AI Video Tools

Explore the best AI Video Tools — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

StatMuse

StatMuse Inc. is an American artificial intelligence company founded in 2014. It operates an eponymous website that hosts a database of sports statistics covering the four major North American sports leagues, the Women's National Basketball Association (WNBA), NCAA Division I men's basketball, NCAA Division I Football Bowl Subdivision, the Big Five association football leagues in Europe, and various professional golf tours. == History == The company was founded by friends Adam Elmore and Eli Dawson in 2014. In email correspondence to the Springfield News-Leader, Elmore detailed that he and Dawson, fans of the National Basketball Association (NBA), were compelled to create StatMuse after they realized there was no online platform where they could search "Lebron James most points" [sic] and quickly get a result "showing his highest scoring games." As a startup, the company's goal was to utilize a type of artificial intelligence called natural language processing (NLP) for sports. In 2015, the company was part of the second group of startups accepted into the Disney Accelerator program. The company secured support from several investors, including The Walt Disney Company, Techstars, Allen & Company, the NFL Players Association, Greycroft and NBA Commissioner David Stern. As part of their partnership with Disney, StatMuse signed a content deal with ESPN (owned by Disney) to provide stats content on social media and television during the 2015–16 NBA season. Initially, the company only had stats available for the NBA, but eventually expanded to provide stats for the other major North American sports leagues. The company's initial demographic was players of fantasy sports, but it eventually expanded to target general sports fans as well. StatMuse offers responses to user queries in the voices of sports-related public figures. Dawson shared with VentureBeat that StatMuse brings people in and records them saying different words and phrases. These celebrity voices were made accessible through Google's Google Assistant service, Microsoft's Cortana virtual assistant, and Amazon's Echo devices. The company launched its phone app in September 2017. The app allows users to access StatMuse's sports statistics database by submitting queries in their natural language. Upon the launch of the phone app, Fitz Tepper of TechCrunch wrote that: "The technology isn't perfect – some of the pauses between words are a bit awkward, making it clear that some phrases are being stitched together on the fly. But this is the exception, and on the whole, most responses sound pretty good." StatMuse plug-ins for Slack and Facebook Messenger were also made, providing text-based sports stats. In 2019, StatMuse received investment from the Google Assistant Investment program. The service launched a premium option dubbed StatMuse+ in May 2023, offering options that had previously been included for free, such as unlimited searches and full results in data tables. The premium version also included early access to new features and a personalized search history, as well as not having ads. The app received a variety of feedback. In January 2024, the service launched a Premier League version of the website dubbed StatMuse FC. It is planned to introduce more leagues on the website.
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Eline Van der Velden

Eline van der Velden is a Dutch comedian, writer, actress and producer based in London, England. She is best known for her work creating Tilly Norwood, an AI-generated "actress". == Early life == Van der Velden was born on the Dutch island of Curaçao, Netherlands Antilles to Dutch businessman Steven van der Velden and physiotherapist Quirine van der Velden. She moved to the United Kingdom at age 14 to study drama and musical theatre at Tring Park School for the Performing Arts. She graduated with an MSc in physics from Imperial College London in 2008. == Career == She was nominated by the International Academy of Digital Arts and Sciences for the Lovie Awards and won Best Online Comedy in 2013 for two of her submitted entries. She has created multiple online shows such as Sketch My Life with London Hughes and Emily Hartridge and Match.com Parody. She became managing director of Makers Channel (makerschannel.co.uk), the first curated video platform in Europe in 2015. Makers Channel has been recently acquired by a Belgian media company De Persgroep, due to its success in the Netherlands. In 2016, she appeared in adverts for the Dutch shampoo brand Andrelon. Miss Holland, a comedy character created by Van der Velden, made headlines in 2016 as she asked the British public to teach her the national anthem. As an actress, she has starred in Dutch TV series De Troon, Beatrix and the Golden Calf-winning series Overspel. In Belgium, she appeared opposite Jamie Dornan in Flying Home. Van der Velden starred in the BBC Three series Putting It Out There, in which she challenges social perceptions of body hair, heels, spit, personal space, and authority figures. In 2018, she starred in the BBC One comedy series Soft Border Patrol and the BBC Three comedy series Miss Holland. In 2025, Particle6 Group, which Van der Velden founded in 2016, introduced Tilly Norwood, an AI-generated "actress" at the Zurich Film Festival. The announcement was met with outrage and a condemnation by the American actors' union SAG-AFTRA. == Awards and recognition == Miss Holland won the Best Online Comedy at the 2013 Lovie Awards, judged by Stephen Fry. The Match.com Parody video won Best Online Comedy People's Lovie Award, the people's vote. Miss Holland and Match.com Parody Date 1 were also featured in the 2013 Google Lovie Letters.
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D3web

