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Swish function : SiLU was first proposed alongside the GELU in 2016, then again proposed in 2017 as the Sigmoid-weighted Linear Unit (SiL) in reinforcement learning. The SiLU/SiL was then again proposed as the SWISH over a year after its initial discovery, originally proposed without the learnable parameter β, so that ... |
Swish function : Activation function Gating mechanism == References == |
Natural language : In neuropsychology, linguistics, and philosophy of language, a natural language or ordinary language is any language that occurs naturally in a human community by a process of use, repetition, and change. It can take different forms, typically either a spoken language or a sign language. Natural lang... |
Natural language : Natural language can be broadly defined as different from artificial and constructed languages, e.g. computer programming languages constructed international auxiliary languages non-human communication systems in nature such as whale and other marine mammal vocalizations or honey bees' waggle dance. ... |
Natural language : Controlled natural languages are subsets of natural languages whose grammars and dictionaries have been restricted in order to reduce ambiguity and complexity. This may be accomplished by decreasing usage of superlative or adverbial forms, or irregular verbs. Typical purposes for developing and imple... |
Natural language : Being constructed, International auxiliary languages such as Esperanto and Interlingua are not considered natural languages, with the possible exception of true native speakers of such languages. Natural languages evolve, through fluctuations in vocabulary and syntax, to incrementally improve human c... |
Natural language : Language acquisition – Process in which a first language is being acquired Origin of language – Relationship between language and human evolution Formal semantics (natural language) – Study of meaning in natural languages Whistled language – Emulation of speech by whistling |
Natural language : == References == |
LaMDA : LaMDA (Language Model for Dialogue Applications) is a family of conversational large language models developed by Google. Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following yea... |
LaMDA : LaMDA is a decoder-only Transformer language model. It is pre-trained on a text corpus that includes both documents and dialogs consisting of 1.56 trillion words, and is then trained with fine-tuning data generated by manually annotated responses for "sensibleness, interestingness, and safety". LaMDA was retrie... |
LaMDA : BERT (language model) Chinese room Ethics of artificial intelligence Gemini (language model) Natural language processing Philosophy of artificial intelligence Prompt engineering |
Social data science : Social data science is an interdisciplinary field that addresses social science problems by applying or designing computational and digital methods. As the name implies, Social Data Science is located primarily within the social science, but it relies on technical advances in fields like data scie... |
Social data science : Social data science employs a wide range of quantitative - both established methods in social science as well as new methods developed in computer science and interdisciplinary data science fields such as natural language processing (NLP) and network science. Social Data Science is closely related... |
Social data science : The origin of term "social data science" coincided with the emergence of a number of research centers and degree programs. In 2016, the Copenhagen Center for Social Data Science (SODAS) - the first academic institution using the SDS name - was launched at the University of Copenhagen. The plan for... |
Social data science : Data scientists have played a vital role in the data revolution, both during the original tech-optimist phase where big data and the Internet was seen as the solution to many societal and scientific problems, and as participants in the tech-lash that followed in its wake as result of, among other ... |
Social data science : Social data science research is typically published in multidisciplinary journals, including top general journals Science, Nature, and PNAS, as well as notable specialized journals such as: Nature Human Behaviour Nature Computational Science The Journal of Computational Social Science Big Data and... |
Social data science : There are multiple specific definitions of social data science, but several institutions around the world currently offer degree and research programs under the rubric of Social Data Science. |
Social data science : Social data scientists are in high demand across society, specifically for employers valuing interdisciplinary skills, and can be found working as: 1. Industry Researchers: Typical workplaces: governments, companies and corporations, independent research institutes, foundations, NGOs. Typical titl... |
Social data science : Social data science is still a new field, with developing branches. Broadly speaking the field can be divided into a range of method-based sub-fields: |
