AI Generator Youtube Channel Name

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  • Elements of AI

    Elements of AI

    Elements of AI is a massive open online course (MOOC) teaching the basics of artificial intelligence. The course, originally launched in 2018, is designed and organized by the University of Helsinki and learning technology company MinnaLearn. The course includes modules on machine learning, neural networks, the philosophy of artificial intelligence, and using artificial intelligence to solve problems. It consists of two parts: Introduction to AI and its sequel, Building AI, that was released in late 2020. In November 2019, the course was named one of four winners of MIT’s Inclusive Innovation Challenge. University of Helsinki's computer science department is known as the alma mater of Linus Torvalds, a Finnish-American software engineer who is the creator of the Linux kernel, which is the kernel for Linux operating systems. == EU’s AI pledge == The government of Finland has pledged to offer the course for all EU citizens by the end of 2021, as the course is made available in all the official EU languages. The initiative was launched as part of Finland's Presidency of the Council of the European Union in 2019, with the European Commission providing translations of the course materials. In 2017, Finland launched an AI strategy to stay competitive in the field of AI amid growing competition between China and the United States. With the support of private companies and the government, Finland's now-realized goal was to get 1 percent of its citizens to participate in Elements of AI. Other governments have also given their support to the course. For instance, Germany's Federal Minister for Economic Affairs and Energy Peter Altmeier has encouraged citizens to take part in the course to help Germany gain a competitive advantage in AI. Sweden's Minister for Energy and Minister for Digital Development Anders Ygeman has said that Sweden aims to teach 1 percent of its population the basics of AI like Finland has. == Participants == Elements of AI had enrolled more than 1 million students from more than 110 countries by May 2023. A quarter of the course's participants are aged 45 and over, and some 40 percent are women. Among Nordic participants, the share of women is nearly 60 percent. In September 2022, the course was available in Finnish, Swedish, Estonian, English, German, Latvian, Norwegian, French, Belgian, Czech, Greek, Slovakian, Slovenian, Latvian, Lithuanian, Portuguese, Spanish, Irish, Icelandic, Maltese, Croatian, Romanian, Italian, Dutch, Polish, and Danish.

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  • Fifth Generation Computer Systems

    Fifth Generation Computer Systems

    The Fifth Generation Computer Systems (FGCS; Japanese: 第五世代コンピュータ, romanized: daigosedai konpyūta) was a 10-year initiative launched in 1982 by Japan's Ministry of International Trade and Industry (MITI) to develop computers based on massively parallel computing and logic programming. The project aimed to create an "epoch-making computer" with supercomputer-like performance and to establish a platform for future advancements in artificial intelligence. Although FGCS was noted as ahead of its time, and its ambitious goals contributed significantly to the development of concurrent logic programming, it ultimately ended in commercial failure. The term "fifth generation" was chosen to emphasize the system's advanced nature. In the history of computing hardware, there had been four prior "generations" of computers: the first generation utilized vacuum tubes; the second, transistors and diodes; the third, integrated circuits; and the fourth, microprocessors. While earlier generations focused on increasing the number of logic elements within a single CPU, it was widely believed at the time that the fifth generation would achieve enhanced performance through the use of massive numbers of CPUs. == Background == In the late 1960s until the early 1970s, there was much talk about "generations" of computer hardware, then usually organized into three generations First generation: Thermionic vacuum tubes. Mid-1940s. IBM pioneered the arrangement of vacuum tubes in pluggable modules. The IBM 650 was a first-generation computer. Second generation: Transistors. 1956. The era of miniaturization begins. Transistors are much smaller than vacuum tubes, draw less power, and generate less heat. Discrete transistors are soldered to circuit boards, with interconnections accomplished by stencil-screened conductive patterns on the reverse side. The IBM 7090 was a second-generation computer. Third generation: Integrated circuits (silicon chips containing multiple transistors). 1964. A pioneering example is the ACPX module used in the IBM 360/91, which, by stacking layers of silicon over a ceramic substrate, accommodated over 20 transistors per chip; the chips could be packed together onto a circuit board to achieve unprecedented logic densities. The IBM 360/91 was a hybrid second and third-generation computer. Omitted from this taxonomy is the "zeroth-generation" computer based on metal gears (such as the IBM 407) or mechanical relays (such as the Mark I), and the post-third-generation computers based on Very Large Scale Integrated (VLSI) circuits. There was also a parallel set of generations for software: First generation: Machine language. Second generation: Low-level programming languages such as Assembly language. Third generation: Structured high-level programming languages such as C, COBOL and FORTRAN. Fourth generation: "Non-procedural" high-level programming languages (such as object-oriented languages). Throughout these multiple generations up to the 1970s, Japan built computers following U.S. and British leads. In the mid-1970s, the Ministry of International Trade and Industry stopped following western leads and started looking into the future of computing on a small scale. They asked the Japan Information Processing Development Center (JIPDEC) to indicate a number of future directions, and in 1979 offered a three-year contract to carry out more in-depth studies along with industry and academia. It was during this period that the term "fifth-generation computer" started to be used. Prior to the 1970s, MITI guidance had successes such as an improved steel industry, the creation of the oil supertanker, the automotive industry, consumer electronics, and computer memory. MITI decided that the future was going to be information technology. However, the Japanese language, particularly in its written form, presented and still presents obstacles for computers. As a result of these hurdles, MITI held a conference to seek assistance from experts. The primary fields for investigation from this initial project were: Inference computer technologies for knowledge processing Computer technologies to process large-scale data bases and knowledge bases High-performance workstations Distributed functional computer technologies Super-computers for scientific calculation == Project launch == The aim was to build parallel computers for artificial intelligence applications using concurrent logic programming. The project imagined an "epoch-making" computer with supercomputer-like performance running on top of large databases (as opposed to a traditional filesystem) using a logic programming language to define and access the data using massively parallel computing/processing. They envisioned building a prototype machine with performance between 100M and 1G LIPS, where a LIPS is a Logical Inference Per Second. At the time typical workstation machines were capable of about 100k LIPS. They proposed to build this machine over a ten-year period, 3 years for initial R&D, 4 years for building various subsystems, and a final 3 years to complete a working prototype system. In 1982 the government decided to go ahead with the project, and established the Institute for New Generation Computer Technology (ICOT) through joint investment with various Japanese computer companies. After the project ended, MITI would consider an investment in a new "sixth generation" project. Ehud Shapiro captured the rationale and motivations driving this project: "As part of Japan's effort to become a leader in the computer industry, the Institute for New Generation Computer Technology has launched a revolutionary ten-year plan for the development of large computer systems which will be applicable to knowledge information processing systems. These Fifth Generation computers will be built around the concepts of logic programming. In order to refute the accusation that Japan exploits knowledge from abroad without contributing any of its own, this project will stimulate original research and will make its results available to the international research community." === Logic programming === The target defined by the FGCS project was to develop "Knowledge Information Processing systems" (roughly meaning, applied Artificial Intelligence). The chosen tool to implement this goal was logic programming. Logic programming approach as was characterized by Maarten Van Emden – one of its founders – as: The use of logic to express information in a computer. The use of logic to present problems to a computer. The use of logical inference to solve these problems. More technically, it can be summed up in two equations: Program = Set of axioms. Computation = Proof of a statement from axioms. The Axioms typically used are universal axioms of a restricted form, called Horn-clauses or definite-clauses. The statement proved in a computation is an existential statement. The proof is constructive, and provides values for the existentially quantified variables: these values constitute the output of the computation. Logic programming was thought of as something that unified various gradients of computer science (software engineering, databases, computer architecture and artificial intelligence). It seemed that logic programming was a key missing connection between knowledge engineering and parallel computer architectures. == Results == After having influenced the consumer electronics field during the 1970s and the automotive world during the 1980s, the Japanese had developed a strong reputation. The launch of the FGCS project spread the belief that parallel computing was the future of all performance gains, producing a wave of apprehension in the computer field. Soon parallel projects were set up in the US as the Strategic Computing Initiative and the Microelectronics and Computer Technology Corporation (MCC), in the UK as Alvey, and in Europe as the European Strategic Program on Research in Information Technology (ESPRIT), as well as the European Computer‐Industry Research Centre (ECRC) in Munich, a collaboration between ICL in Britain, Bull in France, and Siemens in Germany. The project ran from 1982 to 1994, spending a little less than ¥57 billion (about US$320 million) total. After the FGCS Project, MITI stopped funding large-scale computer research projects, and the research momentum developed by the FGCS Project dissipated. However MITI/ICOT embarked on a neural-net project which some called the Sixth Generation Project in the 1990s, with a similar level of funding. Per-year spending was less than 1% of the entire R&D expenditure of the electronics and communications equipment industry. For example, the project's highest expenditure year was 7.2 million yen in 1991, but IBM alone spent 1.5 billion dollars (370 billion yen) in 1982, while the industry spent 2150 billion yen in 1990. === Concurrent logic programming === In 1982, during a visit to the ICOT, Ehud Shapiro invented Concurrent Prolog, a novel programming language t

