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AltStore

AltStore is an alternative app store for the iOS and iPadOS[1] mobile operating systems, which allows users to download applications that are not available on the App Store, most commonly tweaked apps, jailbreak apps, and apps including paid apps on the app store. It was publicly announced on September 25, 2019, and launched on September 28. == History == Riley Testut is an American developer who began to work on AltStore after Apple declined to allow his Nintendo emulator Delta on the App Store. Since Xcode allowed him to temporarily install his Delta app to his iOS device for 7 days of testing, he created AltStore in 2019 to replicate this functionality, which could be extended to other .ipa files. As of 2022, AltStore had been downloaded 1.5 million times. In the following years, AltStore expanded beyond its initial sideloading functionality. The platform was founded by Testut, with Shane Gill later joining as co-founder. AltStore was initially supported through Patreon contributions from its user community, and later saw increased adoption following regulatory developments in the European Union that enabled broader third-party app distribution. The project has also been involved in notable industry collaborations, including a partnership with Epic Games. == Features == AltStore exploits a loophole in the Xcode developer platform, which allows developers to sideload their own apps which they are working on without needing to jailbreak. Sideloaded apps are signed like a developer project for testing and will expire after 7 days with a free account or one year with a paid developer account, by which they will need to be refreshed or reinstalled.
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Information Processing Language

Information Processing Language (IPL) is a programming language created by Allen Newell, Cliff Shaw, and Herbert A. Simon at RAND Corporation and the Carnegie Institute of Technology about 1956. Newell had the job of language specifier-application programmer, Shaw was the system programmer, and Simon had the job of application programmer-user. IPL included features to facilitate AI programming, specifically problem solving. such as lists, dynamic memory allocation, data types, recursion, functions as arguments, generators, and cooperative multitasking. IPL also introduced the concepts of symbol processing and list processing. Unfortunately, all of these innovations were cast in a difficult assembly-language style. Nonetheless, IPL-V (the only public version of IPL) ran on many computers through the mid 1960s. == Basics of IPL == An IPL computer has: A set of symbols. All symbols are addresses, and name cells. Unlike symbols in later languages, symbols consist of a character followed by a number, and are written H1, A29, 9–7, 9–100. Cell names beginning with a letter are regional, and are absolute addresses. Cell names beginning with "9-" are local, and are meaningful within the context of a single list. One list's 9-1 is independent of another list's 9–1. Other symbols (e.g., pure numbers) are internal. A set of cells. Lists are made from several cells including mutual references. Cells have several fields: P, a 3-bit field used for an operation code when the cell is used as an instruction, and unused when the cell is data. Q, a 3-valued field used for indirect reference when the cell is used as an instruction, and unused when the cell is data. SYMB, a symbol used as the value in the cell. A set of primitive processes, which would be termed primitive functions in modern languages. The data structure of IPL is the list, but lists are more intricate structures than in many languages. A list consists of a singly linked sequence of symbols, as might be expected—plus some description lists, which are subsidiary singly linked lists interpreted as alternating attribute names and values. IPL provides primitives to access and mutate attribute value by name. The description lists are given local names (of the form 9–1). So, a list named L1 containing the symbols S4 and S5, and described by associating value V1 to attribute A1 and V2 to A2, would be stored as follows. 0 indicates the end of a list; the cell names 100, 101, etc. are automatically generated internal symbols whose values are irrelevant. These cells can be scattered throughout memory; only L1, which uses a regional name that must be globally known, needs to reside in a specific place. IPL is an assembly language for manipulating lists. It has a few cells which are used as special-purpose registers. H1, for example, is the program counter. The SYMB field of H1 is the name of the current instruction. However, H1 is interpreted as a list; the LINK of H1 is, in modern terms, a pointer to the beginning of the call stack. For example, subroutine calls push the SYMB of H1 onto this stack. H2 is the free-list. Procedures which need to allocate memory grab cells off of H2; procedures which are finished with memory put it on H2. On entry to a function, the list of parameters is given in H0; on exit, the results should be returned in H0. Many procedures return a Boolean result indicating success or failure, which is put in H5. Ten cells, W0-W9, are reserved for public working storage. Procedures are "morally bound" (to quote the CACM article) to save and restore the values of these cells. There are eight instructions, based on the values of P: subroutine call, push/pop S to H0; push/pop the symbol in S to the list attached to S; copy value to S; conditional branch. In these instructions, S is the target. S is either the value of the SYMB field if Q=0, the symbol in the cell named by SYMB if Q=1, or the symbol in the cell named by the symbol in the cell named by SYMB if Q=2. In all cases but conditional branch, the LINK field of the cell tells which instruction to execute next. IPL has a library of some 150 basic operations. These include such operations as: Test symbols for equality Find, set, or erase an attribute of a list Locate the next symbol in a list; insert a symbol in a list; erase or copy an entire list Arithmetic operations (on symbol names) Manipulation of symbols; e.g., test if a symbol denotes an integer, or make a symbol local I/O operations "Generators", which correspond to iterators and filters in functional programming. For example, a generator may accept a list of numbers and produce the list of their squares. Generators could accept suitably designed functions—strictly, the addresses of code of suitably designed functions—as arguments. == History == IPL was first utilized to demonstrate that the theorems in Principia Mathematica which were proven laboriously by hand, by Bertrand Russell and Alfred North Whitehead, could in fact be proven by computation. According to Simon's autobiography Models of My Life, this application was originally developed first by hand simulation, using his children as the computing elements, while writing on and holding up note cards as the registers which contained the state variables of the program. IPL was used to implement several early artificial intelligence programs, also by the same authors: the Logic Theorist (1956), the General Problem Solver (1957), and their computer chess program NSS (1958). Several versions of IPL were created: IPL-I (never implemented), IPL-II (1957 for JOHNNIAC), IPL-III (existed briefly), IPL-IV, IPL-V (1958, for IBM 650, IBM 704, IBM 7090, Philco model 212, many others. Widely used). IPL-VI was a proposal for an IPL hardware. A co-processor “IPL-VC” for the CDC 3600 at Argonne National Libraries was developed which could run IPL-V commands. It was used to implement another checker-playing program. This hardware implementation did not improve running times sufficiently to “compete favorably with a language more directly oriented to the structure of present-day machines”. IPL was soon displaced by Lisp, which had much more powerful features, a simpler syntax, and the benefit of automatic garbage collection. == Legacy to computer programming == IPL arguably introduced several programming language features: List manipulation—but only lists of atoms, not general lists Property lists—but only when attached to other lists Higher-order functions—while assembly programming had always allowed computing with the addresses of functions, IPL was an early attempt to generalize this property of assembly language in a principled way Computation with symbols—though symbols have a restricted form in IPL (letter followed by number) Virtual machine Many of these features were generalized, rationalized, and incorporated into Lisp and from there into many other programming languages during the next several decades.
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InRule Technology

