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Lexical Markup Framework : The ISO number is 24613. The LMF specification has been published officially as an International Standard on 17 November 2008. |
Lexical Markup Framework : The ISO/TC 37 standards are currently elaborated as high level specifications and deal with word segmentation (ISO 24614), annotations (ISO 24611 a.k.a. MAF, ISO 24612 a.k.a. LAF, ISO 24615 a.k.a. SynAF, and ISO 24617-1 a.k.a. SemAF/Time), feature structures (ISO 24610), multimedia containers... |
Lexical Markup Framework : The linguistics constants like /feminine/ or /transitive/ are not defined within LMF but are recorded in the Data Category Registry (DCR) that is maintained as a global resource by ISO/TC 37 in compliance with ISO/IEC 11179-3:2003. And these constants are used to adorn the high level structur... |
Lexical Markup Framework : LMF is composed of the following components: The core package that is the structural skeleton which describes the basic hierarchy of information in a lexical entry. Extensions of the core package which are expressed in a framework that describes the reuse of the core components in conjunction... |
Lexical Markup Framework : In the following example, the lexical entry is associated with a lemma clergyman and two inflected forms clergyman and clergymen. The language coding is set for the whole lexical resource. The language value is set for the whole lexicon as shown in the following UML instance diagram. The elem... |
Lexical Markup Framework : The first publication about the LMF specification as it has been ratified by ISO (this paper became (in 2015) the 9th most cited paper within the Language Resources and Evaluation conferences from LREC papers): Language Resources and Evaluation LREC-2006/Genoa: Gil Francopoulo, Monte George, ... |
Lexical Markup Framework : There is a book published in 2013: LMF Lexical Markup Framework which is entirely dedicated to LMF. The first chapter deals with the history of lexicon models, the second chapter is a formal presentation of the data model and the third one deals with the relation with the data categories of t... |
Lexical Markup Framework : Computational lexicology Lexical semantics Morphology (linguistics) for explanations concerning paradigms and morphosyntax Machine translation for a presentation of the different types of multilingual notations (see section Approaches) Morphological pattern for the difference between a paradi... |
Stochastic Neural Analog Reinforcement Calculator : The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural-net machine designed by Marvin Lee Minsky. Prompted by a letter from Minsky, George Armitage Miller gathered the funding (a few thousand dollars) for the project from the Office of Naval Researc... |
Stochastic Neural Analog Reinforcement Calculator : Citations Works cited Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3. Russell, Stuart; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach. London, England: Pearson Education. IS... |
Stochastic Neural Analog Reinforcement Calculator : Levy, Steven (2010). Hackers. Sebastapol, California: O'Reilly. ISBN 978-1-449-38839-3. "A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement" (Document). Cambridge, Massachusetts: Harvard University Psychological Laboratories. January 8, 1952.... |
Stochastic Neural Analog Reinforcement Calculator : 1951 – SNARC Maze Solver – Minsky / Edmonds (American) at Cyberneticzoo.com 2011 oral history interview with Marvin Minsky. Relevant segments that concern SNARC: Building my randomly wired neural network machine (136/151) Show and tell: My neural network machine (137/... |
Deep Instinct : Deep Instinct is a cybersecurity company that applies deep learning to cybersecurity. The company implements artificial intelligence to the task of preventing and detecting malware. The company was the recipient of the Technology Pioneer by The World Economic Forum in 2017. |
Deep Instinct : In 2015, Deep Instinct was founded by Guy Caspi, Dr. Eli David, and Nadav Maman. The headquarters of the company is located in New York City. In July 2017, NVIDIA became an investor. According to Tom's Hardware, NVIDIA’s investment enabled access to a GPU-based neural network and CUDA platform, which th... |
Deep Instinct : Official website |
Synaptic weight : In neuroscience and computer science, synaptic weight refers to the strength or amplitude of a connection between two nodes, corresponding in biology to the amount of influence the firing of one neuron has on another. The term is typically used in artificial and biological neural network research. |
Synaptic weight : In a computational neural network, a vector or set of inputs x and outputs y , or pre- and post-synaptic neurons respectively, are interconnected with synaptic weights represented by the matrix w , where for a linear neuron y j = ∑ i w i j x i or y = w x =\sum _w_x_~~~~=w . where the rows of the sy... |
Synaptic weight : For biological networks, the effect of synaptic weights is not as simple as for linear neurons or Hebbian learning. However, biophysical models such as BCM theory have seen some success in mathematically describing these networks. In the mammalian central nervous system, signal transmission is carried... |
Synaptic weight : Neural network Synaptic plasticity Hebbian theory == References == |
