The Communications Over Various Feeds Electronically for Engagement Act (COVFEFE Act), House Bill H.R. 2884, was introduced in the United States House of Representatives on June 12, 2017, during the 115th United States Congress. The bill was intended to amend the Presidential Records Act to preserve Twitter posts and other social media interactions of the President of the United States and require the National Archives to store such items. H.R. 2884 was assigned to the House Oversight and Reform Committee for consideration. While in committee, there were no roll call votes related to the bill. The bill died in committee. U.S. Representative Mike Quigley, Democrat of Illinois, introduced the legislation due to Donald Trump's routine use of Twitter, stating "In order to maintain public trust in government, elected officials must answer for what they do and say; this includes 140-character tweets. If the president is going to take to social media to make sudden public policy proclamations, we must ensure that these statements are documented and preserved for future reference". If enacted, the bill "would bar the prolifically tweeting president from deleting his posts, as he has sometimes done". The COVFEFE Act would have also treated a president's personal social media accounts (e.g., Trump's "@realDonaldTrump" Twitter account) the same as official social media accounts (e.g., the "@POTUS" Twitter account). == Background == The bill title refers to "covfefe", a word in a May 31, 2017 tweet that Trump sent at 12:06 AM EDT, reading "Despite the constant negative press covfefe". This incomplete tweet was liked and retweeted hundreds of thousands of times, making it one of the most popular tweets of 2017, as people speculated on its meaning. The tweet was deleted at 5:48 AM EDT. At 6:09 AM EDT, Trump's account tweeted "Who can figure out the true meaning of 'covfefe' ??? Enjoy!" During the May 31 White House press briefing, Hunter Walker of Yahoo! News asked White House press secretary Sean Spicer about the tweet and if there was any concern about the president sending out incoherent tweets that stay up for hours. Spicer responded, "I think the president and a small group of people know exactly what he meant" and offered no other explanation. This unexpected response spawned additional media attention and criticism for its cryptic meaning, with commentators unsure whether or not Spicer was joking. Callum Borchers of The Washington Post's The Fix noted that the Trump administration deliberately responded in a way that encouraged the media and the public to focus on covfefe instead of other controversies like the Russia investigation, resignation of White House communications director Michael Dubke, or U.S.-Germany relations. == Legal significance of Trump's tweeting == Trump's tweets have been legally significant in the past. White House Press Secretary Sean Spicer stated that Trump's tweets are "considered official statements by the President of the United States". Some of his tweets have contradicted his agenda by undercutting or contradicting statements of public officials as well as the arguments of U.S. Department of Justice attorneys seeking to defend Trump's decisions in court. A federal appellate court cited one of Trump's tweets in upholding a lower court's order blocking Trump's Executive Order 13780 from going into effect in 2017. Courts have been clear that Twitter statements can be used as evidence of intent. Before Trump's "@realDonaldTrump" Twitter account was suspended, he blocked a number of users, preventing them from viewing his tweets or posting public replies. A group associated with Columbia University filed a lawsuit on behalf of blocked users, called Knight First Amendment Institute v. Trump. Plaintiffs successfully argued that @realDonaldTrump reply threads constituted a "designated public forum" akin to a public meeting, and therefore blocking users based on their political viewpoints violated their constitutional right to freedom of speech. The Second Circuit upheld this ruling on July 9, 2019. Regardless of the failure of the bill, Trump's tweets have been archived in accordance with the Presidential and Federal Records Act Amendments of 2014.
