AI Detector Xero

AI Detector Xero — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Nouvelle AI

    Nouvelle AI

    Nouvelle artificial intelligence (Nouvelle AI) is an approach to artificial intelligence pioneered in the 1980s by Rodney Brooks, who was then part of MIT artificial intelligence laboratory. Nouvelle AI differs from classical AI by aiming to produce robots with intelligence levels similar to insects. Researchers believe that intelligence can emerge organically from simple behaviors as these intelligences interacted with the "real world", instead of using the constructed worlds which symbolic AIs typically needed to have programmed into them. == Motivation == The differences between nouvelle AI and symbolic AI are apparent in early robots Shakey and Freddy. These robots contained an internal model (or "representation") of their micro-worlds consisting of symbolic descriptions. As a result, this structure of symbols had to be renewed as the robot moved or the world changed. Shakey's planning programs assessed the program structure and broke it down into the necessary steps to complete the desired action. This level of computation required a large amount time to process, so Shakey typically performed its tasks very slowly. Symbolic AI researchers had long been plagued by the problem of updating, searching, and otherwise manipulating the symbolic worlds inside their AIs. A nouvelle system refers continuously to its sensors rather than to an internal model of the world. It processes the external world information it needs from the senses when it is required. As Brooks puts it, "the world is its own best model--always exactly up to date and complete in every detail." A central idea of nouvelle AI is that simple behaviors combine to form more complex behaviors over time. For example, simple behaviors can include elements like "move forward" and "avoid obstacles." A robot using nouvelle AI with simple behaviors like collision avoidance and moving toward a moving object could possibly come together to produce a more complex behavior like chasing a moving object. === The frame problem === The frame problem describes an issue with using first-order logic (FOL) to express facts about a robot in the world. Representing the state of a robot with traditional FOL requires the use of many axioms (symbolic language) to imply that things about an environment do not change arbitrarily. Nouvelle AI seeks to sidestep the frame problem by dispensing with filling the AI or robot with volumes of symbolic language and instead letting more complex behaviors emerge by combining simpler behavioral elements. === Embodiment === The goal of traditional AI was to build intelligences without bodies, which would only have been able to interact with the world via keyboard, screen, or printer. However, nouvelle AI attempts to build embodied intelligence situated in the real world. Brooks quotes approvingly from the brief sketches that Turing gave in 1948 and 1950 of the "situated" approach. Turing wrote of equipping a machine "with the best sense organs that money can buy" and teaching it "to understand and speak English" by a process that would "follow the normal teaching of a child." This approach was contrasted to the others where they focused on abstract activities such as playing chess. == Brooks' robots == === Insectoid robots === Brooks focused on building robots that acted like simple insects while simultaneously working to remove some traditional AI characteristics. He created insect-like robots, named Allen and Herbert after cognitive science and AI pioneers Allen Newell and Herbert A. Simon. Brooks's insectoid robots contained no internal models of the world. Herbert, for example, discarded a high volume of the information received from its sensors and never stored information for more than two seconds. ==== Allen ==== Allen had a ring of twelve ultrasonic sonars as its primary sensors and three independent behavior-producing modules. These modules were programmed to avoid both stationary and moving objects. With only this module activated, Allen stayed in the middle of a room until an object approached and then it ran away while avoiding obstacles in its way. ==== Herbert ==== Herbert used infrared sensors to avoid obstacles and a laser system to collect 3D data over a distance of about 12 feet. Herbert also carried a number of simple sensors in its "hand." The robot's testing ground was the real world environment of the busy offices and workspaces of the MIT AI lab where it searched for empty soda cans and carried them away, a seemingly goal-oriented activity that emerged as a result of 15 simple behavior units combining. As a parallel, Simon noted that an ant's complicated path is due to the structure of its environment rather than the depth of its thought processes. ==== Other insectoid robots ==== Other robots by Brooks' team were Genghis and Squirt. Genghis had six legs and was able to walk over rough terrain and follow a human. Squirt's behavior modules had it stay in dark corners until it heard a noise, then it would begin to follow the source of the noise. Brooks agreed that the level of nouvelle AI had come near the complexity of a real insect, which raised a question about whether or not insect level-behavior was and is a reasonable goal for nouvelle AI. === Humanoid robots === Brooks' own recent work has taken the opposite direction to that proposed by Von Neumann in the quotations "theorists who select the human nervous system as their model are unrealistically picking 'the most complicated object under the sun,' and that there is little advantage in selecting instead the ant, since any nervous system at all exhibits exceptional complexity." ==== Cog ==== In the 1990s, Brooks decided to pursue the goal of human-level intelligence and, with Lynn Andrea Stein, built a humanoid robot called Cog. Cog is a robot with an extensive collection of sensors, a face, and arms (among other features) that allow it to interact with the world and gather information and experience so as to assemble intelligence organically in the manner described above by Turing. The team believed that Cog would be able to learn and able to find a correlation between the sensory information it received and its actions, and to learn common sense knowledge on its own. As of 2003, all development of the project had ceased.

