AI Essay Stock Market

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Augmented Analytics

Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. Augmented analytics is based on business intelligence and analytics. In the graph extraction step, data from different sources are investigated. == Defining Augmented Analytics == Machine Learning – a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules. Natural language generation (NLG) – a software capability that takes unstructured data and translates it into plain-English, readable, language. Automating Insights – using machine learning algorithms to automate data analysis processes. Natural Language Query – enabling users to query data using business terms that are either typed onto a search box or spoken. == Data Democratization == Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented alongside a method for users to make sense of the data. This process is used in hopes of speeding up company decision making and uncovering opportunities hidden in data. There are three aspects to democratising data: Data Parameterisation and Characterisation. Data Decentralisation using an OS of blockchain and DLT technologies, as well as an independently governed secure data exchange to enable trust. Consent Market-driven Data Monetisation. When it comes to connecting assets, there are two features that will accelerate the adoption and usage of data democratisation: decentralized identity management and business data object monetization of data ownership. It enables multiple individuals and organizations to identify, authenticate, and authorize participants and organizations, enabling them to access services, data or systems across multiple networks, organizations, environments, and use cases. It empowers users and enables a personalized, self-service digital onboarding system so that users can self-authenticate without relying on a central administration function to process their information. Simultaneously, decentralized identity management ensures the user is authorized to perform actions subject to the system’s policies based on their attributes (role, department, organization, etc.) and/ or physical location. == Use cases == Agriculture – Farmers collect data on water use, soil temperature, moisture content and crop growth, augmented analytics can be used to make sense of this data and possibly identify insights that the user can then use to make business decisions. Smart Cities – Many cities across the United States, known as Smart Cities collect large amounts of data on a daily basis. Augmented analytics can be used to simplify this data in order to increase effectiveness in city management (transportation, natural disasters, etc.). Analytic Dashboards – Augmented analytics has the ability to take large data sets and create highly interactive and informative analytical dashboards that assist in many organizational decisions. Augmented Data Discovery – Using an augmented analytics process can assist organizations in automatically finding, visualizing and narrating potentially important data correlations and trends. Data Preparation – Augmented analytics platforms have the ability to take large amounts of data and organize and "clean" the data in order for it to be usable for future analyses. Business – Businesses collect large amounts of data, daily. Some examples of types of data collected in business operations include; sales data, consumer behavior data, distribution data. An augmented analytics platform provides access to analysis of this data, which could be used in making business decisions.
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Oxa

Oxa (formerly Oxbotica) is an autonomous vehicle software company, headquartered in Oxfordshire, England, and founded by Paul Newman and Ingmar Posner. == History == In 2013, Newman and Posner led the RobotCar UK project as part of Oxford University's Department of Engineering Science Mobile Robotics Group. RobotCar became the first autonomous vehicle on UK roads. In 2014, the pair used the newly developed technology to found Oxbotica. Oxbotica has raised over $18 million to date and is backed by the IP Group, Parkwalk Advisors and AXA XL. In 2018, Uber's former EMEA business head, Fraser Robinson, was appointed to the board of directors. In May 2019, Ozgur Tohumcu replaced Dr Graeme Smith as Oxbotica's CEO. Also in 2019, the company opened an office in Toronto, Canada. In January 2021, Oxbotica announced it had raised $47 million in a Series B round. In August 2021, the company achieved a safety landmark as the first company to have its autonomy safety case assessed by BSI (British Standards Institution) against the requirements of the UK Code of Practice 2019, PAS 1881:2020 and PAS 1883:2020, certifying the safety conformity of its autonomous vehicle trials and testing. The assessment was completed as part of Project Endeavour, the UK's first multi-city demonstration of autonomous vehicle services and capability. In December 2021, Gavin Jackson was named CEO. In January 2023, the company raised $140 million in a Series C round. In May 2023, the company changed its name to Oxa. Oxa raised $103 million (£77 million) in March 2026, including $50 million from the UK National Wealth Fund. Nvidia's venture capital division, NVentures, also invested in the Series D funding round, along with existing Oxa shareholders IP Group, Australian pension fund Hostplus, and BP Ventures, a division of the UK oil company. == Technology == Oxa designs software and hardware for the conversion of industrial vehicles into autonomous ones. Its full stack, end-to-end Universal Autonomy software is both vehicle and platform-agnostic, with no dependence on external infrastructure such as GPS. It can be deployed in any environment and on any terrain. In addition to underground uses, the technology is also useful in natural canyons and forests, where GPS signals are weak or non-existent, but also in "urban canyons" — cities with tall buildings that obstruct GPS signals for proper navigation. == Public deployments == The LUTZ Pathfinder pod had its first public demonstration in February 2015 in Milton Keynes. The Government-funded project was designed to ensure that autonomous vehicles would comply with the Highway Code. The pod featured autonomous control software from Oxbotica, including 19 sensors, cameras, radar and Lidar. As part of the GATEway Project in 2017, Oxbotica trialled seven autonomous shuttle buses in Greenwich, navigating a two-mile riverside path near London's O2 Arena on a route that is also used by pedestrians and cyclists. Oxbotica ran the UK's first trial of autonomous grocery deliveries that year, with British online supermarket Ocado in London, as the next step in the GATEway Project. In 2018, Oxbotica deployed its autonomous vehicle software at London's Gatwick Airport, which subsequently became the first airport in the world to trial an autonomous shuttle service. The electric-powered vehicles transported staff via airside roads between the airport's North and South terminals. An airside trial of Oxbotica's autonomous driving technology was then successfully completed at Heathrow Airport in partnership with IAG Cargo, the first airside trial of an autonomous vehicle at a UK airport. The Oxbotica-designed CargoPod ran autonomously along a cargo route around the airside perimeter for three weeks. As part of the UK Centre for Connected and Autonomous Vehicles-funded DRIVEN project, Oxbotica is developing and deploying a fleet of Ford Fusion autonomous vehicles running in both London and Oxford on public roads, and in conjunction with its consortium partners, running real-time insurance. AXA XL is partnering with Oxbotica on the development of smart insurance products using Oxbotica's autonomy technology to improve road safety. In 2018, Oxbotica announced a partnership with London private taxi firm Addison Lee to develop and deploy autonomous taxis in the city of London by 2021. A 3D street mapping exercise was conducted in London's Canary Wharf. In 2019, Oxbotica deployed a fleet of their autonomous technology within Ford Mondeo cars on public roads in Stratford, London to test their use in city environments. The £13.2 million project is in collaboration with The DRIVEN Project to develop self-driving cars. == Awards == 2019 Royal Academy of Engineering Silver Medal - Paul Newman 2017 Financial Times ArcelorMittal Boldness in Business Award Barclays Award for Innovation 2016 Frost & Sullivan Award, Technology Leadership for Autonomous Driving Software
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Legal expert system

