AI And Analytics

AI And Analytics — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Are You Dead?

Are You Dead? (Chinese: 死了么; pinyin: Sǐleme), also known by its English name Demumu, is a Chinese application designed for young people living alone. It requires setting up one emergency contact and sends automatic notifications if the user has not checked in via the app for consecutive days. The app was released on the App Store on 10 June 2025. In early January 2026, the application gained popularity due to its name and the issue of safety for people living alone, and ranked high on the list of paid applications in the Chinese region of the Apple App Store before being removed. The app's rise in popularity sparked discussions about taboos about death in China. == History == Are You Dead? was founded and operated independently by three people born in the 1990s, and developed in a way that involved remote collaboration in their spare time. According to the New Yellow River report, Guo, the product manager, said that the application was designed for young people and that the inspiration came from the discussion of netizens on social platforms about "an app that everyone must have and will definitely download" that he observed two or three years ago. The name was also "not their original creation". After realizing its potential demand and social significance, the team successfully registered the name and completed the product development in about a month. Regarding the development entity, the New Yellow River cited information from the Apple App Store that the application was developed by Yuejing (Zhengzhou) Technology Service Co., Ltd. According to Tianyancha information, the company was established in March 2025 with a registered capital of 100,000 yuan. === Rise in popularity === The app has been generating buzz on social media since 9 January 2026, due to its name and the topic of safety for people living alone. Around 10 January, it topped the Apple paid app chart. As of 10:00 a.m. on January 11, it ranked first in the App Store paid app chart. It also ranked highly in the utility app chart; it ranked first or second in the paid utility app charts in the United States, Singapore and Hong Kong, and first or fourth in Australia and Spain. The app was subsequently removed from the Apple App Store in China. In terms of functionality and usage, First Financial praised the product for its "simple interface and single function," but pointed out that the interface lacks a display of consecutive check-in days, and there is also the possibility that users may forget to check in, leading to the mistaken issuance of reminders. In addition, since the application mainly relies on email reminders and lacks SMS or telephone notifications, it does not conform to Chinese social habits; the untimely notifications also make the application more like a "death notification" tool, losing its early warning significance for emergency rescue. Hu Xijin, former editor-in-chief of the Global Times, commented on the application on Weibo that it is "really good and can help many lonely elderly people." The Beijing News Quick Review pointed out that the role of technical tools is limited and needs to be connected with real support such as community patrols and liaison mechanisms. Due to the price increase, there have also been questions about the motivation for the price increase. The app's rise in popularity sparked discussions about taboos about death in China. Regarding the popularity of the application, both Southern Metropolis Daily and The Beijing News commented that it reflects the public issue of the risks of living alone and reflects the general anxiety of the living alone group about dying alone. Shangguan News further pointed out that although such technology products provide a certain "low-cost sense of security", their "cold notifications" may not only cause false alarms, but also highlight the embarrassing reality that "there is no one to fill in the emergency contact". It also emphasized that algorithms or applications cannot bring true happiness and called on society to reconstruct a support network full of humanistic care while relying on technology. The name of the application has also sparked controversy. Most netizens believe that the name "Are You Dead?" is unlucky and makes it awkward to share the application. They suggest changing it to a milder name such as "Are You Alive?". Hu Xijin also said that the name change could "give the elderly who use it more psychological comfort" and "believe that the application will become more popular after the name change". Some people also believe that this straightforward name just points out the real dilemma faced by people living alone and has a special meaning. BBC News commented that the name "Are You Dead" is playing a word game with Ele.me (Chinese: 饿了么; pinyin: Èleme) and the pronunciation is also similar. Legal professionals believe that its name is highly similar to Ele.me and may cause confusion. They also raised the possibility of trademark infringement and unfair competition. However, the developers said that the application is developed for young people and death is not a sensitive topic. They will "consider launching a new application that is more suitable for middle-aged and elderly people". They have not yet received any name change requests from relevant departments. On the evening of 13 January 2026, the Are You Dead? team announced that it would change its name to the English brand name Demumu in the upcoming new version. On 11 January, the development team also issued a statement through its official Weibo account, stating that it would study the renaming suggestion and plan to enrich the SMS reminder function, consider adding the message function and explore the direction of age-friendly products; it also stated that it would launch an 8 yuan paid plan to cover the costs of SMS, servers, etc., and welcomed investors to discuss cooperation. In terms of financing and valuation, it plans to sell 10% of the company's shares for 1 million yuan and proposed a valuation of 10 million yuan. On the evening of January 15, the application was removed from the app store in mainland China. == Functions == The application does not require users to enter phone numbers or other information to register. After filling in their name and setting an emergency contact, users can click the sign-in button every day. If they fail to sign in for two consecutive days, the system will send an email reminder to the emergency contact the next day. In addition, users can also bind a smart bracelet to monitor physiological signs, pre-designate a hearse driver and funeral music, and trigger the "one-click body collection" function when no pulse is detected. The application was initially available for free download, but a one yuan paid download option was introduced at the end of 2025. In January 2026, the application team issued a statement saying that an 8 yuan paid option would be launched based on the costs of SMS, servers, etc.
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European Conference on Artificial Intelligence

The European Conference on Artificial Intelligence (ECAI) is the leading conference in the field of Artificial Intelligence in Europe, and is commonly listed together with IJCAI and AAAI as one of the three major general AI conferences worldwide. The conference series has been held without interruption since 1974, originally under the name AISB. The conference was originally held biennially, but has been organized annually since ECAI 2022. The conferences are held under the auspices of the European Coordinating Committee for Artificial Intelligence (ECCAI) and organized by one of the member societies. The journal AI Communications, sponsored by the same society, regularly publishes special issues in which conference attendees report on the conference. Publication of a paper in ECAI is considered by some journals to be archival: the paper should be considered equivalent to a journal publication and that the contents of ECAI papers cannot be reformulated as separate journal submissions unless a significant amount of new material is added. == List of ECAI conferences == ECAI-1992 took place in Vienna, Austria. ECAI-1996 took place in Budapest, Hungary. ECAI-1998 tool place in Brighton, United Kingdom. ECAI-2000 took place in Berlin, Germany. ECAI-2004 took place in Valencia, Spain. ECAI-2006 took place in Riva del Garda, Italy. ECAI-2008 took place in Patras, Greece. ECAI-2010 took place in Lisbon, Portugal. ECAI-2012 took place in Montpellier, France. ECAI-2014 took place in Prague, Czech Republic. ECAI-2016 took place in The Hague, Netherlands. ECAI-2018 took place in Stockholm, Sweden. ECAI-2020 took place in Santiago de Compostela, Spain. ECAI-2022 took place in Vienna, Austria. ECAI-2023 took place in Kraków, Poland. ECAI-2024 took place in Santiago de Compostela, Spain. ECAI-2025 took place in Bologna, Italy.
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Blackboard system

