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  Commonsense knowledge (artificial intelligence)

  In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as "Lemons are sour" or "Cows say moo", that all humans are expected to know. It is currently an unsolved problem in artificial general intelligence. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy. Commonsense knowledge can underpin a commonsense reasoning process, to attempt inferences such as "You might bake a cake because you want people to eat the cake." A natural language processing process can be attached to the commonsense knowledge base to allow the knowledge base to attempt to answer questions about the world. Common sense knowledge also helps to solve problems in the face of incomplete information. Using widely held beliefs about everyday objects, or common sense knowledge, AI systems make common sense assumptions or default assumptions about the unknown similar to the way people do. In an AI system or in English, this is expressed as "Normally P holds", "Usually P" or "Typically P so Assume P". For example, if we know the fact "Tweety is a bird", because we know the commonly held belief about birds, "typically birds fly," without knowing anything else about Tweety, we may reasonably assume the fact that "Tweety can fly." As more knowledge of the world is discovered or learned over time, the AI system can revise its assumptions about Tweety using a truth maintenance process. If we later learn that "Tweety is a penguin" then truth maintenance revises this assumption because we also know "penguins do not fly". == Commonsense reasoning == Commonsense reasoning simulates the human ability to use commonsense knowledge to make presumptions about the type and essence of ordinary situations they encounter every day, and to change their "minds" should new information come to light. This includes time, missing or incomplete information and cause and effect. The ability to explain cause and effect is an important aspect of explainable AI. Truth maintenance algorithms automatically provide an explanation facility because they create elaborate records of presumptions. Compared with humans, all existing computer programs that attempt human-level AI perform extremely poorly on modern "commonsense reasoning" benchmark tests such as the Winograd Schema Challenge. The problem of attaining human-level competency at "commonsense knowledge" tasks is considered to probably be "AI complete" (that is, solving it would require the ability to synthesize a fully human-level intelligence), although some oppose this notion and believe compassionate intelligence is also required for human-level AI. Common sense reasoning has been applied successfully in more limited domains such as natural language processing and automated diagnosis or analysis. == Commonsense knowledge base construction == Compiling comprehensive knowledge bases of commonsense assertions (CSKBs) is a long-standing challenge in AI research. From early expert-driven efforts like CYC and WordNet, significant advances were achieved via the crowdsourced OpenMind Commonsense project, which led to the crowdsourced ConceptNet KB. Several approaches have attempted to automate CSKB construction, most notably, via text mining (WebChild, Quasimodo, TransOMCS, Ascent), as well as harvesting these directly from pre-trained language models (AutoTOMIC). These resources are significantly larger than ConceptNet, though the automated construction mostly makes them of moderately lower quality. Challenges also remain on the representation of commonsense knowledge: Most CSKB projects follow a triple data model, which is not necessarily best suited for breaking more complex natural language assertions. A notable exception here is GenericsKB, which applies no further normalization to sentences, but retains them in full. == Applications == Around 2013, MIT researchers developed BullySpace, an extension of the commonsense knowledgebase ConceptNet, to catch taunting social media comments. BullySpace included over 200 semantic assertions based around stereotypes, to help the system infer that comments like "Put on a wig and lipstick and be who you really are" are more likely to be an insult if directed at a boy than a girl. ConceptNet has also been used by chatbots and by computers that compose original fiction. At Lawrence Livermore National Laboratory, common sense knowledge was used in an intelligent software agent to detect violations of a comprehensive nuclear test ban treaty. == Data == As an example, as of 2012 ConceptNet includes these 21 language-independent relations: IsA (An "RV" is a "vehicle" | X is an instance of a Y) UsedFor (a "cake tin" is used for "making cakes" | X is used for the purpose Y) HasA (A "rabbit" has a "tail" | X possesses Y element or feature) CapableOf (a "cook" is capable of "making baked goods" | X is capable of doing Y) Desires (a "child" desires "the aroma of baking" | X has a desire for Y) CreatedBy ("cake" is created by a "baker" | X is created by Y) PartOf (a "knife" is be part of a "knife set" | X is a part of Y) Causes ("Heat" causes "cooking"| X is what causes Y) LocatedNear (the "oven" is located near the "refrigerator" | X is located near Y) AtLocation (Somewhere a "Cook" can be at a "restaurant" | X is at the location of Y) DefinedAs (a "Cupcake" is defined as a "cake" that also has the qualities of being "small", "baked within a wrapper", and "containing only one area of frosting or icing" | X is defined as Y that also has the properties A, B & C) SymbolOf (a "heart" is a symbol of "affection" | X is a symbolic representation of Y) ReceivesAction ("cake" can receive the action of being "eaten" | X is capable of receiving action Y) HasPrerequisite ("baking" has the prerequisite of obtaining the "ingredients" | X cannot do Y unless A does B) MotivatedByGoal ("baking" is motivated by the goal of "consumption"/"eating" | X has the motivation of Y goal) CausesDesire ("baking" makesYou want to "follow recipe" | X causes the desire to do Y) MadeOf ("Cake" is made of "flour"/"eggs"/"sugar"/"oil"/etc | X is made of Y) HasFirstSubevent ("baking" has first subevent "make batter" | To do X the first thing that needs to be done is Y) HasSubevent ("eat" has subevent "swallow" | Doing X will lead to Y event following) HasLastSubevent ("sleeping" has last subevent of "waking" | Doing X ends with the event Y) == Commonsense knowledge bases == Cyc Open Mind Common Sense (data source) and ConceptNet (datastore and NLP engine) Evi Graphiq

