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International Speech Communication Association

The International Speech Communication Association (ISCA) is a non-profit organization and one of the two main professional associations for speech communication science and technology, the other association being the IEEE Signal Processing Society. == Purpose == The purpose of the International Speech Communication Association (ISCA) is to promote the study and application of automatic speech processing, including speech recognition and synthesis, as well as related areas such as speaker recognition and speech compression. The association's activities cover all aspects of speech processing, including computational, linguistic, and theoretical aspects. The primary goal of the International Speech Communication Association (ISCA) is to advance the field of automatic speech processing and communication technology through research, education, and collaboration. By promoting the study and application of speech technologies such as speech recognition, speech synthesis, speaker recognition, and speech compression, ISCA aims to foster innovation and development in the areas of human-computer interaction, telecommunications, and multimedia applications. ISCA serves as a platform for researchers, academics, industry professionals, and students to exchange knowledge, share best practices, and foster interdisciplinary dialogue in the field of speech communication science. Through conferences, workshops, publications, and educational initiatives, ISCA seeks to enhance the understanding of speech processing mechanisms, improve the accuracy and efficiency of speech technologies, and explore new frontiers in the realm of human language communication. Furthermore, ISCA plays a crucial role in promoting international collaboration and networking among professionals in the speech communication community. By facilitating partnerships and cooperation between individuals and organizations worldwide, ISCA seeks to drive global progress in speech technology research and application, ultimately contributing to the advancement of communication systems, accessibility tools, and interactive interfaces that benefit society as a whole. == Conferences == ISCA organizes yearly the Interspeech conference. Most recent Interspeech: 2013 Lyon, France 2014 Singapore 2015 Dresden, Germany 2016 San Francisco, US 2017 Stockholm, Sweden 2018 Hyderabad, India 2019 Graz, Austria 2020 Shanghai, China (fully virtual) 2021 Brno, Czechia (hybrid) 2022 Incheon, South Korea 2023 Dublin, Ireland 2023 Kos Island, Greece Forthcoming Interspeech: 2025 Rotterdam, the Netherlands == ISCA board == The ISCA president for 2023-2025 is Odette Scharenborg. The vice president is Bhuvana Ramabhadran and the other members are professionals in the field. == History of ISCA == The precursor to Interspeech was a conference called Eurospeech, first held in 1989 and organised by Jean-Pierre Tubach. It was the conference of the European Speech Communication Association (ESCA), itself the precursor of the International Speech Communication Association (ISCA). A year later another conference on speech science and technology was started: the International Conference on Spoken Language Processing (ICSLP), which was founded in 1990 by Hiroya Fujisaki. The first ISCA (vs. ESCA) event was the merging of Eurospeech and ICSLP to create ICSLP-Interspeech, held in Beijing, China in 2000. This was followed by Eurospeech-Interspeech, which was held in Aalborg, Denmark in 2001. In 2007, the Eurospeech and ICSLP parts of the conference names were dropped and Interspeech became the name of the yearly conference (first Interspeech location: Antwerp, Belgium).
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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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Is an AI Writing Assistant Worth It 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. We tested the leading options and ranked them by quality, value, and ease of use.
