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GPT4-Chan : The release of GPT-4chan to the public caused a lot of reactions and responses from various audiences. On the /pol/ board, the model’s posts and replies attracted a lot of attention and engagement from other users, who were mostly unaware of the model’s identity and nature. Some users praised the model for ... |
Microsoft Copilot : Microsoft Copilot (or simply Copilot) is a generative artificial intelligence chatbot developed by Microsoft. Based on the GPT-4 series of large language models, it was launched in 2023 as Microsoft's primary replacement for the discontinued Cortana. The service was introduced in February 2023 under... |
Microsoft Copilot : In 2019, Microsoft partnered with OpenAI and began investing billions of dollars into the organization. Since then, OpenAI systems have run on an Azure-based supercomputing platform from Microsoft. In September 2020, Microsoft announced that it had licensed OpenAI's GPT-3 exclusively. Others can sti... |
Microsoft Copilot : Tom Warren, a senior editor at The Verge, has noted the conceptual similarity of Copilot and other Microsoft assistant features like Cortana and Clippy. Warren also believes that large language models, as they develop further, could change how users work and collaborate. Rowan Curran, an analyst at ... |
Microsoft Copilot : Tabnine – Coding assistant Tay (chatbot) – Chatbot developed by Microsoft Zo (chatbot) – Chatbot developed by MicrosoftPages displaying short descriptions of redirect targets |
Microsoft Copilot : Official website Media related to Microsoft Copilot at Wikimedia Commons Microsoft Copilot Terms of Use (Archive -- 2024-10-01 -- Wayback Machine, Archive Today, Megalodon, Ghostarchive) Past versions |
Artificial intelligence and elections : As artificial intelligence (AI) has become more mainstream, there is growing concern about how this will influence elections. Potential targets of AI include election processes, election offices, election officials and election vendors. |
Artificial intelligence and elections : Generative AI capabilities allow creation of misleading content. Examples of this include text-to-video, deepfake videos, text-to-image, AI-altered image, text-to-speech, voice cloning, and text-to-text. In the context of an election, a deepfake video of a candidate may propagate... |
Artificial intelligence and elections : AI has begun to be used in election interference by foreign governments. Governments thought to be using AI to interfere in external elections include Russia, Iran and China. Russia was thought to be the most prolific nation targeting the 2024 presidential election with their inf... |
Artificial intelligence and elections : As the use of AI and its associated tools in political campaigning and messaging increases, many ethical concerns have been raised. Campaigns have used AI in a number of ways, including speech writing, fundraising, voter behaviour prediction, fake robocalls and the generation of ... |
Artificial intelligence and elections : Chinese interference in the 2024 United States elections List of elections in 2025 Donald Trump 2024 presidential campaign § Use of artificial intelligence Russian interference in the 2024 United States elections |
Artificial intelligence and elections : "Smashing Security: Keeping the lights on after a ransomware attack" - podcast including discussion on the use of AI in the Indian elections (17m37s - 29m11s). 25 April 2024. |
XLNet : The XLNet was an autoregressive Transformer designed as an improvement over BERT, with 340M parameters and trained on 33 billion words. It was released on 19 June 2019, under the Apache 2.0 license. It achieved state-of-the-art results on a variety of natural language processing tasks, including language modeli... |
XLNet : The main idea of XLNet is to model language autoregressively like the GPT models, but allow for all possible permutations of a sentence. Concretely, consider the following sentence:My dog is cute.In standard autoregressive language modeling, the model would be tasked with predicting the probability of each word... |
XLNet : Two models were released: XLNet-Large, cased: 110M parameters, 24-layer, 1024-hidden, 16-heads XLNet-Base, cased: 340M parameters, 12-layer, 768-hidden, 12-heads. It was trained on a dataset that amounted to 32.89 billion tokens after tokenization with SentencePiece. The dataset was composed of BooksCorpus, and... |
XLNet : BERT (language model) Transformer (machine learning model) Generative pre-trained transformer == References == |
Algorithmic bias : Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Bias can emerge from many factors, including but not limited to the design of ... |
Algorithmic bias : Algorithms are difficult to define, but may be generally understood as lists of instructions that determine how programs read, collect, process, and analyze data to generate output.: 13 For a rigorous technical introduction, see Algorithms. Advances in computer hardware have led to an increased abili... |
Algorithmic bias : Bias can be introduced to an algorithm in several ways. During the assemblage of a dataset, data may be collected, digitized, adapted, and entered into a database according to human-designed cataloging criteria.: 3 Next, programmers assign priorities, or hierarchies, for how a program assesses and so... |
