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Artificial intelligence in fraud detection : Artificial intelligence is used by many different businesses and organizations. It is widely used in the financial sector, especially by accounting firms, to help detect fraud. In 2022, PricewaterhouseCoopers reported that fraud has impacted 46% of all businesses in the worl... |
Deep reinforcement learning : Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowi... |
Deep reinforcement learning : Along with rising interest in neural networks beginning in the mid 1980s, interest grew in deep reinforcement learning, where a neural network is used in reinforcement learning to represent policies or value functions. Because in such a system, the entire decision making process from senso... |
Deep reinforcement learning : Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to l... |
Deep reinforcement learning : Deep reinforcement learning is an active area of research, with several lines of inquiry. |
Deeplearning4j : Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoenc... |
Deeplearning4j : Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics... |
Deeplearning4j : Training with Deeplearning4j occurs in a cluster. Neural nets are trained in parallel via iterative reduce, which works on Hadoop-YARN and on Spark. Deeplearning4j also integrates with CUDA kernels to conduct pure GPU operations, and works with distributed GPUs. |
Deeplearning4j : Deeplearning4j includes an n-dimensional array class using ND4J that allows scientific computing in Java and Scala, similar to the functions that NumPy provides to Python. It's effectively based on a library for linear algebra and matrix manipulation in a production environment. |
Deeplearning4j : DataVec vectorizes various file formats and data types using an input/output format system similar to Hadoop's use of MapReduce; that is, it turns various data types into columns of scalars termed vectors. DataVec is designed to vectorize CSVs, images, sound, text, video, and time series. |
Deeplearning4j : Deeplearning4j includes a vector space modeling and topic modeling toolkit, implemented in Java and integrating with parallel GPUs for performance. It is designed to handle large text sets. Deeplearning4j includes implementations of term frequency–inverse document frequency (tf–idf), deep learning, and... |
Deeplearning4j : Real-world use cases for Deeplearning4j include network intrusion detection and cybersecurity, fraud detection for the financial sector, anomaly detection in industries such as manufacturing, recommender systems in e-commerce and advertising, and image recognition. Deeplearning4j has integrated with ot... |
Deeplearning4j : Deeplearning4j serves machine-learning models for inference in production using the free developer edition of SKIL, the Skymind Intelligence Layer. A model server serves the parametric machine-learning models that makes decisions about data. It is used for the inference stage of a machine-learning work... |
Deeplearning4j : Deeplearning4j is as fast as Caffe for non-trivial image recognition tasks using multiple GPUs. For programmers unfamiliar with HPC on the JVM, there are several parameters that must be adjusted to optimize neural network training time. These include setting the heap space, the garbage collection algor... |
Deeplearning4j : Deeplearning4j can be used via multiple API languages including Java, Scala, Python, Clojure and Kotlin. Its Scala API is called ScalNet. Keras serves as its Python API. And its Clojure wrapper is known as DL4CLJ. The core languages performing the large-scale mathematical operations necessary for deep ... |
Deeplearning4j : Tensorflow, Keras and Deeplearning4j work together. Deeplearning4j can import models from Tensorflow and other Python frameworks if they have been created with Keras. |
Deeplearning4j : Comparison of deep learning software Artificial intelligence Machine learning Deep learning == References == |
Semantic space : 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 in... |
Semantic space : Word embedding Semantic folding Distributional–relational database == References == |
Computational heuristic intelligence : Computational heuristic intelligence (CHI) refers to specialized programming techniques in computational intelligence (also called artificial intelligence, or AI). These techniques have the express goal of avoiding complexity issues, also called NP-hard problems, by using human-li... |
Computational heuristic intelligence : NP-hard Heuristics Computational cybernetics Top-down and bottom-up design == References == |
Spatial neural network : Spatial neural networks (SNNs) constitute a supercategory of tailored neural networks (NNs) for representing and predicting geographic phenomena. They generally improve both the statistical accuracy and reliability of the a-spatial/classic NNs whenever they handle geo-spatial datasets, and also... |
Spatial neural network : Openshaw (1993) and Hewitson et al. (1994) started investigating the applications of the a-spatial/classic NNs to geographic phenomena. They observed that a-spatial/classic NNs outperform the other extensively applied a-spatial/classic statistical models (e.g. regression models, clustering algo... |
