intelligence. Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of the mind, or, as narrow AI, is focused on one narrow task. Weak AI is contrasted with strong AI, which can be interpreted in various ways: Artificial general intelligence (AGI): a m... | Wikipedia - Weak artificial intelligence - Summary | 166 | 860 | null |
Section: Applications and risks. Some examples of narrow AI are AlphaGo, self-driving cars, robot systems used in the medical field, and diagnostic doctors. Narrow AI systems are sometimes dangerous if unreliable. And the behavior that it follows can become inconsistent. It could be difficult for the AI to grasp comple... | Wikipedia - Weak artificial intelligence - Applications and risks | 344 | 1,767 | null |
Section: Weak AI versus strong AI. John Searle contests the possibility of strong AI (by which he means conscious AI). He further believes that the Turing test (created by Alan Turing and originally called the "imitation game", used to assess whether a machine can converse indistinguishably from a human) is not accurat... | Wikipedia - Weak artificial intelligence - Weak AI versus strong AI | 194 | 966 | null |
Section: Usage. The prefix "wet" is a reference to the water found in living creatures. Wetware is used to describe the elements equivalent to hardware and software found in a person, especially the central nervous system (CNS) and the human mind. The term wetware finds use in works of fiction, in scholarly publication... | Wikipedia - Wetware (brain) - Usage | 178 | 955 | null |
Section: History. Although the exact definition has shifted over time, the term Wetware and its fundamental reference to "the physical mind" has been around at least since the mid-1950s. Mostly used in relatively obscure articles and papers, it was not until the heyday of cyberpunk, however, that the term found broad a... | Wikipedia - Wetware (brain) - History | 324 | 1,434 | null |
With the term getting traction in trendsetting publications, it became a buzzword in the early 1990s. In 1991, Dutch media theorist Geert Lovink organized the Wetware Convention in Amsterdam, which was supposed to be an antidote to the "out-of-body" experiments conducted in high-tech laboratories, such as experiments i... | Wikipedia - Wetware (brain) - History | 190 | 873 | null |
Article: Wetware computer. A wetware computer is an organic computer (which can also be known as an artificial organic brain or a neurocomputer) composed of organic material "wetware" such as "living" neurons. Wetware computers composed of neurons are different than conventional computers because they use biological ma... | Wikipedia - Wetware computer - Summary | 328 | 1,814 | null |
Section: Overview. The concept of wetware is an application of specific interest to the field of computer manufacturing. Moore's law, which states that the number of transistors which can be placed on a silicon chip is doubled roughly every two years, has acted as a goal for the industry for decades, but as the size of... | Wikipedia - Wetware computer - Overview | 214 | 1,123 | null |
Section: Background. The concept of wetware is distinct and unconventional and draws slight resonance with both hardware and software from conventional computers. While hardware is understood as the physical architecture of traditional computational devices, comprising integrated circuits and supporting infrastructure,... | Wikipedia - Wetware computer - Background | 323 | 1,911 | null |
Section: Background > The cell as a model of wetware. Cells in many ways can be seen as their form of naturally occurring wetware, similar to the concept that the human brain is the preexisting model system for complex wetware. In his book Wetware: A Computer in Every Living Cell (2009) Dennis Bray explains his theory ... | Wikipedia - Wetware computer - Background > The cell as a model of wetware | 245 | 1,251 | null |
Section: Background > Biorobotics. Wetware and biorobotics are closely related concepts, which both borrow from similar overall principles. A biorobotic structure can be defined as a system modeled from a preexisting organic complex or model such as cells (neurons) or more complex structures like organs (brain) or whol... | Wikipedia - Wetware computer - Background > Biorobotics | 185 | 960 | null |
Section: Effects on users. Wetware technologies such as BCIs and neuromorphic chips offer new possibilities for user autonomy. For those with disabilities, such systems could restore motor or sensory functions and enhance quality of life. However, these technologies raise ethical questions: cognitive privacy, consent o... | Wikipedia - Wetware computer - Effects on users | 178 | 980 | null |
Section: Applications and goals > Basic neurocomputer composed of leech neurons. In 1999 William Ditto and his team of researchers at Georgia Institute of Technology and Emory University created a basic form of a wetware computer capable of simple addition by harnessing leech neurons. Leeches were used as a model organ... | Wikipedia - Wetware computer - Applications and goals > Basic neurocomputer composed of leech neurons | 305 | 1,655 | null |
