A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media. == Uses == Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement. Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff. Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends. Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user. Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms. == History == Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions. With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular. Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. == Legal regulation == Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns. In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots. The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior. == Detection == The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are: cartoon figures as user pictures sometimes also random real user pictures are captured (identity fraud) reposting rate temporal patterns sentiment expression followers-to-friends ratio length of user names variability in (re)posted messages engagement rate (like/followers rate) analysis of the time series of social media posts Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further a
Lawbot
Lawbots are a broad class of customer-facing legal AI applications that are used to automate specific legal tasks, such as document automation and legal research. The terms robot lawyer and lawyer bot are used as synonyms to lawbot. A robot lawyer or a robo-lawyer refers to a legal AI application that can perform tasks that are typically done by paralegals or young associates at law firms. However, there is some debate on the correctness of the term. Some commentators say that legal AI is technically speaking neither a lawyer nor a robot and should not be referred to as such. Other commentators believe that the term can be misleading and note that the robot lawyer of the future will not be one all-encompassing application but a collection of specialized bots for various tasks. Lawbots use various artificial intelligence techniques or other intelligent systems to limit humans' direct ongoing involvement in certain steps of a legal matter. The user interfaces on lawbots vary from smart searches and step-by-step forms to chatbots. Consumer and enterprise-facing lawbot solutions often do not require direct supervision from a legal professional. Depending on the task, some client-facing solutions used at law firms operate under an attorney supervision. == Levels of autonomy == The following levels of autonomy (LoA) are suggested for automated AI legal reasoning: Level 0 (LoA0): No automation for AI legal reasoning Level 1 (LoA1): Simple assistance automation Level 2 (LoA2): Advanced assistance automation Level 3 (LoA3): Semi-autonomous automation Level 4 (LoA4): Domain automation Level 5 (LoA5): Fully-autonomous automation Level 6 (LoA6): Superhuman automation == Examples == Some legal AI solutions are developed and marketed directly to the customers or consumers, whereas other applications are tools for the attorneys at law firms. There are already hundreds of legal AI solutions that operate in multitude of ways varying in sophistication and dependence on scripted algorithms. One notable legal technology chatbot application is DoNotPay. It had started off as an app for contesting parking tickets, but has since expanded to include features that help users with many different types of legal issues, ranging from consumer protection to immigration rights and other social issues. == Impact on the legal industry == In the 2016 report, Deloitte estimated that more than 110,000 law jobs in just the United Kingdom alone could disappear within the next twenty years due to automation. This change could result in the creation of more highly skilled jobs and in the reduction of paralegal and temporary positions. Deloitte's report asserts that "there is significant potential for high-skilled roles that involve repetitive processes to be automated by smart and self-learning algorithms". According to Lawyers to Engage, between 22% of a lawyer’s work and 35% of a legal assistant’s work can be automated in the US. Top law schools like Harvard have already begun to integrate Artificial Intelligence into the curriculum. Legal tech start-up companies have begun developing applications that assist law firms with completing low-risk legal processes. These applications can enable lawyers to focus on more work that requires their specific expertise. The automation of processes like contract reviewing, enforcement of negotiations (smart contracts) and client intake (expert systems) allows law firms to streamline their procedures and improve efficiency. In addition, automation benefits small-to-medium law firms that do not have the resources to utilize junior talent on such routine tasks. The increase of law firms utilizing automated applications could result into legal tech becoming a necessity in the industry. Digital Reason CEO, Tim Estes, stated that those who refuse the opportunity to integrate AI in their workflow are “most at risk.” In 2018, Forbes reported a 713% increase in investments in legal tech. This rapid growth is reflective of law firms beginning to “cede business to… new model legal providers… that meld technological, business and legal expertise.” == Access to law and justice == It has been widely estimated for at least the last generation that all the programs and resources devoted to ensuring access to justice address only 20% of the civil legal needs of low-income people in the United States. Drawing on this experience, in late 2011, the U.S. government-funded Legal Services Corporation decided to convene a summit of leaders to explore how best to use technology in the access-to-justice community. The group adopted a mission for The Summit on the Use of Technology to Expand Access to Justice (Summit) consistent with the magnitude of the challenge: "to explore the potential of technology to move the United States toward providing some form of effective assistance to 100% of persons otherwise unable to afford an attorney for dealing with essential civil legal needs". In April 2017, joined by Microsoft and Pro Bono Net, the Legal Services Corporation (LSC) announced a pilot program to develop online, statewide legal portals to direct individuals with civil legal needs to the most appropriate forms of assistance. == Technological limitations == Current research in subjects such as computational privacy, explainable machine learning, Bayesian deep learning, knowledge-intensive machine learning, and transfer learning reveals that we do not yet have the technology to enable Level 4 to 6 AI lawbots. In 2023, OpenLaw began developing a model called Law Bot, which interacts in a conversational way as an attorney. The dialogue format makes it possible for Law Bot to answer follow-up questions, challenge incorrect premises, and reject inappropriate requests. Currently, they try to ensure it is in full compliance with all laws and regulations while conducting further beta testing before releasing it to the general public.
