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SAP StreamWork

SAP StreamWork is an enterprise collaboration tool from SAP SE released in March 2010, and discontinued in December 2015. StreamWork allowed real-time collaboration like Google Wave, but focused on business activities such as analyzing data, planning meetings, and making decisions. It incorporated technology from Box.net and Evernote to allow users to connect to online files and documents, and document-reader technology from Scribd allowed users to view documents directly within its environment. StreamWork supported the OpenSocial set of application programming interfaces (APIs), allowing it to connect to tools built by third-party developers, such as Google Docs. A version of StreamWork intended for large enterprises used a virtual appliance based on Novell's SUSE Linux Enterprise to connect it to business systems, including those from SAP.
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Apache cTAKES

Apache cTAKES: clinical Text Analysis and Knowledge Extraction System is an open-source Natural Language Processing (NLP) system that extracts clinical information from electronic health record unstructured text. It processes clinical notes, identifying types of clinical named entities — drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity has attributes for the text span, the ontology mapping code, context (family history of, current, unrelated to patient), and negated/not negated. cTAKES was built using the UIMA Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. == Components == Components of cTAKES are specifically trained for the clinical domain, and create rich linguistic and semantic annotations that can be utilized by clinical decision support systems and clinical research. These components include: Named Section identifier Sentence boundary detector Rule-based tokenizer Formatted list identifier Normalizer Context dependent tokenizer Part-of-speech tagger Phrasal chunker Dictionary lookup annotator Context annotator Negation detector Uncertainty detector Subject detector Dependency parser patient smoking status identifier Drug mention annotator == History == Development of cTAKES began at the Mayo Clinic in 2006. The development team, led by Dr. Guergana Savova and Dr. Christopher Chute, included physicians, computer scientists and software engineers. After its deployment, cTAKES became an integral part of Mayo's clinical data management infrastructure, processing more than 80 million clinical notes. When Dr. Savova's moved to Boston Children's Hospital in early 2010, the core development team grew to include members there. Further external collaborations include: University of Colorado Brandeis University University of Pittsburgh University of California at San Diego Such collaborations have extended cTAKES' capabilities into other areas such as Temporal Reasoning, Clinical Question Answering, and coreference resolution for the clinical domain. In 2010, cTAKES was adopted by the i2b2 program and is a central component of the SHARP Area 4. In 2013, cTAKES released their first release as an Apache Software Foundation incubator project: cTAKES 3.0. In March 2013, cTAKES became an Apache Software Foundation Top Level Project (TLP).
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Angelo Dalli

