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  • Cybersecurity in space

    Cybersecurity in space

    Cybersecurity in space involves the defense of all space assets (e.g. navigation systems, satellites, ground antennas, networks, etc.). The security of space can be affected by attacks such as disruption, corruption as well as the destruction of depended-upon assets/collected data. Government (e.g. militaries) and non-government sectors (e.g. financial industries) have started to become more reliant on numerous space-based services. Due to the criticality of these services, space security experts have identified these assets as high-value targets (HVT) that can cause detrimental consequences to all of Earth. == Scope and definitions == Space assets are broken down by three sub-sectors: the space component, the ground component, and the individual user component. The architecture of space assets is extremely complex and allows for a frequent attack vector utilized, the disruption by radio frequency (RF) cyber-attacks. In 2020, a memorandum was published by President Donald Trump, Space Policy Directive‑5 (SPD‑5). It established principles to ensure the safeguarding of all space assets. In 2023, the National Institute of Standards and Technology’s (NIST) published IR 8270, Introduction to Cybersecurity for Commercial Satellite Operations. This report established a baseline risk-management framework (RMF) to be implemented into space operations. == History == During the Cold War in the 1950s-1960s, the United States and Russia entered what was called the “Space Race”. By 1957, the Soviet Union successfully launched the first satellite into space named Sputnik. By 1961, the first key milestone was accomplished when the Soviet Union’s Yuri Gagarin became the first human to orbit Earth. This was later followed by the first American, Alan Shepard, to be launched into space; this was followed by John Glenn becoming the first American to orbit Earth in 1962. In 1969, a pinnacle milestone was reached when Apollo 11 launched into space and Neil Armstrong became the first man to walk on the moon. As space operations furthered, Commercial off-the-shelf products became increasingly popular but resulted in a rapid increase to the cyber-attack surface. Public awareness of space security did not increase until 2022, when the Viasat KA-SAT incident occurred, resulting in the disruption of a large number of modems across Europe. The attack was later accredited to Russia by the U.S. and the U.K. Policy and standards started to rapidly increase by 2020. The establishment of SPD-5 was released in 2020 followed by asset hardening instructions in 2022, and NIST’s IR 8270 in 2023. It was not until 2025 that Europe published their own findings in the Space Threat Landscape 2025 Report. This document led to the EU’s security proposals and standards. == Threats == === Radio-frequency Interference and Global Navigation Satellite Systems (GNSS) Spoofing === Space services are highly dependent on RF links for systems such as GNSS, however, a consequence of this dependency on RF is denial of service and deception. In 2017, the Black Sea maritime event occurred when numerous ships were subject to spoofing. Space services depend on RF links susceptible to jamming (denial) and spoofing (deception), including for GNSS/Positioning, Navigation, and Timing (PNT). Annotated incidents include the 2017 Black Sea maritime spoofing event affecting numerous ships, and extensive aviation GNSS spoofing patterns surveyed in various regions during 2024–2025. === Network intrusion and malware === Cyber threats can intrude and infect assets with malware. They do this by finding misconfiguration vulnerabilities, remote-management interfaces, and/or supply-chain vulnerabilities mainly in ground networks and user terminals. When KA-SAT occurred, it resulted from bulk modem disturbances. Forensic analysts later suggested malicious management controls and wiper malware as the root cause. === Supply-chain and lifecycle risks === The outsource of COTS components, external vendors, and software defined payloads allowed for vulnerabilities to emerge in the System/Product Lifecycle. In response, EU recommended the implementation of lifecycle-wide controls as mitigating factors. === Espionage, disruption, and influence === As Advanced Persistent Threats (APTs), Global Positioning System (GPS) intervention, and information warfare increased, assets like transponders became more frequent targets of attack. == Noteworthy incidents == The Viasat KA‑SAT incident of 2022, where a large number of modems in Europe were disrupted, resulted in the loss of telemetry access to a significant amount of wind turbines in Germany. The mass GNSS deception of the Black Sea in 2017 affected numerous ships when they started to convey fake central locations in Russia. Between 2024 and 2025, there was a mass, repetitive aviation GNSS spoofing that affected the aircraft of various regions. == Standards, guidelines, and best practices == SPD‑5 (U.S.) – This established risk-based engineering, verifying and ensuring positive control, and the implementation of risk mitigation controls. NIST IR 8270 – This created a RMF for COTS satellites. CISA/FBI SATCOM Advisory (AA22‑076) – Provided guidance on hardening techniques such as least-privileged, access control, encryption, etc.). ENISA Space Threat Landscape 2025 – It established the categorization of assets to organize threats, ensuring the observation of system/product lifecycle, and an RMF for COTS satellites. ECSS‑E‑ST‑80C (2024) – This established a standard for securing lifecycles in space, covering all segments (e.g. ground, launch, etc.). == Regulation and governance == As of 2025, there is no international regulations established for space assets, but the U.S., EU, and ESA institutional initiatives have published standards to address security concerns. The U.S. implemented SPD-5 and the Federal Communications Commission (FCC); the FCC addressed orbital debris. While the EU created standards to address technological mandates and support the implementation of NIS2. Lastly, the ESA created a special operations center to safeguard their satellites. International governance is still evolving, but forums have been held by the United Nations Committee on the Peaceful Uses of Outer Space. International conversations under forums such as the UN Committee on the Peaceful Uses of Outer Space (COPUOS) progressively note the cyber–space safety relationship, though formal global norms specific to space cybersecurity continue evolving. == Risk management approaches == Through RMF, mitigation controls have been implemented to reduce the risk of exploitation while increasing the security of space. Controls addressing mitigation include proper configuration, system hardening, zero-trust architectures, encryption, etc. Both the government and industries have placed an emphasis on incident response procedures to identify, contain, and remediate breaches.