InRule Technology is a software company that offers Business Rule Management System (BRMS) enterprise software products. == History == InRule Technology's Chief Executive Officer Rik Chomko and Chief Technology Officer Loren Goodman founded InRule Technology in Chicago in 2002. Paul Hessinger joined InRule Technology in 2004 as chief executive officer and chairman of the board and served until his retirement in 2015. They work with companies in several markets, including financial services, public sector, healthcare, and insurance. In 2007, InRule Technology became a charter member of the Microsoft Business Process Alliance. In August 2019, InRule was acquired by Open Gate Capital. == Products == On October 29, 2012, InRule Technology launched InRule for Microsoft Dynamics CRM. The program provides components to enable creation and update of rules within Microsoft Dynamics CRM, InRule for Microsoft Dynamics CRM provides a platform for shops that prefer to work with Microsoft's platforms. With the availability of InRule 4.6 in 2014, the company introduced deployment of InRule through REST services and allowed REST services to be called from InRule. This enables access to data exposed as a REST service and to package up a rule service for RESTful access. The product launch reflected the move of the company's core audience to use a broader array of technologies despite an earlier focus on .NET. In 2017, InRule introduced InRule for the Salesforce Platform, as well as a technology partnership with Work-Relay, a Business Process Management (BPM) application built on the Salesforce Platform. One year earlier the company introduced InRule for JavaScript, allowing enterprises to run rules on the client-side, server-side or both. The software architecture includes multiple components, including irAuthor, the primary authoring tool for creating and maintaining rules; irVerify, a real-time test environment to run and debug rule applications; and irSDK, a set of APIs that allows developers to integrate inRule into their applications. Additionally, irSOA allows users to access the InRule rule engine as a service. irSOA is now called the irServer Execution Service.
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Omar Al Olama

Omar Sultan Al Olama (Arabic: عمر سلطان العلماء; born 16 February 1990) is Minister of State for Artificial Intelligence, Digital Economy, and Remote Work Applications in the United Arab Emirates. He was appointed in October 2017 by Vice President and Prime Minister of the UAE and Ruler of Dubai, Sheikh Mohammed bin Rashid Al Maktoum. The UAE was the first country to appoint a minister for artificial intelligence. == Early life and education == Al Olama was born on 16 February 1990 in Dubai. He has a bachelor's degree in Business and Administration and Management from the American University in Dubai, and a Diploma in Excellence and Project Management from the American University in Sharjah. == Career == Between February 2012 and May 2014, Al Olama was member of the corporate planning at the UAE's Prime Minister's Office. From November 2015 to November 2016, he was Deputy Head of Minister's Office at the UAE's Prime Minister's Office. Between December 2015 and October 2017, he was Secretary General of the World Organization of Racing Drones. In November 2017, he was appointed member of the Board of Trustees of Dubai Future Foundation and Deputy Managing Director of the Foundation. In July 2016, Al Olama was appointed the managing director, and later in 2021 appointed Vice-Chair of the World Government Summit. In 2021, Al Olama was appointed as the Chairman of the Dubai Chamber of Digital Economy, a sub-section of Dubai Chamber of Commerce and Industry. During the cabinet reshuffle in 2023, Al Olama was appointed as the Director General of the Prime Minister's Office, concurrently maintaining his role as the Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications. == Memberships == In November 2017, Al Olama was appointed as a member of the Future of Digital Economy and Society Council, part of the World Economic Forum (WEF). Later in 2023, the World Economic Forum selected Al Olama to join the steering committee of the AI Governance Alliance, a group comprising 10 global leaders in the digital and technological fields. In 2019, Al Olama was appointed as Chair of the Advisory Board of the Mohamed bin Zayed University of Artificial Intelligence. In 2022, Al Olama was appointed by the UAE Cabinet as Vice-Chair of the Higher Committee for Government Digital Transformation, and also appointed by the Government of Dubai as Vice-Chair of the Higher Committee for Future Technology. In 2022, Al Olama was appointed Chairman of the oversight committee of the Dubai Future District Fund. Since 2023, Al Olama has been on the High-Level Advisory Body on Artificial Intelligence. In 2023, Al Olama, recognized as the world's first minister for artificial intelligence, was included in Time Magazine's inaugural list of the 100 most influential people in AI.
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VideoPoet

VideoPoet is a large language model developed by Google Research in 2023 for video making. It can be asked to animate still images. The model accepts text, images, and videos as inputs, with a program to add feature for any input to any format generated content. VideoPoet was publicly announced on December 19, 2023. It uses an autoregressive language model.
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TD-Gammon