Algorithmic bias : Several problems impede the study of large-scale algorithmic bias, hindering the application of academically rigorous studies and public understanding.: 5 |
Algorithmic bias : A study of 84 policy guidelines on ethical AI found that fairness and "mitigation of unwanted bias" was a common point of concern, and were addressed through a blend of technical solutions, transparency and monitoring, right to remedy and increased oversight, and diversity and inclusion efforts. |
Algorithmic bias : Algorithmic wage discrimination Ethics of artificial intelligence Fairness (machine learning) Hallucination (artificial intelligence) Misaligned goals in artificial intelligence Predictive policing SenseTime |
Algorithmic bias : Baer, Tobias (2019). Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists. New York: Apress. ISBN 9781484248843. Noble, Safiya Umoja (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press. ISBN 97814798372... |
Automatic acquisition of sense-tagged corpora : The knowledge acquisition bottleneck is perhaps the major impediment to solving the word-sense disambiguation (WSD) problem. Unsupervised learning methods rely on knowledge about word senses, which is barely formulated in dictionaries and lexical databases. Supervised lea... |
Automatic acquisition of sense-tagged corpora : Therefore, one of the most promising trends in WSD research is using the largest corpus ever accessible, the World Wide Web, to acquire lexical information automatically. WSD has been traditionally understood as an intermediate language engineering technology which could ... |
Pedagogical agent : A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agen... |
Pedagogical agent : The history of Pedagogical Agents is closely aligned with the history of computer animation. As computer animation progressed, it was adopted by educators to enhance computerized learning by including a lifelike interface between the program and the learner. The first versions of a pedagogical agent... |
Pedagogical agent : It has been suggested by researchers that pedagogical agents may take on different roles in the learning environment. Examples of these roles are: supplanting, scaffolding, coaching, testing, or demonstrating or modelling a procedure. A pedagogical agent as a tutor has not been demonstrated to add a... |
Pedagogical agent : AI: Artificial Intelligence Research at USC Information Science Institute Stanford University: Interactive Animated Pedagogical Agents |
Bioserenity : BioSerenity is a medtech company created in 2014 that develops ambulatory medical devices to help diagnose and monitor patients with chronic diseases such as epilepsy. The medical devices are composed of medical sensors, smart clothing, a smartphone app for Patient Reported Outcome, and a web platform to ... |
Bioserenity : BioSerenity was founded in 2014, by Pierre-Yves Frouin. The company was initially hosted at the ICM Institute (Institute du Cerveau et de la Moëlle épinière), in Paris, France. Fund Raising June 8, 2015 : The company raises a $4 million seed round with Kurma Partners and IdInvest Partners September 20, 20... |
Bioserenity : BioSerenity is one of the Disrupt 100 BioSerenity joined the Next40 BioSerenity was selected by Microsoft and AstraZeneca in their initiative AI Factory for Health BioSerenity accelerated at Stanford's University StartX program |
Bioserenity : Official website FDA Clearance Neuronaute FDA Clearance Cardioskin FDA Clearance Accusom |
Data-driven model : Data-driven models are a class of computational models that primarily rely on historical data collected throughout a system's or process' lifetime to establish relationships between input, internal, and output variables. Commonly found in numerous articles and publications, data-driven models have e... |
Data-driven model : These models have evolved from earlier statistical models, which were based on certain assumptions about probability distributions that often proved to be overly restrictive. The emergence of data-driven models in the 1950s and 1960s coincided with the development of digital computers, advancements ... |
Data-driven model : Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, neural networks for approximating functions, global optimization and evolutionary compu... |
Self-organizing map : A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data. For example, a data ... |
Self-organizing map : Self-organizing maps, like most artificial neural networks, operate in two modes: training and mapping. First, training uses an input data set (the "input space") to generate a lower-dimensional representation of the input data (the "map space"). Second, mapping classifies additional input data us... |
Self-organizing map : The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. This is partly motivated by how visual, auditory or other sensory information is handled in separate parts of the cerebral cortex in the human brain. The weigh... |
