AI Chat Character Apps

AI Chat Character Apps — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Anderson's rule (computer science)

  In the field of computer security, Anderson's rule refers to a principle formulated by Ross J. Anderson: systems that handle sensitive personal information involve a trilemma of security, functionality, and scale, of which you can choose any two. A system that has information on many data subjects and to which many people require access is hard to secure unless its functionality is severely restricted. If it has rich functionality, you may have to restrict the number of people with access, or accept that some information will leak.

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  Recommender system

  A recommender system, also called a recommendation algorithm, recommendation engine, or recommendation platform, is a type of information filtering system that suggests items most relevant to a particular user. The value of these systems becomes particularly evident in scenarios where users must select from a large number of options, such as products, media, or content. Major social media platforms and streaming services rely on recommender systems that employ machine learning to analyze user behavior and preferences, thereby enabling personalized content feeds. Typically, the suggestions refer to a variety decision-making processes, including the selection of a product, musical selection, or online news source to read. The implementation of recommender systems is pervasive, with commonly recognised examples including the generation of playlist for video and music services, the provision of product recommendations for e-commerce platforms, and the recommendation of content on social media platforms and the open web. These systems can operate using a single type of input, such as music, or multiple inputs from diverse platforms, including news, books and search queries. Additionally, popular recommender systems have been developed for specific topics, such as restaurants and online dating services. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. A content discovery platform is a software recommendation platform that employs recommender system tools. It utilizes user metadata in order to identify and suggest relevant content, whilst reducing ongoing maintenance and development costs. A content discovery platform delivers personalized content to websites, mobile devices, and set-top boxes. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and academic journal articles to television. As operators compete to serve as the gateway to home entertainment, personalized television emerges as a key service differentiator. Academic content discovery has recently become another area of interest, the emergence of numerous companies dedicated to assisting academic researchers in keeping up to date with relevant academic content and facilitating serendipitous discovery of new content. == Overview == Recommender systems usually make use of either or both collaborative filtering and content-based filtering, as well as other systems such as knowledge-based systems. Collaborative filtering approaches build a model from a user's past behavior (e.g., items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties. === Example === The differences between collaborative and content-based filtering can be demonstrated by comparing two early music recommender systems, Last.fm and Pandora Radio. We can also look at how these methods are applied in e-commerce, for example, on platforms like Amazon. Last.fm creates a "station" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. Last.fm will play tracks that do not appear in the user's library, but are often played by other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique. Pandora uses the properties of a song or artist (a subset of the 450 attributes provided by the Music Genome Project) to seed a "station" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user "dislikes" a particular song and emphasizing other attributes when a user "likes" a song. This is an example of a content-based approach. In e-commerce, Amazon's well-known "customers who bought X also bought Y" feature is a prime example of collaborative filtering. It also uses content-based filtering when it recommends a book by the same author you've previously read or a pair of shoes in a similar style to ones you've viewed. Each type of system has its strengths and weaknesses. In the above example, Last.fm requires a large amount of information about a user to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems. Whereas Pandora needs very little information to start, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed). === Alternative implementations === Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise. Of note, recommender systems are often implemented using search engines indexing non-traditional data. In some cases, like in the Gonzalez v. Google Supreme Court case, may argue that search and recommendation algorithms are different technologies. Recommender systems have been the focus of several granted patents, and there are more than 50 software libraries that support the development of recommender systems including LensKit, RecBole, ReChorus and RecPack. == History == Elaine Rich created the first recommender system in 1979, called Grundy. She looked for a way to recommend users books they might like. Her idea was to create a system that asks users specific questions and classifies them into classes of preferences, or "stereotypes", depending on their answers. Depending on users' stereotype membership, they would then get recommendations for books they might like. Another early recommender system, called a "digital bookshelf", was described in a 1990 technical report by Jussi Karlgren at Columbia University, and implemented at scale and worked through in technical reports and publications from 1994 onwards by Jussi Karlgren, then at SICS, and research groups led by Pattie Maes at MIT, Will Hill at Bellcore, and Paul Resnick, also at MIT, whose work with GroupLens was awarded the 2010 ACM Software Systems Award. Montaner provided the first overview of recommender systems from an intelligent agent perspective. Adomavicius provided a new, alternate overview of recommender systems. Herlocker provides an additional overview of evaluation techniques for recommender systems, and Beel et al. discussed the problems of offline evaluations. Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges. == Approaches == === Collaborative filtering === One approach to the design of recommender systems that has wide use is collaborative filtering. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past. The system generates recommendations using only information about rating profiles for different users or items. By locating peer users/items with a rating history similar to the current user or item, they generate recommendations using this neighborhood. This approach is a cornerstone for e-commerce sites that analyze the purchasing patterns of thousands of users to suggest what you might like. Collaborative filtering methods are classified as memory-based and model-based. A well-known example of memory-based approaches is the user-based algorithm, while that of model-based approaches is matrix factorization (recommender systems). A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation as first implemented by Allen. When building a model from a user's behavior, a distinction is often made between explicit and implicit forms of data collection. Examples of explicit data collection include the following: Asking a user to rate an item on a sliding scale. Asking a user to search. Asking a user to rank a collection of items from favorite to least favorite. Presenting two items to a user and asking him/her to choose the better one of them. Asking a user to create a list of items that he/she likes (see Rocchio classification or other similar techniques). Examples of implicit data collection include the following: Observing the items that a user views in an online store, media library, or other repository of med

