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Kórsafn : Nature Manifesto – 2024 sound installation by Björk == References == |
Neuro-symbolic AI : Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant and others, the effective construction of rich comp... |
Neuro-symbolic AI : Approaches for integration are diverse. Henry Kautz's taxonomy of neuro-symbolic architectures follows, along with some examples: Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of ... |
Neuro-symbolic AI : Gary Marcus argues that "...hybrid architectures that combine learning and symbol manipulation are necessary for robust intelligence, but not sufficient", and that there are ...four cognitive prerequisites for building robust artificial intelligence: hybrid architectures that combine large-scale lea... |
Neuro-symbolic AI : Garcez and Lamb described research in this area as ongoing at least since the 1990s. At that time, the terms symbolic and sub-symbolic AI were popular. A series of workshops on neuro-symbolic AI has been held annually since 2005 Neuro-Symbolic Artificial Intelligence. In the early 1990s, an initial ... |
Neuro-symbolic AI : Key research questions remain, such as: What is the best way to integrate neural and symbolic architectures? How should symbolic structures be represented within neural networks and extracted from them? How should common-sense knowledge be learned and reasoned about? How can abstract knowledge that ... |
Neuro-symbolic AI : Implementations of neuro-symbolic approaches include: AllegroGraph: an integrated Knowledge Graph based platform for neuro-symbolic application development. Scallop: a language based on Datalog that supports differentiable logical and relational reasoning. Scallop can be integrated in Python and wit... |
Neuro-symbolic AI : Symbolic AI Connectionist AI Hybrid intelligent systems |
Neuro-symbolic AI : Bader, Sebastian; Hitzler, Pascal (2005-11-10). "Dimensions of Neural-symbolic Integration – A Structured Survey". arXiv:cs/0511042. Garcez, Artur S. d'Avila; Broda, Krysia; Gabbay, Dov M.; Gabbay (2002). Neural-Symbolic Learning Systems: Foundations and Applications. Springer Science & Business Med... |
Neuro-symbolic AI : Artificial Intelligence: Workshop series on Neural-Symbolic Learning and Reasoning |
Sample complexity : The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function. More precisely, the sample complexity is the number of training-samples that we need to supply to the algorithm, so that the function return... |
Sample complexity : Let X be a space which we call the input space, and Y be a space which we call the output space, and let Z denote the product X × Y . For example, in the setting of binary classification, X is typically a finite-dimensional vector space and Y is the set . Fix a hypothesis space H of functio... |
Sample complexity : One can ask whether there exists a learning algorithm so that the sample complexity is finite in the strong sense, that is, there is a bound on the number of samples needed so that the algorithm can learn any distribution over the input-output space with a specified target error. More formally, one ... |
Sample complexity : The latter approach leads to concepts such as VC dimension and Rademacher complexity which control the complexity of the space H . A smaller hypothesis space introduces more bias into the inference process, meaning that E H ∗ _^ may be greater than the best possible risk in a larger space. However,... |
Sample complexity : In addition to the supervised learning setting, sample complexity is relevant to semi-supervised learning problems including active learning, where the algorithm can ask for labels to specifically chosen inputs in order to reduce the cost of obtaining many labels. The concept of sample complexity al... |
Sample complexity : A high sample complexity means that many calculations are needed for running a Monte Carlo tree search. It is equivalent to a model-free brute force search in the state space. In contrast, a high-efficiency algorithm has a low sample complexity. Possible techniques for reducing the sample complexity... |
Sample complexity : Active learning (machine learning) == References == |
Figure AI : Figure AI, Inc. is a United States-based robotics company specializing in the development of AI-powered humanoid robots. It was founded in 2022, by Brett Adcock, the founder of Archer Aviation and Vettery. Figure AI's team is composed of experts from robotics, artificial intelligence, sensing, perception, a... |
Figure AI : In 2022, the company introduced its prototype, Figure 01, a bipedal robot designed for manual labor, initially targeting the logistics and warehousing sectors. In May 2023 the company raised $70 million from investors led by Parkway Venture Capital. On January 18 2024 Figure announced a partnership with BMW... |
