Glossary of artificial intelligence
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This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.
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| Approaches |
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A[edit]
- Abductive logic programming – Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some predicates to be incompletely defined, declared as abducible predicates.
- Abductive reasoning – Abductive reasoning (also called abduction,[1] abductive inference,[1] or retroduction[2]) is a form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it.
- Abstract data type – is a mathematical model for data types, where a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations.
- Abstraction – is the process of removing physical, spatial, or temporal details[3] or attributes in the study of objects or systems in order to more closely attend to other details of interest[4]
- Accelerating change – is a perceived increase in the rate of technological change throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change.
- Action language – is a language for specifying state transition systems, and is commonly used to create formal models of the effects of actions on the world.[5] Action languages are commonly used in the artificial intelligence and robotics domains, where they describe how actions affect the states of systems over time, and may be used for automated planning.
- Action model learning – is an area of machine learning concerned with creation and modification of software agent's knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners.
- Action selection – is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment.
- Activation function – In artificial neural networks, the activation function of a node defines the output of that node, or "neuron," given an input or set of inputs. This output is then used as input for the next node and so on until a desired solution to the original problem is found.[6]
- Adaptive algorithm – an algorithm that changes its behavior at the time it is run, based on a priori defined reward mechanism or criterion.
- Adaptive neuro fuzzy inference system –
- Admissible heuristic – In computer science, specifically in algorithms related to pathfinding, a heuristic function is said to be admissible if it never overestimates the cost of reaching the goal, i.e. the cost it estimates to reach the goal is not higher than the lowest possible cost from the current point in the path.[7]
- Affective computing – (sometimes called artificial emotional intelligence, or emotion AI)[8] is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science.[9]
- Agent architecture – in computer science is a blueprint for software agents and intelligent control systems, depicting the arrangement of components. The architectures implemented by intelligent agents are referred to as cognitive architectures.[10]
- AI accelerator –
- AI-complete –
- Algorithm – is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing and automated reasoning tasks.
- Algorithmic efficiency –
- Algorithmic probability –
- AlphaGo –
- Ambient intelligence –
- Analysis of algorithms –
- Analytics – the discovery, interpretation, and communication of meaningful patterns in data.
- Answer set programming –
- Anytime algorithm – an algorithm that can return a valid solution to a problem even if it is interrupted before it ends.
- Application programming interface –
- Approximate string matching –
- Approximation error –
- Argumentation framework –
- Artificial immune system –
- Artificial intelligence –
- Artificial Intelligence Markup Language –
- Artificial neural network –
- Association for the Advancement of Artificial Intelligence –
- Asymptotic computational complexity –
- Attributional calculus –
- Augmented reality –
- Automata theory –
- Automated planning and scheduling –
- Automated reasoning –
- Autonomic computing –
- Autonomous car –
- Autonomous robot –
B[edit]
- Backpropagation – is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network.[11] Backpropagation is shorthand for "the backward propagation of errors," since an error is computed at the output and distributed backwards throughout the network’s layers.[12] It is commonly used to train deep neural networks,[13] a term referring to neural networks with more than one hidden layer.[14]
- Backpropagation through time – (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers[15][16][17]
- Backward chaining – (or backward reasoning) is an inference method described colloquially as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, and other artificial intelligence applications.[18]
- Bag-of-words model – is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision.[19] The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier[20].
- Bag-of-words model in computer vision –
- Batch normalization – is a technique for improving the performance and stability of artificial neural networks. It is a technique to provide any layer in a neural network with inputs that are zero mean/unit variance.[21] Batch normalization was introduced in a 2015 paper.[22][23] It is used to normalize the input layer by adjusting and scaling the activations.[24]
- Bayesian programming –
- Bees algorithm – is a population-based search algorithm which was developed by Pham, Ghanbarzadeh and et al. in 2005.[25] It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.[26][27][28][29]
- Behavior informatics –
- Behavior tree – A Behavior Tree (BT) is a mathematical model of plan execution used in computer science, robotics, control systems and video games. They describe switchings between a finite set of tasks in a modular fashion. Their strength comes from their ability to create very complex tasks composed of simple tasks, without worrying how the simple tasks are implemented. BTs present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state. Its ease of human understanding make BTs less error prone and very popular in the game developer community. BTs have shown to generalize several other control architectures.[30] [31]
- Belief-desire-intention software model –
- Bias–variance tradeoff –
- Big data – is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[32]
- Big O notation –
- Binary tree –
- Bio-inspired computing –
- Blackboard system –
- Boltzmann machine –
- Boolean satisfiability problem –
- Brain technology –
- Branching factor –
- Brute-force search –
C[edit]
- Capsule neural network – A Capsule Neural Network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization.[33]
- Case-based reasoning –
- Chatbot –
- Cloud robotics – is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centred on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low cost, smarter robots have intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.[34][35][36][37]
- Cluster analysis –
- Cobweb – is an incremental system for hierarchical conceptual clustering. COBWEB was invented by Professor Douglas H. Fisher, currently at Vanderbilt University.[38][39] COBWEB incrementally organizes observations into a classification tree. Each node in a classification tree represents a class (concept) and is labeled by a probabilistic concept that summarizes the attribute-value distributions of objects classified under the node. This classification tree can be used to predict missing attributes or the class of a new object.[40]
- Cognitive architecture –
- Cognitive computing –
- Cognitive science –
- Combinatorial optimization –
- Committee machine –
- Commonsense knowledge –
- Commonsense reasoning –
- Computational chemistry –
- Computational complexity theory –
- Computational creativity –
- Computational cybernetics –
- Computational humor –
- Computational intelligence –
- Computational learning theory –
- Computational linguistics –
- Computational mathematics – the mathematical research in areas of science where computing plays an essential role.
