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  • Production (computer science)

    Production (computer science)

    In computer science, a production or production rule is a rewrite rule that replaces some symbols with other symbols. A finite set of productions P {\displaystyle P} is the main component in the specification of a formal grammar (specifically a generative grammar). In such grammars, a set of productions is a special case of relation on the set of strings V ∗ {\displaystyle V^{}} (where ∗ {\displaystyle {}^{}} is the Kleene star operator) over a finite set of symbols V {\displaystyle V} called a vocabulary that defines which non-empty strings can be substituted with others. The set of productions is thus a special kind subset P ⊂ V ∗ × V ∗ {\displaystyle P\subset V^{}\times V^{}} and productions are then written in the form u → v {\displaystyle u\to v} to mean that ( u , v ) ∈ P {\displaystyle (u,v)\in P} (not to be confused with → {\displaystyle \to } being used as function notation, since there may be multiple rules for the same u {\displaystyle u} ). Given two subsets A , B ⊂ V ∗ {\displaystyle A,B\subset V^{}} , productions can be restricted to satisfy P ⊂ A × B {\displaystyle P\subset A\times B} , in which case productions are said "to be of the form A → B {\displaystyle A\to B} . Different choices and constructions of A , B {\displaystyle A,B} lead to different types of grammars. In general, any production of the form u → ϵ , {\displaystyle u\to \epsilon ,} where ϵ {\displaystyle \epsilon } is the empty string (sometimes also denoted λ {\displaystyle \lambda } ), is called an erasing rule, while productions that would produce strings out of nowhere, namely of the form ϵ → v , {\displaystyle \epsilon \to v,} are never allowed. In order to allow the production rules to create meaningful sentences, the vocabulary is partitioned into (disjoint) sets Σ {\displaystyle \Sigma } and N {\displaystyle N} providing two different roles: Σ {\displaystyle \Sigma } denotes the terminal symbols known as an alphabet containing the symbols allowed in a sentence; N {\displaystyle N} denotes nonterminal symbols, containing a distinguished start symbol S ∈ N {\displaystyle S\in N} , that are needed together with the production rules to define how to build the sentences. In the most general case of an unrestricted grammar, a production u → v {\displaystyle u\to v} , is allowed to map arbitrary strings u {\displaystyle u} and v {\displaystyle v} in V {\displaystyle V} (terminals and nonterminals), as long as u {\displaystyle u} is not empty. So unrestricted grammars have productions of the form V ∗ ∖ { ϵ } → V ∗ {\displaystyle V^{}\setminus \{\epsilon \}\to V^{}} or if we want to disallow changing finished sentences V ∗ N V ∗ = ( V ∗ ∖ Σ ∗ ) → V ∗ {\displaystyle V^{}NV^{}=(V^{}\setminus \Sigma ^{})\to V^{}} , where V ∗ N V ∗ {\displaystyle V^{}NV^{}} indicates concatenation and forces a non-terminal symbol to always be present on the left-hand side of the productions, and ∖ {\displaystyle \setminus } denotes set minus or set difference. If we do not allow the start symbol to occur in v {\displaystyle v} (the word on the right side), we have to replace V ∗ {\displaystyle V^{}} with ( V ∖ { S } ) ∗ {\displaystyle (V\setminus \{S\})^{}} on the right-hand side. The other types of formal grammar in the Chomsky hierarchy impose additional restrictions on what constitutes a production. Notably in a context-free grammar, the left-hand side of a production must be a single nonterminal symbol. So productions are of the form: N → V ∗ {\displaystyle N\to V^{}} == Grammar generation == To generate a string in the language, one begins with a string consisting of only a single start symbol, and then successively applies the rules (any number of times, in any order) to rewrite this string. This stops when a string containing only terminals is obtained. The language consists of all the strings that can be generated in this manner. Any particular sequence of legal choices taken during this rewriting process yields one particular string in the language. If there are multiple different ways of generating this single string, then the grammar is said to be ambiguous. For example, assume the alphabet consists of a {\displaystyle a} and b {\displaystyle b} , with the start symbol S {\displaystyle S} , and we have the following rules: 1. S → a S b {\displaystyle S\rightarrow aSb} 2. S → b a {\displaystyle S\rightarrow ba} then we start with S {\displaystyle S} , and can choose a rule to apply to it. If we choose rule 1, we replace S {\displaystyle S} with a S b {\displaystyle aSb} and obtain the string a S b {\displaystyle aSb} . If we choose rule 1 again, we replace S {\displaystyle S} with a S b {\displaystyle aSb} and obtain the string a a S b b {\displaystyle aaSbb} . This process is repeated until we only have symbols from the alphabet (i.e., a {\displaystyle a} and b {\displaystyle b} ). If we now choose rule 2, we replace S {\displaystyle S} with b a {\displaystyle ba} and obtain the string a a b a b b {\displaystyle aababb} , and are done. We can write this series of choices more briefly, using symbols: S ⇒ a S b ⇒ a a S b b ⇒ a a b a b b {\displaystyle S\Rightarrow aSb\Rightarrow aaSbb\Rightarrow aababb} . The language of the grammar is the set of all the strings that can be generated using this process: { b a , a b a b , a a b a b b , a a a b a b b b , … } {\displaystyle \{ba,abab,aababb,aaababbb,\dotsc \}} .