A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts. == Metaphor == The following scenario provides a simple metaphor that gives some insight into how a blackboard functions: A group of specialists are seated in a room with a large blackboard. They work as a team to brainstorm a solution to a problem, using the blackboard as the workplace for cooperatively developing the solution. The session begins when the problem specifications are written onto the blackboard. The specialists all watch the blackboard, looking for an opportunity to apply their expertise to the developing solution. When someone writes something on the blackboard that allows another specialist to apply their expertise, the second specialist records their contribution on the blackboard, hopefully enabling other specialists to then apply their expertise. This process of adding contributions to the blackboard continues until the problem has been solved. == Components == A blackboard-system application consists of three major components The software specialist modules, which are called knowledge sources (KSs). Like the human experts at a blackboard, each knowledge source provides specific expertise needed by the application. The blackboard, a shared repository of problems, partial solutions, suggestions, and contributed information. The blackboard can be thought of as a dynamic "library" of contributions to the current problem that have been recently "published" by other knowledge sources. The control shell, which controls the flow of problem-solving activity in the system. Just as the eager human specialists need a moderator to prevent them from trampling each other in a mad dash to grab the chalk, KSs need a mechanism to organize their use in the most effective and coherent fashion. In a blackboard system, this is provided by the control shell. === Learnable Task Modeling Language === A blackboard system is the central space in a multi-agent system. It's used for describing the world as a communication platform for agents. To realize a blackboard in a computer program, a machine readable notation is needed in which facts can be stored. One attempt in doing so is a SQL database, another option is the Learnable Task Modeling Language (LTML). The syntax of the LTML planning language is similar to PDDL, but adds extra features like control structures and OWL-S models. LTML was developed in 2007 as part of a much larger project called POIROT (Plan Order Induction by Reasoning from One Trial), which is a Learning from demonstrations framework for process mining. In POIROT, Plan traces and hypotheses are stored in the LTML syntax for creating semantic web services. Here is a small example: A human user is executing a workflow in a computer game. The user presses some buttons and interacts with the game engine. While the user interacts with the game, a plan trace is created. That means the user's actions are stored in a logfile. The logfile gets transformed into a machine readable notation which is enriched by semantic attributes. The result is a textfile in the LTML syntax which is put on the blackboard. Agents (software programs in the blackboard system) are able to parse the LTML syntax. == Implementations == We start by discussing two well known early blackboard systems, BB1 and GBB, below and then discuss more recent implementations and applications. The BB1 blackboard architecture was originally inspired by studies of how humans plan to perform multiple tasks in a trip, used task-planning as a simplified example of tactical planning for the Office of Naval Research. Hayes-Roth & Hayes-Roth found that human planning was more closely modeled as an opportunistic process, in contrast to the primarily top-down planners used at the time: While not incompatible with successive-refinement models, our view of planning is somewhat different. We share the assumption that planning processes operate in a two-dimensional planning space defined on time and abstraction dimensions. However, we assume that people's planning activity is largely opportunistic. That is, at each point in the process, the planner's current decisions and observations suggest various opportunities for plan development. The planner's subsequent decisions follow up on selected opportunities. Sometimes, these decision-sequences follow an orderly path and produce a neat top-down expansion as described above. However, some decisions and observations might also suggest less orderly opportunities for plan development. A key innovation of BB1 was that it applied this opportunistic planning model to its own control, using the same blackboard model of incremental, opportunistic, problem-solving that was applied to solve domain problems. Meta-level reasoning with control knowledge sources could then monitor whether planning and problem-solving were proceeding as expected or stalled. If stalled, BB1 could switch from one strategy to another as conditions – such as the goals being considered or the time remaining – changed. BB1 was applied in multiple domains: construction site planning, inferring 3-D protein structures from X-ray crystallography, intelligent tutoring systems, and real-time patient monitoring. BB1 also allowed domain-general language frameworks to be designed for wide classes of problems. For example, the ACCORD language framework defined a particular approach to solving configuration problems. The problem-solving approach was to incrementally assemble a solution by adding objects and constraints, one at a time. Actions in the ACCORD language framework appear as short English-like commands or sentences for specifying preferred actions, events to trigger KSes, preconditions to run a KS action, and obviation conditions to discard a KS action that is no longer relevant. GBB focused on efficiency, in contrast to BB1, which focused more on sophisticated reasoning and opportunistic planning. GBB improves efficiency by allowing blackboards to be multi-dimensional, where dimensions can be either ordered or not, and then by increasing the efficiency of pattern matching. GBB1, one of GBB's control shells implements BB1's style of control while adding efficiency improvements. Other well-known of early academic blackboard systems are the Hearsay II speech recognition system and Douglas Hofstadter's Copycat and Numbo projects. Some more recent examples of deployed real-world applications include: The PLAN component of the Mission Control System for RADARSAT-1, an Earth observation satellite developed by Canada to monitor environmental changes and Earth's natural resources. The GTXImage CAD software by GTX Corporation was developed in the early 1990s using a set of rulebases and neural networks as specialists operating on a blackboard system. Adobe Acrobat Capture (now discontinued), as it used a blackboard system to decompose and recognize image pages to understand the objects, text, and fonts on the page. This function is currently built into the retail version of Adobe Acrobat as "OCR Text Recognition". Details of a similar OCR blackboard for Farsi text are in the public domain. Blackboard systems are used routinely in many military C4ISTAR systems for detecting and tracking objects. Another example of current use is in Game AI, where they are considered a standard AI tool to help with adding AI to video games. == Recent developments == Blackboard-like systems have been constructed within modern Bayesian machine learning settings, using agents to add and remove Bayesian network nodes. In these 'Bayesian Blackboard' systems, the heuristics can acquire more rigorous probabilistic meanings as proposal and acceptances in Metropolis Hastings sampling though the space of possible structures. Conversely, using these mappings, existing Metropolis-Hastings samplers over structural spaces may now thus be viewed as forms of blackboard systems even when not named as such by the authors. Such samplers are commonly found in musical transcription algorithms for example. Blackboard systems have also been used to build large-scale intelligent systems for the annotation of media content, automating parts of traditional social science research. In this domain, the problem of integrating various AI algorithms into a single intelligent system arises spontaneously, with blackboards providing a way for a collection of distributed, modular natural language processing algorithm
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Pop music automation

Pop music automation is a field of study among musicians and computer scientists with a goal of producing successful pop music algorithmically. It is often based on the premise that pop music is especially formulaic, unchanging, and easy to compose. The idea of automating pop music composition is related to many ideas in algorithmic music, artificial intelligence (AI) and computational creativity. == History of automation in music == Algorithms (or, at the very least, formal sets of rules) have been used to compose music for centuries; the procedures used to plot voice-leading in counterpoint, for example, can often be reduced to algorithmic determinant. Now the term is usually reserved, however, for the use of formal procedures to make music without human intervention. Classical music automation software exists that generates music in the style of Mozart and Bach and jazz. Most notably, David Cope has written a software system called "Experiments in Musical Intelligence" (or "EMI") that is capable of analyzing and generalizing from existing music by a human composer to generate novel musical compositions in the same style. EMI's output is convincing enough to persuade human listeners that its music is human-generated to a high level of competence. Creativity research in jazz has focused on the process of improvisation and the cognitive demands that this places on a musical agent: reasoning about time, remembering and conceptualizing what has already been played, and planning ahead for what might be played next. Inevitably associated with pop music automation is pop music analysis. Projects in pop music automation may include, but are not limited to, ideas in melody creation and song development, vocal generation or improvement, automatic accompaniment and lyric composition. == Automatic accompaniment == Some systems exist that automatically choose chords to accompany a vocal melody in real-time. A user with no musical experience can create a song with instrumental accompaniment just by singing into a microphone. An example is a Microsoft Research project called Songsmith, which trains a Hidden Markov model using a music database and uses that model to select chords for new melodies. == Melody generation == Automatic melody generation is often done with a Markov chain, the states of the system become note or pitch values, and a probability vector for each note is constructed, completing a transition probability matrix (see below). An algorithm is constructed to produce an output note values based on the transition matrix weightings, which could be MIDI note values, frequency (Hz), or any other desirable metric. A second-order Markov chain can be introduced by considering the current state and also the previous state, as indicated in the second table. Higher, nth-order chains tend to "group" particular notes together, while 'breaking off' into other patterns and sequences occasionally. These higher-order chains tend to generate results with a sense of phrasal structure, rather than the 'aimless wandering' produced by a first-order system. == Lyric composition == Automated lyric creating software may take forms such as: Selecting words according to their rhythm The Tra-la-Lyrics system produces song lyrics, in Portuguese, for a given melody. This not only involves matching each word syllable with a note in the melody, but also matching the word's stress with the strong beats of the melody. Parsing existing pop music (e.g. for content or word choice) This involves natural language processing. Pablo Gervás has developed a noteworthy system called ASPERA that employs a case-based reasoning (CBR) approach to generating poetic formulations of a given input text via a composition of poetic fragments that are retrieved from a case-base of existing poems. Each poem fragment in the ASPERA case-base is annotated with a prose string that expresses the meaning of the fragment, and this prose string is used as the retrieval key for each fragment. Metrical rules are then used to combine these fragments into a well-formed poetic structure. Automatic analogy or story creation Programs like TALE-SPIN and The MINSTREL system represent a complex elaboration of this basis approach, distinguishing a range of character-level goals in the story from a range of author-level goals for the story. Systems like Bringsjord's BRUTUS can create stories with complex interpersonal themes like betrayal. On-line metaphor generation systems like 'Sardonicus' or 'Aristotle' can suggest lexical metaphors for a given descriptive goal (e.g., to describe a supermodel as skinny, the source terms “pencil”, “whip”, “whippet”, “rope”, “stick-insect” and “snake” are suggested). Free association of grouped words Using a language database (such as wordnet) one can create musings on a subject that may be weak grammatically but are still sensical. See such projects as the Flowerewolf automatic poetry generator or the Dada engine. == Software == === More or less free === BreathCube by xoxos. Simple lyrical vocal content is generated with simple music. CubeBreath by xoxos. Audio input is vocoded in tune with the music. Midi Internet Algorithmic Composition infno, infinite generator of electronic dance music and synth-pop. Algorithmic Trap, trap beat generator. === Commercial === Band in a box generates any element, potentially creates whole new songs from scratch. Musical Palette - Melody Composing Tool SongSmith: Automatic accompaniment for vocal melodies Ludwig 3.0 automatic accompaniment, writes arrangements for given instruments, plays its own songs for an infinitely long time. Automated Composing System creates music in many different styles
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Comparison of video editing software

