MetaMask is a software cryptocurrency wallet developed by ConsenSys for interacting with the Ethereum blockchain and other EVM-compatible networks. It enables users to manage Ethereum accounts and connect to decentralized applications (dApps) via a browser extension or mobile app. As of early 2026, MetaMask reports over 100 million users worldwide. == Overview == MetaMask allows users to store and manage private keys, send and receive Ethereum-based cryptocurrencies and tokens (including ERC-20 and ERC-721 standards), broadcast transactions, and interact with dApps. dApps connect to the wallet via JavaScript interfaces, prompting users to approve signatures or transactions. The wallet features MetaMask Swaps, an in-app token swap aggregator sourcing liquidity from multiple decentralized exchanges (DEXs), with a service fee of 0.875%. In 2025, MetaMask introduced the MetaMask Rewards program (initially mobile-only), where users earn points for activities such as swaps, bridging, and referrals. Season 1 (October 2025 – January 2026) distributed over $30 million in Linea tokens and other perks to participants. == History == MetaMask launched in 2016 as open-source software under the MIT license. It initially supported browser extensions for Chrome and Firefox. Mobile versions were in closed beta from 2019 and publicly released for iOS and Android in September 2020. In August 2020, the license changed to a custom proprietary one. MetaMask Swaps launched on desktop in October 2020 and on mobile in March 2021. The Rewards program launched in late 2025 with Linea integration. == Criticism == MetaMask has faced criticism over privacy, including default analytics settings that share some user data (which can be disabled). Its reliance on Infura (acquired by ConsenSys in 2019) has raised concerns about centralization in Ethereum infrastructure. The wallet regularly issues warnings about phishing scams and fake airdrops impersonating MetaMask.
Artificial intelligence in education
Artificial intelligence in education (often abbreviated as AIEd) is a subfield of educational technology that studies how to use artificial intelligence to create learning environments. Considerations in the field include data-driven decision-making, AI ethics, data privacy and AI literacy. Concerns include the potential for cheating, over-reliance, equity of access, reduced critical thinking, and the perpetuation of misinformation and bias. == History == Efforts to integrate AI into educational contexts have often followed technological advancement in the history of artificial intelligence. In the 1960s, educators and researchers began developing computer-based instruction systems, such as PLATO, developed by the University of Illinois. In the 1970s and 1980s, intelligent tutoring systems (ITS) were being adapted for classroom instruction. The International Artificial Intelligence in Education Society was founded in 1993. Coinciding with the AI boom of the 2020s, the use of large language models in the global north has been promoted and funded by venture capital and big tech. Companies creating AI services have targeted students and educational institutions as customers. Similarly, pre-AI boom educational companies have expanded their use of AI technologies. These commercial incentives for AIEd use may be related to a potential AI bubble. In the U.S., bipartisan support of AI development in K-12 education has been expressed, but specific implementations and best practices remain contentious. == Theory == AIEd applies theory from education studies, machine learning, and related fields. A 2019 review of the previous decade of studies found that most research prioritized technological design over pedagogical integration. Ouyang and Jiao (2021) propose three paradigms for AI in education, which follow roughly from least to most learner-centered and from requiring least to most technical complexity from the AI systems: AI-directed, learner-as-recipient: AIEd systems present a pre-set curriculum based on statistical patterns that do not adjust to learner's feedback. AI-supported, learner-as-collaborator: Systems that incorporate responsiveness to learner's feedback through, for example, natural language processing, wherein AI can support knowledge construction. AI-empowered, learner-as-leader: This model seeks to position AI as a supplement to human intelligence wherein learners take agency and AI provides consistent and actionable feedback. Some scholars place AI in education within a socio-technical framework. This positions AI alongside other emerging educational technologies, such as computing, the internet, and social media. The framework of Tsao, Heinrichs and Camit (2025) draws on new materialism and posthumanism, specifically Donna Haraway's concept of sympoiesis (making-with). This perspective views learning as an entanglement of human and non-human actors (students, teachers, and AI algorithms), where knowledge is co-composed in contact zones between human context and algorithmic prediction. AI agents have been trained on biased datasets, and thus continue to perpetuate societal biases. Since LLMs were created to produce human-like text, algorithmic bias can be introduced and reproduced. AI's data processing and monitoring reinforce neoliberal approaches to education rather than addressing inequalities. == Applications == Uses of generative AI chatbots in education have included assessment and feedback, machine translations, proof-reading exam question generation and copy editing, or as virtual assistants. Emotional AI in education is the study and development of systems that can detect learners' emotions or provide emotional support in learning. == Usage == === Schools and educators === Following the release of ChatGPT in November 2022, some schools and large school districts blocked access to the site and issued warnings that the use of such tools would be seen as cheating. Governmental and non-governmental organizations such as UNESCO, Article 4 of the European Union's AI Act, and the U.S. Department