Spatial neural network : Spatial statistical models (aka geographically weighted models, or merely spatial models) like the geographically weighted regressions (GWRs), SNNs, etc., are spatially tailored (a-spatial/classic) statistical models, so to learn and model the deterministic components of the spatial variability... |
Spatial neural network : There exist several categories of methods/approaches for designing and applying SNNs. One-Size-Fits-all (OSFA) spatial neural networks, use the OSFA method/approach for globally computing the spatial weights and designing a spatial structure from the originally a-spatial/classic neural networks... |
Spatial neural network : There exist case-study applications of SNNs in: energy for predicting the electricity consumption; agriculture for classifying the vegetation; real estate for appraising the premises. |
ETBLAST : eTBLAST was a free text-similarity service now defunct. It was initially developed by Alexander Pertsemlidis and Harold “Skip” Garner in 2005 at The University of Texas Southwestern Medical Center. It offered access to the following databases: MEDLINE National Institutes of Health (NIH) CRISP Institute of Phy... |
ETBLAST : BLAST (Basic Local Alignment Search Tool) Natural language processing Medical literature retrieval |
ETBLAST : Official website |
Morphological pattern : A morphological pattern is a set of associations and/or operations that build the various forms of a lexeme, possibly by inflection, agglutination, compounding or derivation. The term is used in the domain of lexicons and morphology. |
Morphological pattern : It is important to distinguish the paradigm of a lexeme from a morphological pattern. In the context of an inflecting language, an inflectional morphological pattern is not the explicit list of inflected forms. A morphological pattern usually references a prototypical class of inflectional forms... |
Morphological pattern : lexical markup framework morphology (linguistics) Word formation |
Morphological pattern : Aronoff, Mark (1993). "Morphology by Itself". Cambridge, MA: MIT Press. Comrie, Bernard. (1989). Language Universals and Linguistic Typology; 2nd ed. Chicago: University of Chicago Press. ISBN 0-226-11433-3 (pbk). Matthews, Peter. (1991). Morphology; 2nd ed. Cambridge: Cambridge University Press... |
Deep Tomographic Reconstruction : Deep Tomographic Reconstruction is an area where deep learning methods are used for tomographic reconstruction of medical and industrial images. It is a new frontier of the imaging field by utilizing artificial intelligence and machine learning, especially deep artificial neural networ... |
Deep Tomographic Reconstruction : Traditional tomographic reconstruction relies on analytic methods such as filtered back-projection, or iterative methods which incrementally compute inverse transformations from measurement data (e.g., Radon or Fourier transform data). However, these approaches are unsatisfactory in ch... |
DL Boost : Intel's Deep Learning Boost (DL Boost) is a marketing name for instruction set architecture (ISA) features on the x86-64 designed to improve performance on deep learning tasks such as training and inference. |
DL Boost : DL Boost consists of two sets of features: AVX-512 VNNI, 4VNNIW, or AVX-VNNI: fast multiply-accumulation mainly for convolutional neural networks. AVX-512 BF16: lower-precision bfloat16 floating-point numbers for generally faster computation. Operations provided include conversion to/from float32 and dot pro... |
DL Boost : Deep Learning Boost at Intel Andres Rodrigues et al., "Lower Numerical Precision Deep Learning Inference and Training", Intel White paper [3] Intel and ML (2017), from Intel's Developer Relations Division |
Hyena Model (deep learning) : The Hyena model is a neural network architecture that was developed to address the scalability issues associated with traditional self‐attention mechanisms. It is designed to efficiently handle very long sequences by replacing the quadratic-complexity self‐attention with a sub-quadratic op... |
Hyena Model (deep learning) : Traditional Transformer models rely on self-attention to allow each token in a sequence to interact with every other token. Although this mechanism is highly effective for capturing dependencies, its computational cost scales quadratically ( O ( L 2 ) ) ) with the sequence length L. This q... |
Hyena Model (deep learning) : At the core of the Hyena model is the concept of implicit long convolutions. Traditional convolutions use fixed kernels that are explicitly defined and stored, resulting in a parameter count that scales linearly with the kernel size. In contrast, Hyena generates convolutional filters impli... |
Hyena Model (deep learning) : By replacing the quadratic self-attention mechanism with a sequence of FFT-based convolutions and element-wise multiplications, the Hyena operator achieves an overall time complexity of O ( N L log L ) , where N is the number of recurrence steps. This subquadratic scaling is particular... |
Hyena Model (deep learning) : While Transformer models use self-attention to achieve a global receptive field, this comes at the cost of quadratic complexity with respect to the sequence length. In contrast, the Hyena model achieves a similar global context through its recurrence of long convolutions and gating, but wi... |
