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Intelligent agent : "Intelligent agent" is also often used as a vague term, sometimes synonymous with "virtual personal assistant". Some 20th-century definitions characterize an agent as a program that aids a user or that acts on behalf of a user. These examples are known as software agents, and sometimes an "intellige... |
Intelligent agent : Hallerbach et al. explored the use of agent-based approaches for developing and validating automated driving systems. Their method involved a digital twin of the vehicle under test and microscopic traffic simulations using independent agents. Waymo has developed a multi-agent simulation environment,... |
Intelligent agent : Domingos, Pedro (September 22, 2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0465065707. Russell, Stuart J.; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach (2nd ed.). Upper Saddle River, New Jersey: Prentic... |
IPO underpricing algorithm : IPO underpricing is the increase in stock value from the initial offering price to the first-day closing price. Many believe that underpriced IPOs leave money on the table for corporations, but some believe that underpricing is inevitable. Investors state that underpricing signals high inte... |
IPO underpricing algorithm : Underwriters and investors and corporations going for an initial public offering (IPO), issuers, are interested in their market value. There is always tension that results since the underwriters want to keep the price low while the companies want a high IPO price. Underpricing may also be c... |
IPO underpricing algorithm : Evolutionary programming is often paired with other algorithms e.g. artificial neural networks to improve the robustness, reliability, and adaptability. Evolutionary models reduce error rates by allowing the numerical values to change within the fixed structure of the program. Designers pro... |
IPO underpricing algorithm : Currently, many of the algorithms assume homogeneous and rational behavior among investors. However, there's an approach alternative to financial modeling, and it's called agent-based modelling (ABM). ABM uses different autonomous agents whose behavior evolves endogenously which lead to com... |
Astrostatistics : Astrostatistics is a discipline which spans astrophysics, statistical analysis and data mining. It is used to process the vast amount of data produced by automated scanning of the cosmos, to characterize complex datasets, and to link astronomical data to astrophysical theory. Many branches of statisti... |
Brain.js : Brain.js is a JavaScript library used for neural networking, which is released as free and open-source software under the MIT License. It can be used in both the browser and Node.js backends. Brain.js is most commonly used as a simple introduction to neural networking, as it hides complex mathematics and has... |
Brain.js : Creating a feedforward neural network with backpropagation: Creating a recurrent neural network: Train the neural network on RGB color contrast: |
Brain.js : Official website brain.js on GitHub == References == |
Velvet AI : Velvet AI is a family of multilingual generative Artificial Intelligence models developed by Almawave, an Italian company specializing in Data & Artificial Intelligence. The Velvet models, including Velvet 14B and Velvet 2B, are foundational large language models (LLMs) designed and developed entirely in It... |
Velvet AI : CINECA Istituto Italiano per l’Intelligenza Artificiale (AI4I) |
Surrogate model : A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint fu... |
Surrogate model : The scientific challenge of surrogate modeling is the generation of a surrogate that is as accurate as possible, using as few simulation evaluations as possible. The process comprises three major steps which may be interleaved iteratively: Sample selection (also known as sequential design, optimal exp... |
Surrogate model : Popular surrogate modeling approaches are: polynomial response surfaces; kriging; more generalized Bayesian approaches; gradient-enhanced kriging (GEK); radial basis function; support vector machines; space mapping; artificial neural networks and Bayesian networks. Other methods recently explored incl... |
Surrogate model : Recently proposed comparison-based surrogate models (e.g., ranking support vector machines) for evolutionary algorithms, such as CMA-ES, allow preservation of some invariance properties of surrogate-assisted optimizers: Invariance with respect to monotonic transformations of the function (scaling) Inv... |
Surrogate model : An important distinction can be made between two different applications of surrogate models: design optimization and design space approximation (also known as emulation). In surrogate model-based optimization, an initial surrogate is constructed using some of the available budgets of expensive experim... |
Surrogate model : Surrogate Modeling Toolbox (SMT: https://github.com/SMTorg/smt) is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of... |
