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Data preprocessing : Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values... |
Data preprocessing : Online Data Processing Compendium Data preprocessing in predictive data mining. Knowledge Eng. Review 34: e1 (2019) |
Feature engineering : Feature engineering is a preprocessing step in supervised machine learning and statistical modeling which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant information, feature engineering signific... |
Feature engineering : One of the applications of feature engineering has been clustering of feature-objects or sample-objects in a dataset. Especially, feature engineering based on matrix decomposition has been extensively used for data clustering under non-negativity constraints on the feature coefficients. These incl... |
Feature engineering : Feature engineering in machine learning and statistical modeling involves selecting, creating, transforming, and extracting data features. Key components include feature creation from existing data, transforming and imputing missing or invalid features, reducing data dimensionality through methods... |
Feature engineering : Automation of feature engineering is a research topic that dates back to the 1990s. Machine learning software that incorporates automated feature engineering has been commercially available since 2016. Related academic literature can be roughly separated into two types: Multi-relational decision t... |
Feature engineering : The feature store is where the features are stored and organized for the explicit purpose of being used to either train models (by data scientists) or make predictions (by applications that have a trained model). It is a central location where you can either create or update groups of features cre... |
Feature engineering : Feature engineering can be a time-consuming and error-prone process, as it requires domain expertise and often involves trial and error. Deep learning algorithms may be used to process a large raw dataset without having to resort to feature engineering. However, deep learning algorithms still requ... |
Feature engineering : Covariate Data transformation Feature extraction Feature learning Hashing trick Instrumental variables estimation Kernel method List of datasets for machine learning research Scale co-occurrence matrix Space mapping |
Feature engineering : == Further reading == |
CIML community portal : The computational intelligence and machine learning (CIML) community portal is an international multi-university initiative. Its primary purpose is to help facilitate a virtual scientific community infrastructure for all those involved with, or interested in, computational intelligence and machi... |
CIML community portal : The CIML community portal was created to facilitate an online virtual scientific community wherein anyone interested in CIML can share research, obtain resources, or simply learn more. The effort is currently led by Jacek Zurada (principal investigator), with Rammohan Ragade and Janusz Wojtusiak... |
CIML community portal : Jacek M. Zurada, Janusz Wojtusiak, Fahmida Chowdhury, James E. Gentle, Cedric J. Jeannot, and Maciej A. Mazurowski, Computational Intelligence Virtual Community: Framework and Implementation Issues, Proceedings of the IEEE World Congress on Computational Intelligence, Hong Kong, June 1–6, 2008. ... |
CIML community portal : Artificial Intelligence Computational Intelligence Machine Learning National Science Foundation |
Neural gas : Neural gas is an artificial neural network, inspired by the self-organizing map and introduced in 1991 by Thomas Martinetz and Klaus Schulten. The neural gas is a simple algorithm for finding optimal data representations based on feature vectors. The algorithm was coined "neural gas" because of the dynamic... |
Neural gas : Suppose we want to model a probability distribution P ( x ) of data vectors x using a finite number of feature vectors w i , where i = 1 , ⋯ , N . For each time step t Sample data vector x from P ( x ) Compute the distance between x and each feature vector. Rank the distances. Let i 0 be the index... |
Neural gas : A number of variants of the neural gas algorithm exists in the literature so as to mitigate some of its shortcomings. More notable is perhaps Bernd Fritzke's growing neural gas, but also one should mention further elaborations such as the Growing When Required network and also the incremental growing neura... |
Neural gas : To find the ranking i 0 , i 1 , … , i N − 1 ,i_,\ldots ,i_ of the feature vectors, the neural gas algorithm involves sorting, which is a procedure that does not lend itself easily to parallelization or implementation in analog hardware. However, implementations in both parallel software and analog hardware... |
Neural gas : T. Martinetz, S. Berkovich, and K. Schulten. "Neural-gas" Network for Vector Quantization and its Application to Time-Series Prediction. IEEE-Transactions on Neural Networks, 4(4):558–569, 1993. Martinetz, T.; Schulten, K. (1994). "Topology representing networks". Neural Networks. 7 (3): 507–522. doi:10.10... |