Interim Measures for the Management of Generative AI Services
The Interim Measures for the Management of Generative AI Services (Chinese: 生成式人工智能服务管理暂行办法; pinyin: Shēngchéng shì réngōng zhìnéng fúwù guǎnlǐ zànxíng bànfǎ) are a set of regulations governing public-facing generative artificial intelligence services in China. Issued on 10 July 2023 and effective from 15 August 2023, they were China's first binding regulation specifically targeting generative AI. They have been described as among the earliest such regulations adopted by any country. The measures were jointly issued by the Cyberspace Administration of China (CAC) and six other national bodies: the National Development and Reform Commission, the Ministry of Education, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the National Radio and Television Administration. Among the measures' most prominent requirements is that generative AI services must uphold Core Socialist Values and must not generate content that could subvert state power, harm national security, or undermine social stability. The measures also require providers of public-facing generative AI services to undergo security assessments and register their algorithms with the CAC. As of December 2025, 748 generative AI services had completed the filing process at the national level. == Background == The Interim Measures build on two earlier sets of regulations targeting specific algorithm applications. The Administrative Provisions on Algorithm Recommendation for Internet Information Services, effective from March 2022, established China's algorithm registry and required providers of recommendation algorithms with "public opinion properties or social mobilization capabilities" to file with the CAC and undergo security assessments. The Administrative Provisions on Deep Synthesis of Internet Information Services, effective from January 2023, extended similar requirements to algorithms used for generating synthetic media such as deepfakes. In April 2023, the CAC released a draft of the generative AI regulation for public comment. The draft included several requirements that attracted attention, including that generated content should "embody Core Socialist Values" and that training data should be "true and accurate". The public consultation period ran until May 2023. The final version, published in July 2023, was substantially revised from the draft. According to an analysis by the Future of Privacy Forum, changes appeared to reflect feedback from industry stakeholders including Baidu, Xiaomi, SenseTime, and others, as well as input from government-affiliated research institutes. The final measures adopted a more permissive tone, with the CAC describing its approach as "inclusive and prudent" (包容审慎) and emphasising "classified and graded" (分类分级) supervision. == Scope == The measures apply to services that use generative AI technology to provide text, images, audio, video, or other content to the public within mainland China (Article 2). They do not apply to organisations that develop or use generative AI internally without offering services to the domestic public, such as industry associations, enterprises, and research institutions. Overseas providers whose services are accessible to users in China are also subject to the measures. == Key provisions == === Content requirements === Article 4 sets out the core content obligations. Providers and users of generative AI services must uphold the Core Socialist Values. The measures prohibit generating content that incites subversion of national sovereignty or the socialist system, endangers national security or the nation's image, incites separatism, promotes terrorism or extremism, promotes ethnic hatred or discrimination, or contains violence, obscenity, or false information prohibited by law. These content prohibitions largely mirror those in Article 12 of the Cybersecurity Law and in prior regulations governing online content. Article 4 also requires that models be designed and trained to avoid discrimination, that services respect intellectual property rights, and that providers take effective measures to improve the transparency and accuracy of generated content. === Training data and labelling === Article 7 requires providers to ensure that training data is of high quality and legitimately sourced, and that it does not infringe upon intellectual property rights. Where personal information is used, consent must be obtained. The final version of this provision removed language from the draft that would have held providers responsible for the "legitimacy" of all pretraining data, replacing it with a requirement to "employ effective measures to improve the quality of training data". Article 8 requires providers to establish labelling rules for training data and to conduct quality assessments of data annotations. Article 12 requires that generated images, videos, and other synthetic content be labelled as AI-generated. === User rights and privacy === Article 11 requires providers to protect user privacy, to minimise the collection and retention of personal data, and to refrain from unlawfully sharing user information. Users have the right to request review, correction, or deletion of their personal