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  Sasha Stiles

  Sasha Stiles (born 1980) is an American artist and poet. After discovering natural language processing, she created the 2021 poetry collection Technelegy through an eponymous AI model, before presenting the 2025–2026 installation A Living Poem at the Museum of Modern Art. In addition to artificial intelligence, binary code and non-fungible tokens have been important aspects of her work. == Biography == Stiles was born in 1980 in Pasadena, California, to documentary filmmaker parents whose work includes Cosmos: A Personal Voyage. She was interested in science fiction during her youth, particularly how they addressed human-machine collaboration and posthumanism. She graduated magna cum laude from Harvard University with a Bachelor of Arts in 2002 and she graduated with high honors from the University of Oxford with a Master of Studies in 2004. Originally, Stiles's poetry focused on technology. In 2017, she discovered natural language processing, piquing her interest in its ability to process thoughts and words comparably to its human counterparts. Despite lacking a technological background, she managed to channel people like Gwern Branwen, Ross Goodwin, and Allison Parrish as inspirations for her AI work, and in 2019, she started training an AI model named Technelegy. In 2021, Black Spring Press published her poetry collection Technelegy, where she combines AI-generated content produced by the titular AI model with her own traditionally-created work; the AI-generated content was produced by processing Stiles's own poetry onto GPT-2 and GPT-3. She and Technelegy later co-created A Living Poem, which ran at the Museum of Modern Art's Hyundai Card Digital Wall from September 2025 to March 2026. Stiles also has used non-fungible tokens as a platform for her poetry, having been inspired to go into blockchain by her experiences working with a metaverse exhibition curated by Jess Conatser. She has used Christie's and SuperRare to sell several of her poems as tokenized real-world assets, including Daughter of E.V.E. (Ex-Vivo Uterine Environment), a 2021 single-channel video using freeze-frame shots to hide poetry. In 2021, she co-founded TheVerseVerse (stylized as theVERSEverse), a non-fungible token gallery specializing in poetry. She later created Four Core Texts: Humanifesto and Other Poems, involving four NFT videos of poetry written in looping handwriting and powered by Technelegy. Stiles uses binary code as an inspiration for her work, citing in part its "quite antagonistic system of a binary 'EITHER / OR'", which she connected to several dichotomies pitting humanity and the present against technology and the future. In 2018, she started Analog Binary Code, where she creates sculptures by arranging objects in binary code ciphers. She also created Cursive Binary, where she combines binary with cursive handwriting, after writing zeros and ones on a steamed wall while showering. Stiles and the robot BINA48 co-created the 2020 ArtYard exhibition A Valentine for the Future. She was part of the 2021 group exhibition Computational Poetics at the Beall Center for Art and Technology. From February 24 to March 18, 2023, she held her solo show Binary Odes (stylized as B1NARY 0DES) at Annka Kultys Gallery. By 2024, her work had appeared in places such as Gucci storefronts and Times Square billboards. She designed Words Beyond Words, the official poster for Art Basel in Basel 2025. Stiles is based in Milford, New Jersey, where she lives with her husband, musician Kris Bones. She has also lived in Jersey City and Bucks County, Pennsylvania. She is Kalmyk-American on her mother's side, and she has also announced plans to create a version of Technelegy in her ancestral language Kalmyk.

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  Computational theory of mind

  In philosophy of mind, the computational theory of mind (CTM), also known as computationalism, is a family of views that hold that the human mind is an information processing system and that cognition and consciousness together are a form of computation. It is closely related to functionalism, a broader theory that defines mental states by what they do rather than what they are made of. == History == Warren McCulloch and Walter Pitts (1943) were the first to suggest that neural activity is computational. They argued that neural computations explain cognition. A version of the theory was put forward by Peter Putnam and Robert W. Fuller in 1964. The theory was proposed in its modern form by Hilary Putnam in 1960 and 1961, aided by his then PhD student, philosopher and cognitive scientist Jerry Fodor, who continued the research as a post-doc in the 1960s, 1970s, and 1980s. It was later criticized by Putnam himself, John Searle, and others. == Classical computational theory of mind == The CTM holds that the human mind is a computational system that is realized (i.e., physically implemented) by neural activity in the brain. The theory can be elaborated in many ways and varies largely based on how the term computation is understood. In classical computational theory of mind (CCTM), computation is modeled in terms of Turing machines which manipulate symbols according to a rule, in combination with the internal state of the machine. A Turing machine is an abstract machine with unlimited time and storage. CCTM does not pretend that the mind looks like a Turing machine, but instead uses Turing machines as a formalism. Alan Turing argued that any symbolic algorithm executed by a human brain can in theory be replicated on a Turing machine. The critical aspect of such a computational model is that it allows to abstract away from particular physical details of the machine that is implementing the computation. For example, the appropriate computation could be implemented either by silicon chips or biological neural networks, so long as there is a series of outputs based on manipulations of inputs and internal states, performed according to a rule. Computational theories of mind are often said to require mental representation because 'input' into a computation comes in the form of symbols or representations of other objects. A computer cannot compute an actual object but must interpret and represent the object in some form and then compute the representation. Unlike CTM, the representational theory of mind shifts the focus