Angelo Dalli (born 14 April 1978) is a computer scientist specialising in artificial intelligence, a serial entrepreneur, and business angel investor. == Early life and education == Dalli was born in Malta and grew up in the town of Birżebbuġa. Dalli was educated at the Archbishop's Seminary, Malta and represented Malta in the Young European Environmental Research contest held in Cologne in 1994. Dalli represented Malta in the International Olympiad in Informatics held in Eindhoven in 1995, where he won a bronze medal. Dalli started selling computer software as a teenager, and worked for the International Data Group as a freelance contributor for PC World. == Academic work == After graduating from the University of Malta, Dalli spent time lecturing on artificial intelligence and natural language processing before reading for his PhD at the University of Sheffield under the supervision of Yorick Wilks. Dalli has published over 23 peer reviewed papers in the artificial intelligence and natural language processing fields, including one of the earliest methods on timestamp extraction from documents that is now commonly used in most email applications. Angelo has also contributed to the encoding of European languages in Unicode, in particular for the Common Locale Data Repository. In the field of Bioinformatics Dalli has found a particularly useful integer sequence (sequence A062208 in the OEIS) which efficiently computes all alignments of strings of length 3 together with other generalisations (sequence A062204 in the OEIS), (sequence A062205 in the OEIS) for applications in natural language and sequence alignment. Dalli has an Erdős number of 3. Dalli has led the Maltese national informatics team in the International Olympiad in Informatics at IOI 2002 in Seoul, South Korea and IOI 2004 in Athens, Greece. == Artificial intelligence == === Trustworthy AI and Hybrid Intelligence === Angelo has been a vocal proponent of trustworthy AI that impacts society positively and believes that AI should be properly regulated. Angelo has co-founded UMNAI in 2019, with the aim of creating a new form of trustworthy AI that can explain the decisions and steps that the AI has taken to output an answer, based on a neurosymbolic AI architecture that combines neural and symbolic AI in an auditable and certain manner. === AI and society === Angelo led the Government of Malta taskforce that produced Malta's new AI regulation and national AI strategy, and is an active member of the IEEE, AAAI, ACM and the ACL. === AI in transport === Angelo had led the introduction of different machine learning techniques in intelligent transport systems (ITS), including parking, controlled vehicle access zones and dynamic traffic interchange control. His intelligent transport company, Traffiko, operated in Europe, Australia and the Middle East, and was eventually sold to Q-Free in Norway in 2015. === AI in gaming === Angelo is a well known speaker in the online gambling industry. Angelo setup one of the first companies that applied artificial intelligence in the online gambling industry, called Bit8 (now part of Intralot), with the most notable work being on algorithms that estimate and maximise player lifetime value and personalised bonusing systems. These techniques have since been widely adopted by the online gambling industry Intralot subsequently bought Bit8 in 2017. === AI and creativity === Angelo has been collaborating various artists and creatives to teach AI about creativity. The results of this collaboration is the UMA AI entity, short for Universal Machine Artist. Angelo has also co-founded the Creative Science and Arts Institute to act as a foundation for future research into AI, science, technology and creativity. UMA is creating original artwork using a modified Generative adversarial network has a third component, the human artist, to produce different learning results than standard generative AI models. The underlying discriminator in UMA started from an anti-fraud detection system and has now gradually evolved to add stable diffusion and procedural generation methods. The first two artworks generated by UMA were auctioned in October and November 2018 respectively, with all proceeds donated to charity and good causes. Ongoing work in improving UMA and furthering collaboration with other artists is ongoing. Notable exhibitions include Tomorrow's Blossoms with Selina Scerri at Esplora Museum in 2024, which explored the theme of AI and emotions. == Angel investor == Angelo is an angel investor active in the high-tech startup scene, and is a member of EBAN, and World Business Angel Forum senator. Angelo has been encouraging Maltese startups via various public events including the Zest and Budding Rockstars conferences and co-founded BAM, the Malta Business Angel network, in 2019. == Awards and honours == === Entrepreneurial and scientific === Bronze Medal, International Olympiad in Informatics (1995) Malta Top Entrepreneur Award (2019) Malta Top Entrepreneur Award (2014) WIPO IP Enterprise Award for the UMNAI Neuro-symbolic AI architecture (2022) === Corporate awards === Intralot Bit8 EGR Rising Star Award (2014) Intralot Bit8 Malta Communication Authority eBusiness Award for the Best B2B application (2015) Intralot Bit8 Malta iGaming Award for Excellence (2017)
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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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Digital on-screen graphic

A digital on-screen graphic, digitally originated graphic (DOG, bug, network bug, on-screen bug or screenbug) is a watermark-like station logo that most television broadcasters overlay over a portion of the screen area of their programs to identify the channel. They are thus a form of permanent visual station identification, increasing brand recognition and asserting ownership of the video signal. The graphic identifies the source of programming, even if it has been time-shifted or recorded. Many of these technologies allow viewers to skip or omit traditional between-programming station identification; thus the use of a DOG enables the station or network to enforce brand identification even when standard commercials are skipped. DOG watermarking helps to reduce off-the-air copyright infringement—for example, the distribution of a current series' episodes on DVD: the watermarked content is easily differentiated from "official" DVD releases, and can help identify not only the station from which the broadcast was captured, but usually the actual date of the broadcast as well. Graphics may be used to identify if the correct subscription is being used for a type of venue. For example, showing Sky Sports within a pub in the United Kingdom requires a more expensive subscription; a channel authorized under this subscription adds a pint glass graphic to the bottom of the screen for inspectors to see. The graphic changes at certain times, making it harder to counterfeit. On the other hand, watermarks pollute the picture, distract viewers' attention and may cover an important piece of information presented in the television program. Extremely bright watermarks may cause screen burn-in or image persistence on some types of television sets such as the now mostly discontinued and rarely used plasma and CRT displays, and currently commonly used OLED and LCD displays. Usage of visually perceptible embedded watermarks requires the program author to have a separate clean copy for archival purposes, but this practice was not common decades ago when watermarking became popular among broadcasters. Watermarks present an issue when archival videos are used for a documentary that strives to create a coherent story. In some cases, watermarks are blurred or digitally removed if possible to clean up the picture. In the absence of visually perceptible watermarks, content control can be ensured with visually imperceptible digital watermarks. In some cases, the graphic also shows the name of the current program. Some television networks may place additional logos or text alongside their DOG to advertise significant upcoming programs. For example, broadcasters of the Olympic Games (most notably United States broadcaster NBC) often add the Olympic rings to their DOG for a period of time leading up to and during the Games. == Usage == == Connections with sponsor tags == Another graphic on television usually connected with sports (particularly in North America, though not in Europe) is the sponsor tag. It shows the logos of certain sponsors, accompanied by some background relevant to the game, the network logo, announcement and music of some kind. == Usage in ham radio and television == In most countries, the ham station is required to periodically identify their amateur-television transmission. Such stations frequently overlay their callsign on the signal instead of placing a card in the background. Most hams use homebuilt devices or old consumer character generators to generate such identifications rather than using graphical superimposes of high cost to do so. Only rarely one can see real graphics, as the callsign is usually written in the "OSD font". == Live DOGs by hobbyists == One of the easiest and most sought-after devices used to generate DOGs by hobbyists is the 1980s vintage Sony XV-T500 video superimposer. This device can luma-key a signal, capture a still frame into memory and then overlay the keyed graphic in one of eight colors onto any CVBS signal. Another method commonly used by hobbyists and even low-budgeted television stations was Amiga computers with genlock interfaces.
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Karl Steinbuch

