AI Art Discord Server

AI Art Discord Server — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Healthy Together

    Healthy Together

    Healthy Together is a health technology company that provides software for Health & Humans Services Departments. Healthy Together supports a “One Door” approach to eligibility, enrollment, and management for programs like Medicaid, Supplemental Nutrition Assistance Program, TANF and WIC, as well as behavioral health (988), disease surveillance, vital records, child welfare and more. The platform's use is to increase the reach and efficacy of program initiatives, improve health equity and reduce cost. Software is available in the United States of America with current deployments in Florida, Oklahoma. The United States Department of Veterans Affairs also utilizes Healthy Together's mobile platform. == Development == Healthy Together launched in March 2020 and builds software for public health and health and human services departments. The Florida Department of Health began using the platform in September 2020 to deliver real-time test results to residents. Over 50% of households in Florida have adopted the mobile application. On December 6, 2022, the Advanced Technology Academic Research Center (ATARC) awarded Healthy Together and the State of Florida's Department of Health with a Digital Experience Award at their 2022 GITEC Emerging Technology Award Ceremony in Washington, D.C. to recognize success of the project. The partnership was also highlighted on the Federal News Network's show Federal Drive. The platform is also used at universities in Oklahoma. In November 2022, the United States Department of Veterans Affairs and Healthy Together announced a collaboration to expand access to health records for Veterans. The platform provides 18 million Veterans with access to their health information through their smartphones and mobile devices. In December 2022, the integration was recognized as one of Healthcare IT News' Top 10 stories of 2022.

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  • Karen Hao

    Karen Hao

    Karen Hao (born in the United States c. 1993) is an American journalist and author. Currently a freelancer for publications like The Atlantic and previously a foreign correspondent based in Hong Kong for The Wall Street Journal and senior artificial intelligence editor at the MIT Technology Review, she is best known for her coverage on AI research, technology ethics and the social impact of AI. Hao also co-produced the podcast In Machines We Trust and wrote the newsletter The Algorithm. Previously, she worked at Quartz as a tech reporter and data scientist and was an application engineer at the first startup to spin out of X Development. Hao's writing has also appeared in Mother Jones, Sierra Magazine, The New Republic, and other publications. == Early life and education == Hao is the daughter of Chinese immigrant parents, and grew up in New Jersey. She is a native speaker of both English and Mandarin Chinese. She graduated from The Lawrenceville School in 2011. She then studied at the Massachusetts Institute of Technology (MIT), graduating with a B.S. in mechanical engineering and a minor in energy studies in 2015. == Career == Hao is known in the technology world for her coverage of new AI research findings and their societal and ethical impacts. Her writing has spanned research and issues regarding big tech data privacy, misinformation, deepfakes, facial recognition, and AI healthcare tools. In March 2021, Hao published a piece that uncovered previously unknown information about how attempts to combat misinformation by different teams at Facebook using machine learning were impeded and constantly at odds with Facebook's drive to grow user engagement. Upon its release, leaders at Facebook including Mike Schroepfer and Yann LeCun immediately criticized the piece through Twitter responses. AI researchers and AI ethics experts Timnit Gebru and Margaret Mitchell responded in support of Hao's writing and advocated for more change and improvement for all. Hao also co-produced the podcast In Machines We Trust, which discusses the rise of AI with people developing, researching, and using AI technologies. The podcast won the 2020 Front Page Award in investigative reporting. Hao has occasionally created data visualizations that have been featured in her work at the MIT Technology Review and elsewhere. In 2018, her "What is AI?" flowchart visualization was exhibited as an installation at the Museum of Applied Arts in Vienna. She has been an invited speaker at TEDxGateway, the United Nations Foundation, EmTech, WNPR, and many other conferences and podcasts. Her TEDx talk discussed the importance of democratizing how AI is built. In March 2022, she was hired by The Wall Street Journal to cover China technology and society, while being based in Hong Kong. She left the WSJ in 2023. In May 2025, Hao released the book Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI. The book became a New York Times Bestseller and was named a Book of the Year by the Financial Times. In December 2025, after criticism from readers, Hao issued a correction to her book where she had previously overestimated the water consumption of a data center in Chile compared to the community's water consumption by factor of 1,000, due to an error in a government document. In April 2026 the book won the New York Public Library's Helen Bernstein Book Award for Excellence in Journalism. === Selected awards and honors === 2019 Webby Award nominee for best newsletter, as a writer of The Algorithm 2021 Front Page Award in investigative reporting, as a co-producer for In Machines We Trust 2021 Ambies Award nominee for best knowledge and science podcast, as a co-producer for In Machines We Trust 2021 Webby Award nominee for best technology podcast, as a co-producer for In Machines We Trust 2024 American Humanist Media Award 2025 TIME100 AI, named by TIME magazine as one of the 100 most influential people in artificial intelligence 2026 New York Public Library's Helen Bernstein Book Award for Excellence in Journalism 2026 Whiting Award in Non-fiction

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  • Reification (computer science)

    Reification (computer science)