FoundationDB
FoundationDB is a free and open-source multi-model distributed NoSQL database owned by Apple Inc. with a shared-nothing architecture. The product was designed around a "core" database, with additional features supplied in "layers." The core database exposes an ordered key–value store with transactions. The transactions are able to read or write multiple keys stored on any machine in the cluster while fully supporting ACID properties. Transactions are used to implement a variety of data models via layers. The FoundationDB Alpha program began in January 2012 and concluded on March 4, 2013, with their public Beta release. Their 1.0 version was released for general availability on August 20, 2013. On March 24, 2015, it was reported that Apple has acquired the company. A notice on the FoundationDB web site indicated that the company has "evolved" its mission and would no longer offer downloads of the software. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license. == Main features == The main features of FoundationDB include the following: Ordered key–value store In addition to supporting standard key-based reads and writes, the ordering property enables range reads that can efficiently scan large swaths of data. Transactions Transaction processing employs multiversion concurrency control for reads and optimistic concurrency for writes. Transactions can span multiple keys stored on multiple machines. ACID properties FoundationDB guarantees serializable isolation and strong durability via redundant storage on disk before transactions are considered committed. Layers Layers map new data models, APIs, and query languages to the FoundationDB core. They employ FoundationDB's ability to update multiple data elements in a single transaction, ensuring consistency. An example is their SQL layer. Commodity clusters FoundationDB is designed for deployment on distributed clusters of commodity hardware running Linux. Replication FoundationDB stores each piece of data on multiple machines according to a configurable replication factor. Triple replication is the recommended mode for clusters of 5 or more machines. Scalability FoundationDB is designed to support horizontal scaling through the addition of machines to a cluster while automatically handling data replication and partitioning. Systems supported FoundationDB supports packages for Linux, Windows, and macOS. The Linux version supports production clusters, while the Windows and macOS versions support local operation for development purposes. Configurations on Amazon EC2 are also supported. Programming language bindings FoundationDB supports language bindings for Python, Go, Ruby, Node.js, Java, PHP, and C, all of which are made available with the product. == Design limitations == The design of FoundationDB results in several limitations: Long transactions FoundationDB does not support transactions running over five seconds. Large transactions Transaction size cannot exceed 10 MB of total written keys and values. Large keys and values Keys cannot exceed 10 kB in size. Values cannot exceed 100 kB in size. == History == FoundationDB, headquartered in Vienna, Virginia, was started in 2009 by Nick Lavezzo, Dave Rosenthal, and Dave Scherer, drawing on their experience in executive and technology roles at their previous company, Visual Sciences. In March 2015 the FoundationDB Community site was updated to state that the company had changed directions and would no longer be offering downloads of its product. The company was acquired by Apple Inc., which was confirmed March 25, 2015. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license.
Semantic Scholar
Semantic Scholar is a research tool for scientific literature. It is developed at the Allen Institute for AI and was publicly released in November 2015. Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated summaries of scholarly papers. The Semantic Scholar team is actively researching the use of artificial intelligence in natural language processing, machine learning, human–computer interaction, and information retrieval. Semantic Scholar began as a database for the topics of computer science, geoscience, and neuroscience. In 2017, the system began including biomedical literature in its corpus. As of September 2022, it includes over 200 million publications from all fields of science. == Technology == Semantic Scholar provides a one-sentence summary of scientific literature. One of its aims was to address the challenge of reading numerous titles and lengthy abstracts on mobile devices. It also seeks to ensure that the three million scientific papers published yearly reach readers, since it is estimated that only half of this literature is ever read. Artificial intelligence is used to capture the essence of a paper, generating it through an "abstractive" technique. The project uses a combination of machine learning, natural language processing, and machine vision to add a layer of semantic analysis to the traditional methods of citation analysis, and to extract relevant figures, tables, entities, and venues from papers. Another key AI-powered feature is Research Feeds, an adaptive research recommender that uses AI to quickly learn what papers users care about reading and recommends the latest research to help scholars stay up to date. It uses a paper embedding model trained using contrastive learning to find papers similar to those in each Library folder. Semantic Scholar also offers Semantic Reader, an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual. Semantic Reader provides in-line citation cards that allow users to see citations with TLDR (short for Too Long, Didn't Read) automatically generated short summaries as they read and skimming highlights that capture key points of a paper so users can digest faster. In contrast with Google Scholar and PubMed, Semantic Scholar is designed to highlight the most important and influential elements of a paper. The AI technology is designed to identify hidden connections and links between research topics. Like the previously cited search engines, Semantic Scholar also exploits graph structures, which include the Microsoft Academic Knowledge Graph, Springer Nature's SciGraph, and the Semantic Scholar Corpus (originally a 45 million papers corpus in computer science, neuroscience and biomedicine). == Article identifier == Each paper hosted by Semantic Scholar is assigned a unique identifier called the Semantic Scholar Corpus ID (abbreviated S2CID). The following entry is an example: Liu, Ying; Gayle, Albert A; Wilder-Smith, Annelies; Rocklöv, Joacim (March 2020). "The reproductive number of COVID-19 is higher compared to SARS coronavirus". Journal of Travel Medicine. 