Machine Learning (journal) : Machine Learning is a peer-reviewed scientific journal, published since 1986. In 2001, forty editors and members of the editorial board of Machine Learning resigned in order to support the Journal of Machine Learning Research (JMLR), saying that in the era of the internet, it was detrimenta... |
Machine Learning (journal) : J.R. Quinlan (1986). "Induction of Decision Trees". Machine Learning. 1: 81–106. doi:10.1007/BF00116251. Nick Littlestone (1988). "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm" (PDF). Machine Learning. 2 (4): 285–318. doi:10.1007/BF00116827. John R. A... |
Large width limits of neural networks : Artificial neural networks are a class of models used in machine learning, and inspired by biological neural networks. They are the core component of modern deep learning algorithms. Computation in artificial neural networks is usually organized into sequential layers of artifici... |
Large width limits of neural networks : The Neural Network Gaussian Process (NNGP) corresponds to the infinite width limit of Bayesian neural networks, and to the distribution over functions realized by non-Bayesian neural networks after random initialization. The same underlying computations that are used to derive th... |
Intelligent database : Until the 1980s, databases were viewed as computer systems that stored record-oriented and business data such as manufacturing inventories, bank records, and sales transactions. A database system was not expected to merge numeric data with text, images, or multimedia information, nor was it expec... |
Intelligent database : Intelligent Databases, book |
Algorithmic accountability : Algorithmic accountability refers to the allocation of responsibility for the consequences of real-world actions influenced by algorithms used in decision-making processes. Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes. This means they ought to... |
Algorithmic accountability : Algorithms are widely utilized across various sectors of society that incorporate computational techniques in their control systems. These applications span numerous industries, including but not limited to medical, transportation, and payment services. In these contexts, algorithms perform... |
Algorithmic accountability : Algorithms are prevalent across various fields and significantly influence decisions that affect the population at large. Their underlying structures and parameters often remain unknown to those impacted by their outcomes. A notable case illustrating this issue is a recent ruling by the Wis... |
Algorithmic accountability : A notable instance of potential algorithmic bias is highlighted in an article by The Washington Post regarding the ride-hailing service Uber. An analysis of collected data revealed that estimated waiting times for users varied based on the neighborhoods in which they resided. Key factors in... |
Algorithmic accountability : Discussions among experts have sought viable solutions to understand the operations of algorithms, often referred to as "black boxes." It is generally proposed that companies responsible for developing and implementing these algorithms should ensure their reliability by disclosing the inter... |
Algorithmic accountability : Algorithmic transparency Artificial intelligence and elections – Use and impact of AI on political elections Big data ethics Regulation of algorithms |
Algorithmic accountability : Kroll, Joshua A.; Huey, Joanna; Barocas, Solon; Barocas, Solon; Felten, Edward W.; Reidenberg, Joel R.; Robinson, David G.; Robinson, David G.; Yu, Harlan (2016) Accountable Algorithms. University of Pennsylvania Law Review, Vol. 165. Fordham Law Legal Studies Research Paper No. 2765268. |
Exploration–exploitation dilemma : The exploration–exploitation dilemma, also known as the explore–exploit tradeoff, is a fundamental concept in decision-making that arises in many domains. It is depicted as the balancing act between two opposing strategies. Exploitation involves choosing the best option based on curre... |
Exploration–exploitation dilemma : In the context of machine learning, the exploration–exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves training agents to make decisions based on feedback from the environment. Crucially, this feedback may be incomplete or del... |
Exploration–exploitation dilemma : Amin, Susan; Gomrokchi, Maziar; Satija, Harsh; Hoof, van; Precup, Doina (September 1, 2021). "A Survey of Exploration Methods in Reinforcement Learning". arXiv:2109.00157 [cs.LG]. |
Actor-critic algorithm : The actor-critic algorithm (AC) is a family of reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods, and value-based RL algorithms such as value iteration, Q-learning, SARSA, and TD learning. An AC algorithm consists of two main componen... |
Actor-critic algorithm : The actor-critic methods can be understood as an improvement over pure policy gradient methods like REINFORCE via introducing a baseline. |
Actor-critic algorithm : Asynchronous Advantage Actor-Critic (A3C): Parallel and asynchronous version of A2C. Soft Actor-Critic (SAC): Incorporates entropy maximization for improved exploration. Deep Deterministic Policy Gradient (DDPG): Specialized for continuous action spaces. |
Actor-critic algorithm : Reinforcement learning Policy gradient method Deep reinforcement learning |
