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Data pack : Category:Lists Data set Semantic triple |
Data pack : DarkBot @ SourceForge A directory of fact packs == References == |
Data processing unit : A data processing unit (DPU) is a programmable computer processor that tightly integrates a general-purpose CPU with network interface hardware. Sometimes they are called "IPUs" (for "infrastructure processing unit") or "SmartNICs". They can be used in place of traditional NICs to relieve the mai... |
Data processing unit : The introduction of DPUs like Azure Boost reflects a broader shift in the cloud computing industry toward offloading specific functions from general-purpose processors to specialized hardware. Microsoft’s Azure Boost DPU represents its strategy to reduce costs, enhance security, and achieve susta... |
Data processing unit : Compute Express Link (CXL) == References == |
Diagnosis (artificial intelligence) : As a subfield in artificial intelligence, diagnosis is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct. If the system is not functioning correctly, the algorithm should be able to determine, as accu... |
Diagnosis (artificial intelligence) : An example of diagnosis is the process of a garage mechanic with an automobile. The mechanic will first try to detect any abnormal behavior based on the observations on the car and his knowledge of this type of vehicle. If he finds out that the behavior is abnormal, the mechanic wi... |
Diagnosis (artificial intelligence) : The expert diagnosis (or diagnosis by expert system) is based on experience with the system. Using this experience, a mapping is built that efficiently associates the observations to the corresponding diagnoses. The experience can be provided: By a human operator. In this case, the... |
Diagnosis (artificial intelligence) : Model-based diagnosis is an example of abductive reasoning using a model of the system. In general, it works as follows: We have a model that describes the behaviour of the system (or artefact). The model is an abstraction of the behaviour of the system and can be incomplete. In pa... |
Diagnosis (artificial intelligence) : A system is said to be diagnosable if whatever the behavior of the system, we will be able to determine without ambiguity a unique diagnosis. The problem of diagnosability is very important when designing a system because on one hand one may want to reduce the number of sensors to ... |
Diagnosis (artificial intelligence) : Hamscher, W.; L. Console; J. de Kleer (1992). Readings in model-based diagnosis. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. ISBN 1-55860-249-6. |
Diagnosis (artificial intelligence) : Artificial intelligence in healthcare AI effect Applications of artificial intelligence Epistemology List of emerging technologies Outline of artificial intelligence |
Emergent algorithm : An emergent algorithm is an algorithm that exhibits emergent behavior. In essence an emergent algorithm implements a set of simple building block behaviors that when combined exhibit more complex behaviors. One example of this is the implementation of fuzzy motion controllers used to adapt robot mo... |
Emergent algorithm : Emergence Evolutionary computation Fuzzy logic Genetic algorithm Heuristic == References == |
Ensemble averaging (machine learning) : In machine learning, ensemble averaging is the process of creating multiple models (typically artificial neural networks) and combining them to produce a desired output, as opposed to creating just one model. Ensembles of models often outperform individual models, as the various ... |
Ensemble averaging (machine learning) : Ensemble averaging is one of the simplest types of committee machines. Along with boosting, it is one of the two major types of static committee machines. In contrast to standard neural network design, in which many networks are generated but only one is kept, ensemble averaging ... |
Ensemble averaging (machine learning) : The theory mentioned above gives an obvious strategy: create a set of experts with low bias and high variance, and average them. Generally, what this means is to create a set of experts with varying parameters; frequently, these are the initial synaptic weights of a neural networ... |
Ensemble averaging (machine learning) : The resulting committee is almost always less complex than a single network that would achieve the same level of performance The resulting committee can be trained more easily on smaller datasets The resulting committee often has improved performance over any single model The ris... |
Ensemble averaging (machine learning) : Perrone, M. P. (1993), Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization Wolpert, D. H. (1992), "Stacked generalization", Neural Networks, 5 (2): 241–259, CiteSeerX 10.1.1.133.8090, doi:10.1016/S0893-6... |
Mark I Perceptron : The Mark I Perceptron was a pioneering supervised image classification learning system developed by Frank Rosenblatt in 1958. It was the first implementation of an Artificial Intelligence (AI) machine. It differs from the Perceptron which is a software architecture proposed in 1943 by Warren McCullo... |
Mark I Perceptron : The Mark I Perceptron was organized into three layers: A set of sensory units which receive optical input A set of association units, each of which fire based on input from multiple sensory units A set of response units, which fire based on input from multiple association units The connection betwee... |