information. Article 10 requires providers to take measures to prevent excessive dependence on or addiction to generative AI services by minors. === Security assessment and algorithm filing === Article 17 requires that providers of generative AI services with "public opinion properties or the capacity for social mobilization" (具有舆论属性或者社会动员能力) carry out security assessments and complete algorithm filing procedures in accordance with the Administrative Provisions on Algorithm Recommendation for Internet Information Services. == Implementation == === Algorithm filing process === In practice, the filing requirements under the Interim Measures have developed into a two-tier process. The first tier is the standard algorithm filing (算法备案) under the pre-existing Algorithm Recommendation Provisions, which involves submitting information about an algorithm's design, purpose, and data sources to the CAC. This process is primarily a registration mechanism. For public-facing generative AI products, there is an additional, more rigorous process commonly referred to as the "large model filing" (大模型备案). This involves submitting a security self-assessment report, data annotation rules, a keyword blocking list, and evaluation test question sets. The process includes technical testing at the provincial level, followed by review at the national CAC level. The algorithm filing targets specific algorithms, while the large model filing evaluates the broader system architecture, training data, model parameters, and potential social impact. The CAC publishes lists of generative AI services that have successfully completed the filing process. The first such list was published on 2 April 2024. According to the CAC's year-end announcements, 302 generative AI services had completed national-level filing by the end of 2024 (of which 238 were new that year), alongside 105 applications that completed local-level registration. By the end of 2025, the cumulative total had risen to 748 national-level filings and 435 local-level registrations. === Content compliance and testing === According to the Carnegie Endowment, the CAC has conducted compliance audits of generative AI services with a particular focus on ensuring appropriate responses to queries about politically sensitive topics. The large model filing process requires providers to pass both provincial-level and national-level technical testing before their services can be made available to the public. On 1 March 2024, the National Technical Committee 260 on Cybersecurity (TC260) published TC260-003, the Basic Security Requirements for Generative AI Services (生成式人工智能服务安全基本要求), a technical standard that provides detailed guidance on the security assessments required under the Interim Measures. The standard covers requirements for training data safety, model security, and content safety evaluation, and is used as a reference for the filing process. == Analysis == === Relationship to broader Chinese internet regulation === The content requirements in the Interim Measures extend China's existing framework for online information control to generative AI. Legal scholars have noted that the "Core Socialist Values" provision and the specific content prohibitions are consistent with longstanding requirements imposed on internet platforms under the Cybersecurity Law and related regulations. The Asia Society Policy Institute has described the Chinese government's highest regulatory priority in this area as retaining control of information, noting that content-related obligations receive stricter enforcement than other provisions. === Nature of the filing system === The character of the filing system has been debated by scholars. Angela Huyue Zh
Shadowrun
Shadowrun is a science fantasy tabletop role-playing game set in an alternate future in which cybernetics, magic and fantasy creatures co-exist. It combines genres of cyberpunk, urban fantasy, and crime, with occasional elements of conspiracy, horror, and detective fiction. From its inception in 1989, it has spawned a franchise that includes a series of novels, a collectible card game, two miniature-based tabletop wargames, and multiple video games. The title is taken from the game's main premise – a near-future world damaged by a massive magical event, where industrial espionage and corporate warfare runs rampant. A shadowrun – a successful data theft or physical break-in at a rival corporation or organization – is one of the main tools employed by both corporate rivals and underworld figures. Deckers (futuristic hackers) can tap into an immersive, three-dimensional cyberspace on such missions as they seek access, physical or remote, to the power structures of rival groups. They are opposed by rival deckers and lethal, potentially brain-destroying artificial intelligences called "Intrusion Countermeasures" (IC), while they are protected by street fighters and/or mercenaries, often with cyborg implants (called cyberware), magicians, and other exotic figures. Magic has also returned to the world after a series of plagues; dragons who can take human form have returned as well, and are commonly found in high positions of corporate power. == Publication history == Shadowrun was developed and published