A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. Legal expert systems employ a rule base or knowledge base and an inference engine to accumulate, reference and produce expert knowledge on specific subjects within the legal domain. == Purpose == It has been suggested that legal expert systems could help to manage the rapid expansion of legal information and decisions that began to intensify in the late 1960s. Many of the first legal expert systems were created in the 1970s and 1980s. Lawyers were originally identified as primary target users of legal expert systems. Potential motivations for this work included: quicker delivery of legal advice; reduced time spent in repetitive, labour-intensive legal tasks; development of knowledge management techniques that were not dependent on staff; reduced overhead and labour costs and higher profitability for law firms; and reduced fees for clients. Some early development work was oriented toward the creation of automated judges. One of the first use cases was the encoding of the British Nationality Act at Imperial College carried out under the supervision of Marek Sergot and Robert Kowalski. Lance Elliot wrote: "The British Nationality Act was passed in 1981 and shortly thereafter was used as a means of showcasing the efficacy of using Artificial Intelligence (AI) techniques and technologies, doing so to explore how the at-the-time newly enacted statutory law might be encoded into a computerized logic-based formalization." The authors’ seminal article, "The British Nationality Act as a Logic Program," published in 1986 in the Communications of the ACM journal, is one of the first and best-known works in computational law, and one of the most widely cited papers in the field. In 2021, the Inaugural CodeX Prize was awarded to Robert Kowalski, Fariba Sadri, and Marek Sergot in acknowledgment of their groundbreaking work on the application of logic programming to the formalization and analysis of the British Nationality Act. Later work on legal expert systems has identified potential benefits to non-lawyers as a means to increase access to legal knowledge. Legal expert systems can also support administrative processes, facilitate decision-making processes, automate rule-based analyses, and exchange information directly with citizen-users. == Types == === Architectural variations === Rule-based expert systems rely on a model of deductive reasoning that utilizes "If A, then B" rules. In a rule-based legal expert system, information is represented in the form of deductive rules within the knowledge base. In rule-based legal expert systems, logic programming has historically been applied to automate complex compliance paperwork. A notable early example designed for high-volume regulatory filings was the 1999 Intelligent Filing Manager (INTELLIFM), which utilized Prolog rules as its core inference engine to automate the generation, publishing, and population of structured forms via distributed COM interfaces. Case-based reasoning models, which store and manipulate examples or cases, hold the potential to emulate an analogical reasoning process thought to be well-suited for the legal domain. This model effectively draws on known experiences our outcomes for similar problems. A neural net relies on a computer model that mimics that structure of a human brain, and operates in a very similar way to the case-based reasoning model. This expert system model is capable of recognizing and classifying patterns within the realm of legal knowledge and dealing with imprecise inputs. Fuzzy logic models attempt to create 'fuzzy' concepts or objects that can then be converted into quantitative terms or rules that are indexed and retrieved by the system. In the legal domain, fuzzy logic can be used for rule-based and case-based reasoning models. === Theoretical variations === Some legal expert system architects have adopted a very practical approach, employing scientific modes of reasoning within a given set of rules or cases. Others have opted for a broader philosophical approach inspired by jurisprudential reasoning modes emanating from established legal theoreticians. === Functional variations === Some legal expert systems aim to arrive at a particular conclusion in law, while others are designed to predict a particular outcome. An example of a predictive system is one that predicts the outcome of judicial decisions, the value of a case, or the outcome of litigation. == Reception == Many forms of legal expert systems have become widely used and accepted by both the legal community and the users of legal services. == Challenges == === Domain-related problems === The inherent complexity of law as a discipline raises immediate challenges for legal expert system knowledge engineers. Legal matters often involve interrelated facts and issues, which further compound the complexity. Factual uncertainty may also arise when there are disputed versions of factual representations that must be input into an expert system to begin the reasoning process. === Computerized problem solving === The limitations of most computerized problem solving techniques inhibit the success of many expert systems in the legal domain. Expert systems typically rely on deductive reasoning models that have difficulty according degrees of weight to certain principles of law or importance to previously decided cases that may or may not influence a decision in an immediate case or context. === Representation of legal knowledge === Expert legal knowledge can be difficult to represent or formalize within the structure of an expert system. For knowledge engineers, challenges include: Open texture: Law is rarely applied in an exact way to specific facts, and exact outcomes are rarely a certainty. Statutes may be interpreted according to different linguistic interpretations, reliance on precedent cases or other contextual factors including a particular judge's conception of fairness. The balancing of reasons: Many arguments involve considerations or reasons that are not easily represented in a logical way. For instance, many constitutional legal issues are said to balance independently well-established considerations for state interests against individual rights. Such balancing may draw on extra-legal considerations that would be difficult to represent logically in an expert system. Indeterminacy of legal reasoning: In the adversarial arena of law, it is common to have two strong arguments on a single point. Determining the 'right' answer may depend on a majority vote among expert judges, as in the case of an appeal. === Time and cost effectiveness === Creating a functioning expert system requires significant investments in software architecture, subject matter expertise and knowledge engineering. Faced with these challenges, many system architects restrict the domain in terms of subject matter and jurisdiction. The consequence of this approach is the creation of narrowly focused and geographically restricted legal expert systems that are difficult to justify on a cost-benefit basis. Current applications of AI in the legal field utilize machines to review documents, particularly when a high level of completeness and confidence in the quality of document analysis is depended upon, such as in instances of litigation and where due diligence play a role. Among the numerically most quantifiable advantages of AI in the legal field are the time and money saving impact by freeing lawyers from having to spend inordinate amounts of their valuable time on routine tasks, aiding in setting free lawyers’ creative energy by reducing stress. This in turn increases the rate of case load reduction by accomplishing better results in less time, which unlocks potential additional revenue per unit of time spend on a case. The cost of setting up and maintaining AI systems in law is more than offset by the attained savings through increased efficacy; unbalanced cost can be assigned to clients. === Lack of correctness in results or decisions === Legal expert systems may lead non-expert users to incorrect or inaccurate results and decisions. This problem could be compounded by the fact that users may rely heavily on the correctness or trustworthiness of results or decisions generated by these systems. == Examples == ASHSD-II is a hybrid legal expert system that blends rule-based and case-based reasoning models in the area of matrimonial property disputes under English law. CHIRON is a hybrid legal expert system that blends rule-based and case-based reasoning models to support tax planning activities under United States tax law and codes. JUDGE is a rule-based legal expert system that deals with sentencing in the criminal legal domain for offences relating to murder, assault and manslaughter. Legislate is a knowledge graph powered contract management platform whi
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Minne Atairu

