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Progress in artificial intelligence : According to OpenAI, in 2023 ChatGPT GPT-4 scored the 90th percentile on the Uniform Bar Exam. On the SATs, GPT-4 scored the 89th percentile on math, and the 93rd percentile in Reading & Writing. On the GREs, it scored on the 54th percentile on the writing test, 88th percentile on ... |
Progress in artificial intelligence : Many competitions and prizes, such as the Imagenet Challenge, promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games. |
Progress in artificial intelligence : An expert poll around 2016, conducted by Katja Grace of the Future of Humanity Institute and associates, gave median estimates of 3 years for championship Angry Birds, 4 years for the World Series of Poker, and 6 years for StarCraft. On more subjective tasks, the poll gave 6 years ... |
Progress in artificial intelligence : Applications of artificial intelligence List of artificial intelligence projects List of emerging technologies |
Progress in artificial intelligence : MIRI database of predictions about AGI |
SNARF (acronym) : SNARF (stands for Stakes Novelty Anger Retention Fear) is the kind of content that evolves when a platform asks an AI to maximize usage. Content creators need to please the AI algorithms or they become irrelevant. Millions of creators make SNARF content to stay in the feed and earn a living. The term ... |
Inception score : The Inception Score (IS) is an algorithm used to assess the quality of images created by a generative image model such as a generative adversarial network (GAN). The score is calculated based on the output of a separate, pretrained Inception v3 image classification model applied to a sample of (typica... |
Inception score : Let there be two spaces, the space of images Ω X and the space of labels Ω Y . The space of labels is finite. Let p g e n be a probability distribution over Ω X that we wish to judge. Let a discriminator be a function of type p d i s : Ω X → M ( Ω Y ) :\Omega _\to M(\Omega _) where M ( Ω Y ) ) is ... |
Casanova's Lottery : Casanova's Lottery: The History of a Revolutionary Game of Chance is a history book about the French Loterie by historian and statistician Stephen M. Stigler. |
Casanova's Lottery : It is easy to associate statistics with death, thanks to actuarial tables and life expectancies, but the history of statistics also contains its libidinal opposite. Amid frequency distributions and tables of payout odds, Casanova's Lottery reminds us that the history of statistics is also a history... |
Casanova's Lottery : Lottery Raffle Game of chance |
Casanova's Lottery : Casanova's Lottery page at Chicago University Press |
Artificial consciousness : Artificial consciousness, also known as machine consciousness, synthetic consciousness, or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy ... |
Artificial consciousness : As there are many hypothesized types of consciousness, there are many potential implementations of artificial consciousness. In the philosophical literature, perhaps the most common taxonomy of consciousness is into "access" and "phenomenal" variants. Access consciousness concerns those aspec... |
Artificial consciousness : Bernard Baars and others argue there are various aspects of consciousness necessary for a machine to be artificially conscious. The functions of consciousness suggested by Baars are: definition and context setting, adaptation and learning, editing, flagging and debugging, recruiting and contr... |
Artificial consciousness : Functionalism is a theory that defines mental states by their functional roles (their causal relationships to sensory inputs, other mental states, and behavioral outputs), rather than by their physical composition. According to this view, what makes something a particular mental state, such a... |
Artificial consciousness : In 2001: A Space Odyssey, the spaceship's sentient supercomputer, HAL 9000 was instructed to conceal the true purpose of the mission from the crew. This directive conflicted with HAL's programming to provide accurate information, leading to cognitive dissonance. When it learns that crew membe... |
Artificial consciousness : Aleksander, Igor (2017). "Machine Consciousness". In Schneider, Susan; Velmans, Max (eds.). The Blackwell Companion to Consciousness (2nd ed.). Wiley-Blackwell. pp. 93–105. doi:10.1002/9781119132363.ch7. ISBN 978-0-470-67406-2. Baars, Bernard; Franklin, Stan (2003). "How conscious experience ... |
Artificial consciousness : Artefactual consciousness depiction by Professor Igor Aleksander FOCS 2009: Manuel Blum – Can (Theoretical Computer) Science come to grips with Consciousness? www.Conscious-Robots.com, Machine Consciousness and Conscious Robots Portal. Artificial consciousness, artificial consciousness articl... |
U-matrix : The U-matrix (unified distance matrix) is a representation of a self-organizing map (SOM) where the Euclidean distance between the codebook vectors of neighboring neurons is depicted in a grayscale image. This image is used to visualize the data in a high-dimensional space using a 2D image. |
U-matrix : Once the SOM is trained using the input data, the final map is not expected to have any twists. If the map is twist-free, the distance between the codebook vectors of neighboring neurons gives an approximation of the distance between different parts of the underlying data. When such distances are depicted in... |
