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MeaningCloud : Software as a service Natural language processing Computational linguistics Text mining Media monitoring Social media measurement Semantic publishing Semantic technology |
MeaningCloud : MeaningCloud website |
Statistical learning theory : Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to suc... |
Statistical learning theory : The goals of learning are understanding and prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood. Super... |
Statistical learning theory : Take X to be the vector space of all possible inputs, and Y to be the vector space of all possible outputs. Statistical learning theory takes the perspective that there is some unknown probability distribution over the product space Z = X × Y , i.e. there exists some unknown p ( z ) = p... |
Statistical learning theory : The choice of loss function is a determining factor on the function f S that will be chosen by the learning algorithm. The loss function also affects the convergence rate for an algorithm. It is important for the loss function to be convex. Different loss functions are used depending on w... |
Statistical learning theory : In machine learning problems, a major problem that arises is that of overfitting. Because learning is a prediction problem, the goal is not to find a function that most closely fits the (previously observed) data, but to find one that will most accurately predict output from future input. ... |
Statistical learning theory : Consider a binary classifier f : X → \to \ . We can apply Hoeffding's inequality to bound the probability that the empirical risk deviates from the true risk to be a Sub-Gaussian distribution. P ( | R ^ ( f ) − R ( f ) | ≥ ϵ ) ≤ 2 e − 2 n ϵ 2 (|(f)-R(f)|\geq \epsilon )\leq 2e^ But genera... |
Statistical learning theory : Reproducing kernel Hilbert spaces are a useful choice for H . Proximal gradient methods for learning Rademacher complexity Vapnik–Chervonenkis dimension == References == |
Probability matching : Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates. Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, then the observer using a probability-mat... |
Probability matching : Duda, Richard O.; Hart, Peter E.; Stork, David G. (2001), Pattern Classification (2nd ed.), New York: John Wiley & Sons Shanks, D. R., Tunney, R. J., & McCarthy, J. D. (2002). A re‐examination of probability matching and rational choice. Journal of Behavioral Decision Making, 15(3), 233-250. |
VACUUM : VACUUM is a set of normative guidance principles for achieving training and test dataset quality for structured datasets in data science and machine learning. The garbage-in, garbage out principle motivates a solution to the problem of data quality but does not offer a specific solution. Unlike the majority of... |
Residual neural network : A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition, and won the ImageNet Large Scale Visual Recognition Chal... |
Residual neural network : Originally, ResNet was designed for computer vision. All transformer architectures include residual connections. Indeed, very deep transformers cannot be trained without them. The original ResNet paper made no claim on being inspired by biological systems. However, later research has related R... |
Supermind AI : Supermind is a state-funded Chinese artificial intelligence platform that tracks scientists and researchers internationally. The platform is the flagship project of Shenzhen's International Science and Technology Information Center. It mines data from science and technology databases such as Springer, Wi... |
Graph neural network : Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular drug design. Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form t... |
Graph neural network : The architecture of a generic GNN implements the following fundamental layers: Permutation equivariant: a permutation equivariant layer maps a representation of a graph into an updated representation of the same graph. In the literature, permutation equivariant layers are implemented via pairwise... |
Graph neural network : Message passing layers are permutation-equivariant layers mapping a graph into an updated representation of the same graph. Formally, they can be expressed as message passing neural networks (MPNNs). Let G = ( V , E ) be a graph, where V is the node set and E is the edge set. Let N u be the n... |
Graph neural network : Local pooling layers coarsen the graph via downsampling. We present here several learnable local pooling strategies that have been proposed. For each case, the input is the initial graph is represented by a matrix X of node features, and the graph adjacency matrix A . The output is the new ma... |
Graph neural network : Homophily principle, i.e., nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of Graph Neural Networks (GNNs) over traditional Neural Networks (NNs) on graph-structured data, especially on node-lev... |
Graph neural network : A Gentle Introduction to Graph Neural Networks |
Gender digital divide : Gender digital divide is defined as gender biases coded into technology products, technology sector, and digital skills education. It can refer to women's and other gender identity's use of, and professional development in computing work. The gender digital divide has changed throughout history ... |