27 (2). doi:10.1093/jtm/taaa021. PMID 32052846. S2CID 211099356. == Indexing == Semantic Scholar is free to use and unlike similar search engines (e.g., Google Scholar) does not search for material that is behind a paywall. One study compared the index scope of Semantic Scholar to Google Scholar, and found that for the papers cited by secondary studies in computer science, the two indices had comparable coverage, each only missing a handful of the papers. == Number of users and publications == As of January 2018, following a 2017 project that added biomedical papers and topic summaries, the Semantic Scholar corpus included more than 40 million papers from computer science and biomedicine. In March 2018, Doug Raymond, who developed machine learning initiatives for the Amazon Alexa platform, was hired to lead the Semantic Scholar project. As of August 2019, the number of included papers metadata (not the actual PDFs) had grown to more than 173 million after the addition of the Microsoft Academic Graph records. In 2020, a partnership between Semantic Scholar and the University of Chicago Press Journals made all articles published under the University of Chicago Press available in the Semantic Scholar corpus. At the end of 2020, Semantic Scholar had indexed 190 million papers. In 2020, Semantic Scholar reached seven million users per month.
Nature Manifesto
Nature Manifesto is an Immersive sound piece and multimedia installation by Icelandic artist Björk and artist and curator Aleph Molinari, created in collaboration with the French Institute for Research and Coordination in Acoustics/Music (IRCAM). The installation was showcased at the Centre Pompidou in Paris, France from November 20, 2024 to December 9, 2024, as part of the museum's "Biodiversity: Which Culture for Which Future?" forum. It combines natural soundscapes, calls of extinct animals reconstructed through artificial intelligence, and Björk's narration to address damages to biodiversity and the collapse of ecosystems. == Background == Björk's work intricately weaves themes of nature and technology, reflecting her deep engagement with both realms. In 2008, she co-founded the Náttúra campaign to protest the construction of foreign-backed aluminum factories in Iceland, aiming to protect the country's natural landscapes. She released the single "Náttúra" featuring Thom Yorke, with all proceeds supporting this environmental initiative. Her 2011 album Biophilia further exemplifies this synthesis, exploring the relationships between music, nature, and technology through a multimedia project that included interactive apps, custom-made instruments, and educational workshops. Björk's Cornucopia tour (2019-2023) seamlessly integrates themes of nature preservation and environmental activism, and featured a recorded message by Swedish climate activist Greta Thunberg. The tour's fusion of music, technology, and natural imagery reflects Björk's vision of a harmonious coexistence between humanity and nature, advocating for sustainable futures. Björk has previously used artificial intelligence in her works. In 2020, she collaborated with Microsoft to create Kórsafn, a sound installation for the Sister City Hotel lobby in New York City which used an AI-powered model that elaborated choral recordings from her discography through a sensor on the rooftop of the building that would generate music according to data like the weather and the seasons. For her charity single "Oral", featuring Spanish singer Rosalía, she released a music video directed by photographer and visual artist Carlota Guerrero, who used AI-generated deepfake versions of the artists. == Concept == Nature Manifesto is a three-minute and forty-second immersive sound piece. The composition merges Björk's voice, as she articulates a manifesto on biodiversity and the climate crisis, with cries of extinct and endangered animals, harmonizing them with natural soundscapes. The installation was curated by Chloé Siganos and Aleph Molinari, with associate curator Delphine Le Gatt. The primary goal of Nature Manifesto is to foster a deeper understanding of humanity's impact on the natural world. Conceived as a "post-optimistic" manifesto, Aleph Molinari stated that the project's purpose was to "offer a voice to nature". He stated that "the modern concept of nature itself is problematic [...] because it’s a concept born in the Romantic period and, with the rise of the industrial era, became an antithesis to human civilisation and everything urban. Nature came to define what was outside, the savage Other... But nature is everything that we’re part of." The soundscape features recreated calls of extinct and endangered species, developed in collaboration with the French sound research institute IRCAM. Artificial intelligence was employed to simulate the vocalizations of animals that no longer exist in the wild. To save energy and lessen the ecological impact of the use of AI, the research institute developed a "frugal AI" model capable of generating audio in real-time on local servers without a graphics processing unit. The