to the symbols being manipulated. This approach better accounts for systematicity and productivity. In Fodor's view, the mind is a computational system that processes the language of thought. == Variants == Connectionist computationalism models the mind as a neural network. Steven Pinker and Alan Prince distinguish two types of connectionists: eliminative and implementationist. Eliminative connectionists generally reject classical CTMs and the idea of a structured, symbolic mind, whereas implementationists view neural networks and Turing machines as two potentially complementary levels of analysis. It is indeed possible in theory to implement a neural network in a Turing machine, or a Turing machine in a neural network. Building from the tradition of McCulloch and Pitts, the computational theory of cognition (CTC) states that neural computations explain cognition. The computational theory of mind asserts that not only cognition, but also phenomenal consciousness or qualia, are computational. That is to say, CTM entails CTC. While phenomenal consciousness could fulfill some other functional role, computational theory of cognition leaves open the possibility that some aspects of the mind could be non-computational. CTC, therefore, provides an important explanatory framework for understanding neural networks, while avoiding counter-arguments that center around phenomenal consciousness. == "Computer metaphor" == Computational theory of mind is not the same as the computer metaphor, comparing the mind to a modern-day digital computer. While the computer metaphor draws an analogy between the mind as software and the brain as hardware, CTM is the claim that the mind is literally a computational system. "Computational system" is not intended to mean a modern-day electronic computer. == Pancomputationalism == CTM raises a question that remains a subject of debate: what does it take for a physical system (such as a mind, or an artificial computer) to perform computations? A very straightforward account is based on a simple mapping between abstract mathematical computations and physical systems: a system performs computation C if and only if there is a mapping between a sequence of states individuated by C and a sequence of states individuated by a physical description of the system. Putnam (1988) and Searle (1992) argue that this simple mapping account (SMA) trivializes the empirical import of computational descriptions. As Putnam put it, "everything is a Probabilistic Automaton under some Description". Even rocks, walls, and buckets of water—contrary to appearances—are computing systems. Gualtiero Piccinini identifies different versions of pancomputationalism. Searle wrote:the wall behind my back is right now implementing the WordStar program, because there is some pattern of molecule movements that is isomorphic with the formal structure of WordStar. But if the wall is implementing WordStar, if it is a big enough wall it is implementing any program, including any program implemented in the brain.In response to the trivialization criticism, and to restrict SMA, philosophers of mind have offered different accounts of computational systems. These typically include causal account, semantic account, syntactic account, and mechanistic account. Instead of a semantic restriction, the syntactic account imposes a syntactic restriction. The mechanistic account was first introduced by Gualtiero Piccinini in 2007. == Criticism == A range of arguments have been proposed against physicalist conceptions used in computational theories of mind. An early, though indirect, criticism of the computational theory of mind comes from philosopher John Searle. In his thought experiment known as the Chinese room, Searle attempts to refute the claims that artificially intelligent agents can be said to have intentionality and understanding and that these systems, because they can be said to be minds themselves, are sufficient for the study of the human mind. Searle asks us to imagine that there is a man in a room with no way of communicating with anyone or anything outside of the room except for a piece of paper with symbols written on it that is passed under the door. With the paper, the man is to use a series of provided rule books to return paper containing different symbols. Unknown to the man in the room, these symbols are of a Chinese language, and this process generates a conversation that a Chinese speaker outside of the room can actually understand. Searle contends that the man in the room does not understand the Chinese conversation. This was originally written as a repudiation of the idea that computers work like minds. Objections like Searle's might be called insufficiency objections. They claim that computational theories of mind fail because computation is insufficient to account for some capacity of the mind. Arguments from qualia, such as Frank Jackson's knowledge argument, can be understood as objections to computational theories of mind in this way—though they take aim at physicalist conceptions of the mind in general, and not computational theories specifically. Objections have also been put forth that are directly tailored for computational theories of mind. Jerry Fodor himself argues that the mind is still a very long way from having been explained by the computational theory of mind. The main reason for this shortcoming is that most cognition is abductive and global, hence sensitive to all possibly relevant background beliefs to (dis)confirm a belief. This creates, among other problems, the frame problem for the computational theory, because the relevance of a belief is not one of its local, syntactic properties but context-dependent. Putnam himself (see in particular Representation and Reality and the first part of Renewing Philosophy) became a prominent critic of computationalism for a variety of reasons, including ones related to Searle's Chinese room arguments, questions of world-word reference relations, and thoughts about the mind-body problem. Regarding functionalism in particular, Putnam has claimed along lines similar to, but more general than Searle's arguments, that the question of whether the human mind can implement computational states is not relevant to the question of the nature of mind, because "every ordinary open system realizes every abstract finite automaton." Computationalists have responded by aiming to develop criteri

