Patent visualisation is an application of information visualisation. The number of patents has been increasing, encouraging companies to consider intellectual property as a part of their strategy. Patent visualisation, like patent mapping, is used to quickly view a patent portfolio. Software dedicated to patent visualisation began to appear in 2000, for example Aureka from Aurigin (now owned by Thomson Reuters). Many patent and portfolio analytics platforms, such as Questel, Patent Forecast, PatSnap, Patentcloud, Relecura, and Patent iNSIGHT Pro, offer options to visualise specific data within patent documents by creating topic maps, priority maps, IP Landscape reports, etc. Software converts patents into infographics or maps, to allow the analyst to "get insight into the data" and draw conclusions. Also called patinformatics, it is the "science of analysing patent information to discover relationships and trends that would be difficult to see when working with patent documents on a one-and-one basis". Patents contain structured data (like publication numbers) and unstructured text (like title, abstract, claims and visual info). Structured data are processed by data-mining and unstructured data are processed with text-mining. == Data mining == The main step in processing structured information is data-mining, which emerged in the late 1980s. Data mining involves statistics, artificial intelligence, and machine learning. Patent data mining extracts information from the structured data of the patent document. These structured data are bibliographic fields such as location, date or status. === Structured fields === === Advantages === Data mining allows study of filing patterns of competitors and locates main patent filers within a specific area of technology. This approach can be helpful to monitor competitors' environments, moves and innovation trends and gives a macro view of a technology status. == Text-mining == === Principle === Text mining is used to search through unstructured text documents. This technique is widely used on the Internet, it has had success in bioinformatics and now in the intellectual property environment. Text mining is based on a statistical analysis of word recurrence in a corpus. An algorithm extracts words and expressions from title, summary and claims and gathers them by declension. "And" and "if" are labeled as non-information bearing words and are stored in the stopword list. Stoplists can be specialised in order to create an accurate analysis. Next, the algorithm ranks the words by weight, according to their frequency in the patent's corpus and the document frequency containing this word. The score for each word is calculated using a formula such as: W e i g h t = T e r m F r e q u e n c y D o c u m e n t F r e q u e n c y = F r e q u e n c y o f t h e w o r d o r e x p r e s s i o n i n t h e T e x t S e a N u m b e r o f d o c u m e n t s c o n t a i n i n g t h e e x p r e s s i o n o r w o r d {\displaystyle Weight={\frac {Term\ Frequency}{Document\ Frequency}}={\frac {Frequency\ of\ the\ word\ or\ expression\ in\ the\ Text\ Sea}{Number\ of\ documents\ containing\ the\ expression\ or\ word}}} A frequently used word in several documents has less weight than a word used frequently in a few patents. Words under a minimum weight are eliminated, leaving a list of pertinent words or descriptors. Each patent is associated to the descriptors found in the selected document. Further, in the process of clusterisation, these descriptors are used as subsets, in which the patent are regrouped or as tags to place the patents in predetermined categories, for example keywords from International Patent Classifications. Four text parts can be processed with text-mining : Title Abstract Claim Patent Full-Text Software offer different combinations but title, abstract and claim are generally the most used, providing a good balance between interferences and relevancy. === Advantages === Text-mining can be used to narrow a search or quickly evaluate a patent corpus. For instance, if a query produces irrelevant documents, a multi-level clustering hierarchy identifies them in order to delete them and refine the search. Text-mining can also be used to create internal taxonomies specific to a corpus for possible mapping. == Visualisations == Allying patent analysis and informatic tools offers an overview of the environment through value-added visualisations. As patents contain structured and unstructured information, visualisations fall in two categories. Structured data can be rendered with data mining in macrothematic maps and statistical analysis. Unstructured information can be shown in like clouds, cluster maps and 2D keyword maps. === Data mining visualisation === === Text mining visualisation === === Visualisation for both data-mining and text-mining === Mapping visualisations can be used for both text-mining and data-mining results. == Uses == What patent visualisation can highlight: Competitors Partners New innovations Technologic environment description Networks Field application: R&D strategy management Competitive intelligence Licensing Strategy
System requirements specification
A System Requirements Specification (SysRS) (abbreviated SysRS to be distinct from a software requirements specification (SRS)) is a structured collection of information that embodies the requirements of a system. A business analyst (BA), sometimes titled system analyst, is responsible for analyzing the business needs of their clients and stakeholders to help identify business problems and propose solutions. Within the systems development life cycle domain, the BA typically performs a liaison function between the business side of an enterprise and the information technology department or external service providers.
