AI Assistant Examples

AI Assistant Examples — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

YrWall

YrWall is a Digital Graffiti Wall developed by event company Luma, where designs are created on a large wall using a modified spray paint can. The can contains no paint, instead it has an IR light which is tracked by a computer vision system and the image immediately back-projected onto the wall. The inbuilt YrWall software has much of the functionality of a typical computer paint program, with a pop-out interface which enables users to change colour, spray width, opacity, work with stencils and use animated items such as swirls, stars, drips and splats. Recent additions to YrWall include options to email a JPEG of the completed design and create personalised stickers and T-shirts. == Dragons' Den == The inventor of YrWall, Tom Hogan, and his business partner, Tim Williams, appeared on Episode 4 of Series 8 of the BBC show Dragons' Den. Seeking investment in YrWall, the entrepreneurs were successful in gaining £50,000 for 40% of the YrWall parent company Lumacoustics from Dragons Deborah Meaden and Peter Jones. == World's Largest Interactive Graffiti Wall == In September 2009 YrWall was used to create the 'World's Largest Interactive Graffiti Wall' at the Bristol Festival, UK. Artists used the standard 3.5 m2 YrWall to produce artwork which was in turn projected live onto a 26m x 10m space on the side of the iconic Lloyds amphitheatre building.
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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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AI Humanizers: Free vs Paid (2026)

Trying to pick the best AI humanizer? An AI humanizer is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI humanizer 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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AI Presentation Makers Reviews: What Actually Works in 2026

Looking for the best AI presentation maker? An AI presentation maker 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 presentation maker 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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Frame grabber

A frame grabber is an electronic device that captures (i.e., "grabs") individual, digital still frames from an analog video signal or a digital video stream. It is usually employed as a component of a computer vision system, in which video frames are captured in digital form and then displayed, stored, transmitted, analyzed, or combinations of these. Historically, frame grabber expansion cards were the predominant way to interface cameras to PCs. Other interface methods have emerged since then, with frame grabbers (and in some cases, cameras with built-in frame grabbers) connecting to computers via interfaces such as USB, Ethernet and IEEE 1394 ("FireWire"). Early frame grabbers typically had only enough memory to store a single digitized video frame, whereas many modern frame grabbers can store multiple frames. Modern frame grabbers often are able to perform functions beyond capturing a single video input. For example, some devices capture audio in addition to video, and some devices provide, and concurrently capture frames from multiple video inputs. Other operations may be performed as well, such as deinterlacing, text or graphics overlay, image transformations (e.g., resizing, rotation, mirroring), and conversion to JPEG or other compressed image formats. To satisfy the technological demands of applications such as radar acquisition, manufacturing and remote guidance, some frame grabbers can capture images at high frame rates, high resolutions, or both. == Circuitry == Analog frame grabbers, which accept and process analog video signals, include these circuits: Input signal conditioner that buffers the analog video input signal to protect downstream circuitry Video decoder that converts SD analog video (e.g., NTSC, SECAM, PAL) or HD analog video (e.g., AHD, HD-TVI, HD-CVI) to a digital format Digital frame grabbers, which accept and process digital video streams, include these circuits: Digital video decoder that interfaces to and converts a specific type of digital video source, such as Camera Link, CoaXPress, DVI, GigE Vision, LVDS, or SDI Circuitry common to both analog and digital frame grabbers: Memory for storing the acquired image (i.e., a frame buffer) A bus interface through which a processor can control the acquisition and access the data General purpose I/O for triggering image acquisition or controlling external equipment == Applications == === Healthcare === Frame grabbers are used in medicine for many applications, including telenursing and remote guidance. In situations where an expert at another location needs to be consulted, frame grabbers capture the image or video from the appropriate medical equipment, so it can be sent digitally to the distant expert. === Manufacturing === "Pick and place" machines are often used to mount electronic components on circuit boards during the circuit board assembly process. Such machines use one or more cameras to monitor the robotics that places the components. Each camera is paired with a frame grabber that digitizes the analog video, thus converting the video to a form that can be processed by the machine software. === Network security === Frame grabbers may be used in security applications. For example, when a potential breach of security is detected, a frame grabber captures an image or a sequence of images, and then the images are transmitted across a digital network where they are recorded and viewed by security personnel. === Personal use === In recent years with the rise of personal video recorders like camcorders, mobile phones, etc. video and photo applications have gained ascending prominence. Frame grabbing is becoming very popular on these devices. === Astronomy & astrophotography === Amateur astronomers and astrophotographers use frame grabbers when using analog "low light" cameras for live image display and internet video broadcasting of celestial objects. Frame grabbers are essential to connect the analog cameras used in this application to the computers that store or process the images.
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AI Code Generators: Free vs Paid (2026)