Minne Atairu is a Nigerian interdisciplinary artist, a recipient of the 2021 Global South Award Lumen Prize for Art and Technology. She generates synthetic Benin Bronzes through recombination of historical fragments, sculptures, texts, images, and sounds. == Early life and education == Atairu was born in Benin, Nigeria. She holds a bachelor's degree in art history from the University of Maiduguri in Maiduguri, Nigeria; a master's degree in museum studies from the George Washington University in Washington, D.C.; and a doctorate in art education from Teachers College, Columbia University in New York City. Her academic research integrates artificial intelligence, art/museum education and hip-hop based education. == Works == Atairu's artmaking involves using artificial intelligence (AI; such as StyleGAN, GPT-3) to make artwork. She uses tools such as Midjourney and Blender software to develop her works. === Mami Wata === Her first work is a Yoruba goddess called Mami Wata where she used Midjourney in generating the images. === To the Hand === For her 2023 installation To the Hand at The Shed arts center, she worked with Blender to convert text into 3D-printed sculptures made of corn starch or sugarcane infused with bronze. The rings of ground terra-cotta that surround the sculpture represent the walls and deep moats of Benin. == Publications == Atairu, Minne (February 1, 2024). "Reimagining Benin Bronzes using generative adversarial networks". AI & Society. 39 (1): 91–102. doi:10.1007/s00146-023-01761-7. ISSN 1435-5655.
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Image analysis

Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information. On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications — including medicine, security, and remote sensing — human analysts still cannot be replaced by computers. For this reason, many important image analysis tools such as edge detectors and neural networks are inspired by human visual perception models. == Digital == Digital Image Analysis or Computer Image Analysis is when a computer or electrical device automatically studies an image to obtain useful information from it. Note that the device is often a computer but may also be an electrical circuit, a digital camera or a mobile phone. It involves the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing. This field of computer science developed in the 1950s at academic institutions such as the MIT A.I. Lab, originally as a branch of artificial intelligence and robotics. It is the quantitative or qualitative characterization of two-dimensional (2D) or three-dimensional (3D) digital images. 2D images are, for example, to be analyzed in computer vision, and 3D images in medical imaging. The field was established in the 1950s—1970s, for example with pioneering contributions by Azriel Rosenfeld, Herbert Freeman, Jack E. Bresenham, or King-Sun Fu. == Techniques == There are many different techniques used in automatically analysing images. Each technique may be useful for a small range of tasks, however there still aren't any known methods of image analysis that are generic enough for wide ranges of tasks, compared to the abilities of a human's image analysing capabilities. Examples of image analysis techniques in different fields include: 2D and 3D object recognition, image segmentation, motion detection e.g. Single particle tracking, video tracking, optical flow, medical scan analysis, 3D Pose Estimation. == Deep learning == Since the early 2010s, deep learning methods have substantially advanced the field of image analysis. In 2012, a deep convolutional neural network (CNN) known as AlexNet achieved a significant reduction in error rates on the ImageNet large-scale image classification benchmark, demonstrating the effectiveness of deep learning for visual recognition tasks. Subsequent architectures such as ResNet introduced residual connections that enabled training of much deeper networks, further improving accuracy across image analysis tasks. Real-time object detection became practical with frameworks such as YOLO (You Only Look Once), which unified detection and classification into a single network pass. In 2020, the Vision Transformer (ViT) demonstrated that transformer architectures, originally developed for natural language processing, could achieve competitive results on image classification when applied directly to sequences of image patches. More recently, foundation models trained on large-scale datasets have enabled zero-shot generalisation across image analysis tasks. The Segment Anything Model (SAM), trained on over one billion masks, can segment arbitrary objects in images without task-specific fine-tuning. These advances have made image analysis techniques increasingly accessible through browser-based tools and open-source implementations. == Applications == The applications of digital image analysis are continuously expanding through all areas of science and industry, including: anatomy, allows for precise measurements, visualization, and statistical analysis of anatomical structures. assay micro plate reading, such as detecting where a chemical was manufactured. astronomy, such as calculating the size of a planet. automated species identification (e.g. plant and animal species) defense error level analysis filtering machine vision, such as to automatically count items in a factory conveyor belt. materials science, such as determining if a metal weld has cracks. medicine, such as detecting cancer in a mammography scan. metallography, such as determining the mineral content of a rock sample. microscopy, such as counting the germs in a swab. automatic number plate recognition; optical character recognition, such as automatic license plate detection. remote sensing, such as detecting intruders in a house, and producing land cover/land use maps. robotics, such as to avoid steering into an obstacle. security, such as detecting a person's eye color or hair color. == Object-based == Object-based image analysis (OBIA) involves two typical processes, segmentation and classification. Segmentation helps to group pixels into homogeneous objects. The objects typically correspond to individual features of interest, although over-segmentation or under-segmentation is very likely. Classification then can be performed at object levels, using various statistics of the objects as features in the classifier. Statistics can include geometry, context and texture of image objects. Over-segmentation is often preferred over under-segmentation when classifying high-resolution images. Object-based image analysis has been applied in many fields, such as cell biology, medicine, earth sciences, and remote sensing. For example, it can detect changes of cellular shapes in the process of cell differentiation.; it has also been widely used in the mapping community to generate land cover. When applied to earth images, OBIA is known as geographic object-based image analysis (GEOBIA), defined as "a sub-discipline of geoinformation science devoted to (...) partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale". The international GEOBIA conference has been held biannually since 2006. OBIA techniques are implemented in software such as eCognition or the Orfeo toolbox.
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It's the Most Terrible Time of the Year

