AI Data Center Zoning

AI Data Center Zoning — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Ware report

  Security Controls for Computer Systems, commonly called the Ware report, is a 1970 text by Willis Ware that was foundational in the field of computer security. == Development == A defense contractor in St. Louis, Missouri, had bought an IBM mainframe computer, which it was using for classified work on a fighter aircraft. To provide additional income, the contractor asked the Department of Defense (DoD) for permission to sell computer time on the mainframe to local businesses via remote terminals, while the classified work continued. At the time, the DoD did not have a policy to cover this. The DoD's Advanced Research Projects Agency (DARPA) asked Ware - a RAND employee - to chair a committee to examine and report on the feasibility of security controls for computer systems. The committee's report was a classified document given in January 1970 to the Defense Science Board (DSB), which had taken over the project from ARPA. After declassification, the report was published by RAND in October 1979. == Influence == The IEEE Computer Society said the report was widely circulated, and the IEEE Annals of the History of Computing said that it, together with Ware's 1967 Spring Joint Computer Conference session, marked the start of the field of computer security. The report influenced security certification standards and processes, especially in the banking and defense industries, where the report was instrumental in creating the Orange Book.

  Read more →
• 

  Aidoc

  Aidoc Medical is an Israeli technology company that develops computer-aided simple triage and notification systems. Aidoc has obtained U.S. Food and Drug Administration and CE mark approval for its stroke, pulmonary embolism, cervical fracture, intracranial hemorrhage, intra-abdominal free gas, and incidental pulmonary embolism algorithms. Aidoc algorithms are in use in more than 900 hospitals and imaging centers, including Montefiore Nyack Hospital, LifeBridge Health, LucidHealth, Yale New Haven Hospital, Cedars-Sinai Medical Center, University of Rochester Medical Center, and Sheba Medical Center. == History == Aidoc was founded in 2016 by Elad Walach as the CEO, Michael Braginsky as the CTO and Guy Reiner as the VP. In April 2017, the company raised $7M, led by TLV Partners, and in April 2019, the company raised another $27M, led by Square Peg capital. There have been several additional rounds of funding as well, bringing Aidoc's total investment to $370M as of July 2025. In August 2018, Aidoc gained FDA clearance for its intracranial hemorrhage system, and in May 2019 it received clearance for the pulmonary embolism system. In January 2020, the system for detecting large-vessel occlusions (LVOs) in head CTA examinations obtained FDA clearance. In October 2024, it was reported that Aidoc is working with NVIDIA to develop a framework for deployment and integration of artificial intelligence tools in healthcare. The Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE) is a guideline to facilitate AI adoption in the healthcare industry. == Products and market == Aidoc has developed a suite of artificial intelligence products that flag both time-sensitive and time-consuming (for the radiologist) abnormalities across the body. The algorithms are developed with large quantities of data to provide diagnostic aid for a broad set of pathologies. The company offers an array of algorithms that span across the body, including for intracranial hemorrhage, spine fractures (C, T & L), free air in the abdomen, pulmonary embolism, and more. It developed "Always-on AI", a term coined by Elad Walach that refers to a type of artificial intelligence that is "Always-on—constantly running in the background and automatically analyzing medical imaging data, identifying urgent findings, and sparing radiologists from "drowning" in vast amounts of irrelevant data. Aidoc's solutions cover medical conditions prevalent in all settings (ED/inpatient/outpatient), including level 1 trauma centers, outpatient imaging centers, teleradiology groups and, are set up in over 200 medical centers worldwide. Notable customers include the University of Rochester Medical Center and Global Diagnostics Australia. Aidoc announced in 2024 that its new Clinical AI Reasoning Engine (CARE1) had been submitted for FDA approval. In September 2025 Aidoc received a "Breakthrough Device Designation" from the FDA for a new multi-triage solution that spans numerous acute findings in CT scans. Aidoc's CARE1 foundation model was the basis of the workflow on which the designation was made, enabling simultaneous coverage of multiple pathologies. This new designation allows parallel FDA review of multiple indications under a single submission. In April 2026, Aidoc raised million in a Series E funding round led by Growth Equity at Goldman Sachs Alternatives, with participation from General Catalyst and NVentures. The financing brought the company's total funding to over million. == Clinical Research == A clinical study on Aidoc’ accuracy of deep convolutional neural networks for the detection of pulmonary embolism (PE) on CT pulmonary angiograms (CTPAs) was performed by the University Hospital of Basel and presented at the European Congress of Radiology, showing that the Aidoc algorithm reached 93% sensitivity and 95% specificity. Clinical research has also been performed to test the diagnostic performance of Aidoc's deep learning-based triage system for the flagging of acute findings in abdominal computed tomography (CT) examinations. Overall, the algorithm achieved 93% sensitivity (91/98, 7 false negatives) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings. Additional clinical research on Aidoc's Intracranial hemorrhage algorithm accuracy was presented at the European Congress of Radiology by Antwerp University Hospital, evaluating the use of its deep learning algorithm for the detection of intracranial hemorrhage on non-contrast enhanced CT of the brain. The University of Washington completed a study on the accuracy of Aidoc's intracranial hemorrhage algorithm.