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  • AI Text-to-video Tools Reviews: What Actually Works in 2026

    AI Text-to-video Tools Reviews: What Actually Works in 2026

    Looking for 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 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 text-to-video tool 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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  • Emma Brunskill

    Emma Brunskill

    Emma Patricia Brunskill is an American computer scientist. Her research combines machine learning with human–computer interaction by studying the effects of AI systems in human-centered applications including educational software and healthcare, and the theory of reinforcement learning in situations where mistakes impose high risks or costs. She is an associate professor of computer science at Stanford University, where she also holds a courtesy appointment in the Stanford Graduate School of Education and is an affiliate of the King Center on Global Development. == Education and career == Brunskill grew up in Seattle and Edmonds, Washington, and entered the University of Washington at age 15. She graduated magna cum laude in 2000, with a bachelor's degree in computer engineering and physics. A Rhodes Scholarship took her to Magdalen College, Oxford in England, where she received a master's degree in neuroscience in 2002. After a summer working in Rwanda, she became a graduate student of computer science at the Massachusetts Institute of Technology, where she completed her Ph.D. in 2009. Her doctoral dissertation, Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains, was supervised by Nicholas Roy. After working as an NSF Postdoctoral Research Fellow at the University of California, Berkeley, she joined Carnegie Mellon University (CMU) in 2011 as an assistant professor of computer science. She moved from CMU to Stanford University in 2017. == Recognition == Brunskill was a 2014 recipient of the National Science Foundation CAREER Award and a 2015 recipient of the Office of Naval Research Young Investigator Award. She was one of two alumni of the University of Washington's Paul G. Allen School of Computer Science and Engineering to be honored in 2020 by the school's Alumni Impact Awards. She was elected as a Fellow of the Association for the Advancement of Artificial Intelligence in 2025, "for significant contributions to the field of reinforcement learning, and applications for societal benefit, in particular AI for education".