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  • DAYDREAMER

    DAYDREAMER

    DAYDREAMER is a goal-based agent and cognitive architecture developed at the University of California, Los Angeles by Erik T. Mueller and Michael G. Dyer beginning in 1983. The system models the human stream of thought and how it is triggered and directed by emotions, simulating human daydreaming. Taking situational descriptions as input, DAYDREAMER produces English-language daydreams as output and encodes new daydreams, plans, and planning strategies for later reuse. The program comprises five components: a scenario generator based on relaxed planning, a dynamic episodic memory, a collection of personal goals and control goals, an emotion component, and domain knowledge of interpersonal relations and everyday occurrences. The source code was released under a free software license in 2015. == History == Erik Mueller began DAYDREAMER in 1983 while he was a doctoral student in the Artificial Intelligence Laboratory of the Computer Science Department at the University of California, Los Angeles, studying under Michael G. Dyer. Initial development of the project was supported by a grant from the W. M. Keck Foundation with matching funds from the UCLA School of Engineering and Applied Sciences. Additionally, Mueller was supported by an Atlantic Richfield Doctoral Fellowship and Dyer by an IBM Faculty Development Award. The first published descriptions of the program appeared in 1985 at the Ninth International Joint Conference on Artificial Intelligence in Los Angeles and at the Seventh Annual Conference of the Cognitive Science Society in Irvine. Work on the program continued, and a book, Daydreaming in Humans and Machines, was published by Ablex Publishing in 1990. The program was implemented on top of GATE, a knowledge-representation and inference substrate developed by Mueller and Uri Zernik at UCLA, and was originally written in T, a dialect of Scheme. In 2015, Mueller released the DAYDREAMER source code, version 3.5, a Common Lisp rewrite of the original T implementation, on GitHub under the GNU General Public License version 2. The release comprised approximately 12,000 lines of Common Lisp code, along with the GATE knowledge-representation substrate on which DAYDREAMER had originally been built. == Architecture == The program operates in two modes. In daydreaming mode it daydreams continuously until interrupted, while performance mode allows it to demonstrate behavior it has learned through daydreaming. === Emotion and control goals === Emotions and daydreaming form a feedback loop for DAYDREAMER. Emotions activate goals that produce daydreams, and the resulting daydreams modify existing emotions and trigger new ones, which prompt subsequent daydreaming. Recall of a goal success produces a positive emotion whereas recall of a goal failure produces a negative emotion. Emotions activate a set of goals, called control goals, which direct the course of a daydream. The program has four control goals. "Rationalization" generates reasons why an unsatisfactory outcome is in fact acceptable, in order to reduce a negative emotion and maintain self-esteem. "Revenge" is activated by anger when a failure is caused by another and reduces negative emotion through imagined retaliation. "Failure/success reversal" imagines alternative scenarios in which a failure was prevented or a success did not occur as a means of learning planning strategies for future situations. "Preparation" generates hypothetical future scenarios in order to rehearse plans and actions for events that have not yet occurred. === Scenario generator and relaxed planning === The scenario generator produces the sequence of events that make up a daydream. It operates under multiple, often conflicting personal goals rather than pursuing a single goal, applies relaxation rules that permit the generation of non-realistic scenarios, and it draws on episodic memory of past experiences both as subject matter and as a source of planning knowledge. The personal goals that guide the scenario generator include health, food, sex, friendship, love, possessions, self-esteem, social esteem, enjoyment, and achievement. These goals are organized into a goal tree that specifies their relative importance at any given time. Relaxation rules allow the program to set aside its ordinary constraints when generating a scenario. The four constraints that may be relaxed are the behavior of others, the daydreamer's own attributes, physical constraints, and social constraints. The degree of relaxation varies with the active control goal. For example a failure-reversal goal aimed at alternatives uses a low level of relaxation, whereas a revenge goal aimed at a retaliation uses a high level. === Episodic memory and analogy === DAYDREAMER's episodic memory stores its personal and vicarious experiences along with the daydreams it generates. The memory is described as dynamic because it is continually modified during daydreaming such that previously daydreamed episodes become available alongside real ones. As it daydreams, the program indexes daydreams, future plans or actions, and planning strategies into memory. Episodes are organized and retrieved using surface-level similarities, emotions, abstract themes, and Plot Units which are abstract configurations of positive and negative outcomes developed by Wendy Lehnert. A recalled episode is adapted to the current situation through analogy, which requires less effort than generating an equivalent scenario from scratch. == Sample output == In the sample experience from the source code, called LOVERS1, DAYDREAMER begins from an initial situation in which it has a job, is not romantically involved, and is at home. Starting in daydreaming mode, it activates a top-level goal to be in a romantic relationship because it is not currently in one, and a positive motivating emotion of interest becomes associated with that goal. The program then activates a goal to be entertained and pursues seeing a film as a way to achieve it. Facts asserted into memory are converted to English and produced as output, such as "I want to be going out with someone" and "I have to go see a movie". == Reception and influence == DAYDREAMER has been cited in research on computational models of creativity, emotion, and narrative. Linda Wills and Janet Kolodner cite the program as an example of work on opportunism in their study of serendipitous recognition in design. Joseph Bates, A. Bryan Loyall, and W. Scott Reilly of the Carnegie Mellon Oz Project cite DAYDREAMER among prior work in their description of an architecture combining action, emotion, and social behavior. Rafael Pérez y Pérez, Ricardo Sosa, and Christian Lemaitre cite Mueller's DAYDREAMER as one of the few computer models at the time to model daydreaming during the creative process. Jichen Zhu and D. Fox Harrell likewise cite the program in their work on imagining and agency in generative interactive narrative.