Gender digital divide : Education systems are increasingly trying to ensure equitable, inclusive, and high-quality digital skills, education, and training. Though digital skills open pathways to further learning and skills development, women and girls are still being left behind in digital skills education. Globally, d... |
Gender digital divide : Women are less likely to know how to operate a smartphone, navigate the internet, use social media and understand how to safeguard information in digital mediums (abilities that underlie life and work tasks and are relevant to people of all ages) worldwide. There is a gap from the lowest skill p... |
Gender digital divide : In many societies, gender equality does not translate into digital realms and professions. The persistence of growing digital skills gender gaps, even in countries that rank at the top of the World Economic Forum's global gender gap index (reflecting strong gender equality), demonstrates a need ... |
Gender digital divide : Helping women and girls develop digital skills means stronger women, stronger families, stronger communities, stronger economies and better technology. Digital skills are recognized to be essential life skills required for full participation in society. The main benefits for acquiring digital sk... |
Gender digital divide : The digital divide has begun at earlier ages as young adults have lived out their childhoods with personal computers. This has made intervention to prevent further gender divides in the digital realm needed in more early education. Increasing girls' and women's digital skills involves early, var... |
Gender digital divide : Men continue to dominate the technology space, and the disparity serves to perpetuate gender inequalities, as unrecognized bias is replicated and built into algorithms and artificial intelligence (AI). Limited participation of women and girls in the technology sector can stem outward replicating... |
Gender digital divide : AI alignment Artificial intelligence detection software Digital divide Gender disparity in computing Female education Women's empowerment |
Gender digital divide : This article incorporates text from a free content work. Licensed under CC BY-SA 3.0 (license statement/permission). Text taken from Gender-responsive digitalization: A critical component of the COVID-19 response in Africa, FAO, FAO. |
Gender digital divide : This article incorporates text from a free content work. Licensed under CC BY-SA 3.0 IGO. Text taken from I'd blush if I could: closing gender divides in digital skills through education, UNESCO, EQUALS Skills Coalition, UNESCO. UNESCO. |
Cellular neural network : In computer science and machine learning, cellular neural networks (CNN) or cellular nonlinear networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. Typical applications include image proc... |
Cellular neural network : Due to their number and variety of architectures, it is difficult to give a precise definition for a CNN processor. From an architecture standpoint, CNN processors are a system of finite, fixed-number, fixed-location, fixed-topology, locally interconnected, multiple-input, single-output, nonli... |
Cellular neural network : The idea of CNN processors was introduced by Leon Chua and Lin Yang in 1988. In these articles, Chua and Yang outline the underlying mathematics behind CNN processors. They use this mathematical model to demonstrate, for a specific CNN implementation, that if the inputs are static, the process... |
Cellular neural network : CNN processors could be thought of as a hybrid between ANN and Continuous Automata (CA). |
Cellular neural network : The dynamical behavior of CNN processors can be expressed using differential equations, where each equation represents the state of an individual processing unit. The behavior of the entire CNN processor is defined by its initial conditions, inputs, cell interconnections (topology and weights)... |
Cellular neural network : There are toy models simulating CNN processors using billiard balls, but these are used for theoretical studies. In practice, CNN are physically implemented on hardware and current technologies such as semiconductors. There are plans to migrate CNN processors to emerging technologies in the fu... |
Cellular neural network : CNN researchers have diverse interests, ranging from physical, engineering, theoretical, mathematical, computational, and philosophical applications. |
Cellular neural network : There is an ongoing effort to incorporate CNN processors into sensory-computing-actuating machines as part of the emerging field of Cellular Machines. The basic premise is to create an integrated system that uses CNN processors for the sensory signal-processing and potentially the decision-mak... |
Cellular neural network : The Chua Lectures: A 12-Part Series with Hewlett Packard Labs [1] D. Balya, G, Tímar, G. Cserey, and T. Roska, "A New Computational Model for CNN-UMs and its Computational Complexity", Int’l Workshop on Cellular Neural Networks and Their Applications, 2004. L. Chua and L. Yang, "Cellular Neura... |
Convolutional deep belief network : In computer science, a convolutional deep belief network (CDBN) is a type of deep artificial neural network composed of multiple layers of convolutional restricted Boltzmann machines stacked together. Alternatively, it is a hierarchical generative model for deep learning, which is hi... |