d3web is a free, open-source platform for knowledge-based systems (expert systems). Its core is written in Java using XML and/or Office-based formats for the knowledge storage. All of its components are distributed under the terms of the Lesser General Public Licence (LGPL). The d3web diagnostic core implements reasoning and persistence components for problem-solving knowledge including decision trees, (heuristic) rules, set-covering models and diagnostic flowcharts. The software can be integrated into foreign applications (embedded or OEM), but a number of off-the-shelf components already exist. == Components == d3web is a component-based software platform providing applications for authoring and using/executing problem-solving knowledge. The following applications are primarily using d3web: KnowWE (Knowledge Wiki Environment): A semantic wiki building on JSPWiki. Problem-solving knowledge can be authored and executed through the wiki interface. Developed knowledge bases can be exported to be used in OEM or embedded reasoners. Additionally, knowledge exchange via OWL ontologies is provided. KnowME (Knowledge Modelling Environment): A rich-client application for the development of d3web knowledge bases. Problem-solving knowledge can be authored and executed within the desktop application. Developed knowledge bases can be used in OEM or embedded reasoners. The software KnowME is no longer under active development. It is replaced by the KnowWE component (see above). Dialog2: A web-based application for demonstrating the capabilities of the d3web core reasoner. The web servlet is based on Java Server Faces. It can be used out of box or as a starting point for own developments for building knowledge-based interview systems. == Application Domains == A number of industrial and academic projects already used or are currently using the d3web platform. The main application domains are: medical diagnosis, documentation, and therapy: technical fault diagnosis monitoring of technical devices. Some applications (both, commercial and free) created using the d3web diagnostic engine: SmartCare(c): a medical closed-loop system for weaning mechanically ventilated patients, created by Dräger SonoConsult Archived 2011-12-16 at the Wayback Machine: a medical support system for evaluating sonographic examinations (German only) eDOC: a web-based system for self-diagnosing various medical issues (German only) == History == The development of d3web originates from the research work of Prof. Dr. Frank Puppe (University Würzburg, Germany) going back to the 1980s, starting with the medical expert systems MED1 and MED2 . Whereas the original systems were focussed on medical diagnosis the applicability of the approach was generalized by the successor D3 . As the predecessors were implemented in the LISP programming language, d3web is a full Java re-implementation.
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Sense Networks

Sense Networks is a New York City based company with a focus on applications that analyze big data from mobile phones, carrier networks, and taxicabs, particularly by using machine learning technology to make sense of large amounts of location (latitude/longitude) data. In 2009, Sense was named one of "The 25 Most Intriguing Startups in the World" by Bloomberg Businessweek and was called "The Next Google" on the cover of Newsweek. In 2014, Sense Networks was acquired by YP, "the local search and advertising company owned by Cerberus Capital Management and AT&T." It was subsequently sold off to Verve in 2017 == History == Sense Networks was founded by Greg Skibiski in February 2006 (2003?) near his home in Northampton, Massachusetts. After establishing an office in NoHo, New York City near Silicon Alley, Skibiski recruited Alex Pentland, Director of Human Dynamics Research and former Academic Head of the MIT Media Lab, Tony Jebara, Associate Professor and Head of the Machine Learning Laboratory at Columbia University, and Christine Lemke, who would later become co-founders. Sense Networks investors include Intel Capital, Javelin Venture Partners, and Kenan Altunis. Founder Greg Skibiski was pushed out by lead investor Intel Capital in November 2009 following the company's B round of financing. During the same week, the company won the Emerging Communications Conference "Company to Watch" Award. The company has three published patent applications for analyzing sensor data streams: System and Method of Performing Location Analytics (US 20090307263), Comparing Spatial-Temporal Trails in Location Analytics (US 20100079336), and Anomaly Detection in Sensor Analytics (US 20100082301). The company was acquired by the Yellow Pages in 2014. This is a marketing conglomerate under AT&T and Cerberus Capital Management. == Products and services == The Citysense consumer application that shows hotspots of human activity in real-time from mobile phone location and taxicab GPS data was named by ReadWriteWeb (in The New York Times) as "Top 10 Internet of Things Products of 2009". The Cabsense consumer application that shows the best place to catch a New York City taxicab based on GPS data from the vehicle was launched in March 2010. The Macrosense platform is for mobile application providers and mobile phone carriers to analyze billions of customer location data points for predictive analytics in advertising and churn management applications. == Privacy and data ownership == The company allows users to opt-out of their service through their website, and users may monitor their profile through their application. The company does not collect identifiable data (such as phone numbers or names); it collects data received from cellphone to construct anonymous profiles of consumers. This anonymous data/profiles may then be sold to third parties. The company's privacy and data ownership policies are based on The New Deal on Data, as advocated by Alex "Sandy" Pentland, head of the Human Dynamics group at MIT.
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Collateral freedom