TD-Gammon is a computer backgammon program developed in the 1990s by Gerald Tesauro at IBM's Thomas J. Watson Research Center. Its name comes from the fact that it is an artificial neural net trained by a form of temporal-difference learning, specifically TD-Lambda. It explored strategies that humans had not pursued and led to advances in the theory of correct backgammon play. In 1993, TD-Gammon (version 2.1) was trained with 1.5 million games of self-play, and achieved a level of play just slightly below that of the top human backgammon players of the time. In 1998, during a 100-game series, it was defeated by the world champion by a mere margin of 8 points. Its unconventional assessment of some opening strategies had been accepted and adopted by expert players. TD-gammon is commonly cited as an early success of reinforcement learning and neural networks, and was cited in, for example, papers for deep Q-learning and AlphaGo. == Algorithm for play and learning == During play, TD-Gammon examines on each turn all possible legal moves and all their possible responses (lookahead search), feeds each resulting board position into its evaluation function, and chooses the move that leads to the board position that got the highest score. In this respect, TD-Gammon is no different than almost any other computer board-game program. TD-Gammon's innovation was in how it learned its evaluation function. TD-Gammon's learning algorithm consists of updating the weights in its neural net after each turn to reduce the difference between its evaluation of previous turns' board positions and its evaluation of the present turn's board position—hence "temporal-difference learning". The score of any board position is a set of four numbers reflecting the program's estimate of the likelihood of each possible game result: White wins normally, Black wins normally, White wins a gammon, Black wins a gammon. For the final board position of the game, the algorithm compares with the actual result of the game rather than its own evaluation of the board position. The core of TD-gammon is a neural network with 3 layers. The input layer has two types of neurons. One type codes for the board position. They are non-negative integers ranging from 0 to 15, indicating the number of White or Black checkers at each board location. There are 99 input neurons for each, totaling 198 neurons. Another type codes for hand-crafted features previously used in Neurogammon. These features encoded standard concepts used by human experts, such as "advanced anchor," "blockade strength," "home board strength" and the probability of a "blot" (single checker) being hit. The hidden layer contains hidden neurons. Later versions had more of these. The output layer contains 4 neurons, representing the network's estimate of the probability ("equity") that the current board would lead to. The 4 neurons code for: White normal win, White gammon win, Black normal win, Black gammon win. Backgammon win is so rare that Tesauro opted to not represent it. After each turn, the learning algorithm updates each weight in the neural net according to the following rule: w t + 1 − w t = α ( Y t + 1 − Y t ) ∑ k = 1 t λ t − k ∇ w Y k {\displaystyle w_{t+1}-w_{t}=\alpha (Y_{t+1}-Y_{t})\sum _{k=1}^{t}\lambda ^{t-k}\nabla _{w}Y_{k}} where: It was found that picking small λ {\displaystyle \lambda } offered performance roughly equally good, and large λ {\displaystyle \lambda } degraded performance. Because of this, after 1992, TD-Gammon was trained with λ = 0 {\displaystyle \lambda =0} , degenerating into standard TD-learning. This saved compute by a factor of 2. == Development history == Version 1.0 used simple 1-ply search: every next move is scored by the neural net, and the highest-scoring move is selected. Versions 2.0 and 2.1 used 2-ply search: Make a 1-ply analysis to remove unlikely moves ("forward pruning"). Make a 2-play minimax analysis for only the likely moves. Pick the best move, probability-weighted by each of the opponent's 21 possible dice rolls (weighting non-doubles twice as much as doubles). Versions 3.0 and 3.1 used 3-ply search, using 21 2 = 441 {\displaystyle 21^{2}=441} possible dice rolls instead of 21. The last version, 3.1, was trained specifically for an exhibition match against Malcolm Davis at the 1998 AAAI Hall of Champions. It lost at -8 points, mainly due to one blunder, where TD-Gammon opted to double and got gammoned at -32 points. == Experiments and stages of training == Unlike previous neural-net backgammon programs such as Neurogammon (also written by Tesauro), where an expert trained the program by supplying the "correct" evaluation of each position, TD-Gammon was at first programmed "knowledge-free". In early experimentation, using only a raw board encoding with no human-designed features, TD-Gammon reached a level of play comparable to Neurogammon: that of an intermediate-level human backgammon player. Even though TD-Gammon discovered insightful features on its own, Tesauro wondered if its play could be improved by using hand-designed features like Neurogammon's. Indeed, the self-training TD-Gammon with expert-designed features soon surpassed all previous computer backgammon programs. It stopped improving after about 1,500,000 games (self-play) using a three-layered neural network, with 198 input units encoding expert-designed features, 80 hidden units, and one output unit representing predicted probability of winning. == Advances in backgammon theory == TD-Gammon's exclusive training through self-play (rather than imitation learning) enabled it to explore strategies that humans previously had not considered or had ruled out erroneously. Its success with unorthodox strategies had a significant impact on the backgammon community. Late 1991, Bill Robertie, Paul Magriel, and Malcolm Davis, were invited to play against TD-Gammon (version 1.0). A total of 51 games were played, with TD-Gammon losing at -0.25 ppg. Robertie found TD-Gammon to be at the level of a competent advanced player, and better than any previous backgammon program. Robertie subsequently wrote about the use of TD-Gammon for backgammon study. For example, on the opening play, the conventional wisdom was that given a roll of 2-1, 4-1, or 5-1, White should move a single checker from point 6 to point 5. Known as "slotting", this technique trades the risk of a hit for the opportunity to develop an aggressive position. TD-Gammon found that the more conservative play of splitting 24-23 was superior. Tournament players began experimenting with TD-Gammon's move, and found success. Within a few years, slotting had disappeared from tournament play, replaced by splitting, though in 2006 it made a reappearance for 2-1. Backgammon expert Kit Woolsey found that TD-Gammon's positional judgement, especially its weighing of risk against safety, was superior to his own or any human's. TD-Gammon's excellent positional play was undercut by occasional poor endgame play. The endgame requires a more analytical approach, sometimes with extensive lookahead. TD-Gammon's limitation to two-ply lookahead put a ceiling on what it could achieve in this part of the game. TD-Gammon's strengths and weaknesses were the opposite of symbolic artificial intelligence programs and most computer software in general: it was good at matters that require an intuitive "feel" but bad at systematic analysis. It is also poor at doubling strategies. This is likely due to the fact that the neural network is trained without the doubling cube, with the doubling added by feeding the neural network's cubeless equity estimates into theoretically-based heuristic formulae. This was particularly the case in the 1998 exhibition match, where it played 100 games against Malcolm Davis. A single doubling blunder lost the match. TD-gammon was never commercialized or released to the public in some other form, but it inspired commercial backgammon programs based on neural networks, such as JellyFish (1994) and Snowie (1998).
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User modeling