Karl W. Steinbuch (June 15, 1917 in Stuttgart-Bad Cannstatt – June 4, 2005 in Ettlingen) was a German computer scientist, cyberneticist, and electrical engineer. He was an early and influential researcher in German computer science, and was the developer of the Lernmatrix, an early implementation of artificial neural networks. From the late 1960s onwards the focus of his activity shifted from scientific research to right-wing political activism supporting the Neue Rechte. == Biography == Steinbuch joined the National Socialist German Students' League (NSDStB) and the Nazi Party. Steinbuch studied at the University of Stuttgart and in 1944 he received his PhD in physics. In 1948 he joined Standard Elektrik Lorenz (SEL, part of the ITT group) in Stuttgart, as a computer design engineer and later as a director of research and development, where he filed more than 70 patents. Steinbuch completed the first European fully transistorized computer, the ER 56 marketed by SEL. In 1958 he became professor and director of the Institute of Technology for information processing (ITIV) of the University of Karlsruhe, where he retired in 1980. In 1967 he began publishing books, in which he tried to influence German education policy. Together with books from colleagues like Jean Ziegler from Switzerland, Eric J. Hobsbawm from the UK, and John Naisbitt his books predicted what he regarded as the coming education disaster of the emerging civic lobby society. In 1957, together with Helmut Gröttrup, Steinbuch coined the term Informatik, the German word for computer science, which gave informatics, and the term kybernetische Anthropologie. == Awards and recognition == Wilhelm-Boelsche award - medal in Gold German non-fiction book award Gold medal award of the XXI. International Congresses on Aerospace Medicine Konrad Adenauer award of science Jakob Fugger award medal Medal of merit of the state of Baden-Wuerttemberg member, German Academy of Sciences Leopoldina member, International Academy of Science, Munich. grants from a state government grants program, named "Karl-Steinbuch-Stipendium" Steinbuch Centre for Computing at the Karlsruhe Institute of Technology named after him == Books == Steinbuch wrote several books and articles, including: 1957 Informatik: Automatische Informationsverarbeitung ("Informatics: automatic information processing"). 1963 Learning matrices and their applications (together with U. A. W. Piske) 1965 A critical comparison of two kinds of adaptive classification networks (together with Bernard Widrow) 1966 (1969): Die informierte Gesellschaft. Geschichte und Zukunft der Nachrichtentechnik (The informed society. History and Future of telecommunications) 1989: Die desinformierte Gesellschaft (The disinformed society) 1968: Falsch programmiert. Über das Versagen unserer Gesellschaft in der Gegenwart und vor der Zukunft und was eigentlich geschehen müßte. (as a bestseller listet in: Der Spiegel) (Programmed falsely. About our society's failure in the present and with respect to the future and what should be done.) 1969: Programm 2000. (as a bestseller listet in: Der Spiegel) 1971: Automat und Mensch. Auf dem Weg zu einer kybernetischen Anthropologie (Machine and Man. On the way to a cybernetic anthropology; 4th revised edition) 1971: Mensch Technik Zukunft. Probleme von Morgen (German non-fiction book award) (Man Technology Future. Problems of Tomorrow) 1973: Kurskorrektur (Correcting the Course) 1978: Maßlos informiert. Die Enteignung des Denkens (Excessively informed. The Deprivation of Thinking) 1984: Unsere manipulierte Demokratie. Müssen wir mit der linken Lüge leben? (Our Thought-controlled Democracy. Do we have to live with the leftist lie?)
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Halbert White