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  • Top 10 AI Video Editors Compared (2026)

    Top 10 AI Video Editors Compared (2026)

    Looking for the best AI video editor? An AI video editor 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 AI video editor slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • AI Bug Finders Reviews: What Actually Works in 2026

    AI Bug Finders Reviews: What Actually Works in 2026

    Looking for the best AI bug finder? An AI bug finder 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 AI bug finder 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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  • Top 10 AI Text-to-image Tools Compared (2026)

    Top 10 AI Text-to-image Tools Compared (2026)

    Comparing the best AI text-to-image tool? An AI text-to-image tool 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 text-to-image tool 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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  • Artificial intelligence in education

    Artificial intelligence in education

    Artificial intelligence in education (often abbreviated as AIEd) is a subfield of educational technology that studies how to use artificial intelligence to create learning environments. Considerations in the field include data-driven decision-making, AI ethics, data privacy and AI literacy. Concerns include the potential for cheating, over-reliance, equity of access, reduced critical thinking, and the perpetuation of misinformation and bias. == History == Efforts to integrate AI into educational contexts have often followed technological advancement in the history of artificial intelligence. In the 1960s, educators and researchers began developing computer-based instruction systems, such as PLATO, developed by the University of Illinois. In the 1970s and 1980s, intelligent tutoring systems (ITS) were being adapted for classroom instruction. The International Artificial Intelligence in Education Society was founded in 1993. Coinciding with the AI boom of the 2020s, the use of large language models in the global north has been promoted and funded by venture capital and big tech. Companies creating AI services have targeted students and educational institutions as customers. Similarly, pre-AI boom educational companies have expanded their use of AI technologies. These commercial incentives for AIEd use may be related to a potential AI bubble. In the U.S., bipartisan support of AI development in K-12 education has been expressed, but specific implementations and best practices remain contentious. == Theory == AIEd applies theory from education studies, machine learning, and related fields. A 2019 review of the previous decade of studies found that most research prioritized technological design over pedagogical integration. Ouyang and Jiao (2021) propose three paradigms for AI in education, which follow roughly from least to most learner-centered and from requiring least to most technical complexity from the AI systems: AI-directed, learner-as-recipient: AIEd systems present a pre-set curriculum based on statistical patterns that do not adjust to learner's feedback. AI-supported, learner-as-collaborator: Systems that incorporate responsiveness to learner's feedback through, for example, natural language processing, wherein AI can support knowledge construction. AI-empowered, learner-as-leader: This model seeks to position AI as a supplement to human intelligence wherein learners take agency and AI provides consistent and actionable feedback. Some scholars place AI in education within a socio-technical framework. This positions AI alongside other emerging educational technologies, such as computing, the internet, and social media. The framework of Tsao, Heinrichs and Camit (2025) draws on new materialism and posthumanism, specifically Donna Haraway's concept of sympoiesis (making-with). This perspective views learning as an entanglement of human and non-human actors (students, teachers, and AI algorithms), where knowledge is co-composed in contact zones between human context and algorithmic prediction. AI agents have been trained on biased datasets, and thus continue to perpetuate societal biases. Since LLMs were created to produce human-like text, algorithmic bias can be introduced and reproduced. AI's data processing and monitoring reinforce neoliberal approaches to education rather than addressing inequalities. == Applications == Uses of generative AI chatbots in education have included assessment and feedback, machine translations, proof-reading exam question generation and copy editing, or as virtual assistants. Emotional AI in education is the study and development of systems that can detect learners' emotions or provide emotional support in learning. == Usage == === Schools and educators === Following the release of ChatGPT in November 2022, some schools and large school districts blocked access to the site and issued warnings that the use of such tools would be seen as cheating. Governmental and non-governmental organizations such as UNESCO, Article 4 of the European Union's AI Act, and the U.S. Department of Education have published reports advocating for specific AIEd approaches. National higher-education bodies have also published guidance on generative AI, including Ireland's Higher Education Authority, which issued a policy framework for higher education teaching and learning in December 2025. In 2024, UNESCO released updated global guidance for generative AI in education, emphasizing ethical use, teacher training, and data protection to ensure responsible integration of AI tools in learning environments. According to Taso (2025), policy implementation in higher education is interpreted and enacted differently by various organizations. These decentralized policies can lead to inconsistent enforcement and confusion among students regarding what constitutes acceptable use, with the burden of ethical navigation falling on individual teachers and students. AI integration in classrooms has created new forms of invisible labour for educators, who must navigate ambiguous policies, redesign assessments to be AI-resilient, and adjudicate potential academic integrity violations. The use of AI detection tools has also been criticised for creating an adversarial relationship between students and institutions, where students may be falsely accused of misconduct based on probabilistic software. AIEd advocates say that efforts should be made towards increasing global accessibility and training educators to serve underprivileged areas. === Students === Reliance on generative AI has been linked with reduced academic self-esteem and performance, and heightened learned helplessness. Algorithm errors and hallucinations are common flaws in AI agents, making them less trustworthy and reliable. According to a 2025 survey from Inside Higher Ed, 85% of higher education students use generative AI technology in some way, with 25% using AI to complete assignments for them. The most common reason cited for using AI to cheat was pressure to get high grades. 97% of students wanted some form of action from schools on the threat to academic integrity caused by AI, with the most popular options being clearer policies and more education about ethical uses of AI. In September 2025, The Atlantic published an op-ed from a high school senior arguing that the normalization of AI cheating was eroding critical thinking, academic integrity, creativity, and the shared student experience.