Surrogate model : SAEAs are an advanced class of optimization techniques that integrate evolutionary algorithms (EAs) with surrogate models. In traditional EAs, evaluating the fitness of candidate solutions often requires computationally expensive simulations or experiments. SAEAs address this challenge by building a s... |
Surrogate model : Linear approximation Response surface methodology Kriging Radial basis functions Gradient-enhanced kriging (GEK) OptiY Space mapping Surrogate endpoint Surrogate data Fitness approximation Computer experiment Conceptual model Bayesian regression Bayesian model selection |
Surrogate model : Queipo, N.V., Haftka, R.T., Shyy, W., Goel, T., Vaidyanathan, R., Tucker, P.K. (2005), “Surrogate-based analysis and optimization,” Progress in Aerospace Sciences, 41, 1–28. D. Gorissen, I. Couckuyt, P. Demeester, T. Dhaene, K. Crombecq, (2010), “A Surrogate Modeling and Adaptive Sampling Toolbox for ... |
Surrogate model : Matlab code for surrogate modelling Matlab SUrrogate MOdeling Toolbox – Matlab SUMO Toolbox Surrogate Modeling Toolbox -- Python |
LKB : Linguistic Knowledge Builder (LKB) is a free and open source grammar engineering environment for creating grammars and lexicons of natural languages. Any unification-based grammar can be implemented, but LKB is typically used for grammars with typed feature structures such as HPSG. LKB is free software under the ... |
LKB : DELPH-IN LKB wiki |
Language/action perspective : The language/action perspective "takes language as the primary dimension of human cooperative activity," applied not just in person-to-person direct (face-to-face) interactions, but also in the design of systems mediated by information and communication technology. The perspective was deve... |
Language/action perspective : As part of a reflection published in 2006, Terry Winograd describes the language-action perspective as resting on two key orienting principles: The first is its focus on linguistic communication as the basis for understanding what occurs in information systems. Ultimately all information i... |
Language/action perspective : Research on LAP was done in the Advanced Technology Group (ATG) at Apple Computer in the late 1980s. Winograd was invited to present the basic concepts in a seminar at Apple in the winter of 1988. Some Apple ATG researchers, notably Tom Pittard and Brad Hartfield, saw potential for enhanci... |
Language/action perspective : Insights from related work have been applied over the past two decades. At the LAP 2004 - Conference, Kalle Lyytinen discussed the academic/theoretic success of LAP. Yet, these LAP successes have not found entry into the wider stream of applications. In a sense, LAP is now peripheral to co... |
Language/action perspective : Artificial general intelligence Design & Engineering Methodology for Organizations (DEMO) End-user computing Information science |
Language/action perspective : Terry Winograd and Fernando Flores (1987) Understanding Computers and Cognition: A New Foundation for Design. Reading, MA: Addison-Wesley. |
Language/action perspective : Project Theory Gravitates towards the Language Action Perspective A Language/Action Perspective on the Design of Cooperative Work LAP 2005 - Conference Language/Action Perspective summary in the Association for Information Systems (AIS) theory repository "Conversations for action, commitme... |
Mathematics of artificial neural networks : An artificial neural network (ANN) combines biological principles with advanced statistics to solve problems in domains such as pattern recognition and game-play. ANNs adopt the basic model of neuron analogues connected to each other in a variety of ways. |
Mathematics of artificial neural networks : Neural network models can be viewed as defining a function that takes an input (observation) and produces an output (decision) f : X → Y or a distribution over X or both X and Y . Sometimes models are intimately associated with a particular learning rule. A common use of ... |
Mathematics of artificial neural networks : Backpropagation training algorithms fall into three categories: steepest descent (with variable learning rate and momentum, resilient backpropagation); quasi-Newton (Broyden–Fletcher–Goldfarb–Shanno, one step secant); Levenberg–Marquardt and conjugate gradient (Fletcher–Reeve... |
Mathematics of artificial neural networks : To implement the algorithm above, explicit formulas are required for the gradient of the function w ↦ E ( f N ( w , x ) , y ) (w,x),y) where the function is E ( y , y ′ ) = | y − y ′ | 2 . The learning algorithm can be divided into two phases: propagation and weight update. |
Rule-based machine translation : Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morph... |
Rule-based machine translation : The first RBMT systems were developed in the early 1970s. The most important steps of this evolution were the emergence of the following RBMT systems: Systran Japanese MT systems Today, other common RBMT systems include: Apertium GramTrans |