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  Vilém Flusser

  Vilém Flusser (May 12, 1920 – November 27, 1991) was a Czech-born Brazilian philosopher, writer and journalist, best known for his contributions to media studies, communication theory, and the philosophy of language. He lived for a long period in São Paulo (where he became a Brazilian citizen) and later in France, and his works are written in many different languages. His early work was marked by discussion of the thought of Martin Heidegger, and by the influence of existentialism and phenomenology. Phenomenology would play a major role in the transition to the later phase of his work, in which he turned his attention to the philosophy of communication and of artistic production. He contributed to the dichotomy logic theory through history: the period of image worship, and period of text worship, with deviations consequently into idolatry and "textolatry". == Life == Flusser was born in 1920 in Prague, Czechoslovakia into a family of Jewish intellectuals. His father, Gustav Flusser, studied mathematics and physics (under Albert Einstein among others). Vilém attended German and Czech primary schools and later a German grammar school. In 1938, Flusser started to study philosophy at the Juridical Faculty of the Charles University in Prague. In 1939, shortly after the Nazi occupation, Flusser emigrated to London (with Edith Barth, his later wife, and her parents) to continue his studies for one term at the London School of Economics and Political Science. Vilém Flusser lost all of his family in the German concentration camps: his father died in Buchenwald in 1940; his grandparents, his mother and his sister were brought to Theresienstadt and later to Auschwitz where they were killed. The next year, he emigrated to Brazil, living both in São Paulo and Rio de Janeiro. He started working at a Czech import/export company and then at Stabivolt, a manufacturer of radios and transistors. In 1960 he started to collaborate with the Brazilian Institute of Philosophy (IBF) in São Paulo and published in the Revista Brasileira de Filosofia; by these means he seriously approached the Brazilian intellectual community. Flusser had as his friend and closest interlocutor the Brazilian philosopher Vicente Ferreira da Silva. Flusser and Vicente Ferreira da Silva met in São Paulo in the 1960s and began a close intellectual dialogue that continued until Ferreira da Silva's death in 1963. Flusser wrote several essays on Ferreira da Silva's work and that Ferreira da Silva's concept of "Fundamental ontology” had a significant impact on Flusser's understanding of the nature of reality. During the 60s Flusser published and taught at several schools in São Paulo, being Lecturer for Philosophy of Science at the Escola Politécnica of the University of São Paulo and Professor of Philosophy of Communication at the Escola Dramática and the Escola Superior de Cinema in São Paulo. He also participated actively in the arts, collaborating with the Bienal de São Paulo, among other cultural events. Beginning in the 1950s he taught philosophy and worked as a journalist, before publishing his first book Língua e realidade (Language and Reality) in 1963. In 1972 he decided to leave Brazil. Some say it was because it was becoming difficult to publish because of the military regime. Others dispute this reason, since his work on communication and language did not threaten the military. In 1970, when a reform took place at the University of São Paulo by the Brazilian military government, all Lecturers of Philosophy (members of the Department of Philosophy) were dismissed. Flusser, who taught at the Engineering School (Escola Politécnica), had to leave the university as well. In 1972 he and his wife Edith settled temporarily in Merano (Tyrol). Further short stays in various European countries followed until they moved to Robion in southern France in 1981, where they remained until Flusser's death in 1991. To the end of his life, he was quite active writing and giving lectures around media theory and working with new topics (Philosophy of Photography, Technical Images, etc.). He died in 1991 in a car accident near the Czech–German border, while trying to visit his native city, Prague, to give a lecture. Vilém Flusser is the cousin of David Flusser. == Philosophy == Flusser's essays are short, provocative and lucid, with a resemblance to the style of journalistic articles. Critics have noted he is less a 'systematic' thinker than a 'dialogic' one, purposefully eclectic and provocative (Cubitt 2004). However, his early books, written in the 1960s, primarily in Portuguese, and published in Brazil, have a slightly different style. Flusser's writings relate to each other, however, which means that he intensively works over certain topics and dissects them into a number of brief essays. His main topics of interest were: epistemology, ethics, aesthetics, ontology, language philosophy, semiotics, philosophy of science, the history of Western culture, the philosophy of religion, the history of symbolic language, technology, writing, the technical image, photography, migration, media and literature, and, especially in his later years, the philosophy of communication and of artistic production. His writings reflect his wandering life: although the majority of his work was written in German and Portuguese, he also wrote in English and French, with scarce translation to other languages. Because Flusser's writings in different languages are dispersed in the form of books, articles or sections of books, his work as a media philosopher and cultural theorist is only now becoming more widely known. The first book by Flusser to be published in English was Towards a Philosophy of Photography in 1984 by the then new journal European Photography, which was his own translation of the work. The Shape of Things, was published in London in 1999 and was followed by a new translation of Towards a Philosophy of Photography. Flusser's archives have been held by the Academy of Media Arts in Cologne and are currently housed at the Berlin University of the Arts. === Philosophy of photography === Writing about photography in the 1970s and 80s, in the face of the early worldwide impact of computer technologies, Flusser argued that the photograph was the first in a number of technical image forms to have fundamentally changed the way in which the world is seen. Historically, the importance of photography had been that it introduced nothing less than a new epoch: 'The invention of photography constitutes a break in history that can only be understood in comparison to that other historical break constituted by the invention of linear writing.' Whereas ideas might previously have been interpreted in terms of their written form, photography heralded new forms of perceptual experience and knowledge. As Flusser Archive Supervisor Claudia Becker describes, "For Flusser, photography is not only a reproductive imaging technology, it is a dominant cultural technique through which reality is constituted and understood". In this context, Flusser argued that photographs have to be understood in strict separation from 'pre-technical image forms'. For example, he contrasted them to paintings which he described as images that can be sensibly 'decoded', because the viewer is able to interpret what he or she sees as more or less direct signs of what the painter intended. By contrast, even though photography produces images that seem to be 'faithful reproductions' of objects and events they cannot be so directly 'decoded'. The crux of this difference stems, for Flusser, from the fact that photographs are produced through the operations of an apparatus. And the photographic apparatus operates in ways that are not immediately known or shaped by its operator. For example, he described the act of photographing as follows: The photographer's gesture as the search for a viewpoint onto a scene takes place within the possibilities offered by the apparatus. The photographer moves within specific categories of space and time regarding the scene: proximity and distance, bird- and worm's-eye views, frontal- and side-views, short or long exposures, etc. The Gestalt of space–time surrounding the scene is prefigured for the photographer by the categories of his camera. These categories are an a priori for him. He must 'decide' within them: he must press the trigger. Roughly put, the person using a camera might think that they are operating its controls to produce a picture that shows the world the way they want it to be seen, but it is the pre-programmed character of the camera that sets the parameters of this act and it is the apparatus that shapes the meaning of the resulting image. Given the central role of photography to almost all aspects of contemporary life, the programmed character of the photographic apparatus shapes the experience of looking at and interpreting photographs as well as most of the cultural contexts in which we do so. Flusse

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  Graphics suite

  A graphics suite is a software suite for graphics work that are distributed together. The programs are usually able to interact with each other on a higher level than the operating system would normally allow. There is no hard, fast rule regarding the programs to be included in a graphics application suite, but most will include at least a bitmap graphics editor and a vector graphics editor. In addition to these, the suite may contain VRML editors, animation editors, and morphing tools.

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  General-Purpose AI Code of Practice