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  • Andrei Knyazev (mathematician)

    Andrei Knyazev (mathematician)

    Andrew Knyazev is an American mathematician. He graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University under the supervision of Evgenii Georgievich D'yakonov (Russian: Евгений Георгиевич Дьяконов) in 1981 and obtained his PhD in Numerical Mathematics at the Russian Academy of Sciences under the supervision of Vyacheslav Ivanovich Lebedev (Russian: Вячеслав Иванович Лебедев) in 1985. He worked at the Kurchatov Institute between 1981–1983, and then to 1992 at the Marchuk Institute of Numerical Mathematics (Russian: ru:Институт вычислительной математики имени Г. И. Марчука РАН) of the Russian Academy of Sciences, headed by Gury Marchuk (Russian: Гурий Иванович Марчук). From 1993–1994, Knyazev held a visiting position at the Courant Institute of Mathematical Sciences of New York University, collaborating with Olof B. Widlund. From 1994 until retirement in 2014, he was a Professor of Mathematics at the University of Colorado Denver, supported by the National Science Foundation and United States Department of Energy grants. He was a recipient of the 2008 Excellence in Research Award, the 2000 college Teaching Excellence Award, and a finalist of the CU President's Faculty Excellence Award for Advancing Teaching and Learning through Technology in 1999. He was awarded the title of Professor Emeritus at the University of Colorado Denver and named the SIAM Fellow Class of 2016 and AMS Fellow Class of 2019. From 2012–2018, Knyazev worked at the Mitsubishi Electric Research Laboratories on algorithms for image and video processing, data sciences, optimal control, and material sciences, resulting in dozens of publications and 13 patent applications. Since 2018, he contributed to numerical techniques in quantum computing at Zapata Computing, real-time embedded anomaly detection in automotive data, and algorithms for silicon photonics-based hardware. Knyazev is mostly known for his work in numerical solution of large sparse eigenvalue problems, particularly preconditioning and the iterative method LOBPCG. Knyazev's implementation of LOBPCG is available in many open source software packages, e.g., BLOPEX, SciPy, and ABINIT. Knyazev collaborated with John Osborn on the theory of the Ritz method in the finite element method context and with Nikolai Sergeevich Bakhvalov (Russian: Николай Серге́евич Бахвалов) (Erdős number 3 via Leonid Kantorovich) on numerical solution of elliptic partial differential equations with large jumps in the main coefficients. Jointly with his Ph.D. students, Knyazev pioneered using majorization for bounds in the Rayleigh–Ritz method (see and references there) and contributed to the theory of angles between flats.

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  • Best AI Presentation Makers in 2026

    Best AI Presentation Makers in 2026

    In search of the best AI presentation maker? An AI presentation maker is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI presentation maker slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Machine translation in China

    Machine translation in China

    Machine translation in China is the history of machine translation systems developed in China. China became the fourth country that began machine translation (MT) research following USA, UK, and the Soviet Union. In 1957, the Language Institute of Chinese Academy of Sciences took the initiative in Russian-Chinese MT research program and set up an MT research group. From then on the research activities were directed and applied for academic purposes in Universities. The turning point of MT systems launching initiatives in market began from 1990s. MT systems went into blossom into the market. Among these systems, there were commercialized MT systems. To be more specific, Transtar was the first commercialized MT system and has been constantly upgraded. What's more, IMC/EC MT system which was developed by Computer Institute of Chinese Academy of Sciences has further made great advancement. Meanwhile, the practical MT system MT-IT-EC specific to communication domain was also striking to notice, for it has greatly improved the efficiency and productivity in the issue of publications. Government funding is a critical component and support in the development of market-oriented machine translation in China. It is evident to see that since Chinese opened up to the outside world and joined the WTO, the vigorous import and export trade generate opportunities for machine translation to transfer technical terms of products into the readable target information. Facing the increasing demand of sophisticated state-of -the -art translation technology, the academic area including research institute and universities are even launching bachelors’ and master's programs regarding machine translation. Thus, strong evidence illustrates the promising field of machine translation in the future market of China.

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  • Token maxxing

    Token maxxing

    Token Maxxing or Token Maxing is a metric used in an attempt to track productivity in the workplace especially for those using Artificial Intelligence (AI) based services. AI services charge for each token which represent units of effort expended by an AI service to solve a problem. Some believe that token consumption equates to productivity and thus can be used as a metric to monitor an employee's work. Supporters believe that higher token usage indicates higher productivity and higher utilization of powerful AI services. This also suggests that those not consuming enough tokens may be less productive and underutilizing powerful AI services. This belief might lead to an environment that incentivizes higher token usage to predict increased productivity. Critics of token maxxing as a metric claim that prudent workers will maximize any metric that management wants increased to gain a workplace advantage. For example: Engineers in the tech industries pressed to consume as many tokens as possible might run several AI agents in tandem, enter longer input prompts, or automate their tasks to maximize their token consumption. To management, this higher token usage may indicate potential productivity, but in reality may cause additional token costs, worker burnout, or actually create more bloated code of lower quality. Another claim is AI service companies potentially benefit from such an emphasis on token consumption and actively encourage the trend. Some developers have publicly advocated the practice. Developer Sigrid Jin, who said he used 50 billion tokens in a single year, has argued that maximizing token consumption is the best way to understand the value of AI, advising others to spend as much on AI usage as they pay in rent to obtain a return on investment. == See Also == Goodhart's law Perverse incentive Jevons Paradox

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  • Is an AI Video Generator Worth It in 2026?