Looking for the best AI code generator? An AI code 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 code generator 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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Bruno Zamborlin

Bruno Zamborlin (born 1983 in Vicenza) is an AI researcher, entrepreneur and artist based in London, working in the field of human-computer interaction. His work focuses on converting physical objects into touch-sensitive, interactive surfaces using vibration sensors and artificial intelligence. In 2013, he founded Mogees Limited a start-up to transform everyday objects into musical instruments and games using a vibration sensor and a mobile phone. With HyperSurfaces, he converts physical surfaces of any material, shape and form into data-enabled-interactive surfaces using a vibration sensor and a coin-sized chipset. As an artist, he has created art installations around the world, with his most recent work comprising a unique series of "sound furnitures" that was showcased at the Italian Pavilion of the Venice Biennale 2023. He regularly performed with UK-based electronic music duo Plaid (Warp Records). He is also honorary visiting research fellow at Goldsmiths, University of London. == Early life and education == From 2008-2011, Zamborlin worked at the IRCAM (Institute for Research and Coordination Acoustic Musical) – Centre Pompidou as a member of the Sound Music Movement Interaction team. Under the supervision of Frederic Bevilacqua, he started experimenting with the use of artificial intelligence and human movements, and contributed to the creation of Gesture Follower, a software used to analyse body movements of performers and dancers through motion sensors in order to control sound and visual media in real-time, slowing down or speeding up their reproduction based on the speed the gestures are performed. He has lived in London since 2011, where he developed a joint PhD between Goldsmiths, University of London and IRCAM - Centre Pompidou/Pierre and Marie Curie University Paris in AI, focussing on the concept of Interactive Machine Learning applied to digital musical instruments and performing arts. == Career == Zamborlin founded Mogees Limited in 2013 in London, with IRCAM being amongst the early partners. Mogees transform physical objects into musical instruments and games using a vibration sensor and a series of apps for smartphones and desktop. After a campaign on Kickstarter in 2014, Mogees was used both by common users and artists such as Rodrigo y Gabriela, Jean-Michel Jarre and Plaid. The algorithms implemented in these apps employ a special version of physical modelling sound synthesis, where the vibration produced by users when interacting with the physical object are used as exciter for a digital resonator which runs in the app. The result is a hybrid, half acoustic and half digital sound which is a function of both software and acoustic properties of the physical object the users decide to play. In 2017, Zamborlin founded HyperSurfaces together with computational artist Parag K Mital. to merge "the physical and the digital worlds". HyperSurfaces technology converts any surface made of any material, shape and size into data-enabled interactive objects, employing a vibration sensor and proprietary AI algorithms running on a coin-sized chipset. The vibrations generated by people's interactions on the surface are converted into an electric signal by a piezoelectric sensor and analysed in realtime by AI algorithms that run on the chipset. Anytime the AI recognises in the vibration signal one of the events that have been predefined by the user beforehand, a corresponding notification message is generated in realtime and sent to some application. The technology can be applied to anything ranging from button-less human-computer interaction applications for automotive and smart home to the Internet of things. Because the AI algorithms employed by HyperSurfaces run locally on a chipset, without the need to access cloud-based services, they are considered to be part of the field of edge computing. Also, because the AI can be trained beforehand to recognise the events its users are interested in, HyperSurfaces algorithms belong to the field of supervised machine learning. == Selected awards == IRISA Prix Jeune Chercheur, 13 October 2012 NeMoDe, New Economic Models in the Digital Economy, 25 October 2012 == Patents and academic publications == United States pending US10817798B2, Bruno Zamborlin & Carmine Emanuele Cella, "Method to recognize a gesture and corresponding device", published 27 April 2016, assigned to Mogees Limited GB Pending WO/2019/086862, Bruno Zamborlin; Conor Barry & Alessandro Saccoia et al., "A user interface for vehicles", published 9 May 2019, assigned to Mogees Limited GB Pending WO/2019/086863, Bruno Zamborlin; Conor Barry & Alessandro Saccoia et al., "Trigger for game events", published 9 May 2019, assigned to Mogees Limited Bevilacqua, Frédéric; Zamborlin, Bruno; Sypniewski, Anthony; Schnell, Norbert; Guédy, Fabrice; Rasamimanana, Nicolas (2010). "Continuous Realtime Gesture Following and Recognition". Gesture in Embodied Communication and Human-Computer Interaction. Lecture Notes in Computer Science. Vol. 5934. pp. 73–84. doi:10.1007/978-3-642-12553-9_7. ISBN 978-3-642-12552-2. S2CID 16251822. Retrieved 17 January 2021. Rasamimanana, Nicolas; Bevilacqua, Frédéric; Schnell, Norbert; Guédy, Fabrice; Flety, Emmanuel; Maestracci, Come; Zamborlin, Bruno (January 2010). "Modular musical objects towards embodied control of digital music". Proceedings of the fifth international conference on Tangible, embedded, and embodied interaction. Tei '11. pp. 9–12. doi:10.1145/1935701.1935704. ISBN 9781450304788. S2CID 10782645. Retrieved 17 January 2021. Bevilacqua, Frédéric; Schnell, Norbert; Rasamimanana, Nicolas; Zamborlin, Bruno; Guedy, Fabrice (2011). "Online Gesture Analysis and Control of Audio Processing". Musical Robots and Interactive Multimodal Systems. Springer Tracts in Advanced Robotics. Vol. 74. pp. 127–142. doi:10.1007/978-3-642-22291-7_8. ISBN 978-3-642-22290-0. Retrieved 17 January 2021. Zamborlin, Bruno; Bevilacqua, Frédéric; Gillies, Marco; D'Inverno, Mark (15 January 2014). "Fluid gesture interaction design: Applications of continuous recognition for the design of modern gestural interfaces". ACM Transactions on Interactive Intelligent Systems. 3 (4): 22:1–22:30. doi:10.1145/2543921. S2CID 7887245. Retrieved 17 January 2021. Leslie, Grace; Zamborlin, Bruno; Schnell, Norbert; Jodlowski, Pierre (15 June 2010). "A Collaborative, Interactive Sound Installation". Proceedings of the International Computer Music Conference. Retrieved 17 January 2021. Kimura, Mari; Rasamimanana, Nicolas; Bevilacqua, Frédéric; Zamborlin, Bruno; Schnell, Bruno; Flety, Emmanuel (2012). "Extracting Human Expression For Interactive Composition with the Augmented Violin". International Conference on New Interfaces for Musical Expression. Retrieved 17 January 2021. Ferretti, Stefano; Roccetti, Marco; Zamborlin, Bruno (13 January 2009). "On SPAWC: Discussion on a Musical Signal Parser and Well-Formed Composer". 2009 6th IEEE Consumer Communications and Networking Conference. pp. 1–5. doi:10.1109/CCNC.2009.4784966. ISBN 978-1-4244-2308-8. S2CID 14213587. Zamborlin, Bruno; Partesana, Giorgio; Liuni, Marco (15 May 2011). "(LAND)MOVES". Conference on New Interfaces for Musical Expression, NIME: 537–538. Retrieved 17 January 2021.
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The Best Free AI Paragraph Rewriter for Beginners