sounds were then produced and edited by Björk in collaboration with Robin Meier Wiratunga and Bergur Þórisson. The installation was located within the Centre Pompidou's escalator, known as the "caterpillar". The installation was further supported by videos created by visual artist Sam Balfus (also known as Balfua) by using artificial intelligence, and edited by Santiago Molinari. == Activism == To sustain and broaden the themes presented in Nature Manifesto, Björk publicly urged French President Emmanuel Macron to prohibit bottom trawling within France's marine protected areas (MPA). She criticized the French government's claim of protecting 30% of its marine territories, highlighting that over 90% of these MPAs exist only on paper, allowing destructive practices like bottom trawling to continue unchecked. She collaborated with non-governmental organizations Sustainable Ocean Alliance, Ungir umhverfissinnar and Bloom, to advocate for genuine ocean conservation. Björk promoted the cause through her social media profiles by sharing petitions. In November 2024, Björk lent her Instagram account to French environmental activists to directly address Macron. The activists used the platform to call for stronger protection of the ocean, urging Macron to impose stricter restrictions on harmful fishing practices, particularly bottom trawling. == Reception == Nature Manifesto received mixed to positive reviews from critics. Some critiques focused on the installation's setting, suggesting that the movement inherent to the escalator space diminished the immersive potential of the soundscape. The choice of using artificial intelligence was also questioned. Björk and Molinari defended this, as both see AI as a tool that can be used creatively and sustainably, with Björk focusing on the importance of human input to give AI a "soul", and Molinari stressing the need for sustainable technological practices in the broader context of digital life. After the exhibition ended, Björk further opinionated: "this is how we will work in the future. [...] if there is no soul in tomorrow's music made by AI it is because [no one] put it there and we have to speak out and guard this as listeners", further stating that there is already "soulless muzak" [sic] on Spotify, "mass manufactured without the attention of creativity".
Oasis (Minecraft clone)
Oasis is a 2024 video game that attempts to replicate the 2011 sandbox game Minecraft, run entirely using generative artificial intelligence. The project, which began development in 2022 between the AI company Decart and the computer hardware startup Etched, was released by Decart to the public on October 31, 2024. The AI-driven simulation uses "next-frame prediction" to anticipate player actions based on keyboard and mouse inputs, trained on millions of hours of gameplay footage. Without memory or code, the game often outputs unpredictable changes in scenery and inventory, limiting its functionality as a traditional video game. Critics noted its lack of sound, low frame rate, and "dream-like" appearance, though some praised its unpredictability as entertaining. The project is seen as a potential proof of concept for AI-driven video games. == Creation and gameplay == The demo "proof of concept" version of the game was developed by Israeli San Francisco–based AI company Decart and Silicon Valley hardware startup Etched. The idea originated in 2022 when Robert Wachen, a Harvard graduate and co-founder of Etched, met Dean Leitersdorf, an Israel Institute of Technology graduate and co-founder of Decart. Sharing an interest in OpenAI's GPT-3, they collaborated to create the game, naming it after the setting of the novel and film Ready Player One. It was funded by a $21 million grant from Israeli-American billionaire Oren Zeev and New York–based Sequoia Capital. Decart released the game to the public for free on October 31, 2024. The AI replicates Minecraft's gameplay without code using "next-frame prediction", in which the AI tries to predict what the player will see after each keyboard and mouse input, which it was trained to do on millions of hours of Minecraft footage. The game used Nvidia graphics processing units or GPUs for its demo but plans to transition to more energy-efficient Sohu GPUs, under development by Etched, capable of supporting up to 4K graphics. Etched has also suggested the possibility of making the game open source in the future. Alongside Oasis, the company is co-developing AI-generated video and educational content. == Reception == Upon its launch, many players posted videos of their experience with the game online, which often showed Oasis could not maintain coherent logic in its actions or setting. The game also presented low-quality graphics, running between 360p and 720p consistently at 20 FPS, no in-game sound, and could only be played for five minutes at a time before restarting. These issues led some news outlets to refer to the game as a "nightmarish hallucination", and drawing comparisons to dementia and dreams. Despite the negative reviews, Leitersdorf, as well as a number of commentators, have commented that while the game may have fallen short of replicating Minecraft in its demo launch, it was the first step towards something more advanced, which could one day resemble Minecraft or any other game. Online publication The Backdash commented the game could be a "glimpse at the future of game development", while others like Tom's Hardware expressed doubts a game without code could ever look as good as one with, arguing they fail to capture "the point of what makes games fun—or even coherent". In terms of legality, Decart and Etched did not receive permission from Microsoft to create a copy of their game using generative artificial intelligence. No legal actions have been taken by the latter, however, as artificial intelligence and copyright remains largely vague legally.