of Education have published reports advocating for specific AIEd approaches. National higher-education bodies have also published guidance on generative AI, including Ireland's Higher Education Authority, which issued a policy framework for higher education teaching and learning in December 2025. In 2024, UNESCO released updated global guidance for generative AI in education, emphasizing ethical use, teacher training, and data protection to ensure responsible integration of AI tools in learning environments. According to Taso (2025), policy implementation in higher education is interpreted and enacted differently by various organizations. These decentralized policies can lead to inconsistent enforcement and confusion among students regarding what constitutes acceptable use, with the burden of ethical navigation falling on individual teachers and students. AI integration in classrooms has created new forms of invisible labour for educators, who must navigate ambiguous policies, redesign assessments to be AI-resilient, and adjudicate potential academic integrity violations. The use of AI detection tools has also been criticised for creating an adversarial relationship between students and institutions, where students may be falsely accused of misconduct based on probabilistic software. AIEd advocates say that efforts should be made towards increasing global accessibility and training educators to serve underprivileged areas. === Students === Reliance on generative AI has been linked with reduced academic self-esteem and performance, and heightened learned helplessness. Algorithm errors and hallucinations are common flaws in AI agents, making them less trustworthy and reliable. According to a 2025 survey from Inside Higher Ed, 85% of higher education students use generative AI technology in some way, with 25% using AI to complete assignments for them. The most common reason cited for using AI to cheat was pressure to get high grades. 97% of students wanted some form of action from schools on the threat to academic integrity caused by AI, with the most popular options being clearer policies and more education about ethical uses of AI. In September 2025, The Atlantic published an op-ed from a high school senior arguing that the normalization of AI cheating was eroding critical thinking, academic integrity, creativity, and the shared student experience.
Computational law
Computational law is the branch of legal informatics concerned with the automation of legal reasoning. What distinguishes Computational Law systems from other instances of legal technology is their autonomy, i.e. the ability to answer legal questions without additional input from human legal experts. While there are many possible applications of Computational Law, the primary focus of work in the field today is compliance management, i.e. the development and deployment of computer systems capable of assessing, facilitating, or enforcing compliance with rules and regulations. Some systems of this sort already exist. TurboTax is a good example. And the potential is particularly significant now due to recent technological advances – including the prevalence of the Internet in human interaction and the proliferation of embedded computer systems (such as smart phones, self-driving cars, and robots). There are also applications that do not involve governmental laws. The regulations can just as well be the terms of contracts (e.g. delivery schedules, insurance covenants, real estate transactions, financial agreements). They can be the policies of corporations (e.g. constraints on travel, expenditure reporting, pricing rules). They can even be the rules of games (embodied in computer game playing systems). == History == Speculation about potential benefits to legal practice through applying methods from computational science and AI research to automate parts of the law date back at least to the middle 1940s. Further, AI and law and computational law do not seem easily separable, as perhaps most of AI research focusing on the law and its automation appears to utilize computational methods. The forms that speculation took are multiple and not all related in ways to readily show closeness to one another. This history will sketch them as they were, attempting to show relationships where they can be found to have existed. By 1949, a minor academic field aiming to incorporate electronic and computational methods to legal problems had been founded by American legal scholars, called jurimetrics. Though broadly said to be concerned with the application of the "methods of science" to the law, these methods were actually of a quite specifically defined scope. Jurimetrics was to be "concerned with such matters as the quantitative analysis of judicial behavior, the application of communication and information theory to legal expression, the use of mathematical logic in law, the retrieval of legal data by electronic and mechanical means, and the formulation of a calculus of legal predictability". These interests led in 1959 to the founding a journal, Modern Uses of Logic in Law, as a forum wherein articles would be published about the applications of techniques such as mathematical logic, engineering, statistics, etc. to the legal study and development. In 1966, this Journal was renamed as Jurimetrics. Today, however, the journal and meaning of jurimetrics seems to have broadened far beyond what would fit under the areas of applications of computers and computational methods to law. Today the journal not only publishes articles on such practices as found in computational law, but has broadened jurimetrical concerns to mean also things like the use of social science in law or the "policy implications [of] and legislative and administrative control of science". Independently in 1958, at the Conference for the Mechanization of Thought held at the National Physical Laboratory in Teddington, Middlesex, UK, the French jurist Lucien Mehl presented a paper both on the benefits of using computational methods for law and on the potential