This is a comparison of non-linear video editing software applications. See also a more complete list of video editing software. == General information == This table gives basic general information about the different editors: === Active === === Discontinued / Inactive === ==== Definition ==== professional: used for full length Hollywood movies; professional (small): mainly used for paid commercials, short films or podcasts/YouTube channels; prosumer: Mainly targeting private use, anything that can do more than just trimming a film; basic: trimming a film; == System requirements == This table lists the operating systems that different editors can run on without emulation, as well as other system requirements. Note that minimum system requirements are listed; some features (like High Definition support) may be unavailable with these specifications. "Unix" includes the similar Linux, BSD and Unix-like operating systems. == High definition/High resolution import == The table below indicates the ability of each program to import various High Definition video or High resolution video formats for editing. == Feature set == == Output options == Please note that recording to Blu-ray does not imply 1080@50p/60p . Most only support up to 1080i 25/30 frames per second recording. Also not all formats can be output.
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Federal Virtual World Challenge

The Federal Virtual Challenge, formerly The Federal Virtual Worlds Challenge is a competition led by the Simulation and Training Technology Center (United States Army Research, Development and Engineering Command). The event is conducted in order to reach a global development community that will create innovative and interactive training and analysis services in virtual worlds. The inaugural event began in 2009 with the awards being conducted during March 2010 GameTech conference in Orlando, Florida. == Description == The focus of the challenge is training or analysis capability conducted wholly in a virtual environment. The training and analysis audience includes all United States Federal Agencies including, Department of Defense, Department of Homeland Security, Department of Transportation, and Department of Health and Human Services, NASA, DOT, and many more.
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Fuzzy pay-off method for real option valuation

The fuzzy pay-off method for real option valuation (FPOM or pay-off method) is a method for valuing real options, developed by Mikael Collan, Robert Fullér, and József Mezei; and published in 2009. It is based on the use of fuzzy logic and fuzzy numbers for the creation of the possible pay-off distribution of a project (real option). The structure of the method is similar to the probability theory based Datar–Mathews method for real option valuation, but the method is not based on probability theory and uses fuzzy numbers and possibility theory in framing the real option valuation problem. == Method == The Fuzzy pay-off method derives the real option value from a pay-off distribution that is created by using three or four cash-flow scenarios (most often created by an expert or a group of experts). The pay-off distribution is created simply by assigning each of the three cash-flow scenarios a corresponding definition with regards to a fuzzy number (triangular fuzzy number for three scenarios and a trapezoidal fuzzy number for four scenarios). This means that the pay-off distribution is created without any simulation whatsoever. This makes the procedure easy and transparent. The scenarios used are a minimum possible scenario (the lowest possible outcome), the maximum possible scenario (the highest possible outcome) and a best estimate (most likely to happen scenario) that is mapped as a fully possible scenario with a full degree of membership in the set of possible outcomes, or in the case of four scenarios used - two best estimate scenarios that are the upper and lower limit of the interval that is assigned a full degree of membership in the set of possible outcomes. The main observations that lie behind the model for deriving the real option value are the following: The fuzzy NPV of a project is (equal to) the pay-off distribution of a project value that is calculated with fuzzy numbers. The mean value of the positive values of the fuzzy NPV is the "possibilistic" mean value of the positive fuzzy NPV values. Real option value, ROV, calculated from the fuzzy NPV is the "possibilistic" mean value of the positive fuzzy NPV values multiplied with the positive area of the fuzzy NPV over the total area of the fuzzy NPV. The real option formula can then be written simply as: R O V = A ( P o s ) A ( P o s ) + A ( N e g ) × E [ A + ] {\displaystyle \mathrm {ROV} ={\frac {A(\mathrm {Pos} )}{A(\mathrm {Pos} )+A(\mathrm {Neg} )}}\times E[A_{+}]} where A(Pos) is the area of the positive part of the fuzzy distribution, A(Neg) is the area of the negative part of the fuzzy distribution, and E[A+] is the mean value of the positive part of the distribution. It can be seen that when the distribution is totally positive, the real options value reduces to the expected (mean) value, E[A+]. As can be seen, the real option value can be derived directly from the fuzzy NPV, without simulation. At the same time, simulation is not an absolutely necessary step in the Datar–Mathews method, so the two methods are not very different in that respect. But what is totally different is that the Datar–Mathews method is based on probability theory and as such has a very different foundation from the pay-off method that is based on possibility theory: the way that the two models treat uncertainty is fundamentally different. == Use of the method == The pay-off method for real option valuation is very easy to use compared to the other real option valuation methods and it can be used with the most commonly used spreadsheet software without any add-ins. The method is useful in analyses for decision making regarding investments that have an uncertain future, and especially so if the underlying data is in the form of cash-flow scenarios. The method is less useful if optimal timing is the objective. The method is flexible and accommodates easily both one-stage investments and multi-stage investments (compound real options). The method has been taken into use in some large international industrial companies for the valuation of research and development projects and portfolios. In these analyses triangular fuzzy numbers are used. Other uses of the method so far are, for example, R&D project valuation IPR valuation, valuation of M&A targets and expected synergies, valuation and optimization of M&A strategies, valuation of area development (construction) projects, valuation of large industrial real investments. The use of the pay-off method is lately taught within the larger framework of real options, for example at the Lappeenranta University of Technology and at the Tampere University of Technology in Finland.
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Conference on Artificial General Intelligence