by FASA from 1989 until early 2001, when the company closed and Shadowrun was transferred to WizKids, a company founded by former FASA employees. Two years before its closure, FASA sold its videogame branch, FASA Interactive, to Microsoft corporation, keeping rights to publishing novels and pen and paper RPGs. Since then, digital rights to Shadowrun IP have belonged to Microsoft. WizKids licensed the RPG rights to Fantasy Productions, who were already publishing a German version, until WizKids was acquired by Topps in 2003. Catalyst Game Labs, a publishing imprint of InMediaRes Productions, licensed the rights from Topps to publish new products. WizKids itself produced an unsuccessful collectible action figure game based on the property, called Shadowrun Duels. A fifth edition of Shadowrun was announced in December 2012. A limited-edition softcover was sold at the Origins Game Fair in June 2013, and the PDF in July 2013. A hardcover was published in August 2013. Shadowrun Anarchy was published in October 2016 It is a simplified version of the ruleset which allows focus more on the narration than on the rules. The sixth edition, called Shadowrun, Sixth World, was announced on May 1, 2019 to coincide with the game's 30th anniversary, along with a new website at shadowrunsixthworld.com. The game was published on August 26, 2019. The mechanics for this new version are generally similar to those of fifth edition, with some rules reworked for what line developer Jason Hardy describes as streamlining. This new version also progressed the in-game year to 2080. Since 2004, Shadowrun Missions (SRM) has offered fans "living campaigns" that allow for persistent character advancement. SRM is broken down into seasons which are made up of up to 24 individual missions that can be played at home, with special missions available to play exclusively at conventions. Each SRM season develops an overarching plot focused on a specific city from the Shadowrun setting. Missions settings have included the divided city of Denver, the corporate city-state of Manhattan, the Seattle Metroplex city-state, the formerly walled-off wastelands of Chicago, and Neo-Tokyo. For Shadowrun, Sixth World missions returned to Seattle, with twenty-four missions set in 2081, right after Seattle declared independence from the UCAS. The current Shadowrun Missions setting is 2083 New Orleans. The Shadowrun role-playing game has spawned several properties, including Shadowrun: The Trading Card Game, eight video games, an action figure game (Shadowrun Duels), two magazines, an art book and more than 50 novels, starting with the Secrets of Power series which introduces some of the original characters of Shadowrun and provides an introduction to this fictional universe. In addition to the main rule book there have been over 100 published supplements including adventures and expansions to both the rules and the game settings. Catalyst Game Labs announced that 2013 would be "The Year of Shadowrun," and in addition to the release of Shadowrun fifth edition that it has collaborated with publishers on the following properties: Shadowrun: Crossfire, The Adventure Deck-building Game; Shadowrun: Sprawl Gangers, a tactical miniatures wargame; and Shadowrun: Hostile Takeover, a board game designed by Bryan C.P. Steele was planned for release in late 2014/early 2015. Catalyst had been in collaboration with Nordic Games and Cliffhanger Studios to create Shadowrun Chronicles: Boston Lockdown online RPG, however it was shuttered November 30, 2018, with the producers citing lack of funding and the end of the license terms for use of the IP. == Fictional universe == Shadowrun takes place several decades in the future (2050 in the first edition, currently 2088). The end of the Mesoamerican Long Count calendar ushered in the "Sixth World", with once-mythological beings (e.g. dragons) appearing and forms of magic suddenly emerging. Large numbers of humans have "Goblinized" into orks and trolls, while many human children are born as elves, dwarves, and even more exotic creatures. In North America, indigenous peoples discovered that their traditional ceremonies allow them to command powerful spirits, and rituals associated with a new Ghost Dance movement let them take control of much of the western U.S. and Canada, where they formed a federation of Native American Nations. Seattle remains under U.S. control by treaty as a city-state enclave, and most game materials are set there and assume campaigns will use it as their setting. In parallel with these magical developments, the setting's 21st century features technological and social developments associated with cyberpunk science fiction. Megacorporations control the lives of their employees and command their own armies; many of the largest have extraterritoriality, such as currently enjoyed by foreign heads of state. Technological advances make cyberware (mechanical replacement body parts) and bioware (augmented vat-grown body parts implanted in place of or in tandem with natural organs) common. The Computer Crash of 2029 led to the creation of the Matrix, a worldwide computer network that users interact with via direct neural interface. When conflicts arise, corporations, governments, organized