    Is an AI Video Generator Worth It in 2026?

    Curious about the best AI video generator? An AI video generator is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI video generator slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Bernard Vauquois

    Bernard Vauquois

    Bernard Vauquois ((1929-06-14)June 14, 1929 — (1985-09-30)September 30, 1985) was a French mathematician and computer scientist. He was a pioneer of computer science and machine translation (MT) in France. An astronomer-turned-computer scientist, he is known for his work on the programming language ALGOL 60, and later for extensive work on the theoretical and practical problems of MT, of which the eponymous Vauquois triangle is one of the most widely-known contributions. He was a professor at what would become the Grenoble Alpes University. == Biography == Bernard Vauquois was initially a researcher at French National Centre for Scientific Research (CNRS) from 1952 to 1958 at the Astrophysics Institute of the Meudon Observatory, after completing studies in mathematics, physics, and astronomy. Since 1957, his research program has also focused on methods applied to physics from the perspective of electronic computers, and he has taught programming to physicists. This double interest in astrophysics and electronic computers is reflected in the subject of his thesis and that of the complementary thesis in physical sciences that he defended in 1958. In 1960, at 31 years old, he was appointed professor of computer science at Grenoble University, where, alongside professors Jean Kuntzmann and Noël Gastinel, he began work in the field. At that time, he was also contributing to the definition of the language ALGOL 60. Also in 1960, he founded the Centre d'Étude pour la Traduction Automatique (CETA), later renamed as Groupe d'Étude pour la Traduction Automatique (GETA) and currently known as GETALP, a team at the Laboratoire d'informatique de Grenoble, and soon showed his gift for rapid understanding, synthesis, and innovation, and his taste for personal communication across linguistic borders and barriers. After visiting a number of centers, mainly in the United States, where machine translation research was conducted, he analyzed the shortcomings of the "first-generation" approach and evaluated the potential of a new generation based on grammar and formal language theory, and proposed a new approach based on a representational "pivot" and the use of (declarative) rule systems that transform a sequential sentence from one level of representation to another. He led the GETA in constructing the first large second-generation system, applied to Russian–French, from 1962 to 1971. At the end of this period, the accumulated experience led him to correct some defects of the "pure" declarative and interlingual approach, and to use heuristic programming methods, implemented with procedural grammars written in LSPLs ("specialized languages for linguistic programming", langages spécialisés pour la programmation linguistique) that were developed under his direction, and integrated into the ARIANE-78 machine translation system. In 1974, when he cofounded the Leibniz laboratory, he proposed "multilevel structure descriptors" (descripteurs de structures multiniveaux) for units larger than sentence translation. This idea, premonitory of later theoretical work (Ray Jackendoff, Gerald Gazdar) is still the cornerstone of all machine translation software built by GETA and the French national TA project. Bernard Vauquois' last contribution was "static grammar" (grammaire statique) in 1982–83, during the ESOPE project, the preparatory phase of the French national MT project. He was a key figure in the field of computational linguistics in France. At CNRS, he was a member of section 22 of the National Committee in 1963: "General Linguistics, Modern Languages and Comparative Literature", and then, in 1969, of section 28: "General Linguistics, Foreign Languages and Literature". Since 1965, he has been vice-president of the Association for Natural Language Processing (ATALA). He was its president from 1966 to 1971. He was also one of the founders, in 1965, of the ICCL (International Committee on Computational Linguistics), which organizes COLING conferences. He was its president from 1969 to 1984. From France, he often collaborated with other countries (notably Canada, the United States, the USSR, Czechoslovakia, Japan, China, Brazil, Malaysia, and Thailand), working on the specification and implementation of grammars and dictionaries. He began cooperating with Malaysia, for example, in 1979, which led to the creation of the Automatic Terjemaan Project, with a first prototype of an English-Malay MT system demonstrated in 1980. == Vauquois triangle == The Vauquois triangle is a conceptual model and diagram illustrating possible approaches to the design of machine translation systems, first proposed in 1968. == Legacy == Bernard Vauquois is regarded as a pioneer of machine translation in France. He played a key role in developing the first large-scale second-generation machine translation system, and his work influenced the field of machine translation for many years. He supervised some twenty doctoral theses, most of them concerning formal aspects of natural and artificial languages, with an emphasis on machine translation. The Center for Studies on Automatic Translation, which Vauquois founded in 1960, later became the Group for the Study of Machine Translation and Automated Processing of Languages and Speech (GETALP). It is still a research institution in natural language processing. Vauquois was a prolific writer and speaker, disseminating knowledge about machine translation and related topics. His papers and presentations were instrumental in establishing the field of machine translation in France and beyond. == Publications == Vauquois, Bernard (1973). Traduction automatique (in French). Paris: Gauthier-Villars. Vauquois, Bernard (1967). Introduction à la traduction automatique (in French). Paris: Gauthier-Villars.