Model collapse : Model collapse is a phenomenon where machine learning models gradually degrade due to errors coming from uncurated training on the outputs of another model, including prior versions of itself. Such outputs are known as synthetic data. Shumailov et al. coined the term and described two specific stages t... |
Model collapse : Using synthetic data as training data can lead to issues with the quality and reliability of the trained model. Model collapse occurs for three main reasons – functional approximation errors, sampling errors, and learning errors. Importantly, it happens in even the simplest of models, where not all of ... |
Model collapse : Some researchers and commentators on model collapse warn that the phenomenon could fundamentally threaten future generative AI development: As AI-generated data is shared on the Internet, it will inevitably end up in future training datasets, which are often crawled from the Internet. If training on "s... |
Model collapse : In the context of large language models, research found that training LLMs on predecessor-generated text — language models are trained on the synthetic data produced by previous models — causes a consistent decrease in the lexical, syntactic, and semantic diversity of the model outputs through successi... |
Model collapse : Generation loss Generative artificial intelligence |
Model collapse : == References == |
AI-complete : In the field of artificial intelligence (AI), tasks that are hypothesized to require artificial general intelligence to solve are informally known as AI-complete or AI-hard. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm. In the past, problems sup... |
AI-complete : The term was coined by Fanya Montalvo by analogy with NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 PhD dissertation and in Eric Raymond's 1991 Jargon File. Expert systems, that were pop... |
AI-complete : AI-complete problems have been hypothesized to include: AI peer review (composite natural language understanding, automated reasoning, automated theorem proving, formalized logic expert system) Bongard problems Computer vision (and subproblems such as object recognition) Natural language understanding (an... |
AI-complete : Computational complexity theory deals with the relative computational difficulty of computable functions. By definition, it does not cover problems whose solution is unknown or has not been characterized formally. Since many AI problems have no formalization yet, conventional complexity theory does not en... |
AI-complete : Roman Yampolskiy suggests that a problem C is AI-Complete if it has two properties: It is in the set of AI problems (Human Oracle-solvable). Any AI problem can be converted into C by some polynomial time algorithm. On the other hand, a problem H is AI-Hard if and only if there is an AI-Complete problem... |
AI-complete : ASR-complete List of unsolved problems in computer science Synthetic intelligence == References == |
Adversarial stylometry : Adversarial stylometry is the practice of altering writing style to reduce the potential for stylometry to discover the author's identity or their characteristics. This task is also known as authorship obfuscation or authorship anonymisation. Stylometry poses a significant privacy challenge in ... |
Adversarial stylometry : Rao & Rohatgi (2000), an early work in adversarial stylometry, identified machine translation as a possibility, but noted that the quality of translators available at the time presented severe challenges. Kacmarcik & Gamon (2006) is another early work. Brennan, Afroz & Greenstadt (2012) perform... |
Adversarial stylometry : Rao & Rohatgi (2000) suggest that short, unattributed documents (i.e., anonymous posts) are not at risk of stylometric identification, but pseudonymous authors who have not practiced adversarial stylometry in producing corpuses of thousands of words may be vulnerable. Narayanan et al. (2012) at... |
Adversarial stylometry : With imitation, the author attempts to mislead stylometry by matching their style to another author's. An incomplete imitation, where some of the true author's unique characteristics appear alongside the imitated author's, can be a detectable signal for the use of adversarial stylometry. Imitat... |
Adversarial stylometry : How to best mask stylometric characteristics in practice, and what tasks to perform manually, what with tool assistance, and what fully automatically, is an open field of research, especially in short documents with limited potential variability. Manual adversarial stylometry can be preferred o... |
Adversarial stylometry : Adversarial machine learning Author profiling De-identification Digital watermarking Online identity management Operational security Steganography |
Adversarial stylometry : Adelani, David; Zhang, Miaoran; Shen, Xiaoyu; Davody, Ali; Kleinbauer, Thomas; Klakow, Dietrich (2021). "Preventing Author Profiling through Zero-Shot Multilingual Back-Translation". Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 8687–8695. arXiv:210... |
POP-11 : POP-11 is a reflective, incrementally compiled programming language with many of the features of an interpreted language. It is the core language of the Poplog programming environment developed originally by the University of Sussex, and recently in the School of Computer Science at the University of Birmingha... |