    In computer science, reification is the process by which an abstract idea about a program is turned into an explicit data model or other object created in a programming language. A computable/addressable object—a resource—is created in a system as a proxy for a non computable/addressable object. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explicitly formulated and made available to conceptual (logical or computational) manipulation. Informally, reification is often referred to as "making something a first-class citizen" within the scope of a particular system. Some aspect of a system can be reified at language design time, which is related to reflection in programming languages. It can be applied as a stepwise refinement at system design time. Reification is one of the most frequently used techniques of conceptual analysis and knowledge representation. == Reflective programming languages == In the context of programming languages, reification is the process by which a user program or any aspect of a programming language that was implicit in the translated program and the run-time system, are expressed in the language itself. This process makes it available to the program, which can inspect all these aspects as ordinary data. In reflective languages, reification data is causally connected to the related reified aspect such that a modification to one of them affects the other. Therefore, the reification data is always a faithful representation of the related reified aspect . Reification data is often said to be made a first class object. Reification, at least partially, has been experienced in many languages to date: in early Lisp dialects and in current Prolog dialects, programs have been treated as data, although the causal connection has often been left to the responsibility of the programmer. In Smalltalk-80, the compiler from the source text to bytecode has been part of the run-time system since the very first implementations of the language. The C programming language reifies the low-level detail of memory addresses.Many programming language designs encapsulate the details of memory allocation in the compiler and the run-time system. In the design of the C programming language, the memory address is reified and is available for direct manipulation by other language constructs. For example, the following code may be used when implementing a memory-mapped device driver. The buffer pointer is a proxy for the memory address 0xB8000000. Functional programming languages based on lambda-calculus reify the concept of a procedure abstraction and procedure application in the form of the Lambda expression. The Scheme programming language reifies continuations (approximately, the call stack). In C#, reification is used to make parametric polymorphism implemented in the form of generics as a first-class feature of the language. In the Java programming language, there exist "reifiable types" that are "completely available at run time" (i.e. their information is not erased during compilation). REBOL reifies code as data and vice versa. Many languages, such as Lisp, JavaScript, and Curl, provide an eval or evaluate procedure that effectively reifies the language interpreter. Smalltalk and Actor languages permit the reification of blocks and messages, which are equivalent of lambda expressions in Lisp, and thisContext in Smalltalk, which is a reification of the current executing block. Homoiconic languages reify the syntax of the language as data that is understood by the language itself. This allows the user to write programs whose inputs and outputs are code (see macros, eval). Common representations of code include S-expressions (e.g. Clojure, Lisp), and abstract syntax trees (e.g. Rust). == Data reification vs. data refinement == Data reification (stepwise refinement) involves finding a more concrete representation of the abstract data types used in a formal specification. Data reification is the terminology of the Vienna Development Method (VDM) that most other people would call data refinement. An example is taking a step towards an implementation by replacing a data representation without a counterpart in the intended implementation language, such as sets, by one that does have a counterpart (such as maps with fixed domains that can be implemented by arrays), or at least one that is closer to having a counterpart, such as sequences. The VDM community prefers the word "reification" over "refinement", as the process has more to do with concretising an idea than with refining it. For similar usages, see Reification (linguistics). == In conceptual modeling == Reification is widely used in conceptual modeling. Reifying a relationship means viewing it as an entity. The purpose of reifying a relationship is to make it explicit, when additional information needs to be added to it. Consider the relationship type IsMemberOf(member:Person, Committee). An instance of IsMemberOf is a relationship that represents the fact that a person is a member of a committee. The figure below shows an example population of IsMemberOf relationship in tabular form. Person P1 is a member of committees C1 and C2. Person P2 is a member of committee C1 only. The same fact, however, could also be viewed as an entity. Viewing a relationship as an entity, one can say that the entity reifies the relationship. This is called reification of a relationship. Like any other entity, it must be an instance of an entity type. In the present example, the entity type has been named Membership. For each instance of IsMemberOf, there is one and only one instance of Membership, and vice versa. Now, it becomes possible to add more information to the original relationship. As an example, we can express the fact that "person p1 was nominated to be the member of committee c1 by person p2". Reified relationship Membership can be used as the source of a new relationship IsNominatedBy(Membership, Person). For related usages see Reification (knowledge representation). == In Unified Modeling Language (UML) == UML provides an association class construct for defining reified relationship types. The association class is a single model element that is both a kind of association and a kind of class. The association and the entity type that reifies are both the same model element. Note that attributes cannot be reified. == On Semantic Web == === RDF and OWL === In Semantic Web languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), a statement is a binary relation. It is used to link two individuals or an individual and a value. Applications sometimes need to describe other RDF statements, for instance, to record information like when statements were made, or who made them, which is sometimes called "provenance" information. As an example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. The example from the conceptual modeling section describes a particular person with URIref person:p1, who is a member of the committee:c1. The RDF triple from that description is Consider to store two further facts: (i) to record who nominated this particular person to this committee (a statement about the membership itself), and (ii) to record who added the fact to the database (a statement about the statement). The first case is a case of classical reification like above in UML: reify the membership and store its attributes and roles etc.: Additionally, RDF provides a built-in vocabulary intended for describing RDF statements. A description of a statement using this vocabulary is called a reification of the statement. The RDF reification vocabulary consists of the type rdf:Statement, and the properties rdf:subject, rdf:predicate, and rdf:object. Using the reification vocabulary, a reification of the statement about the person's membership would be given by assigning the statement a URIref such as committee:membership12345 so that describing statements can be written as follows: These statements say that the resource identified by the URIref committee:membership12345Stat is an RDF statement, that the subject of the statement refers to the resource identified by person:p1, the predicate of the statement refers to the resource identified by committee:isMemberOf, and the object of the statement refers to the resource committee:c1. Assuming that the original statement is actually identified by committee:membership12345, it should be clear by comparing the original statement with the reification that the reification actually does describe it. The conventional use of the RDF reification vocabulary always involves describing a statement using four statements in this pattern. Therefore, they are sometimes referred to as the "reification quad". Using reification according to this convention, we could record the fact that pe

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  • Horovod (machine learning)

    Horovod (machine learning)

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

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  • VistaCreate

    VistaCreate

    VistaCreate (formerly Crello) is an online graphic design platform for non-designers, launched in 2016. As of 2022, it has more than 10 million users in 192 countries. == Overview == VistaCreate (then known as Crello) was launched in 2016 as a part of Depositphotos. In 2019, the product hit a milestone of 1 million registered users and also launched mobile apps. In 2020, the library of templates and objects became free. A music library and a background remover tool were added to the platform. In May 2021, Moufflons Basketball, in collaboration with VistaCreate, organized a poster design competition in support of gender equality in sports. In October 2021, Vistaprint acquired Crello and its parent company, Depositphotos, for a total price of $85 million. After the acquisition, Crello was rebranded to VistaCreate. Along with Vistaprint and 99designs, it became part of the new Vista parent brand. After Russia started a full-scale war on the territory of Ukraine in February 2022, VistaCreate suspended all business in Russia and Belarus. VistaCreate's team and Depositphotos gathered collections of images and templates dedicated to the war in Ukraine.