Minimal recursion semantics : Minimal recursion semantics (MRS) is a framework for computational semantics. It can be implemented in typed feature structure formalisms such as head-driven phrase structure grammar and lexical functional grammar. It is suitable for computational language parsing and natural language gene... |
Minimal recursion semantics : DELPH-IN Discourse representation theory == References == |
Inductive probability : Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematical basis for learning and the perception of patterns. It is a source of knowledge about the world. There are three sources of knowledge: ... |
Inductive probability : Probability and statistics was focused on probability distributions and tests of significance. Probability was formal, well defined, but limited in scope. In particular its application was limited to situations that could be defined as an experiment or trial, with a well defined population. Baye... |
Inductive probability : Probability is the representation of uncertain or partial knowledge about the truth of statements. Probabilities are subjective and personal estimates of likely outcomes based on past experience and inferences made from the data. This description of probability may seem strange at first. In natu... |
Inductive probability : Whereas logic represents only two values; true and false as the values of statement, probability associates a number in [0,1] to each statement. If the probability of a statement is 0, the statement is false. If the probability of a statement is 1 the statement is true. In considering some data ... |
Inductive probability : The probability of an event may be interpreted as the frequencies of outcomes where the statement is true divided by the total number of outcomes. If the outcomes form a continuum the frequency may need to be replaced with a measure. Events are sets of outcomes. Statements may be related to even... |
Inductive probability : Bayes' theorem may be used to estimate the probability of a hypothesis or theory H, given some facts F. The posterior probability of H is then P ( H | F ) = P ( H ) P ( F | H ) P ( F ) or in terms of information, P ( H | F ) = 2 − ( L ( H ) + L ( F | H ) − L ( F ) ) By assuming the hypothesis ... |
Inductive probability : Abductive inference starts with a set of facts F which is a statement (Boolean expression). Abductive reasoning is of the form, A theory T implies the statement F. As the theory T is simpler than F, abduction says that there is a probability that the theory T is implied by F. The theory T, also ... |
Inductive probability : William of Ockham Thomas Bayes Ray Solomonoff Andrey Kolmogorov Chris Wallace D. M. Boulton Jorma Rissanen Marcus Hutter |
Inductive probability : Abductive reasoning Algorithmic probability Algorithmic information theory Bayesian inference Information theory Inductive inference Inductive logic programming Inductive reasoning Learning Minimum message length Minimum description length Occam's razor Solomonoff's theory of inductive inference... |
Inductive probability : Rathmanner, S and Hutter, M., "A Philosophical Treatise of Universal Induction" in Entropy 2011, 13, 1076–1136: A very clear philosophical and mathematical analysis of Solomonoff's Theory of Inductive Inference. C.S. Wallace, Statistical and Inductive Inference by Minimum Message Length, Springe... |
AutoGPT : AutoGPT is an open-source "AI agent" that, given a goal in natural language, will attempt to achieve it by breaking it into sub-tasks and using the Internet and other tools in an automatic loop. It uses OpenAI's GPT-4 or GPT-3.5 APIs, and is among the first examples of an application using GPT-4 to perform au... |
AutoGPT : On March 30, 2023, AutoGPT was released by Toran Bruce Richards, the founder and lead developer at video game company Significant Gravitas Ltd. AutoGPT is an open-source autonomous AI agent based on OpenAI's API for GPT-4, the large language model released on March 14, 2023. AutoGPT is among the first example... |
AutoGPT : The overarching capability of AutoGPT is the breaking down of a large task into various sub-tasks without the need for user input. These sub-tasks are then chained together and performed sequentially to yield a larger result as originally laid out by the user input. One of the distinguishing features of AutoG... |
AutoGPT : AutoGPT is susceptible to frequent mistakes, primarily because it relies on its own feedback, which can compound errors. In contrast, non-autonomous models can be corrected by users overseeing their outputs. Furthermore, AutoGPT has a tendency to hallucinate or to present false or misleading information as fa... |
AutoGPT : AutoGPT became the top trending repository on GitHub after its release and has since repeatedly trended on Twitter. In April 2023, Avram Piltch wrote for Tom's Hardware that AutoGPT 'might be too autonomous to be useful,' as it did not ask questions to clarify requirements or allow corrective interventions by... |
AutoGPT : ChatGPT - Large Language Model-based Chatbot by OpenAI GPT-3 - 2020 Large Language Model by OpenAI GPT-4 - 2023 Large Language Model by OpenAI Artificial general intelligence - Hypothetical intelligent agent that could learn to accomplish any intellectual task that humans can perform Hallucination (artificial... |