Shopping for the best AI paragraph rewriter? An AI paragraph rewriter is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI paragraph rewriter slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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LRE Map

The LRE Map (Language Resources and Evaluation) is a freely accessible large database on resources dedicated to Natural language processing. The original feature of LRE Map is that the records are collected during the submission of different major Natural language processing conferences. The records are then cleaned and gathered into a global database called "LRE Map". The LRE Map is intended to be an instrument for collecting information about language resources and to become, at the same time, a community for users, a place to share and discover resources, discuss opinions, provide feedback, discover new trends, etc. It is an instrument for discovering, searching and documenting language resources, here intended in a broad sense, as both data and tools. The large amount of information contained in the Map can be analyzed in many different ways. For instance, the LRE Map can provide information about the most frequent type of resource, the most represented language, the applications for which resources are used or are being developed, the proportion of new resources vs. already existing ones, or the way in which resources are distributed to the community. == Context == Several institutions worldwide maintain catalogues of language resources (ELRA, LDC, NICT Universal Catalogue, ACL Data and Code Repository, OLAC, LT World, etc.) However, it has been estimated that only 10% of existing resources are known, either through distribution catalogues or via direct publicity by providers (web sites and the like). The rest remains hidden, the only occasions where it briefly emerges being when a resource is presented in the context of a research paper or report at some conference. Even in this case, nevertheless, it might be that a resource remains in the background simply because the focus of the research is not on the resource per se. == History == The LRE Map originated under the name "LREC Map" during the preparation of LREC 2010 conference. More specifically, the idea was discussed within the FlaReNet project, and in collaboration with ELRA and the Institute of Computational Linguistics of CNR in Pisa, the Map was put in place at LREC 2010. The LREC organizers asked the authors to provide some basic information about all the resources (in a broad sense, i.e. including tools, standards and evaluation packages), either used or created, described in their papers. All these descriptors were then gathered in a global matrix called the LREC Map. The same methodology and requirements from the authors has been then applied and extended to other conferences, namely COLING-2010, EMNLP-2010, RANLP-2011, LREC 2012, LREC 2014 and LREC 2016. After this generalization to other conferences, the LREC Map has been renamed as the LRE Map. == Size and content == The size of the database increases over time. The data collected amount to 4776 entries. Each resource is described according to the following attributes: Resource type, e.g. lexicon, annotation tool, tagger/parser. Resource production status, e.g. newly created finished, existing-updated. Resource availability, e.g. freely available, from data center. Resource modality, e.g. speech, written, sign language. Resource use, e.g. named entity recognition, language identification, machine translation. Resource language, e.g. English, 23 European Union languages, official languages of India. == Uses == The LRE map is a very important tool to chart the NLP field. Compared to other studied based on subjective scorings, the LRE map is made of real facts. The map has a great potential for many uses, in addition to being an information gathering tool: It is a great instrument for monitoring the evolution of the field (useful for funders), if applied in different contexts and times. It can be seen as a huge joint effort, the beginning of an even larger cooperative action not just among few leaders but among all the researchers. It is also an "educational" means towards the broad acknowledgment of the need of meta-research activities with the active involvement of many. It is also instrumental in introducing the new notion of "citation of resources" that could provide an award and a means of scholarly recognition for researchers engaged in resource creation. It is used to help the organization of the conferences of the field like LREC. == Derived matrices == The data were then cleaned and sorted by Joseph Mariani (CNRS-LIMSI IMMI) and Gil Francopoulo (CNRS-LIMSI IMMI + Tagmatica) in order to compute the various matrices of the final FLaReNet reports. One of them, the matrix for written data at LREC 2010 is as follows: English is the most studied language. Secondly, come French and German languages and then Italian and Spanish. == Future == The LRE Map has been extended to Language Resources and Evaluation Journal and other conferences.
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Monica S. Lam