  Read more →
• 

  Synthetic data

  Synthetic data are artificially generated data not produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. In many sensitive applications, datasets theoretically exist but cannot be released to the general public; synthetic data sidesteps the privacy issues that arise from using real consumer information without permission or compensation. == Usefulness == Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. This can be useful when designing many systems, from simulations based on theoretical value, to database processors, etc. This helps detect and solve unexpected issues such as information processing limitations. Synthetic data are often generated to represent the authentic data and allows a baseline to be set. Another benefit of synthetic data is to protect the privacy and confidentiality of authentic data, while still allowing for use in testing systems. Computer security experts claim generated synthetic data "... enables us to create realistic behavior profiles for users and attackers. The data is used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment." In defense and military contexts, synthetic data is seen as a potentially valuable tool to develop and improve complex AI systems, particularly in contexts where high-quality real-world data is scarce. At the same time, synthetic data together with the testing approach can give the ability to model real-world scenarios. == History == Scientific modelling of physical systems has a long history that runs concurrent with the history of physics. For example, research into synthesis of audio and voice can be traced back to the 1930s and before, driven forward by the developments of the telephone and audio recording technologies. Digitization gave rise to software synthesizers from the 1970s onwards. In the context of privacy-preserving statistical analysis, in 1993, the idea of original fully synthetic data was created by Donald Rubin. Rubin originally designed this to synthesize the Decennial Census long form responses for the short form households. He then released samples that did not include any actual long form records - in this he preserved anonymity of the household. Later that year, the idea of original partially synthetic data was created by Little. Little used this idea to synthesize the sensitive values on the public use file. A 1993 work fitted a statistical model to 60,000 MNIST digits, then it was used to generate over 1 million examples. Those were used to train a LeNet-4 to reach state of the art performance. In 1994, Stephen Fienberg introduced 'critical refinement', in which a parametric posterior predictive distribution (instead of a Bayes bootstrap) is used to do the sampling. Later, other important contributors to the development of synthetic data generation were Trivellore Raghunathan, Jerry Reiter, Donald Rubin, John M. Abowd, and Jim Woodcock. Collectively they came up with a solution for how to treat partially synthetic data with missing data. Similarly, they developed the technique of Sequential Regression Multivariate Imputation. == Calculations == Researchers test the framework on synthetic data, which is "the only source of ground truth on which they can objectively assess the performance of their algorithms". Synthetic data can be generated through the use of random lines, having different orientations and starting positions. Datasets can get fairly complicated. A more complicated dataset can be generated by using a synthesizer build. To create a synthesizer build, first use the original data to create a model or equation that fits the data the best. This model or equation will be called a synthesizer build. This build can be used to generate more data. Constructing a synthesizer build involves constructing a statistical model. In a linear regression line example, the original data can be plotted, and a best fit linear line can be created from the data. This line is a synthesizer created from the original data. The next step will be generating more synthetic data from the synthesizer build or from this linear line equation. In this way, the new data can be used for studies and research, and it protects the confidentiality of the original data. David Jensen from the Knowledge Discovery Laboratory explains how to generate synthetic data: "Researchers frequently need to explore the effects of certain data characteristics on their data model." To help construct datasets exhibiting specific properties, such as auto-correlation or degree disparity, proximity can generate synthetic data having one of several types of graph structure: random graphs that are generated by some random process; lattice graphs having a ring structure; lattice graphs having a grid structure, etc. In all cases, the data generation process follows the same process: Generate the empty graph structure. Generate attribute values based on user-supplied prior probabilities. Since the attribute values of one object may depend on the attribute values of related objects, the attribute generation process assigns values collectively. == Applications == === Fraud detection and confidentiality systems === Testing and training fraud detection and confidentiality systems are devised using synthetic data. Specific algorithms and generators are designed to create realistic data, which then assists in teaching a system how to react to certain situations or criteria. For example, intrusion detection software is tested using synthetic data. This data is a representation of the authentic data and may include intrusion instances that are not found in the authentic data. The synthetic data allows the software to recognize these situations and react accordingly. If synthetic data was not used, the software would only be trained to react to the situations provided by the authentic data and it may not recognize another type of intrusion. === Scientific research === Researchers doing clinical trials or any other research may generate synthetic data to aid in creating a baseline for future studies and testing. Real data can contain information that researchers may not want released, so synthetic data is sometimes used to protect the privacy and confidentiality of a dataset. Using synthetic data reduces confidentiality and privacy issues since it holds no personal information and cannot be traced back to any individual. Beyond privacy protection, synthetic data is also being explored for methodological innovation in drug development. For instance, synthetic data may be used to construct synthetic control arms as an alternative to conventional external control arms based on real-world data (RWD) or randomized controlled trials (RCTs). Collectively, regulatory agencies such as the FDA and EMA appear to be at various stages of recognizing and integrating AI-generated synthetic data into their methodologies. While there is growing consensus on the potential of such data to support model development and the broader lifecycle of medicinal products, to date no drug or medical device has been approved using solely or predominantly synthetic data—particularly not as a comparator arm generated entirely via data-driven algorithms. The quality and statistical handling of synthetic data are expected to become more prominent in future regulatory discussions, particularly in contexts such as predictive modeling (e.g., digital twins), where innovative approaches have already been referenced. === Machine learning === Synthetic data is increasingly being used for machine learning applications: a model is trained on a synthetically generated dataset with the intention of transfer learning to real data. Efforts have been made to enable more data science experiments via the construction of general-purpose synthetic data generators, such as the Synthetic Data Vault. In general, synthetic data has several natural advantages: once the synthetic environment is ready, it is fast and cheap to produce as much data as needed; synthetic data can have perfectly accurate labels, including labeling that may be very expensive or impo

  Read more →
• 

  Magic Quadrant

  Magic Quadrant (MQ) is a series of market research reports published by research and advisory firm Gartner that rely on proprietary qualitative data analysis methods to demonstrate market trends, such as direction, maturity, and participants. Their analyses are conducted for several specific technology industries and are updated every 1–2 years: once an updated report has been published, its predecessor is "retired". == Rating == Gartner rates vendors upon two criteria: completeness of vision and ability to execute. Completeness of vision – Reflects the vendor's innovation, and whether the vendor drives or follows the market. Ability to execute – Summarizes factors such as the vendor's financial viability, market responsiveness, product development, sales channels and customer base. The two component scores lead to a vendor position in one of four quadrants: === Leaders === Vendors in the "Leaders" quadrant have the highest composite scores for their completeness of vision and ability to execute. A vendor in the Leaders quadrant has the market share, credibility, and marketing & sales capabilities needed to drive the acceptance of new technologies. These vendors demonstrate a clear understanding of market needs, they are innovators and thought leaders, and they have well-articulated plans that customers and prospects can use when designing their infrastructures and strategies. In addition, they have a presence in the five major geographical regions, consistent financial performance, and broad platform support. === Challengers === Vendors in the "Challengers" quadrant have high scores mainly for their ability to execute. They both participate in the market and execute well enough to be a serious threat to vendors in the "Leaders" quadrant. They have strong products, as well as sufficiently credible market position and resources to sustain continued growth. Financial viability is not an issue for vendors in the "Challengers" quadrant, but they lack the size and influence of vendors in the "Leaders" quadrant due to their relative lack of vision. === Visionaries === Vendors in the "Visionaries" quadrant have high scores mainly for their completeness of vision. They deliver innovative products that address operationally or financially important end-user problems at a broad scale, but have not yet demonstrated the ability to capture market share or maintain sustainable levels of profitability. Visionary vendors are frequently privately held companies and acquisition targets for larger, established companies. The likelihood of acquisition often reduces the risks associated with installing their systems. === Niche Players === Vendors in the "Niche Players" quadrant have relatively low scores for both their ability to execute and their completeness of vision. They are often narrowly focused on specific market or vertical segments. This quadrant often also includes vendors that are adapting their existing products to enter the market under consideration, or larger vendors having difficulty developing and executing on their vision. == Gartner Critical Capabilities == Gartner Critical Capabilities complement Magic Quadrant analysis to offer deeper insight into the products and services offered by multiple vendors by a comparative analysis that scores competing products or services against a set of critical differentiators identified by Gartner. Gartner has periodically ended Magic Quadrant listings for IT Service Management, Web Content Management, and other industries as those markets have fully matured or other factors rendered the analytic framework inapplicable. == Criticism == The Magic Quadrant, and analysts in general, skew the market: according to research, by applying their methodologies to describe a market, they change that marketplace to fit their tools. Another criticism is that open source vendors are not considered sufficiently by analysts like Gartner, as has been published in an online discussion between a VP from Talend and a German Research VP from Gartner. On May 29, 2009 (2009-05-29), software vendor ZL Technologies filed a federal lawsuit against Gartner that challenged the "legitimacy" of Gartner's Magic Quadrant rating system. Gartner filed a motion to dismiss by claiming First Amendment protection since it contends that its MQ reports contain "pure opinion", which legally means opinions that are not based on fact. The court threw out the ZL case because it lacked a specific complaint. The decision was upheld on appeal.