Confusion network : A confusion network (sometimes called a word confusion network or informally known as a sausage) is a natural language processing method that combines outputs from multiple automatic speech recognition or machine translation systems. Confusion networks are simple linear directed acyclic graphs with ... |
Question answering : Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language. |
Question answering : A question-answering implementation, usually a computer program, may construct its answers by querying a structured database of knowledge or information, usually a knowledge base. More commonly, question-answering systems can pull answers from an unstructured collection of natural language document... |
Question answering : Question-answering research attempts to develop ways of answering a wide range of question types, including fact, list, definition, how, why, hypothetical, semantically constrained, and cross-lingual questions. Answering questions related to an article in order to evaluate reading comprehension is ... |
Question answering : Two early question answering systems were BASEBALL and LUNAR. BASEBALL answered questions about Major League Baseball over a period of one year. LUNAR answered questions about the geological analysis of rocks returned by the Apollo Moon missions. Both question answering systems were very effective ... |
Question answering : QA systems are used in a variety of applications, including Fact-checking if a fact is verified, by posing a question like: is fact X true or false? customer service, technical support, market research, generating reports or conducting research. |
Question answering : As of 2001, question-answering systems typically included a question classifier module that determined the type of question and the type of answer. Different types of question-answering systems employ different architectures. For example, modern open-domain question answering systems may use a retr... |
Question answering : Question answering is dependent on a good search corpus; without documents containing the answer, there is little any question answering system can do. Larger collections generally mean better question answering performance, unless the question domain is orthogonal to the collection. Data redundanc... |
Question answering : Question answering systems have been extended in recent years to encompass additional domains of knowledge For example, systems have been developed to automatically answer temporal and geospatial questions, questions of definition and terminology, biographical questions, multilingual questions, and... |
Question answering : Dragomir R. Radev, John Prager, and Valerie Samn. Ranking suspected answers to natural language questions using predictive annotation Archived 2011-08-26 at the Wayback Machine. In Proceedings of the 6th Conference on Applied Natural Language Processing, Seattle, WA, May 2000. John Prager, Eric Bro... |
Question answering : Question Answering Evaluation at TREC Question Answering Evaluation at CLEF |
Learning automaton : A learning automaton is one type of machine learning algorithm studied since 1970s. Learning automata select their current action based on past experiences from the environment. It will fall into the range of reinforcement learning if the environment is stochastic and a Markov decision process (MDP... |
Learning automaton : Research in learning automata can be traced back to the work of Michael Lvovitch Tsetlin in the early 1960s in the Soviet Union. Together with some colleagues, he published a collection of papers on how to use matrices to describe automata functions. Additionally, Tsetlin worked on reasonable and c... |
Learning automaton : A learning automaton is an adaptive decision-making unit situated in a random environment that learns the optimal action through repeated interactions with its environment. The actions are chosen according to a specific probability distribution which is updated based on the environment response the... |
Learning automaton : Finite action-set learning automata (FALA) are a class of learning automata for which the number of possible actions is finite or, in more mathematical terms, for which the size of the action-set is finite. |
Learning automaton : Reinforcement learning Game theory Automata theory |
Learning automaton : Philip Aranzulla and John Mellor (Home page): Mellor J and Aranzulla P (2000): "Using an S-Model Response Environment with Learnng [sic] Automata Based Routing Schemes for IP Networks ", Proc. Eighth IFIP Workshop on Performance Modelling and Evaluation of ATM and IP Networks, pp 56/1-56/12, Ilkley... |
Automated medical scribe : Automated medical scribes (also called artificial intelligence scribes, AI scribes, digital scribes, virtual scribes, ambient AI scribes, AI documentation assistants, and digital/virtual/smart clinical assistants) are tools for transcribing medical speech, such as patient consultations and di... |
Automated medical scribe : Some providers unclear about what happens to user data. Some may sell data to third parties. Some explicitly send user data to for-profit tech companies for secondary purposes, which may not be specified. Some require users to sign consents to such reuse of their data. Some ingest user data t... |