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Marine Carpuat

Marine Carpuat is a computer scientist who works on machine translation and natural language processing. She is known for her research connecting cross-lingual semantics with machine translation. She has been recognized with a NSF Career Award in 2018, a Google Research award in 2016, and Amazon Faculty Awards in 2016 and 2018. == Education == Marine Carpuat obtained her MPhil and PhD from Hong Kong University of Science and Technology in 2008 under the supervision of Dekai Wu. Her PhD thesis was on the topic of machine translation, and demonstrated the first results showing that explicit modeling of lexical semantics could improve the accuracy of a machine translation system. == Career == After completing her education, Carpuat worked at the National Research Council Canada as a researcher. In 2015, she joined University of Maryland as an assistant professor in Computer Science where she is a member of the CLIP lab. Carpuat works in the area of natural language processing with a focus on machine translation and cross-lingual semantics. She has published over 100 peer-reviewed research papers. Her work is published in the proceedings of computer science conferences, including the Annual Meeting of the Association for Computational Linguistics and Empirical Methods in Natural Language Processing. == Selected honors and distinctions == 2016 Google Research Award 2016, 2018 Amazon Research Awards 2018 NSF Career Award
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MoltenVK

MoltenVK is a software library which allows Vulkan applications to run on top of Metal on Apple's macOS, iOS, and tvOS operating systems. It is the first software component to be released for the Vulkan Portability Initiative, a project to have a subset of Vulkan run on platforms lacking native Vulkan drivers. There are some limitations compared with a native Vulkan implementation. == History == MoltenVK was first released as a proprietary and commercially licensed product by The Brenwill Workshop on July 27, 2016. On July 31, 2017, Khronos announced the formation of the Vulkan Portability Technical Subgroup. === Open source === On February 26, 2018, Khronos announced that Vulkan became available on macOS and iOS products through the MoltenVK library. Valve announced that Dota 2 will run on macOS using the Vulkan API with the aid of MoltenVK, and that they had made an arrangement with developer The Brenwill Workshop Ltd to release MoltenVK as open-source software under the Apache License version 2.0. On May 30, 2018, Qt was updated with Vulkan for Qt on macOS using MoltenVK. On May 31, 2018, optional Vulkan support for Dota 2 on macOS was released. Benchmarks for the game were available the following day, showing better performance using Vulkan and MoltenVK compared to OpenGL. On July 20, 2018, Wine was updated with Vulkan support on macOS using MoltenVK. On 29 July 2018, the first app using MoltenVK was accepted onto the App Store, after initially being rejected. On 6 August 2018, Google open-sourced Filament, a crossplatform real-time physically based rendering engine with MoltenVK for macOS/iOS. On November 28, 2018, Valve released Artifact, their first Vulkan-only game on macOS using MoltenVK. === Version 1.0 === On 29 January 2019, MoltenVK 1.0.32 was released with early prototype of Vulkan Portability Extensions. RPCS3 and Dolphin emulators were updated with Vulkan support on macOS using MoltenVK. On 13 April 2019, MoltenVK 1.0.34 was released with support for tessellation. On July 30, 2019, MoltenVK 1.0.36 was released targeting Metal 3.0. On July 31, 2020, MoltenVK 1.0.44 was released, adding support for the tvOS platform. On January 23, 2020, MoltenVK was updated to support for some of the new features of Vulkan 1.2, as of Vulkan SDK 1.2.121. === Version 1.1 === On October 1, 2020, MoltenVK 1.1.0 was released, adding full support for Vulkan 1.1, as of Vulkan SDK 1.2.154. On 9 December 2020, MoltenVK 1.1.1 was released, providing support for Vulkan on Apple silicon GPUs and support for the Mac Catalyst platform for porting iOS/iPadOS apps to macOS. === Version 1.2 === On October 18, 2022, MoltenVK 1.2.0 was released, adding full support for Vulkan 1.2 as of Vulkan SDK 1.3.231. In January 2023, MoltenVK 1.2.2 added support for Vulkan as of SDK 1.3.239, while this version of Vulkan SDK fixed some issues with the interconnectivity with Metal API, while version 1.2.3 supported some additional extensions. === Version 1.3 === On May 1, 2025, MoltenVK 1.3 was released with support for Vulkan 1.3. === Version 1.4 === On August 20, 2025, MoltenVK 1.4 was released with support for Vulkan 1.4.
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Best AI Bug Finders in 2026

In search of the best AI bug finder? An AI bug finder 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 bug finder 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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Jacek M. Zurada

Jacek M. Zurada is a Polish-American computer scientist who is a Professor of the Electrical and Computer Engineering Department at the University of Louisville, Kentucky. His M.S. and Ph.D. degrees are from Politechnika Gdaṅska (Gdansk University of Technology, Poland). He has held visiting appointments at the Swiss Federal Institute of Technology, Zurich, Princeton, Northeastern, and Auburn, and at overseas universities in Australia, Chile, China, France, Germany, Hong Kong, Italy, Japan, Poland, Singapore, Spain, and South Africa. He is a life fellow of IEEE and a fellow of the International Neural Networks Society and Doctor Honoris Causa of Czestochowa Institute of Technology, Poland. == Research == Zurada's research covers neural networks, deep learning, data mining with emphasis on data and feature understanding, rule extraction from semantic and visual information, machine learning, decomposition methods for salient feature extraction, and lambda learning rule for neural networks. == Professional and editorial service == Zurada was the editor-in-chief of IEEE Transactions on Neural Networks (1998–2003), an associate editor of IEEE Transactions on Circuits and Systems, Pt. I and Pt. II, Action Editor in Neural Networks (Elsevier) and on the editorial board of the Proceedings of the IEEE. He is an associate editor of Neurocomputing, Schedae Informaticae, the International Journal of Applied Mathematics and Computer Science, and Editor of the Springer Natural Computing, Advances in Intelligent Systems and Computing and Studies in Computational Intelligence Book series or volumes. == Awards and honours == In 2003 he was given the title of Professor by the President of Poland. Since 2005 he has been an elected Foreign Member of the Polish Academy of Sciences. He also received five honorary professorships from foreign universities, including Sichuan University in Chengdu, China, and Obuda University in Budapest, Hungary.