International Olympiad in Artificial Intelligence
The International Olympiad in Artificial Intelligence (IOAI) is an annual International Science Olympiad in the field of artificial intelligence (AI) for secondary education students under the age of 20. The first IOAI was held in Burgas, Bulgaria, in 2024. Each country or territory may send up to two teams, each consisting of up to four students supported by one leader. Participants are selected through a multi-stage National Olympiad in Artificial Intelligence (NOAI) and/or a Regional Olympiad such as the NAOAI or APOAI. Participants at the IOAI compete on an individual basis. As of 2025, there were 61 countries and territories participating in the IOAI. Three hundred students participated in IOAI 2025. As of 2026, 130 countries and territories are accredited for participation in the IOAI. == Competition Structure == The IOAI consists of three contests: the Individual Contest, the Team Challenge, and the GAITE contest. Medals are awarded based solely on the Individual Contest. === Individual Contest === The Individual Contest is the main competition of the IOAI in which contestants compete individually on separate computers and are not permitted to communicate during the contest. Medals are awarded solely on the basis of the total score from the two-day Individual Contest. The Individual Contest consists of two on-site contest days (six hours per day), preceded by an at-home practice round and an on-site practice session. In IOAI 2025, three at-home problems were released for preparation approximately one month before the on-site contest. Results from this at-home round do not affect final results. The first on-site contest day (Individual Contest 1) comprises three tasks as extensions and continuations of the at-home tasks, while the second day (Individual Contest 2) comprises two or three tasks which are novel and different from the at-home tasks. The Individual Contest tasks span various AI domains such as machine learning, natural language processing, and computer vision. The IOAI 2025 contest rules describe tasks as requiring typical machine-learning workflows, including writing code, fitting models on training data, and running inference on test data, using identical local machines and GPU resources (minimum 24 GB RAM). Tasks, datasets, and submissions are handled through a contest platform (Bohrium), including a web-based Jupyter notebook environment for GPU access. Internet access is restricted to a whitelist of documentation sites and an integrated compact large language model accessible within the platform. The use of external APIs are prohibited unless a task explicitly allows them. In IOAI 2025, each contest task was scored up to 100 points and could include multiple subtasks. Scores are normalized using a baseline solution and a maximum score derived from either a Scientific Committee solution or the best contestant submission. Contestants can view only their own scores during the contest; a live scoreboard may be available publicly outside the contest hall but is not permitted to be viewed by contestants during the contest. For non-English-speaking teams, the IOAI hold a translation session beginning three hours before each contest day in which team leaders review and may amend machine-translated task statements; translations must match the English original and are published after the contest. The IOAI committee also enforces quarantine restrictions during these translation sessions, where neither contestants or team leaders may not use cell phones, laptops, and other communication devices. === Team Challenge === The Team Challenge is a team-based component of the IOAI. The results of this part do not affect the distribution of medals. The IOAI 2025 rules describe it as a “creative and AI-oriented challenge” in which a team's contestants sit together and cooperate, with the format varying by year. In IOAI 2024, teams worked with existing AI image and video generation tools to produce a visual result. In IOAI 2025, teams were assigned to program a robot to complete various tasks. === GAITE Contest === The GAITE (Global AI Talent Empowerment) contest is a simplified version of the individual contest with a separate scoreboard, where participants may ask for hints. It is designed for countries and territories with limited International Science Olympiads history, and it awards alternative prizes instead of medals. == Awards Distribution == The top 50% of the participants in the individual contest receive gold, silver and bronze medals in ratio of 1:2:3, respectively. The top three individuals receive honorary trophies. As in other International Science Olympiads, if an individual is in the top 50% on one of the