POP-11 : Here is an example of a simple POP-11 program: define Double(Source) -> Result; Source*2 -> Result; enddefine; Double(123) => That prints out: ** 246 This one includes some list processing: define RemoveElementsMatching(Element, Source) -> Result; lvars Index; [[% for Index in Source do unless Index = Element ... |
POP-11 : COWSEL (aka POP-1) programming language |
POP-11 : R. Burstall, A. Collins and R. Popplestone, Programming in Pop-2 University Press, Edinburgh, 1968 D.J.M. Davies, POP-10 Users' Manual, Computer Science Report #25, University of Western Ontario, 1976 S. Hardy and C. Mellish, 'Integrating Prolog in the Poplog environment', in Implementations of Prolog, Ed., J.... |
POP-11 : Official website, Free Poplog Portal GetPoplog on GitHub Information about POP-11 teaching materials The Poplog.org website (including partial mirror of Free poplog web site) (currently defunct: see its more recent copy (Jun 17, 2008) @ Internet Archive Wayback Machine) An Overview of POP-11 (Primer for experi... |
Wetware computer : A wetware computer is an organic computer (which can also be known as an artificial organic brain or a neurocomputer) composed of organic material "wetware" such as "living" neurons. Wetware computers composed of neurons are different than conventional computers because they use biological materials,... |
Wetware computer : The concept of wetware is an application of specific interest to the field of computer manufacturing. Moore's law, which states that the number of transistors which can be placed on a silicon chip is doubled roughly every two years, has acted as a goal for the industry for decades, but as the size of... |
Wetware computer : The concept of wetware is distinct and unconventional and draws slight resonance with both hardware and software from conventional computers. While hardware is understood as the physical architecture of traditional computational devices, comprising integrated circuits and supporting infrastructure, s... |
Wetware computer : Artificial neural network Chemical computer Quantum computer Unconventional computing Wetware (brain) Biosensor Biological computing Machine olfaction |
Wetware computer : Biological computer born Neurocomputers - computers are far from comparable to human brain (Discover Magazine, September 2000) New material discovered for organic computers Archived 2010-02-01 at the Wayback Machine Wetware: A Computer in Every Living Cell == References == |
OpenAI Codex : OpenAI Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool for select IDEs, like Visual Studio Code and Neovim. Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for u... |
OpenAI Codex : Based on GPT-3, a neural network trained on text, Codex was additionally trained on 159 gigabytes of Python code from 54 million GitHub repositories. A typical use case of Codex is for a user to type a comment, such as "//compute the moving average of an array for a given window size", then use the AI to... |
OpenAI Codex : OpenAI demonstrations showcased flaws such as inefficient code and one-off quirks in code samples. In an interview with The Verge, OpenAI chief technology officer Greg Brockman said that "sometimes [Codex] doesn't quite know exactly what you're asking" and that it can require some trial and error. OpenAI... |
Lexical choice : Lexical choice is the subtask of Natural language generation that involves choosing the content words (nouns, non-auxiliary verbs, adjectives, and adverbs) in a generated text. Function words (determiners, for example) are usually chosen during realisation. |
Lexical choice : The simplest type of lexical choice involves mapping a domain concept (perhaps represented in an ontology) to a word. For example, the concept Finger might be mapped to the word finger. A more complex situation is when a domain concept is expressed using different words in different situations. For exa... |
Lexical choice : Lexical choice modules must be informed by linguistic knowledge of how the system's input data maps onto words. This is a question of semantics, but it is also influenced by syntactic factors (such as collocation effects) and pragmatic factors (such as context). Hence NLG systems need linguistic models... |
Lexical choice : A number of algorithms and models have been developed for lexical choice in the research community, for example Edmonds developed a model for choosing between near-synonyms (words with similar core meanings but different connotations). However such algorithms and models have not been widely used in app... |
Chinchilla (language model) : Chinchilla is a family of large language models (LLMs) developed by the research team at Google DeepMind, presented in March 2022. |
Chinchilla (language model) : It is named "chinchilla" because it is a further development over a previous model family named Gopher. Both model families were trained in order to investigate the scaling laws of large language models. It claimed to outperform GPT-3. It considerably simplifies downstream utilization beca... |
Chinchilla (language model) : Both the Gopher family and Chinchilla family are families of transformer models. In particular, they are essentially the same as GPT-2, with different sizes and minor modifications. Gopher family uses RMSNorm instead of LayerNorm; relative positional encoding rather than absolute positiona... |