  Read more →
• 

  Resisting AI

  Resisting AI: An Anti-fascist Approach to Artificial Intelligence is a book on artificial intelligence (AI) by Dan McQuillan, published in 2022 by Bristol University Press. == Content == Resisting AI takes the form of an extended essay, which contrasts optimistic visions about AI's potential by arguing that AI may best be seen as a continuation and reinforcement of bureaucratic forms of discrimination and violence, ultimately fostering authoritarian outcomes. For McQuillan, AI's promise of objective calculability is antithetical to an egalitarian and just society. McQuillan uses the expression "AI violence" to describe how – based on opaque algorithms – various actors can discriminate against categories of people in accessing jobs, loans, medical care, and other benefits. The book suggests that AI has a political resonance with soft eugenic approaches to the valuation of life by modern welfare states, and that AI exhibits eugenic features in its underlying logic, as well as in its technical operations. The parallel is with historical eugenicists achieving saving to the state by sterilizing defectives so the state would not have to care for their offspring. The analysis of McQuillan goes beyond the known critique of AI systems fostering precarious labour markets, addressing "necropolitics", the politics of who is entitled to live, and who to die. Although McQuillan offers a brief history of machine learning at the beginning of the book – with its need for "hidden and undercompensated labour", he is concerned more with the social impacts of AI rather than with its technical aspects. McQuillan sees AI as the continuation of existing bureaucratic systems that already marginalize vulnerable groups – aggravated by the fact that AI systems trained on existing data are likely to reinforce existing discriminations, e.g. in attempting to optimize welfare distribution based on existing data patterns, ultimately creating a system of "self-reinforcing social profiling". In elaborating on the continuation between existing bureaucratic violence and AI, McQuillan connects to Hannah Arendt's concept of the thoughtless bureaucrat in Eichmann in Jerusalem: A Report on the Banality of Evil, which now becomes the algorithm that, lacking intent, cannot be accountable, and is thus endowed with an "algorithmic thoughtlessness". McQuillan defends the "fascist" in the title of the work by arguing that while not all AI is fascist, this emerging technology of control may end up being deployed by fascist or authoritarian regimes. For McQuillan, AI can support the diffusion of states of exception, as a technology impossible to properly regulate and a mechanism for multiplying exceptions more widely. An example of a scenario where AI systems of surveillance could bring discrimination to a new high is the initiative to create LGBT-free zones in Poland. Skeptical of ethical regulations to control the technology, McQuillan suggests people's councils and workers' councils, and other forms of citizens' agency to resist AI. A chapter titled "Post-Machine Learning" makes an appeal for resistance via currents of thought from feminist science (standpoint theory), post-normal science (extended peer communities), and new materialism; McQuillan encourages the reader to question the meaning of "objectivity" and calls for the necessity of alternative ways of knowing. Among the virtuous examples of resistance – possibly to be adopted by the AI workers themselves – McQuillan notes the Lucas Plan of the workers of Lucas Aerospace Corporation, in which a workforce declared redundant took control, reorienting the enterprise toward useful products. McQuillan advocates for what he calls decomputing, an opposition to the sweeping application and expansion of artificial intelligence. Similar to degrowth, the approach criticizes AI as an outgrowth of the systemic issues within capitalist systems. McQuillan argues that a different future is possible, in which distance between people is reduced rather than increased through AI intermediaries. The work of McQuillan warns against "watered-down forms of engagement" with AI, such as citizen juries, which superficially look like democratic deliberation but may actually obscure important decisions about AI that are outside the purview of the engagement situation (McQuillan 2022, 128). In an interview about the book, McQuillan describes himself as an "AI abolitionist". == Reception == The book has been praised for how it "masterfully disassembles AI as an epistemological, social, and political paradigm". On the critical side, a review in the academic journal Justice, Power and Resistance took exception to the "nightmarish visions of Big Brother" offered by McQuillan, and argued that while many elements of AI may pose concern, a critique should not be based on a caricature of what AI is, concluding that McQuillan's work is "less of a theory and more of a Manifesto". Another review notes "a disconnect between the technical aspects of AI and the socio-political analysis McQuillan provides." Although the book was published before the ChatGPT and large language model debate heated up, the book has not lost relevance to the AI discussion. It is noted for suggesting a link between beliefs in artificial intelligence and beliefs in a racialised and gendered visions of intelligence overall, whereby a certain type of rational, measurable intelligence is privileged, leading to "historical notions of hierarchies of being". The blog Reboot praised McQuillan for offering a theory of harm of AI (why AI could end up hurting people and society) that does not just encourage tackling in isolation specific predicted problems with AI-centric systems: bias, non-inclusiveness, exploitativeness, environmental destructiveness, opacity, and non-contestability. For educational policies could also look at AI following the reading of McQuillan: In his book Resisting AI, Dan McQuillan argues that "When we're thinking about the actuality of AI, we can't separate the calculations in the code from the social context of its application" .... McQuillan's particular concern is how many contemporary applications of AI are amplifying existing inequalities and injustices as well as deepening social divisions and instabilities. His book makes a powerful case for anticipating these effects and actively resisting them for the good of societies. Videos and podcasts with an interest in AI and emerging technology have discussed the book.

  Read more →
• 

  Source criticism

  Source criticism (or information evaluation) is the process of evaluating an information source, i.e.: a document, a person, a speech, a fingerprint, a photo, an observation, or anything used in order to obtain knowledge. In relation to a given purpose, a given information source may be more or less valid, reliable or relevant. Broadly, "source criticism" is the interdisciplinary study of how information sources are evaluated for given tasks. == Meaning == Problems in translation: The Danish word kildekritik, like the Norwegian word kildekritikk and the Swedish word källkritik, derived from the German Quellenkritik and is closely associated with the German historian Leopold von Ranke (1795–1886). Historian Wolfgang Hardtwig wrote: His [Ranke's] first work Geschichte der romanischen und germanischen Völker von 1494–1514 (History of the Latin and Teutonic Nations from 1494 to 1514) (1824) was a great success. It already showed some of the basic characteristics of his conception of Europe, and was of historiographical importance particularly because Ranke made an exemplary critical analysis of his sources in a separate volume, Zur Kritik neuerer Geschichtsschreiber (On the Critical Methods of Recent Historians). In this work he raised the method of textual criticism used in the late eighteenth century, particularly in classical philology to the standard method of scientific historical writing. (Hardtwig, 2001, p. 12739) Historical theorist Chris Lorenz wrote: The larger part of the nineteenth and twentieth centuries would be dominated by the research-oriented conception of historical method of the so-called Historical School in Germany, led by historians as Leopold Ranke and Berthold Niebuhr. Their conception of history, long been regarded as the beginning of modern, 'scientific' history, harked back to the 'narrow' conception of historical method, limiting the methodical character of history to source criticism. (Lorenz, 2001) In the early 21st century, source criticism is a growing field in, among other fields, library and information science. In this context source criticism is studied from a broader perspective than just, for example, history, classical philology, or biblical studies (but there, too, it has more recently received new attention). == Principles == The following principles are from two Scandinavian textbooks on source criticism, written by the historians Olden-Jørgensen (1998) and Thurén (1997): Human sources may be relics (e.g. a fingerprint) or narratives (e.g. a statement or a letter). Relics are more credible sources than narratives. A given source may be forged or corrupted; strong indications of the originality of the source increases its reliability. The closer a source is to the event which it purports to describe, the more one can trust it to give an accurate description of what really happened A primary source is more reliable than a secondary source, which in turn is more reliable than a tertiary source and so on. If a number of independent sources contain the same message, the credibility of the message is strongly increased. The tendency of a source is its motivation for providing some kind of bias. Tendencies should be minimized or supplemented with opposite motivations. If it can be demonstrated that the witness (or source) has no direct interest in creating bias, the credibility of the message is increased. Two other principles are: Knowledge of source criticism cannot substitute for subject knowledge: "Because each source teaches you more and more about your subject, you will be able to judge with ever-increasing precision the usefulness and value of any prospective source. In other words, the more you know about the subject, the more precisely you can identify what you must still find out". (Bazerman, 1995, p. 304). The reliability of a given source is relative to the questions put to it. "The empirical case study showed that most people find it difficult to assess questions of cognitive authority and media credibility in a general sense, for example, by comparing the overall credibility of newspapers and the Internet. Thus these assessments tend to be situationally sensitive. Newspapers, television and the Internet were frequently used as sources of orienting information, but their credibility varied depending on the actual topic at hand" (Savolainen, 2007). The following questions are often good ones to ask about any source according to the American Library Association (1994) and Engeldinger (1988): How was the source located? What type of source is it? Who is the author and what are the qualifications of the author in regard to the topic that is discussed? When was the information published? In which country was it published? What is the reputation of the publisher? Does the source show a particular cultural or political bias? For literary sources complementing criteria are: Does the source contain a bibliography? Has the material been reviewed by a group of peers, or has it been edited? How does the article/book compare with similar articles/books? == Levels of generality == Some principles of source criticism are universal, other principles are specific for certain kinds of information sources. There is today no consensus about the similarities and differences between source criticism in the natural science and humanities. Logical positivism claimed that all fields of knowledge were based on the same principles. Much of the criticism of logical positivism claimed that positivism is the basis of the sciences, whereas hermeneutics is the basis of the humanities. This was, for example, the position of Jürgen Habermas. A newer position, in accordance with, among others, Hans-Georg Gadamer and Thomas Kuhn, understands both science and humanities as determined by researchers' preunderstanding and paradigms. Hermeneutics is thus a universal theory. The difference is, however, that the sources of the humanities are themselves products of human interests and preunderstanding, whereas the sources of the natural sciences are not. Humanities are thus "doubly hermeneutic". Natural scientists, however, are also using human products (such as scientific papers) which are products of preunderstanding (and can lead to, for example, academic fraud). == Contributing fields == === Epistemology === Epistemological theories are the basic theories about how knowledge is obtained and are thus the most general theories about how to evaluate information sources. Empiricism evaluates sources by considering the observations (or sensations) on which they are based. Sources without basis in experience are not seen as valid. Rationalism provides low priority to sources based on observations. In order to be meaningful, observations must be explained by clear ideas or concepts. It is the logical structure and the well definedness that is in focus in evaluating information sources from the rationalist point of view. Historicism evaluates information sources on the basis of their reflection of their sociocultural context and their theoretical development. Pragmatism evaluate sources on the basis of how their values and usefulness to accomplish certain outcomes. Pragmatism is skeptical about claimed neutral information sources. The evaluation of knowledge or information sources cannot be more certain than is the construction of knowledge. If one accepts the principle of fallibilism then one also has to accept that source criticism can never 100% verify knowledge claims. As discussed in the next section, source criticism is intimately linked to scientific methods. The presence of fallacies of argument in sources is another kind of philosophical criterion for evaluating sources. Fallacies are presented by Walton (1998). Among the fallacies are the ad hominem fallacy (the use of personal attack to try to undermine or refute a person's argument) and the straw man fallacy (when one arguer misrepresents another's position to make it appear less plausible than it really is, in order more easily to criticize or refute it.) === Research methodology === Research methods are methods used to produce scholarly knowledge. The methods that are relevant for producing knowledge are also relevant for evaluating knowledge. An example of a book that turns methodology upside-down and uses it to evaluate produced knowledge is Katzer; Cook & Crouch (1998). === Science studies === Studies of quality evaluation processes such as peer review, book reviews and of the normative criteria used in evaluation of scientific and scholarly research. Another field is the study of scientific misconduct. Harris (1979) provides a case study of how a famous experiment in psychology, Little Albert, has been distorted throughout the history of psychology, starting with the author (Watson) himself, general textbook authors, behavior therapists, and a prominent learning theorist. Harris proposes possible causes for these distortions and analyzes the Albert study as an ex