Mark I Perceptron : In his 1957 proposal for funding for development of the "Cornell Photoperceptron", Rosenblatt claimed:"Devices of this sort are expected ultimately to be capable of concept formation, language translation, collation of military intelligence, and the solution of problems through inductive logic."With... |
Mark I Perceptron : History of artificial intelligence History of artificial neural networks == References == |
Mivar-based approach : The Mivar-based approach is a mathematical tool for designing artificial intelligence (AI) systems. Mivar (Multidimensional Informational Variable Adaptive Reality) was developed by combining production and Petri nets. The Mivar-based approach was developed for semantic analysis and adequate repr... |
Mivar-based approach : While working in the Russian Ministry of Defense, O. O. Varlamov started developing the theory of “rapid logical inference” in 1985. He was analyzing Petri nets and productions to construct algorithms. Generally, mivar-based theory represents an attempt to combine entity-relationship models and t... |
Mivar-based approach : Mivar is the smallest structural element of discrete information space. |
Mivar-based approach : Object-Property-Relation (VSO) is a graph, the nodes of which are concepts and arcs are connections between concepts. Mivar space represents a set of axes, a set of elements, a set of points of space and a set of values of points. A = , n = 1 , … , N , \,n=1,\ldots ,N, where: A is a set of miva... |
Mivar-based approach : Mivar network is a method for representing objects of the subject domain and their processing rules in the form of a bipartite directed graph consisting of objects and rules. A Mivar network is a bipartite graph that can be described in the form of a two-dimensional matrix, in that records inform... |
Mivar-based approach : To implement logical-and-computational data processing the following should be done. First, a formalized subject domain description is developed. The main objects-variables and rules-procedures are specified on the basis of mivar-based approach and then corresponding lists of “objects” and “rules... |
Mivar-based approach : «Mivar» official website. |
Neural architecture search : Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par with or outperform hand-designed architectures. Methods for NAS can be ca... |
Neural architecture search : Reinforcement learning (RL) can underpin a NAS search strategy. Barret Zoph and Quoc Viet Le applied NAS with RL targeting the CIFAR-10 dataset and achieved a network architecture that rivals the best manually-designed architecture for accuracy, with an error rate of 3.65, 0.09 percent bett... |
Neural architecture search : An alternative approach to NAS is based on evolutionary algorithms, which has been employed by several groups. An Evolutionary Algorithm for Neural Architecture Search generally performs the following procedure. First a pool consisting of different candidate architectures along with their v... |
Neural architecture search : Bayesian Optimization (BO), which has proven to be an efficient method for hyperparameter optimization, can also be applied to NAS. In this context, the objective function maps an architecture to its validation error after being trained for a number of epochs. At each iteration, BO uses a s... |
Neural architecture search : Another group used a hill climbing procedure that applies network morphisms, followed by short cosine-annealing optimization runs. The approach yielded competitive results, requiring resources on the same order of magnitude as training a single network. E.g., on CIFAR-10, the method designe... |
Neural architecture search : While most approaches solely focus on finding architecture with maximal predictive performance, for most practical applications other objectives are relevant, such as memory consumption, model size or inference time (i.e., the time required to obtain a prediction). Because of that, research... |
Neural architecture search : RL or evolution-based NAS require thousands of GPU-days of searching/training to achieve state-of-the-art computer vision results as described in the NASNet, mNASNet and MobileNetV3 papers. To reduce computational cost, many recent NAS methods rely on the weight-sharing idea. In this approa... |
Neural architecture search : Neural architecture search often requires large computational resources, due to its expensive training and evaluation phases. This further leads to a large carbon footprint required for the evaluation of these methods. To overcome this limitation, NAS benchmarks have been introduced, from w... |
Neural architecture search : Neural Network Intelligence Automated Machine Learning Hyperparameter Optimization |
Neural architecture search : Survey articles. Wistuba, Martin; Rawat, Ambrish; Pedapati, Tejaswini (2019-05-04). "A Survey on Neural Architecture Search". arXiv:1905.01392 [cs.LG]. Elsken, Thomas; Metzen, Jan Hendrik; Hutter, Frank (August 8, 2019). "Neural Architecture Search: A Survey". Journal of Machine Learning Re... |
Oriented energy filters : Oriented energy filters are used to grant sight to intelligent machines and sensors. The light comes in and is filtered so that it can be properly computed and analyzed by the computer allowing it to “perceive” what it is measuring. These energy measurements are then calculated to take a real ... |