crime syndicates, and even wealthy individuals subcontract their dirty work to specialists, who then perform "shadowruns" or missions undertaken by deniable assets without identities or those that wish to remain unknown. The most skilled of these specialists, called shadowrunners, have earned a reputation for getting the job done. They have developed a knack for staying alive, and prospering, in the world of Shadowrun. The Shadowrun world is cross-genre, incorporating elements of both cyberpunk and urban fantasy. Unlike in a purely cyberpunk game, in the Shadowrun world, magic exists and has "worked" since 2011. Among other things, this split humankind into subtypes, also known as metatypes/metahumans. Some of these metatypes take the form of common fantasy races. Likewise, some animals have turned into familiar monsters of past fantasy and lore and both monsters and human magicians have regained magical powers. By the second half of the 21st century, in the time the game is set, these events are accepted as commonplace. Man, machine, and magic exist in a world where the amazing is among the most common and technology has entered into every facet of human (and metahuman) life. === Races === Characters in Shadowrun can be humans, orks, trolls, elves, dwarves, as well as certain diverging subspecies (known as metavariants) such as gnomes, giants, dryads, etc. In the early days, when magic returned to the world, humans began to either change into, or give birth to, elf and dwarf infants, a phenomenon called Unexplained Genetic Expression (UGE). Later, some juvenile and adult humans "goblinized" into other races (mostly orks, but also some trolls). The term "metahuman" is used either to refer to humanity as a whole, including all races, or to refer specifically to non-human races, depending on context. The return of Halley's Comet brought even further variation in the form of changelings, who have variation atypical to their metatype or even species, such as electroreception. Two of the metahuman races, elves and orks, have fictional languages. Additionally, a virus known as the Human Meta-Human Vampiric Virus (HMHVV), with many variant strains, has been known to cause f
Emma Hart (computer scientist)
Professor Emma Hart, FRSE (born 1967) is an English computer scientist known for her work in artificial immune systems (AIS), evolutionary computation and optimisation. She is a professor of computational intelligence at Edinburgh Napier University, editor-in-chief of the Journal of Evolutionary Computation (MIT Press), and D. Coordinator of the Future & Emerging Technologies (FET) Proactive Initiative, Fundamentals of Collective Adaptive Systems. == Early life and education == Hart was born in Middlesbrough, England in 1967. In 1990 she graduated from the University of Oxford with a first class BA(Hons) in Chemistry. She then continued her studies at the University of Edinburgh, graduating with an MSc in Artificial Intelligence in 1994, followed by a PhD that explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimization and data classification problems. Her dissertation was titled Immunology as a metaphor for computational information processing: Fact or fiction?, and her doctoral advisor was Peter Ross. == Career == In 2000 Hart took a position as a lecturer at Edinburgh Napier University, and was promoted to a Reader, Professor, and in 2008 Chair in Natural Computation. She is now director of the Centre of Algorithms, Visualisation and Evolving Systems (CAVES) group in the School of Computing. She continues to research in the area of developing novel bio-inspired techniques for solving a range of real-world optimisation and classification problems, as well as exploring the fundamental properties of immune-inspired computing through modelling and simulation. She is also involved in editorial activity and currently occupies the position of Editor-in-Chief of the Journal of Evolutionary Computation (MIT Press). Her interests lie in the area of bio-inspired computing, in particular artificial immune systems (AIS). She also undertakes research in three main areas: optimisation, self-organising/self-adaptive systems, and artificial intelligence. Hart is D. Coordinator of Fundamentals of Collective Adaptive Systems (FoCAS), a Future and Emerging Technologies Proactive Initiative funded by the European Commission under FP7. == Selected works == === Conference talks === Hart, Emma. "Lifelong learning in optimization (video)". 28th European Conference on Operational Research. The Association of European Operational Research Societies. Hart, Emma (December 2021). "Self-assembling robots and the potential of artificial evolution". TED talk 2021. === Journal articles === "An immune system approach to scheduling in changing environments". E.Hart, P.Ross. 1999. Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation (2), 1559–1566. "Exploiting the analogy between immunology and sparse distributed memories: A system for clustering non-stationary data". E.Hart, P.Ross. 2002. 1st International Conference on Artificial Immune Systems. "Evolutionary scheduling: A review". E Hart, P Ross, D Corne. 