Self-organizing map : There are two ways to interpret a SOM. Because in the training phase weights of the whole neighborhood are moved in the same direction, similar items tend to excite adjacent neurons. Therefore, SOM forms a semantic map where similar samples are mapped close together and dissimilar ones apart. This... |
Self-organizing map : Banking system financial analysis Financial investment Project prioritization and selection Seismic facies analysis for oil and gas exploration Failure mode and effects analysis Finding representative data in large datasets representative species for ecological communities representative days for ... |
Self-organizing map : The generative topographic map (GTM) is a potential alternative to SOMs. In the sense that a GTM explicitly requires a smooth and continuous mapping from the input space to the map space, it is topology preserving. However, in a practical sense, this measure of topological preservation is lacking.... |
Self-organizing map : Deep learning Hybrid Kohonen self-organizing map Learning vector quantization Liquid state machine Neocognitron Neural gas Sparse coding Sparse distributed memory Topological data analysis |
Self-organizing map : Kohonen, Teuvo (January 2013). "Essentials of the self-organizing map". Neural Networks. 37: 52–65. doi:10.1016/j.neunet.2012.09.018. PMID 23067803. S2CID 17289060. Kohonen, Teuvo (2001). Self-organizing maps: with 22 tables. Springer Series in Information Sciences (3 ed.). Berlin Heidelberg: Spri... |
Self-organizing map : Media related to Self-organizing maps at Wikimedia Commons |
Preference learning : Preference learning is a subfield of machine learning that focuses on modeling and predicting preferences based on observed preference information. Preference learning typically involves supervised learning using datasets of pairwise preference comparisons, rankings, or other preference informatio... |
Preference learning : The main task in preference learning concerns problems in "learning to rank". According to different types of preference information observed, the tasks are categorized as three main problems in the book Preference Learning: |
Preference learning : There are two practical representations of the preference information A ≻ B . One is assigning A and B with two real numbers a and b respectively such that a > b . Another one is assigning a binary value V ( A , B ) ∈ \,\! for all pairs ( A , B ) denoting whether A ≻ B or B ≻ A . Corresp... |
Preference learning : Preference learning can be used in ranking search results according to feedback of user preference. Given a query and a set of documents, a learning model is used to find the ranking of documents corresponding to the relevance with this query. More discussions on research in this field can be foun... |
Multiple instance learning : In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classifi... |
Multiple instance learning : Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple instance learning (MIL) falls under the supervised learning framework, where every traini... |
Multiple instance learning : Keeler et al., in his work in the early 1990s was the first one to explore the area of MIL. The actual term multi-instance learning was introduced in the middle of the 1990s, by Dietterich et al. while they were investigating the problem of drug activity prediction. They tried to create a l... |
Multiple instance learning : Take image classification for example Amores (2013). Given an image, we want to know its target class based on its visual content. For instance, the target class might be "beach", where the image contains both "sand" and "water". In MIL terms, the image is described as a bag X = ,..,X_\ , ... |
Multiple instance learning : If the space of instances is X , then the set of bags is the set of functions N X = ^=\\rightarrow \mathbb \ , which is isomorphic to the set of multi-subsets of X . For each bag B ∈ N X ^ and each instance x ∈ X , B ( x ) is viewed as the number of times x occurs in B . Let Y be... |
Multiple instance learning : Most of the work on multiple instance learning, including Dietterich et al. (1997) and Maron & Lozano-Pérez (1997) early papers, make the assumption regarding the relationship between the instances within a bag and the class label of the bag. Because of its importance, that assumption is of... |
Multiple instance learning : There are two major flavors of algorithms for Multiple Instance Learning: instance-based and metadata-based, or embedding-based algorithms. The term "instance-based" denotes that the algorithm attempts to find a set of representative instances based on an MI assumption and classify future b... |
Multiple instance learning : So far this article has considered multiple instance learning exclusively in the context of binary classifiers. However, the generalizations of single-instance binary classifiers can carry over to the multiple-instance case. One such generalization is the multiple-instance multiple-label pr... |
Multiple instance learning : Supervised learning Multi-label classification |
Multiple instance learning : Recent reviews of the MIL literature include: Amores (2013), which provides an extensive review and comparative study of the different paradigms, Foulds & Frank (2010), which provides a thorough review of the different assumptions used by different paradigms in the literature. Dietterich, T... |