Probabilistic logic network : A probabilistic logic network (PLN) is a conceptual, mathematical and computational approach to uncertain inference. It was inspired by logic programming and it uses probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values... |
Probabilistic logic network : The basic goal of a PLN is to provide accurate probabilistic inference in a way that is compatible with both term logic and predicate logic and scales up to operate in real-time on large dynamic knowledge bases. The goal underlying the theoretical development of PLN has been the creation o... |
Probabilistic logic network : PLN begins with a term logic foundation and then adds on elements of probabilistic and combinatory logic, as well as some aspects of predicate logic and autoepistemic logic, to form a complete inference system, tailored for easy integration with software components embodying other (not exp... |
Probabilistic logic network : Ben Goertzel; Matthew Iklé; Izabela Lyon Freire Goertzel; Ari Heljakka (2008). Probabilistic Logic Networks: A Comprehensive Conceptual, Mathematical and Computational Framework for Uncertain Inference. Springer. pp. 333. ISBN 978-0-387-76871-7. |
Probabilistic logic network : Markov logic network Probabilistic logic |
Structure mapping engine : In artificial intelligence and cognitive science, the structure mapping engine (SME) is an implementation in software of an algorithm for analogical matching based on the psychological theory of Dedre Gentner. The basis of Gentner's structure-mapping idea is that an analogy is a mapping of kn... |
Structure mapping engine : Structure mapping theory is based on the systematicity principle, which states that connected knowledge is preferred over independent facts. Therefore, the structure mapping engine should ignore isolated source-target mappings unless they are part of a bigger structure. The SME, the theory go... |
Structure mapping engine : SME maps knowledge from a source into a target. SME calls each description a dgroup. Dgroups contain a list of entities and predicates. Entities represent the objects or concepts in a description — such as an input gear or a switch. Predicates are one of three types and are a general way to e... |
Structure mapping engine : The algorithm has several steps. The first step of the algorithm is to create a set of match hypotheses between source and target dgroups. A match hypothesis represents a possible mapping between any part of the source and the target. This mapping is controlled by a set of match rules. By cha... |
Structure mapping engine : Once the match hypotheses are generated, SME needs to compute an evaluation score for each hypothesis. SME does so by using a set of intern match rules to calculate positive and negative evidence for each match. Multiple amounts of evidence are correlated using Dempster's rule [Shafer, 1978] ... |
Structure mapping engine : The rest of the SME algorithm is involved in creating maximally consistent sets of match hypotheses. These sets are called gmaps. SME must ensure that any gmaps that it creates are structurally consistent; in other words, that they are one-to-one — such that no source maps to multiple targets... |
Structure mapping engine : Chalmers, French, and Hofstadter [1992] criticize SME for its reliance on manually constructed LISP representations as input. They argue that too much human creativity is required to construct these representations; the intelligence comes from the design of the input, not from SME. Forbus et ... |
Structure mapping engine : Papers by the Qualitative Reasoning Group at Northwestern University Chalmers, D. J., French, R. M., & Hofstadter, D. R.: 1992, High-level perception, representation, and analogy: A critique of artificial intelligence methodology. Journal of Experimental & Theoretical Artificial Intelligence,... |
Thompson sampling : Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that address the exploration–exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief. |
Thompson sampling : Consider a set of contexts X , a set of actions A , and rewards in R . The aim of the player is to play actions under the various contexts, such as to maximize the cumulative rewards. Specifically, in each round, the player obtains a context x ∈ X , plays an action a ∈ A and receives a reward ... |
Thompson sampling : Thompson sampling was originally described by Thompson in 1933. It was subsequently rediscovered numerous times independently in the context of multi-armed bandit problems. A first proof of convergence for the bandit case has been shown in 1997. The first application to Markov decision processes was... |
Distributed artificial intelligence : Distributed artificial intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent ... |
Distributed artificial intelligence : Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to so... |
Distributed artificial intelligence : In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents. Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperat... |
Distributed artificial intelligence : The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objectiv... |
Distributed artificial intelligence : Two types of DAI has emerged: In Multi-agent systems agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive its environment and communicate with other agents. The agent is auton... |