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  • Computer Science Ontology

    Computer Science Ontology

    The Computer Science Ontology (CSO) is an automatically generated taxonomy of research topics in the field of Computer Science. It was produced by the Open University in collaboration with Springer Nature by running an information extraction system over a large corpus of scientific articles. Several branches were manually improved by domain experts. The current version (CSO 3.2) includes about 14K research topics and 160K semantic relationships. CSO is available in OWL, Turtle, and N-Triples. It is aligned with several other knowledge graphs, including DBpedia, Wikidata, YAGO, Freebase, and Cyc. New versions of CSO are regularly released on the CSO Portal. CSO is mostly used to characterise scientific papers and other documents according to their research areas, in order to enable different kinds of analytics. The CSO Classifier is an open-source python tool for automatically annotating documents with CSO. == Applications == Recommender Systems. Computing the semantic similarity of documents. Extracting metadata from video lecture subtitles. Performing bibliometrics analysis.

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  • CloudPassage

    CloudPassage

    CloudPassage is a company that provides an automation platform, delivered via software as a service, that improves security for private, public, and hybrid cloud computing environments. CloudPassage is headquartered in San Francisco. == History == CloudPassage was founded by Carson Sweet, Talli Somekh, and Vitaliy Geraymovych in 2010. The company used cloud computing and big data analytics to implement security monitoring and control in a platform called Halo. CloudPassage spent a year in stealth developing the Halo technology, coming out of stealth mode to a closed beta in January 2011. In June 2012, the company launched the commercial product that included configuration security monitoring, network microsegmentation, and two-factor authentication for privileged access management. By 2013, CloudPassage expanded Halo to support large enterprises with advanced security and compliance requirements with a product called Halo Enterprise. The first round of venture funding for the company raised $6.5 million. In April 2012, CloudPassage raised $14 million. The financing round was led by Tenaya Capital. In February 2014, CloudPassage announced that it had raised $25.5 million in funding led by Shasta Ventures. In total, the company has invested over $30 million in its technology and raised approximately $88 million in capital. == Product == The CloudPassage platform provides cloud workload security and compliance for systems hosted in public or private cloud infrastructure environments, including hybrid cloud and multi-cloud workload hosting models. The flagship product the company offers is called Halo. Halo secures virtual servers in public, private, and hybrid cloud infrastructures and provides file integrity monitoring (FIM) while also administering firewall automation, vulnerability monitoring, network access control, security event alerting, and assessment. The Halo platform also provides security applications such as privileged access management, software vulnerability scanning, multifactor authentication, and log-based IDS. In December 2013, CloudPassage set up six servers with Microsoft Windows and Linux operating systems and combinations of popular programs and invited hackers to attempt to hack into the servers. The top prize was $5,000 and the winning hacker was a novice that completed the task in four hours. CloudPassage programmed the servers to use basic default security settings to show how vulnerable cloud computing programs can be to security threats. == Awards and recognition == In May 2011, Gigaom named CloudPassage in its list of the Top 50 Cloud Innovators. That same month, eWeek recognized CloudPassage as one of 16 Hot Startup Companies Flying Under the Radar. SC Magazine named CloudPassage an Industry Innovator in the Virtualization and Cloud Security category in 2012. Also in 2012, The Wall Street Journal named CloudPassage a runner-up in the Information Security category of its Technology Innovation Awards. The CloudPassage large-scale security program, Halo, won Best Security Solution in 2014 at the SIIA Codie awards.

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  • Ashish Vaswani

    Ashish Vaswani

    Ashish Vaswani is an Indian computer scientist and entrepreneur. He conducted research at Google Brain, co-founded Adept AI, and, as of 2025, was co-founder and chief executive officer of Essential AI. Vaswani is a co-author of the 2017 paper "Attention Is All You Need", which introduced the Transformer neural network architecture. The Transformer model has been used in the development of subsequent NLP models BERT, ChatGPT, and their successors. == Career == Vaswani completed his engineering in Computer Science from Birla Institute of Technology, Mesra (BIT Mesra) in 2002. In 2004, he enrolled at the University of Southern California for graduate studies. He earned his PhD in Computer Science at the University of Southern California supervised by David Chiang. During his research career at Google, Vaswani was part of the Google Brain team, where he conducted the work leading to the 'Attention Is All You Need' publication. Prior to joining Google, he was affiliated with the Information Sciences Institute at the University of Southern California. After Google, Vaswani co-founded Adept AI, a machine learning-focused startup that developed AI agents and tools for software automation. He has since left the company. He later co-founded Essential AI with Niki Parmar. As of 2025, he was chief executive officer of Essential AI. == Notable works == Vaswani's most notable paper, "Attention Is All You Need", was published in 2017. The paper introduced the Transformer model, which uses self-attention mechanisms instead of recurrence for sequence-to-sequence tasks. The Transformer architecture has become foundational to modern language models and NLP systems, including BERT (2018), GPT-2, GPT-3 (2019–2020) and many more recent models. The "Attention Is All You Need" paper is among the most cited papers in machine learning.

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  • HTK (software)

    HTK (software)

    HTK (Hidden Markov Model Toolkit) is a proprietary software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used in many other pattern recognition applications that employ HMMs, including speech synthesis, character recognition and DNA sequencing. Originally developed at the Machine Intelligence Laboratory (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED), HTK is now being widely used among researchers who are working on HMMs.