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  • Hubert Dreyfus

    Hubert Dreyfus

    Hubert Lederer Dreyfus ( DRY-fəs; October 15, 1929 – April 22, 2017) was an American philosopher and a professor of philosophy at the University of California, Berkeley. His main interests included phenomenology, existentialism and the philosophy of both psychology and literature, as well as the philosophical implications of artificial intelligence. He was widely known for his exegesis of Martin Heidegger, which critics labeled "Dreydegger". Dreyfus was featured in Tao Ruspoli's film Being in the World (2010), and was among the philosophers interviewed by Bryan Magee for the BBC Television series The Great Philosophers (1987). The Futurama character Professor Hubert Farnsworth is partly named after him, writer Eric Kaplan having been a former student. == Life and career == Dreyfus was born on 15 October 1929, in Terre Haute, Indiana, to Stanley S. and Irene (Lederer) Dreyfus. He attended Harvard University from 1947. With a senior honors thesis on Causality and Quantum Theory (for which W. V. O. Quine was the main examiner) he was awarded a B.A. summa cum laude in 1951 and joined Phi Beta Kappa. He was awarded a M.A. in 1952. He was a Teaching Fellow at Harvard from 1952 to 1953 (as he was again in 1954 and 1956). Then, on a Harvard Sheldon traveling fellowship, Dreyfus studied at the University of Freiburg from 1953 to 1954. During this time he had an interview with Martin Heidegger. Sean D. Kelly records that Dreyfus found the meeting 'disappointing.' A brief mention of it was made by Dreyfus during his 1987 BBC interview with Bryan Magee in remarks that are revealing of both his and Heidegger's opinion of the work of Jean-Paul Sartre. Between 1956 and 1957, Dreyfus undertook research at the Husserl Archives at the University of Louvain on a Fulbright Fellowship. Towards the end of his stay, his first (jointly authored) paper "Curds and Lions in Don Quijote" would appear in print. After acting as an instructor in philosophy at Brandeis University (1957–1959), he attended the Ecole Normale Supérieure, Paris, on a French government grant (1959–1960). From 1960, first as an instructor, then as an assistant and then associate professor, Dreyfus taught philosophy at the Massachusetts Institute of Technology (MIT). In 1964, with his dissertation Husserl's Phenomenology of Perception, he obtained his Ph.D. from Harvard. (Due to his knowledge of Husserl, Dagfinn Føllesdal sat on the thesis committee but he has asserted that Dreyfus "was not really my student.") That same year, his co-translation (with his first wife) of Sense and Non-Sense by Maurice Merleau-Ponty was published. Also in 1964, and whilst still at MIT, he was employed as a consultant by the RAND Corporation to review the work of Allen Newell and Herbert A. Simon in the field of artificial intelligence (AI). This resulted in the publication, in 1965, of the "famously combative" Alchemy and Artificial Intelligence, which proved to be the first of a series of papers and books attacking the AI field's claims and assumptions. The first edition of What Computers Can't Do would follow in 1972, and this critique of AI (which has been translated into at least ten languages) would establish Dreyfus's public reputation. However, as the editors of his Festschrift noted: "the study and interpretation of 'continental' philosophers... came first in the order of his philosophical interests and influences." === Berkeley === In 1968, although he had been granted tenure, Dreyfus left MIT and became an associate professor of philosophy at the University of California, Berkeley, (winning, that same year, the Harbison Prize for Outstanding Teaching). In 1972 he was promoted to full professor. Though Dreyfus retired from his chair in 1994, he continued as professor of philosophy in the Graduate School (and held, from 1999, a joint appointment in the rhetoric department). He continued to teach philosophy at UC Berkeley until his last class in December 2016. Dreyfus was elected a fellow of the American Academy of Arts and Sciences in 2001. He was also awarded an honorary doctorate for "his brilliant and highly influential work in the field of artificial intelligence" and his interpretation of twentieth century continental philosophy by Erasmus University. Dreyfus died on April 22, 2017. His younger brother and sometimes collaborator, Stuart Dreyfus, is a professor emeritus of industrial engineering and operations research at the University of California, Berkeley. == Dreyfus' criticism of AI == Dreyfus' critique of artificial intelligence (AI) concerns what he considers to be the four primary assumptions of AI research. The first two assumptions are what he calls the "biological" and "psychological" assumptions. The biological assumption is that the brain is analogous to computer hardware and the mind is analogous to computer software. The psychological assumption is that the mind works by performing discrete computations (in the form of algorithmic rules) on discrete representations or symbols. Dreyfus claims that the plausibility of the psychological assumption rests on two others: the epistemological and ontological assumptions. The epistemological assumption is that all activity (either by animate or inanimate objects) can be formalized (mathematically) in the form of predictive rules or laws. The ontological assumption is that reality consists entirely of a set of mutually independent, atomic (indivisible) facts. It's because of the epistemological assumption that workers in the field argue that intelligence is the same as formal rule-following, and it's because of the ontological one that they argue that human knowledge consists entirely of internal representations of reality. On the basis of these two assumptions, workers in the field claim that cognition is the manipulation of internal symbols by internal rules, and that, therefore, human behaviour is, to a large extent, context free (see contextualism). Therefore, a truly scientific psychology is possible, which will detail the 'internal' rules of the human mind, in the same way the laws of physics detail the 'external' laws of the physical world. However, it is this key assumption that Dreyfus denies. In other words, he argues that we cannot now (and never will be able to) understand our own behavior in the same way as we understand objects in, for example, physics or chemistry: that is, by considering ourselves as things whose behaviour can be predicted via 'objective', context free scientific laws. According to Dreyfus, a context-free psychology is a contradiction in terms. Dreyfus's arguments against this position are taken from the phenomenological and hermeneutical tradition (especially the work of Martin Heidegger). Heidegger argued that, contrary to the cognitivist views (on which AI has been based), our being is in fact highly context-bound, which is why the two context-free assumptions are false. Dreyfus doesn't deny that we can choose to see human (or any) activity as being 'law-governed', in the same way that we can choose to see reality as consisting of indivisible atomic facts... if we wish. But it is a huge leap from that to state that because we want to or can see things in this way that it is therefore an objective fact that they are the case. In fact, Dreyfus argues that they are not (necessarily) the case, and that, therefore, any research program that assumes they are will quickly run into profound theoretical and practical problems. Therefore, the current efforts of workers in the field are doomed to failure. Dreyfus argues that to get a device or devices with human-like intelligence would require them to have a human-like being-in-the-world and to have bodies more or less like ours, and social acculturation (i.e. a society) more or less like ours. (This view is shared by psychologists in the embodied psychology (Lakoff and Johnson 1999) and distributed cognition traditions. His opinions are similar to those of robotics researchers such as Rodney Brooks as well as researchers in the field of artificial life.) Contrary to a popular misconception, Dreyfus never predicted that computers would never beat humans at chess. In Alchemy and Artificial Intelligence, he only reported (correctly) the state of the art of the time: "Still no chess program can play even amateur chess." Daniel Crevier writes: "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier." == Webcasting philosophy == When UC Berkeley and Apple began making a selected number of lecture classes freely available to the public as podcasts beginning around 2006, a recording of Dreyfus teaching a course called "Man, God, and Society in Western Literature – From Gods to God and Back" rose to the 58th most popular webcast on iTunes. These webcasts have attracted the attention of many, including non-academics, to Dreyfus and his