Group method of data handling : Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models. GMDH is used in such fields as data mining, knowledge discovery, ... |
Group method of data handling : Like linear regression, which fits a linear equation over data, GMDH fits arbitrarily high orders of polynomial equations over data. To choose between models, two or more subsets of a data sample are used, similar to the train-validation-test split. GMDH combined ideas from: black box mo... |
Group method of data handling : The method was originated in 1968 by Prof. Alexey G. Ivakhnenko in the Institute of Cybernetics in Kyiv. Period 1968–1971 is characterized by application of only regularity criterion for solving of the problems of identification, pattern recognition and short-term forecasting. As referen... |
Group method of data handling : There are many different ways to choose an order for partial models consideration. The very first consideration order used in GMDH and originally called multilayered inductive procedure is the most popular one. It is a sorting-out of gradually complicated models generated from base funct... |
Group method of data handling : Another important approach to partial models consideration that becomes more and more popular is a combinatorial search that is either limited or full. This approach has some advantages against Polynomial Neural Networks, but requires considerable computational power and thus is not effe... |
Group method of data handling : Combinatorial (COMBI) Multilayered Iterative (MIA) GN Objective System Analysis (OSA) Harmonical Two-level (ARIMAD) Multiplicative–Additive (MAA) Objective Computer Clusterization (OCC); Pointing Finger (PF) clusterization algorithm; Analogues Complexing (AC) Harmonical Rediscretization ... |
Group method of data handling : FAKE GAME Project — Open source. Cross-platform. GEvom — Free upon request for academic use. Windows-only. GMDH Shell — GMDH-based, predictive analytics and time series forecasting software. Free Academic Licensing and Free Trial version available. Windows-only. KnowledgeMiner — Commerci... |
Group method of data handling : A.G. Ivakhnenko. Heuristic Self-Organization in Problems of Engineering Cybernetics, Automatica, vol.6, 1970 — p. 207-219. S.J. Farlow. Self-Organizing Methods in Modelling: GMDH Type Algorithms. New-York, Bazel: Marcel Decker Inc., 1984, 350 p. H.R. Madala, A.G. Ivakhnenko. Inductive Le... |
Group method of data handling : Library of GMDH books and articles Group Method of Data Handling |
Category utility : Category utility is a measure of "category goodness" defined in Gluck & Corter (1985) and Corter & Gluck (1992). It attempts to maximize both the probability that two objects in the same category have attribute values in common, and the probability that objects from different categories have differen... |
Category utility : The probability-theoretic definition of category utility given in Fisher (1987) and Witten & Frank (2005) is as follows: C U ( C , F ) = 1 p ∑ c j ∈ C p ( c j ) [ ∑ f i ∈ F ∑ k = 1 m p ( f i k | c j ) 2 − ∑ f i ∈ F ∑ k = 1 m p ( f i k ) 2 ] \sum _\in Cp(c_)\left[\sum _\in F\sum _^p(f_|c_)^-\sum _\in ... |
Category utility : The information-theoretic definition of category utility for a set of entities with size- n binary feature set F = , i = 1 … n \,\ i=1\ldots n , and a binary category C = \ is given in Gluck & Corter (1985) as follows: C U ( C , F ) = [ p ( c ) ∑ i = 1 n p ( f i | c ) log p ( f i | c ) + p ( c ¯... |
Category utility : Like the mutual information, the category utility is not sensitive to any ordering in the feature or category variable values. That is, as far as the category utility is concerned, the category set is not qualitatively different from the category set since the formulation of the category utility do... |
Category utility : This section provides some background on the origins of, and need for, formal measures of "category goodness" such as the category utility, and some of the history that lead to the development of this particular metric. |
Category utility : Category utility is used as the category evaluation measure in the popular conceptual clustering algorithm called COBWEB (Fisher 1987). |
Category utility : Abstraction Concept learning Universals Unsupervised learning == References == |
Artificial intelligence of things : The Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics. In 201... |
Artificial intelligence of things : As defined by the 21st Century Cures Act in 2016, a medical device is a device that performs a function in healthcare with the intention of using it "in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other anim... |