Automated medical scribe : Scribes may operate on desktops, laptop, or mobile computers, under a variety of operating systems. These vary in their risks; for instance, mobiles can be lost. The underlying mobile or desktop operating systems are also part of the trusted computing base, and if they are not secure, the sof... |
Automated medical scribe : Like other LLMs, medical-scribe LLMs are prone to confabulation, where they make up content based on statistically associations between their training data and the transcription audio. LLMs do not distinguish between trying to transcribe the audio and guessing what words will come next, but p... |
Automated medical scribe : Professional organizations generally require that scribes be used only with patient consent; some bodies may require written consent. Medics must also abide by local surveillance laws, which may criminalize recording private conversations without consent. Full information on how data is encry... |
Automated medical scribe : The medical scribe market is, as of 2024, highly competitive, with over 50 products on the market. Many of these products are just proprietary wrappers around the same LLM backends, including backends whose designers have warned they are not to be used for critical applications like medicine.... |
Automated medical scribe : With the exception of fully open-source programs, which are free, medical scribe computer programs are rented rather than sold ("software as a service"). Monthly fees vary from mid-two figures to four figures, in US dollars. Some companies run on a freemium model, where a certain number of tr... |
Automated medical scribe : Medical scribe Large Language Model AI hallucinations List of open-source health software == References == |
How Data Happened : How Data Happened: A History from the Age of Reason to the Age of Algorithms is a 2023 non-fiction book written by Columbia University professors Chris Wiggins and Matthew L. Jones. The book explores the history of data and statistics from the end of the 18th century to the present day. |
How Data Happened : The book starts at the end of the 18th century, when European states began tabulating physical resources, and ends at the present day, when algorithms manipulate our personal information as a commodity. It looks at the rise of data and statistics, and how early statistical methods were used to justi... |
How Data Happened : The wild evolution of data science and how to unpack it, book excerpt on Big Think From Eugenics to Targeted Advertising: The Dark Role of Data in Sorting Humanity, book excerpt on Literary Hub |
Meta-learning (computer science) : Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learn... |
Meta-learning (computer science) : A proposed definition for a meta-learning system combines three requirements: The system must include a learning subsystem. Experience is gained by exploiting meta knowledge extracted in a previous learning episode on a single dataset, or from different domains. Learning bias must be ... |
Meta-learning (computer science) : There are three common approaches: using (cyclic) networks with external or internal memory (model-based) learning effective distance metrics (metrics-based) explicitly optimizing model parameters for fast learning (optimization-based). |
Meta-learning (computer science) : Some approaches which have been viewed as instances of meta-learning: Recurrent neural networks (RNNs) are universal computers. In 1993, Jürgen Schmidhuber showed how "self-referential" RNNs can in principle learn by backpropagation to run their own weight change algorithm, which may ... |
Meta-learning (computer science) : Metalearning article in Scholarpedia Vilalta, R.; Drissi, Y. (2002). "A perspective view and survey of meta-learning" (PDF). Artificial Intelligence Review. 18 (2): 77–95. doi:10.1023/A:1019956318069. Giraud-Carrier, C.; Keller, J. (2002). "Meta-Learning". In Meij, J. (ed.). Dealing w... |
Concept mining : Concept mining is an activity that results in the extraction of concepts from artifacts. Solutions to the task typically involve aspects of artificial intelligence and statistics, such as data mining and text mining. Because artifacts are typically a loosely structured sequence of words and other symbo... |
Concept mining : Traditionally, the conversion of words to concepts has been performed using a thesaurus, and for computational techniques the tendency is to do the same. The thesauri used are either specially created for the task, or a pre-existing language model, usually related to Princeton's WordNet. The mappings o... |
Concept mining : Formal concept analysis Information extraction Compound term processing == References == |
ClearForest : ClearForest was an Israeli software company that developed and marketed text analytics and text mining solutions. |
ClearForest : Founded in 1998, ClearForest had its headquarters just outside Boston and a development center in Or Yehuda. The company was acquired by Reuters in April, 2007. It now markets its services under the names Calais, OpenCalais, and OneCalais. ClearForest was previously venture-backed; its last funding round ... |