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  • Law and Corpus Linguistics

    Law and Corpus Linguistics

    Law and corpus linguistics (LCL) is an academic sub-discipline that uses large databases of examples of language usage equipped with tools designed by linguists called corpora to better get at the meaning of words and phrases in legal texts (statutes, constitutions, contracts, etc.). Thus, LCL is the application of corpus linguistic tools, theories, and methodologies to issues of legal interpretation in much the same way law and economics is the application of economic tools, theories, and methodologies to various legal issues. == History == A 2005 law review article by Lawrence Solan noted in passing that corpus linguistics had potential for its application to interpreting legal texts. But the first systematic exploration and advocacy of applying the tools and methodologies of corpus linguistics to legal interpretive questions of law and corpus linguistics came in the fall of 2010, when the BYU Law Review published a note by Stephen Mouritsen, entitled The Dictionary is Not a Fortress: Definitional Fallacies and a Corpus-Based Approach to Plain Meaning. The note argued that dictionaries are the primary linguistic tool used by judges to determine the plain or ordinary meaning of words and phrases, and highlighted the deficiencies of such an approach. In its stead, the note proposed using corpus linguistics. And the note would be later cited by Adam Liptak in a New York Times article on statutory construction. Law and corpus linguistics (LCL) gained greater legitimacy in July 2011 with the first judicial opinion in American history utilizing corpus linguistics to determine the meaning of a legal text: In re the Adoption of Baby E.Z. In a concurrence in part and in the judgment, Justice Thomas Lee wrote to put forth an alternative ground for the majority's holding—interpreting the phrase "custody determination" by using corpus linguistics. Justice Lee looked at 500 randomized sample sentences from the Corpus of Contemporary American English (COCA) and found that the most common sense of "custody" was in the context of divorce rather than adoption. Further, he found that "custody" is ten times more likely to co-occur (or collocate) with "divorce" than with "adoption". From that evidence Justice Lee concluded that he "would find that the custody proceedings covered by the Act are limited to proceedings resulting in the modifiable custody orders of a divorce", rather than the broader range of custody proceedings. Other jurisprudence and scholarship would follow. In a 2015 concurrence in State v. Rasabout, Justice Lee used a COCA search to determine that "discharge" when used with a firearm (or one of its synonyms) overwhelmingly referred to a single shot rather than emptying the entire magazine of the weapon. And in 2016, four of the five justices joined a footnote in a majority opinion by Justice Lee commending a party for using corpus linguistics in its briefing even though the Court found it unnecessary to resolve the related question. Finally, in 2016 the Michigan Supreme Court became the first court to use a linguist-designed corpus in a majority opinion (COCA), with both the majority and the dissent turning to COCA to determine the meaning of the word "information". In 2020, courts desiring to bolster the legal theory of original intent have sought the opportunity to undertake analyses of statutes utilizing corpus linguistics. In a Ninth Circuit Court of Appeals case, Jones v. Becerra (No. 20-56174), a case involving the Second Amendment and the constitutionality of a California statute which bans the sale of firearms to individuals under the age of 21, a Ninth Circuit panel requested that the parties address three questions: 1) “What is the original public meaning of the Second Amendment phrases: ‘A well regulated Militia’; ‘the right of the people’; and ‘shall not be infringed’? 2) How does the tool of corpus linguistics help inform the determination of the original public meaning of those Second Amendment phrases?” 3) How do the data yielded from corpus linguistics assist in the interpretation of the constitutionality of age-based restrictions under the Second Amendment? As to scholarship, in 2012, Mouritsen followed up his original work with an article in the Columbia Science and Technology Law Review, where he further refined and promoted the use of corpus-based methods for determining questions of legal ambiguity. Additionally, in 2016 two essays and an article on law and corpus linguistics were published. The Yale Law Journal Forum published Corpus Linguistics & Original Public Meaning: A New Tool to Make Originalism More Empirical. Written by Justice Lee and two co-authors, the essay urged originalists to turn to corpus linguistics to improve the rigor and accuracy of originalist scholarship. And in response, the Forum published an essay by Lawrence Solan (a Brooklyn Law professor with a PhD in linguistics), Can Corpus Linguistics Help Make Originalism Scientific? The Boston University Public Interest Law Journal published The Merciful Corpus: The Rule of Lenity, Ambiguity and Corpus Linguistics by Daniel Ortner. In the article Ortner applied corpus linguistics to determining whether sufficient ambiguity exists to trigger the rule of lenity in five Supreme Court cases. Looking forward, in 2017 two more articles are slated for publication. Lee Strang focuses on corpus linguistics and originalism in the U.C. Davis Law Review, and Lawrence Solan and Tammy Gales explore corpus linguistics in the context of finding ordinary meaning in statutory interpretation in the International Journal of Legal Discourse. Lawyers and journalists have also taken notice of corpus linguistics at it relates to the law. In 2010, Neal Goldfarb filed the first known brief in the Supreme Court using corpus linguistics (COCA) to determine whether the ordinary meaning of "personal" referred to corporations in the case FCC v. AT&T. The amicus brief looked at the top collocates (words that co-occur) of "personal" in COHA as well as BYU's Time Magazine Corpus. And writing for The Atlantic, Ben Zimmer took note of this new trend, referring to corpus linguistics in the courts as "Like Lexis on Steroids". On the academic front, in 2013 BYU Law School started the first class on law and corpus linguistics, co-taught by Mouritsen, Lee, and (now Dean) Gordon Smith. The class is currently in its fourth year. And in February 2016, BYU Law School hosted the inaugural conference on LCL, with over two dozen legal and linguistic scholars from around the country discussing and debating the next steps forward for the growing academic movement. The conference has been held regularly in subsequent years. At the 2016 conference BYU Law School announced its plans and progress on the Corpus of Founding Era American English (COFEA), a corpus that covers 1760–1799 and contains more than 120 million words have been collected from founding era letters, diaries, newspapers, non-fiction books, fiction, sermons, speeches, debates, legal cases, and other legal materials.