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Phraselator

The Phraselator is a weatherproof handheld language translation device developed by Applied Data Systems and VoxTec, a former division of the military contractor Marine Acoustics, located in Annapolis, Maryland, USA. It was designed to serve as a handheld computer device that translates English into one of 40 different languages. == The device == The Phraselator is a small speech translation PDA-sized device designed to aid in interpretation. The device does not produce synthesized speech like that utilized by Stephen Hawking; instead, it plays pre-recorded foreign language MP3 files. Users can select the phrase they wish to convey from an English list on the screen or speak into the device. It then uses speech recognition technology called DynaSpeak, developed by SRI International, to play the proper sound file. The accuracy of the speech recognition software is over 70 percent according to software developer Jack Buchanan. The device can also record replies for translation later. Pre-recorded phrases are stored on Secure Digital flash memory cards. A 128 MB card can hold up to 12,000 phrases in four or five languages. Users can download phrase modules from the official website, which contained over 300,000 phrases as of March 2005. Users can also construct their own custom phrase modules. Earlier devices were known to have run on an SA-1110 Strong Arm 206 MHz CPU with 32MB SDRAM and 32MB onboard Flash RAM. A newer model, the P2, was released in 2004 and developed according to feedback from U.S. soldiers. It translates one way from English to approximately 60 other languages. It has a directional microphone, a larger library of phrases and a longer battery life. The 2004 release was created by and utilizes a computer board manufactured by InHand Electronics, Inc. In the future, the device will be able to display pictures so users can ask questions such as "Have you seen this person?" Developer Ace Sarich notes that the device is inferior to human interpreter. Conclusions derived from a Nepal field test conducted by U.S. and Nepal based NGO Himalayan Aid in 2004 seemed to confirm Sarich's comparisons: The very concept of using a machine as a communication point between individuals seemed to actually encourage a more limited form of interaction between tester and respondent. Usually, when limited language skills are present between parties, the genuine struggle and desire to communicate acts as a display of good will – we openly display our weakness in this regard – and the result is a more relaxed and human encounter. This was not necessarily present with the Phraselator as all parties abandoned learning about each other and instead focused on learning how to work with the device. As a tool for bridging any cultural differences or communicating effectively at any length, the Phraselator would not be recommended. This device, at least in the form tested, would best be used in large-scale operations where there is no time for language training and there is a need to communicate fixed ideas, quickly, over the greatest distance by employing large amounts of unskilled users. Large humanitarian or natural disasters in remote areas of third-world countries might be an effective example. == Origin == The original idea for the device came from Lee Morin, a Navy doctor in Operation Desert Storm. To communicate with patients, he played Arabic audio files from his laptop. He informed Ace Sarich, the vice president of VoxTec, about the idea. VoxTec won a DARPA Small Business Innovation Research grant in early 2001 to develop a military-grade handheld phrase translator. During its development, the Phraselator was tested and evaluated by scientists from the Army Research Laboratory. The device was first field tested in Afghanistan in 2001. By 2002, about 500 Phraselators were built for soldiers around the world with another 250 ordered by the U.S. Special Forces. The device cost $2000 to develop and could convert spoken English into one of 200,000 recorded commands and questions in 30 languages. However, the device could only translate one-way. At the time, the only existing two-way voice translator that could convert speech back and forth between languages was the Audio Voice Translation Guide System, or TONGUES, which was developed by Carnegie Mellon University for Lockheed Martin. As part of a DARPA program known as the Spoken Language Communication and Translation System for Tactical Use, SRI International has further developed two-way translation software for use in Iraq called IraqComm in 2006 which contains a vocabulary of 40,000 English words and 50,000 words in Iraqi Arabic. == Notable users == The handheld translator was recently used by U.S. troops while providing relief to tsunami victims in early 2005. About 500 prototypes of the device were provided to U.S. military forces in Operation Enduring Freedom. Units loaded with Haitian dialects have been provided to U.S. troops in Haiti. Army military police have used it in Kandahar to communicate with POWs. In late 2004, the U.S. Navy began to augment some ships with a version of the device attached to large speakers in order to broadcast clear voice instructions up to 400 yards (370 m) away. Corrections officers and law enforcement in Oneida County, New York, have tested the device. Hospital emergency rooms and health departments have also evaluated it. Several Native American tribes such as the Choctaw Nation, the Ponca, and the Comanche Nation have also used the device to preserve their dying languages. Various law enforcement agencies, such as the Los Angeles Police Department, also use the phraselator in their patrol cars. == Awards == In March 2004, DARPA director Dr. Tony Tether presented the Small Business Innovative Research Award to the VoxTec division of Marine Acoustics at DARPATech 2004 in Anaheim, CA. The device was recently listed as one of "Ten Emerging Technologies That Will Change Your World" in MIT's Technology Review. == Pop culture == Software developer Jack Buchanan believes that building a device similar to the fictional universal translator seen in Star Trek would be harder than building the Enterprise. The device was mentioned in a list of "Top 10 Star Trek Tech" on Space.com.