days, but does not receive a medal, they receive an honorary mention during the awards ceremony. The GAITE contest has similar cutoff logic, but receives a reward instead of a medal. The top three teams in the Team Challenge receive trophies. == National selection and regional competitions == National delegations are selected through country-level qualification processes referred to as National Olympiads in Artificial Intelligence (NOAI) or equivalent, which are widely known for their low success rates. Although the total number of participants worldwide is not published, available data indicate exceptionally competitive national pools; for example, Brazil reports over 716,000 competitors, while Russia reports more than 72,000. In addition, Regional Olympiads (for example, APOAI or NAOAI) provide continent-level competition and preparation platforms in most regions. === National Selection (National Olympiads in Artificial Intelligence) === Participating countries and territories select their students for the IOAI through a National Olympiad in Artificial Intelligence (NOAI) or an equivalent process. The names of these selection processes differ by country, but almost all of them (excluding newer countries participating in the GAITE contest) have in common that the process comprises multiple and/or extremely rigorous selection stages. United States / Canada – The USA–North America AI Olympiad (USAAIO) is a three-round process including an invitational in-person round and a subsequent selection camp, after which a national delegation is selected for IOAI. Russia – The Russian Olympiad in Artificial Intelligence is organized as a multi-stage process (training, qualification, main round, final). Organizers reported 72,316 registrations for the training round and 52,260 registrations for the qualifying round in one season, with tasks spanning mathematics, algorithms/programming, and machine learning; 977 students were disqualified following plagiarism checks. Japan – Japan's national selection consists of multiple stages, beginning with the Japan Olympiad in Artificial Intelligence (JOAI), a large-scale Kaggle-style competition. High-performing participants advance through additional assessment stages, including written solution reports and technical interviews. From this process, eight students are selected for the APOAI team, with four ultimately chosen to represent Japan at the IOAI. Brazil – Brazil's National Olympiad in Artificial Intelligence (ONIA) is conducted as a large competition which consists of progressive rounds of evaluation. It identifies 28 top students from over 716,000 competitors, four of which are selected for the IOAI. The competition is held in four phases across two cycles, including a two-step third phase and a final training-and-evaluation phase that selects a four-student national team. Singapore – Singapore's national Olympiad consists of two rounds: an online preliminary round (300 MCQs in 3 hours) selects the top 150 performers to advance to the final assessment, which includes both theory questions and Python programming tasks. Additional training and selection may follow the finals for top performers. Poland – The Polish AI Olympiad adopts a two-stage structure: an open online first stage (at-home tasks) and a second-stage competitive camp with 30 selected participants competing for a four-person IOAI team. France – The Olympiades Françaises d'Intelligence Artificielle (OFIA), organized by France-IOI, follow a three-stage structure consisting of an open online qualification round, a second selection round, and a multi-day national training camp and final in Paris. Bangladesh – The Bangladesh AI Olympiad (BdAIO) selects competitors in three rounds: the online preliminary round, the national finals, and the team selection camp. In 2025, 406 participants competed in the national finals. Norway – The Norwrgian AI Olympiad (NOKI) is a three-stage selection system; however, unlike other countries, its first two rounds are shared with the Norwegian Informatics Olympiad. The national Olympiad reports 1,180 participants in the first round. Hong Kong – The national Olympiad reported more than 800 preliminary-round entrants, narrowing through multiple rounds to 25 finalists, with a subsequent
Minne Atairu