Actor-critic algorithm : Konda, Vijay R.; Tsitsiklis, John N. (January 2003). "On Actor-Critic Algorithms". SIAM Journal on Control and Optimization. 42 (4): 1143–1166. doi:10.1137/S0363012901385691. ISSN 0363-0129. Sutton, Richard S.; Barto, Andrew G. (2018). Reinforcement learning: an introduction. Adaptive computati... |
Gated recurrent unit : Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, but lacks a context vector or output gate, resulting in fewer p... |
Gated recurrent unit : There are several variations on the full gated unit, with gating done using the previous hidden state and the bias in various combinations, and a simplified form called minimal gated unit. The operator ⊙ denotes the Hadamard product in the following. |
Intelligent word recognition : Intelligent Word Recognition, or IWR, is the recognition of unconstrained handwritten words. IWR recognizes entire handwritten words or phrases instead of character-by-character, like its predecessor, optical character recognition (OCR). IWR technology matches handwritten or printed words... |
Intelligent word recognition : AI effect Handwriting recognition Optical character recognition Lists List of emerging technologies Outline of artificial intelligence == References == |
Deep learning in photoacoustic imaging : Photoacoustic imaging (PA) is based on the photoacoustic effect, in which optical absorption causes a rise in temperature, which causes a subsequent rise in pressure via thermo-elastic expansion. This pressure rise propagates through the tissue and is sensed via ultrasonic trans... |
Deep learning in photoacoustic imaging : The first application of deep learning in PACT was by Reiter et al. in which a deep neural network was trained to learn spatial impulse responses and locate photoacoustic point sources. The resulting mean axial and lateral point location errors on 2,412 of their randomly selecte... |
Deep learning in photoacoustic imaging : Photoacoustic microscopy differs from other forms of photoacoustic tomography in that it uses focused ultrasound detection to acquire images pixel-by-pixel. PAM images are acquired as time-resolved volumetric data that is typically mapped to a 2-D projection via a Hilbert transf... |
Deep learning in photoacoustic imaging : Photoacoustic imaging Photoacoustic microscopy Photoacoustic effect |
Deep learning in photoacoustic imaging : Photoacoustic imaging Photoacoustic microscopy Photoacoustic effect |
Artificial reproduction : Artificial reproduction is the re-creation of life brought about by means other than natural ones. It is new life built by human plans and projects. Examples include artificial selection, artificial insemination, in vitro fertilization, artificial womb, artificial cloning, and kinematic replic... |
Artificial reproduction : Humans have aspired to create life since immemorial times. Most theologies and religions have conceived this possibility as exclusive of deities. Christian religions consider the possibility of artificial reproduction, in most cases, as heretical and sinful. |
Artificial reproduction : Although ancient Greek philosophy raised the concept that man could imitate the creative capacity of nature, classic Greeks thought that if possible, human beings would reproduce things as nature does, and vice versa, nature would do the things that man does in the same way. Aristotle, for exa... |
Artificial reproduction : Biology, being the study of cellular life, addresses reproduction in terms of growth and cellular division (i.e., binary fission, mitosis and meiosis); however, the science of artificial reproduction is not restricted by the mirroring of these natural processes.The science of artificial reprod... |
Artificial reproduction : Assisted reproductive technology (ART)'s purpose is to assist the development of a human embryo, commonly because of medical concerns due to fertility limitations. |
Artificial reproduction : Non-assisted reproductive technologies (NART) could have medical motivations but are mostly driven by a wider heterotopic ambition. Although, NARTs are initially designed by humans, they are programed to become independent of humans to a relative or absolute extent. James Lovelock proposed tha... |
Artificial reproduction : Male Pregnancy Artificial Uterus In Vitro Fertilization Xenobot Fertilization Pregnancy The concept of nature sensu Marx Juan David García Bacca == References == |
Backpropagation : In machine learning, backpropagation is a gradient estimation method commonly used for training a neural network to compute its parameter updates. It is an efficient application of the chain rule to neural networks. Backpropagation computes the gradient of a loss function with respect to the weights o... |
Backpropagation : Backpropagation computes the gradient in weight space of a feedforward neural network, with respect to a loss function. Denote: x : input (vector of features) y : target output For classification, output will be a vector of class probabilities (e.g., ( 0.1 , 0.7 , 0.2 ) , and target output is a spe... |
Backpropagation : For the basic case of a feedforward network, where nodes in each layer are connected only to nodes in the immediate next layer (without skipping any layers), and there is a loss function that computes a scalar loss for the final output, backpropagation can be understood simply by matrix multiplication... |