Neural gas : DemoGNG.js Javascript simulator for Neural Gas (and other network models) Java Competitive Learning Applications Unsupervised Neural Networks (including Self-organizing map) in Java with source codes. formal description of Neural gas algorithm A GNG and GWR Classifier implementation in Matlab |
Agentic AI : Agentic AI is a class of artificial intelligence that focuses on autonomous systems that can make decisions and perform tasks without human intervention. The independent systems automatically respond to conditions, to produce process results. The field is closely linked to agentic automation, also known as... |
Agentic AI : The core concept of agentic AI is the use of AI agents to perform automated tasks but without human intervention. While robotic process automation (RPA) and AI agents can be programmed to automate specific tasks or support rule-based decisions, the rules are usually fixed. Agentic AI operates independently... |
Agentic AI : Some scholars trace the conceptual roots of agentic AI to Alan Turing's mid-20th century work with machine intelligence and Norbert Wiener's work on feedback systems. The term agent-based process management system (APMS) was used as far back as 1998 to describe the concept of using autonomous agents for bu... |
Agentic AI : Applications using agentic AI include: Software development - AI coding agents can write large pieces of code, and review it. Agents can even perform non-code related tasks such as reverse engineering specifications from code. Customer support automation - AI agents can improve customer service by improvin... |
Agentic AI : Agentic automation, sometimes referred to as agentic process automation, refers to applying agentic AI to generate and operate workflows. In one example, large language models can construct and execute automated (agentic) workflows, reducing or eliminating the need for human intervention. While agentic AI ... |
Is This What We Want? : Is This What We Want? is an album by various artists, released on 25 February 2025 through Virgin Music Group. It consists of silence recorded in recording studios, protesting the use of unlicensed copyrighted work to train artificial intelligence. The track titles form the sentence "The British... |
Is This What We Want? : Rapid progress in AI technology, constituting an AI boom, was brought to widespread public attention in the early 2020s by text-to-image models such as DALL-E, Midjourney, and Stable Diffusion, which were able to generate complex images that convincingly resembled human-made artworks. The prolif... |
Is This What We Want? : Is This What We Want? consists of 12 tracks, each uncredited. 1,000 artists are credited as co-writers, including Kate Bush, Damon Albarn, Tori Amos, Annie Lennox, Pet Shop Boys, Billy Ocean, the Clash, Ed O'Brien, Dan Smith, Jamiroquai, Mystery Jets, Hans Zimmer, Imogen Heap, Yusuf/Cat Stevens,... |
Is This What We Want? : The album debuted at number 38 on the UK Albums Downloads Chart. |
Is This What We Want? : Sleepify by Vulfpeck, an entirely silent album 4'33", a John Cage composition which instructs the performers to remain silent |
Schema-agnostic databases : Schema-agnostic databases or vocabulary-independent databases aim at supporting users to be abstracted from the representation of the data, supporting the automatic semantic matching between queries and databases. Schema-agnosticism is the property of a database of mapping a query issued wit... |
Schema-agnostic databases : The evolution of data environments towards the consumption of data from multiple data sources and the growth in the schema size, complexity, dynamicity and decentralisation (SCoDD) of schemas increases the complexity of contemporary data management. The SCoDD trend emerges as a central data ... |
Schema-agnostic databases : Schema-agnostic queries can be defined as query approaches over structured databases which allow users satisfying complex information needs without the understanding of the representation (schema) of the database. Similarly, Tran et al. defines it as "search approaches, which do not require ... |
Schema-agnostic databases : As of 2016 the concept of schema-agnostic queries has been developed primarily in academia. Most of schema-agnostic query systems have been investigated in the context of Natural Language Interfaces over databases or over the Semantic Web. These works explore the application of semantic pars... |
Arabic Ontology : Arabic Ontology is a linguistic ontology for the Arabic language, which can be used as an Arabic WordNet with ontologically clean content. People use it also as a tree (i.e. classification) of the concepts/meanings of the Arabic terms. It is a formal representation of the concepts that the Arabic term... |
Arabic Ontology : The ontology structure (i.e., data model) is similar to WordNet structure. Each concept in the ontology is given a unique concept identifier (URI), informally described by a gloss, and lexicalized by one or more of synonymous lemma terms. Each term-concept pair is called a sense, and is given a SenseI... |