Rule-based machine translation : There are three different types of rule-based machine translation systems: Direct Systems (Dictionary Based Machine Translation) map input to output with basic rules. Transfer RBMT Systems (Transfer Based Machine Translation) employ morphological and syntactical analysis. Interlingual R... |
Rule-based machine translation : The main approach of RBMT systems is based on linking the structure of the given input sentence with the structure of the demanded output sentence, necessarily preserving their unique meaning. The following example can illustrate the general frame of RBMT: A girl eats an apple. Source L... |
Rule-based machine translation : An ontology is a formal representation of knowledge that includes the concepts (such as objects, processes etc.) in a domain and some relations between them. If the stored information is of linguistic nature, one can speak of a lexicon. In NLP, ontologies can be used as a source of know... |
Rule-based machine translation : The RBMT system contains: a SL morphological analyser - analyses a source language word and provides the morphological information; a SL parser - is a syntax analyser which analyses source language sentences; a translator - used to translate a source language word into the target langua... |
Rule-based machine translation : No bilingual texts are required. This makes it possible to create translation systems for languages that have no texts in common, or even no digitized data whatsoever. Domain independent. Rules are usually written in a domain independent manner, so the vast majority of rules will "just ... |
Rule-based machine translation : Insufficient amount of really good dictionaries. Building new dictionaries is expensive. Some linguistic information still needs to be set manually. It is hard to deal with rule interactions in big systems, ambiguity, and idiomatic expressions. Failure to adapt to new domains. Although ... |
Rule-based machine translation : Arnold, D.J. et al. (1993): Machine Translation: an Introductory Guide Hutchins, W.J. (1986): Machine Translation: Past, Present, Future |
Rule-based machine translation : First International Workshop on Free/Open-Source Rule-Based Machine Translation https://web.archive.org/web/20120306014535/http://www.inf.ed.ac.uk/teaching/courses/mt/lectures/history.pdf https://web.archive.org/web/20150914205051/http://www.csse.unimelb.edu.au/research/lt/nlp06/materia... |
Manually Annotated Sub-Corpus : Manually Annotated Sub-Corpus (MASC) is a balanced subset of 500K words of written texts and transcribed speech drawn primarily from the Open American National Corpus (OANC). The OANC is a 15 million word (and growing) corpus of American English produced since 1990, all of which is in th... |
Manually Annotated Sub-Corpus : Unlike most freely available corpora including a wide variety of linguistic annotations, MASC contains a balanced selection of texts from a broad range of genres: |
Manually Annotated Sub-Corpus : At present, MASC includes seventeen different types of linguistic annotation (* = in production; ** currently available in original format only): All MASC annotations, whether contributed or produced in-house, are transduced to the Graph Annotation Format (GrAF) defined by ISO TC37 SC4's... |
Manually Annotated Sub-Corpus : MASC is an open data resource that can be used by anyone for any purpose. At the same time, it is a collaborative community resource that is sustained by community contributions of annotations and derived data. It is freely downloadable from the MASC download page or through the Linguist... |
Manually Annotated Sub-Corpus : Ide, N., Baker, C., Fellbaum, C., Passonneau, R. (2010). The Manually Annotated Sub-Corpus: A Community Resource For and By the People. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden. Passonneau, R., Baker, C., Fellbaum, C., Ide, ... |
Manually Annotated Sub-Corpus : MASC website American National Corpus website |
Conditional random field : Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take conte... |
Conditional random field : CRFs are a type of discriminative undirected probabilistic graphical model. Lafferty, McCallum and Pereira define a CRF on observations X and random variables Y as follows: Let G = ( V , E ) be a graph such that Y = ( Y v ) v ∈ V =(_)_ , so that Y is indexed by the vertices of G . Then (... |
Conditional random field : Hammersley–Clifford theorem Maximum entropy Markov model (MEMM) |
Conditional random field : McCallum, A.: Efficiently inducing features of conditional random fields. In: Proc. 19th Conference on Uncertainty in Artificial Intelligence. (2003) Wallach, H.M.: Conditional random fields: An introduction. Technical report MS-CIS-04-21, University of Pennsylvania (2004) Sutton, C., McCallu... |
TidyTuesday : TidyTuesday, also noted as Tidy Tuesday, tidytuesday, or #tidytuesday, is a weekly community of practice that is currently organized by the Data Science Learning Community (DSLC). A new data set is highlighted each week for participants to practice exploring, visualizing, and sharing findings. Participant... |