  Read more →
• 

  Enterprise architecture

  Enterprise architecture (EA) is a business function concerned with the structures and behaviours of a business, especially business roles and processes that create and use business data. The international definition according to the Federation of Enterprise Architecture Professional Organizations is "a well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a comprehensive approach at all times, for the successful development and execution of strategy. Enterprise architecture applies architecture principles and practices to guide organizations through the business, information, process, and technology changes necessary to execute their strategies. These practices utilize the various aspects of an enterprise to identify, motivate, and achieve these changes." The United States Federal Government is an example of an organization that practices EA, in this case with its Capital Planning and Investment Control processes. Companies such as Independence Blue Cross, Intel, Volkswagen AG, and InterContinental Hotels Group also use EA to improve their business architectures as well as to improve business performance and productivity. Additionally, the Federal Enterprise Architecture's reference guide aids federal agencies in the development of their architectures. == Introduction == As a discipline, EA "proactively and holistically lead[s] enterprise responses to disruptive forces by identifying and analyzing the execution of change" towards organizational goals. EA gives business and IT leaders recommendations for policy adjustments and provides best strategies to support and enable business development and change within the information systems the business depends on. EA provides a guide for decision making towards these objectives. The National Computing Centre's EA best practice guidance states that an EA typically "takes the form of a comprehensive set of cohesive models that describe the structure and functions of an enterprise. The individual models in an EA are arranged in a logical manner that provides an ever-increasing level of detail about the enterprise." Important players within EA include enterprise architects and solutions architects. Enterprise architects are at the top level of the architect hierarchy, meaning they have more responsibilities than solutions architects. While solutions architects focus on their own relevant solutions, enterprise architects focus on solutions for and the impact on the whole organization. Enterprise architects oversee many solution architects and business functions. As practitioners of EA, enterprise architects support an organization's strategic vision by acting to align people, process, and technology decisions with actionable goals and objectives that result in quantifiable improvements toward achieving that vision. The practice of EA "analyzes areas of common activity within or between organizations, where information and other resources are exchanged to guide future states from an integrated viewpoint of strategy, business, and technology." === Definitions === The term enterprise can be defined as an organizational unit, organization, or collection of organizations that share a set of common goals and collaborate to provide specific products or services to customers. In that sense, the term enterprise covers various types of organizations, regardless of their size, ownership model, operational model, or geographical distribution. It includes those organizations' complete sociotechnical system, including people, information, processes, and technologies. Enterprise as a sociotechnical system defines the scope of EA. The term architecture refers to fundamental concepts or properties of a system in its environment; and embodied in its elements, relationships, and in the principles of its design and evolution. A methodology for developing and using architecture to guide the transformation of a business from a baseline state to a target state, sometimes through several transition states, is usually known as an enterprise architecture framework. A framework provides a structured collection of processes, techniques, artifact descriptions, reference models, and guidance for the production and use of an enterprise-specific architecture description. Open-source tools supporting EA practice, such as the Essential Project, have also been evaluated for suitability in academic and commercial training contexts. Paramount to changing the EA is the identification of a sponsor. Their mission, vision, strategy, and the governance framework define all roles, responsibilities, and relationships involved in the anticipated transformation. Changes considered by enterprise architects typically include innovations in the structure or processes of an organization; innovations in the use of information systems or technologies; the integration and/or standardization of business processes; and improvement of the quality and timeliness of business information. According to the standard ISO/IEC/IEEE 42010, the product used to describe the architecture of a system is called an architectural description. In practice, an architectural description contains a variety of lists, tables, and diagrams. These are models known as views. In the case of EA, these models describe the logical business functions or capabilities, business processes, human roles and actors, the physical organization structure, data flows and data stores, business applications and platform applications, hardware, and communications infrastructure. The first use of the term "enterprise architecture" is often incorrectly attributed to John Zachman's 1987 A framework for information systems architecture. The first publication to use it was instead a National Institute of Standards (NIST) Special Publication on the challenges of information system integration. The NIST article describes EA as consisting of several levels. Business unit architecture is the top level and might be a total corporate entity or a sub-unit. It establishes for the whole organization necessary frameworks for "satisfying both internal information needs" as well as the needs of external entities, which include cooperating organizations, customers, and federal agencies. The lower levels of the EA that provide information to higher levels are more attentive to detail on behalf of their superiors. In addition to this structure, business unit architecture establishes standards, policies, and procedures that either enhance or stymie the organization's mission. The main difference between these two definitions is that Zachman's concept was the creation of individual information systems optimized for business, while NIST's described the management of all information systems within a business unit. The definitions in both publications, however, agreed that due to the "increasing size and complexity of the [i]mplementations of [i]nformation systems... logical construct[s] (or architecture) for defining and controlling the interfaces and... [i]ntegration of all the components of a system" is necessary. Zachman in particular urged for a "strategic planning methodology." == Overview == === Schools of thought === Within the field of enterprise architecture, there are three overarching schools: Enterprise IT Design, Enterprise Integrating, and Enterprise Ecosystem Adaption. Which school one subscribes to will impact how they see the EA's purpose and scope, as well as the means of achieving it, the skills needed to conduct it, and the locus of responsibility for conducting it. Under Enterprise IT Design, the main purpose of EA is to guide the process of planning and designing an enterprise's IT/IS capabilities to meet the desired organizational objectives, often by greater alignment between IT/IS and business concerns. Architecture proposals and decisions are limited to the IT/IS aspects of the enterprise and other aspects service only as inputs. The Enterprise Integrating school believes that the purpose of EA is to create a greater coherency between the various concerns of an enterprise (HR, IT, Operations, etc.), including the link between strategy formulation and execution. Architecture proposals and decisions here encompass all aspects of the enterprise. The Enterprise Ecosystem Adaption school states that the purpose of EA is to foster and maintain the learning capabilities of enterprises so they may be sustainable. Consequently, a great deal of emphasis is put on improving the capabilities of the enterprise to improve itself, to innovate, and to coevolve with its environment. Typically, proposals and decisions encompass both the enterprise and its environment. === Benefits, challenges, and criticisms === The benefits of EA are achieved through its direct and indirect contributions to organizational goals. Notable benefits include support in the areas related to design and re-design of the organizational structures during mergers, acquisitions, or