Minne Atairu is a Nigerian interdisciplinary artist, a recipient of the 2021 Global South Award Lumen Prize for Art and Technology. She generates synthetic Benin Bronzes through recombination of historical fragments, sculptures, texts, images, and sounds. == Early life and education == Atairu was born in Benin, Nigeria. She holds a bachelor's degree in art history from the University of Maiduguri in Maiduguri, Nigeria; a master's degree in museum studies from the George Washington University in Washington, D.C.; and a doctorate in art education from Teachers College, Columbia University in New York City. Her academic research integrates artificial intelligence, art/museum education and hip-hop based education. == Works == Atairu's artmaking involves using artificial intelligence (AI; such as StyleGAN, GPT-3) to make artwork. She uses tools such as Midjourney and Blender software to develop her works. === Mami Wata === Her first work is a Yoruba goddess called Mami Wata where she used Midjourney in generating the images. === To the Hand === For her 2023 installation To the Hand at The Shed arts center, she worked with Blender to convert text into 3D-printed sculptures made of corn starch or sugarcane infused with bronze. The rings of ground terra-cotta that surround the sculpture represent the walls and deep moats of Benin. == Publications == Atairu, Minne (February 1, 2024). "Reimagining Benin Bronzes using generative adversarial networks". AI & Society. 39 (1): 91–102. doi:10.1007/s00146-023-01761-7. ISSN 1435-5655.
On a Red Station, Drifting
On a Red Station, Drifting is a 2012 science fiction novella by Aliette de Bodard. Set in her Xuya Universe, it focuses on two women aboard a space station with a failing artificial intelligence. It received critical acclaim, becoming a finalist for the 2012 Nebula Award for Best Novella, the 2013 Hugo Award for Best Novella, and the 2013 Locus Award for Best Novella. == Plot == Lê Thi Linh is a magistrate of the Dai Viet Empire who is forced to flee her planet after criticizing the Emperor’s wartime policies. At the same time, rebel groups seize control of her planet and kill most of her subordinates. Linh seeks refuge with her distant relatives on Prosper Station. Prosper is controlled by an artificial intelligence called the Honoured Ancestress. Lê Thi Quyen, Linh’s cousin by marriage, manages the day-to-day operations of Prosper while her husband is away at war. Quyen and Linh immediately fall into conflict. Quyen’s brother-in-law Huu Hieu sells his mem-implants, which are copies of their ancestors’ consciousnesses. Meanwhile, the Honoured Ancestress experiences increasingly severe technical problems. Hieu and Linh become close. Hieu plans use the money from the sale of the implants to leave Prosper and marry his lover on a different station. Linh is upset knowing that she will never be able to leave. A visiting cousin, Lady Oahn, provides schematics for the repair of the Honoured Ancestress. In an effort to hurt Quyen, Linh writes an unflattering poem at a banquet honoring Oanh. In doing so, she reveals that Hieu is trying to leave Prosper. Hieu attempts suicide out of shame, but Linh rescues him. Quyen is able to repair the Honoured Ancestress, restoring her functionality at the expense of erasing many of her memories. The Emperor’s Embroidered Guard arrives at Prosper Station in search of Linh. Linh finds the missing mem-implants and returns them to Quyen. Quyen and Linh briefly reconcile before Linh is arrested and removed from Prosper Station. == Major themes == A review in Kirkus wrote that the novel's "familiar setting" was a "departure point" for the novel to explore its themes. The novel explores family ties; almost everyone on Prosper Station is related in some fashion. Additionally, the use of ancestors' mem-implants further explores the concept of family ties, with some descendants being considered more "worthy" than others due to their higher number of implants. The novel also explores questions of worth, as those who fail at ability tests are often forced to become the "lesser partners" in marriages and are discriminated against due to their perceived lack of achievement. The author notes that it is interesting that gender plays no role in the question of worth, and that the majority of the men in the story are actually the "lesser partner" in their marriage. == Style == The novel is divided into three sections. Liz Bourke wrote that each section builds thematically "towards an emotional crescendo". == Reception == Writing for Locus, Liz Bourke praised the novel's exploration of interpersonal conflict between Linh and Quyen, writing that "essentially subverts the popularly-understood derogatory overtones of 'domestic conflict'". Bourke also praised the story's tension, calling it "so well-strung the prose practically vibrates under its influence". A review for Kirkus stated that the novel is a "beautifully realized story and the characters, plot, theme and writing are expertly crafted." === Awards ===
Motor theory of speech perception