Backpropagation : For more general graphs, and other advanced variations, backpropagation can be understood in terms of automatic differentiation, where backpropagation is a special case of reverse accumulation (or "reverse mode"). |
Backpropagation : The gradient descent method involves calculating the derivative of the loss function with respect to the weights of the network. This is normally done using backpropagation. Assuming one output neuron, the squared error function is E = L ( t , y ) where L is the loss for the output y and target val... |
Backpropagation : Using a Hessian matrix of second-order derivatives of the error function, the Levenberg–Marquardt algorithm often converges faster than first-order gradient descent, especially when the topology of the error function is complicated. It may also find solutions in smaller node counts for which other met... |
Backpropagation : The loss function is a function that maps values of one or more variables onto a real number intuitively representing some "cost" associated with those values. For backpropagation, the loss function calculates the difference between the network output and its expected output, after a training example ... |
Backpropagation : Gradient descent with backpropagation is not guaranteed to find the global minimum of the error function, but only a local minimum; also, it has trouble crossing plateaus in the error function landscape. This issue, caused by the non-convexity of error functions in neural networks, was long thought to... |
Backpropagation : Artificial neural network Neural circuit Catastrophic interference Ensemble learning AdaBoost Overfitting Neural backpropagation Backpropagation through time Backpropagation through structure Three-factor learning |
Backpropagation : Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). "6.5 Back-Propagation and Other Differentiation Algorithms". Deep Learning. MIT Press. pp. 200–220. ISBN 9780262035613. Nielsen, Michael A. (2015). "How the backpropagation algorithm works". Neural Networks and Deep Learning. Determination Pres... |
Backpropagation : Backpropagation neural network tutorial at the Wikiversity Bernacki, Mariusz; Włodarczyk, Przemysław (2004). "Principles of training multi-layer neural network using backpropagation". Karpathy, Andrej (2016). "Lecture 4: Backpropagation, Neural Networks 1". CS231n. Stanford University. Archived from t... |
Way of the Future : Way of the Future (WOTF) is the first known religious organization dedicated to the worship of artificial intelligence (AI). It was founded in 2017 by American engineer Anthony Levandowski. |
Way of the Future : 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 realizat... |
Way of the Future : Some commentators wondered whether the WOTF is a joke parody religion, a potential way to minimize taxation as a religious organization, or a genuine effort to try and deal with the possible psychological and theological aspects of the rise of superhuman AI. |
Way of the Future : Transhumanism Singularitarianism |
Way of the Future : Archived official website |
Automated negotiation : Automated negotiation is a form of interaction in systems that are composed of multiple autonomous agents, in which the aim is to reach agreements through an iterative process of making offers. Automated negotiation can be employed for many tasks human negotiators regularly engage in, such as ba... |
Automated negotiation : Through digitization, the beginning of the 21st century has seen a growing interest in the automation of negotiation and e-negotiation systems, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents being able to negotiate on behalf of human negotiat... |
Automated negotiation : Examples of automated negotiation include: Online dispute resolution, in which disagreements between parties are settled. Sponsored search auction, where bids are placed on advertisement keywords. Content negotiation, in which user agents negotiate over HTTP about how to best represent a web res... |
Feature learning : In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the ... |
Feature learning : Supervised feature learning is learning features from labeled data. The data label allows the system to compute an error term, the degree to which the system fails to produce the label, which can then be used as feedback to correct the learning process (reduce/minimize the error). Approaches include: |
Feature learning : Unsupervised feature learning is learning features from unlabeled data. The goal of unsupervised feature learning is often to discover low-dimensional features that capture some structure underlying the high-dimensional input data. When the feature learning is performed in an unsupervised way, it ena... |
Feature learning : The hierarchical architecture of the biological neural system inspires deep learning architectures for feature learning by stacking multiple layers of learning nodes. These architectures are often designed based on the assumption of distributed representation: observed data is generated by the intera... |
Feature learning : Self-supervised representation learning is learning features by training on the structure of unlabeled data rather than relying on explicit labels for an information signal. This approach has enabled the combined use of deep neural network architectures and larger unlabeled datasets to produce deep f... |