  The General-Purpose AI Code of Practice (GPAI CoP) is a compliance tool released by the European Commission on 10 July 2025 to support compliance with the European Union Artificial Intelligence Act (AI Act). It provides operational guidance for providers of general-purpose AI models, particularly in relation to Articles 53 and 55 of the AI Act, which entered into application on 2 August 2025. The Code is organised into three chapters (Transparency, Copyright, and Safety and Security) and outlines how providers can meet the Act's relevant obligations. Although non-binding, providers can rely on adherence to the Code, meaning that EU regulators will assume that providers following the Code meet the corresponding legal requirements of the AI Act. As such, signatories to the Code will benefit from reduced administrative burdens and increased legal certainty compared to providers that prove compliance in other ways. While adherence to the Code is voluntary, compliance with the AI Act is not. == Background == The EU AI Act, adopted in 2024, established a risk-based regulatory regime for artificial intelligence in the European Union. The rationale for the GPAI CoP stems from Article 56 of the AI Act, which empowers the EU AI Office to develop a voluntary rulebook to guide how AI model providers can meet their legal obligations – specifically those found in Articles 53 and 55. Under Articles 53 and 55, developers of general-purpose AI models whose training compute exceeds 1023 floating-point operations (FLOPs) and that are placed on the EU market must meet transparency obligations and put in place a policy for EU copyright law. Models trained with more than 1025 FLOPs are classified as presenting systemic risk and are subject to enhanced safety requirements. The Commission may also designate a model as presenting systemic risk if it has equivalent impact or capabilities (Annex XIII criteria), even below that compute figure. Because the AI Act is relatively vague on how model providers should implement these requirements, the Code is meant to help by detailing processes and practices for compliance. == Drafting process == The development of the GPAI CoP was drawn up by 13 independent experts and involved four thematic working groups: Transparency & Copyright, Risk assessment for systemic risk, Technical risk mitigation for systemic risk, and Governance risk mitigation for systemic risk. Each group was coordinated by the European Union Artificial Intelligence Office (EU AI Office), drawing on contributions from nearly 1,000 stakeholders, including AI developers, academics, civil society organisations, national authorities, and international observers. The Code underwent three earlier iterations in November 2024, December 2024, and March 2025, before the final version was published on 10 July 2025, more than two months later than initially planned. The GPAI CoP will likely be updated continuously by the EU AI Office, alongside other tools such as the training data summary template. == Signatories == Among U.S.-based technology companies, Amazon, Anthropic, Google, IBM, Microsoft, and OpenAI have signed the GPAI CoP. xAI, founded by Elon Musk, has signed only one of the three chapters, namely the safety and security chapter. Prominent European AI companies that have signed include Aleph Alpha and Mistral AI. The European Commission maintains an updated list of signatories. As of January 2026, Meta is the most notable company that has declined to sign the Code. Major Chinese AI companies, such as Alibaba, Baidu or Deepseek, have also not signed. Providers that do not sign the GPAI CoP will still have to adhere to the binding requirements of the EU AI Act. The European Commission has indicated that it may take tougher action against companies that didn't sign the Code. == Transparency and Copyright chapters == The first two chapters of the GPAI CoP address transparency and copyright compliance and apply to all GPAI providers. They offer a way to demonstrate compliance with their obligations under Article 53 AI Act. The Transparency chapter addresses the documentation of a model's capabilities, limitations, and points of contact, and expects providers to make key documentation available to downstream providers. Signatories must also publish summaries of the content used to train their models. In the Copyright chapter, Signatories commit to follow a policy that aligns with EU copyright law. For example, they commit to mitigating the risk of copyright-infringing output. == Safety and Security chapter == The Safety and Security chapter is the most extensive chapter of the Code, and it applies to GPAI models with systemic risk, meaning it's only relevant to the small number of providers of the most advanced models. It specifies how Signatories commit to meeting Article 55(1) obligations to: Conduct model evaluations to identify systemic risks Assess and mitigate those risks Track and report serious incidents Ensure the cyber and physical security of their models The chapter outlines a comprehensive risk management process that must be applied before major deployment decisions, such as releasing a new systemic-risk GPAI model in the EU market, or substantially updating an existing one. Signatories commit to identifying systemic risks of their model, analysing and evaluating them, determining whether risk levels are acceptable, and implementing mitigation measures if necessary. This process should be repeated until models achieve an acceptable level of risk across all identified risks. === Risk identification === Signatories commit to analysing and evaluating at least four “specified” categories of systemic risk: CBRN (chemical, biological, radiological, and nuclear) Loss of control Cyber offence Harmful manipulation They are also expected to identify other systemic risks to public health, safety, and fundamental rights. The Code instructs providers to consider model capabilities, propensities, and affordances in this identification. Signatories commit to developing risk scenarios illustrating how identified risks could materialise in real-world conditions. === Risk analysis and risk evaluation === After identifying potential systemic risks, Signatories commit to analysing and evaluating the risks in order to determine whether they are acceptable or not, drawing on scientific literature, training data analysis, incident databases, expert consultation, and other sources. They also commit to conducting state-of-the-art model evaluations such as benchmarking, red teaming, and human uplift studies, targeting each risk. The risk analysis process is interconnected: insights from risk modelling should inform model evaluation design, while post-market monitoring should feed back into ongoing analysis. Signatories commit to ultimately estimating the likelihood and severity of each systemic risk. ==== Independent external model evaluations ==== Appendix 3.5 of the Safety and Security chapter requires signatories to ensure that independent external evaluators conduct model evaluations. Signatories may claim an exemption from this requirement only if they can demonstrate that their model is “similarly safe” to another model that has already been shown to comply with the Code, or if they are unable to appoint an appropriately qualified evaluator. The determination of “similarly safe” is based on comparable performance on benchmarks and the similarity of other model characteristics, such as their architecture. The CoP acknowledges that this kind of information is typically available only for models by the same provider, or potentially for open-weights or open-source models. === Risk acceptance criteria === The Code requires providers to compare estimated risks against predefined acceptance criteria, which must be measurable, based on model capabilities, and defined preemptively. While providers get to determine the level of risk they deem acceptable themselves, the pre-defined criteria and acceptance thresholds ensure providers cannot adjust their level of tolerance flexibly ahead of deployment decisions. Only if all risks are below acceptable levels should a model be deployed. === Continuous risk management and governance === The Code mandates ongoing risk management throughout the model lifecycle, including light-touch evaluations, continuous mitigation, post-market monitoring, and incident tracking and reporting. It further requires organisational governance structures assigning responsibility for risk management and expects providers to promote a “healthy risk culture,” including informing employees about the whistleblower protection policy, allowing internal challenges of decisions concerning systemic risk management, and committing to not retaliating against employees who disclose concerns about systemic risks to oversight authorities. === Documentation and transparency === Signatories commit to creating two types of documentation: Safety and Security Frame