2005. Genetic Programming and Evolvable Machines 6(2), 191–220. DOI: https://doi.org/10.1007/s10710-005-7580-7 "Application areas of AIS: The past, the present and the future". E.Hart, J.Timmis. 2008. Applied soft computing 8(1), 191–201. DOI: https://doi.org/10.1016/j.asoc.2006.12.004 "Structure versus function: a topological perspective on immune networks". E.Hart, H.Bersini, F.Santos. 2010. Natural computing 9(3), 603–624. DOI: https://doi.org/10.1007/s11047-009-9138-8 "On the life-long learning capabilities of a nelli: A hyper-heuristic optimisation system". E.Hart, K.Sim. 2014. International Conference on Parallel Problem Solving from Nature, 282–291. DOI: https://doi.org/10.1007/978-3-319-10762-2_28 "A hyper-heuristic ensemble method for static job-shop scheduling". E.Hart, K.Sim. 2016. Evolutionary computation 24(4), 609-635. DOI: https://dx.doi.org/10.1162/EVCO_a_00183 == Awards and recognition == 2016, Featured article on Lifelong Learning in Optimisation, IFORS newsletter 2016, "A Combined Generative and Selective Hyper-heuristic for the Vehicle Routing Problem" presented at GECCO 2016 (Denver, USA), ACM 2016, "A Hybrid Parameter Control Approach Applied to a Diversity-based Multi-objective Memetic Algorithm for Frequency Assignment Problems" presented at WCCI 2016 (Vancouver, Canada), IEEE 2017, Keynote Speaker, 2017 International Joint Conference on Computational Intelligence 2018, Bronze Award in International Human-Competitive Awards (Humies), International Conference on Genetic and Evolutionary Computation, Kyoto Japan 2018, Nomination for best paper award, GECCO 18, Kyoto, Japan 2022, Elected Fellow of the Royal Society of Edinburgh
Developmental robotics
Developmental robotics (DevRob), sometimes called epigenetic robotics, is a scientific field which aims at studying the developmental mechanisms, architectures and constraints that allow lifelong and open-ended learning of new skills and new knowledge in embodied machines. As in human children, learning is expected to be cumulative and of progressively increasing complexity, and to result from self-exploration of the world in combination with social interaction. The typical methodological approach consists in starting from theories of human and animal development elaborated in fields such as developmental psychology, neuroscience, developmental and evolutionary biology, and linguistics, then to formalize and implement them in robots, sometimes exploring extensions or variants of them. The experimentation of those models in robots allows researchers to confront them with reality, and as a consequence, developmental robotics also provides feedback and novel hypotheses on theories of human and animal development. Developmental robotics is related to but differs from evolutionary robotics (ER). ER uses populations of robots that evolve over time, whereas DevRob is interested in how the organization of a single robot's control system develops through experience, over time. DevRob is also related to work done in the domains of robotics and artificial life. == Background == Can a robot learn like a child? Can it learn a variety of new skills and new knowledge unspecified at design time and in a partially unknown and changing environment? How can it discover its body and its relationships with the physical and social environment? How can its cognitive capacities continuously develop without the intervention of an engineer once it is "out of the factory"? What can it learn through natural social interactions with humans? These are the questions at the center of developmental robotics. Alan Turing, as well as a number of other pioneers of cybernetics, already formulated those questions and the general approach in 1950, but it is only since the end of the 20th century that they began to be investigated systematically. Because the concept of adaptive intelligent machines is central to developmental robotics, it has relationships with fields such as artificial intelligence, machine learning, cognitive robotics or computational neuroscience. Yet, while it may reuse some of the techniques elaborated in these fields, it differs from them from many perspectives. It differs from classical artificial intelligence because it does not assume the capability of advanced symbolic reasoning and focuses on embodied and situated sensorimotor and social skills rather than on abstract symbolic problems. It differs from cognitive robotics because it focuses on the processes that allow the formation of cognitive capabilities rather than these capabilities themselves. It differs from computational neuroscience because it focuses on functional modeling of integrated architectures of development and learning. More generally, developmental robotics is uniquely characterized by the following three features: It targets task-independent architectures and learning mechanisms, i.e. the machine/robot has to be able to learn new tasks that are unknown by the engineer; It emphasizes open-ended development and lifelong learning, i.e. the capacity of an organism to acquire continuously novel skills. This should not be understood as a capacity for learning "anything" or even “everything”, but just that the set of skills that is acquired can be infinitely extended at least in some (not all) directions; The