Halbert Lynn White Jr. (November 19, 1950 – March 31, 2012) was the Chancellor's Associates Distinguished Professor of Economics at the University of California, San Diego, and a Fellow of the Econometric Society and the American Academy of Arts and Sciences. == Education and career == White, a native of Kansas City, Missouri, graduated salutatorian from Southwest High School in 1968. He went on to study at Princeton University, receiving his B.A. in economics in 1972. He earned his Ph.D. in economics at the Massachusetts Institute of Technology in 1976, under the supervision of Jerry A. Hausman and Robert Solow. White spent his first years as an assistant professor in the University of Rochester before moving to University of California, San Diego (UCSD) in 1979. He remained at UCSD until his untimely death from cancer. == Research == White was well known in the field of econometrics for his 1980 paper on robust standard errors (which is among the most-cited paper in economics since 1970), and for the heteroscedasticity-consistent estimator and the test for heteroskedasticity that are named after him. A 1982 paper by White contributed strongly to the development of quasi-maximum likelihood estimation. He also contributed to numerous other areas such as neural networks and medicine. In 1999, White co-founded an economic consulting firm, Bates White, which is based in Washington, D.C.
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AI Logo Makers Reviews: What Actually Works in 2026

Shopping for the best AI logo maker? An AI logo maker 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 logo maker 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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Texture compression

Texture compression is a specialized form of image compression designed for storing texture maps in 3D computer graphics rendering systems. Unlike conventional image compression algorithms, texture compression algorithms are optimized for random access. Texture compression can be applied to reduce memory usage at runtime. Texture data is often the largest source of memory usage in a mobile application. == Tradeoffs == In their seminal paper on texture compression, Beers, Agrawala and Chaddha list four features that tend to differentiate texture compression from other image compression techniques. These features are: Decoding Speed It is highly desirable to be able to render directly from the compressed texture data and so, in order not to impact rendering performance, decompression must be fast. Random Access Since predicting the order that a renderer accesses texels would be difficult, any texture compression scheme must allow fast random access to decompressed texture data. This tends to rule out many better-known image compression schemes such as JPEG or run-length encoding. Compression Rate and Visual Quality In a rendering system, lossy compression can be more tolerable than for other use cases. Some texture compression libraries, such as crunch, allow the developer to flexibly trade off compression rate vs. visual quality, using methods such as rate–distortion optimization (RDO). Encoding Speed Texture compression is more tolerant of asymmetric encoding/decoding rates as the encoding process is often done only once during the application authoring process. Given the above, most texture compression algorithms involve some form of fixed-rate lossy vector quantization of small fixed-size blocks of pixels into small fixed-size blocks of coding bits, sometimes with additional extra pre-processing and post-processing steps. Block Truncation Coding is a very simple example of this family of algorithms. Because their data access patterns are well-defined, texture decompression may be executed on-the-fly during rendering as part of the overall graphics pipeline, reducing overall bandwidth and storage needs throughout the graphics system. As well as texture maps, texture compression may also be used to encode other kinds of rendering map, including bump maps and surface normal maps. Texture compression may also be used together with other forms of map processing such as mipmaps and anisotropic filtering. == Availability == Some examples of practical texture compression systems are S3 Texture Compression (S3TC), PVRTC, Ericsson Texture Compression (ETC) and Adaptive Scalable Texture Compression (ASTC); these may be supported by special function units in modern graphics processing units (GPUs). OpenGL and OpenGL ES, as implemented on many video accelerator cards and mobile GPUs, can support multiple common kinds of texture compression - generally through the use of vendor extensions. == Supercompression == A compressed-texture can be further compressed in what is called "supercompression". Fixed-rate texture compression formats are optimized for random access and are much less efficient compared to image formats such as PNG. By adding further compression, a programmer can reduce the efficiency gap. The extra layer can be decompressed by the CPU so that the GPU receives a normal compressed texture, or in newer methods, decompressed by the GPU itself. Supercompression saves the same amount of VRAM as regular texture compression, but saves more disk space and download size. == Neural Texture Compression == Random-Access Neural Compression of Material Textures (Neural Texture Compression) is a Nvidia's technology which enables two additional levels of detail (16× more texels, so four times higher resolution) while maintaining similar storage requirements as traditional texture compression methods. The key idea is compressing multiple material textures and their mipmap chains together, and using a small neural network, that is optimized for each material, to decompress them.
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Best AI Writing Assistants in 2026