Section: Applications and goals > Biological models for conventional computing. After his work creating a basic computer from leech neurons, Ditto continued to work not only with organic molecules and wetware but also on the concept of applying the chaotic nature of biological systems and organic molecules to conventio... | Wikipedia - Wetware computer - Applications and goals > Biological models for conventional computing | 209 | 1,146 | null |
Section: Applications and goals > Impact of wetware in cognitive biology. Cognitive biology evaluates cognition as a basic biological function. W. Tecumseh Fitch, a professor of cognitive biology at the University of Vienna, is a leading theorist on ideas of cellular intentionality. The idea is that not only do whole o... | Wikipedia - Wetware computer - Applications and goals > Impact of wetware in cognitive biology | 301 | 1,588 | null |
He states "If a researcher aims to make a conscious machine, doing it with rigid switches (whether vacuum tubes or static silicon chips) is barking up the wrong tree." Fitch believes that an important aspect of the development of areas such as artificial intelligence is wetware with nano-intentionally, and autonomous a... | Wikipedia - Wetware computer - Applications and goals > Impact of wetware in cognitive biology | 231 | 1,220 | null |
Section: Applications and goals > Ethical and philosophical implications. Wetware computers may have substantial ethical implications, for instance related to possible potentials to sentience and suffering and dual-use technology. Moreover, in some cases the human brain itself may be connected as a kind of "wetware" to... | Wikipedia - Wetware computer - Applications and goals > Ethical and philosophical implications | 268 | 1,488 | null |
Section: Evaluating potential and limitations. The core advantage of wetware is its potential to overcome the rigidity and energy inefficiencies of binary transistor-based systems. Digital systems operate through fixed binary pathways and consume increasing energy as computational loads increase. Wetware, in contrast, ... | Wikipedia - Wetware computer - Evaluating potential and limitations | 331 | 1,991 | null |
Section: Evaluating potential and limitations > Future applications. While there have been few major developments in the creation of an organic computer since the neuron-based calculator developed by Ditto in the 1990s, research continues to push the field forward, and in 2023 a functioning computer was constructed by ... | Wikipedia - Wetware computer - Evaluating potential and limitations > Future applications | 303 | 1,661 | null |
Section: Evaluating potential and limitations > Prototypes. In late 2021, scientists, including two from Cortical Labs, demonstrated that grown brain cells integrated into digital systems can carry out goal-directed tasks with performance-scores. In particular, the human brain cells learned to play a simulated (via ele... | Wikipedia - Wetware computer - Evaluating potential and limitations > Prototypes | 251 | 1,367 | null |
Section: Convergence of AI and wetware. One exciting frontier is the fusion of artificial intelligence (AI) with wetware. Emerging research shows that hybrid systems combining living neural networks with AI can enable self-repair, real-time adaptation, and emotional intelligence. These systems are more flexible than co... | Wikipedia - Wetware computer - Convergence of AI and wetware | 250 | 1,425 | null |
Section: History. In the 1980s and 1990s, many roboticists and cognitive scientists were attempting to find speedier and more efficient alternatives to the traditional world modeling method of action selection. In 1982, Jerome A. Feldman and D.H. Ballard published the "Connectionist Models and Their Properties", refere... | Wikipedia - Winner-take-all in action selection - History | 187 | 960 | null |
Section: Types of winner-take-all architectures > Heterarchy and fully distributed. In the heterarchy and fully distributed architecture, each behavior has a set of pre-conditions to be met before it can be performed, and a set of post-conditions that will be true after the action has been performed. These pre- and pos... | Wikipedia - Winner-take-all in action selection - Types of winner-take-all architectures > Heterarchy and fully distributed | 246 | 1,253 | null |
Section: Types of winner-take-all architectures > Arbiter and centrally coordinated. In the arbiter and centrally coordinated architecture, the action modules are not connected to each other but to a central arbiter. When behaviors are triggered, they begin "voting" by sending signals to the arbiter, and the behavior w... | Wikipedia - Winner-take-all in action selection - Types of winner-take-all architectures > Arbiter and centrally coordinated | 262 | 1,320 | null |
Article: Workplace impact of artificial intelligence. The impact of artificial intelligence on workers includes both applications to improve worker safety and health, and potential hazards that must be controlled. One potential application is using AI to eliminate hazards by removing humans from hazardous situations th... | Wikipedia - Workplace impact of artificial intelligence - Summary | 306 | 1,715 | null |