Collateral freedom is an anti-censorship strategy that attempts to make it economically prohibitive for censors to block content on the Internet. This is achieved by hosting content on cloud services that are considered by censors to be "too important to block", and then using encryption to prevent censors from identifying requests for censored information that is hosted among other content, forcing censors to either allow access to the censored information or take down entire services.
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Thinking Machines Lab

Thinking Machines Lab Inc. is an American artificial intelligence (AI) startup founded by Mira Murati, the former chief technology officer of OpenAI. The company was founded in February 2025, and by July had completed an early-stage funding round led by Andreessen Horowitz, raising $2 billion at a valuation of $12 billion overall from investors such as Nvidia, AMD, Cisco, and Jane Street. The company is based in San Francisco and structured as a public benefit corporation. == History == By its launch in February 2025, Thinking Machines Lab was reported to have hired about 30 researchers and engineers from competitors including OpenAI, Meta AI, and Mistral AI. Its founding team members include Barret Zoph, former OpenAI VP of Research (Post-Training), Lilian Weng, former OpenAI VP, and OpenAI cofounder John Schulman, who joined after a brief stint at the lab's competitor Anthropic. In January 2026, it was reported that Barret Zoph and Luke Metz, departed the startup to return to OpenAI. Other former OpenAI employees who have been hired include Jonathan Lachman and Andrew Tulloch (although Tulloch departed after getting recruited for Meta Superintelligence Labs). Thinking Machines Lab's advisers include Bob McGrew, previously OpenAI's chief research officer, and Alec Radford, who was a lead researcher for OpenAI. On October 1, 2025, it announced Tinker, an API for fine-tuning language models. Users would submit jobs through the API for fine-tuning one of the various open-weight models supported. The Lab would run the jobs on its internal clusters and training infrastructure. == Business structure == Thinking Machines Lab grants Mira Murati a deciding vote on board matters, weighted to provide her with a majority decision-making capability. Additionally, founding shareholders possess votes weighted 100 times greater than those of regular shareholders. In July 2025, Andreessen Horowitz was reported to have led the company's initial funding round, raising "about $2 billion at a valuation of $12 billion". The government of Albania (Murati's country of origin) was also included in this round, making a $10 million investment which required an amendment to the country's 2025 budget. == Partnership == In March 2026, Thinking Machines Lab announced a strategic partnership with NVIDIA involving an undisclosed investment and a multi-year agreement to deploy one gigawatt of Vera Rubin computing capacity.
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Horovod (machine learning)