Distributed artificial intelligence : Areas where DAI have been applied are: Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data Networks, e.g. in telecommunications the DAI system controls the cooperative resources in a ... |
Distributed artificial intelligence : Collective intelligence – Group intelligence that emerges from collective efforts Federated learning – Decentralized machine learning Simulated reality – Concept of a false version of reality Swarm Intelligence – Collective behavior of decentralized, self-organized systemsPages dis... |
Distributed artificial intelligence : Hewitt, Carl; and Jeff Inman (November/December 1991). "DAI Betwixt and Between: From 'Intelligent Agents' to Open Systems Science" IEEE Transactions on Systems, Man, and Cybernetics. Volume: 21 Issue: 6, pps. 1409–1419. ISSN 0018-9472 Grace, David; Zhang, Honggang (August 2012). C... |
Agent mining : Agent mining is a research field that combines two areas of computer science: multiagent systems and data mining. It explores how intelligent computer agents can work together to discover, analyze, and learn from large amounts of data more effectively than traditional methods. |
Agent mining : The interaction and the integration between multiagent systems and data mining have a long history. The very early work on agent mining focused on agent-based knowledge discovery, agent-based distributed data mining, and agent-based distributed machine learning, and using data mining to enhance agent int... |
Distributed multi-agent reasoning system : In artificial intelligence, the distributed multi-agent reasoning system (dMARS) was a platform for intelligent software agents developed at the AAII that makes uses of the belief–desire–intention software model (BDI). The design for dMARS was an extension of the intelligent a... |
Distributed multi-agent reasoning system : dMARS was an agent-oriented development and implementation environment written in C++ for building complex, distributed, time-critical systems. |
Distributed multi-agent reasoning system : Australian Artificial Intelligence Institute Intelligent agent JACK Intelligent Agents AgentSpeak |
Distributed multi-agent reasoning system : d'Inverno, M., Luck, M., Georgeff, M., Kinny, D. and Wooldridge, M. (2004) "The dMARS Architecture: A Specification of the Distributed Multi-Agent Reasoning System". Journal of Autonomous Agents and Multi-Agent Systems. pp. 5–53. Mark d'Inverno, David Kinny, Michael Luck, and ... |
Distributed multi-agent reasoning system : dMARS Product Brief on the AAII website via the Internet Archive |
2016 shooting of Dallas police officers : On July 7, 2016, Micah Xavier Johnson ambushed and shot police officers in Dallas, Texas, killing five, injuring nine others, and wounding two civilians. Johnson, a 25-year-old Army Reserve Afghan War veteran, was angry over white police shootings of black men. He shot the offi... |
2016 shooting of Dallas police officers : Most of the events happened in the streets and buildings around El Centro College, which forms a city block composed of multiple buildings. The block is bordered by Main Street on the south where the protest march was taking place; Lamar Street (now Botham Jean Blvd.) to the ea... |
2016 shooting of Dallas police officers : Five officers were killed; nine other officers and two civilians were injured. Most of the victims were shot during the protests, and at least one other during a shootout. The dead comprised four DPD officers and one Dallas Area Rapid Transit (DART) officer. Four of the injured... |
2016 shooting of Dallas police officers : DART suspended service in downtown Dallas after the shooting, but resumed the next morning with the exception of West End station. The Federal Aviation Administration issued a temporary flight restriction of civilian aircraft for the immediate vicinity in which the shooting occ... |
2016 shooting of Dallas police officers : Texas Governor Greg Abbott ordered the director of the Texas Department of Public Safety to offer any assistance to Dallas when requested. He also said, "In times like this we must remember—and emphasize—the importance of uniting as Americans." Texas Lieutenant Governor Dan Pat... |
2016 shooting of Dallas police officers : 1971 shooting of Dallas police officers Mark Essex 1985 MOVE bombing 2004 shooting of Birmingham police officers 2009 shootings of Oakland police officers 2009 shooting of Pittsburgh police officers 2009 Lakewood shooting Christopher Dorner shootings and manhunt 2014 killings o... |
List of Tesla Autopilot crashes : Tesla Autopilot, a Level 2 advanced driver assistance system (ADAS), was released in October 2015 and the first fatal crashes involving the system occurred less than one year later. The fatal crashes attracted attention from news publications and United States government agencies, incl... |
A.I. Insight forums : The Artificial Intelligence Insight forums, also known as the A.I. Insight forums, are a series of forums to build consensus on how the United States Congress should craft A.I. legislation. Organized by Senate Majority Leader Charles "Chuck" Schumer, the first of nine closed-door forums convened o... |