- Computational neuroscience –
- Computational number theory – also known as algorithmic number theory, it is the study of algorithms for performing number theoretic computations.
- Computational problem –
- Computational statistics –
- Computational vision –
- Computer-automated design –
- Computer science –
- Computer vision –
- Concept drift –
- Confusion matrix –
- Connectionism –
- Consistent heuristic –
- Constrained conditional model –
- Constraint logic programming –
- Constraint programming –
- Constructed language –
- Control theory –
- Convolutional neural network –
- Crossover –
D[edit]
- Darkforest – is a computer go program developed by Facebook, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search.[41][42] The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them.[43] With the update, the system is known as Darkfmcts3.[44]
- Dartmouth workshop – The Dartmouth Summer Research Project on Artificial Intelligence was the name of a 1956 summer workshop now considered by many[45][46] (though not all[47]) to be the seminal event for artificial intelligence as a field.
- Data fusion – is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.[48]
- Data integration – involves combining data residing in different sources and providing users with a unified view of them.[49] This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data[50]) and the need to share existing data explodes.[51] It has become the focus of extensive theoretical work, and numerous open problems remain unsolved.
- Data mining –
- Data science – is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured,[52][53] similar to data mining. Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.[54] It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
- Data set –
- Data warehouse –
- Datalog –
- Decision boundary –
- Decision support system –
- Decision theory –
- Decision tree learning –
- Declarative programming –
- Deductive classifier –
- Deep Blue –
- Deep learning –
- Default logic –
- Description logic –
- Developmental robotics –
- Diagnosis –
- Dialog system –
- Dimensionality reduction –
- Discrete system –
- Distributed artificial intelligence –
- Dynamic epistemic logic –
E[edit]
- Eager learning – is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. [55]
- Ebert test – gauges whether a computer-based synthesized voice[56][57] can tell a joke with sufficient skill to cause people to laugh.[58] It was proposed by film critic Roger Ebert at the 2011 TED conference as a challenge to software developers to have a computerized voice master the inflections, delivery, timing, and intonations of a speaking human.[56] The test is similar to the Turing test proposed by Alan Turing in 1950 as a way to gauge a computer's ability to exhibit intelligent behavior by generating performance indistinguishable from a human being.[59]
- Echo state network – The echo state network (ESN),[60][61] is a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can (re)produce specific temporal patterns. The main interest of this network is that although its behaviour is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system.