Huawei PanGu : Huawei PanGu, PanGu, PanGu-Σ or PanGu-π (Chinese: 盘古大模型; pinyin: pángǔ dà móxíng) is a multimodal large language model developed by Huawei. It was announced on July 7, 2023. The name of the large learning language model, PanGu, was derived from the Chinese mythology and folklore of Pangu, a primordial ch... |
Huawei PanGu : PanGu Large Model 3.0, designed for industry use, was structured with a 5+N+X three-tier architecture. First Layer (L0): Comprises PanGu's five basic large models to provide a variety of capabilities for different industry scenarios. These include Natural Language Processing (NLP) models, Visual models, ... |
Huawei PanGu : Large Language Model Gemini GPT-4 == References == |
Generative AI Copyright Disclosure Act : The Generative AI Copyright Disclosure Act is a piece of legislation introduced by California Representative Adam Schiff in the United States Congress on April 9, 2024. It concerns the transparency of companies regarding their use of copyrighted work to train their generative ar... |
Generative AI Copyright Disclosure Act : The official bill, "Generative AI Copyright Disclosure Act" |
Apple Intelligence : Apple Intelligence is an artificial intelligence system developed by Apple Inc. Relying on a combination of on-device and server processing, it was announced on June 10, 2024, at WWDC 2024, as a built-in feature of Apple's iOS 18, iPadOS 18, and macOS Sequoia, which were announced alongside Apple I... |
Apple Intelligence : Apple Intelligence consists of an on-device model as well as a cloud model running on servers primarily using Apple silicon. Both models consist of a generic foundation model, as well as multiple adapter models that are more specialized to particular tasks like text summarization and tone adjustmen... |
Apple Intelligence : All Macs and iPads with an M-series Apple silicon chip support Apple Intelligence with macOS 15.1 and iPadOS 18.1 and later, respectively. iPhones and iPads with the A17 Pro chip or later are also supported with iOS 18.1 and later Apple claims the less capable Neural Engine of older chips, which is... |
Apple Intelligence : Multimodal large language model – Type of machine learning modelPages displaying short descriptions of redirect targets |
Apple Intelligence : Official website Apple Developer page |
Voice portal : Voice portals are the voice equivalent of web portals, giving access to information through spoken commands and voice responses. Ideally a voice portal could be an access point for any type of information, services, or transactions found on the Internet. Common uses include movie time listings and stock ... |
Voice portal : Voice portals have no dependency on the access device; even low end mobile handsets can access the service. Voice portals talk to users in their local language and there is reduced customer learning required for using voice services compared to Internet/SMS based services. A complex search query that oth... |
Voice portal : Voice is the most natural communication medium, but the information that can be provided is limited compared to visual media. For example, most Internet users try a search term, scan results, then adjust the search term to eliminate irrelevant results. They may take two or three quick iterations to get a... |
Voice portal : Live-agent and Internet-based voice portals are converging, and the range of information they can provide is expanding. Live-agent portals are introducing greater automation through speech recognition and text-to-speech technology, in many cases providing fully automated service, while automated Internet... |
Voice portal : A number of web-based companies are dedicated to providing voice-based access to Internet information to consumers. Quack.com launched its service in March 2000 and has since obtained the first overall voiceportal patent. Quack.com was acquired by AOL in 2000 and relaunched as AOL By Phone later that yea... |
Voice portal : Call avoidance Mobile Search Mobile local search |
Voice portal : Designing the Voice User Interface for Automated Directory Assistance. Amir Mané and Esther Levin 888-TelSurf (beta) Review & Rating | PCMag.com Start-ups dream of a Web that talks VoiceDBC: A semi-automatic tool for writing speech applications. Honours Thesis 2002. Stephen Choularton PhD |
Swish function : The swish function is a family of mathematical function defined as follows: swish β ( x ) = x sigmoid ( β x ) = x 1 + e − β x . _(x)=x\operatorname (\beta x)=. where β can be constant (usually set to 1) or trainable. The swish family was designed to smoothly interpolate between a linear function... |
Swish function : For β = 0, the function is linear: f(x) = x/2. For β = 1, the function is the Sigmoid Linear Unit (SiLU). With β → ∞, the function converges to ReLU. Thus, the swish family smoothly interpolates between a linear function and the ReLU function. Since swish β ( x ) = swish 1 ( β x ) / β _(x)=\operat... |
Swish function : Because swish β ( x ) = swish 1 ( β x ) / β _(x)=\operatorname _(\beta x)/\beta , it suffices to calculate its derivatives for the default case. swish 1 ′ ( x ) = x + sinh ( x ) 4 cosh 2 ( x 2 ) + 1 2 _'(x)=\left(\right)+ so swish 1 ′ ( x ) − 1 2 _'(x)- is odd. swish 1 ″ ( x ) = 1 − ... |
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