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  • Jess (programming language)

    Jess (programming language)

    Jess is a rule engine for the Java computing platform, written in the Java programming language. It was developed by Ernest Friedman-Hill of Sandia National Laboratories. It is a superset of the CLIPS language. It was first written in late 1995. The language provides rule-based programming for the automation of an expert system, and is often termed as an expert system shell. In recent years, intelligent agent systems have also developed, which depend on a similar ability. Rather than a procedural paradigm, where one program has a loop that is activated only one time, the declarative paradigm used by Jess applies a set of rules to a set of facts continuously by a process named pattern matching. Rules can modify the set of facts, or can execute any Java code. It uses the Rete algorithm to execute rules. == License == The licensing for Jess is freeware for education and government use, and is proprietary software, needing a license, for commercial use. In contrast, CLIPS, which is the basis and starting code for Jess, is free and open-source software. == Code examples == Code examples: Sample code:

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  • Versata

    Versata

    Versata is a privately held software company, one of several business units under the ESW Capital umbrella. Versata acquires underperforming or financially struggling enterprise software companies, integrates them into their portfolio, and makes operational changes to improve the viability and performance of the companies. == History == === Early years (1991–2000) === This company was founded in 1991 with the name Image Innovations; Naren Bakshi was co-founder and president, Kevin Fletcher Tweedy was vice president of technology, and they sold a development tool set named Image Application WorkBench that worked with Plexus Software's imaging platform. In 1997, the company name changed to Vision Software. They sold a small suite of software: Vision Builder for accelerated coding; and Vision StoryBoard Pro for creating software documentation. In 1998, their flagship product was a Java development tool named Vision JADE. In January 2000, the company changed names again, this time to Versata, and their e-business automation system, Versata Logic Suite, had three components: Versata Logic Server to host business rules written in Java, Versata Studio for developing the business rules, and Versata Connectors for connecting the logic server to IBM database servers. === Public company (2000–2006) === They went public in March 2000 during the dot-com bubble, raising about $94 million and reaching a market capitalization of over $2.5 billion despite reporting just $13 million in revenue and a $21 million loss in the prior year. In November 2000, Versata expanded into the business workflow area with the acquisition of Verve, Inc. and its workflow management system by the same name. From early 2001 through mid-2003, Versata's revenues were in quarter-over-quarter decline until Alan Baratz took over as CEO. Five consecutive quarters of growth followed until early 2005, when revenues once again took a downward plunge. In mid-2005, the company was notified by NASDAQ that it no longer met NASDAQ's requirements for continued listing, related to maintenance of a minimum amount of shareholder's equity, market value, or net income. In July 2005, Versata was delisted from NASDAQ and publicly traded on the OTC (also known as the Pink Sheets). == Versata, a business unit of ESW Capital == In January 2006, Austin-based Trilogy, Inc. acquired the company and took it private. Trilogy then proceeded to merge portions of Trilogy, specifically, Trilogy Technology Group, into Versata and began acquiring further companies, reorganizing dramatically and offshoring most technical positions to its office in Bangalore, India. From 2006 to 2008, Versata continued to make acquisitions mostly in US. Most of the employees in the acquired companies were laid -off with the majority work being offshored to its India office in Bangalore. In early 2009, Versata made another major overhaul of its business model when it asked all its employees in India to work as contractors through oDesk for a gDev which is an entity incorporated by Trilogy to manage its outsourcing activities. The only employees left in Versata were the ones in US. == Acquisitions == a Corizon was acquired by Metatomix, while Metatomix was part of Versata. b Infopia was acquired by Everest Software, while Everest Software was part of Versata. c Symphony Commerce was acquired by Quantum Retail, while Quantum Retail was part of Versata. == Legal disputes == === Patent infringement and "poison pill" lawsuits with Selectica === The legal disputes with Selectica began in 2004 (before Trilogy acquired Versata in January 2006) and lasted until 2010. While there were many suits and counter-suits, they largely centered around three issues: 2004–2006: Patent infringement in configure, price, and quote (CPQ) software 2005–2007: Patent infringement in contract lifecycle management (CLM) software 2008–2010: The "poison pill" lawsuit In 2004, Selectica and Trilogy had competing CPQ software: Selectica sold Solutions Advisor and Deal Optimization, while Trilogy sold Selling Chain. In April of that year, Trilogy Software sued Selectica for patent infringement. In 2005, before the court ruling, Trilogy made several offers to buy Selectica, but the board rejected them. In January 2006, the court ordered Selectica to pay Trilogy $7.5 million in damages. Four days after the January 2006 judgment in the first lawsuit, Trilogy announced its acquisition of Versata for an undisclosed amount. In 2005, Selectica had acquired the Determine CLM software platform, which included features that overlapped with some offered by Versata. In October 2006, Versata filed a second patent infringement lawsuit. The case was settled in 2007, with Selectica agreeing to pay Trilogy and Versata $10 million, plus up to $7.5 million in additional contingent payments. In 2008, Versata began acquiring Selectica stock. By December, Selectica's board amended its shareholder rights plan to adopt a "poison pill" with an unusually low trigger threshold: if any shareholder acquired more than 4.99% of company stock, their ownership would be diluted. The board explained that the move was meant to protect Selectica's net operating losses (NOLs), which were tax-deductible if the company returned to profitability. Under IRS Section 382, a significant change in stock ownership could cause those NOLs to be disqualified. Versata intentionally triggered the poison pill and also offered to sell back the stocks at a profit (greenmailing them), which prompted a legal dispute over whether Selectica's board had the authority to set such a low threshold and whether defending NOLs justified triggering shareholder dilution. The case ultimately reached the Delaware Supreme Court, which upheld the poison pill in October 2010, ruling in favor of Selectica. === Intellectual property lawsuit over joint development with Sun Microsystems === In 1998, Sun Microsystems hired Trilogy to help Sun's developers in California create a software configurator (later named the WC5 Configurator) that Sun's customers could use to modify products they wanted to buy, customizing products to have the features they wanted. Trilogy worked on the WC5 Configurator for several years, then Sun transferred the work to Oracle to finish. Trilogy believed that they owned the copyright to the work they'd done for Sun, and in 2006 after the merger with Versata they sued Sun for more than $100 million in damages. In April 2009, a jury ruled in favor of Sun and rejected Versata's claims. === Patent lawsuit and ruling on patents of abstract ideas with SAP === SAP developed Pricing Engine, a component in their enterprise resource planning (ERP) system. It competed with an older Trilogy product called Pricer, which was part of Trilogy's Selling Chain platform in the mid-1990s before they merged with Versata. In April 2007—the year after Trilogy acquired Versata—Versata filed a lawsuit against SAP for patent infringement. In August 2009, the jury agreed with Versata and awarded them $139 million. The court granted a new trial on damages and in September 2011, in the retrial, the jury awarded Versata $345 million. This then went to the US Court of Appeals, which in May 2013 affirmed the $345 million damages award, plus interest that had accumulated. In October 2014, Versata and SAP settled their litigation for an undisclosed amount of money. With the dispute between Versata and SAP settled, in June 2013 the Patent Trial and Appeal Board (PTAB) reviewed the validity of the patent itself, and issued a decision in a Covered Business Method (CBM) review, stating that the disputed items were abstract ideas and thus under the US patent law not patentable. In July 2015, the Federal Circuit agreed with PTAB's decision that the challenged items were not patentable. === Trade secrets and damages dispute with Internet Brands === Internet Brands was formerly known as CarsDirect and AutoData Solutions. Like Trilogy, they made software for automakers that helped customers compare vehicles online. In the late 1990s, Trilogy and Internet Brands tried to combine their products but failed to do so, and after a December 1999 lawsuit they made a settlement agreement in May 2001. In 2008, Versata sued Internet Brands claiming they had violated the settlement agreement by making presentations to potential clients stating they had a license from Versata to use and sell Versata technical solutions; and doing so had cost Versata business with Chrysler. Internet Brands' countersuit argued that Versata had misappropriated trade secrets and asked the jury to use Versata's business relationship with Toyota—including revenue from Toyota contracts—as a benchmark to calculate damages. The jury agreed and used that data to determine a $2 million damages award in favor of Internet Brands’ subsidiary, AutoData Solutions. Versata appealed the decision, and in January 2014 the court upheld the $2 million award to Internet Brands. === Patent challenges a