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Amira (software)

Amira (ah-MEER-ah) is a software platform for visualization, processing, and analysis of 3D and 4D data. It is being actively developed by Thermo Fisher Scientific in collaboration with the Zuse Institute Berlin (ZIB), and commercially distributed by Thermo Fisher Scientific — together with its sister software Avizo. == Overview == Amira is an extendable software system for scientific visualization, data analysis, and presentation of 3D and 4D data. It is used by researchers and engineers in academia and industry. It is a tool for processing, analysis and visualization of data from various modalities; e.g. micro-CT, PET, Ultrasound. It is used in many fields, such as microscopy in biology and materials science, molecular biology, quantum physics, astrophysics, computational fluid dynamics (CFD), finite element modeling (FEM), non-destructive testing (NDT), and many more. One of the key features, besides data visualization, is Amira's set of tools for image segmentation and geometry reconstruction. This allows the user to mark (or segment) structures and regions of interest in 3D image volumes using automatic, semi-automatic, and manual tools. The segmentation can then be used for a variety of subsequent tasks, such as volumetric analysis, density analysis, shape analysis, or the generation of 3D computer models for visualization, numerical simulations, or rapid prototyping or 3D printing. Other key Amira features are multi-planar and volume visualization, image registration, filament tracing, cell separation and analysis, tetrahedral mesh generation, fiber-tracking from diffusion tensor imaging (DTI) data, skeletonization, spatial graph analysis, and stereoscopic rendering of 3D data over multiple displays and immersive virtual reality environments, including CAVEs. As a commercial product Amira requires the purchase of a license or an academic subscription. A time-limited, but full-featured evaluation version is available for download free of charge. == History == === 1993–1998: Research software === Amira's roots go back to 1993 and the Department for Scientific Visualization, headed by Hans-Christian Hege at the Zuse Institute Berlin (ZIB). The ZIB is a research institute for mathematics and informatics. The Scientific Visualization department's mission is to help solve computationally and scientifically challenging tasks in medicine, biology, engineering and materials science. For this purpose, it develops algorithms and software for 2D, 3D, and 4D data visualization and visually supported exploration and analysis. At that time, the young visualization group at the ZIB had experience with the extendable, data flow-oriented visualization environments apE, IRIS Explorer, and Advanced Visualization Studio (AVS), but was not satisfied with these products' interactivity, flexibility, and ease-of-use for non-computer scientists. Therefore, the development of a new software system was started in a research project within a medically oriented, multi-disciplinary collaborative research center. Based on experiences that Tobias Höllerer had gained in late 1993 with the new graphics library IRIS Inventor, it was decided to utilize that library. The development of the medical planning system was performed by Detlev Stalling, who later became the chief software architect of Amira. The new software was called "HyperPlan", highlighting its initial target application – a planning system for hyperthermia cancer treatment. The system was being developed on Silicon Graphics (SGI) computers, which at the time were the standard workstations used for high-end graphics computing. The software was based on libraries such as OpenGL (originally IRIS GL), Open Inventor (originally IRIS Inventor), and the graphical user interface libraries X11, Motif (software), and ViewKit. In 1998, X11/Motif/Viewkit were replaced by the Qt toolkit. The HyperPlan framework served as the base for more and more projects at the ZIB and was used by a growing number of researchers in collaborating institutions. The projects included applications in medical image computing, medical visualization, neurobiology, confocal microscopy, flow visualization, molecular analytics and computational astrophysics. === 1998–today: Commercially supported product === The growing number of users of the system started to exceed the capacities that ZIB could spare for software distribution and support, as ZIB's primary mission was algorithmic research. Therefore, the spin-off company Indeed – Visual Concepts GmbH was founded by Hans-Christian Hege, Detlev Stalling, and Malte