The motor theory of speech perception is the hypothesis that people perceive spoken words by identifying the vocal tract gestures with which they are pronounced rather than by identifying the sound patterns that speech generates. It originally claimed that speech perception is done through a specialized module that is innate and human-specific. Though the idea of a module has been qualified in more recent versions of the theory, the idea remains that the role of the speech motor system is not only to produce speech articulations but also to detect them. The hypothesis has gained more interest outside the field of speech perception than inside. This has increased particularly since the discovery of mirror neurons that link the production and perception of motor movements, including those made by the vocal tract. The theory was initially proposed in the Haskins Laboratories in the 1950s by Alvin Liberman and Franklin S. Cooper, and developed further by Donald Shankweiler, Michael Studdert-Kennedy, Ignatius Mattingly, Carol Fowler and Douglas Whalen. == Origins and development == The hypothesis has its origins in research using pattern playback to create reading machines for the blind that would substitute sounds for orthographic letters. This led to a close examination of how spoken sounds correspond to the acoustic spectrogram of them as a sequence of auditory sounds. This found that successive consonants and vowels overlap in time with one another (a phenomenon known as coarticulation). This suggested that speech is not heard like an acoustic "alphabet" or "cipher," but as a "code" of overlapping speech gestures. === Associationist approach === Initially, the theory was associationist: infants mimic the speech they hear and that this leads to behavioristic associations between articulation and its sensory consequences. Later, this overt mimicry would be short-circuited and become speech perception. This aspect of the theory was dropped, however, with the discovery that prelinguistic infants could already detect most of the phonetic contrasts used to separate different speech sounds. === Cognitivist approach === The behavioristic approach was replaced by a cognitivist one in which there was a speech module. The module detected speech in terms of hidden distal objects rather than at the proximal or immediate level of their input. The evidence for this was the research finding that speech processing was special such as duplex perception. === Changing distal objects === Initially, speech perception was assumed to link to speech objects that were both the invariant movements of speech articulators the invariant motor commands sent to muscles to move the vocal tract articulators This was later revised to include the phonetic gestures rather than motor commands, and then the gestures intended by the speaker at a prevocal, linguistic level, rather than actual movements. === Modern revision === The "speech is special" claim has been dropped, as it was found that speech perception could occur for nonspeech sounds (for example, slamming doors for duplex perception). === Mirror neurons === The discovery of mirror neurons has led to renewed interest in the motor theory of speech perception, and the theory still has its advocates, although there are also critics. == Support == === Nonauditory gesture information === If speech is identified in terms of how it is physically made, then nonauditory information should be incorporated into speech percepts even if it is still subjectively heard as "sounds". This is, in fact, the case. The McGurk effect shows that seeing the production of a spoken syllable that differs from an auditory cue synchronized with it affects the perception of the auditory one. In other words, if someone hears "ba" but sees a video of someone pronouncing "ga", what they hear is different—some people believe they hear "da". People find it easier to hear speech in noise if they can see the speaker. People can hear syllables better when their production can be felt haptically. === Categorical perception === Using a speech synthesizer, speech sounds can be varied in place of articulation along a continuum from /bɑ/ to /dɑ/ to /ɡɑ/, or in voice onset time on a continuum from /dɑ/ to /tɑ/ (for example). When listeners are asked to discriminate between two different sounds, they perceive sounds as belonging to discrete categories, even though the sounds vary continuously. In other words, 10 sounds (with the sound on one extreme being /dɑ/ and the sound on the other extreme being /tɑ/, and the ones in the middle varying on a scale) may all be acoustically different from one another, but the listener will hear all of them as either /dɑ/ or /tɑ/. Likewise, the English consonant /d/ may vary in its acoustic details across different phonetic contexts (the /d/ in /du/ does not technically sound the same as the