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  • Spotify Live

    Spotify Live

    Spotify Live, formerly Spotify Greenroom, was a social audio app by Spotify, that allowed users to host or participate in live-audio virtual environments called "room" for conversations. Each room had a maximum capacity of 1000 people. The app was available on Android and iOS, competing with Twitter Spaces and Clubhouse in the social media segment. It was shut down on April 30, 2023. == History == In October 2020, Betty Labs released Locker Room exclusively on the iOS App Store. The app featured virtual audio chat rooms for sports enthusiasts. In late March 2021, Spotify acquired Betty Labs for $50 million and announced plans to rebrand the app with a broader focus on sports, music, and pop culture. On June 16, 2021, Spotify launched the app as Spotify Greenroom on Android (early access) and iOS, expanding its scope beyond just sports. At launch, Spotify introduced the Greenroom Creator Fund to support creators and shows, serving as a rival to Clubhouse's Creator First Accelerator Program. The fund aimed to provide a monetization path for podcasters integrating Greenroom into their verified Spotify accounts. By July 2021, the app had accumulated over 140,000 iOS installs and 100,000 Android installs. In August 2021, Spotify collaborated with the WWE to produce professional wrestling-related podcasts, many of which would be recorded by The Ringer, Spotify's in-house podcasting team, using Greenroom. In March 2022, Spotify Greenroom announced its rebranding as Spotify Live and its migration to the main Spotify app. After a year, Spotify announced it would shut down the Spotify Live app at the end of April 2023. == Features == Greenroom allowed users to create or join a room, which, in the context of the application, was a virtual space for real-time voice chats. Users could only create a room within a pre-defined group, representing either a brand or a generic category. If a user chose to create a room, they became the host, with the ability to invite people, control who could talk, and enable features like recording and the Discussions tab during room creation. Enabling recording displayed a disclaimer informing users that the conversation was being recorded, and the audio, recorded in mp4 format, would be sent to the host via email after the room concluded. If the Discussions tab was enabled, users could send text messages in the public chat section. The host also had the authority to ban users if necessary. When joining a room, a user could opt to be a listener or request to become a speaker. Users had the freedom to follow or block others and join groups at their discretion. Notifications about new rooms in joined groups would be sent to users. Additionally, users could discover new individuals and groups using the search tab. == Partnered creators == By October 2021, Spotify had a variety of partnered creators aimed at boosting traffic and validating its vertically integrated podcast model. These creators primarily focused on Generation Z. In-house Spotify talent, such as The Ringer, produced sports-related content. Simultaneously, the company recruited creators from various social channels to grow Greenroom's audience while also promoting its integration with Spotify and Anchor. Each verified Spotify partner had their Greenroom shows featured in both the Greenroom app and their profiles on the Spotify app. This was part of the company's strategy leading into the 2022 ramp-up to compete with Clubhouse. == Platforms == The app was accessible on both Android and iOS platforms, and users could download the app from their respective app stores. Android users needed Android 8 or above to launch the app, while iOS consumers required iOS 13 or later to run it.