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  • How to Choose an AI Text-to-image Tool

    How to Choose an AI Text-to-image Tool

    Curious about the best AI text-to-image tool? An AI text-to-image tool 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 text-to-image tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • Top 10 AI Video Editors Compared (2026)

    Top 10 AI Video Editors Compared (2026)

    Looking for the best AI video editor? An AI video editor 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 AI video editor slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • Aapo Hyvärinen

    Aapo Hyvärinen

    Aapo Johannes Hyvärinen (born 1970 in Helsinki) is a Finnish professor of computer science at the University of Helsinki and known for his research in independent component analysis. == Education and career == Hyvärinen was born in Helsinki and studied mathematics at the University of Helsinki and received his Doctor of Technology in information science in 1997 at the Helsinki University of Technology under the supervision of Erkki Oja. His doctoral thesis, titled "Independent component analysis: A neural network approach", introduced the FastICA algorithm. Since then, Hyvärinen has conducted research especially in relation to the independent component analysis, as well as score matching (also known as Hyvärinen scoring rule). In November 2007, he was appointed as a professor at the University of Helsinki. Hyvärinen has been a member of the Finnish Academy of Sciences since 2016. From August 2016 to March 2019, he held a professorship in machine learning at the Gatsby Computational Neuroscience Unit of the University College London.