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  Environmental informatics

  Environmental informatics is the science of information applied to environmental science. As such, it provides the information processing and communication infrastructure to the interdisciplinary field of environmental sciences aiming at data, information and knowledge integration, the application of computational intelligence to environmental data as well as the identification of environmental impacts of information technology. Environmental informatics thus acts as a bridge, providing an interdisciplinary means of analysing, describing and understanding the complex interactions between humans, nature and technology. Since each field of applied computer science has its own subject matter, terminology and methods, specialised disciplines, such as environmental, bio- and geoinformatics have emerged, each of which combines computer science with a specific field of application such as environmental, bio- or geosciences. Environmental informatics, bioinformatics and geoinformatics all deal with computer-based processing of environmental phenomena. However, environmental informatics is the only field that pursues normative goals (e.g., political goals of environmental protection, environmental planning, and sustainability). This also influences the choice of methods. This also distinguishes it from application areas such as numerical weather prediction, which is considered an early and important example of computer simulation of environmental phenomena. The UK Natural Environment Research Council defines environmental informatics as the "research and system development focusing on the environmental sciences relating to the creation, collection, storage, processing, modelling, interpretation, display and dissemination of data and information." Kostas Karatzas defined environmental informatics as the "creation of a new 'knowledge-paradigm' towards serving environmental management needs." Karatzas argued further that environmental informatics "is an integrator of science, methods and techniques and not just the result of using information and software technology methods and tools for serving environmental engineering needs." Environmental informatics emerged in early 1990 in Central Europe. Current initiatives to effectively manage, share, and reuse environmental and ecological data are indicative of the increasing importance of fields like environmental informatics and ecoinformatics to develop the foundations for effectively managing ecological information. Examples of these initiatives are National Science Foundation Datanet projects, DataONE and Data Conservancy. == Subject matter and objectives == The subject of environmental informatics are environmental information systems (EIS). An EIS 'is a computer-based system that integrates and stores data collected about the natural environment and provides powerful methods for accessing and evaluating it.' This allows environmental data to be processed by computers for environmental protection, planning, research and technology. According to Jaeschke and Bossel, environmental informatics has three interrelated objectives: Environmental informatics serves to procure data and information for describing the state and development of the environment. Of particular importance is information that is needed to prevent or limit undesirable changes and to support desirable changes. Based on the evaluation and analysis of data, environmental informatics improves our understanding of the environment and the interactions between nature, technology and society. It thus supports environmentally relevant decisions. This enables the influence of development (system correction), the assessment of the effects and side effects of potential measures, and the creation of tools for the routine planning, implementation and monitoring of measures. == History == The simulation model World3, which formed the basis of the highly acclaimed study The Limits to Growth, is considered the starting point of environmental informatics. It incorporated environmental information, among other things, to calculate scenarios for global development. In the mid-1980s, interest grew in structuring environmental protection as an area of application for computer science. One of the first publications in German was the book Informatik im Umweltschutz. Anwendungen und Perspektiven (Computer science in environmental protection. Applications and perspectives) from 1986. The term 'environmental informatics' did not appear until around 1993, which is why the development of environmental informatics is usually referred to as having taken place in the 1990s. In 1993, the first university chair for environmental informatics was established in Cottbus. In 1994, the anthology Umweltinformatik. Informatikmethoden für Umweltschutz und Umweltforschung (Environmental Informatics: Informatics Methods for Environmental Protection and Environmental Research) was published. The development of environmental informatics was 'primarily initiated by German computer science.' In the English-speaking world, the volume Environmental Informatics was published in 1995, mainly based on the German anthology of 1994. An article in the conference proceedings of the World Computer Congress of the International Federation for Information Processing (IFIP) in Hamburg in 1994 describes the initial situation of environmental informatics as follows: 'On the one hand, we suffer from the huge amount of available data – people sometimes speak of data graveyards – on the other hand, the really relevant data may still be missing.' This statement indicates the need that led to the emergence of environmental informatics as a specialised discipline of applied computer science. Furthermore, the specific characteristics and processing requirements of environmental data necessitated the emergence of environmental informatics. The special features of environmental data include: The data structures required are highly heterogeneous due to specific processes and differing perspectives on environmental aspects (e.g., water protection, emission control, hazardous substances). In addition to the heterogeneity of the data, heterogeneous databases also play a role, as environmental data is often obtained and presented in an interdisciplinary manner. Obligations change frequently as a result of new legislation, whether regional (e.g. state regulations on water protection), national (e.g. federal emission control regulations) or international (e.g. Registration, Evaluation, Authorisation and Restriction of Chemicals|REACH). The objects represented are often multidimensional and, therefore, require complex geometric representation using curves or polygons. It is often necessary to process uncertain, imprecise or incomplete data, which is, for example, the result of extrapolations or forecasts. A new "knowledge paradigm" has emerged to meet the requirements of environmental management. Environmental informatics produces its own concepts, methods and techniques and is not merely the result of using information and communication technology methods and tools to meet environmental requirements. The development of environmental informatics since the 1990s has been significantly influenced by the newly established conferences EnviroInfo, ISESS and ITEE and is documented in the respective proceedings. Aspects of sustainability and sustainable development were increasingly integrated into environmental informatics after 2000, thereby expanding the field. In 2004, the Working Group on Sustainable Information Society of the Gesellschaft für Informatik e. V. (German Informatics Society, GI) published the Memorandum on a Sustainable Information Society, which formulates recommendations for an information society that is compatible with human, social and natural needs. Since 2007, environmental informatics has often been described in more detail as informatics for environmental protection, sustainable development and risk management. The increased focus on sustainability has also contributed to the formation of the research focus Information and Communications Technology for Sustainability (ICT4S) and to the emergence of the international conference ICT4S in 2013. ICT-ENSURE, the European Commission's funding measure for the establishment of a European research area on "ICT for Environmental Sustainability Research" (2008–2010), has also contributed to the structuring of environmental informatics. == Environmental informatics and sustainable development == Efforts to place environmental informatics within the context of sustainable development have been growing since 2000 and were significantly influenced by the Memorandum on a Sustainable Information Society. According to this Memorandum, the information society offers great but unevenly distributed opportunities for education, participation and intercultural understanding. In addition, the Memorandum highlighted the material and energy consumption of inf