User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing. == Background == A user model is the collection and categorization of personal data associated with a specific user. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and a user profile is the actual representation in a given user model. The process of obtaining the user profile is called user modeling. Therefore, it is the basis for any adaptive changes to the system's behavior. Which data is included in the model depends on the purpose of the application. It can include personal information such as users' names and ages, their interests, their skills and knowledge, their goals and plans, their preferences and their dislikes or data about their behavior and their interactions with the system. There are different design patterns for user models, though often a mixture of them is used. Static user models Static user models are the most basic kinds of user models. Once the main data is gathered they are normally not changed again, they are static. Shifts in users' preferences are not registered and no learning algorithms are used to alter the model. Dynamic user models Dynamic user models allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models Stereotype based user models are based on demographic statistics. Based on the gathered information users are classified into common stereotypes. The system then adapts to this stereotype. The application therefore can make assumptions about a user even though there might be no data about that specific area, because demographic studies have shown that other users in this stereotype have the same characteristics. Thus, stereotype based user models mainly rely on statistics and do not take into account that personal attributes might not match the stereotype. However, they allow predictions about a user even if there is rather little information about him or her. Highly adaptive user models Highly adaptive user models try to represent one particular user and therefore allow a very high adaptivity of the system. In contrast to stereotype based user models they do not rely on demographic statistics but aim to find a specific solution for each user. Although users can take great benefit from this high adaptivity, this kind of model needs to gather a lot of information first. == Data gathering == Information about users can be gathered in several ways. There are three main methods: Asking for specific facts while (first) interacting with the system Mostly this kind of data gathering is linked with the registration process. While registering users are asked for specific facts, their likes and dislikes and their needs. Often the given answers can be altered afterwards. Learning users' preferences by observing and interpreting their interactions with the system In this case users are not asked directly for their personal data and preferences, but this information is derived from their behavior while interacting with the system. The ways they choose to accomplish a tasks, the combination of things they takes interest in, these observations allow inferences about a specific user. The application dynamically learns from observing these interactions. Different machine learning algorithms may be used to accomplish this task. A hybrid approach which asks for explicit feedback and alters the user model by adaptive learning This approach is a mixture of the ones above. Users have to answer specific questions and give explicit feedback. Furthermore, their interactions with the system are observed and the derived information are used to automatically adjust the user models. Though the first method is a good way to quickly collect main data it lacks the ability to automatically adapt to shifts in users' interests. It depends on the users' readiness to give information and it is unlikely that they are going to edit their answers once the registration process is finished. Therefore, there is a high likelihood that the user models are not up to date. However, this first method allows the users to have full control over the collected data about them. It is their decision which information they are willing to provide. This possibility is missing in the second method. Adaptive changes in a system that learns users' preferences and needs only by interpreting their behavior might appear a bit opaque to the users, because they cannot fully understand and reconstruct why the system behaves the way it does. Moreover, the system is forced to collect a certain amount of data before it is able to predict the users' needs with the required accuracy. Therefore, it takes a certain learning time before a user can benefit from adaptive changes. However, afterwards these automatically adjusted user models allow a quite accurate adaptivity of the system. The hybrid approach tries to combine the advantages of both methods. Through collecting data by directly asking its users it gathers a first stock of information which can be used for adaptive changes. By learning from the users' interactions it can adjust the user models and reach more accuracy. Yet, the designer of the system has to decide, which of these information should have which amount of influence and what to do with learned data that contradicts some of the information given by a user. == System adaptation == Once a system has gathered information about a user it can evaluate that data by preset analytical algorithm and then start to adapt to the user's needs. These adaptations may concern every aspect of the system's behavior and depend on the system's purpose. Information and functions can be presented according to the user's interests, knowledge or goals by displaying only relevant features, hiding information the user does not need, making proposals what to do next and so on. One has to distinguish between adaptive and adaptable systems. In an adaptable system the user can manually change the system's appearance, behavior or functionality by actively selecting the corresponding options. Afterwards the system will stick to these choices. In an adaptive system a dynamic adaption to the user is automatically performed by the system itself, based on the built user model. Thus, an adaptive system needs ways to interpret information about the user in order to make these adaptations. One way to accomplish this task is implementing rule-based filtering. In this case a set of IF... THEN... rules is established that covers the knowledge base of the system. The IF-conditions can check for specific user-information and if they match the THEN-branch is performed which is responsible for the adaptive changes. Another approach is based on collaborative filtering. In this case information about a user is compared to that of other users of the same systems. Thus, if characteristics of the current user match those of another, the system can make assumptions about the current user by presuming that he or she is likely to have similar characteristics in areas where the model of the current user is lacking data. Based on these assumption the system then can perform adaptive changes. == Usages == Adaptive hypermedia: In an adaptive hypermedia system the displayed content and the offered hyperlinks are chosen on basis of users' specific characteristics, taking their goals, interests, knowledge and abilities into account. Thus, an adaptive hypermedia system aims to reduce the "lost in hyperspace" syndrome by presenting only relevant information. Adaptive educational hypermedia: Being a subdivision of adaptive hypermedia the main focus of adaptive educational hypermedia lies on education, displaying content and hyperlinks corresponding to the user's knowledge on the field of study. Intelligent tutoring system: Unlike adaptive educational hypermedia systems intelligent tutoring systems are stand-alone systems. Their aim is to help students in a specific field of study. To do so, they build up a user model where they store information about abilities, knowledge and needs of the user. The system can now adapt to this user by presenting approp
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Aurora (supercomputer)