TidyTuesday : TidyTuesday was started by Tom Mock, a product manager at Posit PBC, on April 1, 2018. The motivations to create this was for newcomers to data and more experienced data scientists to feel less socially isolated and a means to practice skills like acquiring, cleaning, wrangling, visualizing and presenting... |
TidyTuesday : TidyTuesday has also been used by other groups or features published data. R-Ladies Global have used TidyTuesday datasets as a hackathon to practice data skills. In February 2021, Allen Hillery, Athony Starks, and Sekou Tyler, started the #DuboisChallenge. This challenge had participants use modern data v... |
TidyTuesday : Official website GitHub page Python TidyTuesday - GitHub Data Science Learning Community (DSLC) |
Artificial intelligence art : Artificial intelligence art is visual artwork created or enhanced through the use of artificial intelligence (AI) programs. Artists began to create artificial intelligence art in the mid to late 20th century when the discipline was founded. Throughout its history, artificial intelligence a... |
Artificial intelligence art : In addition to the creation of original art, research methods that use AI have been generated to quantitatively analyze digital art collections. This has been made possible due to the large-scale digitization of artwork in the past few decades. According to CETINIC and SHE (2022), using ar... |
Artificial intelligence art : AI has also been used in arts outside of visual arts. Generative AI has been used in video game production beyond imagery, especially for level design (e.g., for custom maps) and creating new content (e.g., quests or dialogue) or interactive stories in video games. AI has also been used in... |
Generative model : In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished, followin... |
Generative model : An alternative division defines these symmetrically as: a generative model is a model of the conditional probability of the observable X, given a target y, symbolically, P ( X ∣ Y = y ) a discriminative model is a model of the conditional probability of the target Y, given an observation x, symbolic... |
Generative model : A generative algorithm models how the data was generated in order to categorize a signal. It asks the question: based on my generation assumptions, which category is most likely to generate this signal? A discriminative algorithm does not care about how the data was generated, it simply categorizes a... |
Generative model : With the rise of deep learning, a new family of methods, called deep generative models (DGMs), is formed through the combination of generative models and deep neural networks. An increase in the scale of the neural networks is typically accompanied by an increase in the scale of the training data, bo... |
Generative model : Discriminative model Graphical model |
Generative model : == External links == |
Synthetic media : Synthetic media (also known as AI-generated media, media produced by generative AI, personalized media, personalized content, and colloquially as deepfakes) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the us... |
Synthetic media : Apart from organizational attack, political organizations and leaders are more suffered from such deep fake videos. In 2022, a deep fake was released where Ukraine president was calling for a surrender the fight against Russia. The video shows Ukrainian president telling his soldiers to lay down their... |
Synthetic media : Synthetic media techniques involve generating, manipulating, and altering data to emulate creative processes on a much faster and more accurate scale. As a result, the potential uses are as wide as human creativity itself, ranging from revolutionizing the entertainment industry to accelerating the res... |
Synthetic media : Must-read: Synthetic Illustration Algorithmic art Artificial imagination Artificial intelligence art Automated journalism Computational creativity Computer music Cybernetic art DALL-E Deepfakes Generative adversarial network Generative art Generative artificial intelligence GPT-3 Human image synthesis... |
Logical Machine Corporation : Logical Machine Corporation (LOMAC), originally John Peers and Company, later Logical Business Machines, Inc., was a computer company active from the mid-1970s to the 1980s. |
Logical Machine Corporation : John Peers (born 1942) founded Logical Machine Corporation as John Peers and Company in September 1974. The company originally occupied a 4,500-square-foot office in Burlingame, California. The company was Peers' fourth; he had recently sold off Allied Business Systems of London to Trafalg... |
Neural scaling law : In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, and training cost. |
Neural scaling law : In general, a deep learning model can be characterized by four parameters: model size, training dataset size, training cost, and the post-training error rate (e.g., the test set error rate). Each of these variables can be defined as a real number, usually written as N , D , C , L (respectively: pa... |
Life-time of correlation : In probability theory and related fields, the life-time of correlation measures the timespan over which there is appreciable autocorrelation or cross-correlation in stochastic processes. |