  Read more →
• 

  Ubiquitous robot

  Ubiquitous robot is a term used in an analogous way to ubiquitous computing. Software useful for "integrating robotic technologies with technologies from the fields of ubiquitous and pervasive computing, sensor networks, and ambient intelligence". The emergence of mobile phone, wearable computers and ubiquitous computing makes it likely that human beings will live in a ubiquitous world in which all devices are fully networked. The existence of ubiquitous space resulting from developments in computer and network technology will provide motivations to offer desired services by any IT device at any place and time through user interactions and seamless applications. This shift has hastened the ubiquitous revolution, which has further manifested itself in the new multidisciplinary research area, ubiquitous robotics. It initiates the third generation of robotics following the first generation of the industrial robot and the second generation of the personal robot. Ubiquitous robot (Ubibot) is a robot incorporating three components including virtual software robot or avatar, real-world mobile robot and embedded sensor system in surroundings. Software robot within a virtual world can control a real-world robot as a brain and interact with human beings. Researchers of KAIST, Korea describe these three components as a Sobot (Software robot), Mobot (Mobile robot), and Embot (Embedded robot).

  Read more →
• 

  Voice user interface

  A voice user interface (VUI) enables spoken human interaction with computers, using speech recognition to understand spoken commands and answer questions, and typically text to speech to play a reply. A voice command device is a device controlled with a voice user interface. Voice user interfaces have been added to automobiles, home automation systems, computer operating systems, home appliances like washing machines and microwave ovens, and television remote controls. They are the primary way of interacting with virtual assistants on smartphones and smart speakers. Older automated attendants (which route phone calls to the correct extension) and interactive voice response systems (which conduct more complicated transactions over the phone) can respond to the pressing of keypad buttons via DTMF tones, but those with a full voice user interface allow callers to speak requests and responses without having to press any buttons. Newer voice command devices are speaker-independent, so they can respond to multiple voices, regardless of accent or dialectal influences. They are also capable of responding to several commands at once, separating vocal messages, and providing appropriate feedback, accurately imitating a natural conversation. == Overview == A VUI is the interface to any speech application. Only a short time ago, controlling a machine by simply talking to it was only possible in science fiction. Until recently, this area was considered to be artificial intelligence. However, advances in technologies like text-to-speech, speech-to-text, natural language processing, and cloud services contributed to the mass adoption of these types of interfaces. VUIs have become more commonplace, and people are taking advantage of the value that these hands-free, eyes-free interfaces provide in many situations. VUIs rely on the ability to process input reliably, inconsistent performance often leads to decreased user engagement and negative feedback. Designing a good VUI requires interdisciplinary talents of computer science, linguistics and human factors such as psychology. Even with advanced development tools, constructing an effective VUI requires understanding of both the tasks to be performed, as well as the target audience that will use the final system. The closer the VUI matches the user's mental model of the task, the easier it will be to use with little or no training, resulting in both higher efficiency and higher user satisfaction. A VUI designed for the general public should emphasize ease of use and provide a lot of help and guidance for first-time callers. In contrast, a VUI designed for a small group of power users (including field service workers), should focus more on productivity and less on help and guidance. Such applications should streamline the call flows, minimize prompts, eliminate unnecessary iterations and allow elaborate "mixed initiative dialogs", which enable callers to enter several pieces of information in a single utterance and in any order or combination. In short, speech applications have to be carefully crafted for the specific business process that is being automated. Not all business processes render themselves equally well for speech automation. In general, the more complex the inquiries and transactions are, the more challenging they will be to automate, and the more likely they will be to fail with the general public. In some scenarios, automation is simply not applicable, so live agent assistance is the only option. A legal advice hotline, for example, would be very difficult to automate. On the flip side, speech is perfect for handling quick and routine transactions, like changing the status of a work order, completing a time or expense entry, or transferring funds between accounts. == History == Early applications for VUI included voice-activated dialing of phones, either directly or through a (typically Bluetooth) headset or vehicle audio system. In 2007, a CNN business article reported that voice command was over a billion dollar industry and that companies like Google and Apple were trying to create speech recognition features. In the years since the article was published, the world has witnessed a variety of voice command devices. Additionally, Google has created a speech recognition engine called Pico TTS and Apple released Siri. Voice command devices are becoming more widely available, and innovative ways for using the human voice are always being created. For example, Business Week suggests that the future remote controller is going to be the human voice. Currently Xbox Live allows such features and Jobs hinted at such a feature on the new Apple TV. == Voice command software products on computing devices == Both Apple Mac and Windows PC provide built in speech recognition features for their latest operating systems. === Microsoft Windows === Two Microsoft operating systems, Windows 7 and Windows Vista, provide speech recognition capabilities. Microsoft integrated voice commands into their operating systems to provide a mechanism for people who want to limit their use of the mouse and keyboard, but still want to maintain or increase their overall productivity. ==== Windows Vista ==== With Windows Vista voice control, a user may dictate documents and emails in mainstream applications, start and switch between applications, control the operating system, format documents, save documents, edit files, efficiently correct errors, and fill out forms on the Web. The speech recognition software learns automatically every time a user uses it, and speech recognition is available in English (U.S.), English (U.K.), German (Germany), French (France), Spanish (Spain), Japanese, Chinese (Traditional), and Chinese (Simplified). In addition, the software comes with an interactive tutorial, which can be used to train both the user and the speech recognition engine. ==== Windows 7 ==== In addition to all the features provided in Windows Vista, Windows 7 provides a wizard for setting up the microphone and a tutorial on how to use the feature. ==== Mac OS X ==== All Mac OS X computers come pre-installed with the speech recognition software. The software is user-independent, and it allows for a user to, "navigate menus and enter keyboard shortcuts; speak checkbox names, radio button names, list items, and button names; and open, close, control, and switch among applications." However, the Apple website recommends a user buy a commercial product called Dictate. === Commercial products === If a user is not satisfied with the built in speech recognition software or a user does not have a built speech recognition software for their OS, then a user may experiment with a commercial product such as Braina Pro or DragonNaturallySpeaking for Windows PCs, and Dictate, the name of the same software for Mac OS. == Voice command mobile devices == Any mobile device running Android OS, Microsoft Windows Phone, iOS 9 or later, or Blackberry OS provides voice command capabilities. In addition to the built-in speech recognition software for each mobile phone's operating system, a user may download third party voice command applications from each operating system's application store: Apple App store, Google Play, Windows Phone Marketplace (initially Windows Marketplace for Mobile), or BlackBerry App World. === Android OS === Google has developed an open source operating system called Android, which allows a user to perform voice commands such as: send text messages, listen to music, get directions, call businesses, call contacts, send email, view a map, go to websites, write a note, and search Google. The speech recognition software is available for all devices since Android 2.2 "Froyo", but the settings must be set to English. Google allows for the user to change the language, and the user is prompted when he or she first uses the speech recognition feature if he or she would like their voice data to be attached to their Google account. If a user decides to opt into this service, it allows Google to train the software to the user's voice. Google introduced the Google Assistant with Android 7.0 "Nougat". It is much more advanced than the older version. Amazon.com has the Echo that uses Amazon's custom version of Android to provide a voice interface. === Microsoft Windows === Windows Phone is Microsoft's mobile device's operating system. On Windows Phone 7.5, the speech app is user independent and can be used to: call someone from your contact list, call any phone number, redial the last number, send a text message, call your voice mail, open an application, read appointments, query phone status, and search the web. In addition, speech can also be used during a phone call, and the following actions are possible during a phone call: press a number, turn the speaker phone on, or call someone, which puts the current call on hold. Windows 10 introduces Cortana, a voice control system that replaces the formerly used voice control on Windows