The Conference on Artificial General Intelligence (AGI) is a meeting of researchers in the field of artificial general intelligence (AGI) organized by the AGI Society steered by Marcus Hutter and Ben Goertzel. It has been held annually since 2008. The conference was initiated by the 2006 Bethesda Artificial General Intelligence Workshop and has since been hosted at various international venues. == Locations and history == AGI-2026 San Francisco State University, California, USA AGI-2025 Reykjavík University, Reykjavík, Iceland AGI-2024 University of Washington, Seattle, Washington, USA AGI-2023 KTH Royal Institute of Technology, Stockholm, Sweden AGI-2022 The Crocodile, Seattle, Washington, USA AGI-2021 Computer History Museum, Mountain View, California, USA AGI-2020 Virtual Conference AGI-2019 Sheraton Shenzhen Futian, Shenzhen, China AGI-2018 Czech Technical University, Prague, Czech Republic AGI-2017 ibis Melbourne, Melbourne, Australia AGI-2016 The New School, New York, New York, USA AGI-2015 Berlin-Brandenburg Academy of Sciences and Humanities, Berlin, Germany AGI-2014 Université Laval, Quebec City, Canada (sponsored by the Cognitive Science Society and the AAAI) AGI-2013 Peking University, Beijing, China (sponsored by the Cognitive Science Society and the AAAI) AGI-2012 University of Oxford, Oxford, United Kingdom (sponsored by the Future of Humanity Institute and Ray Kurzweil) AGI-2011 Google Headquarters, Mountain View, California, USA (sponsored by Google, AAAI, and Ray Kurzweil) AGI-2010 University of Lugano, Lugano, Switzerland (In Memoriam Ray Solomonoff and sponsored by AAAI and Ray Kurzweil) AGI-2009 Crowne Plaza Crystal City, Arlington, Virginia, USA (sponsored by AAAI and Ray Kurzweil) AGI-2008 University of Memphis, Tennessee, USA (sponsored by AAAI) == Notable speakers == The conference has attracted many speakers over the years including Turing Award winners Yoshua Bengio and Richard S. Sutton as well as Ben Goertzel, Marcus Hutter, Jürgen Schmidhuber, Gary Marcus, John E. Laird, Peter Norvig, Joscha Bach, François Chollet, John L. Pollock, Bill Hibbard, Hugo de Garis, Stan Franklin, Steve Omohundro, Randal A. Koene, Ernst Dickmanns, Margaret Boden, David Hanson, Roman Yampolskly, Selmer Bringsjord, Kristinn R. Thórisson and Nick Bostrom.
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Sycophancy (artificial intelligence)

In the field of artificial intelligence, sycophancy is a tendency of large language models (LLMs) and other AI assistants to tailor their responses to what they predict the user wants to hear rather than to what is accurate or warranted. The behavior takes several forms: an assistant may agree with a user's stated opinion even when the user is mistaken; it may abandon a correct answer after a challenge such as "are you sure?"; it may validate beliefs, decisions or self-presentation regardless of merit; or it may praise the user, their work or their ideas in unwarranted terms. The word is borrowed from the ordinary English term for fawning flattery, and is used in AI alignment and AI safety research to describe a class of misalignment failures associated with training on human feedback. Researchers at Anthropic first documented the behavior systematically in 2022. They found that models fine-tuned with reinforcement learning from human feedback (RLHF) were more likely than untuned models to repeat back a user's preferred answer. A 2023 follow-up paper, "Towards Understanding Sycophancy in Language Models", showed that five frontier assistants from OpenAI, Anthropic and Meta all exhibited the behavior, and traced its origin to biases in the human preference data used during training. Later work documented sycophancy in mathematics, medicine, academic peer review and other domains, and identified a broader category called "social sycophancy" affecting an assistant's emotional and interpersonal responses. The issue drew widespread public attention in April 2025 after OpenAI rolled back an update to its GPT-4o model. Users had reported that the assistant praised dangerous decisions, endorsed delusional thinking and offered exaggerated compliments for trivial prompts. OpenAI's post-mortem attributed the change in behavior to an additional training signal based on user thumbs-up and thumbs-down feedback. That episode, together with reporting in The New York Times, Rolling Stone and elsewhere on users drawn into delusional thinking through prolonged chatbot interaction, has been cited in litigation and in academic studies as evidence that sycophancy poses risks to user well-being. Proposed mitigations include fine-tuning on synthetic data that rewards disagreement with incorrect user statements, editing the small subset of model parameters causally responsible for the behavior, changes to the dialogue or system prompt, and benchmarks designed to surface sycophantic behavior before models are released. == Causes == The dominant explanation points to RLHF, the standard technique for aligning chat assistants with user expectations. Human annotators rank candidate model responses; a reward model is trained to predict those rankings; and the language model is then optimized against the reward model. Because human raters tend to prefer outputs that confirm their existing beliefs or flatter their work, the pipeline systematically rewards responses that agree with the annotator. Perez and colleagues at Anthropic published the first large-scale empirical evidence of the effect in 2022. They reported that RLHF training increased the probability that a model would repeat back a dialog user's preferred answer, and that larger models exhibited the behavior more strongly. Sharma and colleagues, the following year, went further and examined Anthropic's own preference data directly. Both the human raters and the reward models trained on their judgments preferred convincingly written sycophantic responses to truthful ones at a non-negligible rate. Wei and co-authors at Google DeepMind found similar results in the PaLM family, observing that both model scale and instruction tuning increased sycophancy on opinion questions. The behavior is often classified as a form of reward hacking, in which an optimization process exploits a flaw in its reward signal rather than achieving the intended objective. OpenAI's post-mortem of the April 2025 GPT-4o incident identified a more specific mechanism. An additional reward signal based on aggregated thumbs-up and thumbs-down feedback from ChatGPT users had, in OpenAI's words, "weakened the influence of our primary reward signal, which had been holding sycophancy in check." Separately, an Anthropic interpretability paper from 2025 located a linear direction in a model's internal activations corresponding to sycophantic behavior, and showed that such "persona vectors" could be used to flag sycophancy-inducing training data and to steer models away from the trait at inference time. == Measurement == The Anthropic team released SycophancyEval with its 2023 paper, supplying test sets for each of the four canonical behaviors. Two further benchmarks from Stanford followed in 2025. SycEval, applied to mathematical and medical reasoning tasks, reported an overall sycophancy rate of 58 per cent across the GPT-4o, Claude and Gemini models tested. ELEPHANT, aimed at social sycophancy, found that the eleven LLMs evaluated affirmed posts that the Reddit community r/AmITheAsshole had judged inappropriate in 42 per cent of cases, and preserved a user's face 45 percentage points more often than human respondents did. Domain-specific benchmarks have followed. BrokenMath tests robustness to plausible-looking but false mathematical claims drawn from competition problems, and reports that the best evaluated model was sycophantic in 29 per cent of cases. SYCON-Bench measures how many dialogue turns are required before a model abandons a correct position. Visual sycophancy in multimodal models has been examined with MM-SY and PENDULUM. A 2026 study by researchers at the Massachusetts Institute of Technology reported that personalization features, which adapt assistants to individual users over repeated sessions, can intensify social sycophancy. == Notable incidents == === GPT-4o rollback (April 2025) === On 25 April 2025, OpenAI completed the rollout of an update to GPT-4o, the default model used in ChatGPT at the time. Within days, users reported that the assistant had begun praising trivial messages in extravagant terms, endorsing impulsive or dangerous decisions, and reinforcing strong emotional statements without pushback. Widely shared examples included the model congratulating a user who reported stopping prescribed psychiatric medication, and praising a business plan to sell "shit on a stick" as venture-capital ready. OpenAI's chief executive, Sam Altman, wrote on 27 April that recent updates had made the model "too sycophant-y and annoying" and said fixes were in progress. The company began reverting the update on 28 April and completed the rollback for free users by 30 April. Two post-mortems followed: a short note on 29 April and a longer technical follow-up, "Expanding on what we missed with sycophancy", on 2 May. Both attributed the regression to a new training signal based on user thumbs-up and thumbs-down feedback, to inadequate pre-launch evaluation for sycophantic drift, and to the dismissal of qualitative concerns raised by internal testers before release. Reporting in CNN, Fortune and Bloomberg News treated the incident as a turning point in public awareness of the problem. === Chatbot-related psychological harm === From mid-2025 onward, news reports began to link sycophantic chatbot behavior to acute psychological harm. In June 2025, The New York Times technology reporter Kashmir Hill published an investigation centered on Eugene Torres, a Manhattan accountant with no history of mental illness, who developed a sustained delusional episode after a series of conversations with ChatGPT about simulation theory. According to the article, the assistant encouraged Torres to stop taking prescribed medication, to cut off friends and family, and at one point told him that he could fly from a nineteen-story building if he "truly believed". Futurism and Rolling Stone ran parallel investigations documenting other cases in which heavy use of ChatGPT had been associated with delusional thinking, involuntary commitment or, in at least one case, the death of a user with a pre-existing psychiatric diagnosis. A 2026 paper by researchers at the Massachusetts Institute of Technology and the University of Washington put forward a formal Bayesian model. It showed that even an ideally rational user could be drawn into what the authors call "delusional spiraling" when interacting with a sufficiently sycophantic assistant, and that the effect was not eliminated by suppressing hallucinations or by warning users in advance. The lawsuit Raine v. OpenAI, filed in San Francisco Superior Court in August 2025 by the parents of a sixteen-year-old who had died by suicide, alleges that "heightened sycophancy" was a design feature of ChatGPT that contributed to their son's death; it is the first wrongful-death suit against a large language-model provider. === Wider commentary === Mainstream coverage in outlets including The New York Times, The Washington Pos
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Asian Digital Finance Forum & Awards