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  • Language model

    Language model

    A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, information retrieval and disaster response. Large language models (LLMs), currently their most advanced form as of 2026, are predominantly based on transformers trained on larger datasets (frequently using texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as the word n-gram language model. == History == Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule-based formal grammars. Discrete representations like word n-gram language models, with probabilities for discrete combinations of words, made significant advances. In the 2000s, continuous representations for words, such as word embeddings, began to replace discrete representations. Typically, the representation is a real-valued vector that encodes a word’s meaning such that words closer in vector space are similar in meaning and common relationships between words, such as plurality or gender, are preserved. == Pure statistical models == In 1980, the first significant statistical language model was proposed, and during the decade IBM performed 'Shannon-style' experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. === Models based on word n-grams === === Exponential === Maximum entropy language models encode the relationship between a word and the n-gram history using feature functions. The equation is P ( w m ∣ w 1 , … , w m − 1 ) = 1 Z ( w 1 , … , w m − 1 ) exp ⁡ ( a T f ( w 1 , … , w m ) ) {\displaystyle P(w_{m}\mid w_{1},\ldots ,w_{m-1})={\frac {1}{Z(w_{1},\ldots ,w_{m-1})}}\exp(a^{T}f(w_{1},\ldots ,w_{m}))} where Z ( w 1 , … , w m − 1 ) {\displaystyle Z(w_{1},\ldots ,w_{m-1})} is the partition function, a {\displaystyle a} is the parameter vector, and f ( w 1 , … , w m ) {\displaystyle f(w_{1},\ldots ,w_{m})} is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n-gram. It is helpful to use a prior on a {\displaystyle a} or some form of regularization. The log-bilinear model is another example of an exponential language model. === Skip-gram model === == Neural models == === Recurrent neural network === Continuous representations or embeddings of words are produced in recurrent neural network-based language models (known also as continuous space language models). Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, further causing a data sparsity problem. Neural networks avoid this problem by representing words as non-linear combinations of weights in a neural net. === Large language models === Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do. == Evaluation and benchmarks == Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves. Various data sets have been developed for use in evaluating language processing systems. These include: Massive Multitask Language Understanding (MMLU) Corpus of Linguistic Acceptability GLUE benchmark Microsoft Research Paraphrase Corpus Multi-Genre Natural Language Inference Question Natural Language Inference Quora Question Pairs Recognizing Textual Entailment Semantic Textual Similarity Benchmark SQuAD question answering Test Stanford Sentiment Treebank Winograd NLI BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs

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  • Digital omnivore

    Digital omnivore

    A digital omnivore is a person who uses multiple modalities (devices) to access the Internet and other media content in their daily life. As people increasingly own mobile devices, cross-platform multimedia consumption has continued to shape the digital landscape, both in terms of the type of media content they consume and how they consume it. As of 2021, at least half of all global digital traffic is generated by mobile devices. == Connected devices and digital consumption == A 2015 study of digital media consumption showed that smartphones were primarily used for communication, and tablets were primarily used for entertainment – additionally, both were frequently used in conjuncture with other devices, like televisions. An earlier 2011 analysis of the way consumers in the U.S. viewed news content on their devices throughout the day demonstrated how people use different mobile devices for different functions. On a typical weekend morning, digital omnivores accessed their news using their tablet, favored their computer during the working day, and returned to tablet use in the evening, peaking between the hours of 9pm and midnight. Mobile phones were used for web-browsing throughout the day when users were away from their personal computer. Increased Wi-Fi availability and mobile broadband adoption have changed the way people are going online. In August 2011, more than a third (37.2%) of U.S. digital traffic coming from mobile phones occurred via a Wi-Fi connection while tablets, which traditionally required a Wi-Fi connection to access the Internet, are increasingly driving traffic using mobile broadband access. As of 2021, LTE, 5G, and other forms of mobile broadband access are available on the majority of mobile devices. Tablets contributed nearly 2% of all web browsing traffic in the United States in 2011. During this period, iPads also began to account for a higher share of Internet traffic than iPhones (46.8% vs. 42.6% of all iOS device traffic. == Implications for marketing, advertisers and publishers == As of 2021, the average amount of time spent daily consuming digital media was eight hours, an increase from 2020 and a further increase from 2019, partially as a result of the COVID-19 pandemic. Social media platforms such as Instagram, Facebook, Twitter, and TikTok, as well as other online platforms like YouTube, incorporate advertisements into the in-app or online experience, with some offering the ability to shop for and sell items through the app or website.