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  • National Security Commission on Artificial Intelligence

    National Security Commission on Artificial Intelligence

    The National Security Commission on Artificial Intelligence (NSCAI) was an independent commission of the United States of America from 2018 to 2021. Its mission was to make recommendations to the President and Congress to "advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States". The commission's 15 members were nominated by the United States Congress. The NSCAI was dissolved on 1 October 2021. == History and reporting == The NSCAI began working in March 2019 and by November 2019 it had received more than 200 classified and unclassified briefings to help with the creation of its final report due in 2021.On 4 November 2019, the NSCAI shared its interim report with Congress, where it explained the 27 initial judgements to base its ongoing work. In the interim report the commission also agreed on seven principles: Global leadership in AI technology is a national security priority AI adoption is an urgent imperative for national security A shared sense of responsibility for the American peoples security must be created from government officials and private sector leaders. It needs to find local AI talent and use it to attract the world’s best minds Actions used for the protection of America’s AI leadership against foreign threats needs to follow the principles of free enterprise, free inquiry and free flow of ideas. The technical limitations of AI are universally known, however, a strong desire remains for powerful, dependable, and secure AI systems. United States used AI must follow American values including the rule of law Fundamental areas of effort for the preservation of U.S. advantages were also agreed upon in the interim report of 2019. The NSCAI released its first report of recommendations in March 2020, most of which were included in the 2021 National Defense Authorization Act. In July 2020, the commission published the second report to Congress. It identified 35 actions for both Executive and Legislative branches, which were focused on six fundamental areas. This report was available to the public. In January 2021, a draft of the final report was presented at a panel led by Schmidt. The report recommended the US to use AI technology for military use and development. It issued its final report in March 2021, saying that the U.S. is not sufficiently prepared to defend or compete against China in the AI era. It was broken up into two parts, the first titled “Defending America in the AI Era”, and the second “Winning the Technology Competition”. The report spoke about China’s efforts and investments into integration and that it could very well take the lead in AI in the next few years. Additional suggestions were made to concentrate on AI in everything we do and to implement it into US national security on multiple levels, as well as focus on bringing in new talent to develop AI and to introduce it to the working force on both civilian and military levels. Another recommendation of the NSCAI report was to develop and provide China and Russia with alternative models that are based on norms and democratic values. The final report also included a proposed $40 billion budget for government spending. On 14 April 2021, NSCAI executive director Ylli Bajraktari and director of Research and Analysis Justin Lynch participated in an event held by the Center for Security and Emerging Technology (CSET) to discuss the final report findings. In October 2021, NSCAI chair Eric Schmidt founded the bipartisan, non-profit Special Competitive Studies Project (SCSP) through his family led non-profit Eric & Wendy Schmidt Fund for Strategic Innovation in order to carry on the NSCAI’s efforts and expand beyond national security. The Foundation for Defense of Democracies held an event in June 2023, called “Thinking Forward After the NSCAI and CSC: A Discussion on AI and Cyber Policy”, with former members of NSCAI on the moderation panel, including Eric Schmidt and Ylli Bajraktari. == Members == Members of the National Security Commission on Artificial Intelligence: Eric Schmidt (chair), former CEO of Google Robert Work (Vice Chair), former Deputy Secretary of Defense Mignon Clyburn, former Commissioner of the Federal Communications Commission Chris Darby, CEO of In-Q-Tel Kenneth M. Ford, CEO of the Florida Institute for Human and Machine Cognition Jose-Marie Griffiths, President of Dakota State University Eric Horvitz, Technical Fellow at Microsoft Katrina G. McFarland, former Assistant Secretary of Defense for Acquisition Jason Matheny, Director of the Center for Security and Emerging Technology at Georgetown University Gilman Louie, partner at Alsop Louie Partners William Mark, vice president at SRI International Andy Jassy, CEO of Amazon Web Services (AWS) Safra Catz, CEO of Oracle Steve Chien, Technical Fellow at Jet Propulsion Laboratory (JPL) Andrew Moore, Google/Alphabet == Recommendations == The report's recommendations include: Dramatically increasing non-defense federal spending on AI research and development, doubling every year from $2 billion in 2022, to $32 billion in 2026. That would bring it up to a level similar to spending on biomedical research A dramatic increase in undergraduate scholarship and graduate studies fellowships in AI Creation of a Digital Corps to bring skilled tech workers into government Founding of a Digital Service Academy: an accredited university providing subsidized education in exchange for a commitment to work for a time in government Include civil rights and civil liberty reports for new AI systems or major updates to existing systems Expanding allocations of employment-based green cards, and giving them to every AI PhD graduate from an accredited U.S. university Reforming the acquisition management system Department of Defense to make it faster and easier to introduce new technologies == Transparency == In December 2019, a ruling was made under the Freedom of Information Act (FOIA) that the NSCAI must also provide historical documents upon request. The Electronic Privacy Information Center (EPIC) filed the lawsuit against the NSCAI in September 2019 after being refused information about the upcoming meetings and prepared records of the commission under FOIA and the Federal Advisory Committee Act (FACA). The U.S. District Court for the District of Columbia ruled in June 2020 that the NSCAI must comply with FACA and therefore hold open meetings and provide records to the public. The lawsuit was also filed by EPIC.