Horovod is a free and open-source distributed deep learning training framework for TensorFlow, Keras, PyTorch and Apache MXNet. It is designed to scale existing single-GPU training scripts to efficiently run on multiple GPUs and computer nodes with minimal code changes, using synchronous data-parallel training based on the ring-allreduce communication pattern. Horovod was initially developed at Uber and released as an open-source project in 2017, and is now hosted by the LF AI & Data Foundation, a project of the Linux Foundation. == History == Horovod was created at Uber as part of the company's internal machine learning platform Michelangelo to simplify scaling TensorFlow models across many GPUs. The first public release of the library, version 0.9.0, was tagged on GitHub in August 2017 under the Apache 2.0 licence. In October 2017, Uber Engineering publicly introduced Horovod as an open-source component of its deep learning toolkit. In February 2018 Alexander Sergeev and Mike Del Balso published a technical paper describing Horovod's design and benchmarking its performance on up to 512 GPUs, showing near-linear scaling for several image-classification models when compared with single-GPU baselines. In December 2018 Uber contributed Horovod to the LF Deep Learning Foundation (later LF AI & Data), making it a Linux Foundation project. Horovod entered incubation under LF AI & Data and graduated as a full foundation project in 2020. Since its initial release the project has expanded beyond TensorFlow to provide APIs for PyTorch, Keras and Apache MXNet, as well as integrations with frameworks such as Apache Spark and Ray, support for elastic training, and tooling for automated performance tuning and profiling. == Design and features == Horovod core principles are based on the MPI concepts size, rank, local rank, allreduce, allgather, broadcast, and alltoall. Horovod implements synchronous data-parallel training, in which each worker process maintains a replica of the model and computes gradients on different mini-batches of data. The gradients are aggregated across workers using the ring-allreduce communication pattern rather than a central parameter server, which reduces communication bottlenecks and can improve scaling on multi-GPU clusters. Communication is built on top of collective-communication libraries such as MPI, NCCL, Gloo and Intel oneCCL, and supports both GPU and CPU training. In the benchmark experiments reported in the original paper, Horovod achieved around 90% scaling efficiency on 512 GPUs for the ResNet-101 and Inception v3 convolutional neural networks, and around 68% scaling efficiency for the VGG-16 model. Horovod can be deployed on-premises or in cloud environments and is distributed as a Python package with optional GPU support via CUDA. The official documentation provides guides for running Horovod with Docker, Kubernetes (including via Kubeflow and the MPI Operator), commercial platforms such as Databricks, and cluster schedulers such as LSF. == Adoption and use cases == Within Uber, Horovod has been used for applications including autonomous driving research, fraud detection and trip forecasting. Major cloud providers have integrated Horovod into their managed machine learning offerings. Amazon Web Services supports distributed training with Horovod in services such as Amazon SageMaker and AWS Deep Learning Containers, while Microsoft Azure documents Horovod-based training workflows for Azure Synapse Analytics. Technical guides from academic and research computing centres, including Purdue University and the NASA Advanced Supercomputing programme, describe Horovod-based workflows for multi-GPU training on supercomputers and clusters. Horovod is also used in conjunction with Apache Spark and dedicated storage systems as part of end-to-end data processing and model-training pipelines. Industry blogs and technical tutorials describe deployments of Horovod on Kubernetes, on-premises clusters and cloud-managed Kubernetes services such as Amazon EKS.
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Knowledge space