means to use such methods to automate law for a discussion that included AI luminaries like Marvin Minsky. Mehl believed that the law could by automated by two basic distinct, though not wholly separable, types of machine. These were the "documentary or information machine", which would provide the legal researcher quick access to relevant case precedents and legal scholarship, and the "consultation machine", which would be "capable of answering any question put to it over a vast field of law". The latter type of machine would be able to basically do much of a lawyer's job by simply giving the "exact answer to a [legal] problem put to it". By 1970, Mehl's first type of machine, one that would be able to retrieve information, had been accomplished but there seems to have been little consideration of further fruitful intersections between AI and legal research. There were, however, still hopes that computers could model the lawyer's thought processes through computational methods and then apply that capacity to solve legal problems, thus automating and improving legal services via increased efficiency as well as shedding light on the nature of legal reasoning. By the late 1970s, computer science and the affordability of computer technology had progressed enough that the retrieval of "legal data by electronic and mechanical means" had been achieved by machines fitting Mehl's first type and were in common use in American law firms. During this time, research focused on improving the goals of the early 1970s occurred, with programs like Taxman being worked on in order to both bring useful computer technology into the law as practical aids and to help specify the exact nature of legal concepts. Nonetheless, progress on the second type of machine, one that would more fully automate the law, remained relatively inert. Research into machines that could answer questions in the way that Mehl's consultation machine would picked up somewhat in the late 1970s and 1980s. A 1979 convention in Swansea, Wales marked the first international effort solely to focus upon applying artificial intelligence research to legal problems in order to "consider how computers can be used to discover and apply the legal norms embedded within the written sources of the law". Considerable progress on the development of the second type of machine was made in the following decade, with the development of a variety of expert systems. According to Thorne McCarty, "these systems all have the following characteristics: They do backward chaining inference from a specified goal; they ask questions to elicit information from the user; and they produce a suggested answer along with a trace of the supporting legal rules." According to Prakken and Sartor the representation of the British Nationality Act as a logic program, which introduced this approach, was "hugely influential for the development of computational representations of legislation, showing how logic programming enables intuitively appealing representations that can be directly deployed to generate automatic inferences". In 2021, this work received the Inaugural CodeX Prize as "one of the first and best-known works in computational law, and one of the most widely cited papers in the field." In a 1988 review of Anne Gardner's book An Artificial Intelligence Approach to Legal Reasoning (1987), the Harvard academic legal scholar and computer scientist Edwina Rissland wrote that "She plays, in part, the role of pioneer; artificial intelligence ("AI") techniques have not yet been widely applied to perform legal tasks. Therefore, Gardner, and this review, first describe and define the field, then demonstrate a working model in the domain of contract offer and acceptance." Eight years after the Swansea conference had passed, and still AI and law researchers merely trying to delineate the field could be described by their own kind as "pioneer[s]". In the 1990s and early 2000s more progress occurred. Computational research generated insights for law. The First International Conference on AI and the Law occurred in 1987, but it is in the 1990s and 2000s that the biannual conference began to build up steam and to delve more deeply into the issues involved with work intersecting computational methods, AI, and law. Classes began to be taught to undergraduates on the uses of computational methods to automating, understanding, and obeying the law. Further, by 2005, a team largely composed of Stanford computer scientists from the Stanford Logic group had devoted themselves to studying the uses of computational techniques to the law. Computational methods in fact advanced enough that members of the legal profession began in the 2000s to both analyze, predict and worry about the potential future of computational law and a new academic field of computational legal studies seems to be now well established. As insight into what such scholars see in the law's future due in part to computational law, here is quote from a recent conference about the "New Normal" for the legal profession: "Over the last 5 years, in the fallout of the Great Recession, the legal profession has entered the era of the New Normal. Notably, a series of forces related to technological change, globalization, and the pressure to do more with less (in both corpo
Pulsar (social listening platform)