  Read more →
• 

  Knowledge organization system

  Knowledge organization system (KOS), concept system, or concept scheme is the generic term used in knowledge organization (KO) for the selection of concepts with an indication of selected semantic relations. Despite their differences in type, coverage, and application, all KOS aim to support the organization of knowledge and information to facilitate their management and retrieval. KOS vary in complexity from simple sorted lists to complex relational networks. They represent both structural and functional features, and serve to eliminate ambiguity, control synonyms, establish relationships, and present properties. From their origins in library and information science (LIS), KOS have been applied to other domains and disciplines within science and industry, although scholarly research and debate remain primarily within the KO field. Challenges of KOS include ambiguity of terminology, repercussions of biased systems, and potential obsolescence. KOS can be expressed in RDF and RDFS as per the Simple Knowledge Organization System (SKOS) recommendation by W3C, which aims to enable the sharing and linking of KOS via the Web. One of the largest collections of KOS is the BARTOC registry. == Types == While different schema of KOS have been proposed, most are generally arranged in terms of the complexity of their construction and maintenance. Some scholars argue that organizing KOS on a spectrum oversimplifies the shared characteristics among them, and may even result in a non-ideal structure being chosen. The following types are not exhaustive, and are often not mutually-exclusive in practice. === Term lists === Term lists are the least structured form of KOS. They include lists, glossaries, dictionaries, and synonym rings. Authority files and gazetteers may also be considered term lists, however other scholars categorize them and directories as "metadata-like models". Examples include the Union List of Artist Names name authority file and the GeoNames gazetteer. === Categorization and classification === KOS that emphasize specific (and often hierarchical) structures include subject headings, taxonomies, categorization schema, and classification schema & systems. Despite inconsistent use of the terms "categorization" and "classification" in some literature, categorization is generally loosely-assembled grouping schema and may include attributes that are not mutually exclusive (or having fuzzy boundaries), while classification is related to the arrangement of non-overlapping and mutually-exclusive classes. Classification schema may be universal (such as Dewey Decimal Classification and Information Coding Classification) or domain-specific (such as the National Library of Medicine Classification). === Relationship models === The types of KOS with greatest complexity and which utilize connections between concepts include thesauri, semantic networks, and ontologies. One of the most prominent examples of a semantic network is WordNet. === Others === Certain structures proposed to be considered types of KOS—but are not consistently included in schema—include folksonomies, topic maps, web directory structures, publication organization systems, and bibliometric maps. Some KOS organize other KOS themselves—for instance, PeriodO is a gazetteer of periodization categories. == Applications == Some early KOS were developed as a support system for abstracting and indexing services to be used by specially-trained searchers. With the growth of information digitization, usability became increasingly accessible, and more complex structures were developed. Prominent examples of KOS outside of LIS include organism taxonomy in biology, the periodic table of elements in chemistry, SIC and NAICS classification systems for industry & business, and AGROVOC agricultural controlled vocabulary. == Challenges == The study and design of KOS is an ongoing topic of discussion among KO scholars. === Terminology === [There is] a serious lack of vocabulary control in the literature on controlled vocabulary. Inconsistency of terminology within the study of KOS is a common issue. For instance, "ontology" is used for both a specific type of KOS as well as a generic term for any KOS. The terms "taxonomy", "classification", and "categorization" are also sometimes used interchangeably. === Bias === As knowledge can be historically and culturally biased, scholars have also discussed how KOS themselves can perpetuate harmful practices or stereotypes. For example, a number of concerns and criticisms about the classification of mental disorders in the Diagnostic and Statistical Manual of Mental Disorders have been raised, contributing to ongoing revisions. Ethical and intentional design approaches have been proposed for multi-perspective KOS in efforts to mitigate bias and other harmful practices. === Obsolescence === The possible obsolescence of the thesaurus and other simpler KOS has been the topic of debate, especially in the face of increasingly complex ontologies, the growing usage of "Google-like retrieval systems", and the move of KO theory and research away from LIS and toward computer science. Supporters of thesauri argue its continued usefulness for metadata enrichment, vocabulary mapping, and web services, as well as its usage in specific domains such as corporate intranets and digital image libraries.

  Read more →
• 

  Interviewer effect

  The interviewer effect (also called interviewer variance or interviewer error) is the distortion of response to an interviewer-administered data collection effort which results from differential reactions to the social style and personality of interviewers or to their presentation of particular questions. The use of fixed-wording questions is one method of reducing interviewer bias. Anthropological research and case-studies are also affected by the problem, which is exacerbated by the self-fulfilling prophecy, when the researcher is also the interviewer it is also any effect on data gathered from interviewing people that is caused by the behavior or characteristics (real or perceived) of the interviewer. Interviewer effects can also be associated with the characteristics of the interviewer, such as race. Whether black respondents are interviewed by white interviewers or black interviewers has a strong impact on their responses to both attitude questions and behavioral ones. In the latter case, for example, if black respondents are interviewed by black interviewers in pre-election surveys, they are more likely to actually vote in the upcoming election than if they are interviewed by white interviewers. Furthermore, the race of the interviewer can also affect answers to factual questions that might take the form of a test of how informed the respondent is. Black respondents in a survey of political knowledge, for example, get fewer correct answers to factual questions about politics when interviewed by white interviewers than when interviewed by black interviewers. This is consistent with the research literature on stereotype threat, which finds diminished test performance of potentially stigmatised groups when the interviewer or test supervisor is from a perceived higher status group. Interviewer effects can be mitigated somewhat by randomly assigning subjects to different interviewers, or by using tools such as computer-assisted telephone interviewing (CATI).