A.I. Insight forums : Amid a surge in the popularity and advancement of artificial intelligence, senator Chuck Schumer launched an effort to establish a framework for the regulation of A.I. in April. By the end of June, a preliminary framework – dubbed the "SAFE Innovation Framework" – was established and presented to ... |
A.I. Insight forums : The overarching consensus following the conclusion of the September 13 forum was that there "should be" regulations regarding the use and advancement of A.I., but it should not be made "too fast". Many tech executives who attended the forum also warned senators of the risks and threats that A.I. c... |
A.I. Insight forums : The second of nine forums was hosted on October 24, 2023, as federal A.I. regulation drew nearer. According to Schumer's office, the forum was centered mainly on how A.I. could "enable innovation", and the innovation that is needed for the safe progression of A.I. At the forum, Senators Brian Scha... |
A.I. Insight forums : Over the course of fall 2023, there is slated to be a total of nine forums on the topic of A.I., with the first hosted on September 13. == References == |
AAAI Conference on Artificial Intelligence : The AAAI Conference on Artificial Intelligence (AAAI) is a leading international academic conference in artificial intelligence held annually. It ranks 4th in terms of H5 Index in Google Scholar's list of top AI publications, after ICLR, NeurIPS, and ICML. It is supported by... |
AAAI Conference on Artificial Intelligence : AAAI-2025 Pennsylvania Convention Center, Philadelphia, Pennsylvania, United States AAAI-2024 Vancouver Convention Centre, Vancouver, British Columbia, Canada AAAI-2023 Washington Convention Center, Washington, D.C., United States AAAI-2022 Virtual Conference AAAI-2021 Virtu... |
AAAI Conference on Artificial Intelligence : ICML ICLR Journal of Machine Learning Research Machine Learning (journal) NeurIPS |
AI Action Summit : The Artificial Intelligence (AI) Action Summit was held at the Grand Palais in Paris, France, from 10 to 11 February 2025. The summit was co-chaired by French President Emmanuel Macron and Indian Prime Minister Narendra Modi. The 2025 AI Action Summit followed the 2023 AI Safety Summit hosted at Blet... |
AI Action Summit : The First International AI Safety Report was published on 29 January 2025. Commissioned after the Bletchley Park AI Safety Summit, the report focused on the risks and threats posed by general-purpose AI, and was slated for discussion at the Paris summit as part of the "Trust in AI" pillar. Whereas th... |
AI Action Summit : At the summit, the European Union made several announcements related to planned investments supporting AI development. President Ursula von der Leyen of the European Commission launched InvestAI, a €200 billion initiative, including €20 billion to build four AI gigafactories to train highly complex, ... |
AI Action Summit : The Financial Times editorial board noted that the Paris summit "highlighted a shift in the dynamics towards geopolitical competition", which it characterised as "a new AI arms race" between the US and China, with Europe "trying to carve out its role". Fortune.com AI editor Jeremy Kahn described the ... |
AI Action Summit : At the summit, 58 countries, including France, China, and India, signed a joint declaration, the Statement on Inclusive and Sustainable Artificial Intelligence for People and the Planet. The statement outlines general principles such as accessibility and overcoming the digital divide; developing AI t... |
AI Action Summit : Artificial Intelligence Action Summit |
AI Now Institute : The AI Now Institute (AI Now) is an American research institute studying the social implications of artificial intelligence and policy research that addresses the concentration of power in the tech industry. AI Now has partnered with organizations such as the Distributed AI Research Institute (DAIR),... |
AI Now Institute : AI Now grew out of a 2016 symposium spearheaded by the Obama White House Office of Science and Technology Policy. The event was led by Meredith Whittaker, the founder of Google's Open Research Group, and Kate Crawford, a principal researcher at Microsoft Research. The event focused on near-term impli... |
AI Now Institute : AI Now publishes an annual reports on the state of AI, and its integration into society. Its 2017 Report stated that, "current framings of AI ethics are failing", and provided ten strategic recommendations for the field - including pre-release trials of AI systems, and increased research into bias an... |
AI Now Institute : Banjo (application) Clearview AI |
2023 Bilderberg Conference : The 2023 Bilderberg Conference or Bilderberg Club was held between May 18–21, 2023 at the Pestana Palace hotel in Lisbon, Portugal. The 2023 meeting was the 69th edition of the event. A Bilderberg Group press release stated that there were approximately 130 participants from 23 countries. E... |
2023 Bilderberg Conference : The key topics for discussion at the 2023 Bilderberg Conference were announced on the Bilderberg website shortly before the meeting. These topics included: |
2023 Bilderberg Conference : A list of 127 participants was published on the Bilderberg website. This list may not be complete, as a source connected to the Bilderberg group told The Daily Telegraph in 2013 that some attendees do not have their names publicized. Oscar Stenström, Sweden’s chief negotiator for NATO membe... |
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