Aurora is an exascale supercomputer that was sponsored by the United States Department of Energy (DOE) and designed by Intel and Cray for Argonne National Laboratory. It was briefly the second fastest supercomputer in the world from November 2023 to June 2024. The cost was estimated in 2019 to be US$500 million. Olivier Franza is the chief architect and principal investigator of this design. == History == In 2013 DOE presented a proposal for an "exascale" supercomputer, capable of speeds in the neighborhood of 1 exaFLOP (1018 floating point mathematical operations per second) with a maximum power consumption of 20 megawatts (MW) by 2020. Aurora was first announced in 2015 and to be finished in 2018. It was expected to have a speed of 180 petaFLOPS which would be around the speed of Summit. Aurora was meant to be the most powerful supercomputer at the time of its launch and to be built by Cray with Intel processors. Later, in 2017, Intel announced that Aurora would be delayed to 2021 but scaled up to 1 exaFLOP. In March 2019, DOE said that it would build the first supercomputer with a performance of one exaFLOP in the United States in 2021. In October 2020, DOE said that Aurora would be delayed again for a further six months, and would no longer be the first exascale computer in the US. In late October 2021 Intel announced that Aurora would now exceed 2 exaFLOPS in peak double-precision compute – That claim however never was realized. The system was fully installed on June 22, 2023. In May 2024, Aurora appeared at number two on the Top500 supercomputer list, with a performance of 1.012 exaFLOPS, marking the second entry of an exascale capable system on the Top500. == Usage == Functions include research on brain structure, nuclear fusion, low carbon technologies, subatomic particles, cancer and cosmology. It will also develop new materials that will be useful for batteries and more efficient solar cells. It is to be available to the general scientific community. == Architecture == Aurora has 10,624 nodes, with each node being composed of two Intel Xeon Max processors, six Intel Max series GPUs and a unified memory architecture, providing a maximum computing power of 130 teraFLOPS per node. It has around 10 petabytes of memory and 230 petabytes of storage. The machine is stated to consume around 39 MW of power. For comparison, the fastest computer in the world today, El Capitan uses 30 MW, while another Top 500 System, Frontier uses 24 MW.
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PropBank

PropBank is a corpus that is annotated with verbal propositions and their arguments—a "proposition bank". Although "PropBank" refers to a specific corpus produced by Martha Palmer et al., the term propbank is also coming to be used as a common noun referring to any corpus that has been annotated with propositions and their arguments. The PropBank project has played a role in research in natural language processing, and has been used in semantic role labelling. == Comparison == PropBank differs from FrameNet, the resource to which it is most frequently compared, in several ways. PropBank is a verb-oriented resource, while FrameNet is centered on the more abstract notion of frames, which generalizes descriptions across similar verbs (e.g. "describe" and "characterize") as well as nouns and other words (e.g. "description"). PropBank does not annotate events or states of affairs described using nouns. PropBank commits to annotating all verbs in a corpus, whereas the FrameNet project chooses sets of example sentences from a large corpus and only in a few cases has annotated longer continuous stretches of text. PropBank-style annotations often remain close to the syntactic level, while FrameNet-style annotations are sometimes more semantically motivated. From the start, PropBank was developed with the idea of serving as training data for machine learning-based semantic role labeling systems in mind. It requires that all arguments to a verb be syntactic constituents and different senses of a word are only distinguished if the differences bear on the arguments. Due to such differences, semantic role labeling with respect to PropBank is often a somewhat easier task than producing FrameNet-style annotations.
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Group concept mapping

Group concept mapping is a structured methodology for organizing the ideas of a group on any topic of interest and representing those ideas visually in a series of interrelated maps. It is a type of integrative mixed method, combining qualitative and quantitative approaches to data collection and analysis. Group concept mapping allows for a collaborative group process with groups of any size, including a broad and diverse array of participants. Since its development in the late 1980s by William M.K. Trochim at Cornell University, it has been applied to various fields and contexts, including community and public health, social work, health care, human services,, instructional interventions, and biomedical research and evaluation. == Overview == Group concept mapping integrates qualitative group processes with multivariate analysis to help a group organize and visually represent its ideas on any topic of interest through a series of related maps. It combines the ideas of diverse participants to show what the group thinks and values in relation to the specific topic of interest. It is a type of structured conceptualization used by groups to develop a conceptual framework, often to help guide evaluation and planning efforts. Group concept mapping is participatory in nature, allowing participants to have an equal voice and to contribute through various methods. A group concept map visually represents all the ideas of a group and how they relate to each other, and depending on the scale, which ideas are more relevant, important, or feasible. == Process == Group concept mapping involves a structured multi-step process, including brainstorming, sorting and rating, multidimensional scaling and cluster analysis, and the generation and interpretation of multiple maps. The first step requires participants to brainstorm a large set of statements relevant to the topic of interest, usually in response to a focus prompt. Participants are then asked to individually sort those statements into categories based on their perceived similarity and rate each statement on one or more scales, such as importance or feasibility. The data is then analyzed using The Concept System software, which creates a series of interrelated maps using multidimensional scaling (MDS) of the sort data, hierarchical clustering of the MDS coordinates applying Ward's method, and the computation of average ratings for each statement and cluster of statements. The resulting maps display the individual statements in two-dimensional space with more similar statements located closer to each other, and grouped into clusters that partition the space on the map. The Concept System software also creates other maps that show the statements in each cluster rated on one or more scales, and absolute or relative cluster ratings between two cluster sets. As a last step in the process, participants are led through a structured interpretation session to better understand and label all the maps. == History == Group concept mapping was developed as a methodology in the late 1980s by William M.K. Trochim at Cornell University. Trochim is considered to be a leading evaluation expert, and he has taught evaluation and research methods at Cornell since 1980. Originally called "concept mapping", the methodology has evolved since its inception with the maturation of the field and the continued advancement of the software, which is now a Web application. == Uses == Group concept mapping can be used with any group for any topic of interest. It is often used by government agencies, academic institutions, national associations, not-for-profit and community-based organizations, and private businesses to help turn the ideas of the group into measurable actions. This includes in the areas of organizational development, strategic planning, needs assessment, curriculum development, research, and evaluation. Group concept mapping is well-documented, well-established methodology, and it has been used in hundreds of published papers. == Versus concept mapping and mind mapping == More generally, concept mapping is any process used for visually representing relationships between ideas in pictures or diagrams. A concept map is typically a diagram of multiple ideas, often represented as boxes or circles, linked in a graph (network) structure through arrows and words where each idea is connected to another. The technique was originally developed in the 1970s by Joseph D. Novak at Cornell University. Concept mapping may be done by an individual or a group. A mind map is a diagram used to visually represent information, centering on one word or idea with categories and sub-categories radiating off of it in a tree structure. Popularized by Tony Buzan in the 1970s, mind mapping is often a spontaneous exercise done by an individual or group to gather information about what they think around a single topic. Unlike Novak's concept maps and Buzan's mind maps, group concept mapping has a structured mathematical process (sorting and rating, multidimensional scaling and cluster analysis) for organizing and visually representing multiple ideas of a group through a series of specific steps. In other words, in group concept mapping, the resulting visual representations are mathematically generated from mixed (qualitative and quantitative) data collected from a group of research subjects, whereas in Novak's concept maps and Buzan's mind maps the visual representations are drawn directly by the subjects resulting in diagrams that are qualitative data and final product at the same time.
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Dendral