Cognitive computing : Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition... |
Cognitive computing : At present, there is no widely agreed upon definition for cognitive computing in either academia or industry. In general, the term cognitive computing has been used to refer to new hardware and/or software that mimics the functioning of the human brain (2004). In this sense, cognitive computing is... |
Cognitive computing : Cognitive computing-branded technology platforms typically specialize in the processing and analysis of large, unstructured datasets. |
Cognitive computing : Education Even if cognitive computing can not take the place of teachers, it can still be a heavy driving force in the education of students. Cognitive computing being used in the classroom is applied by essentially having an assistant that is personalized for each individual student. This cogniti... |
Cognitive computing : Cognitive computing in conjunction with big data and algorithms that comprehend customer needs, can be a major advantage in economic decision making. The powers of cognitive computing and artificial intelligence hold the potential to affect almost every task that humans are capable of performing. ... |
Cognitive computing : Automation Affective computing Analytics Artificial intelligence Artificial neural network Brain computer interface Cognitive computer Cognitive reasoning Cognitive science Enterprise cognitive system Semantic Web Social neuroscience Synthetic intelligence Usability Neuromorphic engineering AI acc... |
Cognitive computing : Russell, John (February 15, 2016). "Mapping Out a New Role for Cognitive Computing in Science". HPCwire. Retrieved April 21, 2016. |
Incremental heuristic search : Incremental heuristic search algorithms combine both incremental and heuristic search to speed up searches of sequences of similar search problems, which is important in domains that are only incompletely known or change dynamically. Incremental search has been studied at least since the ... |
Incremental heuristic search : Incremental heuristic search has been extensively used in robotics, where a larger number of path planning systems are based on either D* (typically earlier systems) or D* Lite (current systems), two different incremental heuristic search algorithms. |
Incremental heuristic search : Maxim Likhachev's page Sven Koenig's web page Anthony Stentz's web page |
Cover's theorem : Cover's theorem is a statement in computational learning theory and is one of the primary theoretical motivations for the use of non-linear kernel methods in machine learning applications. It is so termed after the information theorist Thomas M. Cover who stated it in 1965, referring to it as counting... |
Cover's theorem : Let the number of homogeneously linearly separable sets of N points in d dimensions be defined as a counting function C ( N , d ) of the number of points N and the dimensionality d . The theorem states that C ( N , d ) = 2 ∑ k = 0 d − 1 ( N − 1 k ) ^ . It requires, as a necessary and sufficient c... |
Cover's theorem : By induction with the recursive relation C ( N + 1 , d ) = C ( N , d ) + C ( N , d − 1 ) . To show that, with fixed N , increasing d may turn a set of points from non-separable to separable, a deterministic mapping may be used: suppose there are N points. Lift them onto the vertices of the simplex... |
Cover's theorem : The 1965 paper contains multiple theorems. Theorem 6: Let X ∪ = =\left\,x_,\cdots ,x_,y\right\ be in ϕ -general position in d -space, where ϕ = ( ϕ 1 , ϕ 2 , ⋯ , ϕ d ) ,\phi _,\cdots ,\phi _\right) . Then y is ambiguous with respect to C ( N , d − 1 ) dichotomies of X relative to the class of a... |
Cover's theorem : Support vector machine Kernel method |
Cover's theorem : Haykin, Simon (2009). Neural Networks and Learning Machines (Third ed.). Upper Saddle River, New Jersey: Pearson Education Inc. pp. 232–236. ISBN 978-0-13-147139-9. Cover, T.M. (1965). "Geometrical and Statistical properties of systems of linear inequalities with applications in pattern recognition" (... |
Latent semantic mapping : Latent semantic mapping (LSM) is a data-driven framework to model globally meaningful relationships implicit in large volumes of (often textual) data. It is a generalization of latent semantic analysis. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead... |
Latent semantic mapping : Latent semantic analysis |
Latent semantic mapping : Bellegarda, J.R. (2005). "Latent semantic mapping [information retrieval]". IEEE Signal Processing Magazine. 22 (5): 70–80. Bibcode:2005ISPM...22...70B. doi:10.1109/MSP.2005.1511825. S2CID 17327041. J. Bellegarda (2006). "Latent semantic mapping: Principles and applications". ICASSP 2006. Arch... |
Human Problem Solving : Human Problem Solving (1972) is a book by Allen Newell and Herbert A. Simon. |
Human Problem Solving : Problem solving == References == |
Intelligent control : Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms. |