ClearForest : ClearForest offers several hosted solutions, including: OpenCalais, a free web service and open API (for commercial and non-commercial use) that performs named-entity recognition and enables automatic metadata generation using the ClearForest financial module. Semantic Web Services (SWS), an on-demand ser... |
ClearForest : Economy of Israel |
ClearForest : ClearForest web site ClearForest semantic web services and Gnosis Firefox extension web site |
Hyperdimensional computing : Hyperdimensional computing (HDC) is an approach to computation, particularly Artificial General Intelligence. HDC is motivated by the observation that the cerebellum cortex operates on high-dimensional data representations. In HDC, information is thereby represented as a hyperdimensional (l... |
Hyperdimensional computing : Data is mapped from the input space to sparse HD space under an encoding function φ : X → H. HD representations are stored in data structures that are subject to corruption by noise/hardware failures. Noisy/corrupted HD representations can still serve as input for learning, classification, ... |
Hyperdimensional computing : HDC algebra reveals the logic of how and why systems makes decisions, unlike artificial neural networks. Physical world objects can be mapped to hypervectors, to be processed by the algebra. |
Hyperdimensional computing : HDC is suitable for "in-memory computing systems", which compute and hold data on a single chip, avoiding data transfer delays. Analog devices operate at low voltages. They are energy-efficient, but prone to error-generating noise. HDC's can tolerate such errors. Various teams have develope... |
Hyperdimensional computing : HDC is robust to errors such as an individual bit error (a 0 flips to 1 or vice versa) missed by error-correcting mechanisms. Eliminating such error-correcting mechanisms can save up to 25% of compute cost. This is possible because such errors leave the result "close" to the correct vector.... |
Hyperdimensional computing : A simple example considers images containing black circles and white squares. Hypervectors can represent SHAPE and COLOR variables and hold the corresponding values: CIRCLE, SQUARE, BLACK and WHITE. Bound hypervectors can hold the pairs BLACK and CIRCLE, etc. |
Hyperdimensional computing : High-dimensional space allows many mutually orthogonal vectors. However, If vectors are instead allowed to be nearly orthogonal, the number of distinct vectors in high-dimensional space is vastly larger. HDC uses the concept of distributed representations, in which an object/observation is ... |
Hyperdimensional computing : HDC can combine hypervectors into new hypervectors using well-defined vector space operations. Groups, rings, and fields over hypervectors become the underlying computing structures with addition, multiplication, permutation, mapping, and inverse as primitive computing operations. All compu... |
Hyperdimensional computing : Vector symbolic architectures (VSA) provided a systematic approach to high-dimensional symbol representations to support operations such as establishing relationships. Early examples include holographic reduced representations, binary spatter codes, and matrix binding of additive terms. HD ... |
Hyperdimensional computing : Kleyko, Denis; Rachkovskij, Dmitri A.; Osipov, Evgeny; Rahimi, Abbas (2023-07-31). "A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations". ACM Computing Surveys. 55 (6): 1–40. arXiv:2111.06077. doi:10.1145/3538531. ISSN 0360-0300.... |
Hyperdimensional computing : Kanerva, Pentti (2009-06-01). "Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors". Cognitive Computation. 1 (2): 139–159. doi:10.1007/s12559-009-9009-8. ISSN 1866-9964. S2CID 733980. Neubert, Peer; Schubert, Stefan; P... |
Removal of Sam Altman from OpenAI : On November 17, 2023, OpenAI's board of directors ousted co-founder and chief executive Sam Altman after the board had no confidence in his leadership. The removal was caused by concerns about his handling of artificial intelligence safety, and allegations of abusive behavior. Altman... |
Removal of Sam Altman from OpenAI : The resignation of LinkedIn co-founder Reid Hoffman, venture capitalist Shivon Zilis, and former Republican representative Will Hurd from the board allowed the remaining members to remove Altman. According to Kara Swisher and The Wall Street Journal, Sutskever was instrumental in Alt... |
Removal of Sam Altman from OpenAI : On November 17, 2023, at approximately noon PST, OpenAI's board of directors ousted Altman effective immediately following a "deliberative review process". The board concluded that Altman was not "consistently candid in his communications". Altman was informed of his removal five to ... |
Removal of Sam Altman from OpenAI : Tiger Global Management and Sequoia Capital had attempted to reinstate Altman, according to The Information; Bloomberg News reported that Microsoft and Thrive Capital were seeking for Altman's reinstatement. On November 18, The Verge reported that OpenAI's board of directors discusse... |
Removal of Sam Altman from OpenAI : Edwards, Benj (November 18, 2023). "Details emerge of surprise board coup that ousted CEO Sam Altman at OpenAI". Ars Technica. Retrieved November 18, 2023. Elder, Bryce (November 20, 2023). "How to talk to an elderly relative about Altman, OpenAI and Microsoft". Financial Times. Retr... |
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