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  • Color gradient

    Color gradient

    In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to fill a region. In assigning colors to a set of values, a gradient is a continuous colormap, a type of color scheme. In computer graphics, the term swatch has come to mean a palette of active colors. == Definitions == Color gradient is a set of colors arranged in a linear order (ordered) A continuous colormap is a curve through a colorspace === Strict definition === A colormap is a function which associate a real value r with point c in color space C {\displaystyle C} f : [ r m i n , r m a x ] ⊂ R → C {\displaystyle f:[r_{min},r_{max}]\subset \mathbf {R} \to C} which is defined by: a colorspace C an increasing sequence of sampling points r 0 < . . . < r m ∈ [ r m i n , r m a x ] {\displaystyle r_{0}<... Read more →

  • International Computer Archive of Modern and Medieval English

    International Computer Archive of Modern and Medieval English

    The International Computer Archive of Modern and Medieval English (ICAME) is an international group of linguists and data scientists working in corpus linguistics to digitise English texts. The organisation was founded in Oslo, Norway in 1977 as the International Computer Archive of Modern English, before being renamed to its current title. Its primary objectives were: collecting and distributing information on English language material available for computer processing; and linguistic research completed or in progress on this material; compiling an archive of corpora to be located at the University of Bergen, from where copies of the material can be obtained at cost. The portal to their materials is hosted at the University of Bergen, where they have set out the aim of the organization to "collect and distribute information on English language material available for computer processing and on linguistic research to compile an archive of English text corpora in machine-readable form, and to make material available to research institutions." Creating computer corpora, i.e. collections of texts in machine-readable form, is the most accessible way to study both transcribed spoken language and various genres of written texts for modern scholars, including both "descriptive and more theoretically-minded linguists". The ICAME group hosts academic conferences that focus on corpus linguistic studies of historical changes and contemporary grammatical descriptions of English, and makes corpora of different varieties of English available to scholars, starting with editions of the 1960s Brown Corpus. Their first academic conference was held in Bergen, Norway in 1979, and scholars who were interested in corpus linguistics continued to meet each spring in different European and English-speaking countries. At these meetings, the compilation and distribution of corpora they enabled played a key role in the creation of the field of corpus linguistics in the 20th century, a precursor to current big data analytics. In summarizing the field, Kennedy's Introduction to Corpus Linguistics notes that "for corpus linguists with an interest in the description of English, the International Computer Archive of Modern and Medieval English has been the major resource". The influence of ICAME on the field has also be laid out in Facchinetti's history, Corpus Linguistics Twenty-five Years On. One influential resource that ICAME made available was a CD of 20 different corpora, including those covering different regional Englishes (such as the Australian Corpus of English, the Wellington Corpus of Spoken New Zealand English, the Kolhapur Corpus of Indian English, the Bergen Corpus of London Teenage Language (COLT), the Helsinki Corpus of Older Scots, and the International Corpus of English—East-African component), as well as versions of the Brown Corpus and the Lancaster-Bergen-Oslo (LOB) corpus tagged for part of speech. ICAME also published an annual journal, the ICAME Journal, formerly ICAME News, that contains articles, conference reports, reviews and notices related to corpus linguistics. The current editors of the ICAME Journal are Merja Kytö and Anna-Brita Stenström.I am wearing a tie clip in the shape of a monkey wrench... The story behind this peculiar piece of jewelry goes back to the early 60s when I was assembling the notorious Brown Corpus and others were using computers to make concordances of William Butler Yeats and other poets. One of my colleagues, a specialist in modem Irish literature, was heard to remark that anyone who would use a computer on good literature was nothing but a plumber. Some of my students responded by forming a linguistic plumber's union, the symbol of which was, of course, a monkey wrench.