Feature learning : Dynamic representation learning methods generate latent embeddings for dynamic systems such as dynamic networks. Since particular distance functions are invariant under particular linear transformations, different sets of embedding vectors can actually represent the same/similar information. Therefor... |
Feature learning : Automated machine learning (AutoML) Deep learning geometric feature learning Feature detection (computer vision) Feature extraction Word embedding Vector quantization Variational autoencoder == References == |
Pattern theory : Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery to recognize and classify patterns; rather, it prescribe... |
Pattern theory : Abductive reasoning Algebraic statistics Computational anatomy Formal concept analysis Grammar induction Image analysis Induction Lattice theory Spatial statistics |
Pattern theory : 2007. Ulf Grenander and Michael Miller Pattern Theory: From Representation to Inference. Oxford University Press. Paperback. (ISBN 9780199297061) 1994. Ulf Grenander General Pattern Theory. Oxford Science Publications. (ISBN 978-0198536710) 1996. Ulf Grenander Elements of Pattern Theory. Johns Hopkins ... |
Pattern theory : Pattern Theory Group at Brown University Pattern Theory: Grenander's Ideas and Examples - a video lecture by David Mumford Pattern Theory and Applications - graduate course page with material by a Brown University alumnus |
Neural cryptography : Neural cryptography is a branch of cryptography dedicated to analyzing the application of stochastic algorithms, especially artificial neural network algorithms, for use in encryption and cryptanalysis. |
Neural cryptography : Artificial neural networks are well known for their ability to selectively explore the solution space of a given problem. This feature finds a natural niche of application in the field of cryptanalysis. At the same time, neural networks offer a new approach to attack ciphering algorithms based on ... |
Neural cryptography : In 1995, Sebastien Dourlens applied neural networks to cryptanalyze DES by allowing the networks to learn how to invert the S-tables of the DES. The bias in DES studied through Differential Cryptanalysis by Adi Shamir is highlighted. The experiment shows about 50% of the key bits can be found, all... |
Neural cryptography : The most used protocol for key exchange between two parties A and B in the practice is Diffie–Hellman key exchange protocol. Neural key exchange, which is based on the synchronization of two tree parity machines, should be a secure replacement for this method. Synchronizing these two machines is s... |
Neural cryptography : Neural Network Stochastic neural network Shor's algorithm |
Neural cryptography : Neuro-Cryptography 1995 - The first definition of the Neuro-Cryptography (AI Neural-Cryptography) applied to DES cryptanalysis by Sebastien Dourlens, France. Neural Cryptography - Description of one kind of neural cryptography at the University of Würzburg, Germany. Kinzel, W.; Kanter, I. (2002). ... |
Weak artificial intelligence : Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of the mind, or, as Artificial Narrow Intelligence, is focused on one narrow task. Weak AI is contrasted with strong AI, which can be interpreted in various ways: Artificial general intelligen... |
Weak artificial intelligence : 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 complex ... |
Weak artificial intelligence : 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 accurate or... |
Weak artificial intelligence : A.I. Rising – 2018 film directed by Lazar Bodroža Artificial intelligence – Intelligence of machines Artificial general intelligence – Type of AI with wide-ranging abilities Deep learning – Branch of machine learning Expert system – Computer system emulating the decision-making ability of... |
Gabbay's separation theorem : In mathematical logic and computer science, Gabbay's separation theorem, named after Dov Gabbay, states that any arbitrary temporal logic formula can be rewritten in a logically equivalent "past → future" form. I.e. the future becomes what must be satisfied. This form can be used as execut... |
GPT4-Chan : Generative Pre-trained Transformer 4Chan (GPT-4chan) is a controversial AI model that was developed and deployed by YouTuber and AI researcher Yannic Kilcher in June 2022. The model is a large language model, which means it can generate text based on some input, by fine-tuning GPT-J with a dataset of millio... |
GPT4-Chan : The development of GPT-4chan began in May 2022, when Kilcher announced his project on his YouTube channel. Notably, at the time before ChatGPT, he explained that he wanted to create a large language model that could generate realistic and coherent text in the style of /pol/, one of the most notorious online... |
GPT4-Chan : In June 2022, Kilcher deployed his model on the /pol/ board itself, using a bot that he programmed to post and reply to threads. He did not reveal the model’s identity, and that he let it run autonomously, without any human supervision or intervention. He wanted to conduct a natural experiment, and to obser... |
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