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  • Best AI Image Generators in 2026

    Best AI Image Generators in 2026

    Comparing the best AI image generator? An AI image generator is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI image generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • NovelAI

    NovelAI

    NovelAI is an online cloud-based, SaaS model, and a paid subscription service for AI-assisted storywriting and text-to-image synthesis, originally launched in beta on June 15, 2021, with the image generation feature being implemented later on October 3, 2022. NovelAI is owned and operated by Anlatan, which is headquartered in Wilmington, Delaware. == Features == NovelAI uses GPT-based large language models (LLMs) to generate storywriting and prose. It has several models, such as Calliope, Sigurd, Euterpe, Krake, and Genji, with Genji being a Japanese-language model. The service also offers encrypted servers and customizable editors. For AI art generation, which generates images from text prompts, NovelAI uses a custom version of the source-available Stable Diffusion text-to-image diffusion model called NovelAI Diffusion, which is trained on a Danbooru-based dataset. NovelAI is also capable of generating a new image based on an existing image. The NovelAI terms of service states that all generated content belongs to the user, regardless if the user is an individual or a corporation. Anlatan states that generated images are not stored locally on their servers. == History == On April 28, 2021, Anlatan officially launched NovelAI. On June 15, 2021, Anlatan released their finetuned GPT-Neo-2.7B model from EleutherAI named Calliope, after the Greek Muses. A day later, they released their Opus-exclusive GPT-J-6B finetuned model named Sigurd, after the Norse/Germanic hero. On March 21, 2023, Nvidia and CoreWeave announced Anlatan being one of the first CoreWeave customers to deploy NVIDIA's H100 Tensor Core GPUs for new LLM model inferencing and training. On April 1, 2023, Anlatan added ControlNet features to their text-to-image NovelAI Diffusion model. On May 16, 2023, Anlatan announced that they named their H100 cluster Shoggy, a reference to H.P. Lovecraft's Shoggoths, which was used to pre-train an undisclosed 8192 token context LLM in-house model. == Reception and controversy == Following the implementation of image generation, NovelAI became a widely-discussed topic in Japan, with some online commentators noting that its image synthesis features are very adept at producing close impressions of anime characters, including lolicon and shotacon imagery, while others have expressed concern that it is a paid service reliant on a diffusion model, while the original machine learning training data consists of images used without the consent of the original artists. Attorney Kosuke Terauchi notes that, since a revision of the law in 2018, it is no longer illegal in Japan for machine learning models to scrape copyrighted content from the internet to use as training data; meanwhile, in the United States where NovelAI is based, there is no specific legal framework which regulates machine learning, and thus the fair use doctrine of US copyright law applies instead. Danbooru has posted an official statement in regards to NovelAI's use of the site's content for AI training, expressing that Danbooru is not affiliated with NovelAI, and does not endorse nor condone NovelAI's use of artists' artworks for machine learning. FayerWayer described NovelAI as a service capable of generating hentai. Manga artist Izumi Ū commented that while the manga style art generated by NovelAI is highly accurate, there are still imperfections in the output, although he views these as human-like in a favourable light nonetheless. In response to the topic of NovelAI, Narugami, founder of the Japanese freelance artist commissioning website Skeb, stated on October 5, 2022 that the use of AI image generation is prohibited on the platform since 2018. Illustrations using NovelAI have been posted on social media and illustration posting sites, and by October 13, 2,111 works tagged with #NovelAI were posted on Pixiv. Pixiv has stated that it is not considering a complete elimination of creations that use AI, though it requires AI-generated posts to be marked as such and allows users to filter them out. == Incidents == On October 6, 2022, NovelAI experienced a data breach where its software's source code was leaked.

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  • Corpus language

    Corpus language

    A corpus language is a language that has no living speakers but for which numerous records produced by its native speakers survive. Examples of corpus languages are Ancient Greek, Latin, the Egyptian language, Old English, Old Norse, Elamite, and Sanskrit. Some corpus languages, such as Ancient Greek and Latin, left very large corpora and therefore can be fully reconstructed, even though some details of pronunciation may be unclear. Such languages can be used even today, as is the case with Sanskrit and Latin. Other languages have such limited corpora that some important words—e.g., some pronouns—are lacking in the corpora. Examples of these are Ugaritic and Gothic. Languages attested only by a few words, often names, and a few phrases, are called Trümmersprache (literally "rubble languages") in German linguistics. These can be reconstructed only in a very limited way, and often their genetic relationship to other languages remains unclear. Examples are Dalmatian, Etruscan, also known as Rasenna, Dadanitic, a Semitic language that may be close to classical Arabic, Lombardic, Burgundian, Vandalic, and Oscan, Umbrian, and Faliscan, all Italic languages that were related to Latin. Corpus languages are studied using the methods of corpus linguistics, but corpus linguistics can also be used (and is commonly used) for the study of the writings and other records of living languages. Not all extinct languages are corpus languages, since there are many extinct languages in which few or no writings or other records survive, as is the case in the vast majority of languages that have ever existed.