- Embodied agent –
- Embodied cognitive science –
- Error-driven learning –
- Ensemble averaging –
- Ethics of artificial intelligence –
- Evolutionary algorithm –
- Evolutionary computation –
- Evolving classification function –
- Existential risk –
- Expert systems –
F[edit]
- Fast-and-frugal trees – is a type of classification tree. Fast-and-frugal trees can be used as decision-making tools which operate as lexicographic classifiers, and, if required, associate an action (decision) to each class or category.[62]
- Feature extraction –
- Feature learning –
- Feature selection –
- First-order logic – —also known as first-order predicate calculus and predicate logic—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects and allows the use of sentences that contain variables, so that rather than propositions such as Socrates is a man one can have expressions in the form "there exists X such that X is Socrates and X is a man" and there exists is a quantifier while X is a variable.[63] This distinguishes it from propositional logic, which does not use quantifiers or relations.[64]
- Fluent –
- Formal language –
- Forward chaining – (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. The opposite of forward chaining is backward chaining. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data.[65]
- Frame –
- Frame language –
- Frame problem –
- Friendly artificial intelligence –
- Futures studies –
- Fuzzy control system –
- Fuzzy logic –
- Fuzzy rule –
- Fuzzy set –
G[edit]
- Game theory –
- Genetic algorithm –
- Genetic operator –
- Glowworm swarm optimization –
- Google DeepMind –
- Graph –
- Graph –
- Graph database –
- Graph theory –
- Graph traversal –
H[edit]
- Heuristic – is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut. A heuristic function, also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.[66]
- Hidden layer – an internal layer of neurons in an artificial neural network, not dedicated to input or output
- Hidden unit – an neuron in a hidden layer in an artificial neural network
- Hyper-heuristic – is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.[67][68][69]
I[edit]
- IEEE Computational Intelligence Society –
- Incremental learning –
- Inference engine –
- Information integration –
- Information Processing Language –
- Intelligence amplification –
- Intelligence explosion –
- Intelligent agent –
- Intelligent control –
- Intelligent personal assistant –
- Interpretation –
- Issue trees –
J[edit]
K[edit]
- Kernel method –
- KL-ONE –
- Knowledge acquisition –
- Knowledge-based systems –
- Knowledge engineering –
- Knowledge extraction –
- Knowledge Interchange Format –
- Knowledge representation and reasoning –
L[edit]
M[edit]
- Machine vision –
- Markov chain –
- Markov decision process –
- Mathematical optimization –
- Machine learning –
- Machine listening –
- Machine perception –
- Mechanism design –
- Mechatronics –
- Metabolic network modelling –
- Metaheuristic –
- Model checking –
- Modus ponens –
- Modus tollens –
- Monte Carlo tree search –
- Multi-agent system –
- Multi-swarm optimization –
- Mutation –
- Mycin –
N[edit]
- Naive Bayes classifier –
- Naive semantics –
- Name binding –
- Named-entity recognition –
- Named graph –
- Natural language generation –
- Natural language processing –
- Natural language programming –
- Network motif –
- Neural machine translation –
- Neural Turing machine –
- Neuro-fuzzy –
- Neurocybernetics –
- Neuromorphic engineering –
- Node –
- Nondeterministic algorithm –
- Nouvelle AI –
- NP –
- NP-completeness –
- NP-hardness –
O[edit]
- Occam's razor –
- Offline learning –
- Online learning –
- Ontology engineering –
- Ontology learning –
- OpenAI –
- OpenCog –
- Open Mind Common Sense –
- Open-source software –
P[edit]
- Partial order reduction –
- Partially observable Markov decision process –
- Particle swarm optimization –
- Pathfinding –
- Pattern recognition –
- Planner –
- Predicate logic –
- Predictive analytics –
- Principal component analysis –
- Principle of rationality –
- Probabilistic programming language –
- Production Rule Representation –
- Production system –
- Programming language –
- Prolog –
- Propositional calculus –
- Python –
Q[edit]
R[edit]
- R programming language –
- Radial basis function network –
- Random forest –
- Reasoning system –
- Recurrent neural network –
- Region connection calculus –
- Reinforcement learning –
- Reservoir computing –
- Resource Description Framework –
- Restricted Boltzmann machine –
- Rete algorithm –
- Robotics –
- Rule-based system –
S[edit]
- Satisfiability –
- Search algorithm –
- Selection –
- Self-management –
- Semantic network –
- Semantic reasoner –
- Semantic query –
- Semantics –
- Sensor fusion –
- Separation logic –
- Similarity learning –
- Simulated annealing –
- Situated approach –
- Situation calculus –
- SLD resolution –
- Soft computing –
- Software –
- Software engineering –
- Spatial-temporal reasoning –
- SPARQL –
- Speech recognition –
- Spiking neural network –
- State –
- Statistical classification –
- Statistical relational learning –
- Stochastic optimization –
- Stochastic semantic analysis
- STRIPS –
- Subject-matter expert –
- Superintelligence –
- Supervised learning –
- Swarm intelligence –
- Symbolic artificial intelligence –
- Synthetic intelligence –
- Systems neuroscience –
T[edit]
- Technological singularity –
- Temporal difference learning –
- Tensor network theory –
- TensorFlow –
- Theoretical computer science –
- Theory of computation –
- Thompson sampling –
- Time complexity –
- Transhumanism –
- Transition system –
- Tree traversal –
- True quantified Boolean formula –
- Turing test –
- Type system –
U[edit]
V[edit]
W[edit]
X[edit]
Y[edit]
Z[edit]
See also[edit]
References and notes[edit]
- ^ a b For example: Josephson, John R.; Josephson, Susan G., eds. (1994). Abductive Inference: Computation, Philosophy, Technology. Cambridge, UK; New York: Cambridge University Press. doi:10.1017/CBO9780511530128. ISBN 0521434610. OCLC 28149683.