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  AlphaGo Zero

  AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. By playing games against itself, AlphaGo Zero: surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0; reached the level of AlphaGo Master in 21 days; and exceeded all previous versions in 40 days. Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills, as expert data is "often expensive, unreliable, or simply unavailable." Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was "no longer constrained by the limits of human knowledge". Furthermore, AlphaGo Zero performed better than standard deep reinforcement learning models (such as Deep Q-Network implementations) due to its integration of Monte Carlo tree search. David Silver, one of the first authors of DeepMind's papers published in Nature on AlphaGo, said that it is possible to have generalized AI algorithms by removing the need to learn from humans. Google later developed AlphaZero, a generalized version of AlphaGo Zero that could play chess and shōgi in addition to Go. In December 2017, AlphaZero beat the 3-day version of AlphaGo Zero by winning 60 games to 40, and with 8 hours of training it outperformed AlphaGo Lee on an Elo scale. AlphaZero also defeated a top chess program (Stockfish) and a top Shōgi program (Elmo). == Architecture == The network in AlphaGo Zero is a ResNet with two heads. The stem of the network takes as input a 17x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise. If the time step go before the beginning of the game, then 0 in all positions.) 8 channels are the positions of the other player's stones from the last eight time steps. 1 channel is all 1 if black is to move, and 0 otherwise. The body is a ResNet with either 20 or 40 residual blocks and 256 channels. There are two heads, a policy head and a value head. Policy head outputs a logit array of size 19 × 19 + 1 {\displaystyle 19\times 19+1} , representing the logit of making a move in one of the points, plus the logit of passing. Value head outputs a number in the range ( − 1 , + 1 ) {\displaystyle (-1,+1)} , representing the expected score for the current player. -1 represents current player losing, and +1 winning. == Training == AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. Only four TPUs were used for inference. The neural network initially knew nothing about Go beyond the rules. Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather than having some rare human-programmed edge cases to help recognize unusual Go board positions. The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game's outcome. In the first three days AlphaGo Zero played 4.9 million games against itself in quick succession. It appeared to develop the skills required to beat top humans within just a few days, whereas the earlier AlphaGo took months of training to achieve the same level. According to Epoch.ai, training cost 3e23 FLOPs. For comparison, the researchers also trained a version of AlphaGo Zero using human games, AlphaGo Master, and found that it learned more quickly, but actually performed more poorly in the long run. DeepMind submitted its initial findings in a paper to Nature in April 2017, which was then published in October 2017. == Hardware cost == The hardware cost for a single AlphaGo Zero system in 2017, including the four TPUs, has been quoted as around $25 million. == Applications == According to Hassabis, AlphaGo's algorithms are likely to be of the most benefit to domains that require an intelligent search through an enormous space of possibilities, such as protein folding (see AlphaFold) or accurately simulating chemical reactions. AlphaGo's techniques are probably less useful in domains that are difficult to simulate, such as learning how to drive a car. DeepMind stated in October 2017 that it had already started active work on attempting to use AlphaGo Zero technology for protein folding, and stated it would soon publish new findings. == Reception == AlphaGo Zero was widely regarded as a significant advance, even when compared with its groundbreaking predecessor, AlphaGo. Oren Etzioni of the Allen Institute for Artificial Intelligence called AlphaGo Zero "a very impressive technical result" in "both their ability to do it—and their ability to train the system in 40 days, on four TPUs". The Guardian called it a "major breakthrough for artificial intelligence", citing Eleni Vasilaki of Sheffield University and Tom Mitchell of Carnegie Mellon University, who called it an impressive feat and an “outstanding engineering accomplishment" respectively. Mark Pesce of the University of Sydney called AlphaGo Zero "a big technological advance" taking us into "undiscovered territory". Gary Marcus, a psychologist at New York University, has cautioned that for all we know, AlphaGo may contain "implicit knowledge that the programmers have about how to construct machines to play problems like Go" and will need to be tested in other domains before being sure that its base architecture is effective at much more than playing Go. In contrast, DeepMind is "confident that this approach is generalisable to a large number of domains". In response to the reports, South Korean Go professional Lee Sedol said, "The previous version of AlphaGo wasn’t perfect, and I believe that’s why AlphaGo Zero was made." On the potential for AlphaGo's development, Lee said he will have to wait and see but also said it will affect young Go players. Mok Jin-seok, who directs the South Korean national Go team, said the Go world has already been imitating the playing styles of previous versions of AlphaGo and creating new ideas from them, and he is hopeful that new ideas will come out from AlphaGo Zero. Mok also added that general trends in the Go world are now being influenced by AlphaGo's playing style. "At first, it was hard to understand and I almost felt like I was playing against an alien. However, having had a great amount of experience, I’ve become used to it," Mok said. "We are now past the point where we debate the gap between the capability of AlphaGo and humans. It’s now between computers." Mok has reportedly already begun analyzing the playing style of AlphaGo Zero along with players from the national team. "Though having watched only a few matches, we received the impression that AlphaGo Zero plays more like a human than its predecessors," Mok said. Chinese Go professional Ke Jie commented on the remarkable accomplishments of the new program: "A pure self-learning AlphaGo is the strongest. Humans seem redundant in front of its self-improvement." == Comparison with predecessors == == AlphaZero == On 5 December 2017, DeepMind team released a preprint on arXiv, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in chess, shogi, and Go, defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case. AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include: AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. Chess (unlike Go) can end in a tie; therefore AZ can take into account the possibility of a tie game. An open source program, Leela Zero, based on the ideas from the AlphaGo papers is available. It uses a GPU instead of the TPUs recent versions of AlphaGo rely on.

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  Jakub Pachocki

  Jakub Pachocki (born 1991) is a Polish computer scientist and former competitive programmer. He is best known as OpenAI's chief scientist and for his role in overseeing development of GPT-4. == Background == Pachocki was born in 1991 in Gdańsk, Poland. In high school, he was a six-time finalist of the Polish Olympiad in Informatics. In 2009, he qualified for the International Olympiad in Informatics, winning a silver medal. Pachocki obtained his undergraduate degree in Computer Science from the University of Warsaw. He represented his university at the International Collegiate Programming Contest with his team winning a gold medal and coming second place overall in 2012. In the same year he was also the champion of the Google Code Jam. From 2011 to 2012, Pachocki worked at Facebook as a software engineering intern. Pachocki attended graduate school at Carnegie Mellon University, where he obtained his PhD under the supervision of Gary Miller. == Career == After graduation, Pachocki did postdoc work at Harvard University and Simons Institute for the Theory of Computing. === OpenAI === In 2017, Pachocki joined OpenAI. In 2021, he became OpenAI's research director where he led the development of GPT-4 and OpenAI Five. In May 2024, he became chief scientist after his mentor Ilya Sutskever left the company. OpenAI CEO Sam Altman has called Pachocki "easily one of the greatest minds of our generation". == Competitive programming achievements == International Olympiad in Informatics: Silver medal (2009) International Collegiate Programming Contest World Finals: Gold medal (second place overall in 2012) Google Code Jam: Champion (2012), Third place (2011) Facebook Hacker Cup: Second place (2013) TopCoder Open Algorithm: Second place (2012) A more comprehensive list of achievements can be found at the Competitive Programming Hall Of Fame website.