Dendral was a project in artificial intelligence (AI) of the 1960s, and the computer software expert system that it produced. Its primary aim was to study hypothesis formation and discovery in science. For that, a specific task in science was chosen: help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra and using knowledge of chemistry. It was done at Stanford University by Edward Feigenbaum, Bruce G. Buchanan, Joshua Lederberg, and Carl Djerassi, along with a team of highly creative research associates and students. It began in 1964 and spans approximately half the history of AI research. The software program Dendral is considered the first expert system because it automated the decision-making process and problem-solving behavior of organic chemists. The project consisted of research on two main programs Heuristic Dendral and Meta-Dendral, and several sub-programs. It was written in the Lisp programming language, which was considered the language of AI because of its flexibility. Many systems were derived from Dendral, including MYCIN, MOLGEN, PROSPECTOR, XCON, and STEAMER. There are many other programs today for solving the mass spectrometry inverse problem, see List of mass spectrometry software, but they are no longer described as 'artificial intelligence', just as structure searchers. The name Dendral is an acronym of the term "Dendritic Algorithm". == Heuristic Dendral == Heuristic Dendral is a program that uses mass spectra or other experimental data together with a knowledge base of chemistry to produce a set of possible chemical structures that may be responsible for producing the data. A mass spectrum of a compound is produced by a mass spectrometer, and is used to determine its molecular weight, the sum of the masses of its atomic constituents. For example, the compound water (H2O), has a molecular weight of 18 since hydrogen has a mass of 1.01 and oxygen 16.00, and its mass spectrum has a peak at 18 units. Heuristic Dendral would use this input mass and the knowledge of atomic mass numbers and valence rules, to determine the possible combinations of atomic constituents whose mass would add up to 18. As the weight increases and the molecules become more complex, the number of possible compounds increases drastically. Thus, a program that is able to reduce this number of candidate solutions through the process of hypothesis formation is essential. New graph-theoretic algorithms were invented by Lederberg, Harold Brown, and others that generate all graphs with a specified set of nodes and connection-types (chemical atoms and bonds) -- with or without cycles. Moreover, the team was able to prove mathematically that the generator is complete, in that it produces all graphs with the specified nodes and edges, and that it is non-redundant, in that the output contains no equivalent graphs (e.g., mirror images). The CONGEN program, as it became known, was developed largely by computational chemists Ray Carhart, Jim Nourse, and Dennis Smith. It was useful to chemists as a stand-alone program to generate chemical graphs showing a complete list of structures that satisfy the constraints specified by a user. == Meta-Dendral == Meta-Dendral is a machine learning system that receives the set of possible chemical structures and corresponding mass spectra as input, and proposes a set of rules of mass spectrometry that correlate structural features with processes that produce the mass spectrum. These rules would be fed back to Heuristic Dendral (in the planning and testing programs described below) to test their applicability. Thus, "Heuristic Dendral is a performance system and Meta-Dendral is a learning system". The program is based on two important features: the plan-generate-test paradigm and knowledge engineering. === Plan-generate-test paradigm === The plan-generate-test paradigm is the basic organization of the problem-solving method, and is a common paradigm used by both Heuristic Dendral and Meta-Dendral systems. The generator (later named CONGEN) generates potential solutions for a particular problem, which are then expressed as chemical graphs in Dendral. However, this is feasible only when the number of candidate solutions is minimal. When there are large numbers of possible solutions, Dendral has to find a way to put constraints that rules out large sets of candidate solutions. This is the primary aim of Dendral planner, which is a “hypothesis-formation” program that employs “task-specific knowledge to find constraints for the generator”. Last but not least, the tester analyzes each proposed candidate solution and discards those that fail to fulfill certain criteria. This mechanism of plan-generate-test paradigm is what holds Dendral together. === Knowledge Engineering === The primary aim of knowledge engineering is to attain a productive interaction between the available knowledge base and problem solving techniques. This is possible through development of a procedure in which large amounts of task-specific information is encoded into heuristic programs. Thus, the first essential component of knowledge engineering is a large “knowledge base.” Dendral has specific knowledge about the mass spectrometry technique, a large amount of information that forms the basis of chemistry and graph theory, and information that might be helpful in finding the solution of a particular chemical structure elucidation problem. This “knowledge base” is used both to search for possible chemical structures that match the input data, and to learn new “general rules” that help prune searches. The benefit Dendral provides the end user, even a non-expert, is a minimized set of possible solutions to check manually. == Heuristics == A heuristic is a rule of thumb, an algorithm that does not guarantee a solution, but reduces the number of possible solutions by discarding unlikely and irrelevant solutions. The use of heuristics to solve problems is called "heuristics programming", and was used in Dendral to allow it to replicate in machines the process through which human experts induce the solution to problems via rules of thumb and specific information. Heuristics programming was a major approach and a giant step forward in artificial intelligence, as it allowed scientists to finally automate certain traits of human intelligence. It became prominent among scientists in the late 1940s through George Polya’s book, How to Solve It: A New Aspect of Mathematical Method. As Herbert A. Simon said in The Sciences of the Artificial, "if you take a heuristic conclusion as certain, you may be fooled and disappointed; but if you neglect heuristic conclusions altogether you will make no progress at all." == History == During the mid 20th century, the question "can machines think?" became intriguing and popular among scientists, primarily to add humanistic characteristics to machine behavior. John McCarthy, who was one of the prime researchers of this field, termed this concept of machine intelligence as "artificial intelligence" (AI) during the Dartmouth summer in 1956. AI is usually defined as the capacity of a machine to perform operations that are analogous to human cognitive capabilities. Much research to create AI was done during the 20th century. Also around the mid 20th century, science, especially biology, faced a fast-increasing need to develop a "man-computer symbiosis", to aid scientists in solving problems. For example, the structural analysis of myoglobin, hemoglobin, and other proteins relentlessly needed instrumentation development due to its complexity. In the early 1960s, Joshua Lederberg started working with computers and quickly became tremendously interested in creating interactive computers to help him in his exobiology research. Specifically, he was interested in designing computing systems to help him study alien organic compounds. Lederberg had been heading a team designing instruments for the Mars Viking lander to search for precursor molecules of life in samples of the Mars surface, using a mass spectrometer coupled with a minicomputer. As he was not an expert in either chemistry or computer programming, he collaborated with Stanford chemist Carl Djerassi to help him with chemistry, and Edward Feigenbaum with programming, to automate the process of determining chemical structures from raw mass spectrometry data. Feigenbaum was an expert in programming languages and heuristics, and helped Lederberg design a system that replicated the way Djerassi solved structure elucidation problems. They devised a system called Dendritic Algorithm (Dendral) that was able to generate possible chemical structures corresponding to the mass spectrometry data as an output. Dendral then was still very inaccurate in assessing spectra of ketones, alcohols, and isomers of chemical compounds. Thus, Djerassi "taught" general rules to Dendral that could help eliminate most of the "chemically implausible" structures, and p
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Reason maintenance