It's the Most Terrible Time of the Year is an AI-generated television commercial created for McDonald's Netherlands by TBWA\Neboko and The Sweetshop. It was released on 6 December 2025 before being pulled four days later due to negative reception over its use of generative artificial intelligence and its cynical, negative depiction of the holiday season. == Plot == On a bleak, snowy day, various people in the city experience different kinds of mishaps during the Christmas season. Among other incidents, families struggle with their huge loads of presents; Santa Claus gets stuck in traffic; a Christmas tree "redecorates" a man's home, sending him through the window; another family puts up with annoying relatives and a burnt Christmas dinner. Because of all this chaos, a man decides to find refuge in a McDonald's outlet. A Christmas choir finishes singing the jingle "It's the Most Terrible Time of the Year" with the call to action to "hide out in McDonald's till January's here". == Campaign == It's the Most Terrible Time of the Year is a 45-second television commercial made by Dutch agency TBWA\Neboko with involvement of United States-based film production studio The Sweetshop. The advertisement was produced heavily with generative artificial intelligence (AI) following the trend set by other brands such as Coca-Cola and Toys "R" Us. McDonald's Netherlands, the client, released a statement that the commercial was meant to depict "the stressful moments during the holidays in the Netherlands". The commercial also used Andy Williams's "It's the Most Wonderful Time of the Year" with lyrics changed to fit with the concept of the advertisement. According to The Sweetshop, the production of the advertisement took "seven weeks". It also added that much effort was put into the commercial compared to the traditional process. Ten people of its in-house AI engine The Gardening Club worked on the project. Los Angeles-based directors Mark Potoka and Matt Spicer were initially credited to be involved in the film but they resigned due to being sidelined from the production process. == Reception == The advertisement was released on McDonald's Netherlands' YouTube channel on 6 December 2025. It had a negative reception over the use of generative AI and the "cynical" concept of the work's story. The video was made private on 9 December 2025. The Sweetshop stated that the production of the advertisement took human effort. McDonald's Netherlands, while stating the original intent of the commercial, released a statement after its pullout that, for many of its customers, the holiday season is the "most wonderful time of the year".
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Kialo

Kialo is an online structured debate platform with argument maps in the form of debate trees. It is a collaborative reasoning tool for thoughtful discussion, understanding different points of view, and collaborative decision-making, showing arguments for and against claims underneath user-submitted theses or questions. The deliberative discourse platform is designed to present hundreds of supporting or opposing arguments in a dynamic argument tree and is streamlined for rational civil debate on topics such as philosophical questions, policy deliberations, entertainment, ethics, science questions, and unsolved problems or subjects of disagreement in general. Argument-boxes are structured into hierarchical branches where the root is the main thesis (or theses) of the debate, enabling deliberation and navigable debates between opposing perspectives. A debate is divided into Pro (supporting) and Con (refuting or devaluing) columns where registered users can add arguments and rate the impact on the weight or validity of the parent claim. The arguments are sorted according to the rating average. Its argument tree structure enables detailed scrutiny of claims at all levels of the tree and allows users to for example quickly understand why a decision was made or which of the aggregated arguments swayed it this way. Newcomers can join a debate at any time and look back at the structured discussion history, and then weigh in at the right place with their new argument or their comment on a specific argument. The design presets a structure on debates "that allows participants to easily see, process, and ultimately assess the many facets of competing claims". The word Kialo is Esperanto for "reason". The platform is the world's largest argument mapping and structured debate site. == Overview == Users can comment on every Pro or Con, for example for requesting sources or expansions. Recent activities of a debate are shown in a panel on the right side of the respective debate. Debates can be found through the search or on the Explore page through their descriptions and topic-tags. Mere comments that do not make a constructive point (a self-contained argument backed by reasoning) are not allowed and are picked up by other users and moderators. "Civil language and sensible observations from opposing perspectives" can be seen also in debates about controversial topics. The site by-design incentivizes fair, rigorous, open-minded dialogue. Contributors making claims often also write counterpoints to their own contribution. Claims need to be shorter than 500 characters and can link to external sources. Debate trees can also start off with multiple theses – such as different policy options or hypotheses. Claims can link to related debates or include segments of them. In the discussion tab of each claim, users can make edit proposals (e.g. for accuracy, improving sources, or changing scope), decide if the argument should be moved or copied to another branch, call for archiving a claim, and ask for extra evidence or clarification. Debates can grow large and complex for which a sunburst diagram visualization of the topology of the debate and the search functionality can be useful. Each debate also has a chat-box. In cases where e.g. a "Con" is a point against multiple in the "Pros", users – through moderators – can link these arguments at the respective places to avoid duplication of content and allowing a clean chain for people to understand which points are arguments against each other. Contributions of users are tracked, enabling a board of thought-leaders for every debate. Other gamification elements include a feature to thank users for their contributions. The "Perspectives" feature allows users to see 'Impact' ratings of supporters and opposers of a thesis as well as of the debate's moderators and individual contributors. It thereby enables participants to see a debate from other participants' perspectives and to sort by them. In Kialo Edu, this feature lets teachers view votes for a whole class, individuals, or supporters/opponents of a specific thesis. Users in both versions of Kialo can vote on the overall debate topic as well as on individual claims to express their perspectives or conclusions, with the rationale (i.e. the main causal arguments) why they voted on the veracity of the thesis as they did not being captured. Voting can be done by any registered user while navigating through any debate that has voting enabled or via using the Guided Voting wizard user interface that automatically walks through branches. As of 2021, Kialo doesn't have a mobile app. == Contents == A 2018 report stated the collaborative argument platform hosts more than 10,000 debates in various languages. It also hosts private debates. The website claims that it has over 18,000 public debates as of July 2023, as well as over 1 million votes and over 720,000 claims. Debates can be found via the site's internal search and up to six tags per debate. Preprint studies have scraped public debates on over 1.4K issues with over 130K statements as of October 2019 and 1628 debates, related to over 1120 categories, with 124,312 unique claims as of June 26, 2020. == Kialo Inc. == The site is run by Kialo Inc. It was founded by German-born entrepreneur and London School of Economics and Political Science graduate Errikos Pitsos in August 2017 and is based in Brooklyn and Berlin. According to a 2018 report, the site does not show advertisements and does not sell user's data. The for-profit company was founded in 2011, Pitsos began to develop the concept in 2012 and described various specifics of the system in 2014. In 2018, he stated that they intend to make money by selling the platform to companies as a deliberation and decision-making tool. The site is free to use for the public and in education. According to the site, as of 2023 Kialo.com is a non-revenue generating site with no ads and no reselling of user data. == Applications and adoption == === Adopted applications === Applications of its content or the platform in society include: Teachers and professors, especially in high schools – including the universities Harvard and Princeton, are using Kialo for class discussions and exercises in critical thinking and reasoning, as consolidating understanding of materials covered in recent classes, more useful and engaging learning experiences, for remote/e-learning, for clearing up misconceptions, teaching logical fallacies and rational argumentation, for academic dialogue, teaching media literacy, and for teaching to sufficiently reflect or research before posting online. Like for debaters of the main site, access for schools and universities is free. Kialo Edu is the custom version of Kialo specifically designed for classroom use where debates are private and locked to invited students. Kialo allows teachers to provide feedback to students on their ideas, argument structure, and research quality while it is left to other students to rate the impacts of their peers' arguments. Students can be allowed to contribute anonymously which may be useful for controversial issues as well as for safeguarding privacy in education. Students are or can be encouraged to back up their claims with evidence which can foster digital literacy and research skills. Students and teachers can use it to arrange their thoughts when structuring an essay or project. The site's name was decided on internally using the software. === Prototypical and theoretical applications === Potential, theoretical, prototypical or little-used applications include: Education Improving critical thinking skills of society at large as well as facilitating deep or efficient thinking and deepening research and debates where e.g. discussions are less shallow and the well-known or many arguments have already been made and in many cases aren't unreasonably over- or underrated. Pitsos claimed that "we're training students to be very good test-takers instead of critical thinkers", suggesting teaching people to think things through may be more important or neglected compared to essay writing skills. Many young people and adults are "submerged into a sea of dispersed information", "[b]rowsing and engaging in superficial thinking activities". Kialo could counteract this issue and help people develop good sane reasoning. Academia, R&D and policy Three scholars from three prestigious U.S. universities outlined possible benefits in this domain, including applications beyond higher education such as for academic communication. They suggest the debate platform could be used for structuring the communication of open peer-review by helping those giving feedback to "hone in on[sic] core arguments and pieces of evidence in an even more direct way" than annotated commenting. It could be used to evaluate extracted argument structures and sequences from raw texts, as in a Semantic Web for arguments. Such "argument mining", to which Kialo is the lar
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Conference on Artificial General Intelligence