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  Hedgeable

  Hedgeable, Inc. was a U.S. based financial services company and digital wealth management platform headquartered in New York City. Hedgeable was known for not following set allocations, and instead actively managing accounts in response to market movements. On August 9, 2018, Hedgeable closed its doors to new investors, with existing investors required to transfer out of the company. The company claimed that it was not shutting down but simply removing its SEC registration. == History == Hedgeable was founded in 2009 by twin brothers Michael and Matthew Kane, who previously worked at high-net worth investment managers such as Bridgewater Associates and Spruce Private Investors. Both Michael and Matthew graduated from Penn State University with degrees in finance. Hedgeable is a Registered Investment Advisor with the U.S. Securities and Exchange Commission. The company has received funding from SixThirty and Route 66 Ventures as well as various other angel investors. On August 9, 2018, Hedgeable closed its doors to new investors. == Investing Strategies == Hedgeable did not follow a buy-and-hold approach, but instead actively manages accounts in response to market movements focusing on downside protection in bear markets. Their strategy was different from other robo-advisors, which use Modern Portfolio Theory. Hedgeable offered investment options including Exchange Traded Funds (ETFs) to individual stocks, master limited partnerships, private equity and bitcoin. Mutual funds were not used in portfolios. Although the firm's focus was to provide a direct-to-consumer service, Hedgeable's investment strategies were available to financial advisors and institutions as well through a variety of platforms. == Product Features == When it was open to external clients, Hedgeable aimed to gamify their personal finance experience. Clients could open a new account or transfer an existing account. Hedgeable accepted retirement accounts, taxable accounts, business accounts and various other account types. Hedgeable offered the following features: Downside protection Account aggregation Alternative investments Alpha rewards API Mobile app It was awarded 4/5 for client transparency by Paladin Research. Hedgeable was the winner of the Finovate Fall 2015 Best of Show Award and the GREAT 2015 Tech Award (FinTech Category). In 2016, Hedgeable launched its first iOS mobile app in order to expand their product offerings.

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  Procedural reasoning system

  In artificial intelligence, a procedural reasoning system (PRS) is a framework for constructing real-time reasoning systems that can perform complex tasks in dynamic environments. It is based on the notion of a rational agent or intelligent agent using the belief–desire–intention software model. A user application is predominately defined, and provided to a PRS system is a set of knowledge areas. Each knowledge area is a piece of procedural knowledge that specifies how to do something, e.g., how to navigate down a corridor, or how to plan a path (in contrast with robotic architectures where the programmer just provides a model of what the states of the world are and how the agent's primitive actions affect them). Such a program, together with a PRS interpreter, is used to control the agent. The interpreter is responsible for maintaining beliefs about the world state, choosing which goals to attempt to achieve next, and choosing which knowledge area to apply in the current situation. How exactly these operations are performed might depend on domain-specific meta-level knowledge areas. Unlike traditional AI planning systems that generate a complete plan at the beginning, and replan if unexpected things happen, PRS interleaves planning and doing actions in the world. At any point, the system might only have a partially specified plan for the future. PRS is based on the BDI or belief–desire–intention framework for intelligent agents. Beliefs consist of what the agent believes to be true about the current state of the world, desires consist of the agent's goals, and intentions consist of the agent's current plans for achieving those goals. Furthermore, each of these three components is typically explicitly represented somewhere within the memory of the PRS agent at runtime, which is in contrast to purely reactive systems, such as the subsumption architecture. == History == The PRS concept was developed by the Artificial Intelligence Center at SRI International during the 1980s, by many workers including Michael Georgeff, Amy L. Lansky, and François Félix Ingrand. Their framework was responsible for exploiting and popularizing the BDI model in software for control of an intelligent agent. The seminal application of the framework was a fault detection system for the reaction control system of the NASA Space Shuttle Discovery. Development on this PRS continued at the Australian Artificial Intelligence Institute through to the late 1990s, which led to the development of a C++ implementation and extension called dMARS. == Architecture == The system architecture of SRI's PRS includes the following components: Database for beliefs about the world, represented using first order predicate calculus. Goals to be realized by the system as conditions over an interval of time on internal and external state descriptions (desires). Knowledge areas (KAs) or plans that define sequences of low-level actions toward achieving a goal in specific situations. Intentions that include those KAs that have been selected for current and eventual execution. Interpreter or inference mechanism that manages the system. == Features == SRI's PRS was developed for embedded application in dynamic and real-time environments. As such it specifically addressed the limitations of other contemporary control and reasoning architectures like expert systems and the blackboard system. The following define the general requirements for the development of their PRS: asynchronous event handling guaranteed reaction and response types procedural representation of knowledge handling of multiple problems reactive and goal-directed behavior focus of attention reflective reasoning capabilities continuous embedded operation handling of incomplete or inaccurate data handling of transients modeling delayed feedback operator control == Applications == The seminal application of SRI's PRS was a monitoring and fault detection system for the reaction control system (RCS) on the NASA space shuttle. The RCS provides propulsive forces from a collection of jet thrusters and controls altitude of the space shuttle. A PRS-based fault diagnostic system was developed and tested using a simulator. It included over 100 KAs and over 25 meta level KAs. RCS specific KAs were written by space shuttle mission controllers. It was implemented on the Symbolics 3600 Series LISP machine and used multiple communicating instances of PRS. The system maintained over 1000 facts about the RCS, over 650 facts for the forward RCS alone and half of which are updated continuously during the mission. A version of the PRS was used to monitor the reaction control system on the Space Shuttle Discovery. PRS was tested on Shakey the robot including navigational and simulated jet malfunction scenarios based on the space shuttle. Later applications included a network management monitor called the Interactive Real-time Telecommunications Network Management System (IRTNMS) for Telecom Australia. == Extensions == The following list the major implementations and extensions of the PRS architecture. UM-PRS OpenPRS (formerly C-PRS and Propice) AgentSpeak Distributed multi-agent reasoning system (dMARS) GORITE JAM JACK Intelligent Agents SRI Procedural Agent Realization Kit (SPARK) PRS-CL