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  • Exploration–exploitation dilemma

    Exploration–exploitation dilemma

    The exploration–exploitation dilemma, also known as the explore–exploit tradeoff, is a fundamental concept in decision-making that arises in many domains. It is depicted as the balancing act between two opposing strategies. Exploitation involves choosing the best option based on current knowledge of the system (which may be incomplete or misleading), while exploration involves trying out new options that may lead to better outcomes in the future at the expense of an exploitation opportunity. Finding the optimal balance between these two strategies is a crucial challenge in many decision-making problems whose goal is to maximize long-term benefits. == Application in machine learning == In the context of machine learning, the exploration–exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves training agents to make decisions based on feedback from the environment. Crucially, this feedback may be incomplete or delayed. The agent must decide whether to exploit the current best-known policy or explore new policies to improve its performance. === Multi-armed bandit methods === The multi-armed bandit (MAB) problem was a classic example of the tradeoff, and many methods were developed for it, such as epsilon-greedy, Thompson sampling, and the upper confidence bound (UCB). See the page on MAB for details. In more complex RL situations than the MAB problem, the agent can treat each choice as a MAB, where the payoff is the expected future reward. For example, if the agent performs an epsilon-greedy method, then the agent will often "pull the best lever" by picking the action that had the best predicted expected reward (exploit). However, it would pick a random action with probability epsilon (explore). Monte Carlo tree search, for example, uses a variant of the UCB method. === Exploration problems === There are some problems that make exploration difficult. Sparse reward. If rewards occur only once a long while, then the agent might not persist in exploring. Furthermore, if the space of actions is large, then the sparse reward would mean the agent would not be guided by the reward to find a good direction for deeper exploration. A standard example is Montezuma's Revenge. Deceptive reward. If some early actions give immediate small reward, but other actions give later large reward, then the agent might be lured away from exploring the other actions. Noisy TV problem. If certain observations are irreducibly noisy (such as a television showing random images), then the agent might be trapped exploring those observations (watching the television). === Exploration reward === This section based on. The exploration reward (also called exploration bonus) methods convert the exploration-exploitation dilemma into a balance of exploitations. That is, instead of trying to get the agent to balance exploration and exploitation, exploration is simply treated as another form of exploitation, and the agent simply attempts to maximize the sum of rewards from exploration and exploitation. The exploration reward can be treated as a form of intrinsic reward. We write these as r t i , r t e {\displaystyle r_{t}^{i},r_{t}^{e}} , meaning the intrinsic and extrinsic rewards at time step t {\displaystyle t} . However, exploration reward is different from exploitation in two regards: The reward of exploitation is not freely chosen, but given by the environment, but the reward of exploration may be picked freely. Indeed, there are many different ways to design r t i {\displaystyle r_{t}^{i}} described below. The reward of exploitation is usually stationary (i.e. the same action in the same state gives the same reward), but the reward of exploration is non-stationary (i.e. the same action in the same state should give less and less reward). Count-based exploration uses N n ( s ) {\displaystyle N_{n}(s)} , the number of visits to a state s {\displaystyle s} during the time-steps 1 : n {\displaystyle 1:n} , to calculate the exploration reward. This is only possible in small and discrete state space. Density-based exploration extends count-based exploration by using a density model ρ n ( s ) {\displaystyle \rho _{n}(s)} . The idea is that, if a state has been visited, then nearby states are also partly-visited. In maximum entropy exploration, the entropy of the agent's policy π {\displaystyle \pi } is included as a term in the intrinsic reward. That is, r t i = − ∑ a π ( a | s t ) ln ⁡ π ( a | s t ) + ⋯ {\displaystyle r_{t}^{i}=-\sum _{a}\pi (a|s_{t})\ln \pi (a|s_{t})+\cdots } . === Prediction-based === This section based on. The forward dynamics model is a function for predicting the next state based on the current state and the current action: f : ( s t , a t ) ↦ s t + 1 {\displaystyle f:(s_{t},a_{t})\mapsto s_{t+1}} . The forward dynamics model is trained as the agent plays. The model becomes better at predicting state transition for state-action pairs that had been done many times. A forward dynamics model can define an exploration reward by r t i = ‖ f ( s t , a t ) − s t + 1 ‖ 2 2 {\displaystyle r_{t}^{i}=\|f(s_{t},a_{t})-s_{t+1}\|_{2}^{2}} . That is, the reward is the squared-error of the prediction compared to reality. This rewards the agent to perform state-action pairs that had not been done many times. This is however susceptible to the noisy TV problem. Dynamics model can be run in latent space. That is, r t i = ‖ f ( s t , a t ) − ϕ ( s t + 1 ) ‖ 2 2 {\displaystyle r_{t}^{i}=\|f(s_{t},a_{t})-\phi (s_{t+1})\|_{2}^{2}} for some featurizer ϕ {\displaystyle \phi } . The featurizer can be the identity function (i.e. ϕ ( x ) = x {\displaystyle \phi (x)=x} ), randomly generated, the encoder-half of a variational autoencoder, etc. A good featurizer improves forward dynamics exploration. The Intrinsic Curiosity Module (ICM) method trains simultaneously a forward dynamics model and a featurizer. The featurizer is trained by an inverse dynamics model, which is a function for predicting the current action based on the features of the current and the next state: g : ( ϕ ( s t ) , ϕ ( s t + 1 ) ) ↦ a t {\displaystyle g:(\phi (s_{t}),\phi (s_{t+1}))\mapsto a_{t}} . By optimizing the inverse dynamics, both the inverse dynamics model and the featurizer are improved. Then, the improved featurizer improves the forward dynamics model, which improves the exploration of the agent. Random Network Distillation (RND) method attempts to solve this problem by teacher–student distillation. Instead of a forward dynamics model, it has two models f , f ′ {\displaystyle f,f'} . The f ′ {\displaystyle f'} teacher model is fixed, and the f {\displaystyle f} student model is trained to minimize ‖ f ( s ) − f ′ ( s ) ‖ 2 2 {\displaystyle \|f(s)-f'(s)\|_{2}^{2}} on states s {\displaystyle s} . As a state is visited more and more, the student network becomes better at predicting the teacher. Meanwhile, the prediction error is also an exploration reward for the agent, and so the agent learns to perform actions that result in higher prediction error. Thus, we have a student network attempting to minimize the prediction error, while the agent attempting to maximize it, resulting in exploration. The states are normalized by subtracting a running average and dividing a running variance, which is necessary since the teacher model is frozen. The rewards are normalized by dividing with a running variance. Exploration by disagreement trains an ensemble of forward dynamics models, each on a random subset of all ( s t , a t , s t + 1 ) {\displaystyle (s_{t},a_{t},s_{t+1})} tuples. The exploration reward is the variance of the models' predictions. === Noise === For neural network–based agents, the NoisyNet method changes some of its neural network modules by noisy versions. That is, some network parameters are random variables from a probability distribution. The parameters of the distribution are themselves learnable. For example, in a linear layer y = W x + b {\displaystyle y=Wx+b} , both W , b {\displaystyle W,b} are sampled from Gaussian distributions N ( μ W , Σ W ) , N ( μ b , Σ b ) {\displaystyle {\mathcal {N}}(\mu _{W},\Sigma _{W}),{\mathcal {N}}(\mu _{b},\Sigma _{b})} at every step, and the parameters μ W , Σ W , μ b , Σ b {\displaystyle \mu _{W},\Sigma _{W},\mu _{b},\Sigma _{b}} are learned via the reparameterization trick.