Behavior-based robotics
Behavior-based robotics (BBR) or behavioral robotics is an approach in robotics that focuses on robots that are able to exhibit complex-appearing behaviors despite little internal variable state to model its immediate environment, mostly gradually correcting its actions via sensory-motor links. == Principles == Behavior-based robotics sets itself apart from traditional artificial intelligence by using biological systems as a model. Classic artificial intelligence typically uses a set of steps to solve problems, it follows a path based on internal representations of events compared to the behavior-based approach. Rather than use preset calculations to tackle a situation, behavior-based robotics relies on adaptability. This advancement has allowed behavior-based robotics to become commonplace in researching and data gathering. Most behavior-based systems are also reactive, which means they need no programming of what a chair looks like, or what kind of surface the robot is moving on. Instead, all the information is gleaned from the input of the robot's sensors. The robot uses that information to gradually correct its actions according to the changes in immediate environment. Behavior-based robots (BBR) usually show more biological-appearing actions than their computing-intensive counterparts, which are very deliberate in their actions. A BBR often makes mistakes, repeats actions, and appears confused, but can also show the anthropomorphic quality of tenacity. Comparisons between BBRs and insects are frequent because of these actions. BBRs are sometimes considered examples of weak artificial intelligence, although some have claimed they are models of all intelligence. == Features == Most behavior-based robots are programmed with a basic set of features to start them off. They are given a behavioral repertoire to work with dictating what behaviors to use and when, obstacle avoidance and battery charging can provide a foundation to help the robots learn and succeed. Rather than build world models, behavior-based robots simply react to their environment and problems within that environment. They draw upon internal knowledge learned from their past experiences combined with their basic behaviors to resolve problems. == History == The school of behavior-based robots owes much to work undertaken in the 1980s at the Massachusetts Institute of Technology by Rodney Brooks, who with students and colleagues built a series of wheeled and legged robots utilizing the subsumption architecture. Brooks' papers, often written with lighthearted titles such as "Planning is just a way of avoiding figuring out what to do next", the anthropomorphic qualities of his robots, and the relatively low cost of developing such robots, popularized the behavior-based approach. Brooks' work builds—whether by accident or not—on two prior milestones in the behavior-based approach. In the 1950s, W. Grey Walter, an English scientist with a background in neurological research, built a pair of vacuum tube-based robots that were exhibited at the 1951 Festival of Britain, and which have simple but effective behavior-based control systems. The second milestone is Valentino Braitenberg's 1984 book, "Vehicles – Experiments in Synthetic Psychology" (MIT Press). He describes a series of thought experiments demonstrating how simply wired sensor/motor connections can result in some complex-appearing behaviors such as fear and love. Later work in BBR is from the BEAM robotics community, which has built upon the work of Mark Tilden. Tilden was inspired by the reduction in the computational power needed for walking mechanisms from Brooks' experiments (which used one microcontroller for each leg), and further reduced the computational requirements to that of logic chips, transistor-based electronics, and analog circuit design. A different direction of development includes extensions of behavior-based robotics to multi-robot teams. The focus in this work is on developing simple generic mechanisms that result in coordinated group behavior, either implicitly or explicitly.
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".