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  • Visual servoing

    Visual servoing

    Visual servoing, also known as vision-based robot control and abbreviated VS, is a technique which uses feedback information extracted from a vision sensor (visual feedback) to control the motion of a robot. One of the earliest papers that talks about visual servoing was from the SRI International Labs in 1979. == Visual servoing taxonomy == There are two fundamental configurations of the robot end-effector (hand) and the camera: Eye-in-hand, or end-point open-loop control, where the camera is attached to the moving hand and observing the relative position of the target. Eye-to-hand, or end-point closed-loop control, where the camera is fixed in the world and observing the target and the motion of the hand. Visual Servoing control techniques are broadly classified into the following types: Image-based (IBVS) Position/pose-based (PBVS) Hybrid approach IBVS was proposed by Weiss and Sanderson. The control law is based on the error between current and desired features on the image plane, and does not involve any estimate of the pose of the target. The features may be the coordinates of visual features, lines or moments of regions. IBVS has difficulties with motions very large rotations, which has come to be called camera retreat. PBVS is a model-based technique (with a single camera). This is because the pose of the object of interest is estimated with respect to the camera and then a command is issued to the robot controller, which in turn controls the robot. In this case the image features are extracted as well, but are additionally used to estimate 3D information (pose of the object in Cartesian space), hence it is servoing in 3D. Hybrid approaches use some combination of the 2D and 3D servoing. There have been a few different approaches to hybrid servoing 2-1/2-D Servoing Motion partition-based Partitioned DOF Based == Survey == The following description of the prior work is divided into 3 parts Survey of existing visual servoing methods. Various features used and their impacts on visual servoing. Error and stability analysis of visual servoing schemes. === Survey of existing visual servoing methods === Visual servo systems, also called servoing, have been around since the early 1980s , although the term visual servo itself was only coined in 1987. Visual Servoing is, in essence, a method for robot control where the sensor used is a camera (visual sensor). Servoing consists primarily of two techniques, one involves using information from the image to directly control the degrees of freedom (DOF) of the robot, thus referred to as Image Based Visual Servoing (IBVS). While the other involves the geometric interpretation of the information extracted from the camera, such as estimating the pose of the target and parameters of the camera (assuming some basic model of the target is known). Other servoing classifications exist based on the variations in each component of a servoing system , e.g. the location of the camera, the two kinds are eye-in-hand and hand–eye configurations. Based on the control loop, the two kinds are end-point-open-loop and end-point-closed-loop. Based on whether the control is applied to the joints (or DOF) directly or as a position command to a robot controller the two types are direct servoing and dynamic look-and-move. Being one of the earliest works the authors proposed a hierarchical visual servo scheme applied to image-based servoing. The technique relies on the assumption that a good set of features can be extracted from the object of interest (e.g. edges, corners and centroids) and used as a partial model along with global models of the scene and robot. The control strategy is applied to a simulation of a two and three DOF robot arm. Feddema et al. introduced the idea of generating task trajectory with respect to the feature velocity. This is to ensure that the sensors are not rendered ineffective (stopping the feedback) for any the robot motions. The authors assume that the objects are known a priori (e.g. CAD model) and all the features can be extracted from the object. The work by Espiau et al. discusses some of the basic questions in visual servoing. The discussions concentrate on modeling of the interaction matrix, camera, visual features (points, lines, etc..). In an adaptive servoing system was proposed with a look-and-move servoing architecture. The method used optical flow along with SSD to provide a confidence metric and a stochastic controller with Kalman filtering for the control scheme. The system assumes (in the examples) that the plane of the camera and the plane of the features are parallel., discusses an approach of velocity control using the Jacobian relationship s˙ = Jv˙ . In addition the author uses Kalman filtering, assuming that the extracted position of the target have inherent errors (sensor errors). A model of the target velocity is developed and used as a feed-forward input in the control loop. Also, mentions the importance of looking into kinematic discrepancy, dynamic effects, repeatability, settling time oscillations and lag in response. Corke poses a set of very critical questions on visual servoing and tries to elaborate on their implications. The paper primarily focuses the dynamics of visual servoing. The author tries to address problems like lag and stability, while also talking about feed-forward paths in the control loop. The paper also, tries to seek justification for trajectory generation, methodology of axis control and development of performance metrics. Chaumette in provides good insight into the two major problems with IBVS. One, servoing to a local minima and second, reaching a Jacobian singularity. The author show that image points alone do not make good features due to the occurrence of singularities. The paper continues, by discussing the possible additional checks to prevent singularities namely, condition numbers of J_s and Jˆ+_s, to check the null space of ˆ J_s and J^T_s . One main point that the author highlights is the relation between local minima and unrealizable image feature motions. Over the years many hybrid techniques have been developed. These involve computing partial/complete pose from Epipolar Geometry using multiple views or multiple cameras. The values are obtained by direct estimation or through a learning or a statistical scheme. While others have used a switching approach that changes between image-based and position-based on a Lyapnov function. The early hybrid techniques that used a combination of image-based and pose-based (2D and 3D information) approaches for servoing required either a full or partial model of the object in order to extract the pose information and used a variety of techniques to extract the motion information from the image. used an affine motion model from the image motion in addition to a rough polyhedral CAD model to extract the object pose with respect to the camera to be able to servo onto the object (on the lines of PBVS). 2-1/2-D visual servoing developed by Malis et al. is a well known technique that breaks down the information required for servoing into an organized fashion which decouples rotations and translations. The papers assume that the desired pose is known a priori. The rotational information is obtained from partial pose estimation, a homography, (essentially 3D information) giving an axis of rotation and the angle (by computing the eigenvalues and eigenvectors of the homography). The translational information is obtained from the image directly by tracking a set of feature points. The only conditions being that the feature points being tracked never leave the field of view and that a depth estimate be predetermined by some off-line technique. 2-1/2-D servoing has been shown to be more stable than the techniques that preceded it. Another interesting observation with this formulation is that the authors claim that the visual Jacobian will have no singularities during the motions. The hybrid technique developed by Corke and Hutchinson, popularly called portioned approach partitions the visual (or image) Jacobian into motions (both rotations and translations) relating X and Y axes and motions related to the Z axis. outlines the technique, to break out columns of the visual Jacobian that correspond to the Z axis translation and rotation (namely, the third and sixth columns). The partitioned approach is shown to handle the Chaumette Conundrum discussed in. This technique requires a good depth estimate in order to function properly. outlines a hybrid approach where the servoing task is split into two, namely main and secondary. The main task is keep the features of interest within the field of view. While the secondary task is to mark a fixation point and use it as a reference to bring the camera to the desired pose. The technique does need a depth estimate from an off-line procedure. The paper discusses two examples for which depth estimates are obtained from robot odometry and by assuming that all

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  • Mittens (chess)

    Mittens (chess)

    Mittens is a chess engine developed by Chess.com. It was released on January 1, 2023, alongside four other engines, all of them given cat-related names. The engine became a viral sensation in the chess community due to exposure through content made by chess streamers and a social media marketing campaign, later contributing to record levels of traffic to the Chess.com website and causing issues with database scalability. Mittens was given a rating of one point by Chess.com, although it was evidently stronger than that. Various chess masters played matches against the engine, with players such as Hikaru Nakamura and Levy Rozman drawing and losing their games respectively. A month after its release, Mittens was removed from the website on February 1, as expected through Chess.com's monthly bot cycles. In December 2023, Mittens was brought back in a group of Chess.com's most popular bots of 2023. In January 2024, Mittens was removed again. == Release == Mittens was released on January 1, 2023, as part of a New Year event on Chess.com. It was one of five engines released, all with names related to cats. The other engines released were named Scaredy Cat, rated 800; Angry Cat, rated 1000; Mr. Grumpers, rated 1200 and Catspurrov (a pun on Garry Kasparov), rated 1400. As part of the announcement, a picture of each engine was accompanied by a short description of its character. The description given for Mittens suggested that the engine was hiding something, reading: Mittens likes chess… But how good is she? Of the five engines released, Mittens was by far the most popular. In December 2023, Chess.com re-released Mittens as part of a "best of 2023" group of chess bots made to showcase their most popular bots of the year. == Design == Mittens was conceptualized by Chess.com employee Will Whalen. Appearing as a kitten, Mittens trash talked its opponents with a selection of voice lines: these lines included quotes from J. Robert Oppenheimer, Vincent van Gogh and Friedrich Nietzsche, as well as the 1967 film Le Samouraï. The engine's "personality" was devised by a writing team headed by Sean Becker, and Marija Casic provided the engine's graphics. Chess.com did not disclose any information about the software running the engine. It may be based on Chess.com's Komodo Dragon 3 engine. Mittens' strategy was to slowly grind down an opponent, a tactic likened to the playing style of Anatoly Karpov. Becker stated that the design team believed it would be "way more demoralizing and funny" for the engine to play this way. According to Hikaru Nakamura, Mittens sometimes missed the best move (or winning positions). == Rating == On Chess.com, Mittens had a rating of one point. However, the engine's playing style and tactics showed that it was stronger than that; Mittens was able to beat or draw against many top human players. In an interview with CNN Business, Whalen stated that the idea behind giving Mittens a rating of one was to surprise its opponents, giving it the upper hand psychologically. Estimates of Mittens' true rating range from an Elo of 3200 to 3500, because of its ability to beat other engines of around that level. An upper bound of the engine's rating was found after Levy Rozman made Mittens play against Stockfish 15, a 3700 rated engine. Mittens lost the two games that the engines played. The range of Mittens' possible ratings was summarized by Dot Esports, who stated: It seems like she’s around the 3200–3500 rating range (in Chess.com terms, where the best human players, like Magnus Carlsen and Hikaru Nakamura, sport a 3000–3100 rating in the faster formats), as evidenced by her victories over the site’s otherwise strongest, 3200-rated bots, and her defeat to Stockfish 15, which is currently rated around 3700. == Games == Against human players, Mittens won over 99 percent of the millions of games it played. Chess players such as Hikaru Nakamura, Benjamin Bok, Levy Rozman and Eric Rosen struggled against Mittens; while Rozman and Rosen both lost against the engine, Nakamura and Bok were both able to make a draw. In particular, Nakamura's game against the engine lasted 166 moves; he was playing as White. Bok, Benjamin Finegold and Rozman later went on to win against Mittens, the latter with engine assistance from Stockfish. Magnus Carlsen publicly refused to play the engine, calling it a "transparent marketing trick" and "a soulless computer". Against other chess engines, Mittens participated in the Chess.com Computer Chess Championship as a side act. In the competition, Mittens played 150 games against an engine named after the film M3GAN and won overall with a score of 81.5 to 68.5. This equated to 54 percent of the games played. During the event, an estimate of Mittens' rating was made at 3515 points. == Impact == Mittens went viral in the chess community due to its concept and design: according to an announcement by Chess.com, a combined total of 120 million games were played against the cat engines over the course of January, with around 40 million played against Mittens. The popularity of the engine was helped by the social media exposure created by Chess.com. This included creating an official Twitter account to promote the engine. Chess streamers like Rozman and Nakamura helped cultivate this by creating content around the engine. A video by Nakamura entitled "Mittens the chess bot will make you quit chess" gained over 3.5 million views on YouTube. On January 11, Chess.com reported issues with database scalability due to record levels of traffic: 40 percent more games had been played on Chess.com in January 2023 than any other month since the website's release. According to The Wall Street Journal, the popularity spike was more than the similar surge following the release of Netflix's The Queen's Gambit. The popularity of Mittens was cited by Chess.com as a reason for this instability. The problems continued throughout January; Chess.com stated that they would have to upgrade their servers and invest more in cloud computing to solve the problems caused by the website's popularity surge. On February 1, 2023, Mittens and the other cat engines were removed from the computer section of Chess.com. They were replaced with five new engines themed around artificial intelligence. A tweet was posted on the Mittens's Twitter account after the engine's removal, reading "This is just the beginning. Goodbye for now."