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  Sedona Canada Principles

  The Sedona Canada Principles are a set of authoritative guidelines published by The Sedona Conference to aid members of the Canadian legal community involved in the identification, collection, preservation, review and production of electronically stored information (ESI). The principles were drafted by a small group of lawyers, judges and technologists called the Sedona Working Group 7 or Sedona Canada. Sedona Canada is an offshoot of The Sedona Conference which is an American "non-profit ... research and educational institute dedicated to the advanced study of law and policy in the areas of antitrust law, complex litigation, and intellectual property rights". == Background == Civil procedure in Canada is jurisdictional with each province following its own rules of civil procedure. However, each province must address the fact that due to the advancement of technology the discovery process enshrined in the rules of civil procedure can be potentially derailed due to the sheer volume of electronically stored information (ESI). When dealing with litigation matters that involve electronically stored information (ESI), the discovery process is commonly called e-discovery. The problems associated with e-discovery in Canada led to the creation of the Sedona Canada Principles. Rule 29.1.03(4) of the wikibooks:Ontario Rules of Civil Procedure specifically refers to the Sedona Canada Principles in referencing Principles re Electronic Discovery although it has been reported that this rule has been largely ignored in practice. == Summary == The Sedona Canada Principles largely refer to the processes found in the Electronic Discovery Reference Model. The principles urge proportionality due to the potentially enormous volumes of documents that may be discoverable when dealing with ESI. They also encourage good faith in the document preservation stage and regular meetings between parties to discuss the scope of the litigation. Parties are urged to be aware of the potential costs involved in producing relevant ESI but are advised that only reasonably accessible ESI need be produced. The principles stipulate that parties should not be required to search for or collect deleted material unless there is an agreement or court order related to those terms. The use of electronic tools and processes such as data sampling and web harvesting are acceptable practices. Parties are encouraged to agree early in the litigation process on production format required for the exchange of relevant documents as part of the discovery process (native files, pdf, tiff, metadata requirements etc.). Agreements or direction should be sought, if necessary, with respect to privilege or other confidential information related to production of electronic documents and data. Parties should be aware that legal precedents can be formed as a result of e-discovery practices and sanctions can be considered for a party's failure to meet their discovery obligations unless it can be demonstrated that the failure was not intentional. All parties must bear the “reasonable” costs associated with e-discovery but other arrangements can be agreed upon by the parties or by court order. == Caselaw == In Warman v. National Post Company proportionality was at issue in a case where the plaintiff was suing the defendant for libel. A motion was brought by the defendant to have the plaintiff provide a mirror image of his hard drive in an effort to prove an internet article was indeed authored by the plaintiff. Issues of proportionality and the work of the Sedona Conference and Sedona Canada Principles were factored in to the decision to grant the defendant only limited access to the hard drive. In Innovative Health Group Inc. v. Calgary Health Region the plaintiff's legal obligation to produce imaged hard drives is in question. Justice Conrad refers to the advice of Sedona Canada on proportionality and problems associated with time and expense related to the difficulties associated with electronically stored information. In York University v. Michael Markicevic Justice Brown specifically refers to the need for the parties to agree upon a formal e-discovery plan to be drafted in consultation with Sedona Canada Principles. In Friends of Lansdowne v. Ottawa Master MacLeod refers to the need for Sedona Canada principles and states “This is particularly true in the current information age when e-mail is ubiquitous and multiple copies or variants of messages may be held on various kinds of data storage devices including individual hard drives, e-mail and Blackberry servers. Even documents that ultimately exist in paper form normally begin their life on computers and negotiations frequently involve exchanges of electronic drafts. To find every scrap of paper and every electronic trace of relevant information has become a nightmarish task that threatens to render any kind of litigation extravagantly expensive.” == Criticism == Critics of the Sedona Canada Principles believe they should address system integrity and that the true history of any file preserved cannot be identified without proof of the integrity of the electronic record systems management it comes from. Other criticism is more directed to the Sedona Canada working group and complaints that it is insular and irrelevant.

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  Apache Hama

  Apache Hama is a distributed computing framework based on bulk synchronous parallel computing techniques for massive scientific computations e.g., matrix, graph and network algorithms. Originally a sub-project of Hadoop, it became an Apache Software Foundation top level project in 2012. It was created by Edward J. Yoon, who named it (short for "Hadoop Matrix Algebra"), and Hama also means hippopotamus in Yoon's native Korean language (하마), following the trend of naming Apache projects after animals and zoology (such as Apache Pig). Hama was inspired by Google's Pregel large-scale graph computing framework described in 2010. When executing graph algorithms, Hama showed a fifty-fold performance increase relative to Hadoop. Retired in April 2020, project resources are made available as part of the Apache Attic. Yoon cited issues of installation, scalability, and a difficult programming model for its lack of adoption. == Architecture == Hama consists of three major components: BSPMaster, GroomServers and Zookeeper. === BSPMaster === BSPMaster is responsible for: Maintaining groom server status Controlling super steps in a cluster Maintaining job progress information Scheduling jobs and assigning tasks to groom servers Disseminating execution class across groom servers Controlling fault Providing users with the cluster control interface. A BSP Master and multiple grooms are started by the script. Then, the bsp master starts up with a RPC server for groom servers. Groom servers starts up with a BSPPeer instance and a RPC proxy to contact the bsp master. After started, each groom periodically sends a heartbeat message that encloses its groom server status, including maximum task capacity, unused memory, and so on. Each time the BSP master receives a heartbeat message, it brings the groom server status up-to-date. The bsp master makes use of groom servers' status in order to assign tasks to idle groom servers - and returns a heartbeat response containing assigned tasks and others actions for a groom server to do. Currently BSP master has a FIFO job scheduler and simple task assignment algorithms. === GroomServer === A groom server (shortly referred to as groom) is a process that performs BSP tasks assigned by BSPMaster. Each groom contacts the BSPMaster, and it takes assigned tasks and reports its status by means of periodical piggybacks with BSPMaster. Each groom is designed to run with HDFS or other distributed storages. Basically, a groom server and a data node should be run on one physical node. === Zookeeper === A Zookeeper is used to manage the efficient barrier synchronisation of the BSPPeers.