Artificial intelligence of things : When integrating AI into cloud engineering, it can help multiple professional fields in maximizing data collection. It can improve performance and efficiency through digital management. Cloud engineering follows engineering methods to apply to cloud computing and focuses on technolog... |
Artificial intelligence of things : Artificial intelligence Medical Device - Artificial Intelligence Cloud Computing - Cloud Engineering Internet of things Edge Computing == References == |
Attributional calculus : Attributional calculus is a logic and representation system defined by Ryszard S. Michalski. It combines elements of predicate logic, propositional calculus, and multi-valued logic. Attributional calculus provides a formal language for natural induction, which is an inductive learning process w... |
Attributional calculus : Michalski, R.S., "ATTRIBUTIONAL CALCULUS: A Logic and Representation Language for Natural Induction," Reports of the Machine Learning and Inference Laboratory, MLI 04–2, George Mason University, Fairfax, VA, April, 2004. |
The Fable of Oscar : The Fable of Oscar is a fable proposed by John L. Pollock in his book How to Build a Person (ISBN 9780262161138) to defend the idea of token physicalism, agent materialism, and strong AI. It ultimately illustrates what is needed for an Artificial Intelligence to be built and why humans are just lik... |
The Fable of Oscar : Once in a distant land there lived a race of Engineers. They have all their physical needs provided by the machines they have invented. One of the Engineers decide that he will create an "intelligent machine" that is much more ingenious than the more machines, in that it can actually sense, learn, ... |
The Fable of Oscar : Mind–body problem Robot |
The Fable of Oscar : http://johnpollock.us/ftp/OSCAR-web-page/oscar.html http://philpapers.org/rec/POLOAC == References == |
Deep lambertian networks : Deep Lambertian Networks (DLN) is a combination of Deep belief network and Lambertian reflectance assumption which deals with the challenges posed by illumination variation in visual perception. Lambertian Reflectance model gives an illumination invariant representation which can be used for ... |
Semantic analysis (machine learning) : In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages base... |
Semantic analysis (machine learning) : Explicit semantic analysis Information extraction Semantic similarity Stochastic semantic analysis Ontology learning == References == |
Software agent : In computer science, a software agent is a computer program that acts for a user or another program in a relationship of agency. The term agent is derived from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action ... |
Software agent : The basic attributes of an autonomous software agent are that agents: are not strictly invoked for a task, but activate themselves, may reside in wait status on a host, perceiving context, may get to run status on a host upon starting conditions, do not require interaction of user, may invoke other tas... |
Software agent : Software agents may offer various benefits to their end users by automating complex or repetitive tasks. However, there are organizational and cultural impacts of this technology that need to be considered prior to implementing software agents. |
Software agent : Issues to consider in the development of agent-based systems include how tasks are scheduled and how synchronization of tasks is achieved how tasks are prioritized by agents how agents can collaborate, or recruit resources, how agents can be re-instantiated in different environments, and how their inte... |
Software agent : Agent architecture Chatbot Data loss prevention Endpoint detection and response Software bot |
Software agent : Software Agents: An Overview Archived July 17, 2011, at the Wayback Machine, Hyacinth S. Nwana. Knowledge Engineering Review, 11(3):1–40, September 1996. Cambridge University Press. FIPA The Foundation for Intelligent Physical Agents JADE Java Agent Developing Framework, an Open Source framework develo... |
Syman : SYMAN is an artificial intelligence technology that uses data from social media profiles to identify trends in the job market. SYMAN is designed to organize actionable data for products and services including recruiting, human capital management, CRM, and marketing. SYMAN was developed with a $21 million series... |
Syman : Workday |
Multitask optimization : Multi-task optimization is a paradigm in the optimization literature that focuses on solving multiple self-contained tasks simultaneously. The paradigm has been inspired by the well-established concepts of transfer learning and multi-task learning in predictive analytics. The key motivation beh... |