In search of the best AI writing assistant? An AI writing assistant 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 writing assistant 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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Best AI Logo Makers in 2026

Looking for the best AI logo maker? An AI logo 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 logo 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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The Best Free AI Headshot Generator for Beginners

Shopping for the best AI headshot generator? An AI headshot 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 headshot generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Tiki Wiki CMS Groupware

Tiki Wiki CMS Groupware or simply Tiki, originally known as TikiWiki, is a free and open source Wiki-based content management system and online office suite written primarily in PHP and distributed under the GNU Lesser General Public License (LGPL-2.1-only) license. In addition to enabling websites and portals on the internet and on intranets and extranets, Tiki contains a number of collaboration features allowing it to operate as a Geospatial Content Management System (GeoCMS) and Groupware web application. Tiki includes all the basic features common to most CMSs such as the ability to register and maintain individual user accounts within a flexible and rich permission / privilege system, create and manage menus, RSS-feeds, customize page layout, perform logging, and administer the system. All administration tasks are accomplished through a browser-based user interface. Tiki features an all-in-one design, as opposed to a core+extensions model followed by other CMSs. This allows for future-proof upgrades (since all features are released together), but has the drawback of an extremely large codebase (more than 1,000,000 lines). Tiki can run on any computing platform that supports both a web server capable of running PHP 5 (including Apache HTTP Server, IIS, Lighttpd, Hiawatha, Cherokee, and nginx) and a MySQL/MariaDB database to store content and settings. == Major components == Tiki has four major categories of components: content creation and management tools, content organization tools and navigation aids, communication tools, and configuration and administration tools. These components enable administrators and users to create and manage content, as well as letting them communicate to others and configure sites. In addition, Tiki allows each user to choose from various visual themes. These themes are implemented using CSS and the open source Smarty template engine. Additional themes can be created by a Tiki administrator for branding or customization as well. == Internationalization == Tiki is an international project, supporting many languages. The default interface language in Tiki is English, but any language that can be encoded and displayed using the UTF-8 encoding can be supported. Translated strings can be included via an external language file, or by translating interface strings directly, through the database. As of 29 September 2005, Tiki had been fully translated into eight languages and reportedly 90% or more translated into another five languages, as well as partial translations for nine additional languages. Tiki also supports interactive translation of actual wiki pages and was the initial wiki engine used in the Cross Lingual Wiki Engine Project. This allows Tiki-based web sites to have translated content — not just the user interface. == Implementation == Tiki is developed primarily in PHP with some JavaScript code. It uses MySQL/MariaDB as a database. It will run on any server that provides PHP 5, including Apache and Microsoft's IIS. Tiki components make extensive use of other open source projects, including Zend Framework, Smarty, jQuery, HTML Purifier, FCKeditor, Raphaël, phpCAS, and Morcego. When used with Mapserver Tiki can become a Geospatial Content Management System. == Project team == Tiki is under active development by a large international community of over 300 developers and translators, and is one of the largest open-source teams in the world. Project members have donated the resources and bandwidth required to host the tiki.org website and various subdomains. The project members refer to this dependence on their own product as "eating their own dogfood", which they have been doing since the early days of the project. Tiki community members also participate in various related events such as WikiSym and the Libre Software Meeting. == History == Tiki has been hosted on SourceForge.net since its initial release (Release 0.9, named Spica) in October 2002. It was primarily the development of Luis Argerich (Buenos Aires, Argentina), Eduardo Polidor (São Paulo, Brazil), and Garland Foster (Green Bay, WI, United States). In July 2003, Tiki was named the SourceForge.net July 2003 Project of the Month. In late 2003, a fork of Tiki was used to create Bitweaver. In 2006, Tiki was named to CMS Report's Top 30 Web Applications. In 2008, Tiki was named to EContent magazine's Top 100 In 2009, Tiki adopted a six-month release cycle and announced the selection of a Long Term Support (LTS) version and the Tiki Software Community Association was formed as the legal steward for Tiki. The Tiki Software Association is a not-for-profit entity established in Canada. Previously, the entire project was run entirely by volunteers. In 2010, Tiki received Best of Open Source Software Applications Award (BOSSIE) from InfoWorld, in the Applications category. In 2011, Tiki was named to CMS Report's Top 30 Web Applications. In 2012, Tiki was named "Best Web Tool" by WebHostingSearch.com, and "People's Choice: Best Free CMS" by CMS Critic. In 2016, Tiki was named as one of the "10 Best Open Source Collaboration Software Tools" by Small Business Computing. == Name == The name TikiWiki is written in CamelCase, a common Wiki syntax indicating a hyperlink within the Wiki. It is most likely a compound word combining two Polynesian terms, Tiki and Wiki, to create a self-rhyming name similar to wikiwiki, a common variant of wiki. A backronym has also been formed for Tiki: Tightly Integrated Knowledge Infrastructure. == Release Information and History == In general, the Tiki Software Community Association releases a new major version of Tiki Wiki every 8 months where prior, non-LTS, major versions are supported until the first minor version release of the next major version (i.e., 16.0 ⇒ 17.1). Starting with version 12.x, Tiki Wiki LTS is supported for 5 years where it enters a security/maintenance release cycle upon the release of the next LTS version. Tiki Wiki's release history is outlined below.
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Hans Uszkoreit