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  AI-driven design automation

  AI-driven design automation is the use of artificial intelligence (AI) to automate and improve different parts of the electronic design automation (EDA) process. It is particularly important in the design of integrated circuits (chips) and complex electronic systems, where it can potentially increase productivity, decrease costs, and speed up design cycles. AI Driven Design Automation uses several methods, including machine learning, expert systems, and reinforcement learning. These are used for many tasks, from planning a chip's architecture and logic synthesis to its physical design and final verification. == History == === 1980s–1990s: Expert systems and early experiments === The use of AI for design automation originated in the 1980s and 1990s, mainly with the creation of expert systems. These systems tried to capture the knowledge and practical rules used by human design experts, and used these rules, along with reasoning engines, to direct the design process. A notable early project was the ULYSSES system from Carnegie Mellon University. ULYSSES was a CAD tool integration environment that let expert designers turn their design methods into scripts that could be run automatically. It treated design tools as sources of knowledge that a scheduler could manage. Another example was the ADAM (Advanced Design AutoMation) system at the University of Southern California, which used an expert system called the Design Planning Engine. This engine figured out design strategies on the fly and handled different design jobs by organizing specialized knowledge into structured formats called frames. Other systems like DAA (Design Automation Assistant) used a rule-based approach for specific jobs, such as register transfer level (RTL) design for systems like the IBM 370. Researchers at Carnegie Mellon University also created TALIB, an expert system for mask layout that used over 1200 rules, and EMUCS/DAA for CPU architectural design which used about 70 rules. These projects showed that AI worked better for problems where relatively few rules were required to describe much larger amounts of data. At the same time, there was a surge of tools called silicon compilers like MacPitts, Arsenic, and Palladio. They used algorithms and search techniques to explore different design paradigms. This was another way to automate design, even if it was not always based on expert systems. Early tests with neural networks in VLSI design also happened during this time, although they were not as common as systems based on rules. === 2000s: Introduction of machine learning === In the 2000s, interest in AI for design automation increased. This was mostly because of better machine learning (ML) algorithms and more available data from design and manufacturing. For example, they were used to model and reduce the effects of small manufacturing differences in semiconductor devices. This became very important as the size of components on chips became smaller. The large amount of data created during chip design provided the foundation needed to train smarter ML models. This allowed for predicting outcomes and optimizing in areas that were hard to automate before. === 2016–2020: Reinforcement learning and large scale initiatives === A major turning point happened in the mid to late 2010s, sparked by successes in other areas of AI. The success of DeepMind's AlphaGo in mastering the game of Go inspired researchers. They began to apply reinforcement learning (RL) to difficult EDA problems. These problems often require searching through many options and making a series of decisions. In 2018, the U.S. DARPA started the Intelligent Design of Electronic Assets (IDEA) program. A main goal of IDEA was to create a fully automated layout generator that required no human intervention, able to produce a chip design ready for manufacturing from RTL specifications in 24 hours. Another big initiative was the OpenROAD project, a large effort under IDEA led by UC San Diego with industry and university partners, aimed to build an open source, independent toolchain. It used machine learning, parallelization and divide and conquer approaches. A much-publicized but controversial demonstration of RL's potential came from Google researchers between 2020 and 2021. They created a deep reinforcement learning method for planning the layout of a chip, known as floorplanning. They reported that this method created layouts that were as good as or better than those made by human experts, and it did so in less than six hours. This method used a type of network called a graph convolutional neural network. It showed that it could learn general patterns that could be applied to new problems, getting better as it saw more chip designs. The technology was later used to design Google's Tensor Processing Unit (TPU) accelerators. However, in the original paper, the improvement (if any) from AI was not demonstrated. There was no comparison with existing non-AI tools performing the same task, and since the data is proprietary, no ability for anyone else to perform this comparison. Various efforts to reproduce the AI algorithm, and compare its results with various commercial and academic tools, have yielded mixed results with no conclusive advantage to AI. === 2020s: Autonomous systems and agents === Entering the 2020s, the industry saw the commercial launch of autonomous AI driven EDA systems. For example, Synopsys launched DSO.ai (Design Space Optimization AI) in early 2020, calling it the first autonomous artificial intelligence application for chip design in the industry. This system uses reinforcement learning to search for the best ways to optimize a design within the huge number of possible solutions, trying to improve power, performance, and area (PPA). By 2023, DSO.ai had been used to produce over 100 commercial chips, showing mainstream adoption. Synopsys later grew its AI tools into a suite called Synopsys.ai. The goal was to use AI in the entire EDA workflow, including verification and testing. These advancements, which combine modern AI methods with cloud computing and large data resources, have led to talks about a new phase in EDA. Industry experts and participants sometimes call this 'EDA 4.0'. This new era is defined by the widespread use of AI and machine learning to deal with growing design complexity, automate more of the design process, and help engineers handle the huge amounts of data that EDA tools create. The purpose of EDA 4.0 is to optimize product performance, get products to market faster and make development and manufacturing smoother through intelligent automation. == Applications == Artificial intelligence (AI) is now used in many stages of the electronic design workflow. It aims to improve productivity, get better results, and handle the growing complexity of modern integrated circuits. AI helps designers from the very first ideas about architecture all the way to manufacturing and testing. === High level synthesis and architectural exploration === In the first phases of chip design, AI helps with High Level Synthesis (HLS) and exploring different system level design options (DSE). These processes are key for turning general ideas into detailed hardware plans. AI algorithms, often using supervised learning, are used to build simpler, substitute models. These models can quickly guess important design measurements like area, performance, and power for many different architectural options or HLS settings. For example, the Ithemal tool uses deep neural networks to estimate how fast basic code blocks will run, which helps in making processor architecture decisions. Similarly, PRIMAL uses machine learning estimate power use at the register transfer level (RTL), giving early information about how much power the chip will use. Reinforcement learning (RL) and Bayesian optimization are also used to guide the DSE process. They help search through the many parameters to find the best HLS settings or architectural details like cache sizes. LLMs are also being tested for creating architectural plans or initial C code for HLS, as seen with GPT4AIGChip. === Logic synthesis and optimization === Logic synthesis starts from a high level hardware description and generates an optimized list of electronic gates, known as a gate level netlist, that is ready for placement, routing, and then construction in a specific manufacturing process. AI methods help with different parts of this process, including logic optimization, technology mapping, and making improvements after mapping. Supervised learning, especially with Graph Neural Networks (GNNs), is good at handling data or problems that can be represented as graphs. Since circuit diagrams are instances of directed graphs, supervised learning can help create models that predict design properties like power or error rates in circuits. In logic synthesis and optimization reinforcement learning is used to perform logic optimization directly. In some cases ag