Intelligent control : Intelligent control can be divided into the following major sub-domains: Neural network control Machine learning control Reinforcement learning Bayesian control Fuzzy control Neuro-fuzzy control Expert Systems Genetic control New control techniques are created continuously as new models of intelli... |
Intelligent control : Action selection AI effect Applications of artificial intelligence Artificial intelligence systems integration Function approximation Hybrid intelligent system Lists List of emerging technologies Outline of artificial intelligence |
Intelligent control : Antsaklis, P.J. (1993). Passino, K.M. (ed.). An Introduction to Intelligent and Autonomous Control. Kluwer Academic Publishers. ISBN 0-7923-9267-1. Archived from the original on 10 April 2009. Liu, J.; Wang, W.; Golnaraghi, F.; Kubica, E. (2010). "A Novel Fuzzy Framework for Nonlinear System Contr... |
Intelligent control : Jeffrey T. Spooner, Manfredi Maggiore, Raul Ord onez, and Kevin M. Passino, Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques, John Wiley & Sons, NY; Farrell, J.A., Polycarpou, M.M. (2006). Adaptive Approximation Based Control: Unifying Neural, ... |
Multilayer perceptron : In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear activation functions, organized in layers, notable for being able to distinguish data that is not linearly separable. Modern neural networks are ... |
Multilayer perceptron : In 1943, Warren McCulloch and Walter Pitts proposed the binary artificial neuron as a logical model of biological neural networks. In 1958, Frank Rosenblatt proposed the multilayered perceptron model, consisting of an input layer, a hidden layer with randomized weights that did not learn, and an... |
Multilayer perceptron : Weka: Open source data mining software with multilayer perceptron implementation. Neuroph Studio documentation, implements this algorithm and a few others. |
Computational intelligence : In computer science, computational intelligence (CI) refers to concepts, paradigms, algorithms and implementations of systems that are designed to show "intelligent" behavior in complex and changing environments. These systems are aimed at mastering complex tasks in a wide variety of techni... |
Computational intelligence : Artificial intelligence (AI) is used in the media, but also by some of the scientists involved, as a kind of umbrella term for the various techniques associated with it or with CI. Craenen and Eiben state that attempts to define or at least describe CI can usually be assigned to one or more... |
Computational intelligence : In 1950, Alan Turing, one of the founding fathers of computer science, developed a test for computer intelligence known as the Turing test. In this test, a person can ask questions via a keyboard and a monitor without knowing whether his counterpart is a human or a computer. A computer is c... |
Computational intelligence : The main applications of Computational Intelligence include computer science, engineering, data analysis and bio-medicine. |
Computational intelligence : According to bibliometrics studies, computational intelligence plays a key role in research. All the major academic publishers are accepting manuscripts in which a combination of Fuzzy logic, neural networks and evolutionary computation is discussed. On the other hand, Computational intelli... |
Computational intelligence : IEEE Transactions on Neural Networks and Learning Systems IEEE Transactions on Fuzzy Systems IEEE Transactions on Evolutionary Computation IEEE Transactions on Emerging Topics in Computational Intelligence IEEE Transactions on Autonomous Mental Development IEEE/ACM Transactions on Computati... |
Computational intelligence : Computational Intelligence: An Introduction by Andries Engelbrecht. Wiley & Sons. ISBN 0-470-84870-7 Computational Intelligence: A Logical Approach by David Poole, Alan Mackworth, Randy Goebel. Oxford University Press. ISBN 0-19-510270-3 Computational Intelligence: A Methodological Introduc... |
U-Net : U-Net is a convolutional neural network that was developed for image segmentation. The network is based on a fully convolutional neural network whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation. Segmentation of a 512 × 512 image takes less tha... |
U-Net : The U-Net architecture stems from the so-called "fully convolutional network". The main idea is to supplement a usual contracting network by successive layers, where pooling operations are replaced by upsampling operators. Hence these layers increase the resolution of the output. A successive convolutional laye... |
U-Net : The network consists of a contracting path and an expansive path, which gives it the u-shaped architecture. The contracting path is a typical convolutional network that consists of repeated application of convolutions, each followed by a rectified linear unit (ReLU) and a max pooling operation. During the contr... |
U-Net : There are many applications of U-Net in biomedical image segmentation, such as brain image segmentation (''BRATS'') and liver image segmentation ("siliver07") as well as protein binding site prediction. U-Net implementations have also found use in the physical sciences, for example in the analysis of micrograph... |
U-Net : U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic s... |
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