International Panel on the Information Environment : The International Panel on the Information Environment (IPIE) is an international consortium of over 250 experts from 55 countries dedicated to providing actionable scientific knowledge on threats to the global information environment. The organization has been compa... |
International Panel on the Information Environment : The first panel was a Scientific Panel on Global Standards on AI Auditing, chaired by Professor Wendy Chun; science and technology policy expert Professor Alondra Nelson served as a member of the panel. At the UN Summit of the Future in September 2024 the IPIE announ... |
International Panel on the Information Environment : The concept was proposed in 2021 during the first Nobel Prize Summit organized by the US National Academy of Sciences and the Nobel Foundation, involving Dr. Sheldon Himelfarb, then head of PeaceTech Lab, and Professor Philip N Howard, a Professor at Oxford Universit... |
International Panel on the Information Environment : The IPIE is currently led by Oxford University Professor Philip N. Howard, who serves a President, and Professor Sebastian Valenzuela from the Catholic University of Chile, who serves as Chief Science Officer. The IPIE Secretariat includes 10 staff based out of Zuric... |
International Panel on the Information Environment : Amelia Arsenault; Sheldon Himelfarb; Susan Abbott (2011), Evaluating media interventions in conflict countries: Toward developing common principles and a community of practice (PDF), United States Institute of Peace, Wikidata Q124692340 Guardian Nigeria (22 May 2023)... |
Operation Serenata de Amor : Operation Serenata de Amor is an artificial intelligence project designed to analyze public spending in Brazil. The project has been funded by a recurrent financing campaign since September 7, 2016, and came in the wake of major scandals of misappropriation of public funds in Brazil, such a... |
Operation Serenata de Amor : Throughout development of the project, new modules have been newly introduced in addition to the main repository: The main repository, serenata-de-amor, serves as the starting point for investigative work. Rosie is the robot programmed to identify public funds expenses with discrepancies, s... |
Operation Serenata de Amor : Operation Serenata de Amor is an Artificial intelligence project for analysis of public expenditures. It was conceived in March 2016 by data scientist Irio Musskopf, sociologist Eduardo Cuducos and entrepreneur Felipe Cabral. The project was financed collectively in the Catarse platform, wh... |
Operation Serenata de Amor : In January 2017, concluding the period financed by the initial campaign, the group carried out an investigation into the suspicious activities found by the data analysis system. 629 complaints were made to the Ombudsman's Office of the Chamber of Deputies, questioning expenses of 216 federa... |
Operation Serenata de Amor : Internet activism List of scandals in Brazil Open government |
Operation Serenata de Amor : Operation Serenata de Amor official website Github repository |
Manus (AI agent) : Manus (hands in Latin) is an autonomous artificial intelligence agent developed by Chinese startup company Monica. The agent is designed to independently carry out complex online tasks without direct/continuous human guidance. |
Manus (AI agent) : Manus was founded to create artificial intelligence agents capable of operating independently, based on large language models (LLM). The official launch of Manus on March 6, 2025, drew international attention. Experts and media described Manus as a major advance because it could autonomously handle c... |
Manus (AI agent) : Manus is claimed as a fully autonomous AI agent, designed to handle tasks like website creation, stock analysis, travel planning, and schedule management. It has demonstrated performance on the GAIA benchmark, a test of real-world problem-solving skills, with reports indicating a score around 86.5%, ... |
Socially assistive robot : A socially assistive robot (SAR) aids users through social engagement and support rather than through physical tasks and interactions. |
Socially assistive robot : The field of socially assistive robotics emerged in the early 2000s, following the emergence of the field of social robots. In contrast to social robots, SARs aid users with specific goals related to behavior change rather than serving as purely social entities. The term "Socially assistive r... |
Socially assistive robot : SARs rely on artificial intelligence (AI) to generate real-time, responsive, natural, and meaningful robot behaviors during interactions with humans. The robots employ various forms of communication, such as facial expressions, gestures, body movements, and speech. In contrast to robots inten... |
Socially assistive robot : SARs have been developed and validated in a wide array of applications, including healthcare, elder care, education, and training. For example, SARs have been developed to support children on the autism spectrum in acquiring and practicing social and cognitive skills, to motivate and coach st... |