Reason maintenance is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. It encompasses a variety of techniques that share a common architecture: two components—a reasoner and a reason maintenance system—communicate with each other via an interface. The reasoner uses the reason maintenance system to record its inferences and justifications of ("reasons" for) the inferences. The reasoner also informs the reason maintenance system which are the currently valid base facts (assumptions). The reason maintenance system uses the information to compute the truth value of the stored derived facts and to restore consistency if an inconsistency is derived. == Truth maintenance system == A truth maintenance system, or TMS, is a knowledge representation method for representing both beliefs and their dependencies and an algorithm called the "truth maintenance algorithm" that manipulates and maintains the dependencies. The name truth maintenance is due to the ability of these systems to restore consistency. A truth maintenance system maintains consistency between old believed knowledge and current believed knowledge in the knowledge base (KB) through revision. If the current believed statements contradict the knowledge in the KB, then the KB is updated with the new knowledge. It may happen that the same data will again be believed, and the previous knowledge will be required in the KB. If the previous data are not present, but may be required for new inference. But if the previous knowledge was in the KB, then no retracing of the same knowledge is needed. The use of TMS avoids such retracing; it keeps track of the contradictory data with the help of a dependency record. This record reflects the retractions and additions which makes the inference engine (IE) aware of its current belief set. == Algorithm == Each statement having at least one valid justification is made a part of the current belief set. When a contradiction is found, the statement(s) responsible for the contradiction are identified and the records are appropriately updated. This process is called dependency-directed backtracking. The TMS algorithm maintains the records in the form of a dependency network. Each node in the network is an entry in the KB (a premise, antecedent, or inference rule etc.) Each arc of the network represent the inference steps through which the node was derived. A premise is a fundamental belief which is assumed to be true. They do not need justifications. The set of premises are the basis from which justifications for all other nodes will be derived. == Justification == There are two types of justification for a node. They are: Support list [SL] Conditional proof (CP) == Examples == Many kinds of truth maintenance systems exist. Two major types are single-context and multi-context truth maintenance. In single context systems, consistency is maintained among all facts in memory (KB) and relates to the notion of consistency found in classical logic. Multi-context systems support paraconsistency by allowing consistency to be relevant to a subset of facts in memory, a context, according to the history of logical inference. This is achieved by tagging each fact or deduction with its logical history. Multi-agent truth maintenance systems perform truth maintenance across multiple memories, often located on different machines. de Kleer's assumption-based truth maintenance system (ATMS, 1986) was utilized in systems based upon KEE on the Lisp Machine. The first multi-agent TMS was created by Mason and Johnson. It was a multi-context system. Bridgeland and Huhns created the first single-context multi-agent system.
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Endomondo

Endomondo is a health and wellness website. It allows users to track their health statistics and provides insights on fitness trends. Originally launched in 2007, Endomondo was acquired by Under Armour in 2015. Under Armour shut down Endomondo in 2020, but, by 2024, Endomondo re-launched as its own entity. == History == Endomondo started in Denmark in 2007 by Mette Lykke, Christian Birk and Jakob Nordenhof Jønck. In 2011, the company opened an office in Silicon Valley, USA, but kept its research and development department in Denmark. In 2013, Endomondo LLC was listed in Red Herring as a European finalists for promising start-ups. The same year, Christian Birk and Jakob Nordenhof Jønck left the daily operation of the company, but kept co-ownership. In February 2015, Endomondo LLC was acquired by athletic apparel maker Under Armour for $85 million. Endomondo, at that time, had over 20 million users. In October 2020, Under Armour announced that Endomondo would be shutting down and selling off MyFitnessPal to the private equity firm Francisco Partners for $345 million. Service stopped on 31 December 2020, giving customers until 15 February 2021 to download an archive of their historic data. In 2024, Endomondo.com was brought back online as a professional fitness guidance website. == Features == Endomondo provides numerous workouts, guidance on exercises, performance-enhancing nutrition, and tips. Previously, Endomondo was able to track numerous fitness attributes such as running routes, distance, duration, and calories. The software helped analyze performance and recommend improvements. There was a free and a paid version available of Endomondo. The free version had advertisements. The paid Premium version was free of advertisements and included additional features such as the possibility to create one's own training plan. The offering of additional features was different between the Android, IOS and Windows platforms, and had significantly better features for tracking performance over time than UnderArmours suggested replacement. Endomondo offered challenges of various types to the user and allowed users to create their own challenges.
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Microelectronics and Computer Technology Corporation