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  • Learning vector quantization

    Learning vector quantization

    In computer science, learning vector quantization (LVQ) is a prototype-based supervised classification algorithm. LVQ is the supervised counterpart of vector quantization systems. LVQ can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all Hebbian learning-based approach. It is a precursor to self-organizing maps (SOM) and related to neural gas and the k-nearest neighbor algorithm (k-NN). LVQ was invented by Teuvo Kohonen. == Definition == An LVQ system is represented by prototypes W = ( w ( i ) , . . . , w ( n ) ) {\displaystyle W=(w(i),...,w(n))} which are defined in the feature space of observed data. In winner-take-all training algorithms one determines, for each data point, the prototype which is closest to the input according to a given distance measure. The position of this so-called winner prototype is then adapted, i.e. the winner is moved closer if it correctly classifies the data point or moved away if it classifies the data point incorrectly. An advantage of LVQ is that it creates prototypes that are easy to interpret for experts in the respective application domain. LVQ systems can be applied to multi-class classification problems in a natural way. A key issue in LVQ is the choice of an appropriate measure of distance or similarity for training and classification. Recently, techniques have been developed which adapt a parameterized distance measure in the course of training the system, see e.g. (Schneider, Biehl, and Hammer, 2009) and references therein. LVQ can be a valuable aid in classifying text documents. == Algorithm == The algorithms are presented as in. Set up: Let the data be denoted by x i ∈ R D {\displaystyle x_{i}\in \mathbb {R} ^{D}} , and their corresponding labels by y i ∈ { 1 , 2 , … , C } {\displaystyle y_{i}\in \{1,2,\dots ,C\}} . The complete dataset is { ( x i , y i ) } i = 1 N {\displaystyle \{(x_{i},y_{i})\}_{i=1}^{N}} . The set of code vectors is w j ∈ R D {\displaystyle w_{j}\in \mathbb {R} ^{D}} . The learning rate at iteration step t {\displaystyle t} is denoted by α t {\displaystyle \alpha _{t}} . The hyperparameters w {\displaystyle w} and ϵ {\displaystyle \epsilon } are used by LVQ2 and LVQ3. The original paper suggests ϵ ∈ [ 0.1 , 0.5 ] {\displaystyle \epsilon \in [0.1,0.5]} and w ∈ [ 0.2 , 0.3 ] {\displaystyle w\in [0.2,0.3]} . === LVQ1 === Initialize several code vectors per label. Iterate until convergence criteria is reached. Sample a datum x i {\displaystyle x_{i}} , and find out the code vector w j {\displaystyle w_{j}} , such that x i {\displaystyle x_{i}} falls within the Voronoi cell of w j {\displaystyle w_{j}} . If its label y i {\displaystyle y_{i}} is the same as that of w j {\displaystyle w_{j}} , then w j ← w j + α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}+\alpha _{t}(x_{i}-w_{j})} , otherwise, w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} . === LVQ2 === LVQ2 is the same as LVQ3, but with this sentence removed: "If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} and w k ← w k + α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\alpha _{t}(x_{i}-w_{k})} .". If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then nothing happens. === LVQ3 === Initialize several code vectors per label. Iterate until convergence criteria is reached. Sample a datum x i {\displaystyle x_{i}} , and find out two code vectors w j , w k {\displaystyle w_{j},w_{k}} closest to it. Let d j := ‖ x i − w j ‖ , d k := ‖ x i − w k ‖ {\displaystyle d_{j}:=\|x_{i}-w_{j}\|,d_{k}:=\|x_{i}-w_{k}\|} . If min ( d j d k , d k d j ) > s {\displaystyle \min \left({\frac {d_{j}}{d_{k}}},{\frac {d_{k}}{d_{j}}}\right)>s} , where s = 1 − w 1 + w {\displaystyle s={\frac {1-w}{1+w}}} , then If w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have the same class, and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have different classes, then w j ← w j + α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}+\alpha _{t}(x_{i}-w_{j})} and w k ← w k − α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}-\alpha _{t}(x_{i}-w_{k})} . If w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, and w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have different classes, then w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} and w k ← w k + α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\alpha _{t}(x_{i}-w_{k})} . If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then w j ← w j − ϵ α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\epsilon \alpha _{t}(x_{i}-w_{j})} and w k ← w k + ϵ α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\epsilon \alpha _{t}(x_{i}-w_{k})} . If w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have different classes, and w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have different classes, then the original paper simply does not explain what happens in this case, but presumably nothing happens in this case. Otherwise, skip. Note that condition min ( d j d k , d k d j ) > s {\displaystyle \min \left({\frac {d_{j}}{d_{k}}},{\frac {d_{k}}{d_{j}}}\right)>s} , where s = 1 − w 1 + w {\displaystyle s={\frac {1-w}{1+w}}} , precisely means that the point x i {\displaystyle x_{i}} falls between two Apollonian spheres.