Flux (machine-learning framework) : Flux is an open-source machine-learning software library and ecosystem written in Julia. Its current stable release is v0.15.0 . It has a layer-stacking-based interface for simpler models, and has a strong support on interoperability with other Julia packages instead of a monolithic ... |
Flux (machine-learning framework) : Differentiable programming Comparison of deep-learning software == References == |
Ablation (artificial intelligence) : In artificial intelligence (AI), particularly machine learning (ML), ablation is the removal of a component of an AI system. An ablation study aims to determine the contribution of a component to an AI system by removing the component, and then analyzing the resultant performance of... |
Ablation (artificial intelligence) : The term is credited to Allen Newell, one of the founders of artificial intelligence, who used it in his 1974 tutorial on speech recognition, published in Newell (1975). The term is by analogy with ablation in biology. The motivation was that, while individual components are enginee... |
Semantic folding : Semantic folding theory describes a procedure for encoding the semantics of natural language text in a semantically grounded binary representation. This approach provides a framework for modelling how language data is processed by the neocortex. |
Semantic folding : Semantic folding theory draws inspiration from Douglas R. Hofstadter's Analogy as the Core of Cognition which suggests that the brain makes sense of the world by identifying and applying analogies. The theory hypothesises that semantic data must therefore be introduced to the neocortex in such a form... |
Semantic folding : Analogous to the structure of the neocortex, Semantic Folding theory posits the implementation of a semantic space as a two-dimensional grid. This grid is populated by context-vectors in such a way as to place similar context-vectors closer to each other, for instance, by using competitive learning p... |
Semantic folding : Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed ... |
Semantic folding : The topological distribution over a two-dimensional grid (outlined above) lends itself to a bitmap type visualization of the semantics of any word or text, where each active semantic feature can be displayed as e.g. a pixel. As can be seen in the images shown here, this representation allows for a di... |
Semantic folding : == References == |
Pattern language (formal languages) : In theoretical computer science, a pattern language is a formal language that can be defined as the set of all particular instances of a string of constants and variables. Pattern Languages were introduced by Dana Angluin in the context of machine learning. |
Pattern language (formal languages) : Given a finite set Σ of constant symbols and a countable set X of variable symbols disjoint from Σ, a pattern is a finite non-empty string of symbols from Σ∪X. The length of a pattern p, denoted by |p|, is just the number of its symbols. The set of all patterns containing exactly n... |
Pattern language (formal languages) : The problem of deciding whether s ∈ L(p) for an arbitrary string s ∈ Σ+ and pattern p is NP-complete (see picture), and so is hence the problem of deciding p ≤ q for arbitrary patterns p, q. The class of pattern languages is not closed under ... union: e.g. for Σ = as above, L(01)... |
Pattern language (formal languages) : In a refined Chomsky hierarchy, the class of pattern languages is a proper superclass and subclass of the singleton and the indexed languages, respectively, but incomparable to the language classes in between; due to the latter, the pattern language class is not explicitly shown in... |
Pattern language (formal languages) : Given a sample set S of strings, a pattern p is called descriptive of S if S ⊆ L(p), but not S ⊆ L(q) ⊂ L(p) for any other pattern q. Given any sample set S, a descriptive pattern for S can be computed by enumerating all patterns (up to variable renaming) not longer than the shorte... |
Stability (learning theory) : Stability, also known as algorithmic stability, is a notion in computational learning theory of how a machine learning algorithm output is changed with small perturbations to its inputs. A stable learning algorithm is one for which the prediction does not change much when the training data... |
Stability (learning theory) : A central goal in designing a machine learning system is to guarantee that the learning algorithm will generalize, or perform accurately on new examples after being trained on a finite number of them. In the 1990s, milestones were reached in obtaining generalization bounds for supervised l... |
Stability (learning theory) : Early 1900s - Stability in learning theory was earliest described in terms of continuity of the learning map L , traced to Andrey Nikolayevich Tikhonov. 1979 - Devroye and Wagner observed that the leave-one-out behavior of an algorithm is related to its sensitivity to small changes in the... |