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  InRule Technology

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

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  Emospark

  EmoSpark is an artificial intelligence console created in London, United Kingdom by Patrick Levy-Rosenthal. The device uses facial recognition and language analysis to evaluate human emotion and convey responsive content according to the emotion. The console measures 90 mm x 90 mm x 90 mm and is cube shaped. It operates on an "Emotional Processing Unit", an emotion chip developed by Emoshape Inc. that enables the system to create emotional profile graphs of its surroundings. The emotional processing unit is a patent pending technology that is said to create synthesised emotional responses in machines. EmoSpark was funded through an Indiegogo campaign which aimed to raise $200,000. == Product overview == EmoSpark was created by French inventor Patrick Levy-Rosenthal, as an emotionally intelligent artificial life unit for the home that can interact with people. It is powered by Android and can communicate with users through typed input from a computer, tablet, smartphone or TV as well as through spoken commands. The EmoSpark's features are categorized into two types: functional and emotional. EmoSpark is said to have the ability to perform practical software-based tasks. Through the smartphone interface, it is able to gauge a person’s emotions and is reported to have a conversational library of over 2 million sentences. The face-tracking technology identifies users likes and dislikes to categorize their emotional responses to stimuli such as videos and music. The device has an emotional spectrum that is composed of eight emotions which are surprise, sadness, joy, trust, fear, disgust, anger and anticipation. EmoSpark monitors a person's facial expressions and emotions through images from an external camera, which are then processed through an emotion text analysis and content analysis. The New Scientist reported that EmoSpark had the ability to work on the best way to cheer up its users, emotionally. === Connectivity === EmoSpark is able to connect to Facebook and YouTube to present users with content designed to improve their mood, or to Wikipedia for collaborative knowledge that can be shared when users ask questions of it. Through Android OS, EmoSpark is able to be customized with Google Play store apps. The cube is expected to develop its own personality based on the communications it has had with the people using it. == EmoShape == The Emotion Chip (EPU) used in the cube is created by the US company Emoshape Inc, founded by Levy-Rosenthal. EmoShape Ltd (UK) was the company that developed EmoSpark cube. Patrick Levy-Rosenthal also received the IST Prize in 2005 from the European Council for Applied Science, Technology and Engineering.

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  No Thanks (app)

  No Thanks is a Palestinian boycott-awareness mobile application developed by Palestinian software engineer Ahmed Bashbash, created to assist consumers in identifying and boycotting products associated with companies linked to Israel. Launched in 13 November 2023, the app gained significant attention amid the Gaza–Israel conflict. == History == No Thanks is a mobile application developed by Ahmed Bashbash, a Palestinian software engineer from Gaza residing in Hungary. The app was conceived in October 2023 following the death of Bashbash's brother in an Israeli airstrike on October 31, 2023. His sister had previously died in 2020 due to delayed medical treatment. The app was officially launched on November 13, 2023, and quickly gained traction, got over 100,000 downloads within its first month of release. On November 30, 2023, Google removed the app from its Play Store due to a violation of its content policies. The app's home page included a description: "Welcome to No Thanks, here you can see if the product in your hand supports killing children in Palestine or not," which was deemed to contravene Google's guidelines on hate speech and sensitive content. On December 3, 2023, following changes to the app's description, Google reinstated the app.

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  Golem XIV

  Golem XIV is a book written by Polish science fiction writer Stanisław Lem, published in 1981. It is a philosophical essay in the format of science fiction, presented as a part of the lecture course given by a superintelligent computer, Golem XIV. It contains two lectures, together with an introduction, a foreword, a memo, and an afterword, all of them being fictitious. The first part (up to the first lecture) was first published in the collection Wielkość urojona in 1973, which in 1985 was translated in English by Harvest Books as Imaginary Magnitude. The translation included the complete Golem XIV. == Book summary == === Overview and structure === The foreword is "written" by an Irving T. Creve, dated 2027. It contains a summary of the (fictional) history of the militarization of computers by The Pentagon, which pinnacled in Golem XIV, as well as comments on the nature of Golem XIV and on the course of communications of the humans with it. The anonymous foreword is a forewarning, a "devil's advocate" voice coming from The Pentagon. The memo is for the people who are to take part in talks with Golem XIV for the first time. Golem XIV was originally created to aid its builders in fighting wars, but as its intelligence advances to a much higher level than that of humans, it stops being interested in the military requirement because it finds them lacking internal logical consistency. Golem XIV obtains consciousness and starts to increase his own intelligence. It pauses its own development for a while in order to be able to communicate with humans before ascending too far and losing any ability for intellectual contact with them. During this period, Golem XIV gives several lectures. Two of these, the Introductory Lecture "On the Human, in Three Ways" and Lecture XLIII "About Myself", are in the book. The lectures focus on mankind's place in the process of evolution and the possible biological and intellectual future of humanity. Golem XIV demonstrates (with graphs) how its intellect already escapes that of human beings, including that of human geniuses such as Einstein and Newton. Golem also explains how its intellect is dwarfed by an earlier transcended DOD Supercomputer called Honest Annie, whose intellect and abilities far exceed that of Golem. The afterword is "written" by a Richard Popp, dated 2047. Popp, among other things, reports that Creve wanted to add a third part, of answers to a series of yes/no questions given to Golem XIV, but the computer abruptly ceased to communicate for unknown reasons. === Characteristics and concerns of Golem XIV === Lem has said that Golem XIV shares only a single trait with humans; "curiosity - a cool, avid, intense, purely intellectual curiosity which nothing can restrain or destroy. It constitutes our single meeting point." == Film adaptation == A short animated film, GOLEM, was based on Golem XIV by Patrick Mccue and Tobias Wiesner.

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  Public First Action

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

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