In mathematical psychology and education theory, a knowledge space is a combinatorial structure used to formulate mathematical models describing the progression of a human learner. Knowledge spaces were introduced in 1985 by Jean-Paul Doignon and Jean-Claude Falmagne, and remain in extensive use in the education theory. Modern applications include two computerized tutoring systems, ALEKS and the defunct RATH. Formally, a knowledge space assumes that a domain of knowledge is a collection of concepts or skills, each of which must be eventually mastered. Not all concepts are interchangeable; some require other concepts as prerequisites. Conversely, competency at one skill may ease the acquisition of another through similarity. A knowledge space marks out which collections of skills are feasible: they can be learned without mastering any other skills. Under reasonable assumptions, the collection of feasible competencies forms the mathematical structure known as an antimatroid. Researchers and educators usually explore the structure of a discipline's knowledge space as a latent class model. == Motivation == Knowledge Space Theory attempts to address shortcomings of standardized testing when used in educational psychometry. Common tests, such as the SAT and ACT, compress a student's knowledge into a very small range of ordinal ranks, in the process effacing the conceptual dependencies between questions. Consequently, the tests cannot distinguish between true understanding and guesses, nor can they identify a student's particular weaknesses, only the general proportion of skills mastered. The goal of knowledge space theory is to provide a language by which exams can communicate What the student can do and What the student is ready to learn. == Model structure == Knowledge Space Theory-based models presume that an educational subject S can be modeled as a finite set Q of concepts, skills, or topics. Each feasible state of knowledge about S is then a subset of Q; the set of all such feasible states is K. The precise term for the information (Q, K) depends on the extent to which K satisfies certain axioms: A knowledge structure assumes that K contains the empty set (a student may know nothing about S) and Q itself (a student may have fully mastered S). A knowledge space is a knowledge structure that is closed under set union: if, for each topic, there is an expert in a class on that topic, then it is possible, with enough time and effort, for each student in the class to become an expert on all those topics simultaneously. A quasi-ordinal knowledge space is a knowledge space that is also closed under set intersection: if student a knows topics A and B; and student c knows topics B and C; then it is possible for another student b to know only topic B. A well-graded knowledge space or learning space is a knowledge space satisfying the following axiom: If S∈K, then there exists x∈S such that S\{x}∈K In educational terms, any feasible body of knowledge can be learned one concept at a time. === Prerequisite partial order === The more contentful axioms associated with quasi-ordinal and well-graded knowledge spaces each imply that the knowledge space forms a well-understood (and heavily studied) mathematical structure: A quasi-ordinal knowledge space can be associated with a distributive lattice under set union and set intersection. The name "quasi-ordinal" arises from Birkhoff's representation theorem, which explains that distributive lattices uniquely correspond to partial orders. A well-graded knowledge space is an antimatroid, a type of mathematical structure that describes certain problems solvable with a greedy algorithm. In either case, the mathematical structure implies that set inclusion defines partial order on K, interpretable as an educational prerequirement: if a(⪯)b in this partial order, then a must be learned before b. === Inner and outer fringe === The prerequisite partial order does not uniquely identify a curriculum; some concepts may lead to a variety of other possible topics. But the covering relation associated with the prerequisite partial does control curricular structure: if students know a before a lesson and b immediately after, then b must cover a in the partial order. In such a circumstance, the new topics covered between a and b constitute the outer fringe of a ("what the student was ready to learn") and the inner fringe of b ("what the student just learned"). == Construction of knowledge spaces == In practice, there exist several methods to construct knowledge spaces. The most frequently used method is querying experts. There exist several querying algorithms that allow one or several experts to construct a knowledge space by answering a sequence of simple questions. Another method is to construct the knowledge space by explorative data analysis (for example by item tree analysis) from data. A third method is to derive the knowledge space from an analysis of the problem solving processes in the corresponding domain.
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Calais (Reuters product)

Calais is a service created by Thomson Reuters that automatically extracts semantic information from web pages in a format that can be used on the semantic web. Calais was launched in January 2008, and is free to use. The technology is now available via the website of Refinitiv, a provider of financial market data and infrastructure founded in 2018, that is a subsidiary of London Stock Exchange Group. The Calais Web service reads unstructured text and returns Resource Description Framework formatted results identifying entities, facts and events within the text. The service appears to be based on technology acquired when Reuters purchased ClearForest in 2007. The technology has also been used to automatically tag blog articles, and organize museum collections. Calais uses natural language processing technologies delivered via a web service interface.
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Ontology components