complexity of acquired knowledge and skills shall increase (and the increase be controlled) progressively. Developmental robotics emerged at the crossroads of several research communities including embodied artificial intelligence, enactive and dynamical systems cognitive science, connectionism. Starting from the essential idea that learning and development happen as the self-organized result of the dynamical interactions among brains, bodies and their physical and social environment, and trying to understand how this self-organization can be harnessed to provide task-independent lifelong learning of skills of increasing complexity, developmental robotics strongly interacts with fields such as developmental psychology, developmental and cognitive neuroscience, developmental biology (embryology), evolutionary biology, and cognitive linguistics. As many of the theories coming from these sciences are verbal and/or descriptive, this implies a crucial formalization and computational modeling activity in developmental robotics. These computational models are then not only used as ways to explore how to build more versatile and adaptive machines but also as a way to evaluate their coherence and possibly explore alternative explanations for understanding biological development. == Research directions == === Skill domains === Due to the general approach and methodology, developmental robotics projects typically focus on having robots develop the same types of skills as human infants. A first category that is important being investigated is the acquisition of sensorimotor skills. These include the discovery of one's own body, including its structure and dynamics such as hand-eye coordination, locomotion, and interaction with objects as well as tool use, with a particular focus on the discovery and learning of affordances. A second category of skills targeted by developmental robots are social and linguistic skills: the acquisition of simple social behavioural games such as turn-taking, coordinated interaction, lexicons, syntax and grammar, and the grounding of these linguistic skills into sensorimotor skills (sometimes referred as symbol grounding). In parallel, the acquisition of associated cognitive skills are being investigated such as the emergence of the self/non-self distinction, the development of attentional capabilities, of categorization systems and higher-level representations of affordances or social constructs, of the emergence of values, empathy, or theories of mind. === Mechanisms and constraints === The sensorimotor and social spaces in which humans and robot live are so large and complex that only a small part of potentially learnable skills can actually be explored and learnt within a life-time. Thus, mechanisms and constraints are necessary to guide developmental organisms in their development and control of the growth of complexity. There are several important families of these guiding mechanisms and constraints which are studied in developmental robotics, all inspired by human development: Motivational systems, generating internal reward signals that drive exploration and learning, which can be of two main types: extrinsic motivations push robots/organisms to maintain basic specific internal properties such as food and water level, physical integrity, or light (e.g. in phototropic systems); intrinsic motivations push robot to search for novelty, challenge, compression or learning progress per se, thus generating what is sometimes called curiosity-driven learning and exploration, or alternatively active learning and exploration; Social guidance: as humans learn a lot by interacting with their peers, developmental robotics investigates mechanisms that can allow robots to participate to human-like social interaction. By perceiving and interpreting social cues, this may allow robots both to learn from humans (through diverse means such as imitation, emulation, stimulus enhancement, demonstration, etc. ...) and to trigger natural human pedagogy. Thus, social acceptance of developmental robots is also investigated; Statistical inference biases and cumulative knowledge/skill reuse: biases characterizing both representations/encodings and inference mechanisms can typically allow considerable improvement of the efficiency of learning and are thus studied. Related to this, mechanisms allowing to infer new knowledge and acquire new skills by reusing previously learnt structures is also an essential field of study; The properties of embodiment, including geometry, materials, or innate motor primitives/synergies often encoded as dynamical systems, can considerably simplify the acquisition of sensorimotor or social skills, and is sometimes referred as morphological computation. The interaction of these constraints with other constraints is an important axis of investigation; Maturational constraints: In human infants, both the body and the neural system grow progressively, rather than being full-fledged already at birth. This implies, for example, that new degrees of freedom, as well as increases of the volume and resolution of available sensorimotor signals, may appear as learning and development unfold. Transposing these mechanisms in developmental robots, and understanding how it may hinder or on the contrary ease the acquisition of novel complex skills is a central questi