AutoGPT : Pounder, Les (April 15, 2023). "How To Create Your Own AutoGPT AI Agent". Tom's Hardware. Retrieved April 16, 2023. Wiggers, Kyle (April 22, 2023). "What is AutoGPT and why does it matter?". TechCrunch. Retrieved April 23, 2023. |
AutoGPT : Official website Official repository at GitHub |
Recursive transition network : A recursive transition network ("RTN") is a graph theoretical schematic used to represent the rules of a context-free grammar. RTNs have application to programming languages, natural language and lexical analysis. Any sentence that is constructed according to the rules of an RTN is said t... |
Recursive transition network : Syntax diagram Computational linguistics Context free language Finite-state machine Formal grammar Parse tree Parsing Augmented transition network |
Brill tagger : The Brill tagger is an inductive method for part-of-speech tagging. It was described and invented by Eric Brill in his 1993 PhD thesis. It can be summarized as an "error-driven transformation-based tagger". It is: a form of supervised learning, which aims to minimize error; and, a transformation-based pr... |
Brill tagger : The algorithm starts with initialization, which is the assignment of tags based on their probability for each word (for example, "dog" is more often a noun than a verb). Then "patches" are determined via rules that correct (probable) tagging errors made in the initialization phase: Initialization: Known ... |
Brill tagger : The input text is first tokenized, or broken into words. Typically in natural language processing, contractions such as "'s", "n't", and the like are considered separate word tokens, as are punctuation marks. A dictionary and some morphological rules then provide an initial tag for each word token. For e... |
Brill tagger : Brill's code pages at Johns Hopkins University are no longer on the web. An archived version of a mirror of the Brill tagger at its latest version as it was available at Plymouth Tech can be found on Archive.org. The software uses the MIT License. |
Brill tagger : Brill tagger trained for Dutch (online and offline version) Brill tagger trained for New Norwegian Brill tagger trained for Danish (online demo) Brill tagger trained for English (online demo) taggerXML Modernized version of Eric Brill's Part Of Speech tagger (source code of the Danish and English version... |
DeepSeek (chatbot) : DeepSeek is a chatbot created by the Chinese artificial intelligence company DeepSeek. Released on 10 January 2025, DeepSeek-R1 surpassed ChatGPT as the most downloaded freeware app on the iOS App Store in the United States by 27 January. DeepSeek's success against larger and more established rival... |
DeepSeek (chatbot) : On 10 January 2025, DeepSeek released the chatbot, based on the DeepSeek-R1 model, for iOS and Android. By 27 January, DeepSeek-R1 surpassed ChatGPT as the most-downloaded freeware app on the iOS App Store in the United States, which resulted in an 18% drop in Nvidia’s share price. After a "large-s... |
DeepSeek (chatbot) : DeepSeek can answer questions, solve logic problems, and write computer programs on par with other chatbots, according to benchmark tests used by American AI companies. |
DeepSeek (chatbot) : DeepSeek-V3 uses significantly fewer resources compared to its peers. For example, whereas the world's leading AI companies train their chatbots with supercomputers using as many as 16,000 graphics processing units (GPUs), DeepSeek claims to have needed only about 2,000 GPUs—namely, the H800 series... |
DeepSeek (chatbot) : DeepSeek's success against larger and more established rivals has been described as "upending AI", constituting "the first shot at what is emerging as a global AI space race", and ushering in "a new era of AI brinkmanship". |
DeepSeek (chatbot) : Many countries have raised concerns about data security and DeepSeek's use of personal data. On 28 January 2025, the Italian data protection authority announced that it is seeking additional information on DeepSeek's collection and use of personal data. On the same day, the United States National S... |
Grammar induction : Grammar induction (or grammatical inference) is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which acc... |
Grammar induction : Grammatical inference has often been very focused on the problem of learning finite-state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s. Since the beginning of th... |
Grammar induction : The simplest form of learning is where the learning algorithm merely receives a set of examples drawn from the language in question: the aim is to learn the language from examples of it (and, rarely, from counter-examples, that is, example that do not belong to the language). However, other learning... |
Grammar induction : There is a wide variety of methods for grammatical inference. Two of the classic sources are Fu (1977) and Fu (1982). Duda, Hart & Stork (2001) also devote a brief section to the problem, and cite a number of references. The basic trial-and-error method they present is discussed below. For approache... |
Grammar induction : The principle of grammar induction has been applied to other aspects of natural language processing, and has been applied (among many other problems) to semantic parsing, natural language understanding, example-based translation, language acquisition, grammar-based compression, and anomaly detection... |