Contemporary ontologies share many structural similarities, regardless of the ontology language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations. == List == Common components of ontologies include: Individuals instances or objects (the basic or "ground level" objects; the tokens). Classes sets, collections, concepts, types of objects, or kinds of things. Attributes aspects, properties, features, characteristics, or parameters that individuals (and classes and relations) can have. Relations ways in which classes and individuals can be related to one another. Relations can carry attributes that specify the relation further. Function terms complex structures formed from certain relations that can be used in place of an individual term in a statement. Restrictions formally stated descriptions of what must be true in order for some assertion to be accepted as input. Rules statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form. Axioms assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In these disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements. Events the changing of attributes or relations. Actions types of events. Ontologies are commonly encoded using ontology languages. == Individuals == Individuals (instances) are the basic, "ground level" components of an ontology. The individuals in an ontology may include concrete objects such as people, animals, tables, automobiles, molecules, and planets, as well as abstract individuals such as numbers and words (although there are differences of opinion as to whether numbers and words are classes or individuals). Strictly speaking, an ontology need not include any individuals, but one of the general purposes of an ontology is to provide a means of classifying individuals, even if those individuals are not explicitly part of the ontology. In formal extensional ontologies, only the utterances of words and numbers are considered individuals – the numbers and names themselves are classes. In a 4D ontology, an individual is identified by its spatio-temporal extent. Examples of formal extensional ontologies are BORO, ISO 15926 and the model in development by the IDEAS Group. == Classes == == Attributes == Objects in an ontology can be described by relating them to other things, typically aspects or parts. These related things are often called attributes, although they may be independent things. Each attribute can be a class or an individual. The kind of object and the kind of attribute determine the kind of relation between them. A relation between an object and an attribute express a fact that is specific to the object to which it is related. For example, the Ford Explorer object has attributes such as: ⟨has as name⟩ Ford Explorer ⟨as by definition as part⟩ 6-speed transmission ⟨as by definition as part⟩ door (with as minimum and maximum cardinality: 4) ⟨as by definition as part one of⟩ {4.0L engine, 4.6L engine} The value of an attribute can be a complex data type; in this example, the related engine can only be one of a list of subtypes of engines, not just a single thing. Ontologies are only true ontologies if concepts are related to other concepts (the concepts do have attributes). If that is not the case, then you would have either a taxonomy (if hyponym relationships exist between concepts) or a controlled vocabulary. These are useful, but are not considered true ontologies. == Relations == Relations (also known as relationships) between objects in an ontology specify how objects are related to other objects. Typically a relation is of a particular type (or class) that specifies in what sense the object is related to the other object in the ontology. For example, in the ontology that contains the concept Ford Explorer and the concept Ford Bronco might be related by a relation of type ⟨is defined as a successor of⟩. The full expression of that fact then becomes: Ford Explorer is defined as a successor of : Ford Bronco This tells us that the Explorer is the model that replaced the Bronco. This example also illustrates that the relation has a direction of expression. The inverse expression expresses the same fact, but with a reverse phrase in natural language. Much of the power of ontologies comes from the ability to describe relations. Together, the set of relations describes the semantics of the domain: that is, its various semantic relations, such as synonymy, hyponymy and hypernymy, coordinate relation, and others. The set of used relation types (classes of relations) and their subsumption hierarchy describe the expression power of the language in which the ontology is expressed. An important type of relation is the subsumption relation (is-a-superclass-of, the converse of is-a, is-a-subtype-of or is-a-subclass-of). This defines which objects are classified by which class. For example, we have already seen that the class Ford Explorer is-a-subclass-of 4-Wheel Drive Car, which in turn is-a-subclass-of Car. The addition of the is-a-subclass-of relationships creates a taxonomy; a tree-like structure (or, more generally, a partially ordered set) that clearly depicts how objects relate to one another. In such a structure, each object is the 'child' of a 'parent class' (Some languages restrict the is-a-subclass-of relationship to one parent for all nodes, but many do not). Another common type of relations is the mereology relation, written as part-of, that represents how objects combine to form composite objects. For example, if we extended our example ontology to include concepts like Steering Wheel, we would say that a "Steering Wheel is-by-definition-a-part-of-a Ford Explorer" since a steering wheel is always one of the components of a Ford Explorer. If we introduce meronymy relationships to our ontology, the hierarchy that emerges is no longer able to be held in a simple tree-like structure since now members can appear under more than one parent or branch. Instead this new structure that emerges is known as a directed acyclic graph. As well as the standard is-a-subclass-of and is-by-definition-a-part-of-a relations, ontologies often include additional types of relations that further refine the semantics they model. Ontologies might distinguish between different categories of relation types. For example: relation types for relations between classes relation types for relations between individuals relation types for relations between an individual and a class relation types for relations between a single object and a collection relation types for relations between collections Relation types are sometimes domain-specific and are then used to store specific kinds of facts or to answer particular types of questions. If the definitions of the relation types are included in an ontology, then the ontology defines its own ontology definition language. An example of an ontology that defines its own relation types and distinguishes between various categories of relation types is the Gellish ontology. For example, in the domain of automobiles, we might need a made-in type relationship which tells us where each car is built. So the Ford Explorer is made-in Louisville. The ontology may also know that Louisville is-located-in Kentucky and Kentucky is-classified-as-a state and is-a-part-of the U.S. Software using this ontology could now answer a question like "which cars are made in the U.S.?"
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D3web