Hazard controls include cybersecurity and information privacy measures, communication and transparency with workers about data usage, and limitations on collaborative robots. From a workplace safety and health perspective, only "weak" or "narrow" AI that is tailored to a specific task is relevant, as there are many exa... | Wikipedia - Workplace impact of artificial intelligence - Summary | 231 | 1,186 | null |
Section: Health and safety applications > Eliminating hazardous tasks. AI may increase the scope of work tasks where a worker can be removed from a situation that carries risk. In a sense, while traditional automation can replace the functions of a worker's body with a robot, AI effectively replaces the functions of th... | Wikipedia - Workplace impact of artificial intelligence - Health and safety applications > Eliminating hazardous tasks | 171 | 849 | null |
Section: Health and safety applications > Analytics to reduce risk. Machine learning is used for people analytics to make predictions about worker behavior to assist management decision-making, such as hiring and performance assessment. These could also be used to improve worker health. The analytics may be based on in... | Wikipedia - Workplace impact of artificial intelligence - Health and safety applications > Analytics to reduce risk | 257 | 1,456 | null |
Section: Health and safety applications > Streamlining safety and health workflows. AI can also be used to make the workplace safety and health workflow more efficient. Digital assistants, like Amazon Alexa, Google Assistant, and Apple Siri, are increasingly adopted in workplaces to enhance productivity by automating r... | Wikipedia - Workplace impact of artificial intelligence - Health and safety applications > Streamlining safety and health workflows | 281 | 1,578 | null |
Section: Hazards. There are several broad aspects of AI that may give rise to specific hazards. The risks depend on implementation rather than the mere presence of AI.: 2–3 Systems using sub-symbolic AI such as machine learning may behave unpredictably and are more prone to inscrutability in their decision-making. This... | Wikipedia - Workplace impact of artificial intelligence - Hazards | 243 | 1,200 | null |
Section: Hazards > Psychosocial > Changes in work practices. AI is expected to lead to changes in the skills required of workers, requiring training of existing workers, flexibility, and openness to change. The requirement for combining conventional expertise with computer skills may be challenging for existing workers... | Wikipedia - Workplace impact of artificial intelligence - Hazards > Psychosocial > Changes in work practices | 196 | 1,004 | null |
Section: Hazards > Physical. Physical hazards in the form of human–robot collisions may arise from robots using AI, especially collaborative robots (cobots). Cobots are intended to operate in close proximity to humans, which makes impossible the common hazard control of isolating the robot using fences or other barrier... | Wikipedia - Workplace impact of artificial intelligence - Hazards > Physical | 175 | 855 | null |
Section: Hazard controls. AI, in common with other computational technologies, requires cybersecurity measures to stop software breaches and intrusions,: 17 as well as information privacy measures. Communication and transparency with workers about data usage is a control for psychosocial hazards arising from security a... | Wikipedia - Workplace impact of artificial intelligence - Hazard controls | 195 | 1,044 | null |
Section: Risk management. Both applications and hazards arising from AI can be considered as part of existing frameworks for occupational health and safety risk management. As with all hazards, risk identification is most effective and least costly when done in the design phase. Workplace health surveillance, the colle... | Wikipedia - Workplace impact of artificial intelligence - Risk management | 199 | 1,153 | null |
Section: Standards and regulation. As of 2019, ISO was developing a standard on the use of metrics and dashboards, information displays presenting company metrics for managers, in workplaces. The standard is planned to include guidelines for both gathering data and displaying it in a viewable and useful manner.: 11 In ... | Wikipedia - Workplace impact of artificial intelligence - Standards and regulation | 165 | 788 | null |
Article: Xiao-i. Xiao-i (or Xiao-I Corporation; Chinese: 小i机器人) is a Chinese cognitive artificial intelligence enterprise founded in 2001. On June 29, 2023, Xiao-i launched its generative model Hua Zang Universal Large Language Model. In the same year on October 26, Xiao-i launched the Hua Zang Ecosystem and showcased ... | Wikipedia - Xiao-i - Summary | 258 | 1,093 | null |
Section: History. Xiao-i (Shanghai Xiao-i Robot Technology Co., Ltd) was founded in Shanghai, China, in 2001. In 2004, it released the world's first chatbot on MSN and Tencent QQ. In the same year, the company applied for an invention patent for this technology, and it was officially granted authorization in 2009, titl... | Wikipedia - Xiao-i - History | 325 | 1,491 | null |