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  • Tractable (company)

    Tractable (company)

    Tractable is a technology company specializing in the development of Artificial Intelligence (AI) to assess damage to property and vehicles. The AI allows users to appraise damage digitally. == Technology == Tractable's technology uses computer vision and deep learning to automate the appraisal of visual damage in accident and disaster recovery, for example to a vehicle. Drivers can be directed to use the application by their insurer after an accident, with the aim of settling their claim more quickly. The AI evaluates the damage from images, and therefore doesn't assess what isn't visible (such as, for example, interior damage to a vehicle or property). == History == Alexandre Dalyac and Razvan Ranca founded Tractable in 2014, and Adrien Cohen joined as co-founder in 2015. The company employs more than 300 staff members, largely in the United Kingdom. Tractable was named one of the 100 leading AI companies in the world in 2020 and 2021 by CB Insights. It won the Best Technology Award in the 2020 British Insurance Awards. In June 2021, Tractable announced a venture round that valued the company at $1 billion. Tractable was the UK's 100th billion-dollar tech company, or unicorn. In July 2023, the company received a $65 million investment from SoftBank Group, through its Vision Fund 2.

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  • Concurrent MetateM

    Concurrent MetateM

    Concurrent MetateM is a multi-agent language in which each agent is programmed using a set of (augmented) temporal logic specifications of the behaviour it should exhibit. These specifications are executed directly to generate the behaviour of the agent. As a result, there is no risk of invalidating the logic as with systems where logical specification must first be translated to a lower-level implementation. The root of the MetateM concept is Gabbay's separation theorem; any arbitrary temporal logic formula can be rewritten in a logically equivalent past → future form. Execution proceeds by a process of continually matching rules against a history, and firing those rules when antecedents are satisfied. Any instantiated future-time consequents become commitments which must subsequently be satisfied, iteratively generating a model for the formula made up of the program rules. == Temporal Connectives == The Temporal Connectives of Concurrent MetateM can divided into two categories, as follows: Strict past time connectives: '●' (weak last), '◎' (strong last), '◆' (was), '■' (heretofore), 'S' (since), and 'Z' (zince, or weak since). Present and future time connectives: '◯' (next), '◇' (sometime), '□' (always), 'U' (until), and 'W' (unless). The connectives {◎,●,◆,■,◯,◇,□} are unary; the remainder are binary. === Strict past time connectives === ==== Weak last ==== ●ρ is satisfied now if ρ was true in the previous time. If ●ρ is interpreted at the beginning of time, it is satisfied despite there being no actual previous time. Hence "weak" last. ==== Strong last ==== ◎ρ is satisfied now if ρ was true in the previous time. If ◎ρ is interpreted at the beginning of time, it is not satisfied because there is no actual previous time. Hence "strong" last. ==== Was ==== ◆ρ is satisfied now if ρ was true in any previous moment in time. ==== Heretofore ==== ■ρ is satisfied now if ρ was true in every previous moment in time. ==== Since ==== ρSψ is satisfied now if ψ is true at any previous moment and ρ is true at every moment after that moment. ==== Zince, or weak since ==== ρZψ is satisfied now if (ψ is true at any previous moment and ρ is true at every moment after that moment) OR ψ has not happened in the past. === Present and future time connectives === ==== Next ==== ◯ρ is satisfied now if ρ is true in the next moment in time. ==== Sometime ==== ◇ρ is satisfied now if ρ is true now or in any future moment in time. ==== Always ==== □ρ is satisfied now if ρ is true now and in every future moment in time. ==== Until ==== ρUψ is satisfied now if ψ is true at any future moment and ρ is true at every moment prior. ==== Unless ==== ρWψ is satisfied now if (ψ is true at any future moment and ρ is true at every moment prior) OR ψ does not happen in the future.

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  • Metaclass (knowledge representation)

    Metaclass (knowledge representation)