Pulsar is a software platform for social media monitoring, audience intelligence and social listening that allows organizations to monitor and analyze online conversations across social media, news, and other digital sources. The platform combines social media listening, media monitoring, trend analysis, and audience segmentation to help users understand public discussions and audience behavior in real time. The platform is a social listening platform, which aggregates data from networks such as X, Facebook, Instagram, and forums) and applies artificial intelligence for text and sentiment analysis. Pulsar is offered as a cloud-based Software as a Service (SaaS) tool and insights consultancy. It has been part of Pulsar Group (formerly Access Intelligence), a publicly listed group of communications software products, since 2019. As well as commercial uses, the platform has been used in peer-reviewed academic research analysing online discourse. The platform is listed on the UK government's G-Cloud 14 Digital Marketplace for the provision of social listening and audience intelligence services. == History == Pulsar originated in the early 2010s as a project within Face, a London-based innovation and market research consultancy. The platform's first product, Pulsar TRAC, launched in 2013 as a social media analytics tool. Pulsar TRAC was designed to measure the reach of conversations, mapping brand audiences, and tracking how content spreads through networks. The development was led by Dr Francesco D'Orazio, who created the Pulsar brand and led the development of the platform while serving as VP of Product and Innovation at Face. Face itself had been acquired by the Cello Group Plc (a UK-based advisory firm) in 2012, and Pulsar became part of Cello's portfolio of research and data tools. In January 2017, Cello Group made a significant investment to scale Pulsar and announced the merger of Face's qualitative research business into Pulsar, unifying both under the Pulsar brand for global expansion. In 2018, Pulsar opened an office in Los Angeles to better serve its growing U.S. client base in media, healthcare, and entertainment sectors and Francesco D'Orazio was appointed CEO. The company focused on developing new products amid a wave of consolidation in the social listening industry. In October 2019, Pulsar was acquired by Access Intelligence Plc (now Pulsar Group), an AIM-listed communications software company. The group, which also owns PR and media tools Isentia, Vuelio and ResponseSource, integrated Pulsar to their end-to-end marketing and communications insights offering. Pulsar established a new office in Sydney, Australia in 2022 as part of this global expansion, adding to its existing offices in London and Los Angeles. In 2023, Pulsar Group (then Access Intelligence) was recognised as one of Europe's fastest growing companies by the Financial Times. In May 2024, Access Intelligence PLC changed its name to Pulsar Group PLC. The company has since continued to develop its platform. In March 2025 it introduced new tool Narratives AI, described as a "search engine for public opinion" and the first of its kind for analyzing public narratives and their evolutions in both social media and the news. In October 2025, Pulsar launched Insight Agents, a set of AI agents embedded into the platform advertised to "proactively anticipate user needs or issues, carry out routine tasks, uncover anomalies in your datasets, and prompt responses at scale, 24/7." == Products == Pulsar's architecture integrates four main products into a single interface. The core product suite is often broken into three main components: Pulsar TRAC (for social listening and audience analysis), Pulsar TRENDS (for trend discovery and analysis), and Pulsar CORE (for owned-channel and web analytics). Pulsar's fourth product is Narratives AI. === Pulsar TRAC === Pulsar TRAC is a social listening and audience intelligence platform that allows users to configure searches that track public conversations and measure audience behaviour. Pulsar TRAC is focused on conversation insights and audience segmentations - the platform is reported to collect and analyse data from a wide range of sources, including major social networks, forums, news and review sites, and ecommerce platforms, with real-time visualisations and AI-supported analytics used to find patterns and communities of interest. Pulsar TRAC can be incorporated into workflows with other audience tools, such as an integration with Audiense that connects TRAC's conversation insights to external audience-segmentation datasets. === Pulsar CORE === Pulsar CORE centres on the analysis of owned-channel data, such as brand social media profiles, website interaction and other in-house digital assets, to generate audience and content insights. CORE can monitor published content, evaluate competitors, and extract demographic and behavioural segmentation from owned channels. === Narratives AI === Narratives AI is a tool within the Pulsar audience intelligence platform that uses artificial intelligence to detect, cluster and analyse narratives forming across social and news media. It was launched in March 2025 as a standalone search interface that processes real-time and historical data to find cultural trends, behaviours and beliefs. It uses clustering algorithms and visualisation to show how conversations form and spread online, and their relative importance within wider discourse. == Notable features == === Insight Agents === Pulsar's Insight Agents are AI-powered agents within the Pulsar platform designed to automate and augment common tasks in media, social, audience and narrative intelligence. Branded as TeamMates, these agents are grouped into four functional types: Sentinels for real-time monitoring, anomaly detection and alerting Oracles for forecasting and scenario planning Custodians for governance, compliance and policy enforcement Analysts for research, reporting and recommendations Each agent is trained on Pulsar's multi-source data and domain-specific workflows. In February 2026, Pulsar introduced 'Crisis Oracle,' an AI-driven system designed to quantify narrative momentum and predict reputational risk. == Academic research == Pulsar has been used as a data collection and analysis tool in peer-reviewed academic research across public health, infodemiology, veterinary science, and policy research. Published uses include a World Health Organization report on infodemic management, a Journal of Medical Internet Research study on headache and migraine discourse across Japan, Germany, and France, a Frontiers in Big Data study of Long COVID narratives, and Frontiers in Veterinary Science studies on canine chronic kidney disease and oral medication administration in dogs.