  Read more →
• 

  ARMA International

  ARMA International (formerly the Association of Records Managers and Administrators) is an American not-for-profit professional association for information professionals – primarily information management (including records management) and information governance, and related industry practitioners and vendors. The association provides educational opportunities and publications covering aspects of information management broadly. == History == The Association was founded in 1955. In 1975, the Association of Records Executives and Administrators (AREA) and the American Records Management Association merged to form ARMA International. The headquarters for ARMA International is located in Overland Park, Kansas. == Operations == ARMA International services professionals in the United States, Canada, Japan, and the United Kingdom. Its members include records managers, attorneys, information technology professionals, consultants, and archivists involved in various aspects of managing records and information assets. ARMA hosts an annual conference with the goal of bringing together record and information management professionals from around the world – In 2023, ARMA hosted conferences in both the United States and Canada. Topics addressed in the 120+ educational sessions include advanced technology, creating information structure, ediscovery and information law, information management fundamentals, information project management, and reducing organizational information risk. The expo features exhibitors displaying records and information technologies, products, and services.

  Read more →
• 

  Multi-armed bandit

  In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is named from imagining a gambler at a row of slot machines (sometimes known as "one-armed bandits"), who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. More generally, it is a problem in which a decision maker iteratively selects one of multiple fixed choices (i.e., arms or actions) when the properties of each choice are only partially known at the time of allocation, and may become better understood as time passes. A fundamental aspect of bandit problems is that choosing an arm does not affect the properties of the arm or other arms. Instances of the multi-armed bandit problem include the task of iteratively allocating a fixed, limited set of resources between competing (alternative) choices in a way that minimizes the regret. A notable alternative setup for the multi-armed bandit problem includes the "best arm identification (BAI)" problem where the goal is instead to identify the best choice by the end of a finite number of rounds. The multi-armed bandit problem is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma. In contrast to general reinforcement learning, the selected actions in bandit problems do not affect the reward distribution of the arms. The multi-armed bandit problem also falls into the broad category of stochastic scheduling. In the problem, each machine provides a random reward from a probability distribution specific to that machine, that is not known a priori. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls. The crucial tradeoff the gambler faces at each trial is between "exploitation" of the machine that has the highest expected payoff and "exploration" to get more information about the expected payoffs of the other machines. The trade-off between exploration and exploitation is also faced in machine learning. In practice, multi-armed bandits have been used to model problems such as managing research projects in a large organization, like a science foundation or a pharmaceutical company. In early versions of the problem, the gambler begins with no initial knowledge about the machines. Herbert Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in "some aspects of the sequential design of experiments". A theorem, the Gittins index, first published by John C. Gittins, gives an optimal policy for maximizing the expected discounted reward. == Empirical motivation == The multi-armed bandit problem models an agent that simultaneously attempts to acquire new knowledge (called "exploration") and optimize their decisions based on existing knowledge (called "exploitation"). The agent attempts to balance these competing tasks in order to maximize their total value over the period of time considered. There are many practical applications of the bandit model, for example: clinical trials investigating the effects of different experimental treatments while minimizing patient losses, adaptive routing efforts for minimizing delays in a network, financial portfolio design In these practical examples, the problem requires balancing reward maximization based on the knowledge already acquired with attempting new actions to further increase knowledge. This is known as the exploitation vs. exploration tradeoff in machine learning. The model has also been used to control dynamic allocation of resources to different projects, answering the question of which project to work on, given uncertainty about the difficulty and payoff of each possibility. Originally considered by Allied scientists in World War II, it proved so intractable that, according to Peter Whittle, the problem was proposed to be dropped over Germany so that German scientists could also waste their time on it. The version of the problem now commonly analyzed was formulated by Herbert Robbins in 1952. == The multi-armed bandit model == The multi-armed bandit (short: bandit or MAB) can be seen as a set of real distributions B = { R 1 , … , R K } {\displaystyle B=\{R_{1},\dots ,R_{K}\}} , each distribution being associated with the rewards delivered by one of the K ∈ N + {\displaystyle K\in \mathbb {N} ^{+}} levers. Let μ 1 , … , μ K {\displaystyle \mu _{1},\dots ,\mu _{K}} be the mean values associated with these reward distributions. The gambler iteratively plays one lever per round and observes the associated reward. The objective is to maximize the sum of the collected rewards. The horizon H {\displaystyle H} is the number of rounds that remain to be played. The bandit problem is formally equivalent to a one-state Markov decision process. The regret ρ {\displaystyle \rho } after T {\displaystyle T} rounds is defined as the expected difference between the reward sum associated with an optimal strategy and the sum of the collected rewards: ρ = T μ ∗ − ∑ t = 1 T r ^ t {\displaystyle \rho =T\mu ^{}-\sum _{t=1}^{T}{\widehat {r}}_{t}} , where μ ∗ {\displaystyle \mu ^{}} is the maximal reward mean, μ ∗ = max k { μ k } {\displaystyle \mu ^{}=\max _{k}\{\mu _{k}\}} , and r ^ t {\displaystyle {\widehat {r}}_{t}} is the reward in round t {\displaystyle t} . A zero-regret strategy is a strategy whose average regret per round ρ / T {\displaystyle \rho /T} tends to zero with probability 1 when the number of played rounds tends to infinity. Intuitively, zero-regret strategies are guaranteed to converge to a (not necessarily unique) optimal strategy if enough rounds are played. == Variations == A common formulation is the Binary multi-armed bandit or Bernoulli multi-armed bandit, which issues a reward of one with probability p {\displaystyle p} , and otherwise a reward of zero. Another formulation of the multi-armed bandit has each arm representing an independent Markov machine. Each time a particular arm is played, the state of that machine advances to a new one, chosen according to the Markov state evolution probabilities. There is a reward depending on the current state of the machine. In a generalization called the "restless bandit problem", the states of non-played arms can also evolve over time. There has also been discussion of systems where the number of choices (about which arm to play) increases over time. Computer science researchers have studied multi-armed bandits under worst-case assumptions, obtaining algorithms to minimize regret in both finite and infinite (asymptotic) time horizons for both stochastic and non-stochastic arm payoffs. === Best arm identification === An important variation of the classical regret minimization problem in multi-armed bandits is best arm identification (BAI), also known as pure exploration. This problem is crucial in various applications, including clinical trials, adaptive routing, recommendation systems, and A/B testing. In BAI, the objective is to identify the arm having the highest expected reward. An algorithm in this setting is characterized by a sampling rule, a decision rule, and a stopping rule, described as follows: Sampling rule: ( a t ) t ≥ 1 {\displaystyle (a_{t})_{t\geq 1}} is a sequence of actions at each time step Stopping rule: τ {\displaystyle \tau } is a (random) stopping time which suggests when to stop collecting samples Decision rule: a ^ τ {\displaystyle {\hat {a}}_{\tau }} is a guess on the best arm based on the data collected up to time τ {\displaystyle \tau } There are two predominant settings in BAI: Fixed budget setting: Given a time horizon T ≥ 1 {\displaystyle T\geq 1} , the objective is to identify the arm with the highest expected reward a ⋆ ∈ arg ⁡ max k μ k {\displaystyle a^{\star }\in \arg \max _{k}\mu _{k}} minimizing probability of error δ {\displaystyle \delta } . Fixed confidence setting: Given a confidence level δ ∈ ( 0 , 1 ) {\displaystyle \delta \in (0,1)} , the objective is to identify the arm with the highest expected reward a ⋆ ∈ arg ⁡ max k μ k {\displaystyle a^{\star }\in \arg \max _{k}\mu _{k}} with the least possible amount of trials and with probability of error P ( a ^ τ ≠ a ⋆ ) ≤ δ {\displaystyle \mathbb {P} ({\hat {a}}_{\tau }\neq a^{\star })\leq \delta } . For example using a decision rule, we could use m 1 {\displaystyle m_{1}} where m {\displaystyle m} is the machine no.1 (you can use a different variable respectively) and 1 {\displaystyle 1} is the amount for each time an attempt is made at pulling the lever, where ∫ ∑ m 1 , m 2 , ( . . . ) = M {\displaystyle \int \sum m_{1},m_{2},(...)=M} , identify M {\displaystyle M} as the sum of each attempts m 1 + m 2 {\displaystyle m_{1}+m_{2}} , (...) as needed, and from there you can get a ratio, sum or mean as quantitative probability and sample your formulation for each slots. You can also do ∫ ∑ k ∝ i N − (