The Conference on Artificial General Intelligence (AGI) is a meeting of researchers in the field of artificial general intelligence (AGI) organized by the AGI Society steered by Marcus Hutter and Ben Goertzel. It has been held annually since 2008. The conference was initiated by the 2006 Bethesda Artificial General Intelligence Workshop and has since been hosted at various international venues. == Locations and history == AGI-2026 San Francisco State University, California, USA AGI-2025 Reykjavík University, Reykjavík, Iceland AGI-2024 University of Washington, Seattle, Washington, USA AGI-2023 KTH Royal Institute of Technology, Stockholm, Sweden AGI-2022 The Crocodile, Seattle, Washington, USA AGI-2021 Computer History Museum, Mountain View, California, USA AGI-2020 Virtual Conference AGI-2019 Sheraton Shenzhen Futian, Shenzhen, China AGI-2018 Czech Technical University, Prague, Czech Republic AGI-2017 ibis Melbourne, Melbourne, Australia AGI-2016 The New School, New York, New York, USA AGI-2015 Berlin-Brandenburg Academy of Sciences and Humanities, Berlin, Germany AGI-2014 Université Laval, Quebec City, Canada (sponsored by the Cognitive Science Society and the AAAI) AGI-2013 Peking University, Beijing, China (sponsored by the Cognitive Science Society and the AAAI) AGI-2012 University of Oxford, Oxford, United Kingdom (sponsored by the Future of Humanity Institute and Ray Kurzweil) AGI-2011 Google Headquarters, Mountain View, California, USA (sponsored by Google, AAAI, and Ray Kurzweil) AGI-2010 University of Lugano, Lugano, Switzerland (In Memoriam Ray Solomonoff and sponsored by AAAI and Ray Kurzweil) AGI-2009 Crowne Plaza Crystal City, Arlington, Virginia, USA (sponsored by AAAI and Ray Kurzweil) AGI-2008 University of Memphis, Tennessee, USA (sponsored by AAAI) == Notable speakers == The conference has attracted many speakers over the years including Turing Award winners Yoshua Bengio and Richard S. Sutton as well as Ben Goertzel, Marcus Hutter, Jürgen Schmidhuber, Gary Marcus, John E. Laird, Peter Norvig, Joscha Bach, François Chollet, John L. Pollock, Bill Hibbard, Hugo de Garis, Stan Franklin, Steve Omohundro, Randal A. Koene, Ernst Dickmanns, Margaret Boden, David Hanson, Roman Yampolskly, Selmer Bringsjord, Kristinn R. Thórisson and Nick Bostrom.
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List of video editing software

The following is a list of video editing software. The criterion for inclusion in this list is the ability to perform non-linear video editing. Most modern transcoding software supports transcoding a portion of a video clip, which would count as cropping and trimming. However, items in this article have one of the following conditions: Can perform other non-linear video editing function such as montage or compositing Can do the trimming or cropping without transcoding == Free (libre) or open-source == The software listed in this section is either free software or open source, and may or may not be commercial. === Active and stable === === Inactive === == Proprietary (non-commercial) == The software listed in this section is proprietary, and freeware or freemium. === Active === === Discontinued === == Proprietary (commercial) == The software listed in this section is proprietary and commercial. === Active === === Discontinued ===
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Gundam Build Metaverse