Hans Uszkoreit is a German computational linguist. Hans Uszkoreit studied Linguistics and Computer Science at Technische Universität Berlin and the University of Texas at Austin. While he was studying in Austin, he also worked as a research associate in a large machine translation project at the Linguistics Research Center. After he received his Ph.D. in linguistics from the University of Texas, he worked as a computer scientist at the Artificial Intelligence Center and was affiliated with the Center for the Study of Language and Information at Stanford University. Nowadays, he is teaching as a professor of Computational Linguistics at Saarland University. Moreover, he serves as a Scientific Director at the German Research Center for Artificial Intelligence (DFKI) where he heads the DFKI Language Technology Lab. == Life and career == Hans Uszkoreit, a native of East Berlin, was actively involved in a group of young individuals who opposed the East Germany regime. His protesting against the 1968 invasion of Czechoslovakia led to his expulsion from high school and subsequent imprisonment for a period of fifteen months on charges of subversive agitation. Realizing that continuing his education in East Germany was not feasible, Uszkoreit made the decision to escape to West Berlin. There, he completed his high school education and pursued a degree in Linguistics and Computer Science at Technische Universität Berlin. During his time as a student, he worked part-time as an editor and writer for Zitty, a city magazine, which he co-founded. In 1977, Uszkoreit was granted a Fulbright Grant to further his studies at the University of Texas at Austin. During his time in Austin, he concurrently served as a research associate in a significant machine translation project. Subsequently, he received a second Fulbright grant, which enabled him to pursue a Ph.D. program in linguistics. In 1984, he successfully completed his doctoral studies, earning a Ph.D. in linguistics. Between 1982 and 1986, Uszkoreit held the position of a computer scientist at the Artificial Intelligence Center of SRI International in Menlo Park, California. In 1988, he created the Department of Computational Linguistics and Phonetics at Saarland University. In 1989 he was elected head of the Language Technology Lab at DFKI. In 2012, Uszkoreit's achievements in the domain of relation extraction led to his receipt of a Google Faculty Research Award, acknowledging the substantial progress made by Uszkoreit and his team in advancing the field. In 2013, Uszkoreit, in collaboration with Feiyu Xu and Roberto Navigli, was granted an additional Google Research Award, which provided support for a targeted project within Google's Language Understanding Program, focusing on the augmentation of language comprehension and analysis. == Personal life == He is father of a son Jakob Uszkoreit, machine learning researcher scientist, an author of the landmark paper "Attention Is All You Need", and daughter Lena Uszkoreit. == Awards == 2002 Elected Member of the European Academy of Sciences 2012 Google Faculty Research Award 2013 Google Focused Research Award
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Is an AI Clip Maker Worth It in 2026?

Trying to pick the best AI clip maker? An AI clip maker 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 clip maker 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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