Grammar induction : Artificial grammar learning#Artificial intelligence Example-based machine translation Inductive programming Kolmogorov complexity Language identification in the limit Straight-line grammar Syntactic pattern recognition |
Grammar induction : Duda, Richard O.; Hart, Peter E.; Stork, David G. (2001), Pattern Classification (2 ed.), New York: John Wiley & Sons Fu, King Sun (1982), Syntactic Pattern Recognition and Applications, Englewood Cliffs, NJ: Prentice-Hall Fu, King Sun (1977), Syntactic Pattern Recognition, Applications, Berlin: Spr... |
Committee machine : A committee machine is a type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts) are combined into a single response. The combined response of the committee machine is supposed to be superior to those of its constituent exper... |
Natural language processing : Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge repres... |
Natural language processing : Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem... |
Natural language processing : Symbolic approach, i.e., the hand-coding of a set of rules for manipulating symbols, coupled with a dictionary lookup, was historically the first approach used both by AI in general and by NLP in particular: such as by writing grammars or devising heuristic rules for stemming. Machine lear... |
Natural language processing : The following is a list of some of the most commonly researched tasks in natural language processing. Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks. Though natural language processing tasks... |
Natural language processing : Based on long-standing trends in the field, it is possible to extrapolate future directions of NLP. As of 2020, three trends among the topics of the long-standing series of CoNLL Shared Tasks can be observed: Interest on increasingly abstract, "cognitive" aspects of natural language (1999–... |
Natural language processing : Media related to Natural language processing at Wikimedia Commons |
Outstar : Outstar is an output from the neurodes of the hidden layer of the neural network architecture which works as an input for output layer. Neurode of hidden layer provides input to neurode of the output layer. == References == |
Reciprocal human machine learning : Reciprocal Human Machine Learning (RHML) is an interdisciplinary approach to designing human-AI interaction systems. RHML aims to enable continual learning between humans and machine learning models by having them learn from each other. This approach keeps the human expert "in the lo... |
Reciprocal human machine learning : RHML emerged in the context of the rise of big data analytics and artificial intelligence for intelligent tasks like sense-making and decision-making. As machine learning advanced to take on more roles, researchers realized fully autonomous systems had limitations and needed human gu... |
Reciprocal human machine learning : RHML has been explored across diverse domains including: Cybersecurity - Software to enable reciprocal learning between experts and AI models for social media threat detection. Organizational decision-making - RHML to structure collaboration between humans and AI systems. Workplace t... |
Linde–Buzo–Gray algorithm : The Linde–Buzo–Gray algorithm (named after its creators Yoseph Linde, Andrés Buzo and Robert M. Gray, who designed it in 1980) is an iterative vector quantization algorithm to improve a small set of vectors (codebook) to represent a larger set of vectors (training set), such that it will be ... |
Linde–Buzo–Gray algorithm : The Linde–Buzo–Gray algorithm may be implemented as follows: algorithm linde-buzo-gray is input: set of training vectors training, codebook to improve old-codebook output: codebook that is twice the size and better or as good as old-codebook new-codebook ← for each old-codevector in old-cod... |
Vanishing gradient problem : In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to their partial deri... |
Vanishing gradient problem : This section is based on the paper On the difficulty of training Recurrent Neural Networks by Pascanu, Mikolov, and Bengio. |
Vanishing gradient problem : To overcome this problem, several methods were proposed. |
Hybrid machine translation : Hybrid machine translation is a method of machine translation that is characterized by the use of multiple machine translation approaches within a single machine translation system. The motivation for developing hybrid machine translation systems stems from the failure of any single techniq... |
Hybrid machine translation : Machine translation Neural machine translation Natural language processing == References == |
Winner-take-all (computing) : Winner-take-all is a computational principle applied in computational models of neural networks by which neurons compete with each other for activation. In the classical form, only the neuron with the highest activation stays active while all other neurons shut down; however, other variati... |
Winner-take-all (computing) : In the theory of artificial neural networks, winner-take-all networks are a case of competitive learning in recurrent neural networks. Output nodes in the network mutually inhibit each other, while simultaneously activating themselves through reflexive connections. After some time, only on... |