Westerhoff. In Feb 1998 the HyperPlan software was given the new, application-neutral name "Amira". This name is not an acronym, but was chosen for being pronounceable in different languages and providing a suitable connotation, namely "to look at" or "to wonder at", from the Latin verb "admirare" (to admire), which reflects a basic situation in data visualization. A major re-design of the software was undertaken by Detlev Stalling and Malte Westerhoff in order to make it a commercially supportable product and to make it available on non-SGI computers as well. In March 1999, the first version of the commercial Amira was exhibited at the CeBIT tradeshow in Hannover, Germany on SGI IRIX and Hewlett-Packard UniX (HP-UX) booths. Versions for Linux and Microsoft Windows followed within the following twelve months. Later Mac OS X support was added. Indeed – Visual Concepts GmbH selected the Bordeaux, France and San Diego, United States based company TGS, Inc. as the worldwide distributor for Amira and completed five major releases (up to version 3.1) in the subsequent four years. In 2003 both Indeed – Visual Concepts GmbH, as well as TGS, Inc. were acquired by Massachusetts-based Mercury Computer Systems, Inc. (NASDAQ:MRCY) and became part of Mercury's newly formed life sciences business unit, later branded Visage Imaging. In 2009, Mercury Computer Systems, Inc. spun off Visage Imaging again and sold it to Melbourne, Australia based Promedicus Ltd (ASX:PME), a leading provider of radiology information systems and medical IT solutions. During this time, Amira continued to be developed in Berlin, Germany and in close collaboration with the ZIB, still headed by the original creators of Amira. TGS, located in Bordeaux, France was sold by Mercury Computer systems to a French investor and renamed to Visualization Sciences Group (VSG). VSG continued the work on a complementary product named Avizo, based on the same source code but customized for material sciences. In August 2012, FEI, to that date the largest OEM reseller of Amira, purchased VSG and the Amira business from Promedicus. This brought the two software sisters Amira and Avizo back into one hand. In August 2013, Visualization Sciences Group (VSG) became a business unit of FEI. In 2016 FEI has been bought by Thermo Fisher Scientific and became part of its Materials & Structural Analysis division in early 2017. Amira and Avizo are still being marketed as two different products; Amira for life sciences and Avizo for materials science, but the development efforts are now joined once again. In the meantime, the number of scientific articles using the Amira / Avizo software, is in the order of 10 thousands. == Amira options == === Microscopy option === Specific readers for microscopy data Image deconvolution Exploration of 3D imagery obtained from virtually any microscope Extraction and editing of filament networks from microscopy images === DICOM reader === Import of clinical and preclinical data in DICOM format === Mesh option === Generation of 3D finite element (FE) meshes from segmented image data Support for many state-of-the-art FE solver formats High-quality visualization of simulation mesh-based results, using scalar, vector, and tensor field display modules === Skeletonization option === Reconstruction and analysis of neural and vascular networks Visualization of skeletonized networks Length and diameter quantification of network segments Ordering of segments in a tree graph Skeletonization of very large image stacks === Molecular option === Advanced tools for the visualization of molecule models Hardware-accelerated volume rendering Powerful molecule editor Specific tools for complex molecular visualization === Developer option === Creation of new custom components for visualizing or data processing Implementation of new file readers or writers C++ programming language Development wizard for getting started quickly === Neuro option === Medical image analysis for DTI and brain perfusion Fiber tracking supporting several stream-line based algorithms Fiber separation into fiber bundles based on user defined source and destination regions Computation of tensor fields, diffusion weighted maps Eigenvalue decomposition of tensor fields Computation of mean transit time, cerebral blood flow, and cerebral blood volume === VR option === Visualization of data on large tiled displays
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Jun'ichi Tsujii