Arabic Ontology : Concepts in the Arabic Ontology are mapped to synsets in WordNet, as well as to BFO and DOLCE. Terms used in the Arabic Ontology are mapped to lemmas in the LDC's SAMA database. |
Arabic Ontology : The Arabic Ontology can be seen as a next generation of WordNet - or as an ontologically clean Arabic WordNet. It follows the same structure (i.e., data model) as WordNet, and it is fully mapped to WordNet. However, there are critical foundational differences between them: The ontology is benchmarked ... |
Arabic Ontology : The Arabic Ontology can be used in many application domains; such as: Information retrieval, to enrich queries (e.g., in search engines) and improve the quality of the results, i.e. meaningful search rather than string-matching search; Machine translation and word-sense disambiguation, by finding the ... |
Arabic Ontology : The URLs in the Arabic Ontology are designed according to the W3C's Best Practices for Publishing Linked Data, as described in the following URL schemes. This allows one to also explore the whole database like exploring a graph: Ontology Concept: Each concept in the Arabic Ontology has a ConceptID and... |
GPT-4o : GPT-4o ("o" for "omni") is a multilingual, multimodal generative pre-trained transformer developed by OpenAI and released in May 2024. GPT-4o is free, but ChatGPT Plus subscribers have higher usage limits. It can process and generate text, images and audio. Its application programming interface (API) is faster... |
GPT-4o : Multiple versions of GPT-4o were originally secretly launched under different names on Large Model Systems Organization's (LMSYS) Chatbot Arena as three different models. These three models were called gpt2-chatbot, im-a-good-gpt2-chatbot, and im-also-a-good-gpt2-chatbot. On 7 May 2024, OpenAI CEO Sam Altman t... |
GPT-4o : When released in May 2024, GPT-4o achieved state-of-the-art results in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. GPT-4o scored 88.7 on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5 for GPT-4. Unlike GPT-3.5 and ... |
GPT-4o : On July 18, 2024, OpenAI released a smaller and cheaper version, GPT-4o mini. According to OpenAI, its low cost is expected to be particularly useful for companies, startups, and developers that seek to integrate it into their services, which often make a high number of API calls. Its API costs $0.15 per milli... |
GPT-4o : Llama (language model) Apple Intelligence == References == |
Text watermarking : Text watermarking is a technique for embedding hidden information within textual content to verify its authenticity, origin, or ownership. With the rise of generative AI systems using large language models (LLM), there has been significant development focused on watermarking AI-generated text. Poten... |
Text watermarking : Digital watermarking == References == |
Kernel embedding of distributions : In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which a probability distribution is represented as an element of a reproducing kernel Hilbert space (RKHS). A generalization of the indiv... |
Kernel embedding of distributions : Let X denote a random variable with domain Ω and distribution P . Given a symmetric, positive-definite kernel k : Ω × Ω → R the Moore–Aronszajn theorem asserts the existence of a unique RKHS H on Ω (a Hilbert space of functions f : Ω → R equipped with an inner product ⟨ ⋅ , ... |
Kernel embedding of distributions : The expectation of any function f in the RKHS can be computed as an inner product with the kernel embedding: E [ f ( X ) ] = ⟨ f , μ X ⟩ H [f(X)]=\langle f,\mu _\rangle _ In the presence of large sample sizes, manipulations of the n × n Gram matrix may be computationally demanding... |
Kernel embedding of distributions : This section illustrates how basic probabilistic rules may be reformulated as (multi)linear algebraic operations in the kernel embedding framework and is primarily based on the work of Song et al. The following notation is adopted: P ( X , Y ) = joint distribution over random variab... |
Kernel embedding of distributions : In this simple example, which is taken from Song et al., X , Y are assumed to be discrete random variables which take values in the set and the kernel is chosen to be the Kronecker delta function, so k ( x , x ′ ) = δ ( x , x ′ ) . The feature map corresponding to this kernel is ... |
Kernel embedding of distributions : Information Theoretical Estimators toolbox (distribution regression demonstration). |
Symbol level : In knowledge-based systems, agents choose actions based on the principle of rationality to move closer to a desired goal. The agent is able to make decisions based on knowledge it has about the world (see knowledge level). But for the agent to actually change its state, it must use whatever means it has ... |
Symbol level : Knowledge level modeling == References == |
Matchbox Educable Noughts and Crosses Engine : The Matchbox Educable Noughts and Crosses Engine (sometimes called the Machine Educable Noughts and Crosses Engine or MENACE) was a mechanical computer made from 304 matchboxes designed and built by artificial intelligence researcher Donald Michie in 1961. It was designed ... |