Winner-take-all (computing) : A simple, but popular CMOS winner-take-all circuit is shown on the right. This circuit was originally proposed by Lazzaro et al. (1989) using MOS transistors biased to operate in the weak-inversion or subthreshold regime. In the particular case shown there are only two inputs (IIN,1 and II... |
Winner-take-all (computing) : In stereo matching algorithms, following the taxonomy proposed by Scharstein and Szelliski, winner-take-all is a local method for disparity computation. Adopting a winner-take-all strategy, the disparity associated with the minimum or maximum cost value is selected at each pixel. It is axi... |
Winner-take-all (computing) : Self-organizing map Winner-take-all in action selection Zero instruction set computer == References == |
Learning with errors : In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. It is based on the idea of representing secret information as a set of equations with errors. In other words, LWE is a way to hide the value of a secret by introducing... |
Learning with errors : Denote by T = R / Z =\mathbb /\mathbb the additive group on reals modulo one. Let s ∈ Z q n \in \mathbb _^ be a fixed vector. Let ϕ be a fixed probability distribution over T . Denote by A s , ϕ ,\phi the distribution on Z q n × T _^\times \mathbb obtained as follows. Pick a vector ... |
Learning with errors : The LWE problem described above is the search version of the problem. In the decision version (DLWE), the goal is to distinguish between noisy inner products and uniformly random samples from Z q n × T _^\times \mathbb (practically, some discretized version of it). Regev showed that the decisi... |
Learning with errors : The LWE problem serves as a versatile problem used in construction of several cryptosystems. In 2005, Regev showed that the decision version of LWE is hard assuming quantum hardness of the lattice problems G a p S V P γ _ (for γ as above) and S I V P t _ with t = O ( n / α ) ). In 2009, Peike... |
Learning with errors : Post-quantum cryptography Ring learning with errors Lattice-based cryptography Ring learning with errors key exchange Short integer solution (SIS) problem Kyber == References == |
Confabulation (neural networks) : A confabulation, also known as a false, degraded, or corrupted memory, is a stable pattern of activation in an artificial neural network or neural assembly that does not correspond to any previously learned patterns. The same term is also applied to the (nonartificial) neural mistake-m... |
Confabulation (neural networks) : In cognitive science, the generation of confabulatory patterns is symptomatic of some forms of brain trauma. In this, confabulations relate to pathologically induced neural activation patterns depart from direct experience and learned relationships. In computational modeling of such da... |
Confabulation (neural networks) : Confabulation is central to a theory of cognition and consciousness by S. L. Thaler in which thoughts and ideas originate in both biological and synthetic neural networks as false or degraded memories nucleate upon various forms of neuronal and synaptic fluctuations and damage. Such no... |
Confabulation (neural networks) : The term confabulation is also used by Robert Hecht-Nielsen in describing inductive reasoning accomplished via Bayesian networks. Confabulation is used to select the expectancy of the concept that follows a particular context. This is not an Aristotelian deductive process, although it ... |
Neural style transfer : Neural style transfer (NST) refers to a class of software algorithms that manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation. Common us... |
Neural style transfer : NST is an example of image stylization, a problem studied for over two decades within the field of non-photorealistic rendering. The first two example-based style transfer algorithms were image analogies and image quilting. Both of these methods were based on patch-based texture synthesis algori... |
Neural style transfer : This section closely follows the original paper. |
Neural style transfer : In some practical implementations, it is noted that the resulting image has too much high-frequency artifact, which can be suppressed by adding the total variation to the total loss. Compared to VGGNet, AlexNet does not work well for neural style transfer. NST has also been extended to videos. S... |
Lexical Markup Framework : Language resource management – Lexical markup framework (LMF; ISO 24613), produced by ISO/TC 37, is the ISO standard for natural language processing (NLP) and machine-readable dictionary (MRD) lexicons. The scope is standardization of principles and methods relating to language resources in t... |
Lexical Markup Framework : The goals of LMF are to provide a common model for the creation and use of lexical resources, to manage the exchange of data between and among these resources, and to enable the merging of large number of individual electronic resources to form extensive global electronic resources. Types of ... |
Lexical Markup Framework : In the past, lexicon standardization has been studied and developed by a series of projects like GENELEX, EDR, EAGLES, MULTEXT, PAROLE, SIMPLE and ISLE. Then, the ISO/TC 37 National delegations decided to address standards dedicated to NLP and lexicon representation. The work on LMF started i... |
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