Microelectronics and Computer Technology Corporation, originally the Microelectronics and Computer Consortium and widely seen by the acronym MCC, was the first, and at one time one of the largest, computer industry research and development consortia in the United States. MCC ceased operations in 2000 and was formally dissolved in 2004. == Divisions == MCC did research and development in the following areas: [1] System Architecture and Design (optimise hardware and software design, provide for scalability and interoperability, allow rapid prototyping for improved time-to-market, and support the re-engineering of existing systems for open systems). Advanced Microelectronics Packaging and Interconnection (smaller, faster, more powerful, and cost-competitive). Hardware Systems Engineering (tools and methodologies for cost-efficient, up-front design of advanced electronic systems, including modelling and design-for-test techniques to improve cost, yield, quality, and time-to-market). Environmentally Conscious Technologies (process control and optimisation tools, information management and analysis capabilities, and non-hazardous material alternatives supporting cost-efficient production, waste minimisation, and reduced environmental impact). Distributed Information Technology (managing and maintaining physically distributed corporate information resources on different platforms, building blocks for the national information infrastructure, networking tools and services for integration within and between companies, and electronic commerce). Intelligent Systems (systems that "intelligently" support business processes and enhance performance, including decision support, data management, forecasting and prediction). == History == The MCC was a response to the announcement of Japan's Fifth Generation Project, a large Japanese research project launched in 1982 aimed at producing a new kind of computer by 1991. The Japanese had formed similar industrial research consortia as early as 1956.[2] Many European and American computer companies saw this new Japanese initiative as an attempt to take full control of the world's high-end computer market, and MCC was created, in part, as a defensive move against that threat. In late 1982, several major computer and semiconductor manufacturers in the United States banded together and founded MCC under the leadership of Admiral Bobby Ray Inman, whose previous positions had been Director of the National Security Agency and deputy director of the Central Intelligence Agency. Such formations were illegal in the United States until the 1984 Congressional passage of the "National Cooperative Research Act". Several sites with relevant universities were considered, including Atlanta, Georgia (Georgia Tech), the Research Triangle, N.C. (UNC), the Washington, D.C. area (George Mason), Stanford University and Austin, Texas (UT) which was the final selection. The University of Texas offered land upon which they would construct a new building specifically designed for the MCC within their Austin campus. Ross Perot also offered the use of his private plane for 2 years for staff recruitment. Austin was selected as the site for MCC in 1983. Despite this purpose and the background of Inman and his senior staff, MCC accepted no government funding for many years and was a refuge for some avoiding work on Strategic Defense Initiative projects. MCC was part of the Artificial Intelligence boom of the 1980s, reportedly the single largest customer of both Symbolics and Lisp Machines, Inc. (and like Symbolics, was one of the first companies to register a .com domain). In the 1980s its major programs were packaging, software engineering, CAD, and advanced computer architectures. The latter comprised artificial intelligence, human interface, database, and parallel processing, the latter two merging in the late 1980s. Many of the early shareholder companies were mainframe computer companies under stress in the 1980s. Over the years, MCC's membership diversified to include a broad range of high-profile corporations involved in information technology products, as well as government research and development agencies and leading universities. In June, 2000 the MCC Board of Directors voted to dissolve the consortium, and the few remaining employees held a wake at Scholz's Beer Garden in Austin on October 25. Formal dissolution papers were reportedly not filed until 2004. == Spinoffs == While multiple technologies were transferred to member companies and government agencies in the final years, fourteen companies were spun out of MCC. Those spinoffs include: TeraVicta Technologies, Austin's first MEMS company; its focus was to develop microscopic switch technology for fiber optic switching and radiofrequency switching in mobile phones specifically to dynamically switch between the future 3G-4GLTE-future5G wireless communication frequencies and ensure mobile phones were communicating over the strongest wireless signal to reduce dropped calls. Robert Miracky was the founding CEO who spun out the first commercial metal micromachining technology developed by MCC researchers Brent Lunceford, Jason Reed, Richard Nelson, K.Hu, and C. Hilbert in a collaborative development program with IBM in a novel implementation and operational paradigm for solid-state integrated circuit coolers integrated with conductive MEMS switches. TeraVicta was liquidated under Chapter 7 bankruptcy proceedings in 2015. The Austin region subsequently built up a MEMS & Sensors value chain in the billions of dollars comprising companies such as 3M, Cypress Semiconductor, NXP Semiconductor, Cirrus Logic, Silicon Labs, and the Austin division of the now-defunct Silicon Valley Technology Center. Portelligent, a company that provides reverse engineering teardown services. At the time, Portelligent was the first company to commercialize such services; they had been provided by MCC to its member companies. Today, there are at least twelve companies worldwide that sell reports known as "reverse engineering teardown reports." Modern day teardown reports provide detailed information about technology products such as the bill of materials, microchip, and printed circuit board design specifics, manufacturing details including manufacturing location details for the entire value chain responsible for making electronics, including the iPhone and Samsung Galaxy smartphones. Portelligent was acquired by CMP Technology in 2007. Evolutionary Technologies International, a company focused on developing database tools and data warehousing. It was spun off from MCC in 1990.
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Public First Action

Public First Action is a 501(c)(4) nonprofit organization focused on United States public policy related to artificial intelligence. Public First Action is a bipartisan group that advocates for AI transparency, safeguards, and export controls on advanced AI chips. The organization is aligned with the political action committees Jobs and Democracy, Defending Our Values and Public First. == History == Public First Action was formed in 2025 by former Congressmen Brad Carson, a Democrat, and Chris Stewart, a Republican, to advocate for federal, state, and local regulations related to AI. The group's formation followed the founding of a super PAC network, Leading the Future, which advocates for deregulation of the AI industry and faster development of the new technology. Public First Action supports measures that would increase transparency at frontier AI companies and impose export controls on advanced AI chips, in addition to opposing the preemption of state-level AI laws. In February 2026, Public First Action received $20 million from the AI company Anthropic. That same month, the group announced plans to support 30 to 50 Democrats and Republicans in state and federal races, with Public First Action and aligned super PACs launching advertisements in Nebraska, Tennessee, and other states. In one ad, Public First Action touted Senator Marsha Blackburn for her work on child online safety. As of 2026, the group plans to raise between $50 and $75 million for public oversight of AI and related reforms. == Organization == === Leadership and funding === Public First Action is led by Carson and Stewart. The group has raised nearly $50 million in funding with a goal of raising $75 million during the 2026 midterms. Anthropic has contributed $20 million to the group. === Structure === Public First Action is aligned with three political action committees: "Jobs and Democracy", which supports Democratic candidates; "Defending Our Values", which supports Republican candidates; and "Public First", which supports both Republicans and Democrats.
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