Matchbox Educable Noughts and Crosses Engine : Donald Michie (1923–2007) had been on the team decrypting the German Tunny Code during World War II. Fifteen years later, he wanted to further display his mathematical and computational prowess with an early convolutional neural network. Since computer equipment was not ob... |
Matchbox Educable Noughts and Crosses Engine : MENACE was made from 304 matchboxes glued together in an arrangement similar to a chest of drawers. Each box had a code number, which was keyed into a chart. This chart had drawings of tic-tac-toe game grids with various configurations of X, O, and empty squares, correspon... |
Matchbox Educable Noughts and Crosses Engine : MENACE played first, as O, since all matchboxes represented permutations only relevant to the "X" player. To retrieve MENACE's choice of move, the opponent or operator located the matchbox that matched the current game state, or a rotation or mirror image of it. For exampl... |
Matchbox Educable Noughts and Crosses Engine : Donald Michie's MENACE proved that a computer could learn from failure and success to become good at a task. It used what would become core principles within the field of machine learning before they had been properly theorised. For example, the combination of how MENACE s... |
Matchbox Educable Noughts and Crosses Engine : Michie, D.; Chambers, R. A. (1968), "BOXES: An Experiment in Adaptive Control", Machine Intelligence, Edinburgh, UK: Oliver and Boyd, S2CID 18229198 – via Semantic Scholar, Michie and R. A Chambers' paper on the AI implications of BOXES and MENACE. Russell, David W. (2012)... |
Automated machine learning : Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready fo... |
Automated machine learning : In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-process... |
Automated machine learning : Automated machine learning can target various stages of the machine learning process. Steps to automate are: Data preparation and ingestion (from raw data and miscellaneous formats) Column type detection; e.g., Boolean, discrete numerical, continuous numerical, or text Column intent detecti... |
Automated machine learning : There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry". This phrase refers to the issue in machine learning where development relies on manual decisions and biases of exper... |
Automated machine learning : Artificial intelligence Artificial intelligence and elections Neural architecture search Neuroevolution Self-tuning Neural Network Intelligence ModelOps Hyperparameter optimization |
Automated machine learning : "Open Source AutoML Tools: AutoGluon, TransmogrifAI, Auto-sklearn, and NNI". Bizety. 2020-06-16. Ferreira, Luís, et al. "A comparison of AutoML tools for machine learning, deep learning and XGBoost." 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. https://reposit... |
Evolutionary developmental robotics : Evolutionary developmental robotics (evo-devo-robo for short) refers to methodologies that systematically integrate evolutionary robotics, epigenetic robotics and morphogenetic robotics to study the evolution, physical and mental development and learning of natural intelligent syst... |
Evolutionary developmental robotics : Artificial life Cognitive robotics Morphogenetic robotics Developmental robotics Evolutionary robotics == References == |
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 |
Belief–desire–intention model : For popular psychology, the belief–desire–intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental model... |
Belief–desire–intention model : BDI was part of the inspiration behind the BDI software architecture, which Bratman was also involved in developing. Here, the notion of intention was seen as a way of limiting time spent on deliberating about what to do, by eliminating choices inconsistent with current intentions. BDI h... |
Belief–desire–intention model : Bratman, M. E. (1999) [1987]. Intention, Plans, and Practical Reason. CSLI Publications. ISBN 1-57586-192-5. |
Algorithm selection : Algorithm selection (sometimes also called per-instance algorithm selection or offline algorithm selection) is a meta-algorithmic technique to choose an algorithm from a portfolio on an instance-by-instance basis. It is motivated by the observation that on many practical problems, different algori... |
Algorithm selection : Given a portfolio P of algorithms A ∈ P \in , a set of instances i ∈ I and a cost metric m : P × I → R \times \to \mathbb , the algorithm selection problem consists of finding a mapping s : I → P \to from instances I to algorithms P such that the cost ∑ i ∈ I m ( s ( i ) , i ) m(s(i),i) ac... |
Algorithm selection : The algorithm selection problem is mainly solved with machine learning techniques. By representing the problem instances by numerical features f , algorithm selection can be seen as a multi-class classification problem by learning a mapping f i ↦ A \mapsto for a given instance i . Instance feat... |