Multitask optimization : There are several common approaches for multi-task optimization: Bayesian optimization, evolutionary computation, and approaches based on Game theory. |
Multitask optimization : Algorithms for multi-task optimization span a wide array of real-world applications. Recent studies highlight the potential for speed-ups in the optimization of engineering design parameters by conducting related designs jointly in a multi-task manner. In machine learning, the transfer of optim... |
Multitask optimization : Multi-objective optimization Multi-task learning Multicriteria classification Multiple-criteria decision analysis == References == |
AZFinText : Arizona Financial Text System (AZFinText) is a textual-based quantitative financial prediction system written by Robert P. Schumaker of University of Texas at Tyler and Hsinchun Chen of the University of Arizona. |
AZFinText : This system differs from other systems in that it uses financial text as one of its key means of predicting stock price movement. This reduces the information lag-time problem evident in many similar systems where new information must be transcribed (e.g., such as losing a costly court battle or having a pr... |
AZFinText : The foundation of AZFinText can be found in the ACM TOIS article. Within this paper, the authors tested several different prediction models and linguistic textual representations. From this work, it was found that using the article terms and the price of the stock at the time the article was released was th... |
AZFinText : AZFinText has been the topic of discussion by numerous media outlets. Some of the more notable ones include The Wall Street Journal, MIT's Technology Review, Dow Jones Newswire, WBIR in Knoxville, TN, Slashdot and other media outlets. |
AZFinText : https://blogs.wsj.com/digits/2010/06/21/using-artificial-intelligence-to-digest-financial-news/ slashdot.org/story/10/06/12/1341212/Quant-AI-Picks-Stocks-Better-Than-Humans www.technologyreview.com/blog/guest/25308/ |
AI Dungeon : AI Dungeon is a single-player/multiplayer text adventure game which uses artificial intelligence (AI) to generate content and allows players to create and share adventures and custom prompts. The game's first version was made available in May 2019, and its second version (initially called AI Dungeon 2) was... |
AI Dungeon : AI Dungeon is a text adventure game that uses artificial intelligence to generate random storylines in response to player-submitted stimuli. In the game, players are prompted to choose a setting for their adventure (e.g. fantasy, mystery, apocalyptic, cyberpunk, zombies), followed by other options relevant... |
AI Dungeon : Approximately two thousand people played the original version of the game within the first month of its May 2019 release. Within a week of its December 2019 relaunch, the game reached over 100,000 players and over 500,000 play-throughs, and reached 1.5 million players by June 2020. As of December 2019, the... |
AI Dungeon : Official website Original open-source code for AI Dungeon on GitHub (archived) |
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 |
Learning to rank : Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some parti... |
Learning to rank : For the convenience of MLR algorithms, query-document pairs are usually represented by numerical vectors, which are called feature vectors. Such an approach is sometimes called bag of features and is analogous to the bag of words model and vector space model used in information retrieval for represen... |
Learning to rank : There are several measures (metrics) which are commonly used to judge how well an algorithm is doing on training data and to compare the performance of different MLR algorithms. Often a learning-to-rank problem is reformulated as an optimization problem with respect to one of these metrics. Examples ... |
Learning to rank : Learning to Rank approaches are often categorized using one of three approaches: pointwise (where individual documents are ranked), pairwise (where pairs of documents are ranked into a relative order), and listwise (where an entire list of documents are ordered). Tie-Yan Liu of Microsoft Research Asi... |
Learning to rank : Norbert Fuhr introduced the general idea of MLR in 1992, describing learning approaches in information retrieval as a generalization of parameter estimation; a specific variant of this approach (using polynomial regression) had been published by him three years earlier. Bill Cooper proposed logistic ... |
Learning to rank : Similar to recognition applications in computer vision, recent neural network based ranking algorithms are also found to be susceptible to covert adversarial attacks, both on the candidates and the queries. With small perturbations imperceptible to human beings, ranking order could be arbitrarily alt... |