Jun'ichi Tsujii (辻井潤一, Tsujii Jun'ichi; born 7 February 1949) is a Japanese computer scientist specializing in natural language processing and text mining, particularly in the field of biology and bioinformatics. == Education == Tsujii received his Bachelor of Engineering, Master of Engineering and PhD degrees in electrical engineering from Kyoto University in 1971, 1973, and 1978 respectively. He was Assistant Professor and Associate Professor at Kyoto University, before accepting a position as Professor of Computational Linguistics at the University of Manchester Institute of Science and Technology (UMIST) in 1988. He was President of the Association for Computational Linguistics (ACL) in 2006, and has been a permanent member of the International Committee on Computational Linguistics (ICCL) since 1992, and the chair of the committee since 2014. == Research == Since May 2015, Tsujii has been the director of the Artificial Intelligence Research Center at the National Institute of Advanced Industrial Science and Technology, Japan. Tsujii was previously a Principal Researcher at Microsoft Research Asia (MSRA). Before joining MSRA, he was a professor at the University of Tokyo, where he belonged to both the School of Inter-faculty Initiative on Informatics and the Graduate School of Information Science and Technology. Tsujii is also a Visiting Professor and Scientific Advisor at the National Centre for Text Mining (NaCTeM) at the University of Manchester in the United Kingdom. == Awards == On 14 May 2010, Tsujii was awarded the Medals of Honor with Purple Ribbon, one of Japan's highest awards, presented to influential contributors in the fields of art, academics or sports. In September 2014, Tsujii was awarded the FUNAI Achievement Award at the Forum on Information Technology (FIT), which took place at the University of Tsukuba. The award is presented to distinguished individuals engaged in research or related business activities in the field of Information Technology who have produced excellent achievements in the field, are still active in leading positions and have strong impact on young students and researchers. In December 2014, Tsujii was named as an ACL Fellow, in recognition of his significant contributions to MT, parsing by unification-based grammar and text mining for biology. In March 2016, Tsujii was awarded Okawa Prize for his contribution to the field of Natural Language Processing, Machine Translation and Text Mining, together with Professor Jaime Carbonnel of CMU. In August 2021, Tsujii received ACL Lifetime Achievement Award, which is considered the most prestigious award in the field of Computational Linguistics and Natural Language Processing. In May 2022, Tsujii received the Order of the Sacred Treasure, Gold Rays and Neck Ribbon, from the Japanese government. In October 2024, Tsujii was designated a Person of Cultural Merit. == Selected publications == Oiwa, Hidekazu; Tsujii, Jun'ichi (2014). Common Space Embedding of Primal-Dual Relation Semantic Spaces. COLING 2014. Dublin. pp. 1579–1590. Taura, K.; Matsuzaki, T.; Miwa, M.; Kamoshida, Y.; Yokoyama, D.; Dun, N.; Shibata, T.; Jun, C. S.; Tsujii, J. (2013). "Design and implementation of GXP make – A workflow system based on make". Future Generation Computer Systems. 29 (2): 662–672. doi:10.1016/j.future.2011.05.026. S2CID 31627886. Sun, X.; Zhang, Y.; Matsuzaki, T.; Tsuruoka, Y.; Tsujii, J. (2013). "Probabilistic Chinese word segmentation with non-local information and stochastic training". Information Processing & Management. 49 (3): 626–636. doi:10.1016/j.ipm.2012.12.003. Mu, T.; Goulermas, J. Y.; Tsujii, J.; Ananiadou, S. (2012). "Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data". IEEE Transactions on Pattern Analysis and Machine Intelligence. 34 (11): 2216–2232. Bibcode:2012ITPAM..34.2216M. doi:10.1109/TPAMI.2012.20. PMID 23289130. S2CID 711467. Miwa, M.; Sætre, R.; Kim, J. D.; Tsujii, J. (2010). "Event Extraction with Complex Event Classification Using Rich Features". Journal of Bioinformatics and Computational Biology. 08 (1): 131–146. doi:10.1142/S0219720010004586. PMID 20183879. Kim, J. D.; Ohta, T.; Tsujii, J. (2008). "Corpus annotation for mining biomedical events from literature". BMC Bioinformatics. 9 10. doi:10.1186/1471-2105-9-10. PMC 2267702. PMID 18182099. Miyao, Y.; Tsujii, J. (2008). "Feature Forest Models for Probabilistic HPSG Parsing". Computational Linguistics. 34: 35–80. doi:10.1162/coli.2008.34.1.35. S2CID 885002. Sagae, Kenji; Tsujii, Jun'ichi (2007). Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles. EMNLP-CoNLL. pp. 1044–1050. Ananiadou, S; Pyysalo, S; Tsujii, J; Kell, D. B. (2010). "Event extraction for systems biology by text mining the literature". Trends in Biotechnology. 