- ^ "Retroduction | Dictionary | Commens". Commens – Digital Companion to C. S. Peirce. Mats Bergman, Sami Paavola & João Queiroz. Retrieved 2014-08-24.
- ^ Colburn, Timothy; Shute, Gary (2007-06-05). "Abstraction in Computer Science". Minds and Machines. 17 (2): 169–184. doi:10.1007/s11023-007-9061-7. ISSN 0924-6495.
- ^ Kramer, Jeff (2007-04-01). "Is abstraction the key to computing?". Communications of the ACM. 50 (4): 36–42. doi:10.1145/1232743.1232745. ISSN 0001-0782.
- ^ Michael Gelfond, Vladimir Lifschitz (1998) "Action Languages", Linköping Electronic Articles in Computer and Information Science, vol 3, nr 16.
- ^ "What is an Activation Function?". deepai.org.
- ^ Russell, S.J.; Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall. ISBN 0-13-790395-2.
- ^ Rana el Kaliouby (Nov–Dec 2017). "We Need Computers with Empathy". Technology Review. 120 (6). p. 8.
- ^ Tao, Jianhua; Tieniu Tan (2005). "Affective Computing: A Review". Affective Computing and Intelligent Interaction. LNCS 3784. Springer. pp. 981–995. doi:10.1007/11573548.
- ^ Comparison of Agent Architectures Archived August 27, 2008, at the Wayback Machine.
- ^ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaaron (2016) Deep Learning. MIT Press. p. 196. ISBN 9780262035613
- ^ "What is Backpropagation?". deepai.org.
- ^ Nielsen, Michael A. (2015). "Chapter 6". Neural Networks and Deep Learning.
- ^ "Deep Networks: Overview - Ufldl". ufldl.stanford.edu. Retrieved 2017-08-04.
- ^ Mozer, M. C. (1995). "A Focused Backpropagation Algorithm for Temporal Pattern Recognition". In Chauvin, Y.; Rumelhart, D. Backpropagation: Theory, architectures, and applications. ResearchGate. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 137–169. Retrieved 2017-08-21.
- ^ Robinson, A. J. & Fallside, F. (1987). The utility driven dynamic error propagation network (Technical report). Cambridge University, Engineering Department. CUED/F-INFENG/TR.1.
- ^ Werbos, Paul J. (1988). "Generalization of backpropagation with application to a recurrent gas market model". Neural Networks. 1 (4): 339–356. doi:10.1016/0893-6080(88)90007-x.
- ^ Feigenbaum, Edward (1988). The Rise of the Expert Company. Times Books. p. 317. ISBN 0-8129-1731-6.
- ^ Sivic, Josef (April 2009). "Efficient visual search of videos cast as text retrieval" (PDF). IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31, NO. 4. IEEE. pp. 591–605.
- ^ McTear et al 2016, p. 167.
- ^ "Understanding the backward pass through Batch Normalization Layer". kratzert.github.io. Retrieved 24 April 2018.
- ^ Ioffe, Sergey; Szegedy, Christian. "Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift" (PDF).
- ^ "Glossary of Deep Learning: Batch Normalisation". medium.com. Retrieved 24 April 2018.
- ^ "Batch normalization in Neural Networks". towardsdatascience.com. Retrieved 24 April 2018.
- ^ Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
- ^ Pham, D.T., Castellani, M. (2009), The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Proc. ImechE, Part C, 223(12), 2919-2938.
- ^ Pham, D.T. and Castellani, M. (2013), Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms, Soft Computing, 1-33.
- ^ Pham, D.T. and Castellani, M. (2015), A comparative study of the bees algorithm as a tool for function optimisation, Cogent Engineering 2(1), 1091540.
- ^ Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm, Computers 2017, 6(1), 5; (doi: 10.3390/computers6010005)
- ^ Colledanchise Michele, and Ögren Petter 2016. How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees. In IEEE Transactions on Robotics vol.PP, no.99, pp.1-18 (2016)
- ^ Colledanchise Michele, and Ögren Petter 2017. Behavior Trees in Robotics and AI: An Introduction.
- ^ Breur, Tom (July 2016). "Statistical Power Analysis and the contemporary "crisis" in social sciences". Journal of Marketing Analytics. 4 (2–3): 61–65. doi:10.1057/s41270-016-0001-3. ISSN 2050-3318.