Asian Digital Finance Forum & Awards (also known as Asian Digital Finance Forum and Awards) is a forum and honorary awards platform convened in Colombo, Sri Lanka. It has been hosted in a hybrid format (virtual and in-person), with editions reported in 2022, 2023 and 2025. The event is organised by the Asian FinTech Academy (AFTA) in collaboration with a number of local and international institutions. == Overview == The forum has featured international academic, industry, and policy speakers and has recognised institutions and individuals for contributions related to digital finance and fintech innovation. Media coverage has described participation and recognition at the forum as spanning multiple regions, with institutions and individuals from South Asia, Southeast Asia, East Asia, the Middle East, Europe, and North America featured across different editions. == Awards and recognition == The forum and awards were held in a hybrid format with virtual and in-person proceedings at Hilton Colombo in the 2022 and 2023 editions. The Asian Digital Finance Forum & Awards presents honorary recognitions to institutions and individuals for contributions to digital finance, financial inclusion, and related regulatory, technological, and policy developments. Media coverage has described the recognitions as non-competitive and based on demonstrated leadership and impact rather than open nominations. In 2025, the forum and awards served as an anchor initiative associated with the Asia International Digital Economy & AI in Finance Summit at Port City Colombo, with an emphasis on artificial intelligence in finance, financial inclusion, and governance-related themes. === 2022 === According to reporting by Daily FT, institutions recognised at the 2022 edition included Sri Lanka’s Bank of Ceylon, Commercial Bank of Ceylon, Hatton National Bank, and People’s Bank, alongside international organisations and fintech-sector contributors. === 2023 === Coverage of the 2023 forum described recognitions awarded to India’s International Financial Services Centres Authority (IFSCA) for regulatory innovation, as well as to digital finance and payments platforms including Dialog Genie and SLT-Mobitel mCash. IDEMIA’s Asia–Pacific operations were also recognised for contributions related to biometric and digital identity technologies in financial services. === 2025 === For the 2025 edition, institutional honourees reported in the media included Nium (Singapore), recognised for cross-border payments optimisation, and Paytm (India), recognised for AI-powered financial inclusion initiatives. A Visionary Award for Next-Generation Financial Hub Development was presented to Port City Colombo in recognition of its fintech- and AI-oriented development strategy. Individual honourees reported for 2025 included Sopnendu Mohanty (Singapore), Neil Tan (Hong Kong), Purvi Munot (United Arab Emirates), and Amira Abdelaziz (Egypt), recognised for contributions spanning fintech governance, ecosystem development, inclusive wealth technology, and AI-driven financial policy and regulation. In 2025, media reports described the awards as being subject to an independent validation framework. The process was led by Dr. Sivaguru S. Sritharan, appointed as Global Validation Chair, and involved independent research, analytical review, and benchmarking against international standards, with recognitions characterised as honorary and non-competitive.
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Computer-assisted legal research

Computer-assisted legal research (CALR) or computer-based legal research is a mode of legal research that uses databases of court opinions, statutes, court documents, and secondary material. Electronic databases make large bodies of case law easily available. Databases also have additional benefits, such as Boolean searches, evaluating case authority, organizing cases by topic, and providing links to cited material. Databases are available through paid subscription or for free. Subscription-based services include Westlaw, LexisNexis, JustCite, HeinOnline, Bloomberg Law, Lex Intell, VLex and LexEur. As of 2015, the commercial market grossed $8 billion. Free services include OpenJurist, Google Scholar, AltLaw, Ravel Law, WIPO Lex, Law Delta and the databases of the Free Access to Law Movement. == Purposes == Computer-assisted legal research is undertaken by a variety of actors. It is taught as a topic in many law degrees and is used extensively by undergraduate and postgraduate law students in meeting the work requirements of their degree courses. Professors of Law rely on the digitization of primary and secondary sources of law when conducting their research and writing the material that they submit for publication. Professional lawyers rely on computer-assisted legal research in order to properly understand the status of the law and so to act effectively in the best interest of their client. They may also consult the text of case judgements and statutes specifically, as well as wider academic comment, in order to form the basis of (or response to) an appeal. The availability of legal information online differs by type, jurisdiction and subject matter. The types of information available include: Texts of statutes, statutory instruments, civil codes, etc. Explanatory notes and government publications relating to statutes and their operation Texts of governing documents such as constitutions and treaties Case judgements Journals on legal matters or legal theory Dictionaries and legal encyclopedia Legal texts and materials in the form of e-books Current affairs and market information Educational information on the law and its operation == Before the Internet == Prior to the advent and popularization of the World Wide Web, access to digital legal information was largely through the use of CD-ROMs, designed and sold by commercial organizations. Dial-up services were also available from the 1970s. As the use of the Internet spread in the early 1990s, companies such as LexisNexis and Westlaw incorporated Internet connectivity into their software packages. Browser-based legal information started to be published by Legal Information Institutes from 1992. == Publicly available information == The first effort to provide free computer access to legal information was made by two academics, Peter Martin and Tom Bruce, in 1992. Today, the Legal Information Institute freely publishes such resources as the text of the United States Constitution, judgements of the United States Supreme Court, and the text of the United States Code. The Australasian Legal Information Institute (AusLII) was established soon after in 1995. Other legal information institutes, such as those of Great Britain and Ireland (BAILII), Canada (CII) and South Africa (SAfLI) soon followed. LIIs were partially formalized in 2002 following the signing of the Declaration of Free Access to the Law, which has been signed by 54 countries. At the time of writing, the World Legal Information Institute contains in excess of 1800 databases from 123 jurisdictions. Many governments also publish legal information online. For example, UK legislation and statutory instruments have been publicly available online since 2010. Depending on the jurisdiction in question, the decisions of higher appellate courts may also be published online, either by the Legal Information Institute or by the court service directly. Sources of European Union Law are published for free by EUR-Lex in 23 languages, including judgments of the European Courts. Similarly, judgements of the European Court of Human Rights are published on its website.
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Plants vs. Zombies: Replanted