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  • Pretext

    Pretext

    A pretext (adj.: pretextual) is an excuse to do something or say something that is not accurate. Pretexts may be based on a half-truth or developed in the context of a misleading fabrication. Pretexts have been used to conceal the true purpose or rationale behind actions and words. They are often heard in political speeches. In US law, a pretext usually describes false reasons that hide the true intentions or motivations for a legal action. If a party can establish a prima facie case for the proffered evidence, the opposing party must prove that these reasons were "pretextual" or false. This can be accomplished by directly demonstrating that the motivations behind the presentation of evidence is false, or indirectly by evidence that the motivations are not "credible". In Griffith v. Schnitzer, an employment discrimination case, a jury award was reversed by a Court of Appeals because the evidence was not sufficient that the defendant's reasons were "pretextual". That is, the defendant's evidence was either undisputed, or the plaintiff's was "irrelevant subjective assessments and opinions". A "pretextual" arrest by law enforcement officers is one carried out for illegal purposes such as to conduct an unjustified search and seizure. As one example of pretext, in the 1880s, the Chinese government raised money on the pretext of modernizing the Chinese navy. Instead, these funds were diverted to repair a ship-shaped, two-story pavilion which had been originally constructed for the mother of the Qianlong Emperor. This pretext and the Marble Barge are famously linked with Empress Dowager Cixi. This architectural folly, known today as the Marble Boat (Shifang), is "moored" on Lake Kunming in what the empress renamed the "Garden for Cultivating Harmony" (Yiheyuan). Another example of pretext was demonstrated in the speeches of the Roman orator Cato the Elder (234–149 BC). For Cato, every public speech became a pretext for a comment about Carthage. The Roman statesman had come to believe that the prosperity of ancient Carthage represented an eventual and inevitable danger to Rome. In the Senate, Cato famously ended every speech by proclaiming his opinion that Carthage had to be destroyed (Carthago delenda est). This oft-repeated phrase was the ultimate conclusion of all logical argument in every oration, regardless of the subject of the speech. This pattern persisted until his death in 149, which was the year in which the Third Punic War began. In other words, any subject became a pretext for reminding his fellow senators of the dangers Carthage represented. == Uses in warfare == The early years of Japan's Tokugawa shogunate were unsettled, with warring factions battling for power. The causes for the fighting were in part pretextual, but the outcome brought diminished armed conflicts after the Siege of Osaka in 1614–1615. The next two-and-a-half centuries of Japanese history were comparatively peaceful under the successors of Tokugawa Ieyasu and the bakufu government he established. === United States === During the War of 1812, US President James Madison was often accused of using impressment of American sailors by the Royal Navy as a pretext to invade Canada. The sinking of the USS Maine in 1898 was blamed on the Spanish, despite early reports of it having been an accident, contributing to U.S. entry into the Spanish–American War. The slogan "Remember the Maine! To hell with Spain!" was used as a rallying cry. Some have argued that United States President Franklin D. Roosevelt used the attack on Pearl Harbor by Japanese forces on December 7, 1941, as a pretext to enter World War II. American soldiers and supplies had been assisting British and Soviet operations for almost a year by this point, and the United States had thus "chosen a side", but due to the political climate in the States at the time and some campaign promises made by Roosevelt that he would not send American troops to fight in foreign wars, Roosevelt could not declare war for fear of public backlash. The attack on Pearl Harbor united the American people's resolve against the Axis powers and created the bellicose atmosphere in which to declare war. The 1964 Gulf of Tonkin incident, later revealed to have been partly provoked and partly not to have happened, was used to bring the United States fully into the Vietnam War. United States President George W. Bush used the September 11 attacks and faulty intelligence about the existence of weapons of mass destruction as a pretext for the war in Iraq. == Social engineering == A type of social engineering called pretexting uses a pretext to elicit information fraudulently from a target. The pretext in this case includes research into the identity of a certain authorized person or personality type in order to establish legitimacy in the mind of the target.

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  • RightsCon

    RightsCon

    RightsCon is an annual conference on digital rights hosted by Access Now. It convenes international leaders and organizations to discuss global problems including internet censorship, the regulation of algorithms, electronic surveillance, the ethics of technology, online hate speech, content moderation, cyberwarfare, and more. == History == The conference was first convened by Access (today, Access Now) in Silicon Valley in 2011, with the intention of gathering civil society to discuss impacts of the growing tech industry on digital rights and human rights. It sought the participation of leaders from both industry (including companies such as Twitter, Google, Mozilla, and Comcast) and civil society organizations (such as the Electronic Frontier Foundation and New America). Keynote speakers included the then-Assistant Secretary of State, Michael Posner; Egyptian blogger and political prisoner, Alaa Abd El-Fattah; and then-director of public policy at Google, Bob Boorstin. RightsCon organizers have sought to ensure the event is accessible to attendees from across the globe, particularly global majority countries, informing the decision to hold the conference in Asia, the Middle East, and Latin America. === Online convenings === In 2020, RightsCon was to be held in San José, Costa Rica, but due to the COVID-19 pandemic, the meeting took place in an online format. In 2021, the 10th edition of RightsCon was again held online from Monday, June 7 to Friday, June 11, 2021, due to the continued global COVID-19 pandemic which altered several digital rights physical meetings. The topics for RightsCon2021 included: Artificial Intelligence (AI), automation, data protection and user control, digital futures, democracy, elections, new business models, content control, peacebuilding, censorship, internet shutdowns, freedom of the media and many others were discussed by several digital rights organizations and individuals. === 2026 cancellation === The 14th RightsCon was scheduled to be held in Zambia from May 5 to 8, 2026. On April 29, 2026, the Zambian government abruptly postponed the conference, writing in a statement that the postponement was "necessitated by the need for comprehensive disclosure […] relating to key thematic issues proposed for discussion during the Summit." In May 2026, the conference was cancelled due to pressure from the Chinese government. In a statement the same day, Access Now wrote that it was "told that diplomats from the People's Republic of China (PRC) were putting pressure on the Government of Zambia because Taiwanese civil society participants were planning to join us in person." == List of conferences == Past RightsCon conferences include:

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  • Simple Knowledge Organization System