- ^ Sabour, Sara; Frosst, Nicholas; Hinton, Geoffrey E. (2017-10-26). "Dynamic Routing Between Capsules". arXiv:1710.09829 [cs.CV].
- ^ "Cloud Robotics and Automation A special issue of the IEEE Transactions on Automation Science and Engineering". IEEE. Retrieved 7 December 2014.
- ^ "RoboEarth".
- ^ Goldberg, Ken. "Cloud Robotics and Automation".
- ^ Li, R. "Cloud Robotics-Enable cloud computing for robots". Retrieved 7 December 2014.
- ^ Fisher, Douglas (1987). "Knowledge acquisition via incremental conceptual clustering" (PDF). Machine Learning. 2 (2): 139–172. doi:10.1007/BF00114265.
- ^ Fisher, Douglas H. (July 1987). "Improving inference through conceptual clustering". Proceedings of the 1987 AAAI Conferences. AAAI Conference. Seattle Washington. pp. 461–465.
- ^ William Iba and Pat Langley. "Cobweb models of categorization and probabilistic concept formation". In Emmanuel M. Pothos and Andy J. Wills,. Formal approaches in categorization. Cambridge: Cambridge University Press. pp. 253–273. ISBN 9780521190480.
- ^ Tian, Yuandong; Zhu, Yan (2015). "Better Computer Go Player with Neural Network and Long-term Prediction". arXiv:1511.06410v1 [cs.LG].
- ^ "How Facebook's AI Researchers Built a Game-Changing Go Engine". MIT Technology Review. December 4, 2015. Retrieved 2016-02-03.
- ^ "Facebook AI Go Player Gets Smarter With Neural Network And Long-Term Prediction To Master World's Hardest Game". Tech Times. 2016-01-28. Retrieved 2016-04-24.
- ^ "Facebook's artificially intelligent Go player is getting smarter". VentureBeat. Retrieved 2016-04-24.
- ^ Solomonoff, R.J.The Time Scale of Artificial Intelligence; Reflections on Social Effects, Human Systems Management, Vol 5 1985, Pp 149-153
- ^ Moor, J., The Dartmouth College Artificial Intelligence Conference: The Next Fifty years, AI Magazine, Vol 27, No., 4, Pp. 87-9, 2006
- ^ Kline, Ronald R., Cybernetics, Automata Studies and the Dartmouth Conference on Artificial Intelligence, IEEE Annals of the History of Computing, October–December, 2011, IEEE Computer Society
- ^ M. Haghighat, M. Abdel-Mottaleb, & W. Alhalabi (2016). Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Transactions on Information Forensics and Security, 11(9), 1984-1996.
- ^ Maurizio Lenzerini (2002). "Data Integration: A Theoretical Perspective" (PDF). PODS 2002. pp. 233–246.
- ^ Big Data Integration
- ^ Frederick Lane (2006). "IDC: World Created 161 Billion Gigs of Data in 2006".
- ^ Dhar, V. (2013). "Data science and prediction". Communications of the ACM. 56 (12): 64. doi:10.1145/2500499.
- ^ Jeff Leek (2013-12-12). "The key word in "Data Science" is not Data, it is Science". Simply Statistics.
- ^ Hayashi, Chikio (1998-01-01). "What is Data Science? Fundamental Concepts and a Heuristic Example". In Hayashi, Chikio; Yajima, Keiji; Bock, Hans-Hermann; Ohsumi, Noboru; Tanaka, Yutaka; Baba, Yasumasa. Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer Japan. pp. 40–51. doi:10.1007/978-4-431-65950-1_3. ISBN 9784431702085.
- ^ Hendrickx, Iris; Van den Bosch, Antal (October 2005). "Hybrid algorithms with Instance-Based Classification". Machine Learning: ECML2005. Springer. pp. 158–169.
- ^ a b Adam Ostrow (March 5, 2011). "Roger Ebert's Inspiring Digital Transformation". Mashable Entertainment. Retrieved 2011-09-12.
With the help of his wife, two colleagues and the Alex-equipped MacBook that he uses to generate his computerized voice, famed film critic Roger Ebert delivered the final talk at the TED conference on Friday in Long Beach, California....
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Now perhaps, there is the Ebert Test, a way to see if a synthesized voice can deliver humor with the timing to make an audience laugh.... He proposed the Ebert Test as a way to gauge the humanness of a synthesized voice.
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Meanwhile, the technology that enables Ebert to “speak” continues to see improvements – for example, adding more realistic inflection for question marks and exclamation points. In a test of that, which Ebert called the “Ebert test” for computerized voices,
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He calls it the “Ebert Test,” after Turing’s AI standard...
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