Section: Products and Services. Xiao-i has launched the Hua Zang Universal Large Language Model, and building on its foundation, Xiao-i has also launched the Hua Zang Ecosystem. Xiao-i's industry-specific solutions include "Hua Zang+Customer Service Center," "Hua Zang+Finance," "Hua Zang+Urban Public Service," "Hua Zan... | Wikipedia - Xiao-i - Products and Services | 293 | 1,210 | null |
Section: Patent Case. In 2004, Xiao-i filed an application for an invention patent titled "A Chatbot System." The patent was officially granted authorization in 2009, numbered ZL200410053749.9. In June 2012, Xiao-i contended that Apple Inc.'s integration of Siri in its iPhone 4S has infringed on its patented technology... | Wikipedia - Xiao-i - Patent Case | 332 | 1,550 | null |
In 2016, following the decision of the Beijing High People’s Court on the patent invalidity, the First Intermediate People’s Court of Shanghai dismissed Xiao-i’s lawsuit filed back in 2012. On June 28, 2020, the Supreme People's Court of the People's Republic of China rendered a verdict affirming the validity of Xiao-i... | Wikipedia - Xiao-i - Patent Case | 190 | 848 | null |
Article: Zeuthen strategy. The Zeuthen strategy in cognitive science is a negotiation strategy used by some artificial agents. Its purpose is to measure the willingness to risk conflict. An agent will be more willing to risk conflict if it does not have much to lose in case that the negotiation fails. In contrast, an a... | Wikipedia - Zeuthen strategy - Summary | 185 | 919 | null |
Section: Three key questions. The Zeuthen strategy answers three open questions that arise when using the monotonic concession protocol, namely: Which deal should be proposed at first? On any given round, who should concede? In case of a concession, how much should the agent concede? The answer to the first question is... | Wikipedia - Zeuthen strategy - Three key questions | 185 | 868 | null |
Section: Risk. Risk ( i , t ) = { 1 U i ( δ ( i , t ) ) = 0 U i ( δ ( i , t ) ) − U i ( δ ( j , t ) ) U i ( δ ( i , t ) ) otherwise {\displaystyle {\text{Risk}}(i,t)={\begin{cases}1&U_{i}(\delta (i,t))=0\\{\frac {U_{i}(\delta (i,t))-U_{i}(\delta (j,t))}{U_{i}(\delta (i,t))}}&{\text{otherwise}}\end{cases}}} Risk(i,t) is... | Wikipedia - Zeuthen strategy - Risk | 273 | 758 | null |
Section: Minimal sufficient concession. δ ′ = arg max δ ∈ S C ( A , t ) { U A ( δ ) } {\displaystyle \delta '=\arg \max _{\delta \in {SC(A,t)}}\{U_{A}(\delta )\}} is the minimal sufficient concession of agent A in step t. Agent A begins the negotiation by proposing δ ( A , 0 ) = arg max δ ∈ N S U A ( δ ) {\displays... | Wikipedia - Zeuthen strategy - Minimal sufficient concession | 326 | 902 | null |
{\displaystyle Risk(A,t)\leq Risk(B,t).} That is, if and only if U A ( δ A ) − U A ( δ B ) U A ( δ A ) ≤ U B ( δ B ) − U B ( δ A ) U B ( δ B ) {\displaystyle {\frac {U_{A}(\delta _{A})-U_{A}(\delta _{B})}{U_{A}(\delta _{A})}}\leq {\frac {U_{B}(\delta _{B})-U_{B}(\delta _{A})}{U_{B}(\delta _{B})}}} U B ( δ B ) ( U A ( δ... | Wikipedia - Zeuthen strategy - Minimal sufficient concession | 350 | 604 | null |
δ A ) U B ( δ A ) {\displaystyle U_{A}(\delta _{A})U_{B}(\delta _{B})-U_{A}(\delta _{B})U_{B}(\delta _{B})\leq U_{A}(\delta _{A})U_{B}(\delta _{B})-U_{A}(\delta _{A})U_{B}(\delta _{A})} − U A ( δ B ) U B ( δ B ) ≤ − U A ( δ A ) U B ( δ A ) {\displaystyle -U_{A}(\delta _{B})U_{B}(\delta _{B})\leq -U_{A}(\delta _{A})U_{B... | Wikipedia - Zeuthen strategy - Minimal sufficient concession | 349 | 603 | null |
Section: Areas > Detecting and recognizing emotional information. Detecting emotional information usually begins with passive sensors that capture data about the user's physical state or behavior without interpreting the input. The data gathered is analogous to the cues humans use to perceive emotions in others. For ex... | Wikipedia - Affective computing - Areas > Detecting and recognizing emotional information | 245 | 1,367 | null |
Section: Areas > Emotion in machines. Another area within affective computing is the design of computational devices proposed to exhibit either innate emotional capabilities or that are capable of convincingly simulating emotions. A more practical approach, based on current technological capabilities, is the simulation... | Wikipedia - Affective computing - Areas > Emotion in machines | 209 | 1,189 | null |
Section: Technologies > Emotional speech. Various changes in the autonomic nervous system can indirectly alter a person's speech, and affective technologies can leverage this information to recognize emotion. For example, speech produced in a state of fear, anger, or joy becomes fast, loud, and precisely enunciated, wi... | Wikipedia - Affective computing - Technologies > Emotional speech | 252 | 1,361 | null |
Section: Technologies > Emotional speech > Algorithms. The process of speech/text affect detection requires the creation of a reliable database, knowledge base, or vector space model, broad enough to fit every need for its application, as well as the selection of a successful classifier which will allow for quick and a... | Wikipedia - Affective computing - Technologies > Emotional speech > Algorithms | 341 | 1,708 | null |