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  Enterprise information system

  An Enterprise Information System (EIS) is any kind of information system which improves the functions of enterprise business processes through integration. This means typically offering high quality service, dealing with large volumes of data and capable of supporting some large and possibly complex organization or enterprise. An EIS must be able to be used by all parts and all levels of an enterprise. The word enterprise can have various connotations. Frequently the term is used only to refer to very large organizations such as multi-national companies or public-sector organizations. However, the term may be used to mean virtually anything, by virtue of it having become a corporate-speak buzzword. == Purpose == Enterprise information systems provide a technology platform that enables organizations to integrate and coordinate their business processes on a robust foundation. An EIS is currently used in conjunction with customer relationship management and supply chain management to automate business processes. An enterprise information system provides a single system that is central to the organization that ensuring information can be shared across all functional levels and management hierarchies. An EIS can be used to increase business productivity and reduce service cycles, product development cycles and marketing life cycles. It may be used to amalgamate existing applications. Other outcomes include higher operational efficiency and cost savings. Financial value is not usually a direct outcome from the implementation of an enterprise information system. == Design stage == At the design stage the main characteristic of EIS efficiency evaluation is the probability of timely delivery of various messages such as command, service, etc. == Information systems == Enterprise systems create a standard data structure and are invaluable in eliminating the problem of information fragmentation caused by multiple information systems within an organization. An EIS differentiates itself from legacy systems in that it is self-transactional, self-helping and adaptable to general and specialist conditions. Unlike an enterprise information system, legacy systems are limited to department-wide communications. A typical enterprise information system would be housed in one or more data centers, would run enterprise software, and could include applications that typically cross organizational borders such as content management systems.

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  Mobile content management system

  A mobile content management system (MCMs) is a type of content management system (CMS) capable of storing and delivering content and services to mobile devices, such as mobile phones, smart phones, and PDAs. Mobile content management systems may be discrete systems, or may exist as features, modules or add-ons of larger content management systems capable of multi-channel content delivery. Mobile content delivery has unique, specific constraints including widely variable device capacities, small screen size, limitations on wireless bandwidth, sometimes small storage capacity, and (for some devices) comparatively weak device processors. Demand for mobile content management increased as mobile devices became increasingly ubiquitous and sophisticated. MCMS technology initially focused on the business to consumer (B2C) mobile market place with ringtones, games, text-messaging, news, and other related content. Since, mobile content management systems have also taken root in business-to-business (B2B) and business-to-employee (B2E) situations, allowing companies to provide more timely information and functionality to business partners and mobile workforces in an increasingly efficient manner. A 2008 estimate put global revenue for mobile content management at US$8 billion. == Key features == === Multi-channel content delivery === Multi-channel content delivery capabilities allow users not to manage a central content repository while simultaneously delivering that content to mobile devices such as mobile phones, smartphones, tablets and other mobile devices. Content can be stored in a raw format (such as Microsoft Word, Excel, PowerPoint, PDF, Text, HTML etc.) to which device-specific presentation styles can be applied. === Content access control === Access control includes authorization, authentication, access approval to each content. In many cases the access control also includes download control, wipe-out for specific user, time specific access. For the authentication, MCM shall have basic authentication which has user ID and password. For higher security many MCM supports IP authentication and mobile device authentication. === Specialized templating system === While traditional web content management systems handle templates for only a handful of web browsers, mobile CMS templates must be adapted to the very wide range of target devices with different capacities and limitations. There are two approaches to adapting templates: multi-client and multi-site. The multi-client approach makes it possible to see all versions of a site at the same domain (e.g. sitename.com), and templates are presented based on the device client used for viewing. The multi-site approach displays the mobile site on a targeted sub-domain (e.g. mobile.sitename.com). === Location-based content delivery === Location-based content delivery provides targeted content, such as information, advertisements, maps, directions, and news, to mobile devices based on current physical location. Currently, GPS (global positioning system) navigation systems offer the most popular location-based services. Navigation systems are specialized systems, but incorporating mobile phone functionality makes greater exploitation of location-aware content delivery possible.

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  Line Drawing System-1

  LDS-1 (Line Drawing System-1) was a calligraphic (vector, rather than raster) display processor and display device created by Evans & Sutherland in 1969. This model was known as the first graphics device with a graphics processing unit. == Features == It was controlled by a variety of host computers. Straight lines were smoothly rendered in real-time animation. General principles of operation were similar to the systems used today: 4x4 transformation matrices, 1x4 vertices. Possible uses included flight simulation (in the product brochure there are screenshots of landing on a carrier), scientific imaging and GIS systems. == History == The first LDS-1 was shipped to the customer (BBN) in August 1969. Only a few of these systems were ever built. One was used by the Los Angeles Times as their first typesetting/layout computer. One went to NASA Ames Research Center for Human Factors Research. Another was bought by the Port Authority of New York to develop a tugboat pilot trainer for navigation in the harbor. The MIT Dynamic Modeling had one, and there was a program for viewing an ongoing game of Maze War.