one in /di/, for example), but all /d/'s as perceived by a listener fall within one category (voiced alveolar plosive) and that is because "linguistic representations are abstract, canonical, phonetic segments or the gestures that underlie these segments." This suggests that humans identify speech using categorical perception, and thus that a specialized module, such as that proposed by the motor theory of speech perception, may be on the right track. === Speech imitation === If people can hear the gestures in speech, then the imitation of speech should be very fast, as in when words are repeated that are heard in headphones as in speech shadowing. People can repeat heard syllables more quickly than they would be able to produce them normally. === Speech production === Hearing speech activates vocal tract muscles, and the motor cortex and premotor cortex. The integration of auditory and visual input in speech perception also involves such areas. Disrupting the premotor cortex disrupts the perception of speech units such as plosives. The activation of the motor areas occurs in terms of the phonemic features which link with the vocal track articulators that create speech gestures. The perception of a speech sound is aided by pre-emptively stimulating the motor representation of the articulators responsible for its pronunciation . Auditory and motor cortical coupling is restricted to a specific range of neuronal firing frequency. === Perception-action meshing === Evidence exists that perception and production are generally coupled in the motor system. This is supported by the existence of mirror neurons that are activated both by seeing (or hearing) an action and when that action is carried out. Another source of evidence is that for common coding theory between the representations used for perception and action. == Criticisms == The motor theory of speech perception is not widely held in the field of speech perception, though it is more popular in other fields, such as theoretical linguistics. As three of its advocates have noted, "it has few proponents within the field of speech perception, and many authors cite it primarily to offer critical commentary".p. 361 Several critiques of it exist. === Multiple sources === Speech perception is affected by nonproduction sources of information, such as context. Individual words are hard to understand in isolation but easy when heard in sentence context. It therefore seems that speech perception uses multiple sources that are integrated together in an optimal way. === Production === The motor theory of speech perception would predict that speech motor abilities in infants predict their speech perception abilities, but in actuality it is the other way around. It would also predict that defects in speech production would impair speech perception, but they do not. However, this only affects the first and already superseded behaviorist version of the theory, where infants were supposed to learn all production-perception patterns by imitation early in childhood. This is no longer the mainstream view of motor-speech theorists. === Speech module === Several sources of evidence for a specialized speech module have failed to be supported. Duplex perception can be observed with door slams. The McGurk effect can also be achieved with nonlinguistic stimuli, such as showing someone a video of a basketball bouncing but playing the sound of a ping-pong ball bouncing. As for categorical perception, listeners can be sensitive to acoustic differences within single phonetic categories. As a result, this part of the theory has been dropped by some researchers. === Sublexical tasks === The evidence provided for the motor theory of speech perception is limited to tasks such as syllable discrimination that use speech units not full spoken words or spoken sentences. As a result, "speech perception is sometimes interpreted as referring to the perception of speech at the sublexical level. However, the ultimate goal of these studies is presumably to understand the neural processes supporting the ability to process spee
Dreams of Violets
Dreams of Violets is a film entirely generated by artificial intelligence, produced and directed by brothers Ash and Pooya Koosha. The film will be screened at the Tribeca Film Festival on 10 June 2026. All images and characters in the film were generated using AI-powered video tools and based on journalistic reports, photographs, and eyewitness accounts. == Plot == The film is a fictionalized dramatization of the events surrounding the massacre of Iranian civilians in January 2026. International organizations estimate the death toll at over 7,000, amidst protests and state violence that unfolded during a communications blackout.