Monica Sin-Ling Lam is an American computer scientist. She is a professor in the Computer Science Department at Stanford University. == Education == Monica Lam received a B.Sc. from University of British Columbia in 1980 and a Ph.D. in computer science from Carnegie Mellon University in 1987. == Career == Lam joined the faculty of Computer Science at Stanford University in 1988. She has contributed to the research of a wide range of computer systems topics including compilers, program analysis, operating systems, security, computer architecture, and high-performance computing. More recently, she is working in natural language processing, and virtual assistants with an emphasis on privacy protection. She is the faculty director of the Open Virtual Assistant Lab, which organized the first workshop for the World Wide Voice Web. The lab developed the open-source Almond voice assistant, which is sponsored by the National Science Foundation. Almond received Popular Science's Best of What's New award in 2019. Previously, Lam led the SUIF (Stanford University Intermediate Format) Compiler project, which produced a widely used compiler infrastructure known for its locality optimizations and interprocedural parallelization. Many of the compiler techniques she developed have been adopted by industry. Her other research projects included the architecture and compiler for the CMU Warp machine, a systolic array of VLIW processors, and the Stanford DASH distributed shared memory machine. In 1998, she took a sabbatical leave from Stanford to help start Tensilica Inc., a company that specializes in configurable processor cores. In another research project, her program analysis group developed a collection of tools for improving software security and reliability. They developed the first scalable context-sensitive inclusion-based pointer analysis and a freely available tool called BDDBDDB, that allows programmers to express context-sensitive analyses simply by writing Datalog queries. Other tools developed include Griffin, static and dynamic analysis for finding security vulnerabilities in Web applications such as SQL injection, a static and dynamic program query language called QL, a static memory leak detector called Clouseau, a dynamic buffer overrun detector called CRED, and a dynamic error diagnosis tool called DIDUCE. In the Collective project, her research group and she developed the concept of a livePC: subscribers of the livePC will automatically run the latest of the published PC virtual images with each reboot. This approach allows computers to be managed scalably and securely. In 2005, the group started a company called MokaFive to transfer the technology to industry. She also directed the MobiSocial laboratory at Stanford, as part of the Programmable Open Mobile Internet 2020 initiative. Lam is also the cofounder of Omlet, which launched in 2014. Omlet is the first product from MobiSocial. Omlet is an open, decentralized social networking tool, based on an extensible chat platform. Lam chaired the ACM SIGPLAN Programming Languages Design and Implementation Conference in 2000, served on the Editorial Board of ACM Transactions on Computer Systems and numerous program committees for conferences on languages and compilers (PLDI, POPL), operating systems (SOSP), and computer architecture (ASPLOS, ISCA). == Awards and honors == National Academy of Engineering member, 2019 University of British Columbia Computer Science 50th Anniversary Research Award, 2018 Fellow of the ACM, 2007 ACM Programming Language Design and Implementation Best Paper Award in 2004 ACM SIGSOFT Distinguished Paper Award in 2002 ACM Most Influential Programming Language Design and Implementation Paper Award in 2001 NSF Young Investigator award in 1992 Two of her papers were recognized in "20 Years of PLDI--a Selection (1979-1999)" One of her papers was recognized in the "25 Years of the International Symposia on Computer Architecture", 1988. == Selected works == Compilers: Principles, Techniques and Tools (2d Ed) (2006) (the "Dragon Book") by Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman (ISBN 0-321-48681-1) A Systolic Array Optimizing Compiler (1989) (ISBN 0-89838-300-5) Monica Lam, Dissertation
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Top 10 AI Video Generators Compared (2026)

Shopping for the best AI video generator? An AI video generator is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI video generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Is an AI Image Generator Worth It 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. We tested the leading options and ranked them by quality, value, and ease of use.
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Text Database and Dictionary of Classic Mayan