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  StoredIQ

  StoredIQ was a company founded for information lifecycle management (ILM) of unstructured data. Founded in 2001 as Deepfile in Austin, Texas by Jeff Erramouspe, Jeff Bone, Russell Turpin, Rudy Rouhana, Laura Arbilla and Brett Funderburg, the company changed its name in 2005 to StoredIQ. It continued to operate successfully for over a decade until it was acquired in 2012 by IBM. It now serves as a platform for IBM's information life cycle governance, big data governance and enterprise content management technologies. StoredIQ was awarded five patents by the USPTO. The first, originally filed in 2003, enabled unstructured data in file systems to be manipulated in a similar way to information stored in databases. Subsequent patents built upon the patented actionable file system with further enhancements specific to Enterprise Policy Management and expanding the reach of StoredIQ's management capability all the way to individual desktops. In 2008 StoredIQ was recognized as "Best in Compliance" by Network Products Guide. At the same time, StoredIQ was being recognized as a "Top 5 Provider" by the prestigious Socha-Gelbmann eDiscovery survey. There were takeover negotiations with EMC Corporation, initially a strategic investor in StoredIQ, however, the company rejected the approach, leaving EMC to acquire a competitor. The company published a whitepaper titled The Truth About Big Data. This promotion combined with StoredIQ's patented technology led to IBM selecting StoredIQ as the basis for some products.

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  Ontology merging

  Ontology merging defines the act of bringing together two conceptually divergent ontologies or the instance data associated to two ontologies. This is similar to work in database merging (schema matching). This merging process can be performed in a number of ways, manually, semi automatically, or automatically. Manual ontology merging although ideal is extremely labour-intensive and current research attempts to find semi or entirely automated techniques to merge ontologies. These techniques are statistically driven often taking into account similarity of concepts and raw similarity of instances through textual string metrics and semantic knowledge. These techniques are similar to those used in information integration employing string metrics from open source similarity libraries.

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  Two-phase commit protocol

  In transaction processing, databases, and computer networking, the two-phase commit protocol (2PC, tupac) is a type of atomic commitment protocol (ACP). It is a distributed algorithm that coordinates all the processes that participate in a distributed atomic transaction on whether to commit or abort (roll back) the transaction. This protocol (a specialised type of consensus protocol) achieves its goal even in many cases of temporary system failure (involving either process, network node, communication, etc. failures), and is thus widely used. However, it is not resilient to all possible failure configurations, and in rare cases, manual intervention is needed to remedy an outcome. To accommodate recovery from failure (automatic in most cases) the protocol's participants use logging of the protocol's states. Log records, which are typically slow to generate but survive failures, are used by the protocol's recovery procedures. Many protocol variants exist that primarily differ in logging strategies and recovery mechanisms. Though usually intended to be used infrequently, recovery procedures compose a substantial portion of the protocol, due to many possible failure scenarios to be considered and supported by the protocol. In a "normal execution" of any single distributed transaction (i.e., when no failure occurs, which is typically the most frequent situation), the protocol consists of two phases: The commit-request phase (or voting phase), in which a coordinator process attempts to prepare all the transaction's participating processes (named participants, cohorts, or workers) to take the necessary steps for either committing or aborting the transaction and to vote, either "Yes": commit (if the transaction participant's local portion execution has ended properly), or "No": abort (if a problem has been detected with the local portion), and The commit phase, in which, based on voting of the participants, the coordinator decides whether to commit (only if all have voted "Yes") or abort the transaction (otherwise), and notifies the result to all the participants. The participants then follow with the needed actions (commit or abort) with their local transactional resources (also called recoverable resources; e.g., database data) and their respective portions in the transaction's other output (if applicable). The two-phase commit (2PC) protocol should not be confused with the two-phase locking (2PL) protocol, a concurrency control protocol. == Assumptions == The protocol works in the following manner: one node is a designated coordinator, which is the master site, and the rest of the nodes in the network are designated the participants. The protocol assumes that: there is stable storage at each node with a write-ahead log, no node crashes forever, the data in the write-ahead log is never lost or corrupted in a crash, and any two nodes can communicate with each other. The last assumption is not too restrictive, as network communication can typically be rerouted. The first two assumptions are much stronger; if a node is totally destroyed then data can be lost. The protocol is initiated by the coordinator after the last step of the transaction has been reached. The participants then respond with an agreement message or an abort message depending on whether the transaction has been processed successfully at the participant. == Basic algorithm == === Commit request (or voting) phase === The coordinator sends a query to commit message to all participants and waits until it has received a reply from all participants. The participants execute the transaction up to the point where they will be asked to commit. They each write an entry to their undo log and an entry to their redo log. Each participant replies with: either an agreement message (participant votes Yes to commit), if the participant's actions succeeded; or an abort message (participant votes No to commit), if the participant experiences a failure that will make it impossible to commit. === Commit (or completion) phase === ==== Success ==== If the coordinator received an agreement message from all participants during the commit-request phase: The coordinator sends a commit message to all the participants. Each participant completes the operation, and releases all the locks and resources held during the transaction. Each participant sends an acknowledgement to the coordinator. The coordinator completes the transaction when all acknowledgements have been received. ==== Failure ==== If any participant votes No during the commit-request phase (or the coordinator's timeout expires): The coordinator sends a rollback message to all the participants. Each participant undoes the transaction using the undo log, and releases the resources and locks held during the transaction. Each participant sends an acknowledgement to the coordinator. The coordinator undoes the transaction when all acknowledgements have been received. ==== Message flow ==== Coordinator Participant QUERY TO COMMIT --------------------------------> VOTE YES/NO prepare/abort <------------------------------- commit/abort COMMIT/ROLLBACK --------------------------------> ACKNOWLEDGEMENT commit/abort <-------------------------------- end An next to the record type means that the record is forced to stable storage. == Disadvantages == The greatest disadvantage of the two-phase commit protocol is that it is a blocking protocol. If the coordinator fails permanently, some participants will never resolve their transactions: After a participant has sent an agreement message as a response to the commit-request message from the coordinator, it will block until a commit or rollback is received. A two-phase commit protocol cannot dependably recover from a failure of both the coordinator and a cohort member during the commit phase. If only the coordinator had failed, and no cohort members had received a commit message, it could safely be inferred that no commit had happened. If, however, both the coordinator and a cohort member failed, it is possible that the failed cohort member was the first to be notified, and had actually done the commit. Even if a new coordinator is selected, it cannot confidently proceed with the operation until it has received an agreement from all cohort members, and hence must block until all cohort members respond. == Implementing the two-phase commit protocol == === Common architecture === In many cases the 2PC protocol is distributed in a computer network. It is easily distributed by implementing multiple dedicated 2PC components similar to each other, typically named transaction managers (TMs; also referred to as 2PC agents or Transaction Processing Monitors), that carry out the protocol's execution for each transaction (e.g., The Open Group's X/Open XA). The databases involved with a distributed transaction, the participants, both the coordinator and participants, register to close TMs (typically residing on respective same network nodes as the participants) for terminating that transaction using 2PC. Each distributed transaction has an ad hoc set of TMs, the TMs to which the transaction participants register. A leader, the coordinator TM, exists for each transaction to coordinate 2PC for it, typically the TM of the coordinator database. However, the coordinator role can be transferred to another TM for performance or reliability reasons. Rather than exchanging 2PC messages among themselves, the participants exchange the messages with their respective TMs. The relevant TMs communicate among themselves to execute the 2PC protocol schema above, "representing" the respective participants, for terminating that transaction. With this architecture the protocol is fully distributed (does not need any central processing component or data structure), and scales up with number of network nodes (network size) effectively. This common architecture is also effective for the distribution of other atomic commitment protocols besides 2PC, since all such protocols use the same voting mechanism and outcome propagation to protocol participants. === Protocol optimizations === Database research has been done on ways to get most of the benefits of the two-phase commit protocol while reducing costs by protocol optimizations and protocol operations saving under certain system's behavior assumptions. ==== Presumed abort and presumed commit ==== Presumed abort or Presumed commit are common such optimizations. An assumption about the outcome of transactions, either commit, or abort, can save both messages and logging operations by the participants during the 2PC protocol's execution. For example, when presumed abort, if during system recovery from failure no logged evidence for commit of some transaction is found by the recovery procedure, then it assumes that the transaction has been aborted, and acts accordingly. This means that it does not matter if aborts are logged at all, and such logging can be saved under this assumption. Typical