    Simple Knowledge Organization System

    Simple Knowledge Organization System (SKOS) is a W3C recommendation designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary. SKOS is part of the Semantic Web family of standards built upon RDF and RDFS, and its main objective is to enable easy publication and use of such vocabularies as linked data. == History == === DESIRE II project (1997–2000) === The most direct ancestor to SKOS was the RDF Thesaurus work undertaken in the second phase of the EU DESIRE project . Motivated by the need to improve the user interface and usability of multi-service browsing and searching, a basic RDF vocabulary for Thesauri was produced. As noted later in the SWAD-Europe workplan, the DESIRE work was adopted and further developed in the SOSIG and LIMBER projects. A version of the DESIRE/SOSIG implementation was described in W3C's QL'98 workshop, motivating early work on RDF rule and query languages: A Query and Inference Service for RDF. === LIMBER (1999–2001) === SKOS built upon the output of the Language Independent Metadata Browsing of European Resources (LIMBER) project funded by the European Community, and part of the Information Society Technologies programme. In the LIMBER project CCLRC further developed an RDF thesaurus interchange format which was demonstrated on the European Language Social Science Thesaurus (ELSST) at the UK Data Archive as a multilingual version of the English language Humanities and Social Science Electronic Thesaurus (HASSET) which was planned to be used by the Council of European Social Science Data Archives CESSDA. === SWAD-Europe (2002–2004) === SKOS as a distinct initiative began in the SWAD-Europe project, bringing together partners from both DESIRE, SOSIG (ILRT) and LIMBER (CCLRC) who had worked with earlier versions of the schema. It was developed in the Thesaurus Activity Work Package, in the Semantic Web Advanced Development for Europe (SWAD-Europe) project. SWAD-Europe was funded by the European Community, and part of the Information Society Technologies programme. The project was designed to support W3C's Semantic Web Activity through research, demonstrators and outreach efforts conducted by the five project partners, ERCIM, the ILRT at Bristol University, HP Labs, CCLRC and Stilo. The first release of SKOS Core and SKOS Mapping were published at the end of 2003, along with other deliverables on RDF encoding of multilingual thesauri and thesaurus mapping. === Semantic web activity (2004–2005) === Following the termination of SWAD-Europe, SKOS effort was supported by the W3C Semantic Web Activity in the framework of the Best Practice and Deployment Working Group. During this period, focus was put both on consolidation of SKOS Core, and development of practical guidelines for porting and publishing thesauri for the Semantic Web. === Development as W3C Recommendation (2006–2009) === The SKOS main published documents — the SKOS Core Guide, the SKOS Core Vocabulary Specification, and the Quick Guide to Publishing a Thesaurus on the Semantic Web — were developed through the W3C Working Draft process. Principal editors of SKOS were Alistair Miles, initially Dan Brickley, and Sean Bechhofer. The Semantic Web Deployment Working Group, chartered for two years (May 2006 – April 2008), put in its charter to push SKOS forward on the W3C Recommendation track. The roadmap projected SKOS as a Candidate Recommendation by the end of 2007, and as a Proposed Recommendation in the first quarter of 2008. The main issues to solve were determining its precise scope of use, and its articulation with other RDF languages and standards used in libraries (such as Dublin Core). === Formal release (2009) === On August 18, 2009, W3C released the new standard that builds a bridge between the world of knowledge organization systems – including thesauri, classifications, subject headings, taxonomies, and folksonomies – and the linked data community, bringing benefits to both. Libraries, museums, newspapers, government portals, enterprises, social networking applications, and other communities that manage large collections of books, historical artifacts, news reports, business glossaries, blog entries, and other items can now use SKOS to leverage the power of linked data. === Historical view of components === SKOS was originally designed as a modular and extensible family of languages, organized as SKOS Core, SKOS Mapping, and SKOS Extensions, and a Metamodel. The entire specification is now complete within the namespace http://www.w3.org/2004/02/skos/core#. == Overview == In addition to the reference itself, the SKOS Primer (a W3C Working Group Note) summarizes the Simple Knowledge Organization System. The SKOS defines the classes and properties sufficient to represent the common features found in a standard thesaurus. It is based on a concept-centric view of the vocabulary, where primitive objects are not terms, but abstract notions represented by terms. Each SKOS concept is defined as an RDF resource. Each concept can have RDF properties attached, including: one or more preferred index terms (at most one in each natural language) alternative terms or synonyms definitions and notes, with specification of their language Concepts can be organized in hierarchies using broader-narrower relationships, or linked by non-hierarchical (associative) relationships. Concepts can be gathered in concept schemes, to provide consistent and structured sets of concepts, representing whole or part of a controlled vocabulary. === Element categories === The principal element categories of SKOS are concepts, labels, notations, documentation, semantic relations, mapping properties, and collections. The associated elements are listed in the table below. === Concepts === The SKOS vocabulary is based on concepts. Concepts are the units of thought—ideas, meanings, or objects and events (instances or categories)—which underlie many knowledge organization systems. As such, concepts exist in the mind as abstract entities which are independent of the terms used to label them. In SKOS, a Concept (based on the OWL Class) is used to represent items in a knowledge organization system (terms, ideas, meanings, etc.) or such a system's conceptual or organizational structure. A ConceptScheme is analogous to a vocabulary, thesaurus, or other way of organizing concepts. SKOS does not constrain a concept to be within a particular scheme, nor does it provide any way to declare a complete scheme—there is no way to say the scheme consists only of certain members. A topConcept is (one of) the upper concept(s) in a hierarchical scheme. === Labels and notations === Each SKOS label is a string of Unicode characters, optionally with language tags, that are associated with a concept. The prefLabel is the preferred human-readable string (maximum one per language tag), while altLabel can be used for alternative strings, and hiddenLabel can be used for strings that are useful to associate, but not meant for humans to read. A SKOS notation is similar to a label, but this literal string has a datatype, like integer, float, or date; the datatype can even be made up (see 6.5.1 Notations, Typed Literals and Datatypes in the SKOS Reference). The notation is useful for classification codes and other strings not recognizable as words. === Documentation === The Documentation or Note properties provide basic information about SKOS concepts. All the properties are considered a type of skos:note; they just provide more specific kinds of information. The property definition, for example, should contain a full description of the subject resource. More specific note types can be defined in a SKOS extension, if desired. A query for skos:note ? will obtain all the notes about , including definitions, examples, and scope, history and change, and editorial documentation. Any of these SKOS Documentation properties can refer to several object types: a literal (e.g., a string); a resource node that has its own properties; or a reference to another document, for example using a URI. This enables the documentation to have its own metadata, like creator and creation date. Specific guidance on SKOS documentation properties can be found in the SKOS Primer Documentary Notes. === Semantic relations === SKOS semantic relations are intended to provide ways to declare relationships between concepts within a concept scheme. While there are no restrictions precluding their use with two concepts from separate schemes, this is discouraged because it is likely to overstate what can be known about the two schemes, and perhaps link them inappropriately. The property related simply makes an association relationship between two concepts; no hierarchy or generality relation is implied. The properties broader and narrower are used to assert a direct hierarchical link between two concepts. The meaning may be unexpected; the relat

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  • Artificial Linguistic Internet Computer Entity