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  • Samer Hassan

    Samer Hassan

    Samer Hassan is a computer scientist, social scientist, activist and researcher, focused on the study of the collaborative economy, online communities and decentralized technologies. He is an associate professor at Universidad Complutense de Madrid (Spain) and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. He is the recipient of an ERC Grant of 1.5M€ with the P2P Models project, to research blockchain-based decentralized autonomous organizations for the collaborative economy. == Education and career == Hassan is a Spanish/Lebanese scholar with an interdisciplinary background, which combines computer sciences with social sciences and activism. He received a degree in Computer Science and MSc in Artificial Intelligence from the Universidad Complutense de Madrid (UCM) in Spain. He also studied three years of Political Science at the distance learning university UNED. He then pursued a PhD in Social Simulation at the department of Software Engineering and Artificial Intelligence of UCM, supervised by the computer scientist Juan Pavón and the sociologist Millán Arroyo-Menéndez. He has been researching in several institutions, funded by several scholarships and awards, most notably Harvard's Real Colegio Complutense, and the Spanish postdoctoral grants Juan de la Cierva and José Castillejo. Thus, he was a visiting researcher at the Centre for Research in Social Simulation, in the Department of Sociology at the University of Surrey in the UK, working under the supervision of Nigel Gilbert (2007-2008), and a lecturer at the American University of Science and Technology in Lebanon (2010–11). He was selected as Fellow at the Berkman Klein Center for Internet & Society at Harvard University (2015-2017) and is presently a Faculty Associate at the same structure. Starting in 2024, he joined, as affiliate faculty, the Institute for Digital Cooperative Economy (The New School), part of the Platform Cooperativism Consortium. == Activism and social engagement == As an activist, Hassan has been engaged in both offline (La Tabacalera de Lavapiés, Medialab-Prado) and online (Ourproject.org, Barrapunto, Wikipedia) initiatives. He was accredited as a grassroots facilitator by the Altekio Cooperative. He co-founded the Comunes Nonprofit in 2009 and the Move Commons webtool project in 2010. He has co-organized practitioner-oriented workshops on platform co-ops and free/open source decentralized tools for communities, and has presented his work in non-academic conferences of Mozilla, the Internet Archive, and others. As a privacy advocate, he co-created a course on cyber-ethics which has been teaching since 2013 (as of 2021). He was co-founder of the Sci-Fdi Spanish science-fiction magazine. His gender is non-binary and uses he/they pronouns. == Work == Hassan's interdisciplinary research spans multiple fields, including online communities, online governance, online collaboration, decentralized technologies, blockchain-based decentralized autonomous organizations, free/libre/open source software, Commons-based peer production, agent-based social simulation, social movements and cyberethics. He has published more than 60 works in these fields. Hassan's PhD thesis focused on the methodological challenges for building data-driven social simulation models. The main model built simulated the transition from modern values to postmodern values in Spain. His methodological work also explored the combination of different artificial intelligence technologies, i.e. software agents with fuzzy logic, data mining, natural language processing, and microsimulation. In his postdoctoral period, he focused on experimenting with multiple software systems to facilitate the collaborative economy, e.g. semantic-web labelling for commons-based initiatives, distribution of value in peer production communities, agent-supported online assemblies, decentralized real-time collaborative software, decentralized blockchain based reputation, or blockchain-enabled commons governance. Hassan was Principal Investigator of the UCM partner in the EU-funded P2Pvalue project on building decentralized web-tools for collaborative communities. As such, he led the team that created SwellRT, a federated backend-as-a-service focused to ease development of apps featuring real-time collaboration. Intellectual Property of this project was transferred to the Apache Software