The Murderbot Diaries
The Murderbot Diaries is a science fiction series by American author Martha Wells, published by Tor Books. The series is told from the perspective of the titular cyborg guard, a "SecUnit" owned by a futuristic megacorporation. SecUnits include "governor" modules that control and punish the constructs if they take any actions not approved by the company. The ironically self-named "Murderbot" hacked and disabled the module but pretends to be a normal SecUnit, staving off the boredom of security work by watching media. As it spends more time with a series of caring entities (both humans and artificial intelligences), it develops genuine friendships and emotional connections, which it finds inconvenient. The TV series Murderbot is based on the novels by Martha Wells. == Books == === Setting === In an advanced largely hyper-capitalist space-faring society, travel between star systems is routine due to now-stable wormhole technology. Initially, wormhole travel was unreliable, but has since improved to the point where "lost" colonies are being found. People reside on planets, some of which have been terraformed, or on space habitats which have full life support and artificial gravity. Most people who can afford it have technology that allows them to tap into ubiquitous data feeds supplying all kinds of information, including entertainment. This technology can be worn, or be implanted into the body. Sentient and semi-sentient artificial intelligences perform tasks such as operating starships, mining, controlling habitats, moving cargo, waging corporate warfare, providing physical pleasure and comfort, or security. Most of these purposes are fulfilled by "bots" of varying complexity and intelligence, but the last three are respectively performed by CombatUnits, ComfortUnits, and SecUnits. The characters and narrator of the book call these conscious entities "constructs", but they are functionally cyborgs (cybernetic organisms): part machine, part organic. A significant distinction, however, is that they are manufactured entities, not born and later modified. The Corporation Rim is a profit-oriented, cutthroat part of this society that indulges in espionage, assassination, indentured slavery, and ruthless exploitation of resources. One particular target of the corporations is illegal "alien remnant" exploitation. These remnants are often extremely dangerous to people and machines. The laws are enforced by other corporations. Outside the Corporation Rim are colonies, such as Preservation, that have established their right to exist under various laws that, at least for the time being, the corporations are unwilling to test. Wells noted in 2017 that All Systems Red, Artificial Condition, Rogue Protocol, and Exit Strategy "have an overarching story, with the fourth one bringing the arc to a conclusion". === Story chronology === "Compulsory" All Systems Red Artificial Condition Rogue Protocol Exit Strategy "Rapport" "Home" Fugitive Telemetry Network Effect System Collapse Platform Decay === All Systems Red (2017) === A scientific expedition on an alien planet goes awry when one of its members is attacked by a giant native creature. She is saved by the expedition's SecUnit (Security Unit), a security construct with a mixture of robot and human features. The SecUnit has secretly hacked the governor module allowing it to be controlled by humans and has named itself Murderbot, as it is heavily armed and designed for combat. However, it prefers to spend its time watching space operas and is uncomfortable interacting with humans. The SecUnit has a vested interest in keeping its human clients safe and alive, since it wants to avoid discovery of its autonomy and has an especially grisly expedition on its record. Murderbot soon discovers information regarding hazardous fauna has been deleted from their survey packet of the planet. Further investigation reveals some sections on their maps are missing as well. Meanwhile, the PreservationAux survey team, led by Dr. Mensah, navigate their mixed feelings about the part machine, part human nature of their SecUnit. As members of an egalitarian, independent planet outside of the Corporation Rim, the survey team struggles with the system of indentured servitude (and in many cases de facto slavery) the rim operates under. When they lose contact with the only other known expedition on the planet, the DeltFall Group, Mensah leads a team to the opposite side of the planet to investigate. At the DeltFall habitat, Murderbot discovers everyone there has been brutally murdered, and one of their three SecUnits has been destroyed. Murderbot disables the remaining two as they attack it but is surprised when two additional SecUnits appear. Murderbot destroys one, and Mensah takes the other. During these encounters, Murderbot is seriously injured. It also realizes one of the rogue SecUnits has installed a combat override module into its neck. The Preservation scientists are able to remove it before it completes the data upload which would put Murderbot under the control of whoever has command over the other SecUnits. The team discovers Murderbot is autonomous, and had once malfunctioned and murdered 57 people. The Preservation scientists mostly agree, based on its protective behavior thus far, the SecUnit can be trusted. Remembering small incidents which appear to be attempted sabotage, Murderbot and the group determine there must be a third expedition on the planet, whose members are trying to eliminate DeltFall and Preservation for some reason. The Preservation scientists confirm their HubSystem has been hacked. They flee their habitat before the mystery expedition they have dubbed EvilSurvey comes to kill them. The EvilSurvey team—GrayCris—leaves a message in the Preservation habitat inviting its scientists to meet at a rendezvous point to negotiate terms for their survival. Murderbot knows GrayCris will never