    In knowledge representation, particularly in the Semantic Web, a metaclass is a class whose instances can themselves be classes. Similar to their role in programming languages, metaclasses in ontology languages can have properties otherwise applicable only to individuals, while retaining the same class's ability to be classified in a concept hierarchy. This enables knowledge about instances of those metaclasses to be inferred by semantic reasoners using statements made in the metaclass. Metaclasses thus enhance the expressivity of knowledge representations in a way that can be intuitive for users. While classes are suitable to represent a population of individuals, metaclasses can, as one of their feature, be used to represent the conceptual dimension of an ontology. Metaclasses are supported in the Web Ontology Language (OWL) and the data-modeling vocabulary RDFS. Metaclasses are often modeled by setting them as the object of claims involving rdf:type and rdfs:subClassOf—built-in properties commonly referred to as instance of and subclass of. Instance of entails that the subject of the claim is an instance, i.e. an individual that is a member of a class. Subclass of entails that the subject is a class. In the context of instance of and subclass of, the key difference between metaclasses and ordinary classes is that metaclasses are the object of instance of claims used on a class, while ordinary classes are not objects of such claims. (e.g. in a claim Bob instance of Human, Bob is the subject and an Instance, while the object, Human, is an ordinary class; but a further claim that Human instance of Animal species makes "Animal species" a metaclass because it has a member, "Human", that is also a Class). OWL 2 DL supports metaclasses by a feature called punning, in which one entity is interpreted as two different types of thing—a class and an individual—depending on its syntactic context. For example, through punning, an ontology could have a concept hierarchy such as Harry the eagle instance of golden eagle, golden eagle subclass of bird, and golden eagle instance of species. In this case, the punned entity would be golden eagle, because it is represented as a class (second claim) and an instance (third claim); whereas the metaclass would be species, as it has an instance that is a class. Punning also enables other properties that would otherwise be applicable only to ordinary instances to be used directly on classes, for example "golden eagle conservation status least concern." Having arisen from the fields of knowledge representation, description logic and formal ontology, Semantic Web languages have a closer relationship to philosophical ontology than do conventional programming languages such as Java or Python. Accordingly, the nature of metaclasses is informed by philosophical notions such as abstract objects, the abstract and concrete, and type-token distinction. Metaclasses permit concepts to be construed as tokens of other concepts while retaining their ontological status as types. This enables types to be enumerated over, while preserving the ability to inherit from types. For example, metaclasses could allow a machine reasoner to infer from a human-friendly ontology how many elements are in the periodic table, or, given that number of protons is a property of chemical element and isotopes are a subclass of elements, how many protons exist in the isotope hydrogen-2. Metaclasses are sometime organized by levels, in a similar way to the simple Theory of types where classes that are not metaclasses are assigned the first level, classes of classes in the first level are in the second level, classes of classes in the second level on the next and so on. == Examples == Following the type-token distinction, real world objects such as Abraham Lincoln or the planet Mars are regrouped into classes of similar objects. Abraham Lincoln is said to be an instance of human, and Mars is an instance of planet. This is a kind of is-a relationship. Metaclasses are class of classes, such as for example the nuclide concept. In chemistry, atoms are often classified as elements and, more specifically, isotopes. The glass of water one last drank has many hydrogen atoms, each of which is an instance of hydrogen. Hydrogen itself, a class of atoms, is an instance of nuclide. Nuclide is a class of classes, hence a metaclass. == Implementations == === RDF and RDFS === In RDF, the rdf:type property is used to state that a resource is an instance of a class. This enables metaclasses to be easily created by using rdf:type in a chain-like fashion. For example, in the two triples the resource species is a metaclass, because golden eagle is used as a class in the first statement and the class golden eagle is said to be an instance of the class species in the second statement. This way of doing allows :species to have non-class instances. RDF also provides rdf:Property as a way to create properties beyond those defined in the built-in vocabulary. Properties can be used directly on metaclasses, for example "species quantity 8.7 million", where quantity is a property defined via rdf:Property and species is a metaclass per the preceding example above. RDFS, an extension of RDF, introduced rdfs:Class and rdfs:subClassOf and enriched how vocabularies can classify concepts. Whereas rdf:type enables vocabularies to represent instantiation, the property rdfs:subClassOf enables vocabularies to represent subsumption. RDFS thus makes it possible for vocabularies to represent taxonomies, also known as subsumption hierarchies or concept hierarchies, which is an important addition to the type–token distinction made possible by RDF. Notably, the resource rdfs:Class is an instance of itself, demonstrating both the use of metaclasses in the language's internal implementation and a reflexive usage of rdf:type. RDFS is its own metamodel. This allows a second way to express that a resource is a metaclass. A triple to instantiate rdfs:Class, for example :golden_eagle rdf:type rdfs:Class will declare :golden_eagle as a class. It's also possible to subclass the rdfs:Class resource to declare a meta-class resource, for example :species rdfs:SubclassOf. By deduction, any instance of :species is then a class, so it is a class with class-instances, a meta-class.. This second way does not allows non-class instances of species and explicitly declares :tpecies as a meta-class. === OWL === In some OWL flavors like OWL1-DL, entities can be either classes or instances, but cannot be both. This limitations forbids metaclasses and metamodeling. This is not the case in the OWL1 full flavor, but this allows the model to be computationally undecidable. In OWL2, metaclasses can implemented with punning, that is a way to treat classes as if they were individuals. Other approaches have also been proposed and used to check the properties of ontologies at a meta level. ==== Punning ==== OWL 2 supports metaclasses through a feature called punning. In metaclasses implemented by punning, the same subject is interpreted as two fundamentally different types of thing—a class and an individual—depending on its syntactic context. This is similar to a pun in natural language, where different senses of the same word are emphasized to illustrate a point. Unlike in natural language, where puns are typically used for comedic or rhetorical effect, the main goal of punning in Semantic Web technologies is to make concepts easier to represent, closer to how they are discussed in everyday speech or academic literature. Although OWL 2 permits the same symbol to assume different roles, its standard semantics (known as Direct Semantics) still interprets the symbol differently depending on whether it is used as an individual, a class, or a property. === Protégé === In the ontology editor Protégé, metaclasses are templates for other classes who are their instances. == Classification == Some ontologies like the Cyc AI project's classifies classes and metaclasses. Classes are divided into fixed-order classes and variable-order classes. In the case of fixed-order classes, an order is attributed for metaclasses by measuring the distance to individuals with respect to the number of "instance of" triples that are necessary to find an individual. Classes that are not metaclasses are classes of individuals, so their order is "1" (first-order classes). Metaclasses that are classes of first-order classes' order is "2" (second-order classes), and so on. Variable-order metaclasses, on the other hand, can have instances; one example of variable-order metaclass is the class of all fixed-order classes.

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  • Knowledge processing for robots

    Knowledge processing for robots

    KnowRob (Knowledge processing for robots) is a system which combines knowledge representation and reasoning methods to acquire and ground knowledge. This system is the backbone of openEASE. both under developing at the Institute for Artificial Intelligence at the University of Bremen, Germany. == The framework == KnowRob can serve as a common sense framework for the integration of knowledge. This knowledge can be static encyclopedic knowledge, common sense knowledge, task descriptions, environment models, object information, observed actions, etc., which can come from different sources, like manually axiomatized, derived from observations, or imported from the web. KnowRob has been used by different research groups, as the Rice University using the ontological knowledge base in a robotic platform. As well by the Eindhoven University of Technology research group competing in the RoboCup league, in the "at Home" category, with the RoboEarth project. As well, KnowRob is mentioned in the work of some research groups from the Lucian Blaga University of Sibiu, Middle East Technical University in their combination of different knowledge bases, Keio University as related work because of the ontology service, University of Texas at Austin as related work as well because of the relation with the work presented, Hanyang University as related work as an OWL based knowledge processing framework. == Representations == To represent the knowledge, KnowRob uses the OWL ontology language and an extended first-order logic knowledge representation with computable predicates. To give the order of subactions, KnowRob includes a pair-wise ordering constrain, which gives a partial ordering. KnowRob adopts the closed-world assumption Prolog, and an open-world assumption by the use of computables. To include reasoning rules into Prolog, KnowRob uses an inference procedure beyond the capabilities of OWL to extract information about tasks executions. In its second version, KnowRob provides a logic interface to the hybrid reasoning kernel as a logic based language. This language presents the hybrid reasoning kernel as if everything were entities retrievable by providing partial descriptions for them. This entities descriptions include objects, their parts, and articulation models, environments composed of objects, software components, actions, and events. === Episodic memories === Episodic memory is related to the experience information, which is organized temporally and spatially, alongside combined with context information. In KnowRob, an episodic memory is understood as a recording that the agent makes of the ongoing activity, which includes very detailed information about the actions, motions, their purposes, effects and the behavior they generate, it also includes the images captured during execution, etc. == Usage == The knowledge is computed by external methods using Prolog queries. In the second version of the KnowRob system, is included a better structure of the packages and documentations. Which includes some extensions from the previous version, as well as a logic based language. For example, a cup description from perception can be represented in this language as: entity(Cup,[an, object, [type, cup], [shape, cylinder], [color, orange]]) As well, a controller could represent the same object as: entity(Cup, [an, object, [type, cup], [proper_physical_parts, [an, object, [type, handle], [grasp−pose, G−pose]]]]) The interface language is comparable to other query languages for symbolic knowledge bases. KnowRob's query language integrates reasoning methods, such as the simulation-based reasoning. == Goals == The goal of the KnowRob framework is to make semantic knowledge available for service robots. It is able to answer queries about missing information in vague instructions for tasks. This is possible with the actions hierarchical representation and information about objects which can be included in certain action.

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