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  • Be My Eyes

    Be My Eyes

    Be My Eyes is a Danish mobile app that aims to help blind and visually impaired people to recognize objects and manage everyday situations. An online community of sighted volunteers receive photos or videos from randomly assigned affected individuals and assist via live chat. In 2023, the company launched Be My AI, an AI-based interface to help blind and visually impaired users describe images. The app is currently available for Android, iOS, and Windows. == History == === Founding and early years === The app was developed and marketed by Hans Jørgen Wiberg. He had demonstrated that although there are video chat software such as Skype and FaceTime, none is tailored for the visually impaired. For development, he joined forces with the Danish Association of the Blind, and other organizations. The app was first presented at an event for start-up companies in 2012 and first released in 2015. A version for Android was released in 2017, in addition to the iOS version. Praise was given for easy use of the app. The lack of sufficient data protection, which makes it possible to pass on data to third parties, was criticized. === Recent developments === The company has raised over $650,000, including funding from Silicon Valley, Microsoft, and other angel investors. In February 2020, $2.8 million in Series A funding was raised, allowing the company to further develop its business model while keeping visual support services free for visually impaired users. The investment allows the company to further develop its unique "purpose and profit" business model while keeping the visual support service free and unlimited for all visually impaired users. === User base and accessibility === Over 9.3 million volunteers and 900,000 blind or visually impaired people use the app. == Features == === Human-based assistance === A visually impaired person starts a live stream showing their view from their cellphone camera. They are assigned, through a phone call or chat, a random volunteer who speaks the same language and who is in the same time zone. This allows the volunteer to describe an object and assist the visually impaired person, such as guiding the person to move their camera, read instructions, or clean up a spill. Through speech synthesis, content can be read out loud. This process encourages a more independent life for blind and visually impaired people. === Be My AI === In March of 2023, Be My Eyes launched Be My AI, an AI-based virtual assistant. Be My AI is accessible through the Be My Eyes app, and is based on OpenAI's GPT-4 large language model. Through the interface, the app allows blind and visually impaired users to send images from a variety of devices to be described. The app allows users to then follow up with questions to further tailor the image description. Blind users report using Be My AI for a variety of tasks, including reading menus, identifying clothing, and describing people. The Be My AI interface is available on Android, iOS, and Windows. Within a few weeks of the interface's roll out, the company reported that it had been used one million times, and it was named among Time's best inventions of 2023. Be My AI is part of a growing number of AI-based apps and devices designed to help blind and visually impaired individuals. == Partnerships == === Microsoft === In November 2023, Be My Eyes entered a partnership with Microsoft to share data to help improve accessibility-focused AI models. === Meta === In 2024, Be My Eyes integrated with Ray-Ban Meta smart glasses, a wearable product developed by Meta and EssilorLuxottica. The partnership enabled users to receive hands-free, real-time visual descriptions and volunteer assistance by using voice commands through the smart glasses. === Hilton === In October 2024, Hilton partnered with Be My Eyes to provide live video assistance for blind and low-vision guests. The free service connects travelers to a Hilton team member that can guide them through tasks like adjusting thermostats, opening window shades, or navigating hotel amenities. This collaboration progressed from a prior arrangement where Hilton helped train Be My Eyes' GPT-4 powered AI model to better recognize objects and layouts in hotel rooms. === Tesco === In October 2025, retailer Tesco announced its partnership with Be My Eyes to launch a six-month pilot aimed at improving in-store accessibility in the UK. The initiative was launched on World Sight Day, 9 October, enabling Be My Eyes users to connect directly with Tesco staff via the app for personalised visual assistance while shopping, Euronewsweek reported. == Awards == Nordic Startup Awards for "Best Social Entrepreneurial Tech Startup" in Denmark 2021 Apple Design Award for best social impact