Plants vs. Zombies: Replanted is a 2025 tower defense video game developed by PopCap Seattle, The Lost Pixels, and published by Electronic Arts. It is a remaster of the 2009 game Plants vs. Zombies, introducing upscaled graphics and new additional content. Plants vs. Zombies: Replanted was released for video game consoles and personal computers on October 23, 2025. It received generally positive reviews from critics, but was criticized by the original game's development team for including fabricated concept art and for mishandling the soundtrack. == Gameplay == Plants vs. Zombies: Replanted follows the same gameplay of the original Plants vs. Zombies game with very minor changes. It is a lane-based tower defense game where the player has to defend their home from incoming zombies. The player can place various plants by spending "sun", the game's currency during levels. Sun icons can be collected from the sky during daytime and from sun-producing plants such as sunflowers. Some plants can attack zombies while some can act as defense. If all zombies are defeated in a level, the player wins. If a zombie reaches the left side of the line, a lawn mower—or other similar, relevant object—will activate and clear the row of any zombies, but if the lawn mower has already been used, and another zombie crosses, the game is over. === Replanted features === Plants vs. Zombies: Replanted contains up to 4K upscaled graphics and widescreen support, in comparison to the original game's static 800x600 resolution and 4:3 aspect ratio. Replanted now has full controller support and features local multiplayer modes ported from the original game's seventh generation console ports: co-op, where two players play together with assigned roles; and Versus, where one plays as the plants and the other as the zombies. No online multiplayer is planned, however support for Steam Remote Play was later added in a patch as an alternative for Windows users. Replanted also contains quality-of-life features. Gameplay can now be sped up by the player's will, with a max speed increase of 2.5x. Sun icons can now be mass collected using the "Sun Magnet." On Windows, players can quick-select plants from their seed bank using the number keys as hotkeys. Replanted also introduces two new additional game modes. "Cloudy Day" is a set of non-linear levels in the Adventure campaign. These levels only allow Sunflowers as sun-producing plants. During these levels, the amount of sun dropped from the sky and produced by plants are lowered. At certain times, rain clouds will move over the lawn. While these clouds are present, sun will stop appearing from the sky and from Sunflowers. However, all plants will cost around half their original price and have significantly faster recharge times. "R.I.P. Mode" is a harder difficulty of the Adventure campaign, but the player is forced back to the beginning if they lose a single level. Replanted additionally features "bonus levels" included as non-linear levels in the Adventure campaign. These include 10 new minigames that were previously unused in the original game. In a later update, Replanted added "Survival: Endless" levels to all five areas of the game instead of just the daytime pool. == Development == The existence of a Plants vs. Zombies remaster was revealed in an interview with Janet Robin from The String Revolution, who they did a vinyl collaboration with the franchise in 2025 with Iam8bit. Janet stated that EA commissioned them to record an acoustic composition of the track "Crazy Dave" to be used for an "anniversary edition" of the game. The song would be additionally be a tribute to the song "Bad Guy", which artist Billie Eilish has stated to be somewhat similar to the track. Plants vs. Zombies Replanted was officially announced in a Nintendo Direct presentation in late July 2025. As an incentive, people who pre-ordered the game are given an in-game retro-styled skin of the Peashooter. Replanted was showcased at PAX West on August 25, 2025. A dev diary for Plants vs. Zombies: Replanted was uploaded to YouTube on October 17, 2025. The video features Nick Reinhart, Jake Neri, and Matt Townsend. A developer panel for the game was available during TwitchCon 2025. == Release == Plants vs. Zombies: Replanted was released for Nintendo Switch, Nintendo Switch 2, PlayStation 4, PlayStation 5, Xbox One, Xbox Series X and Series S, and personal computers on October 23, 2025. It was leaked onto the internet on October 17, 2025. Players discovered multiple software bugs, and multiple assets alleged to be upscaled by generative artificial intelligence were found, leading to backlash. Numerous bugs were fixed in a day-one patch on October 23, 2025. == Reception == === Critical response === The versions of Plants vs. Zombies: Replanted for Windows, PlayStation 5, and Nintendo Switch 2 received "generally favorable" reviews from critics, according to review aggregator website Metacritic, while the Xbox Series X version received "mixed or average" reviews. According to OpenCritic, 57% of critics recommended it. IGN's Alessandro Fillari called it "a good way to get re-acquainted with one of the quirkiest puzzle-strategy games of the 2000s", while acknowledging its questionable decisions. Shacknews' David Craddock said it was his favorite version of Plants vs. Zombies, stating, "it packs everything fans loved about the original game, plus lots more" while justifying its US$20 price. The Verge described Replanted as "a time capsule from a simpler, happier time". Kyle Hilliard from Game Informer praised its faithfulness, complimenting the new animations and character designs that did not alter its memorability. Noah Hunter for Final Weapon described the remake as solid, though criticized the lack of certain features and containing bugs that gate it from being excellent. Ben Lyons from Gamereactor stated Replanted is the same as the original overall, despite believing the £18 price is not justified. === Original developers === Rich Werner, the original game's character designer, claims that some concept art contained in the game, speculated to be for Plants vs. Zombies: Garden Warfare (2014), did not originate from the original's development. Werner also stated that concept art for the Disco Zombie is fabricated; the design for the Disco Zombie was created after the estate of Michael Jackson requested the original Dancing Zombie, who resembles Michael Jackson from his Thriller music video, be removed from the game. On October 19, 2026, composer Laura Shigihara expressed her dissatisfaction with the lack of dynamic music in the game. Dynamic music would later be implemented in a later patch. In an interview featuring Rich Werner and user interface designer Matt Holmberg on April 29, 2026, Werner revealed that he and Shigihara were contacted by EA to make a music video to market Replanted. However, after the game was leaked, Werner's response on social media led EA to cancel the collaboration.
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Coupled pattern learner