Gundam Build Metaverse (Japanese: ガンダムビルドメタバース, Hepburn: Gandamu Birudo Metabāzu) is a Japanese original net animation anime mini-series produced by Sunrise Beyond, and the fifth series within the Gundam Build Series sub-series. The series celebrates the 10th anniversary of the Gundam Build franchise, including characters from the previous installments. == Plot == The story is set in the same universe of the Gundam Build series in an online metaverse space where users can use avatars to move around and interact with other users, including conducting Gunpla (Gundam plastic model) battles with them. The story centers on Rio Hōjō, a boy who lives in Hawaii, and who learns how to build Gunpla from a local hobbyist named Seria Urutsuki. In the metaverse, a figure known as Mask Lady teaches him the art of Gunpla battling, and he strives to get better at it every day. With his custom Lah Gundam, he seeks out ever stronger opponents. == Characters == === Main characters === Rio Hojo (ホウジョウ・リオ, Hōjō Rio) Voiced by: Chika Anzai A young boy from Hawaii who is an enthusiast of Gunpla Battle and is an apprentice of the mysterious Diver "Mask Lady". Rio's Gunpla is the Lah Gundam, modeled after an entry-grade RX-78-2 Gundam, from the original Mobile Suit Gundam anime series. Seria Urutsuki (ウルツキ・セリア, Urutsuki Seria) / Mask Lady (マスクレディー, Masuku Reidi) Voiced by: Rio Tsuchiya A clerk at a local hobby shop and the instructor at their Gunpla class, Seria becomes Rio's Gunpla mentor using the alias "Mask Lady". Seria's Gunpla is the ZGMF-X20A-PF Gundam Perfect Strike Freedom Rouge, based on both the MBF-02 Strike Rouge and the GAT-X105+AQM/E-YM1 Perfect Strike Gundam from Mobile Suit Gundam Seed and the ZGMF-X20A Strike Freedom Gundam from Mobile Suit Gundam Seed Destiny. === Returning characters === Fumina Hoshino (ホシノ・フミナ, Hoshino Fumina) Voiced by: Yui Makino A veteran Gunpla Battler from the early days of the sport and the Leader of "Team Try Fighters", she works as an advertiser and announcer within the Metaverse realm. Tatsuya Yuuki (ユウキ・タツヤ, Yūki Tatsuya) / Meijin Kawaguchi III (三代目メイジン・カワグチ, Sandaime Meijin Kawaguchi) Voiced by: Takuya Satō A builder and three-times Gunpla Battle world champion who inherited the name of the legendary Meijin Kawaguchi, known as "Meijin Kawaguchi III", and still the current title holder. His newest Gunpla is the Gundam Amazing Barbatos Lupus based on the ASW-G-08 Gundam Barbatos Lupus from Mobile Suit Gundam: Iron-Blooded Orphans. Riku Mikami (ミカミ・リク, Mikami Riku) / Riku (リク) Voiced by: Yūsuke Kobayashi The Founder and former leader of the legendary force, "Build Divers". His Gunpla is the Gundam 00 Diver Arc, the latest version of the original GN-0000DVR Gundam 00 Diver from Gundam Build Divers, incorporating elements from the 00 Gundam from Mobile Suit Gundam 00 and the Gundam AGE-FX from Mobile Suit Gundam AGE. Sarah (サラ, Sara) Voiced by: Haruka Terui An EL-Diver and member of the Build Divers. Momoka Yashiro (ヤシロ・モモカ, Yashiro Momoka) / Momo (モモ) Voiced by: Nene Hieda Member of Build Divers. Her gunpla is the MOMOKAPOOL (R×R), an upgraded version of her PEN-01M Momokapool from Gundam Build Divers Aya Fujisawa (フジサワ・アヤ, Fujisawa Aya) / Ayame (アヤメ) Voiced by: Manami Numakura Member of Build Divers. Her Gunpla is the F-Kunoichi Kai, an SD Gunpla based on the F91 Gundam F91 from Mobile Suit Gundam F91. Sei Iori (イオリ・セイ, Iori Sei) Voiced by: Mikako Komatsu A builder and one time Gunpla Battle World Champion. His current Gunpla is the GAT-X105B/EG Build Strike Exceed Galaxy, the latest version of the original GAT-X105B Build Strike Gundam from Gundam Build Fighters. Aria von Reiji Asuna (アリーア・フォン・レイジ・アスナ, Arīa fon Reiji Asuna) Voiced by: Sachi Kokuryu A prince from the country called Arian that exists within a space colony in another dimension, who became friends with Sei Iori and together won the Gunpla Battle World Championship. He somehow manages to log into the metaverse to reunite with his friend, piloting the SB-011 Star Burning Gundam. Sekai Kamiki (カミキ・セカイ, Kamiki Sekai) Voiced by: Kazumi Togashi A veteran builder and former member of Team Try Fighters. He is currently the Japanese National representative Champion. In the series he develops a rivalry relationship with Hiroto similar to that of Kyoya and Rommel. His current Gunpla is the Shin Burning Gundam, the latest version of the original KMK-B01 Kamiki Burning Gundam from Gundam Build Fighters Try which is based on the Burning Gundam and Master Gundam. Hiroto Kuga (クガ・ヒロト, Kuga Hiroto) / Hiroto (ヒロト, Hiroto) Voiced by: Chiaki Kobayashi A veteran diver, the one responsible for discovering more EL-Divers, and a former member of the legendary force "Avalon", who later joined the unofficial, "BUILD DiVERS" and eventually became the current Force Leader, and as well as the current title holder of "Hero of Gunpla". In the third episode he is the only Build Diver member who participates in the tournament, while his fellow force-mates are in the audience routing for him and Rio. His Gunpla is the Plutine Gundam, which is a combination of his Core Gundam II Plus, upgraded from the Core Gundam II featured in Gundam Build Divers Re:Rise equipped with the Pluto Armor. Magee (マギー, Magī) Voiced by: Taishi Murata A flamboyant veteran Diver who owns a shop in the metaverse and is an acquaintance of Seria's. Freddie (フレディ, Furedi) Voiced by: Ai Kakuma An alien anthropomorphic dog boy from planet Eldora, a support member to both Build Diver teams, who manages to access the metaverse from his home planet along his fellow Eldorans. Ogre (オーガ, Ōga) Voiced by: Wataru Hatano Kyoya Kisugi (キスギ・キョウヤ, Kisugi Kyōya) / Kyoya Kujo (クジョウ・キョウヤ, Kujō Kyōya) Voiced by: Jun Kasama Leader of the legendary force "Avalon" and the reigning and current title holder of "World Champion". He along with Hiroto Kuga, Maria Urutsuki, and Tatsuya Yuuki are currently at the top of the entire gunpla world community. His current gunpla is an recolored version of his AGE-TRYMAG Gundam TRY AGE Magnum from Gundam Build Divers Re:Rise. Susumu Sazaki (サザキ・ススム, Sazaki Susumu) Voiced by: Ryo Hirohashi Kaoruko Sazaki (サザキ・カオルコ, Sazaki Kaoruko) Voiced by: Ryo Hirohashi Mahiru Shigure (シグレ・マヒル, Shigure Mahiru) Voiced by: Rinko Natsuhi Keiko Sano (サノ・ケイコ, Sano Keiko) Voiced by: Ami Naito === Others === Maria Urutsuki (ウルツキ・マリア, Urutsuki Maria) / Mascarilla (マスカリージャ, Masukarīja) Voiced by: Ai Kakuma A mysterious masked woman with a harsh rivalry with Seria and a similar avatar as hers, she is later revealed as Seria's younger sister Maria, who began to loathe her sister after she quit on their dream to fight for the title of Lady Kawaguchi. She later obtains the title, becoming "Lady Kawaguchi VII". Jeff (ジェフさん, Jefu-san) Voiced by: Kenta Miyake A distant relative of Seria and Maria's and owner of the hobby shop where Seria lives. Mellow Neige (メロウ・ネージュ, Merō Nēju) Voiced by: Chikano Ibuki A sentient A.I. who is the current publicity face of the Gunpla Metaverse. == Episodes ==
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Stanhope Demonstrator