Life-time of correlation : The correlation coefficient ρ, expressed as an autocorrelation function or cross-correlation function, depends on the lag-time between the times being considered. Typically such functions, ρ(t), decay to zero with increasing lag-time, but they can assume values across all levels of correlatio... |
Knowledge level : In artificial intelligence, knowledge-based agents draw on a pool of logical sentences to infer conclusions about the world. At the knowledge level, we only need to specify what the agent knows and what its goals are; a logical abstraction separate from details of implementation. This notion of knowle... |
Knowledge level : Knowledge level modeling Knowledge relativity |
Knowledge level : T. Menzies. Applications of Abduction: Knowledge-Level Modeling. November 1996. A. Newell. The Knowledge Level. Artificial Intelligence, 18(1):87-127, 1982. |
Random neural network : The random neural network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals. It was invented by Erol Gelenbe and is linked to the G-network model of queueing networks as well as to Gene Regulatory Network models. Each cell stat... |
Random neural network : Linear-nonlinear-Poisson cascade model |
Random neural network : References Sources E. Gelenbe, Random neural networks with negative and positive signals and product form solution, Neural Computation, vol. 1, no. 4, pp. 502–511, 1989. E. Gelenbe, Stability of the random neural network model, Neural Computation, vol. 2, no. 2, pp. 239–247, 1990. E. Gelenbe, A.... |
Bidirectional recurrent neural networks : Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. With this form of generative deep learning, the output layer can get information from past (backwards) and future (forward) states simultaneously. Invented in 199... |
Bidirectional recurrent neural networks : The principle of BRNN is to split the neurons of a regular RNN into two directions, one for positive time direction (forward states), and another for negative time direction (backward states). Those two states' output are not connected to inputs of the opposite direction states... |
Bidirectional recurrent neural networks : BRNNs can be trained using similar algorithms to RNNs, because the two directional neurons do not have any interactions. However, when back-propagation through time is applied, additional processes are needed because updating input and output layers cannot be done at once. Gene... |
Bidirectional recurrent neural networks : Applications of BRNN include : Speech Recognition (Combined with Long short-term memory) Translation Handwritten Recognition Industrial Soft sensor Protein Structure Prediction Part-of-speech tagging Dependency Parsing Entity Extraction |
Bidirectional recurrent neural networks : [1] Implementation of BRNN/LSTM in Python with Theano |
Text Retrieval Conference : The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activit... |
Text Retrieval Conference : Encourage retrieval search based on large text collections Increase communication among industry, academia, and government by creating an open forum for the exchange of research ideas Speed the transfer of technology from research labs into commercial products by demonstrating substantial im... |
Text Retrieval Conference : TREC defines relevance as: "If you were writing a report on the subject of the topic and would use the information contained in the document in the report, then the document is relevant." Most TREC retrieval tasks use binary relevance: a document is either relevant or not relevant. Some TREC... |
Text Retrieval Conference : In 1992 TREC-1 was held at NIST. The first conference attracted 28 groups of researchers from academia and industry. It demonstrated a wide range of different approaches to the retrieval of text from large document collections .Finally TREC1 revealed the facts that automatic construction of ... |
Text Retrieval Conference : NIST claims that within the first six years of the workshops, the effectiveness of retrieval systems approximately doubled. The conference was also the first to hold large-scale evaluations of non-English documents, speech, video and retrieval across languages. Additionally, the challenges h... |
Text Retrieval Conference : The conference is made up of a varied, international group of researchers and developers. In 2003, there were 93 groups from both academia and industry from 22 countries participating. |
Text Retrieval Conference : List of computer science awards |
Text Retrieval Conference : TREC website at NIST TIPSTER The TREC book (at Amazon) |
Labeled data : Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio ... |
Labeled data : In 2006, Fei-Fei Li, the co-director of the Stanford Human-Centered AI Institute, initiated research to improve the artificial intelligence models and algorithms for image recognition by significantly enlarging the training data. The researchers downloaded millions of images from the World Wide Web and a... |
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