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  • Salvatore J. Stolfo

    Salvatore J. Stolfo

    Salvatore J. Stolfo is an academic and professor of computer science at Columbia University, specializing in computer security. == Early life == Born in Brooklyn, New York, Stolfo received a Bachelor of Science degree in Computer Science and Mathematics from Brooklyn College in 1974. He received his Ph.D. from NYU Courant Institute in 1979 and has been on the faculty of Columbia ever since, where he's taught courses in Artificial Intelligence, Intrusion and Anomaly Detection Systems, Introduction to Programming, Fundamental Algorithms, Data Structures, and Knowledge-Based Expert Systems. == Academic research == While at Columbia, Stolfo has received close to $50M in funding for research that has broadly focused on Security, Intrusion Detection, Anomaly Detection, Machine Learning and includes early work in parallel computing and artificial intelligence. He has published or co-authored over 250 papers and has over 46,000 citations with an H-index of 102. In 1996 he proposed a project with DARPA that applies machine learning to behavioral patterns to detect fraud or intrusion in networks. DADO, developed by in part by Stolfo, introduced the parallel computing primitive: “Broadcast, Resolve, Report”, a hardwire implemented mechanism that today is called MapReduce. Among his earliest work, Stolfo along with colleague Greg Vesonder of Bell Labs, developed a large-scale expert data analysis system, called ACE (Automated Cable Expertise) for the nation's phone system. AT&T Bell Labs distributed ACE to a number of telephone wire centers to improve the management and scheduling of repairs in the local loop. Stolfo coined the term FOG computing (not to be confused with fog computing) where technology is used “to launch disinformation attacks against malicious insiders, preventing them from distinguishing the real sensitive customer data from fake worthless data.” In 2005 Stolfo received funding from the Army Research Office to conduct a workshop to bring together a group of researchers to help identify a research program to focus on insider threats. He was elevated to IEEE Fellow in 2018 "for his contributions to machine learning based cybersecurity." He was elected as an ACM Fellow in 2019 "for contributions to machine-learning-based cybersecurity and parallel hardware for database inference systems". == Career == Founded in 2011, Red Balloon Security (or RBS) is a cyber security company founded by Dr Sal Stolfo and Dr Ang Cui. A spinout from the IDS lab, RBS developed a symbiote technology called FRAK as a host defense for embedded systems under the sponsorship of DARPA's Cyber Fast Track program. Created based on their IDS lab research for the DARPA Active Authentication and the Anomaly Detection at Multiple Scales program, Dr Sal Stolfo and Dr. Angelos Keromytis founded Allure Security Technologies. Using active behavioral authentication and decoy technology Stolfo pioneered and patented in 1996. Founded in 2009, Allure Security Technology was created based on work done under DARPA sponsorship in Columbia's IDS lab based on DARPA prompts to research how to detect hackers once they are inside an organization's perimeter and how to continuously authenticate a user without a password. Stolfo's company Electronic Digital Documents produced a “DataBlade” technology, which Informix marketed during their strategy of acquisition and development in the mid 80's. Stolfo's patented merge/purge technology called EDD DataCleanser DataBlade was licensed by Informix. Since its acquisition by IBM in 2005, IBM Informix is one of the world's most widely used database servers, with users ranging from the world's largest corporations to startups. System Detection was one of the companies founded by Prof. Stolfo to commercialize the Anomaly Detection technology developed in the IDS lab. The company ultimately reorganized and was rebranded as Trusted Computer Solutions. That company was recently acquired by Raytheon. Recently a jury awarded Columbia University $185 million for patent infringement for one of Prof. Stolfo's inventions, the Application Communities technology. https://news.columbia.edu/news/columbia-university-awarded-185-million-patent-infringement-nortonlifelock-inc. The final order from the judge applied nearly treble damages: https://www.reuters.com/legal/litigation/gen-digital-owes-columbia-481-mln-us-patent-fight-judge-says-2023-10-02/

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  • Top 10 AI Code-review Tools Compared (2026)

    Top 10 AI Code-review Tools Compared (2026)

    In search of the best AI code-review tool? An AI code-review tool 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 code-review tool 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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  • Apertium