The Stanhope Demonstrator was the first machine to solve problems in logic. It was designed by Charles Stanhope, 3rd Earl Stanhope to demonstrate consequences in logic symbolically. The first model was constructed in 1775. It consisted of two slides coloured red and gray mounted in a square brass frame. This could be used to demonstrate the solution to a syllogistic type of problem in which objects might have two different properties and the question was how many would have both properties. Scales marked zero to ten were used to set the numbers or proportions of objects with the two properties. This form of inference anticipated the numerically definite syllogism which Augustus De Morgan laid out in his book, Formal Logic, in 1847. == Construction == The device was a brass plate about four inches square which was mounted on a piece of mahogany which was three-quarters of an inch thick. There was an opening with a depression in the wood about one and a half inches square and half an inch deep. This opening was called the holon, meaning "whole", and represented the full set of objects under consideration. A slide of red translucent glass could be inserted from the right across the holon. A slide of gray wood could be slid under the red slide. When the device was used for the "Rule for the Logic of Certainty", the gray slider was inserted from the left. When it was used for the "Rule for the Logic of Probability", the gray slider was inserted from above. The red and the gray sliders represented the two affirmative propositions which were being combined. Stanhope called these ho and los. At least four of the devices with this square style were built. In 1879, Robert Harley wrote that he had one which he had been given by Stanhope's great-grandson, Arthur, who had kept one. The other two were owned by Henry Prevost Babbage – the son of Charles Babbage, who continued his work on the Analytical Engine. One of the devices was donated to the Science Museum, London by the last Earl in 1953. Other styles, such as circular models, were constructed, but these were less convenient.
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Uncertain inference

Uncertain inference was first described by C. J. van Rijsbergen as a way to formally define a query and document relationship in Information retrieval. This formalization is a logical implication with an attached measure of uncertainty. == Definitions == Rijsbergen proposes that the measure of uncertainty of a document d to a query q be the probability of its logical implication, i.e.: P ( d → q ) {\displaystyle P(d\to q)} A user's query can be interpreted as a set of assertions about the desired document. It is the system's task to infer, given a particular document, if the query assertions are true. If they are, the document is retrieved. In many cases the contents of documents are not sufficient to assert the queries. A knowledge base of facts and rules is needed, but some of them may be uncertain because there may be a probability associated to using them for inference. Therefore, we can also refer to this as plausible inference. The plausibility of an inference d → q {\displaystyle d\to q} is a function of the plausibility of each query assertion. Rather than retrieving a document that exactly matches the query we should rank the documents based on their plausibility in regards to that query. Since d and q are both generated by users, they are error prone; thus d → q {\displaystyle d\to q} is uncertain. This will affect the plausibility of a given query. By doing this it accomplishes two things: Separate the processes of revising probabilities from the logic Separate the treatment of relevance from the treatment of requests Multimedia documents, like images or videos, have different inference properties for each datatype. They are also different from text document properties. The framework of plausible inference allows us to measure and combine the probabilities coming from these different properties. Uncertain inference generalizes the notions of autoepistemic logic, where truth values are either known or unknown, and when known, they are true or false. == Example == If we have a query of the form: q = A ∧ B ∧ C {\displaystyle q=A\wedge B\wedge C} where A, B and C are query assertions, then for a document D we want the probability: P ( D → ( A ∧ B ∧ C ) ) {\displaystyle P(D\to (A\wedge B\wedge C))} If we transform this into the conditional probability P ( ( A ∧ B ∧ C ) | D ) {\displaystyle P((A\wedge B\wedge C)|D)} and if the query assertions are independent we can calculate the overall probability of the implication as the product of the individual assertions probabilities. == Further work == Croft and Krovetz applied uncertain inference to an information retrieval system for office documents they called OFFICER. In office documents the independence assumption is valid since the query will focus on their individual attributes. Besides analysing the content of documents one can also query about the author, size, topic or collection for example. They devised methods to compare document and query attributes, infer their plausibility and combine it into an overall rating for each document. Besides that uncertainty of document and query contents also had to be addressed. Probabilistic logic networks is a system for performing uncertain inference; crisp true/false truth values are replaced not only by a probability, but also by a confidence level, indicating the certitude of the probability. Markov logic networks allow uncertain inference to be performed; uncertainties are computed using the maximum entropy principle, in analogy to the way that Markov chains describe the uncertainty of finite-state machines.
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TIMIT

TIMIT is a corpus of phonemically and lexically transcribed speech of American English speakers of different sexes and dialects. Each transcribed element has been delineated in time. TIMIT was designed to further acoustic-phonetic knowledge and automatic speech recognition systems. It was commissioned by DARPA and corpus design was a joint effort between the Massachusetts Institute of Technology, SRI International, and Texas Instruments (TI). The speech was recorded at TI, transcribed at MIT, and verified and prepared for publishing by the National Institute of Standards and Technology (NIST). There is also a telephone bandwidth version called NTIMIT (Network TIMIT). TIMIT and NTIMIT are not freely available — either membership of the Linguistic Data Consortium, or a monetary payment, is required for access to the dataset. == Data == TIMIT contains ~5 hours of speech, of 10 sentences spoken by each of 630 speakers. The sentences were randomly sampled from a corpus of 2342 sentences. The speakers were native speakers of American English, classified under 8 major dialect regions: New England, Northern, North Midland, South Midland, Southern, New York City, Western, Army Brat (moved around). The speakers were 70% male and 30% female. Recordings were made in a noise-isolated recording booth at Texas Instrument, using a semi-automatic computer system (STEROIDS) to control the presentation of prompts to the speaker and the recording. Two-channel recordings were made using a Sennheiser HMD 414 headset-mounted microphone and a Brüel & Kjær 1/2" far-field pressure microphone (#4165). The speech was digitized at a sample rate of 20 kHz then and downsampled to 16 kHz. == History == The TIMIT telephone corpus was an early attempt to create a database with speech samples. It was published in the year 1988 on CD-ROM and consists of only 10 sentences per speaker. Two 'dialect' sentences were read by each speaker, as well as another 8 sentences selected from a larger set Each sentence averages 3 seconds long and is spoken by 630 different speakers. It was the first notable attempt in creating and distributing a speech corpus and the overall project has produced costs of 1.5 million US$. An update was released in October 1990. It included full 630-speaker corpus; checked and corrected transcriptions; word-alignment transcriptions; NIST SPHERE-headered waveform files and header manipulation software; phonemic dictionary; new test and training subsets balanced for dialectal and phonetic coverage; more extensive documentation. The full name of the project is DARPA-TIMIT Acoustic-Phonetic Continuous Speech Corpus and the acronym TIMIT stands for Texas Instruments/Massachusetts Institute of Technology. The main reason why a corpus of telephone speech was created was to train speech recognition software. In the Blizzard challenge, different software has the obligation to convert audio recordings into textual data and the TIMIT corpus was used as a standardized baseline.
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Distributed artificial intelligence