Stability (learning theory) : We define several terms related to learning algorithms training sets, so that we can then define stability in multiple ways and present theorems from the field. A machine learning algorithm, also known as a learning map L , maps a training data set, which is a set of labeled examples ( x ... |
Stability (learning theory) : From Bousquet and Elisseeff (02): For symmetric learning algorithms with bounded loss, if the algorithm has Uniform Stability with the probabilistic definition above, then the algorithm generalizes. Uniform Stability is a strong condition which is not met by all algorithms but is, surprisi... |
Stability (learning theory) : This is a list of algorithms that have been shown to be stable, and the article where the associated generalization bounds are provided. Linear regression k-NN classifier with a loss function. Support Vector Machine (SVM) classification with a bounded kernel and where the regularizer is a... |
The Master Algorithm : The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015. Domingos wrote the book in order to generate interest from people outside the field. |
The Master Algorithm : The book outlines five approaches of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem and analogical modelling. The author explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natur... |
The Master Algorithm : In 2016 Bill Gates recommended the book, alongside Nick Bostrom's Superintelligence, as one of two books everyone should read to understand AI. In 2018 the book was noted to be on Chinese Communist Party general secretary Xi Jinping's bookshelf. |
The Master Algorithm : https://www.wsj.com/articles/the-sum-of-human-knowledge-1442610803 http://www.kdnuggets.com/2015/09/book-master-algorithm-pedro-domingos.html http://www.kdnuggets.com/2014/08/interview-pedro-domingos-master-algorithm-new-deep-learning.html (interview) |
BERT (language model) : Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improv... |
BERT (language model) : BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules: Tokenizer: This module converts a piece of English text into a sequence of integers ("tokens"). Embedding: This module converts the sequence of tokens into an array of real-valued vectors representin... |
BERT (language model) : Language models like ELMo, GPT-2, and BERT, spawned the study of "BERTology", which attempts to interpret what is learned by these models. Their performance on these natural language understanding tasks are not yet well understood. Several research publications in 2018 and 2019 focused on invest... |
BERT (language model) : BERT was originally published by Google researchers Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, generative pre-training, ELMo, and ULMFit. Unlike previous m... |
BERT (language model) : The BERT models were influential and inspired many variants. RoBERTa (2019) was an engineering improvement. It preserves BERT's architecture (slightly larger, at 355M parameters), but improves its training, changing key hyperparameters, removing the next-sentence prediction task, and using much ... |
BERT (language model) : Rogers, Anna; Kovaleva, Olga; Rumshisky, Anna (2020). "A Primer in BERTology: What we know about how BERT works". arXiv:2002.12327 [cs.CL]. |
BERT (language model) : Official GitHub repository |
Empirical Methods in Natural Language Processing : Empirical Methods in Natural Language Processing (EMNLP) is a leading conference in the area of natural language processing and artificial intelligence. Along with the Association for Computational Linguistics (ACL) and the North American Chapter of the Association for... |
Empirical Methods in Natural Language Processing : EMNLP 2025, Suzhou, China EMNLP 2024, Miami, Florida, United States EMNLP 2023, Singapore EMNLP 2022, Abu Dhabi, United Arab Emirates (Hybrid) EMNLP 2021, Punta Cana, Dominican Republic or online EMNLP 2020, Punta Cana, Dominican Republic (Virtual conference due to COV... |
Cross-language information retrieval : Cross-language information retrieval (CLIR) is a subfield of information retrieval dealing with retrieving information written in a language different from the language of the user's query. The term "cross-language information retrieval" has many synonyms, of which the following a... |
Cross-language information retrieval : EXCLAIM (EXtensible Cross-Linguistic Automatic Information Machine) CLEF (Conference and Labs of the Evaluation Forum, formerly known as Cross-Language Evaluation Forum) |
Cross-language information retrieval : A resource page for CLIR A search engine for CLIR |
Reparameterization trick : The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization. It allows for the efficient computation of gradients through random var... |
Reparameterization trick : Let z be a random variable with distribution q ϕ ( z ) (z) , where ϕ is a vector containing the parameters of the distribution. |
Reparameterization trick : For some common distributions, the reparameterization trick takes specific forms: Normal distribution: For z ∼ N ( μ , σ 2 ) (\mu ,\sigma ^) , we can use: z = μ + σ ϵ , ϵ ∼ N ( 0 , 1 ) (0,1) Exponential distribution: For z ∼ Exp ( λ ) (\lambda ) , we can use: z = − 1 λ log ( ϵ ) , ϵ ∼ Unifo... |