Learning to rank : Content-based image retrieval Multimedia information retrieval Image retrieval Triplet loss |
Learning to rank : Competitions and public datasets LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval Yandex's Internet Mathematics 2009 Yahoo! Learning to Rank Challenge Microsoft Learning to Rank Datasets |
Testsigma : Testsigma is a low-code, AI-driven automated testing platform for software testing, CI/CD, and agile teams. It provides testing products and solutions for web, mobile, and API applications and can be integrated with popular CI/CD tools. |
Testsigma : Testsigma was founded by Rukmangada Kandyala in 2019. Testsigma has multiple products to let software testing teams test web apps, mobile apps, APIs and ERP applications like Salesforce. Testsigma claims Nagra, Samsung, Cisco, Bosch, NTUC Fairprice as customers. |
Testsigma : In 2022 Testsigma raised $4.6 Million in funding led by Accel and Strive. In June 2024 Testsigma raised $8.2M led by MassMutual Ventures. |
Testsigma : Testsigma offers many continuous testing capabilities as part of its cloud testing platform, including: Mobile Testing API Testing Web Testing Salesforce testing |
Testsigma : Official website |
DABUS : DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) is an artificial intelligence (AI) system created by Stephen Thaler. It reportedly conceived of two novel products — a food container constructed using fractal geometry, which enables rapid reheating, and a flashing beacon for attracting atten... |
DABUS : The Artificial Inventor Project The latest news on the DABUS patent case (ipstars.com) |
Living Intelligence : Living Intelligence is the convergence of artificial intelligence, biotechnology, and advanced sensors. |
Living Intelligence : The conceptual framework of Living Intelligence was introduced in 2024 with a report published by Amy Webb and Sam Jordan from Future Today Institute. The report described it as a convergence of three technologies (artificial intelligence, biotechnology, and advanced sensors) for systems capable o... |
Living Intelligence : Living Intelligence can be used in a variety of industries, including business and education. In education, it focuses on human cognition to personalize learning experiences. It can also assist in training AI models with empathy for applications of customer service and healthcare. Notable early de... |
Toronto Declaration : The Toronto Declaration: Protecting the Rights to Equality and Non-Discrimination in Machine Learning Systems is a declaration that advocates responsible practices for machine learning practitioners and governing bodies. It is a joint statement issued by groups including Amnesty International and ... |
Toronto Declaration : The Toronto Declaration consists of 59 articles, broken into six sections, concerning international human rights law, duties of states, responsibilities of private sector actors, and the right to an effective remedy. |
Machine learning in video games : Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control and procedural content generation (PCG). Machine learning is a subset of artificial intelligence that uses historical data to bu... |
Machine learning in video games : Machine learning agents have been used to take the place of a human player rather than function as NPCs, which are deliberately added into video games as part of designed gameplay. Deep learning agents have achieved impressive results when used in competition with both humans and other... |
Machine learning in video games : Computer vision focuses on training computers to gain a high-level understanding of digital images or videos. Many computer vision techniques also incorporate forms of machine learning, and have been applied on various video games. This application of computer vision focuses on interpr... |
Machine learning in video games : Machine learning has seen research for use in content recommendation and generation. Procedural content generation is the process of creating data algorithmically rather than manually. This type of content is used to add replayability to games without relying on constant additions by h... |
Machine learning in video games : Music is often seen in video games and can be a crucial element for influencing the mood of different situations and story points. Machine learning has seen use in the experimental field of music generation; it is uniquely suited to processing raw unstructured data and forming high lev... |
Learning curve (machine learning) : In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. Typically, the nu... |
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