    Apertium

    Apertium is a free/open-source rule-based machine translation platform. It is free software and released under the terms of the GNU General Public License. == Overview == Apertium is a transfer-based machine translation system, which uses finite state transducers for all of its lexical transformations, and Constraint Grammar taggers as well as hidden Markov models or Perceptrons for part-of-speech tagging / word category disambiguation. A structural transfer component is responsible for word movement and agreement; most Apertium language pairs up until now have used "chunking" or shallow transfer rules, though newer pairs use (possibly recursive) rules defined in a Context-free grammar. Many existing machine translation systems available at present are commercial or use proprietary technologies, which makes them very hard to adapt to new usages. Apertium code and data is free software and uses a language-independent specification, to allow for the ease of contributing to Apertium, more efficient development, and enhancing the project's overall growth. At present (December 2020), Apertium has released 51 stable language pairs, delivering fast translation with reasonably intelligible results (errors are easily corrected). Being an open-source project, Apertium provides tools for potential developers to build their own language pair and contribute to the project. == History == Apertium originated as one of the machine translation engines in the project OpenTrad, which was funded by the Spanish government, and developed by the Transducens research group at the Universitat d'Alacant. It was originally designed to translate between closely related languages, although it has recently been expanded to treat more divergent language pairs. To create a new machine translation system, one just has to develop linguistic data (dictionaries, rules) in well-specified XML formats. Language data developed for it (in collaboration with the Universidade de Vigo, the Universitat Politècnica de Catalunya and the Universitat Pompeu Fabra) currently support (in stable version) the Arabic, Aragonese, Asturian, Basque, Belarusian, Breton, Bulgarian, Catalan, Crimean Tatar, Danish, English, Esperanto, French, Galician, Hindi, Icelandic, Indonesian, Italian, Kazakh, Macedonian, Malaysian, Maltese, Northern Sami, Norwegian (Bokmål and Nynorsk), Occitan, Polish, Portuguese, Romanian, Russian, Sardinian, Serbo-Croatian, Silesian, Slovene, Spanish, Swedish, Tatar, Ukrainian, Urdu, and Welsh languages. A full list is available below. Several companies are also involved in the development of Apertium, including Prompsit Language Engineering, Imaxin Software and Eleka Ingeniaritza Linguistikoa. The project has taken part in the 2009, 2010, 2011, 2012, 2013 and 2014 editions of Google Summer of Code and the 2010, 2011, 2012, 2013, 2014, 2015, 2016 and 2017 editions of Google Code-In. == Translation methodology == This is an overall, step-by-step view how Apertium works. The diagram displays the steps that Apertium takes to translate a source-language text (the text we want to translate) into a target-language text (the translated text). Source language text is passed into Apertium for translation. The deformatter removes formatting markup (HTML, RTF, etc.) that should be kept in place but not translated. The morphological analyser segments the text (expanding elisions, marking set phrases, etc.), and looks up segments in the language dictionaries, returning dictionary forms and tags for all matches. In pairs that involve agglutinative morphology, including a number of Turkic languages, a Helsinki Finite State Transducer (HFST) is used. Otherwise, an Apertium-specific finite state transducer system called lttoolbox, is used. The morphological disambiguator (the morphological analyser and the morphological disambiguator together form the part of speech tagger) resolves ambiguous segments (i.e., when there is more than one match) by choosing one match. Apertium uses Constraint Grammar rules (with the vislcg3 parser) for most of its language pairs. Retokenisation uses a finite state transducer to match sequences of lexical units and may reorder or translate tags (often used for translating idiomatic expressions into something that more approaches the target language grammar) Lexical transfer looks up disambiguated source-language basewords to find their target-language equivalents (i.e., mapping source language to target language). For lexical transfer, Apertium uses an XML-based dictionary format called bidix. Lexical selection chooses between alternative translations when the source text word has alternative meanings. Apertium uses a specific XML-based technology, apertium-lex-tools, to perform lexical selection. Structural transfer (i.e., it is an XML format that allows writing complex structural transfer rules) can consist of one-step chunking transfer, three-step chunking transfer or a CFG-based transfer module. The chunking modules flag grammatical differences between the source language and target language (e.g. gender or number agreement) by creating a sequence of chunks containing markers for this. They then reorder or modify chunks in order to produce a grammatical translation in the target-language. The newer CFG-based module matches input sequences into possible parse trees, selecting the best-ranking one and applying transformation rules on the tree. The morphological generator uses the tags to deliver the correct target language surface form. The morphological generator is a morphological transducer, just like the morphological analyser. A morphological transducer both analyses and generates forms. The post-generator makes any necessary orthographic changes due to the contact of words (e.g. elisions). The reformatter replaces formatting markup (HTML, RTF, etc.) that was removed by the deformatter in the first step. Apertium delivers the target-language translation. == Supported languages == As of June 2026, the following 108 pairs and 51 languages and languages varieties are supported by Apertium.