let them live, so the SecUnit formulates a plan. It makes an overture to GrayCris to negotiate for its own freedom, but this is a distraction while the Preservation scientists access the GrayCris HubSystem to activate their emergency beacon. The plan works, but Murderbot is injured protecting Mensah from the explosion of the launch. Later, the SecUnit finds itself repaired retaining its memories and disabled governor module. Mensah has bought its contract, and she plans to bring it back to Preservation's home base where it can legally live autonomously. Though grateful, Murderbot is reluctant to have its decisions made for it, and it slips away on a cargo ship. === Artificial Condition (2018) === Murderbot makes deals with bots piloting unmanned cargo ships to travel toward the mining facility where it once malfunctioned—resulting in the death of 57 people. It hopes to learn more about the initial incident in which it went rogue, of which it has little memory. Murderbot boards the final ship and discovers the bot pilot is an unexpectedly powerful, intrusive artificial intelligence. They come to a tentative truce and watch media together during the final leg of the journey to RaviHyral, the station where the incident occurred. Murderbot learns the ship is a deep-space research vessel assigned to cargo runs during downtime, which explains why the bot pilot is so sophisticated. Murderbot reluctantly allows this artificial intelligence—which it has dubbed ART (Asshole Research Transport) due to its sarcastic personality—to make physical modifications to the SecUnit's body to allow it to pass for an augmented human, and to disconnect the data port at the back of its neck which had been used to insert a combat override module in the previous book. To gain access to the RaviHyral facility, Murderbot takes a contract as a security consultant for three scientists who are meeting with their former employer, the head and namesake of Tlacey Excavations, to negotiate the return of their research, which they believe was illegally seized by the company. Their transport craft is sabotaged, but with ART's help, Murderbot is able to land it safely. Now aware Tlacey is actively trying to kill the scientists rather than comply with their demands, Murderbot guides them through their meeting with Tlacey and thwarts another assassination attempt. Murderbot returns to the site of the massacre and learns it was the result of another mining operation's sabotage attempt using malware, which made all of the facility's SecUnits go berserk. The facility's ComfortUnits—weaponless, anatomically correct constructs sometimes disparagingly called "sexbots"—died attempting to stop the massacre. Tlacey's ComfortUnit voices its desire for freedom and willingness to help Murderbot thwart Tlacey. While the SecUnit meets with a Tlacey employee to secretly retrieve a copy of the research, Tlacey abducts one of the scientists, Tapan. Murderbot goes after her, accepting a combat override module intended to control the SecUnit but actually has no effect, due
Deep Zoom
Deep Zoom is a technology developed by Microsoft for efficiently transmitting and viewing images. It allows users to pan around and zoom in on a large, high resolution image or a large collection of images. It reduces the time required for initial load by downloading only the region being viewed or only at the resolution it is displayed at. Subsequent regions are downloaded as the user pans to (or zooms into) them; animations are used to hide any jerkiness in the transition. The libraries are also available in other platforms including Java and Flash. == History == The Deep Zoom file format is very similar to the Google Maps image format where images are broken into tiles and then displayed as required. The tiling typically follows a quadtree pattern of increasing resolution of image (in other words twice the zoom and twice the resolution). The main difference is that with Google Maps the actual details on the image change from one zoom level to another, while with Deep Zoom the same image is displayed at each zoom level. Seadragon Software, formerly Sand Codex, first created the Seadragon technology and its implementation of what is now called Deep Zoom. This technology was then absorbed into the Microsoft Live Labs when Seadragon Software was acquired. Engineers from Seadragon now work with Microsoft to integrate their work into technology such as Silverlight and Photosynth. == Deep Zoom examples == The most famous implementation of Deep Zoom was probably the first: the memorabilia collection at the Hard Rock website. Conceived and designed by Duncan/Channon and built by Vertigo, it was demonstrated for the first time in March 2008 at the Microsoft MIX convention in Las Vegas. In 2010, Microsoft Live Labs partnered with the University of California, Berkeley to create ChronoZoom, a DeepZoom-powered time visualization tool that pushed the limits of DeepZoom, since it required zooming from the scale of 13 billion years down to a single day. The project has since graduated to development under Microsoft Research. Another example is the Deep Earth project. It is described by its creators as "a community project focused on creating a rich interactive mapping control using Silverlight2 Deep Zoom. Concentrating on Microsoft Virtual Earth imagery and data the project offers team members the opportunity to learn and share while creating something cool and useful." A paintings collection project http://galleryzoom.co.uk/ shows 1000 high resolution/sensor images individually indexed. (Using Deep Zoom Composer). Blaise Aguera y Arcas gave a demonstration of Seadragon and Photosynth at the 2007 TED conference. In November 2009, 352 Media Group, a Silverlight developer in the Microsoft Silverlight Partner Program, created an example of Deep Zoom using Microsoft Silverlight version 3. It is online at 352 Media Group's Web site. The