ANN – is a mathematical model, inspired by biological neural networks, that can better grasp possible non-linearities of the feature space. Decision tree algorithms – work based on following a decision tree in which leaves represent the classification outcome, and branches represent the conjunction of subsequent featur... | Wikipedia - Affective computing - Technologies > Emotional speech > Algorithms | 331 | 1,717 | null |
Section: Technologies > Emotional speech > Databases. The vast majority of present systems are data-dependent. This creates one of the biggest challenges in detecting emotions based on speech, as it implicates choosing an appropriate database used to train the classifier. Most of the currently possessed data was obtain... | Wikipedia - Affective computing - Technologies > Emotional speech > Databases | 297 | 1,596 | null |
Section: Technologies > Emotional speech > Speech descriptors. The complexity of the affect recognition process increases with the number of classes (affects) and speech descriptors used within the classifier. It is, therefore, crucial to select only the most relevant features in order to assure the ability of the mode... | Wikipedia - Affective computing - Technologies > Emotional speech > Speech descriptors | 239 | 1,285 | null |
Section: Technologies > Facial affect detection > Facial expression databases. Creation of an emotion database is a difficult and time-consuming task. However, database creation is an essential step in the creation of a system that will recognize human emotions. Most of the publicly available emotion databases include ... | Wikipedia - Affective computing - Technologies > Facial affect detection > Facial expression databases | 226 | 1,328 | null |
Section: Technologies > Facial affect detection > Challenges in facial detection. As with every computational practice, in affect detection by facial processing, some obstacles need to be surpassed, in order to fully unlock the hidden potential of the overall algorithm or method employed. In the early days of almost ev... | Wikipedia - Affective computing - Technologies > Facial affect detection > Challenges in facial detection | 279 | 1,453 | null |
Section: Technologies > Body gesture. Gestures could be efficiently used as a means of detecting a particular emotional state of the user, especially when used in conjunction with speech and face recognition. Depending on the specific action, gestures could be simple reflexive responses, like lifting your shoulders whe... | Wikipedia - Affective computing - Technologies > Body gesture | 247 | 1,313 | null |
Section: Technologies > Physiological monitoring > Blood volume pulse > Overview. A subject's blood volume pulse (BVP) can be measured by a process called photoplethysmography, which produces a graph indicating blood flow through the extremities. The peaks of the waves indicate a cardiac cycle where the heart has pumpe... | Wikipedia - Affective computing - Technologies > Physiological monitoring > Blood volume pulse > Overview | 170 | 790 | null |
Section: Technologies > Physiological monitoring > Facial electromyography. Facial electromyography is a technique used to measure the electrical activity of the facial muscles by amplifying the tiny electrical impulses that are generated by muscle fibers when they contract. The face expresses a great deal of emotion, ... | Wikipedia - Affective computing - Technologies > Physiological monitoring > Facial electromyography | 151 | 771 | null |
Section: Technologies > Physiological monitoring > Galvanic skin response. Galvanic skin response (GSR) is an outdated term for a more general phenomenon known as Electrodermal activity or EDA. EDA is a general phenomena whereby the skin's electrical properties change. The skin is innervated by the [sympathetic nervous... | Wikipedia - Affective computing - Technologies > Physiological monitoring > Galvanic skin response | 215 | 1,051 | null |
Section: Potential applications > Education. Affection influences learners' learning state. Using affective computing technology, computers can judge the learners' affection and learning state by recognizing their facial expressions. In education, the teacher can use the analysis result to understand the student's lear... | Wikipedia - Affective computing - Potential applications > Education | 152 | 902 | null |
Section: Potential applications > Other applications. Affective computing has potential applications in human–computer interaction, such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting trac... | Wikipedia - Affective computing - Potential applications > Other applications | 206 | 1,074 | null |
Section: Cognitivist vs. interactional approaches. Within the field of human–computer interaction, Rosalind Picard's cognitivist or "information model" concept of emotion has been criticized by and contrasted with the "post-cognitivist" or "interactional" pragmatist approach taken by Kirsten Boehner and others which vi... | Wikipedia - Affective computing - Cognitivist vs. interactional approaches | 320 | 1,589 | null |
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