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  • Collision detection

    Collision detection

    Collision detection is the computational problem of detecting an intersection of two or more objects in virtual space. More precisely, it deals with the questions of if, when, and where two or more objects intersect. Collision detection is a classic problem of computational geometry with applications in computer graphics, physical simulation, video games, robotics (including autonomous driving), and computational physics. Collision detection algorithms can be divided into operating on 2D or 3D spatial objects. == Overview == Collision detection is closely linked to calculating the distance between objects, as objects collide when the distance between them is less than or equal to zero. Negative distances indicate that one object has penetrated another. Performing collision detection requires more context than just the distance between the objects. Accurately identifying the points of contact on both objects' surfaces is also essential for computing a physically accurate collision response. The complexity of this task increases with the level of detail in the objects' representations: the more intricate the model, the greater the computational cost. Collision detection frequently involves dynamic objects, adding a temporal dimension to distance calculations. Instead of simply measuring distance between static objects, collision detection algorithms often aim to determine whether the objects' motion will bring them to a point in time when their distance is zero—an operation that adds significant computational overhead. In collision detection involving multiple objects, a naive approach would require detecting collisions for all pairwise combinations of objects. As the number of objects increases, the number of required comparisons grows rapidly: for n {\displaystyle n} objects, n ( n − 1 ) / 2 {n(n-1)}/{2} intersection tests are needed with a naive approach. This quadratic growth makes such an approach computationally expensive as n {\displaystyle n} increases. Due to the complexity mentioned above, collision detection is a computationally intensive process. Nevertheless, it is essential for interactive applications like video games, robotics, and real-time physics engines. To manage these computational demands, extensive efforts have gone into optimizing collision detection algorithms. A commonly used approach towards accelerating the required computations is to divide the process into two phases: the broad phase and the narrow phase. The broad phase aims to answer the question of whether objects might collide, using a conservative but efficient approach to rule out pairs that clearly do not intersect, thus avoiding unnecessary calculations. Objects that cannot be definitively separated in the broad phase are passed to the narrow phase. Here, more precise algorithms determine whether these objects actually intersect. If they do, the narrow phase often calculates the exact time and location of the intersection. == Broad phase == This phase aims at quickly finding objects or parts of objects for which it can be quickly determined that no further collision test is needed. A useful property of such approach is that it is output sensitive. In the context of collision detection this means that the time complexity of the collision detection is proportional to the number of objects that are close to each other. An early example of that is the I-COLLIDE where the number of required narrow phase collision tests was O ( n + m ) {\displaystyle O(n+m)} where n {\displaystyle n} is the number of objects and m {\displaystyle m} is the number of objects at close proximity. This is a significant improvement over the quadratic complexity of the naive approach. === Spatial partitioning === Several approaches can be grouped under the spatial partitioning umbrella, which includes octrees (for 3D), quadtrees (for 2D), binary space partitioning (or BSP trees) and other, similar approaches. If one splits space into a number of simple cells, and if two objects can be shown not to be in the same cell, then they need not be checked for intersection. Dynamic scenes and deformable objects require updating the partitioning which can add overhead. === Bounding volume hierarchy === Bounding Volume Hierarchy (BVH) is a tree structure over a set of bounding volumes. Collision is determined by doing a tree traversal starting from the root. If the bounding volume of the root doesn't intersect with the object of interest, the traversal can be stopped. If, however there is an intersection, the traversal proceeds and checks the branches for each there is an intersection. Branches for which there is no intersection with the bounding volume can be culled from further intersection test. Therefore, multiple objects can be determined to not intersect at once. BVH can be used with deformable objects such as cloth or soft-bodies but the volume hierarchy has to be adjusted as the shape deforms. For deformable objects we need to be concerned about self-collisions or self intersections. BVH can be used for that end as well. Collision between two objects is computed by computing intersection between the bounding volumes of the root of the tree as there are collision we dive into the sub-trees that intersect. Exact collisions between the actual objects, or its parts (often triangles of a triangle mesh) need to be computed only between intersecting leaves. The same approach works for pair wise collision and self-collisions. === Exploiting temporal coherence === During the broad-phase, when the objects in the world move or deform, the data-structures used to cull collisions have to be updated. In cases where the changes between two frames or time-steps are small and the objects can be approximated well with axis-aligned bounding boxes, the sweep and prune algorithm can be a suitable approach. Several key observation make the implementation efficient: Two bounding-boxes intersect if, and only if, there is overlap along all three axes; overlap can be determined, for each axis separately, by sorting the intervals for all the boxes; and lastly, between two frames updates are typically small (making sorting algorithms optimized for almost-sorted lists suitable for this application). The algorithm keeps track of currently intersecting boxes, and as objects move, re-sorting the intervals helps keep track of the status. === Pairwise pruning === Once a pair of physical bodies has been selected for further investigation, collisions need to be checked more carefully. However, in many applications, individual objects (if they are not too deformable) are described by a set of smaller primitives, mainly triangles. So there are two sets of triangles, S = S 1 , S 2 , … , S n {\displaystyle S={S_{1},S_{2},\dots ,S_{n}}} and T = T 1 , T 2 , … , T n {\displaystyle T={T_{1},T_{2},\dots ,T_{n}}} (for simplicity, each set has the same number of triangles.) The obvious thing to do is to check all triangles S j {\displaystyle S_{j}} against all triangles T k {\displaystyle T_{k}} for collisions, but this involves n 2 {\displaystyle n^{2}} comparisons, which is highly inefficient. If possible, it is desirable to use a pruning algorithm to reduce the number of pairs of triangles that need to be checked. The most widely used family of algorithms is known as the hierarchical bounding volumes method. As a preprocessing step, for each object (e.g., S {\displaystyle S} and T {\displaystyle T} ) calculates a hierarchy of bounding volumes. Then, at each time step, when collisions need to be checked between S {\displaystyle S} and T {\displaystyle T} , the hierarchical bounding volumes are used to reduce the number of pairs of triangles under consideration. For simplicity, provide an example using bounding spheres, although it has been noted that spheres are undesirable in many cases. If E {\displaystyle E} is a set of triangles, a bounding sphere is pre-calculated. B ( E ) {\displaystyle B(E)} . There are many ways of choosing B ( E ) {\displaystyle B(E)} , B ( E ) {\displaystyle B(E)} is a sphere that completely contains E {\displaystyle E} and is as small as possible. Ahead of time, B ( S ) {\displaystyle B(S)} and B ( T ) {\displaystyle B(T)} can be computed. Clearly, if these two spheres do not intersect (and that is very easy to test), then neither do S {\displaystyle S} and T {\displaystyle T} . This is not much better than an n-body pruning algorithm, however. If E = E 1 , E 2 , … , E m {\displaystyle E={E_{1},E_{2},\dots ,E_{m}}} is a set of triangles, then split it into two halves L ( E ) := E 1 , E 2 , … , E m / 2 {\displaystyle L(E):={E_{1},E_{2},\dots ,E_{m/2}}} and R ( E ) := E m / 2 + 1 , … , E m − 1 , E m {\displaystyle R(E):={E_{m/2+1},\dots ,E_{m-1},E_{m}}} . Apply this to S {\displaystyle S} and T {\displaystyle T} , and calculate (ahead of time) the bounding spheres B ( L ( S ) ) , B ( R ( S ) ) {\displaystyle B(L(S)),B(R(S))} and B ( L ( T ) ) , B ( R ( T ) ) {\displaystyle B(L(T)),B(R(T))} . T