Winston Churchill Deep Zoom Archived 2010-07-04 at the Wayback Machine mosaic, created by Silverlight developers Shoothill, features as both an online interactive deep zoom and a standalone deep zoom which forms part of the Churchill exhibit in the Churchill War Rooms in Whitehall. In 2010, Shoothill built the Sumatran Tiger Deep Zoom - the largest seen to date - for worldwide conservation charity Fauna and Flora International, featuring thousands of images of endangered species. An early example of Deep Zoom-like technology was implemented at The Department of Maori Affairs in New Zealand in 1997. The technology was used to display Maori land ownership. == Deep Zoom images == The file format used by Deep Zoom (as well as Photosynth and Seadragon Ajax) is XML based. Users can specify a single large image (dzi) or a collection of images (dzc). It also allows for "Sparse Images"; where some parts of the image have greater resolution than others, an example of which can be found on the Seadragon Ajax home page; The bike image displayed is a sparse image. Though used in the proprietary Deep Zoom, the dzi format is open and able to be used by anyone. === Deep Zoom image (dzi) === A DZI has two parts: a DZI file (with either a .dzi or .xml extension) and a subdirectory of image folders. Each folder in the image subdirectory is labeled with its level of resolution. Higher numbers correspond to a higher resolution level; inside each folder are the image tiles corresponding to that level of resolution, numbered consecutively in columns from top left to bottom right. === Deep Zoom collection (dzc) === A DZC is a collection of some number of DZIs linked and referenced by a DZC file (with either a .dzc or .xml extension). At a high level, a collection is a number of image thumbnails whose location is kept track of by the .dzc/.xml file, when zooming into an image, it accesses greater resolutions tiles. A DZC's structure is similar to that of a DZI; the .dzc/.xml file defines the collection and the subdirectory of folders maps to the DZI file structure, each with their set of .dzi/.xml and image tiles. The DZC is used in Microsoft's Pivot, but not in SeaDragon per se. === Sparse Images === Sparse images are a sub-classification of the DZI file type. A sparse image is normally a number of separate photographs with varying resolution levels that have been placed in a single DZI instead of a DZC. Sparse images have no different file structure than that of a DZI and differ only in that there is not a single "highest resolution" level for the entire DZI. == Software that uses Deep Zoom == Image Composite Editor - image stitching tool created by Microsoft Research Deep Zoom Composer - collage maker and simple panorama tool created by Microsoft. Images' resolution is maintained when exporting for web use (via Silverlight Deep Zoom or JavaScript using a third-party template). No longer available for download from Microsoft though it can be found on various other sources such as Internet Archive. == iPhone OS development == Microsoft Live Labs has created an application for the App Store called Seadragon Mobile. It is run over the internet and includes Deep Zoom on the following categories; art, history, maps, photos, Photosynth which anybody can upload to, space and technology & web.
Shyster (expert system)
SHYSTER is a legal expert system developed at the Australian National University in Canberra in 1993. It was written as the doctoral dissertation of James Popple under the supervision of Robin Stanton, Roger Clarke, Peter Drahos, and Malcolm Newey. A full technical report of the expert system, and a book further detailing its development and testing have also been published. SHYSTER emphasises its pragmatic approach, and posits that a legal expert system need not be based upon a complex model of legal reasoning in order to produce useful advice. Although SHYSTER attempts to model the way in which lawyers argue with cases, it does not attempt to model the way in which lawyers decide which cases to use in those arguments. SHYSTER is of a general design, permitting its operation in different legal domains. It was designed to provide advice in areas of case law that have been specified by a legal expert using a bespoke specification language. Its knowledge of the law is acquired, and represented, as information about cases. It produces its advice by examining, and arguing about, the similarities and differences between cases. It derives its name from Shyster: a slang word for someone who acts in a disreputable, unethical, or unscrupulous way, especially in the practice of law and politics. == Methods == SHYSTER is a specific example of a general category of legal expert systems, broadly defined as systems that make use of artificial intelligence (AI) techniques to solve legal problems. Legal AI systems can be divided into two categories: legal retrieval systems and legal analysis systems. SHYSTER belongs to the latter category of legal analysis systems. Legal analysis systems can be further subdivided into two categories: judgment machines and legal expert systems. SHYSTER again belongs to the latter category of legal expert systems. A legal expert system, as Popple uses the term, is a system capable of performing at a level expected of a lawyer: "AI systems which merely assist a lawyer in coming to legal conclusions or preparing legal arguments are not here considered to be legal expert systems; a legal expert system must exhibit some legal expertise itself." Designed to operate in more than one legal domain, and be of specific use to the common law of Australia, SHYSTER accounts for statute law, case law, and the doctrine of precedent in areas of private law. Whilst it accommodates statute law, it is primarily a case-based system, in contradistinction to rule-based systems like MYCIN. More specifically, it was designed in a manner enabling it to be linked with