28 (7): 381–90. doi:10.1016/j.tibtech.2010.04.005. PMID 20570001. Tsuruoka, Y.; Tateishi, Y.; Kim, J. D.; Ohta, T.; McNaught, J.; Ananiadou, S.; Tsujii, J. (2005). "Developing a Robust Part-of-Speech Tagger for Biomedical Text". Advances in Informatics. Lecture Notes in Computer Science. Vol. 3746. p. 382. doi:10.1007/11573036_36. ISBN 978-3-540-29673-7. S2CID 206592413. Tsuruoka, Y.; Tsujii, J. (2005). Bidirectional inference with the easiest-first strategy for tagging sequence data. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 467–474. doi:10.3115/1220575.1220634. Tsujii, J.; Ananiadou, S. (2005). "Thesaurus or Logical Ontology, Which One Do We Need for Text Mining?". Language Resources and Evaluation. 39: 77–90. doi:10.1007/s10579-005-2697-0. S2CID 3204827. Kazama, J. I.; Tsujii, J. I. (2005). "Maximum Entropy Models with Inequality Constraints: A Case Study on Text Categorization". Machine Learning. 60 (1–3): 159–194. doi:10.1007/s10994-005-0911-3. hdl:10119/3305. Matsuzaki, T.; Miyao, Y.; Tsujii, J. I. (2005). Probabilistic CFG with latent annotations. 43rd Annual Meeting on Association for Computational Linguistics - ACL '05. p. 75. doi:10.3115/1219840.1219850. Kim, J. -D.; Ohta, T.; Tateisi, Y.; Tsujii, J. (2003). "GENIA corpus--a semantically annotated corpus for bio-textmining". Bioinformatics. 19: i180–i182. doi:10.1093/bioinformatics/btg1023. PMID 12855455. Hirschman, L.; Park, J. C.; Tsujii, J.; Wong, L.; Wu, C. H. (2002). "Accomplishments and challenges in literature data mining for biology". Bioinformatics. 18 (12): 1553–1561. doi:10.1093/bioinformatics/18.12.1553. PMID 12490438. Torisawa, K.; Tsujii, J. I. (1996). Computing phrasal-signs in HPSG prior to parsing. 16th conference on Computational linguistics -. Vol. 2. p. 949. doi:10.3115/993268.993332.
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Abeba Birhane

Abeba Birhane is an Ethiopian-born cognitive scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay Prabhu uncovered that large-scale image datasets commonly used to develop AI systems, including ImageNet and 80 Million Tiny Images, carried racist and misogynistic labels and offensive images. She has been recognized by VentureBeat as a top innovator in computer vision and named as one of the 100 most influential persons in AI 2023 by TIME magazine. == Early life and education == Birhane was born in Ethiopia. She received her Bachelors of Science in Psychology and a Bachelors of Arts in Philosophy from The Open University. In 2015, she completed her Master of Science in Cognitive Science and, in 2021, her Ph.D. at the Complex Software Lab in the School of Computer Science at University College Dublin. == Career and research == Birhane studied the impacts of emerging AI technologies and how they shape individuals and local communities. She found that AI algorithms tend to disproportionately impact vulnerable groups such as older workers, trans people, immigrants, and children. Her research on relational ethics won the best paper award at NeurIPS’s Black in AI workshop in 2019. She has also studied and written about algorithmic colonization driven by corporate agendas. Her work in decolonizing computational sciences addressed the inherited oppressions in current systems especially towards women of color. In 2020, Birhane and Vinay Prabhu, principal machine learning scientist at UnifyID, published a paper examining the problematic data collection, labelling, classification, and consequences of large image datasets. These datasets, including ImageNet and MIT's 80 Million Tiny Images, have been used to develop thousands of AI algorithms and systems. Birhane and Prabhu found that they contained many racist and misogynistic labels and slurs as well as offensive images. This resulted in MIT voluntarily and formally taking down the 80 Million Tiny Images dataset. More recently, Birhane has worked with Rediet Abebe, George Obaido, and Sekou Remy on researching the barriers to data sharing in Africa. They found that power imbalances are significant in the data sharing process, even when the data comes from Africa. Their research was published at the ACM Conference on Fairness, Accountability, and Transparency. In 2024, Birhane established the AI Accountability Lab research group at Trinity College Dublin. == Selected awards == 2019 NeurIPS Black in AI Workshop Best Paper Award 2020 Venture Beat AI Innovations Award in the category Computer Vision Innovation (received with Vinay Prabhu) 2021 100 Brilliant Women in AI Ethics Hall of Fame Honoree 2022 Lero Director’s Prize for PhD/PostDoctoral Contribution. 2023 100 Most Influential People in AI by TIME magazine
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Is an AI Text-to-video Tool Worth It in 2026?