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  • Nick Frosst

    Nick Frosst

    Nicholas M. W. Frosst is a Canadian computer scientist and musician. He co-founded Cohere, a Toronto-based artificial intelligence company. He is also the lead singer in the indie rock band Good Kid. == Early life and education == Frosst was born on January 5, 1993. Frosst earned a Bachelor of Science degree in computer science and cognitive science from the University of Toronto in 2015. He was a student of Geoffrey Hinton, who also hired Frosst at Google Brain. == Career == Frosst was among Geoffrey Hinton's earliest hires at Google Brain in Toronto, working as a machine learning researcher on deep learning and neural network architectures. He worked there from 2016 to 2020. Frosst co-founded Cohere with Aidan Gomez and Ivan Zhang in 2019. The company builds large language models and enterprise AI tools. Frosst has publicly explained Cohere's focus on industries like finance and health, where there are privacy and other regulatory considerations. Frosst has also spoken openly about his belief that artificial intelligence will not replace humans, but rather streamline and automate mundane tasks, and his belief that AGI is less "imminent" than many in the field claim. Frosst and the other Cohere co-founders were listed first on Maclean's AI Trailblazers Power List and The Logic's Innovation Leaders. == Music == After spending time in a prior band which played "weird" music featuring a glockenspiel, Frosst and fellow computer science students at the University of Toronto formed the indie rock band Good Kid in 2015. Frosst is the lead vocalist for the band. While on tour with the band, Frosst continues his work in the tech industry remotely. Frosst has described the band as way for him to relax and not constantly think about tech. His vocals have been compared to that of Kele Okereke. As of 2026, the band, which has performed at Lollapalooza, has 3.1 million monthly Spotify listeners. In 2024, the band was nominated for the Juno Awards Breakthrough Group of the Year. == Discography == === Good Kid === Can We Hang Out Sometime? (2026)

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    AI Image Generators: Free vs Paid (2026)

    Looking for the best AI image generator? An AI image generator 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 AI image 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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