The project Text Database and Dictionary of Classic Mayan (abbr. TWKM) promotes research on the writing and language of pre-Hispanic Maya culture. It is housed in the Faculty of Arts at the University of Bonn and was established with funding from the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts. The project has a projected run-time of fifteen years and is directed by Nikolai Grube from the Department of Anthropology of the Americas at the University of Bonn. The goal of the project is to conduct computer-based studies of all extant Maya hieroglyphic texts from an epigraphic and cultural-historical standpoint, and to produce and publish a database and a comprehensive dictionary of the Classic Mayan language. == Subject of the Project == The text database, as well as the dictionary that will be compiled by the conclusion of the project, will be assembled based on all known texts from the pre-Hispanic Maya culture. These texts were produced and used between approximately the third century B.C. through A.D. 1500, in a region that today includes parts of the countries of Mexico, Guatemala, Belize, and Honduras. The thousands of hieroglyphic inscriptions on monuments, ceramics, or daily objects that have survived into the present offer insight into the language's vocabulary and structure. The project's database and dictionary will digitally represent original spellings using the logo-syllabic Maya hieroglyphs, as well as their transcription and transliteration in the Roman alphabet. The data will be additionally annotated with various epigraphic analyses, translations, and further object-specific information. == Project Partners == TWKM will employ digital technologies in order to compile and make available the data and metadata, as well as to publish the project's research results. The project thereby methodologically positions itself in the field of the digital humanities. The project will be conducted in cooperation with the project partners (below), the research association for the eHumanities TextGrid, as well as the University and Regional Library of Bonn (ULB). The working environment that is currently under construction, in which the data and metadata will be compiled and annotated, will be realized in theTextGrid Laboratory, a software of the virtual research environment. A further component of this software, the TextGrid Repository, will make the data that are authorized for publication freely available online and ensure their long-term storage. The tools for data compilation and annotation attained from the modularly constructed and extended TextGrid lab thereby provide all the necessary materials for facilitating the research team's the typical epigraphic workflow. The workflow usually begins by documenting the texts and the objects on which they are preserved, and by compiling descriptive data. It then continues with the various levels of epigraphic and linguistic analysis, and concludes in the best case scenario with a translation of the analyzed inscription and a corresponding publication. In cooperation with the ULB, selected data will additionally be made available. The project's Virtual Inscription Archive will present online, in the Digital Collections of the ULB, hieroglyphic inscriptions selected from the published data in the repository, including an image of and brief information about the texts and the objects on which they are written, epigraphic analysis, and translation. == Project Goal == One of the project's goals is to produce a dictionary of Classic Mayan, in both digital and print form, towards the end of the project run-time. Additionally, a database with a corpus of inscriptions, including their translations and epigraphic analyses, will be made freely available online. The database furthermore will provide an ontology-like link of the contextual object data with the inscriptions and with each other, thereby allowing a cultural-historical arrangement of all contents within the periods of pre-Hispanic Maya culture. The contents of the database are additionally linked to citations of relevant literature. As a result, the database will also make freely available to both the scientific community and other interested parties a bibliography representing the research history and a base of knowledge concerning ancient Maya culture and script. In addition, the Classic Maya script, in its temporally defined stages of language development, will be gathered into and documented in a comprehensive language corpus with the aid of the information gathered by the project. In collaboration with all project participants, the corpus data can be used, together with the aid of various comparable analyses and also computational linguistic methods, such as inference-based methods, to confirm readings of some hieroglyphs that are currently only partially confirmed, and to eventually completely decipher the Classic Maya script.
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Separating words problem