In search of the best AI text-to-video tool? An AI text-to-video 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 text-to-video 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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GazoPa

GazoPa was an image search engine that used features from an image to search for and identify similar images which closed in 2011. GazoPa began in TechCrunch50 in 2008 before launching into a state of open beta in 2009. GazoPa branched out and released a flower photo community site called "GazoPa Bloom" in 2010. This site was for exploring flower images and, if users need help identifying a flower, uploading images for other people try to identify them. Both sites closed to the public in 2011 when the company decided to focus on other areas of their business.
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Eurotra

Eurotra was a machine translation project established and funded by the European Commission from 1978 until 1992. == History == In 1976, the European Commission started using the commercially developed machine translation system SYSTRAN with a plan to make it work for further languages than originally developed for (Russian-English and English-French), which however turned out to be difficult. This and the potential in existing systems within European research center, led to the decision in 1978 to start the project Eurotra, first through a preparatory Eurotra Coordination Group. Four years later, the European Commission and coordination group gained the approval of the European Parliament. The goal of the project as to create machine translation system for the official languages of the European Community, which at the time were Danish, Dutch, German, English, French, Italian, later including Greek, Spanish and Portuguese. However, as time passed, expectations became tempered; "Fully Automatic High Quality Translation" was not a reasonably attainable goal. The true character of Eurotra was eventually acknowledged to be in fact pre-competitive research rather than prototype development. The project was motivated by one of the founding principles of the EU: that all citizens had the right to read any and all proceedings of the Commission in their own language. As more countries joined, this produced a combinatorial explosion in the number of language pairs involved, and the need to translate every paper, speech and even set of meeting minutes produced by the EU into the other eight languages meant that translation rapidly became the overwhelming component in the administrative budget. To solve this problem Eurotra was devised. The project was unusual in that rather than consisting of a single research team, it had member groups distributed around the member countries, organised along language rather than national lines (for example, groups in Leuven and Utrecht worked closely together), and the secretariat was based at the European Commission in Luxembourg. The actual design of the project was unusual as MT projects go. Older systems, such as SYSTRAN, were heavily dictionary-based, with minor support for rearranging word order. More recent systems have often worked on a probabilistic approach, based on parallel corpora. Eurotra addressed the constituent structure of the text to be translated, going through first a syntactic parse followed by a second parse to produce a dependency structure followed by a final parse with a third grammar to produce what was referred to internally as Intermediate Representation (IR). Since all three modules were implemented as Prolog programs, it would then in principle be possible to put this structure backwards through the corresponding modules for another language to produce a translated text in any of the other languages. However, in practice this was not in fact how language pairs were implemented. The first "live" translation occupied a 4Mb Microvax running Ultrix and C-Prolog for a complete weekend some time in early 1987. The sentence, translated from English into Danish, was "Japan makes computers". The main problem faced by the system was the generation of so-called "Parse Forests" - often a large number of different grammar rules could be applied to any particular phrase, producing hundreds, even thousands of (often identical) parse trees. This used up huge quantities of computer store, slowing the whole process down unnecessarily. While Eurotra never delivered a "working" MT system, the project made a far-reaching long-term impact on the nascent language industries in European member states, in particular among the southern countries of Greece, Italy, Spain, and Portugal. There is at least one commercial MT system (developed by an academic/commercial consortium in Denmark) derived from Eurotra technology.
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Top 10 AI Virtual Assistants Compared (2026)

Looking for the best AI virtual assistant? An AI virtual assistant 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 virtual assistant 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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