Distributed Artificial Intelligence (DAI) (also called Decentralized Artificial Intelligence) is a melding of artificial intelligence with distributed computing. From artificial intelligence comes the theory and technology for constructing or analyzing an intelligent system. But where artificial intelligence uses psychology as a source of ideas, inspiration, and metaphor, DAI uses sociology, economics, and management science for inspiration. Where the focus of artificial intelligence is on the individual, the focus of DAI is on the group. Distributed computing provides the computational substrate on which this group focus can occur. Using techniques from artificial intelligence, communication theory, control theory, and interaction theory, it produces a cooperative solution to problems by a decentralized group of computational entities (agents). DAI is closely related to and a predecessor of the field of multi-agent systems. They are distinguished generally by multi-agent systems being open, where the entities might arise from different interests and have individual goals, and distributed artificial-intelligence systems, where the entities have common goals. There are numerous applications and tools. == Definition == Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets. DAI systems consist of autonomous learning processing nodes (agents), that are distributed, often at a very large scale. DAI nodes can act independently, and partial solutions are integrated by communication between nodes, often asynchronously. By virtue of their scale, DAI systems are robust and elastic, and by necessity, loosely coupled. Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment. DAI systems do not require all the relevant data to be aggregated in a single location, in contrast to monolithic or centralized Artificial Intelligence systems, which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large datasets. In addition, the source dataset may change or be updated during the course of the execution of a DAI system. == Development == In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents. As a scientific discipline, it progressed through a series of workshops in the USA (International Workshop on Distributed Artificial Intelligence, held in 13 editions from 1978 - 1994), Europe (Workshop on Modelling Autonomous Agents in a Multi-Agent World https://link.springer.com/conference/maamaw), and Asia (Multi-Agent and Cooperative Computation Workshop (MACC) https://sites.google.com/view/sig-macc/macc-workshop?authuser=0). Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence, or by competition. DAI is categorized into multi-agent systems and distributed problem solving. In multi-agent systems the main focus is how agents coordinate their knowledge and activities. For distributed problem solving the major focus is how the problem is decomposed and the solutions are synthesized. == Goals == The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires: A distributed system with robust and elastic computation on unreliable and failing resources that are loosely coupled Coordination of the actions and communication of the nodes Subsamples of large data sets and online machine learning There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems in DAI research include the following: Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation. Distributed problem solving (DPS): the concept of agent, autonomous entities that can communicate with each other, was developed to serve as an abstraction for developing DPS systems. See below for further details. Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at macro level but also at micro level, as it is in many social simulation scenarios. == Approaches == Two types of DAI has emerged: In Multi-agent systems agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive their environment, and communicate with other agents. An agent is autonomous and has skills to achieve goals. The agents change the state of their environment by their actions. There are a number of different coordination techniques. In distributed problem solving the work is divided among nodes and the knowledge is shared. The main concerns are task decomposition and synthesis of the knowledge and solutions. DAI can apply a bottom-up approach to AI, similar to the subsumption architecture as well as the traditional top-down approach of AI. In addition, DAI can also be a vehicle for emergence. === Challenges === The challenges in Distributed AI are: How to carry out communication and interaction of agents and which communication language or protocols should be used. How to ensure the coherency of agents. How to synthesise the results among 'intelligent agents' group by formulation, description, decomposition and allocation. == Applications and tools == Areas where DAI have been applied are: Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data Networks, e.g. in telecommunications the DAI system controls the cooperative resources in a WLAN network Routing, e.g. model vehicle flow in transport networks Scheduling, e.g. flow shop scheduling where the resource management entity ensures local optimization and cooperation for global and local consistency Search engines, e.g. in LLM federated search like Ithy where document retrieval and analysis are distributed to DAI agents before aggregation Multi-Agent systems, e.g. artificial life, the study of simulated life Electric power systems, e.g. Condition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System DAI integration in tools has included: ECStar is a distributed rule-based learning system. == Agents == === Systems: Agents and multi-agents === Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving. Notion of Multi-Agents: Multi-Agent system is defined as a network of agents which are loosely coupled working as a single entity like society for problem solving that an individual agent cannot solve. === Software agents === The key concept used in DPS and MABS is the abstraction called software agents. An agent is a virtual (or physical) autonomous entity that has an understanding of its environment and acts upon it. An agent is usually able to communicate with other agents in the same system to achieve a common goal, that one agent alone could not achieve. This communication system uses an agent communication language. A first classification that is useful is to divide agents into: reactive agent – A reactive agent is not much more than an automaton that receives input, processes it and produces an output. deliberative agent – A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans. hybrid agent – A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation. Well-recognized agent architectures that describe how an agent is internally structured are: ASMO (emergence of distributed modules) BDI (Believe Desire Intention, a general architecture that describes how plans are made) InterRAP (A three-layer architecture, with a reactive, a deliberative and a social layer) PECS (Physics, Emotion, Cognition, Social, describes how those four parts influences the agents behavior). Soar (a rule-based approach)
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Stanhope Demonstrator

The Stanhope Demonstrator was the first machine to solve problems in logic. It was designed by Charles Stanhope, 3rd Earl Stanhope to demonstrate consequences in logic symbolically. The first model was constructed in 1775. It consisted of two slides coloured red and gray mounted in a square brass frame. This could be used to demonstrate the solution to a syllogistic type of problem in which objects might have two different properties and the question was how many would have both properties. Scales marked zero to ten were used to set the numbers or proportions of objects with the two properties. This form of inference anticipated the numerically definite syllogism which Augustus De Morgan laid out in his book, Formal Logic, in 1847. == Construction == The device was a brass plate about four inches square which was mounted on a piece of mahogany which was three-quarters of an inch thick. There was an opening with a depression in the wood about one and a half inches square and half an inch deep. This opening was called the holon, meaning "whole", and represented the full set of objects under consideration. A slide of red translucent glass could be inserted from the right across the holon. A slide of gray wood could be slid under the red slide. When the device was used for the "Rule for the Logic of Certainty", the gray slider was inserted from the left. When it was used for the "Rule for the Logic of Probability", the gray slider was inserted from above. The red and the gray sliders represented the two affirmative propositions which were being combined. Stanhope called these ho and los. At least four of the devices with this square style were built. In 1879, Robert Harley wrote that he had one which he had been given by Stanhope's great-grandson, Arthur, who had kept one. The other two were owned by Henry Prevost Babbage – the son of Charles Babbage, who continued his work on the Analytical Engine. One of the devices was donated to the Science Museum, London by the last Earl in 1953. Other styles, such as circular models, were constructed, but these were less convenient.
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