In theoretical computer science, the separating words problem is the problem of finding the smallest deterministic finite automaton that behaves differently on two given strings, meaning that it accepts one of the two strings and rejects the other string. It is an open problem how large such an automaton must be, in the worst case, as a function of the length of the input strings. == Example == The two strings 0010 and 1000 may be distinguished from each other by a three-state automaton in which the transitions from the start state go to two different states, both of which are terminal in the sense that subsequent transitions from these two states always return to the same state. The state of this automaton records the first symbol of the input string. If one of the two terminal states is accepting and the other is rejecting, then the automaton will accept only one of the strings 0010 and 1000. However, these two strings cannot be distinguished by any automaton with fewer than three states. == Simplifying assumptions == For proving bounds on this problem, it may be assumed without loss of generality that the inputs are strings over a two-letter alphabet. For, if two strings over a larger alphabet differ then there exists a string homomorphism that maps them to binary strings of the same length that also differ. Any automaton that distinguishes the binary strings can be translated into an automaton that distinguishes the original strings, without any increase in the number of states. It may also be assumed that the two strings have equal length. For strings of unequal length, there always exists a prime number p whose value is logarithmic in the smaller of the two input lengths, such that the two lengths are different modulo p. An automaton that counts the length of its input modulo p can be used to distinguish the two strings from each other in this case. Therefore, strings of unequal lengths can always be distinguished from each other by automata with few states. == History and bounds == The problem of bounding the size of an automaton that distinguishes two given strings was first formulated by Goralčík & Koubek (1986), who showed that the automaton size is always sublinear. Later, Robson (1989) proved the upper bound O(n2/5(log n)3/5) on the automaton size that may be required. This was improved by Chase (2020) to O(n1/3(log n)7). There exist pairs of inputs that are both binary strings of length n for which any automaton that distinguishes the inputs must have size Ω(log n). Closing the gap between this lower bound and Chase's upper bound remains an open problem. Jeffrey Shallit has offered a prize of 100 British pounds for any improvement to Robson's upper bound. == Special cases == Several special cases of the separating words problem are known to be solvable using few states: If two binary words have differing numbers of zeros or ones, then they can be distinguished from each other by counting their Hamming weights modulo a prime of logarithmic size, using a logarithmic number of states. More generally, if a pattern of length k appears a different number of times in the two words, they can be distinguished from each other using O(k log n) states. If two binary words differ from each other within their first or last k positions, they can be distinguished from each other using k + O(1) states. This implies that almost all pairs of binary words can be distinguished from each other with a logarithmic number of states, because only a polynomially small fraction of pairs have no difference in their initial O(log n) positions. If two binary words have Hamming distance d, then there exists a prime p with p = O(d log n) and a position i at which the two strings differ, such that i is not equal modulo p to the position of any other difference. By computing the parity of the input symbols at positions congruent to i modulo p, it is possible to distinguish the words using an automaton with O(d log n) states.
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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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