a rule-based system to form a hybrid system. Although case-based reasoning possesses an advantage over rule-based systems by the elimination of complex semantic networks, it suffers from intractable theoretical obstacles: without some further theory it cannot be predicted what features of a case will turn out to be relevant. Users of SHYSTER therefore require some legal expertise. Richard Susskind argues that "jurisprudence can and ought to supply the models of law and legal reasoning that are required for computerized [sic] implementation in the process of building all expert systems in law". Popple, however, believes jurisprudence is of limited value to developers of legal expert systems. He posits that a lawyer must have a model of the law (maybe unarticulated) which includes assumptions about the nature of law and legal reasoning, but that model need not rest on basic philosophical foundations. It may be a pragmatic model, developed through experience within the legal system. Many lawyers perform their work with little or no jurisprudential knowledge, and there is no evidence to suggest that they are worse, or better, at their jobs than lawyers well-versed in jurisprudence. The fact that many lawyers have mastered the process of legal reasoning, without having been immersed in jurisprudence, suggests that it may indeed be possible to develop legal expert systems of good quality without jurisprudential insight. As a pragmatic legal expert system SHYSTER is the embodiment of this belief. A further example of SHYSTER’s pragmatism is its simple knowledge representation structure. This structure was designed to facilitate specification of different areas of case law using a specification language. Areas of case law are specified in terms of the cases and attributes of importance in those areas. SHYSTER weights its attributes and checks for dependence between them. In order to choose cases upon which to construct its opinions, SHYSTER calculates distances between cases and uses these distances to determine which of the leading cases are nearest to the instant case. To this end SHYSTER can be seen to adopt and expand upon nearest neighbor search methods used in pattern recognition. These nearest cases are used to produce an argument (based on similarities and differences between the cases) about the likely outcome in the instant case. This argument relies on the doctrine of precedent; it assumes that the instant case will be decided the same way as was the nearest case. SHYSTER then uses information about these nearest cases to construct a report. The report that SHYSTER generates makes a prediction and justifies that prediction by reference only to cases and their similarities and differences: the calculations that SHYSTER performs in coming to its opinion do not appear in that opinion. Safeguards are employed to warn users if SHYSTER doubts the veracity of its advice. == Results == SHYSTER was tested in four different and disparate areas of case law. Four specifications were written, each representing an area of Australian law: an aspect of the law of trover; the meaning of "authorization [sic]" in copyright law of Australia; the categorisation of employment contracts; and the implication of natural justice in administrative decision-making. SHYSTER was evaluated under five headings: its usefulness, its generality, the quality of its advice, its limitations, and possible enhancements that could be made to it. Despite its simple knowledge representation structure, it has shown itself capable of producing good advice, and its simple structure has facilitated the specification of different areas of law. Appreciating the difficulties encountered by legal expert systems developers in adequately representing legal knowledge can assist in appreciating the shortcomings of digital rights management technologies. Some academics believe future digital rights management systems may become sophisticated enough to permit exceptions to copyright law. To this end SHYSTER's attempt to model "authorization [sic]" in the Copyright Act can be viewed as pioneering work in this field. The term "authorization [sic]" is undefined in the Copyright Act. Consequently, a number of cases have been before the courts seeking answers as to what conduct amounts to authorisation. The main contexts in which the issue has arisen are analogous to permitted exceptions to copyright currently prevented by most digital rights management technologies: "home taping of recorded materials, photocopying in educational institutions and performing works in public". When applied to one case concerning compact cassettes, SHYSTER successfully agreed that Amstrad did not authorise the infringement. 'shyster-myci'n Popple highlighted the most obvious avenue of future research using SHYSTER as the development of a rule-based system, and the linking together of that rule-based system with the existing case-based system to form a hybrid system. This intention was eventually realised by Thomas O’Callaghan, the creator of SHYSTER-MYCIN: a hybrid legal expert system first presented at ICAIL '03, 24–28 June 2003 in Edinburgh, Scotland. MYCIN is an existing medical expert system, which was adapted for use with SHYSTER. MYCIN’s controversial "certainty factor" is not used in SHYSTER-MYCIN. The reason for this is the difficulty in scientifically establishing how certain a fact is in a legal domain. The rule-based approach of the MYCIN part is used to reason with the provisions of an Act of Parliament only. This hybrid system enables the case-based system (SHYSTER) to determine open textured concepts when required by the rule-based system (MYCIN). The ultimate conclusion of this joint endeavour is that a hybrid approach is preferred in the creation of legal expert systems where "it is appropriate to use rule-based reasoning when dealing with statutes, and…case-based reasoning when dealing with cases".