Algorithm selection : The algorithm selection problem can be effectively applied under the following assumptions: The portfolio P of algorithms is complementary with respect to the instance set I , i.e., there is no single algorithm A ∈ P \in that dominates the performance of all other algorithms over I (see figure... |
Algorithm selection : Algorithm selection is not limited to single domains but can be applied to any kind of algorithm if the above requirements are satisfied. Application domains include: hard combinatorial problems: SAT, Mixed Integer Programming, CSP, AI Planning, TSP, MAXSAT, QBF and Answer Set Programming combinat... |
Algorithm selection : Algorithm Selection Library (ASlib) Algorithm selection literature == References == |
IBM Watsonx : Watsonx is IBM's commercial generative AI and scientific data platform based on cloud. It offers a studio, data store, and governance toolkit. It supports multiple large language models (LLMs) along with IBM's own Granite. The platform is described as an AI tool tailored to companies and which can be cust... |
IBM Watsonx : Watsonx was revealed on May 9, 2023, at the annual Think conference of IBM as a platform that includes multiple services. Just like Watson AI computer with the similar name, Watsonx was named after Thomas J. Watson, IBM's founder and first CEO. On February 13, 2024, Anaconda partnered with IBM to embed it... |
IBM Watsonx : IBM Watson Generative AI Large language model ChatGPT |
IBM Watsonx : Official webpage Official introductory video for watsonx AI Prompt Lab |
AFNLP : AFNLP (Asian Federation of Natural Language Processing Associations) is the organization for coordinating the natural language processing related activities and events in the Asia-Pacific region. |
AFNLP : AFNLP was founded on 4 October 2000. |
AFNLP : ALTA – Australasian Language Technology Association ANLP Japan Association of Natural Language Processing ROCLING Taiwan ROC Computational Linguistics Society SIG-KLC Korea SIG-Korean Language Computing of Korea Information Science Society |
AFNLP : NLPRS: Natural Language Processing Pacific Rim Symposium IRAL: International Workshop on Information Retrieval with Asian Languages PACLING: Pacific Association for Computational Linguistics PACLIC: Pacific Asia Conference on Language, Information and Computation PRICAI: Pacific Rim International Conference on ... |
AFNLP : IJCNLP-04: The 1st International Joint Conference on Natural Language Processing in Hainan Island, China IJCNLP-05: The 2nd International Joint Conference on Natural Language Processing in Jeju Island, Korea IJCNLP-08: The 3rd International Joint Conference on Natural Language Processing in Hyderabad, India ACL... |
AFNLP : http://www.afnlp.org/ |
PHerc. Paris. 4 : PHerc. Paris. 4 is a carbonized scroll of papyrus, dating to the 1st century BC to the 1st century AD. Part of a corpus known as the Herculaneum papyri, it was buried by hot-ash in the Roman city of Herculaneum during the eruption of Mount Vesuvius in 79 AD. It was subsequently discovered in excavatio... |
PHerc. Paris. 4 : The Villa of the Papyri was buried during the eruption of Vesuvius in 79 AD, subjecting the scrolls to temperatures of 310–320 °C, compacting them and converting them to charcoal. The first scrolls were uncovered in 1752, with subsequent excavations uncovering more scrolls. There were attempts to unro... |
PHerc. Paris. 4 : The 20th century yielded progress in the readings of Herculaneum texts utilizing microscopes, digital photography and multispectral filters approaching the usage infrared spectroscopy to gain better clarity of the texts. In 2015, PHerc. Paris. 1 and PHerc. Paris. 4 were studied side by side, with Pari... |
Tensor product network : A tensor product network, in artificial neural networks, is a network that exploits the properties of tensors to model associative concepts such as variable assignment. Orthonormal vectors are chosen to model the ideas (such as variable names and target assignments), and the tensor product of t... |
Quantum artificial life : Quantum artificial life is the application of quantum algorithms with the ability to simulate biological behavior. Quantum computers offer many potential improvements to processes performed on classical computers, including machine learning and artificial intelligence. Artificial intelligence ... |
Quantum artificial life : The growing advancement of quantum computers has led researchers to develop quantum algorithms for simulating life processes. Researchers have designed a quantum algorithm that can accurately simulate Darwinian Evolution. Since the complete simulation of artificial life on quantum computers ha... |
Hierarchical navigable small world : The Hierarchical navigable small world (HNSW) algorithm is a graph-based approximate nearest neighbor search technique used in many vector databases. Nearest neighbor search without an index involves computing the distance from the query to each point in the database, which for larg... |
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