Clanker
Clanker is a derogatory term for robots and artificial intelligence (AI) software. The term has been used in Star Wars media, first appearing in the franchise's 2005 video game Star Wars: Republic Commando. In 2025, the term became widely used to express hatred or distaste for machines ranging from delivery robots to large language models. This trend has been attributed to anxiety around the negative societal effects of AI. == In science fiction == The term has been previously used in science fiction literature, first appearing in a 1958 article by William Tenn in which he uses it to describe robots from science fiction films like Metropolis. The Star Wars franchise began using the term as a slur against droids in the 2005 video game Star Wars: Republic Commando before being prominently used in the animated series Star Wars: The Clone Wars, which follows a galaxy-wide war between the Galactic Republic's clone troopers and the Confederacy of Independent Systems' battle droids. In Star Wars media, robots—more commonly known as droids—are routinely depicted as the subjects of discrimination. For example, in the original Star Wars film, C-3PO and R2-D2 are abducted by Jawas and sold to the family of Luke Skywalker. When visiting a cantina in Mos Eisley, both droids are refused service by the bartender, who remarks that "We don't serve their kind." In Star Wars lore, the term clanker had entered use by the time of the franchise's High Republic Era and became prominent during the Clone Wars, in which clone troopers regularly use the phrase against battle droids. == AI backlash == The growing popularity of the term clanker reflects an increase in direct contact between people and AI systems. On sidewalks, delivery robots impede mobility and cause safety issues. In digital spaces, cybersecurity experts have raised concerns about the rising number of bots online, which now make up a large portion of internet traffic. A 2025 report estimated that about one in five social media accounts are automated. The term is also a reaction to AI advocacy from industrialists like Elon Musk and Sam Altman, who have championed the integration of AI into nearly every aspect of modern life. This includes efforts by major companies and startups alike, such as Amazon's development of humanoid robots to replace human workers in service industries. Such initiatives have further fueled public skepticism, reinforcing the association of clanker with unease over automation and the displacement of human roles. A global survey conducted by the research firm Gartner in December 2023 found that 64% of customers would prefer companies to avoid using AI in customer service, with another 53% stating they would consider switching to a different company if they discovered AI was handling their service interactions. Another report by Ernst & Young, published in July 2025, found that 42% of employees across Europe are worried that the use of AI in the workplace may threaten their employment. Criticism has also been directed at the technology itself. Some of the backlash stems from concerns about the resource consumption of AI systems, their frequent reliance on copyrighted material without consent, and questions about the intentions of the corporations behind them. There are also concerns about the potential cognitive effects of relying heavily on AI. A study, authored by researchers at Microsoft and Carnegie Mellon University, warns that regular dependence on AI may leave users mentally unprepared for real-world problem solving, likening the effect to cognitive atrophy. In June 2025, United States Senator Ruben Gallego tweeted that his "new bill makes sure you don't have to talk to a clanker if you don't want to", referring to proposed legislation that would require call centers to disclose their use of automated customer service agents to callers in the United States and offer the option to switch to a human representative. == Analysis == Linguist Adam Aleksic has described clanker as an evolution of racial slurs that anthropomorphize robotic systems. Internet memes incorporating the term often reference historical discrimination against marginalized groups such as African Americans. Based on the work of linguist Geoffrey Nunberg, American news website Axios has argued that clanker is merely a derogatory word, rather than a slur, because it does not perpetuate social inequities. NPR has noted the irony that the word robot was coined by Karel Čapek for his 1920 science-fiction play R.U.R. as a similar criticism of industrialization forcing workers to become devoid of their humanity. Aleksic has observed that robot can be further traced to the Proto-Slavic noun orbъ, which means 'slave'. While other science fiction media include pejoratives for androids and robots, such as skinjob and toaster from the Blade Runner and Battlestar Galactica franchises, respectively, clanker is believed to have gained popularity because its usage is intuitive and flexible. Whereas AI slop describes low-quality output from artificial intelligence, clanker belittles the underlying computer systems.