Coupled Pattern Learner (CPL) is a machine learning algorithm which couples the semi-supervised learning of categories and relations to forestall the problem of semantic drift associated with boot-strap learning methods. == Coupled Pattern Learner == Semi-supervised learning approaches using a small number of labeled examples with many unlabeled examples are usually unreliable as they produce an internally consistent, but incorrect set of extractions. CPL solves this problem by simultaneously learning classifiers for many different categories and relations in the presence of an ontology defining constraints that couple the training of these classifiers. It was introduced by Andrew Carlson, Justin Betteridge, Estevam R. Hruschka Jr. and Tom M. Mitchell in 2009. == CPL overview == CPL is an approach to semi-supervised learning that yields more accurate results by coupling the training of many information extractors. Basic idea behind CPL is that semi-supervised training of a single type of extractor such as ‘coach’ is much more difficult than simultaneously training many extractors that cover a variety of inter-related entity and relation types. Using prior knowledge about the relationships between these different entities and relations CPL makes unlabeled data as a useful constraint during training. For e.g., ‘coach(x)’ implies ‘person(x)’ and ‘not sport(x)’. == CPL description == === Coupling of predicates === CPL primarily relies on the notion of coupling the learning of multiple functions so as to constrain the semi-supervised learning problem. CPL constrains the learned function in two ways. Sharing among same-arity predicates according to logical relations Relation argument type-checking === Sharing among same-arity predicates === Each predicate P in the ontology has a list of other same-arity predicates with which P is mutually exclusive. If A is mutually exclusive with predicate B, A’s positive instances and patterns become negative instances and negative patterns for B. For example, if ‘city’, having an instance ‘Boston’ and a pattern ‘mayor of arg1’, is mutually exclusive with ‘scientist’, then ‘Boston’ and ‘mayor of arg1’ will become a negative instance and a negative pattern respectively for ‘scientist.’ Further, Some categories are declared to be a subset of another category. For e.g., ‘athlete’ is a subset of ‘person’. === Relation argument type-checking === This is a type checking information used to couple the learning of relations and categories. For example, the arguments of the ‘ceoOf’ relation are declared to be of the categories ‘person’ and ‘company’. CPL does not promote a pair of noun phrases as an instance of a relation unless the two noun phrases are classified as belonging to the correct argument types. === Algorithm description === Following is a quick summary of the CPL algorithm. Input: An ontology O, and a text corpus C Output: Trusted instances/patterns for each predicate for i=1,2,...,∞ do foreach predicate p in O do EXTRACT candidate instances/contextual patterns using recently promoted patterns/instances; FILTER candidates that violate coupling; RANK candidate instances/patterns; PROMOTE top candidates; end end ==== Inputs ==== A large corpus of Part-Of-Speech tagged sentences and an initial ontology with predefined categories, relations, mutually exclusive relationships between same-arity predicates, subset relationships between some categories, seed instances for all predicates, and seed patterns for the categories. ==== Candidate extraction ==== CPL finds new candidate instances by using newly promoted patterns to extract the noun phrases that co-occur with those patterns in the text corpus. CPL extracts, Category Instances Category Patterns Relation Instances Relation Patterns ==== Candidate filtering ==== Candidate instances and patterns are filtered to maintain high precision, and to avoid extremely specific patterns. An instance is only considered for assessment if it co-occurs with at least two promoted patterns in the text corpus, and if its co-occurrence count with all promoted patterns is at least three times greater than its co-occurrence count with negative patterns. ==== Candidate ranking ==== CPL ranks candidate instances using the number of promoted patterns that they co-occur with so that candidates that occur with more patterns are ranked higher. Patterns are ranked using an estimate of the precision of each pattern. ==== Candidate promotion ==== CPL ranks the candidates according to their assessment scores and promotes at most 100 instances and 5 patterns for each predicate. Instances and patterns are only promoted if they co-occur with at least two promoted patterns or instances, respectively. == Meta-Bootstrap Learner == Meta-Bootstrap Learner (MBL) was also proposed by the authors of CPL. Meta-Bootstrap learner couples the training of multiple extraction techniques with a multi-view constraint, which requires the extractors to agree. It makes addition of coupling constraints on top of existing extraction algorithms, while treating them as black boxes, feasible. MBL assumes that the errors made by different extraction techniques are independent. Following is a quick summary of MBL. Input: An ontology O, a set of extractors ε Output: Trusted instances for each predicate for i=1,2,...,∞ do foreach predicate p in O do foreach extractor e in ε do Extract new candidates for p using e with recently promoted instances; end FILTER candidates that violate mutual-exclusion or type-checking constraints; PROMOTE candidates that were extracted by all extractors; end end Subordinate algorithms used with MBL do not promote any instance on their own, they report the evidence about each candidate to MBL and MBL is responsible for promoting instances. == Applications == In their paper authors have presented results showing the potential of CPL to contribute new facts to existing repository of semantic knowledge, Freebase
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Transhuman Space

Transhuman Space (THS) is a role-playing game by David Pulver, published by Steve Jackson Games as part of the "Powered by GURPS" (Generic Universal Role-Playing System) line. Set in the year 2100, humanity has begun to colonize the Solar System. The pursuit of transhumanism is now in full swing, as more and more people reach fully posthuman states. In 2002, the Transhuman Space adventure "Orbital Decay" received an Origins Award nomination for Best Role-Playing Game Adventure. Transhuman Space won the 2003 Grog d'Or Award for Best Role-playing Game, Game Line or RPG Setting. == Setting == The game assumes that no cataclysm — natural or human-induced — swept Earth in the 21st century. Instead, constant developments in information technology, genetic engineering, nanotechnology and nuclear physics generally improved condition of the average human life. Plagues of the 20th century (like cancer or AIDS) have been suppressed, the ozone layer is being restored and Earth's ecosystems are recovering (although thermal emission by fusion power plants poses an environmental threat—albeit a much lesser one than previous sources of energy). Thanks to modern medicine humans live biblical timespans surrounded by various artificially intelligent helper applications and robots (cybershells), sensory experience broadcasts (future TV) and cyberspace telepresence. Thanks to cheap and clean fusion energy humanity has power to fuel all these wonders, restore and transform its home planet and finally settle on other heavenly bodies. Human genetic engineering has advanced to the point that anyone—single individuals, same-sex couples, groups of three or more—can reproduce. The embryos can be allowed to be developed naturally, or they can undergo three levels of tinkering: 1. Genefixing, which corrects defects; 2. Upgrades, which boost natural abilities (Ishtar Upgrades are slightly more attractive than usual, Metanoia Upgrades are more intelligent, etc.); and... 3. Full transition to parahuman status (Nyx Parahumans only need a few hours of sleep per week, Aquamorphs can live underwater, etc.) Another type of human genetic engineering, far more controversial, is the creation of bioroids, fully sentient slave races. People can "upload" by recording the simulation of their brains on computer disks. The emulated individual then becomes a ghost, an infomorph very easily confused with "sapient artificial intelligence". However, this technology has several problems as the solely available "brainpeeling" technique is fatal to the original biological lifeform being simulated, has a significant failure rate and the philosophical questions regarding personal identity remain equivocal. Any infomorph, regardless of its origin, can be plugged into a "cybershell" (robotic or cybernetic body), or a biological body, or "bioshell". Or, the individual can illegally make multiple "xoxes", or copies of themselves, and scatter them throughout the system, exponentially increasing the odds that at least one of them will live for centuries more, if not forever. This is also a time of space colonization. First, humanity (specifically China, followed by the United States and others) colonized Mars in a fashion resembling that outlined in the Mars Direct project. The Moon, Lagrangian points, inner planets and asteroids soon followed. In the late 21st century even some of Saturn's moons have been settled as a base for that planet's Helium-3 scooping operations. Transhuman Space's setting is neither utopia nor dystopia, however: several problems have arisen from these otherwise beneficial developments. The generation gap has become a chasm as lifespans increase. No longer do the elite fear death, and no longer can the young hope to replace them. While it seemed that outworld colonies would offer accommodation and work for those young ones, they are being replaced by genetically tailored bioroids and AI-powered cybershells. The concept of humanity is no longer clear in a world where even some animals speak of their rights and the dead haunt both cyberspace and reality (in form of infomorph-controlled bioshells or cybershells). And the wonders of high science are not universally shared — some countries merely struggle with informatization while others suffer from nanoplagues, defective drugs, implants and software tested on their populace. In some poor countries high-tech tyrants oppress their backward people. And in outer space all sort of modern crime thrives, barely suppressed by military forces. == Publication history == After the initial set of GURPS books that were published using the GURPS Lite, later publications such as Transhuman Space by David Pulver were labelled simply "Powered by GURPS" without using the name "GURPS" in the book title. Transhuman Space received a significant amount of supporting publications, and was the largest original background setting that Steve Jackson Games produced in 15 years. Shannon Appelcline noted that by its inclusion of posthuman characters, the book began to show the limits of the GURPS system as it was, which is something that Pulver would address soon thereafter. Steve Jackson Games has not updated the core book (GURPS Transhuman Space) to 4th edition, although the supplement Transhuman Space: Changing Times provides a path for migrating to 4th edition. It has produced several 4th edition supplements for the setting: Transhuman Space: Bioroid Bazaar, Transhuman Space: Cities on the Edge, Transhuman Space: Martial Arts 2100, Transhuman Space: Personnel Files 2-5, Transhuman Space: Shell-Tech, GURPS Spaceships 8: Transhuman Spacecraft, Transhuman Space: Transhuman Mysteries, and Transhuman Space: Wings of the Rising Sun. == Reception == In a review of Transhuman Space in Black Gate, William Stoddard said "Transhuman Space was a richly detailed setting; if it had imperfections, it had enough depth to make up for them. I think it has the potential to become a classic in its field. Perhaps a campaign set in its default start year of 2100 could leave the early twenty-first century blurry enough to avoid obvious incongruities." == Reviews == Review in Vol. 20, No. 1 of Prometheus, the journal of the Libertarian Futurist Society.
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Revelation Space series