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  Vector database

  A vector database, vector store or vector search engine is a database that stores and retrieves embeddings of data in vector space. Vector databases typically implement approximate nearest neighbor algorithms so users can search for records semantically similar to a given input, unlike traditional databases which primarily look up records by exact match. Use-cases for vector databases include similarity search, semantic search, multi-modal search, recommendations engines, object detection, and retrieval-augmented generation (RAG). Vector embeddings are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, with the number of dimensions ranging from a few hundred to tens of thousands, depending on the complexity of the data being represented. Each data item is represented by one vector in this space. Words, phrases, or entire documents, as well as images, audio, and other types of data, can all be vectorized. These feature vectors may be computed from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically similar data items receive feature vectors close to each other. Vector retrieval can be combined with metadata filtering or lexical search to support filtered and hybrid retrieval workflows. == Techniques == Common techniques for similarity search on high-dimensional vectors include: Hierarchical Navigable Small World (HNSW) graphs Locality-sensitive hashing (LSH) and sketching Product quantization (PQ) Inverted files These techniques may also be combined in vector search systems. In recent benchmarks, HNSW-based implementations have been among the best performers. Conferences such as the International Conference on Similarity Search and Applications (SISAP) and the Conference on Neural Information Processing Systems (NeurIPS) have hosted competitions on vector search in large databases. == Applications == Vector databases are used in a wide range of machine learning applications including similarity search, semantic search, multi-modal search, recommendations engines, object detection, and retrieval-augmented generation. === Retrieval-augmented generation === An especially common use-case for vector databases is in retrieval-augmented generation (RAG), a method to improve domain-specific responses of large language models. The retrieval component of a RAG can be any search system, but is most often implemented as a vector database. Text documents describing the domain of interest are collected, and for each document or document section, a feature vector (known as an "embedding") is computed, typically using a deep learning network, and stored in a vector database along with a link to the document. Given a user prompt, the feature vector of the prompt is computed, and the database is queried to retrieve the most relevant documents. These are then automatically added into the context window of the large language model, and the large language model proceeds to create a response to the prompt given this context. == Implementations ==

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  Artificial intuition

  Artificial intuition is a theoretical capacity of an artificial software to function similarly to human consciousness, specifically in the capacity of human consciousness known as intuition. == Comparison of human and the theoretically artificial == Intuition is the function of the mind, the experience of which, is described as knowledge based on "a hunch", resulting (as the word itself does) from "contemplation" or "insight". Psychologist Jean Piaget showed that intuitive functioning within the normally developing human child at the Intuitive Thought Substage of the preoperational stage occurred at from four to seven years of age. In Carl Jung's concept of synchronicity, the concept of "intuitive intelligence" is described as something like a capacity that transcends ordinary-level functioning to a point where information is understood with a greater depth than is available in more simple rationally-thinking entities. Artificial intuition is theoretically (or otherwise) a sophisticated function of an artifice that is able to interpret data with depth and locate hidden factors functioning in Gestalt psychology, and that intuition in the artificial mind would, in the context described here, be a bottom-up process upon a macroscopic scale identifying something like the archetypal (see τύπος). To create artificial intuition supposes the possibility of the re-creation of a higher functioning of the human mind, with capabilities such as what might be found in semantic memory and learning. The transferral of the functioning of a biological system to synthetic functioning is based upon modeling of functioning from knowledge of cognition and the brain, for instance as applications of models of artificial neural networks from the research done within the discipline of computational neuroscience. == Application software contributing to its development == The notion of a process of a data-interpretative synthesis has already been found in a computational-linguistic software application that has been created for use in an internal security context. The software integrates computed data based specifically on objectives incorporating a paradigm described as "religious intuitive" (hermeneutic), functional to a degree that represents advances upon the performance of generic lexical data mining.