d3web is a free, open-source platform for knowledge-based systems (expert systems). Its core is written in Java using XML and/or Office-based formats for the knowledge storage. All of its components are distributed under the terms of the Lesser General Public Licence (LGPL). The d3web diagnostic core implements reasoning and persistence components for problem-solving knowledge including decision trees, (heuristic) rules, set-covering models and diagnostic flowcharts. The software can be integrated into foreign applications (embedded or OEM), but a number of off-the-shelf components already exist. == Components == d3web is a component-based software platform providing applications for authoring and using/executing problem-solving knowledge. The following applications are primarily using d3web: KnowWE (Knowledge Wiki Environment): A semantic wiki building on JSPWiki. Problem-solving knowledge can be authored and executed through the wiki interface. Developed knowledge bases can be exported to be used in OEM or embedded reasoners. Additionally, knowledge exchange via OWL ontologies is provided. KnowME (Knowledge Modelling Environment): A rich-client application for the development of d3web knowledge bases. Problem-solving knowledge can be authored and executed within the desktop application. Developed knowledge bases can be used in OEM or embedded reasoners. The software KnowME is no longer under active development. It is replaced by the KnowWE component (see above). Dialog2: A web-based application for demonstrating the capabilities of the d3web core reasoner. The web servlet is based on Java Server Faces. It can be used out of box or as a starting point for own developments for building knowledge-based interview systems. == Application Domains == A number of industrial and academic projects already used or are currently using the d3web platform. The main application domains are: medical diagnosis, documentation, and therapy: technical fault diagnosis monitoring of technical devices. Some applications (both, commercial and free) created using the d3web diagnostic engine: SmartCare(c): a medical closed-loop system for weaning mechanically ventilated patients, created by Dräger SonoConsult Archived 2011-12-16 at the Wayback Machine: a medical support system for evaluating sonographic examinations (German only) eDOC: a web-based system for self-diagnosing various medical issues (German only) == History == The development of d3web originates from the research work of Prof. Dr. Frank Puppe (University Würzburg, Germany) going back to the 1980s, starting with the medical expert systems MED1 and MED2 . Whereas the original systems were focussed on medical diagnosis the applicability of the approach was generalized by the successor D3 . As the predecessors were implemented in the LISP programming language, d3web is a full Java re-implementation.
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OpenAI Operator

OpenAI Operator was an AI agent developed by OpenAI, capable of autonomously performing tasks through web browser interactions, including filling forms, placing online orders, scheduling appointments, and other repetitive browser-based tasks. It uses OpenAI's advanced models to expand practical automation capabilities for users in daily activities. Operator was launched on January 23, 2025. It was released as a limited-access research preview to ChatGPT Pro-tier subscribers in the United States on February 1, 2025, with future plans to broaden availability. Operator was deprecated after the release of ChatGPT agent, and shut down on August 31, 2025. == Performance and limitations == In benchmark assessments, Operator achieved notable success, scoring 38.1% on OSWorld benchmarks (OS-level tasks) and 58.1% on WebArena benchmarks (web interactions). However, it did not reach human-level accuracy and faced limitations with intricate user interfaces and extended workflows. == Safety and privacy == OpenAI emphasized privacy and safety measures within Operator, including stringent data protection protocols and built-in safety checks designed to prevent unauthorized sensitive actions or information misuse. == Availability == Initially, Operator was only available to ChatGPT Pro subscribers in the U.S., with plans for broader availability to Plus, Team, and Enterprise users in the future.
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TigerGraph

TigerGraph is a private company headquartered in Redwood City, California. It provides graph database and graph analytics software. == History == TigerGraph was founded in 2012 by programmer Yu, Ruoming, Li, Like and Mingxi, under the name GraphSQL. In September 2017, the company came out of stealth mode under the name TigerGraph with $33 million in funding. It raised an additional $32 million in funding in September 2019 and another $105 million in a series C round in February 2021. Cumulative funding as of March 2021 is $170 million. == Products == TigerGraph's hybrid transactional/analytical processing database and analytics software can scale to hundreds of terabytes of data with trillions of edges, and is used for data intensive applications such as fraud detection, customer data analysis (customer 360), IoT, artificial intelligence and machine learning. It is available using the cloud computing delivery model. The analytics uses C++ based software and a parallel processing engine to process algorithms and queries. It has its own graph query language that is similar to SQL. TigerGraph also provides a software development kit for creating graphs and visual representations. As of Mar 2024, TigerGraph version is up to version 4.2.0 TigerGraph offers free Community Edition for developers, researchers, and educators. It can be obtained from https://dl.tigergraph.com/ == Query Language == GSQL , designed by Mingxi Wu and Alin Deutsch in 2015, is a SQL-like Turing complete query language. GSQL includes additions to make it compliant with the Graph Query Language standard.
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Philosophy of information