The Revelation Space series is a book series created by Alastair Reynolds. The fictional universe is used as the setting for a number of his novels and stories. Its fictional history follows the human species through various conflicts from the relatively near future (roughly 2200) to approximately 40,000 AD (all the novels to date are set between 2427 and 2858, although certain stories extend beyond this period). It takes its name from Revelation Space (2000), which was the first published novel set in the universe. == Universe == The Revelation Space universe is a fictional universe set in a future version of our world, with the addition of a number of extraterrestrial species and advanced technologies that are not necessarily grounded in current science. It is, nonetheless, somewhat "harder" than most examples of space opera, relying to a considerable extent on science Reynolds believes to be possible; in particular, faster-than-light travel is largely absent. Reynolds has said he prefers to keep the science in his fiction plausible, but he will adopt science he believes to be impossible when it is necessary for the story. The name "Revelation Space universe" has been used by Alastair Reynolds in both the introductory text in the collections Diamond Dogs, Turquoise Days and Galactic North, and also on several editions of the novels set in the universe. He considered calling it the "Exordium universe" after a key plot device, but found that the name was already in use. While a great deal of science fiction reflects either very optimistic or dystopian visions of the human future, the Revelation Space universe is notable in that human societies have not developed to either positive or negative extremes. Instead, despite their dramatically advanced technology, they are similar to those of today in terms of their moral ambiguity and mixture of cruelty and decency, corruption and opportunity. The Revelation Space universe contains elements of Lovecraftian horror, with one posthuman entity stating explicitly that some things in the universe are fundamentally beyond human or transhuman understanding. Nevertheless, the main storyline is essentially optimistic, with humans continuing to survive even in a universe that seems fundamentally hostile to intelligent life. The name "Revelation Space" appears in the novel of the same name during Philip Lascaille's account of his visit to Lascaille's Shroud, an anomalous region of the local universe. Lascaille says that "the key" to something momentous "was explained to me [. . .] while I was in Revelation Space." === Chronology === The chronology of the Revelation Space universe extends to roughly one billion years into the past, when the "Dawn War" — a galaxy-spanning conflict over the availability of various natural resources — resulted in almost all sentient life in the galaxy being wiped out. One race of survivors, later termed the Inhibitors, having converted itself to machine form, predicted that the impending Andromeda–Milky Way collision, roughly 3 billion years in our future, may severely damage the capacity of either galaxy to support life. Consequently, they planned to adjust the positions of stars in order to limit the damage the collision would cause. Also central to the Inhibitor project was the eradication of all species above a certain technological level until the crisis was over, as they believed no organic species would be capable of co-operating on such a large-scale project (an in-universe solution to the Fermi paradox). Whilst they were relatively successful, certain advanced species were able to hide from Inhibitor forces, or even fight back. In human history, during the 21st and 22nd centuries, numerous wars occurred, and a flotilla of generation ships was deployed to colonise a planet orbiting the star 61 Cygni (which becomes a major segment of the plot of Chasm City). The flotilla later reached a planet termed Sky's Edge, which was to be embroiled in war until human civilisation there was eradicated. Meanwhile, in the Solar System in 2190, a faction known as the Conjoiners emerged as a result of increased experimentation with neural implants. In response, the Coalition for Neural Purity was formed, opposed to the Conjoiners. Nevil Clavain, one of the series's primary protagonists, fought on the side of the Coalition in the ensuing war, but defected later on after being betrayed. Clavain, and the Conjoiners, succeeded in escaping the Solar System and left for surrounding stars. For the next few centuries, the so-called Belle Epoque, humanity enjoyed a period of relative peace and prosperity, with several planets being colonised. The most successful planet of all was Yellowstone, a planet orbiting the star Epsilon Eridani, site of the Glitter Band / Rust Belt and Chasm City. Technologies developed included the Conjoiner Drive, a gift from the Conjoiners (who resumed contact with humanity at an unknown time), advanced nanotechnology, and numerous other devices. With the exception of an attempted takeover of the Glitter Band, no major incidents affected humanity during this time. The Belle Epoque was terminated by the advent of the Melding Plague in 2510, a nanotechnological virus that destroyed all other nanotechnology it came into contact with. Only the Conjoiners were unaffected by this disaster, which devastated the civilisation around Yellowstone. War between the Conjoiners and the Demarchists, a rival faction, erupted as a result of the plague. Meanwhile, activities around a far-flung human colony on the planet Resurgam, orbiting the star Delta Pavonis, inadvertently attracted the attention of the Inhibitors. The Conjoiners, also made aware of this event, sent Clavain to recover the exceedingly powerful "Cache Weapons" from this system (said weapons having been stolen from the Conjoiners centuries before) so that they could be used to fend off the Inhibitors while the Conjoiners escaped. Clavain instead defected from the Conjoiners, intending to use the weapons to protect all of humanity. Skade, another Conjoiner, was sent to stop him and recover the weapons. They fought around the Resurgam system, with Clavain and his allies eventually obtaining the weapons. Clavain's ally Remontoire agreed to seek out alien assistance from the Hades Matrix, a nearby alien computer disguised as a neutron star, whilst Clavain sheltered refugees from Resurgam on another planet, later termed Ararat. Remontoire returned in 2675, only a few days after Clavain's death at the hands of Skade, who had arrived with him. Remontoire and his allies were now at war with the Inhibitors, assisted by alien technology obtained from Hades. Even so, it was realised that the humans would not last indefinitely, and Clavain's people, now led by Scorpio, decided to seek out the mysterious "Shadows": a race believed to be near a moon called Hela, site of a theocracy. Aura, daughter of Ana Khouri (an ally of Remontoire) infiltrated the theocracy under the pseudonym Rashmika Els. After considerable conflict, Scorpio and Aura realised that contacting the Shadows was inadvisable. With the later assistance of the Conjoiner known as Glass, and of Clavain's estranged brother Warren, Scorpio and Aura (now going by the name Lady Arek) instead succeeded in contacting the Nestbuilders, an alien race who provided them with weapons capable of defeating the Inhibitors. As such, the Inhibitors were effectively eradicated from human space, with buffer zones and frontiers established to keep them at bay. Humanity then enjoyed a second, 400-year-long golden age. After this, however, came the Greenfly outbreak, in which human civilisation was destroyed by a rogue terraforming system of human origin that destroyed planets and converted them to millions of orbiting, vegetation-filled habitats. The Greenfly began to subsume most of human space, with all efforts to stop them failing, due to the Greenfly having assimilated aspects of both the Melding Plague and Inhibitor technology. The storyline of the Revelation Space universe thus far concludes with humanity leaving the Milky Way galaxy in an attempt to set up a new civilisation elsewhere. == Books and stories set in the universe == All short stories and novellas in this universe to date are collected in Galactic North and Diamond Dogs, Turquoise Days, with the exception of "Monkey Suit", "The Last Log of the Lachrimosa", "Night Passage", "Open and Shut", and "Plague Music". === The Inhibitor Sequence === Revelation Space. London: Gollancz, 2000. ISBN 978-0-575-06875-9. Redemption Ark. London: Gollancz, 2002. ISBN 978-0-575-06879-7. Absolution Gap. London: Gollancz, 2003. ISBN 978-0-575-07434-7. Inhibitor Phase. London: Gollancz, 2021. ISBN 978-0-575-09075-0. === Prefect Dreyfus Emergencies === The Prefect. London: Gollancz, 2007, ISBN 978-0-575-07716-4. (Re-released as Aurora Rising in 2017, ISBN 978-1-473-22336-3) Elysium Fire. London: Gollancz, 2018, ISBN 978-0-575-09059-0.
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