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  Data management plan

  A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future. DMPs were originally used in 1966 to manage aeronautical and engineering projects' data collection and analysis, and expanded across engineering and scientific disciplines in the 1970s and 1980s. Up until the early 2000s, DMPs were used "for projects of great technical complexity, and for limited mid-study data collection and processing purposes". In the 2000s and later, E-research and economic policies drove the development and uptake of DMPs. == Importance == Preparing a data management plan before data are collected is claimed to ensure that data are in the correct format, organized well, and better annotated. This could arguably save time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It is also claimed to increase research efficiency since both the data collector and other researchers might be able to understand and use well-annotated data in the future. One component of a data management plan is data archiving and preservation. By deciding on an archive ahead of time, the data collector can format data during collection to make its future submission to a database easier. If data are preserved, they are more relevant since they can be re-used by other researchers. It also allows the data collector to direct requests for data to the database, rather than address requests individually. A frequent argument in favor of preservation is that data that are preserved have the potential to lead to new, unanticipated discoveries, and they prevent duplication of scientific studies that have already been conducted. Data archiving also provides insurance against loss by the data collector. In the 2010s, funding agencies increasingly required data management plans as part of the proposal and evaluation process, despite little or no evidence of their efficacy. == Major components == "There is no general and definitive list of topics that should be covered in a DMP for a research project", and researchers are often left to their own devices as to how to fill out a DMP. === Information about data and data format === A description of data to be produced by the project. This might include (but is not limited to) data that are: Experimental Observational Raw or derived Physical collections Models Simulations Curriculum materials Software Images How will the data be acquired? When and where will they be acquired? After collection, how will the data be processed? Include information about Software used Algorithms Scientific workflows File formats that will be used, justify those formats, and describe the naming conventions used. Quality assurance & quality control measures that will be taken during sample collection, analysis, and processing. If existing data are used, what are their origins? How will the data collected be combined with existing data? What is the relationship between the data collected and existing data? How will the data be managed in the short-term? Consider the following: Version control for files Backing up data and data products Security & protection of data and data products Who will be responsible for management === Metadata content and format === Metadata are the contextual details, including any information important for using data. This may include descriptions of temporal and spatial details, instruments, parameters, units, files, etc. Metadata is commonly referred to as "data about data". Issues to be considered include: How detailed has the metadata to be in order to make the data meaningful? How will the metadata be created and/or captured? Examples include lab notebooks, GPS hand-held units, Auto-saved files on instruments, etc. What format will be used for the metadata? What are the metadata standards commonly used in the respective scientific discipline? There should be justification for the format chosen. === Policies for access, sharing, and re-use === Describe any obligations that exist for sharing data collected. These may include obligations from funding agencies, institutions, other professional organizations, and legal requirements. Include information about how data will be shared, including when the data will be accessible, how long the data will be available, how access can be gained, and any rights that the data collector reserves for using data. Address any ethical or privacy issues with data sharing Address intellectual property & copyright issues. Who owns the copyright? What are the institutional, publisher, and/or funding agency policies associated with intellectual property? Are there embargoes for political, commercial, or patent reasons? Describe the intended future uses/users for the data Indicate how the data should be cited by others. How will the issue of persistent citation be addressed? For example, if the data will be deposited in a public archive, will the dataset have a persistent identifier (e.g., ARK, DOI, Handle, PURL, URN) assigned to it? === Long-term storage and data management === Researchers should identify an appropriate archive for the long-term preservation of their data. By identifying the archive early in the project, the data can be formatted, transformed, and documented appropriately to meet the requirements of the archive. Researchers should consult colleagues and professional societies in their discipline to determine the most appropriate database, and include a backup archive in their data management plan in case their first choice goes out of existence. Early in the project, the primary researcher should identify what data will be preserved in an archive. Usually, preserving the data in its most raw form is desirable, although data derivatives and products can also be preserved. An individual should be identified as the primary contact person for archived data, and ensure contact information is always kept up-to-date in case there are requests for data or information about data. === Budget === Data management and preservation costs may be considerable, depending on the nature of the project. By anticipating costs ahead of time, researchers ensure that the data will be properly managed and archived. Potential expenses that should be considered are Human resources and staff as they handle data preparation, management, documentation, and preservation Hardware and/or software needed for data management, backing up, security, documentation, and preservation Costs associated with submitting the data to an archive The data management plan should include how these costs will be paid. == NSF Data Management Plan == All grant proposals submitted to National Science Foundation (NSF) must include a Data Management Plan that is no more than two pages. This is a supplement (not part of the 15-page proposal) and should describe how the proposal will conform to the Award and Administration Guide policy (see below). It may include the following: The types of data The standards to be used for data and metadata format and content Policies for access and sharing Policies and provisions for re-use Plans for archiving data Policy summarized from the NSF Award and Administration Guide, Section 4 (Dissemination and Sharing of Research Results): Promptly publish with appropriate authorship Share data, samples, physical collections, and supporting materials with others, within a reasonable time frame Share software and inventions Investigators can keep their legal rights over their intellectual property, but they still have to make their results, data, and collections available to others Policies will be implemented via Proposal review Award negotiations and conditions Support/incentives == ESRC Data Management Plan == Since 1995, the UK's Economic and Social Research Council (ESRC) have had a research data policy in place. The current ESRC Research Data Policy states that research data created as a result of ESRC-funded research should be openly available to the scientific community to the maximum extent possible, through long-term preservation and high-quality data management. ESRC requires a data management plan for all research award applications where new data are being created. Such plans are designed to promote a structured approach to data management throughout the data lifecycle, resulting in better quality data that is ready to archive for sharing and re-use. The UK Data Service, the ESRC's flagship data service, provides practical guidance on research data management planning suitable for social science researchers in the UK and around the world. ESRC has a longstanding arrangement with the UK Data A

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