Reparameterization trick : Variational autoencoder Stochastic gradient descent Variational inference |
Reparameterization trick : Ruiz, Francisco R.; AUEB, Titsias RC; Blei, David (2016). "The Generalized Reparameterization Gradient". Advances in Neural Information Processing Systems. 29. arXiv:1610.02287. Retrieved September 23, 2024. Zhang, Cheng; Butepage, Judith; Kjellstrom, Hedvig; Mandt, Stephan (2019-08-01). "Adv... |
Bidirectional associative memory : Bidirectional associative memory (BAM) is a type of recurrent neural network. BAM was introduced by Bart Kosko in 1988. There are two types of associative memory, auto-associative and hetero-associative. BAM is hetero-associative, meaning given a pattern it can return another pattern ... |
Bidirectional associative memory : A BAM contains two layers of neurons, which we shall denote X and Y. Layers X and Y are fully connected to each other. Once the weights have been established, input into layer X presents the pattern in layer Y, and vice versa. The layers can be connected in both directions (bidirectio... |
Bidirectional associative memory : The memory or storage capacity of BAM may be given as min ( m , n ) , where " n " is the number of units in the X layer and " m " is the number of units in the Y layer. The internal matrix has n x p independent degrees of freedom, where n is the dimension of the first vector (6 in ... |
Bidirectional associative memory : A pair ( A , B ) defines the state of a BAM. To store a pattern, the energy function value for that pattern has to occupy a minimum point in the energy landscape. The stability analysis of a BAM is based on the definition of Lyapunov function (energy function) E , with each state ( ... |
Bidirectional associative memory : Autoassociative memory Self-organizing feature map |
Bidirectional associative memory : Bidirectional Associative Memory – Python source code for the Wiki article Bidirectional associative memories – ACM Portal Reference |
Artificial intelligence : Artificial intelligence (AI) refers to the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies m... |
Artificial intelligence : The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI rese... |
Artificial intelligence : AI research uses a wide variety of techniques to accomplish the goals above. |
Artificial intelligence : AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdS... |
Artificial intelligence : AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of DeepMind hopes to "solve intelligence, and then use that to solve everything else". However, as the use of AI has become widespread, several unintended co... |
Artificial intelligence : The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of m... |
Artificial intelligence : Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines. Another major focus has been whether machines can be conscious, and the associated ethical implications. Many other topics in philosophy are relevant to AI, such as epis... |
Artificial intelligence : Thought-capable artificial beings have appeared as storytelling devices since antiquity, and have been a persistent theme in science fiction. A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works a... |
Artificial intelligence : Artificial consciousness – Field in cognitive science Artificial intelligence and elections – Use and impact of AI on political elections Artificial intelligence content detection – Software to detect AI-generated content Behavior selection algorithm – Algorithm that selects actions for intell... |
Artificial intelligence : "Artificial Intelligence". Internet Encyclopedia of Philosophy. |
Document-term matrix : A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in each document in a collection. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix is a specific instance of a document-feature ma... |
Document-term matrix : When creating a data-set of terms that appear in a corpus of documents, the document-term matrix contains rows corresponding to the documents and columns corresponding to the terms. Each ij cell, then, is the number of times word j occurs in document i. As such, each row is a vector of term count... |
Document-term matrix : The document-term matrix emerged in the earliest years of the computerization of text. The increasing capacity for storing documents created the problem of retrieving a given document in an efficient manner. While previously the work of classifying and indexing was accomplished by hand, researche... |
Document-term matrix : A point of view on the matrix is that each row represents a document. In the vectorial semantic model, which is normally the one used to compute a document-term matrix, the goal is to represent the topic of a document by the frequency of semantically significant terms. The terms are semantic unit... |
Artificial intelligence industry in Italy : The artificial intelligence industry in Italy is growing and supports industrial development. In 2024 it reached a new record, reaching 1.2 billion euros with a growth of +58% compared to 2023. |
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