The philosophy of information (PI) is a branch of philosophy that studies topics relevant to information processing, representational system and consciousness, cognitive science, computer science, information science and information technology. It includes: the critical investigation of the conceptual nature and basic principles of information, including its dynamics, utilisation and sciences the elaboration and application of information-theoretic and computational methodologies to philosophical problems. == History == The philosophy of information (PI) has evolved from the philosophy of artificial intelligence, logic of information, cybernetics, social theory, ethics and the study of language and information. === Logic of information === The logic of information, also known as the logical theory of information, considers the information content of logical signs and expressions along the lines initially developed by Charles Sanders Peirce. === Study of language and information === Later contributions to the field were made by Fred Dretske, Jon Barwise, Brian Cantwell Smith, and others. The Center for the Study of Language and Information (CSLI) was founded at Stanford University in 1983 by philosophers, computer scientists, linguists, and psychologists, under the direction of John Perry and Jon Barwise. === P.I. === More recently this field has become known as the philosophy of information. The expression was coined in the 1990s by Luciano Floridi, who has published prolifically in this area with the intention of elaborating a unified and coherent, conceptual frame for the whole subject. == Definitions of "information" == The concept information has been defined by several theorists. Charles S. Peirce's theory of information was embedded in his wider theory of symbolic communication he called the semiotic, now a major part of semiotics. For Peirce, information integrates the aspects of signs and expressions separately covered by the concepts of denotation and extension, on the one hand, and by connotation and comprehension on the other. Donald M. MacKay says that information is a distinction that makes a difference. According to Luciano Floridi, four kinds of mutually compatible phenomena are commonly referred to as "information": Information about something (e.g. a train timetable) Information as something (e.g. DNA, or fingerprints) Information for something (e.g. algorithms or instructions) Information in something (e.g. a pattern or a constraint). == Philosophical directions == === Computing and philosophy === Recent creative advances and efforts in computing, such as semantic web, ontology engineering, knowledge engineering, and modern artificial intelligence provide philosophy with fertile ideas, new and evolving subject matters, methodologies, and models for philosophical inquiry. While computer science brings new opportunities and challenges to traditional philosophical studies, and changes the ways philosophers understand foundational concepts in philosophy, further major progress in computer science would only be feasible when philosophy provides sound foundations for areas such as bioinformatics, software engineering, knowledge engineering, and ontologies. Classical topics in philosophy, namely, mind, consciousness, experience, reasoning, knowledge, truth, morality and creativity are rapidly becoming common concerns and foci of investigation in computer science, e.g., in areas such as agent computing, software agents, and intelligent mobile agent technologies. According to Luciano Floridi " one can think of several ways for applying computational methods towards philosophical matters: Conceptual experiments in silico: As an innovative extension of an ancient tradition of thought experiment, a trend has begun in philosophy to apply computational modeling schemes to questions in logic, epistemology, philosophy of science, philosophy of biology, philosophy of mind, and so on. Pancomputationalism: On this view, computational and informational concepts are considered to be so powerful that given the right level of abstraction, anything in the world could be modeled and represented as a computational system, and any process could be simulated computationally. Then, however, pancomputationalists have the hard task of providing credible answers to the following two questions: how can one avoid blurring all differences among systems? what would it mean for the system under investigation not to be an informational system (or a computational system, if computation is the same as information processing)?
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OntoWiki

OntoWiki was a free and open-source semantic wiki application, meant to serve as an ontology editor and a knowledge acquisition system. It is a web-based application written in PHP and using either a MySQL database or a Virtuoso triple store. OntoWiki is form-based rather than syntax-based, and thus tries to hide as much of the complexity of knowledge representation formalisms from users as possible. OntoWiki is mainly being developed by the Agile Knowledge Engineering and Semantic Web (AKSW) research group at the University of Leipzig, a group also known for the DBpedia project among others, in collaboration with volunteers around the world. In 2009 the AKSW research group got a budget of €425,000 from the Federal Ministry of Education and Research of Germany for the development of the OntoWiki. In 2010 OntoWiki became part of the technology stack supporting the LOD2 (linked open data) project. Leipzig University is one of the consortium members of the project, which is funded by a €6.5m EU grant. The development ended in 2016 due to the lack of capacity migrating from PHP 5 to 7 including the required Zend Framework from version 1 to 2.
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