    Artificial Linguistic Internet Computer Entity

    A.L.I.C.E. (Artificial Linguistic Internet Computer Entity), also referred to as Alicebot, or simply Alice, is a natural language processing chatbot—a program that engages in a conversation with a human by applying some heuristical pattern matching rules to the human's input. It was inspired by Joseph Weizenbaum's classical ELIZA program. It is one of the strongest programs of its type and has won the Loebner Prize, awarded to accomplished humanoid, talking robots, three times (in 2000, 2001, and 2004). The program is unable to pass the Turing test, as even the casual user will often expose its mechanistic aspects in short conversations. Alice was originally composed by Richard Wallace; it "came to life" on November 23, 1995. The program was rewritten in Java beginning in 1998. The current incarnation of the Java implementation is Program D. The program uses an XML Schema called AIML (Artificial Intelligence Markup Language) for specifying the heuristic conversation rules. Alice code has been reported to be available as open source. The AIML source is available from ALICE A.I. Foundation on Google Code and from the GitHub account of Richard Wallace. These AIML files can be run using an AIML interpreter like Program O or Program AB. == In popular culture == Spike Jonze has cited ALICE as the inspiration for his academy award-winning film Her, in which a human falls in love with a chatbot. In a New Yorker article titled “Can Humans Fall in Love with Bots?” Jonze said “that the idea originated from a program he tried about a decade ago called the ALICE bot, which engages in friendly conversation.” The Los Angeles Times reported:Though the film’s premise evokes comparisons to Siri, Jonze said he actually had the idea well before the Apple digital assistant came along, after using a program called Alicebot about ten years ago. As geek nostalgists will recall, that intriguing if at times crude software (it flunked the industry-standard Turing Test) would attempt to engage users in everyday chatter based on a database of prior conversations. Jonze liked it, and decided to apply a film genre to it. “I thought about that idea, and what if you had a real relationship with it?” Jonze told reporters. “And I used that as a way to write a relationship movie and a love story.”

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  • Freddy II

    Freddy II

    Freddy (1969–1971) and Freddy II (1973–1976) were experimental robots built in the Department of Machine Intelligence and Perception (later Department of Artificial Intelligence, now part of the School of Informatics at the University of Edinburgh). == Technology == Technical innovations involving Freddy were at the forefront of the 70s robotics field. Freddy was one of the earliest robots to integrate vision, manipulation and intelligent systems as well as having versatility in the system and ease in retraining and reprogramming for new tasks. The idea of moving the table instead of the arm simplified the construction. Freddy also used a method of recognising the parts visually by using graph matching on the detected features. The system used an innovative collection of high level procedures for programming the arm movements which could be reused for each new task. == Lighthill controversy == In the mid 1970s there was controversy about the utility of pursuing a general purpose robotics programme in both the USA and the UK. A BBC TV programme in 1973, referred to as the "Lighthill Debate", pitched James Lighthill, who had written a critical report for the science and engineering research funding agencies in the UK, against Donald Michie from the University of Edinburgh and John McCarthy from Stanford University. The Edinburgh Freddy II and Stanford/SRI Shakey robots were used to illustrate the state-of-the-art at the time in intelligent robotics systems. == Freddy I and II == Freddy Mark I (1969–1971) was an experimental prototype, with 3 degrees-of-freedom created by a rotating platform driven by a pair of independent wheels. The other main components were a video camera and bump sensors connected to a computer. The computer moved the platform so that the camera could see and then recognise the objects. Freddy II (1973–1976) was a 5 degrees of freedom manipulator with a large vertical 'hand' that could move up and down, rotate about the vertical axis and rotate objects held in its gripper around one horizontal axis. Two remaining translational degrees of freedom were generated by a work surface that moved beneath the gripper. The gripper was a two finger pinch gripper. A video camera was added as well as later a light stripe generator. The Freddy and Freddy II projects were initiated and overseen by Donald Michie. The mechanical hardware and analogue electronics were designed and built by Stephen Salter (who also pioneered renewable energy from waves (see Salter's Duck)), and the digital electronics and computer interfacing were designed by Harry Barrow and Gregan Crawford. The software was developed by a team led by Rod Burstall, Robin Popplestone and Harry Barrow which used the POP-2 programming language, one of the world's first functional programming languages. The computing hardware was an Elliot 4130 computer with 384KB (128K 24-bit words) RAM and a hard disk linked to a small Honeywell H316 computer with 16KB of RAM which directly performed sensing and control. Freddy was a versatile system which could be trained and reprogrammed to perform a new task in a day or two. The tasks included putting rings on pegs and assembling simple model toys consisting of wooden blocks of different shapes, a boat with a mast and a car with axles and wheels. Information about part locations was obtained using the video camera, and then matched to previously stored models of the parts. It was soon realised in the Freddy project that the 'move here, do this, move there' style of robot behavior programming (actuator or joint level programming) is tedious and also did not allow for the robot to cope with variations in part position, part shape and sensor noise. Consequently, the RAPT robot programming language was developed by Pat Ambler and Robin Popplestone, in which robot behavior was specified at the object level. This meant that robot goals were specified in terms of desired position relationships between the robot, objects and the scene, leaving the details of how to achieve the goals to the underlying software system. Although developed in the 1970s RAPT is still considerably more advanced than most commercial robot programming languages. The team of people who contributed to the project were leaders in the field at the time and included Pat Ambler, Harry Barrow, Ilona Bellos, Chris Brown, Rod Burstall, Gregan Crawford, Jim Howe, Donald Michie, Robin Popplestone, Stephen Salter, Austin Tate and Ken Turner. Also of interest in the project was the use of a structured-light 3D scanner to obtain the 3D shape and position of the parts being manipulated. The Freddy II robot is currently on display at the Royal Museum in Edinburgh, Scotland, with a segment of the assembly video shown in a continuous loop.

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  • Computational Intelligence (journal)

    Computational Intelligence (journal)

    Computational Intelligence Journal is a peer-reviewed scientific journal covering research on artificial intelligence and computer science. The journal published novel research as well as innovative applications in a broad range of AI, covering Computational Intelligence is an artificial intelligence journal publishing novel research on a broad range of experimental and theoretical topics in AI and computer science. With a broad scope, the journal covers machine learning, knowledge mining, web intelligence, AI language, and philosophical implications. The journal was established in 1985 and is published by Wiley-Blackwell. Currently, the editors-in-chief is Diane Inkpen. The quality of the journal as an academic publishing venue is evaluated according to public citation impact metrics. in 2022, the Computational Intelligence Journal CiteScore of Scopus was 5.3, while Clarivate's Web of Science gives it 0.39 in the Journal Citation Indicator and 2,8 in the Journal Impact Factor.

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