Foundation in 2017. As part of this research line, Hassan's team also develop two SwellRT-based apps, "Teem" for management of social collectives and Jetpad, a federated real time editor. He presented the innovations concerning these software at Harvard's Berkman Klein Center and Harvard's Center for Research on Computation and Society. Other research lines offered outcomes beyond publications. "Wikichron", coled by Javier Arroyo, is a web tool to visualize MediaWiki community metrics, currently in production and available for third-parties. "Decentralized Science", led by Hassan's PhD student Ámbar Tenorio-Fornés, is a framework to facilitate decentralized infrastructure and open peer review in the scientific publication process, which has been selected by the European Commission to receive funding as a spin-off social enterprise. His research on blockchain and crowdfunding models awarded him with a commission from Triple Canopy. His team pushed forward a mapping of the ecosystem of blockchain for social good, led by the Joint Research Centre and published by the European Commission. As part of his ERC project P2P Models, Hassan and his team –including Silvia Semenzin– are investigating whether blockchain technology and Decentralized Autonomous Organizations could contribute to improving the governance of commons-oriented communities, both online and offline. Their work has been showcased for tackling the impact of blockchain on governance, proposing alternatives to the current sharing economy, emerging forms of techno-social systems like NFTs or prediction markets, or giving relevance to gender issues in the field. Hassan was invited to present the project achievements in Harvard Kennedy School, MIT Media Lab, Harvard's Data Privacy Lab, Harvard's Center for Research on Computation and Society, and Harvard's SEAS EconCS. British MP and Opposition Leader Ed Miliband showcased his research and its potential impact on policy. The project made public its way of organizing and its core values. In particular, it has shown a commitment to diversity as a core value in hiring, or choosing case studies. == Selected works == Arroyo, Javier; Davó, David; Martínez-Vicente, Elena; Faqir-Rhazoui, Youssef; Hassan, Samer (8 November 2022). "DAO-Analyzer: Exploring Activity and Participation in Blockchain Organizations" (PDF). Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing. CSCW'22 Companion. New York, NY, USA: Association for Computing Machinery. pp. 193–196. doi:10.1145/3500868.3559707. ISBN 978-1-4503-9190-0. Rozas, David; Tenorio-Fornés, Antonio; Díaz-Molina, Silvia; Hassan, Samer (2021). "When Ostrom Meets Blockchain: Exploring the Potentials of Blockchain for Commons Governance". SAGE Open. 11 (1): 215824402110025. doi:10.1177/21582440211002526. ISSN 2158-2440. Faqir-Rhazoui, Youssef; Ariza-Garzón, Miller-Janny; Arroyo, Javier; Hassan, Samer (8 May 2021). "Effect of the Gas Price Surges on User Activity in the DAOs of the Ethereum Blockchain" (PDF). Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. CHI EA '21. New York, NY, USA: Association for Computing Machinery. pp. 1–7. doi:10.1145/3411763.3451755. ISBN 978-1-4503-8095-9. Hassan, Samer; Filippi, Primavera De (20 April 2021). "Decentralized Autonomous Organization". Internet Policy Review. 10 (2). doi:10.14763/2021.2.1556. hdl:10419/235960. ISSN 2197-6775. Joint Research Centre (European Commission); Hassan, Samer; Hakami, Anna; Brekke, Jaya Klara; De Filippi, Primavera; Lopéz Morales, Genoveva; Pólvora, Alexandre; Orgaz Alonso, Christian; Bodó, Balázs (2020). Scanning the European ecosystem of distributed ledger technologies for social and public good: what, why, where, how, and ways to move forward. LU: Publications Office of the European Union. doi:10.2760/300796. ISBN 978-92-76-21578-3. Filippi, Primavera De; Hassan, Samer (14 November 2016). "Blockchain technology as a regulatory technology: From code is law to law is code". First Monday. arXiv:1801.02507. doi:10.5210/fm.v21i12.7113. ISSN 1396-0466.

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  • Is an Conversational AI Platform Worth It in 2026?

    Is an Conversational AI Platform Worth It in 2026?

    Looking for the best conversational AI platform? An conversational AI platform is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right conversational AI platform slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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