  Read more →
• 

  Information and media literacy

  Information and media literacy (IML) is a combination of information literacy and media literacy. It enables people to show and make informed judgments as users of information and media, as well as to become skillful creators and producers of information and media messages. The transformative nature of IML includes creative works and creating new knowledge; to publish and collaborate responsibly requires ethical, cultural and social understanding. IML is also known as media and information literacy (MIL). UNESCO first adopted the term MIL in 2008 as a "composite concept" combining the competencies of information literacy and media literacy. UNESCO emphasizes the importance of global education in media and information literacy, and in 2013 defined Media and Information Literacy (MIL) as the ability to access, evaluate, use, and create information and media content in critical and ethical ways. Prior to the 1990s, the primary focus of information literacy was research skills. Media literacy, a study that emerged around the 1970s, traditionally focuses on the analysis and the delivery of information through various forms of media. Information literacy, as a skill proposed as early as 1974, centers on an individual's ability to recognize information needs and effectively locate, evaluate, and use information. These days, the study of information literacy has been extended to include the study of media literacy in many countries like the UK, Australia and New Zealand. It is also referred to as information and communication technologies (ICT) in the United States. Educators such as Gregory Ulmer have also defined the field as electracy.Media literacy is the ability to actively inquire into and think critically about information. It includes the ability to understand, evaluate, and create media content, and is an essential skill in today's information society. Livingstone, Van Couvering, and Thumim (2008) described the distinction between media literacy and information literacy: "Media literacy views media as lenses or windows for observing the world and expressing the self, whereas information literacy sees information as a tool for taking action in the world." == Integration of media and information literacy == Historically, the fields of information and media literacy have been separate, but over the course of the 21st century there have been calls to integrate both fields. Most definitions of information and media literacy include not only the abilities to locate, access, and analyze information but also the ability to create information. Only by integrating media literacy with information literacy can students better understand the sources of information and how it is used. Media education has primarily taken place in educational institutions, while information education has primarily occurred in libraries. Discussions surrounding the overlap of information literacy and media literacy came to fruition in the mid-to-late 2000s and 2010s as noted by Marcus Leaning. == In the digital age == The definition of literacy is "the ability to read and write". In practice many more skills are needed to locate, critically assess and make effective use of information. By extension, literacy now also includes the ability to manage and interact with digital information and media, in personal, shared and public domains. Historically, "information literacy" has largely been seen from the relatively top-down, organisational viewpoint of library and information sciences. However the same term is also used to describe a generic "information literacy" skill. The modern digital age has led to the proliferation of information spread across the Internet. Individuals must be able to recognize whether information is true or false and better yet know how to locate, evaluate, use, and communicate information in various formats; this is called information literacy. Towards the end of the 20th century, literacy was redefined to include "new literacies" relating to the new skills needed in everyday experience. "Multiliteracies" recognised the multiplicity of literacies, which were often used in combination. "21st century skills" frameworks link new literacies to wider life skills such as creativity, critical thinking, accountability. What these approaches have in common is a focus on the multiple skills needed by individuals to navigate changing personal, professional and public "information landscapes". As the conventional definition of literacy itself continues to evolve among practitioners, so too has the definition of information literacies. Noteworthy definitions include: Zurkowski defined information literacy as "the ability to find known or knowable content on any subject." CILIP, the Chartered Institute of Library and Information Practitioners, defines information literacy as "the ability to think critically and make balanced judgements about any information we find and use". In the United States, the definition proposed by the Association of College and Research Libraries (ACRL) is the most widely recognized. It defines information literacy as "a set of abilities requiring individuals to recognize when information is needed and to locate, evaluate, and use the needed information effectively." JISC, the Joint Information Systems Committee, refers to information literacy as one of six "digital capabilities", seen as an interconnected group of elements centered on "ICT literacy". Mozilla groups digital and other literacies as "21st century skills", a "broad set of knowledge, skills, habits and traits that are important to succeed in today's world". UNESCO, the United Nations Educational, Scientific and Cultural Organization, recognizing the necessity of teaching and learning both traditional and new types of information, the global importance of education was emphasized in 2008 through the "Teacher Media and Information Literacy (MIL) Curriculum". It defines MIL as a set of competencies that enable citizens to access, retrieve, understand, evaluate, use, create, and share information and media content in all formats through various tools in a critical, ethical, and effective manner, so as to participate in and carry out personal, professional, and social activities. Besides this, UNESCO also asserts information literacy as a "universal human right". == 21st-century students == In modern society, although the overall level of education has improved, the channels for knowledge production and dissemination have become increasingly diverse and commercialized, and traditional authoritative institutions no longer hold a monopoly over knowledge validation. While digital platforms have broadened access to information, they have also weakened trust mechanisms and evaluation standards, making epistemological skepticism a norm. Moreover, with the rise and spread of social media, misinformation and disinformation can be just as easily accessed in both densely and sparsely populated areas. These factors further underscore the importance of information literacy education. The IML learning capacities prepare students to be 21st century literate. According to Jeff Wilhelm (2000), "technology has everything to do with literacy. And being able to use the latest electronic technologies has everything to do with being literate." He supports his argument with J. David Bolter's statement that "if our students are not reading and composing with various electronic technologies, then they are illiterate. They are not just unprepared for the future; they are illiterate right now, in our current time and context". In a broader sense, developing this advanced competency of media and information literacy is essential, as it is crucial for students to exercise their freedom of expression in the 21st century. Wilhelm's statement is supported by the 2005 Wired World Phase II (YCWW II) survey conducted by the Media Awareness Network of Canada on 5000 Grade 4 – 11 students. The key findings of the survey were: 62% of Grade 4 students prefer the Internet. 38% of Grade 4 students prefer the library. 91% of Grade 11 students prefer the Internet. 9% of Grade 11 students prefer the library. Marc Prensky (2001) uses the term "digital native" to describe people who have been brought up in a digital world. The Internet has been a pervasive element of young people's home lives. 94% of kids reported that they had Internet access at home, and a significant majority (61%) had a high-speed connection. By the time kids reach Grade 11, half of them (51 percent) have their own Internet-connected computer, separate and apart from the family computer. The survey also showed that young Canadians are now among the most wired in the world. Contrary to the earlier stereotype of the isolated and awkward computer nerd, today's wired kid is a social kid. In general, many students are better networked through the use of technology than most teachers and parents, who may not understand the abilities of technology.

  Read more →
• 

  Umbrella review

  In medical research, an umbrella review is a review of systematic reviews or meta-analyses. They may also be called overviews of reviews, reviews of reviews, summaries of systematic reviews, or syntheses of reviews. Umbrella reviews are among the highest levels of evidence currently available in medicine. By summarizing information from multiple overview articles, umbrella reviews make it easier to review the evidence and allow for comparison of results between each of the individual reviews. Umbrella reviews may address a broader question than a typical review, such as discussing multiple different treatment comparisons instead of only one. They are especially useful for developing guidelines and clinical practice, and when comparing competing interventions.

  Read more →