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  • Gary B. Fogel

    Gary B. Fogel

    Gary Bryce Fogel (born 1968) is an American biologist and computer scientist. He is the Chief Executive Officer of Natural Selection, Inc. He is most known for his applications of computational intelligence and machine learning to bioinformatics, computational biology, and industrial optimization. == Education and Research == Fogel was born and raised in La Jolla, California, graduating from La Jolla High School. He received a B.A. in biology with a minor in earth sciences from the University of California, Santa Cruz in 1991 and a Ph.D. in biology from the University of California, Los Angeles in 1998. Fogel has published over 150 peer-reviewed publications in conferences and journals, 2 edited books, and 11 patents. As CEO of Natural Selection, Inc., his research focuses on the application of computational intelligence, machine learning, and predictive analytics in areas not limited to: Viral evolution, cellular differentiation, drug discovery, RNA structure, cis-regulatory elements, cancer, and evolutionary game theory as well as the development of evolutionary algorithms and other approaches. == Service == Between 2008–2018 Gary Fogel was editor-in-chief of the Elsevier journal BioSystems. He has served previously as an associate editor for IEEE Transactions on Artificial Intelligence, IEEE Computational Intelligence Magazine (2005–2010), IEEE Transactions on Evolutionary Computation (2001–2013), IEEE Transactions on Emerging Topics in Computational Intelligence (2016–2018), IEEE/ACM Transactions on Computational Biology and Bioinformatics (2004–2008), International Journal of Bioinformatics Research and Applications (2004–2007), International Journal of Data Mining and Bioinformatics (2005–2007), as a consulting editor for the Journal of Computational Intelligence in Bioinformatics (2006–2007), and as an editorial board member of Ecological Informatics (2005–2009) and BMC Big Data Analytics (2015–2020). Within the IEEE Computational Intelligence Society, Fogel founded the Bioinformatics and Bioengineering Technical Committee and established the IEEE Computational Intelligence in Bioinformatics and Computational Biology conference series, chairing the first two meetings in 2004 and 2005 in San Diego. He co-founded the IEEE Conference on Artificial Intelligence in 2023. Fogel served on the IEEE Computational Intelligence Society Administrative Committee (2004–2009, 2014–2022) and served as IEEE CIS Vice President of Conferences (2010–2013, 2019). == Teaching == Gary Fogel also serves as adjunct faculty at San Diego State University in the department of aerospace engineering as well as in the Computational Science Research Center. He has authored four books and numerous articles on the history of early aviation focusing on motorless flight. He is an associate fellow of the American Institute of Aeronautics and Astronautics and serves on the AIAA History Committee. == Awards == 2023 – Outstanding Contribution to Aerospace Education Award, AIAA San Diego Section 2022 – Elected Fellow of the Asia-Pacific Artificial Intelligence Association 2019 – Top 100 AI Leaders in Drug Discovery and Advanced Healthcare by Deep Knowledge Analytics 2019 – Outstanding Contribution to Aerospace Education Award, AIAA San Diego Section 2016 – Meritorious Service Award, IEEE Computational Intelligence Society 2016 – Outstanding Contribution to the Community Award, AIAA San Diego Section 2015 – Outstanding Enhancement of the Image of the Aerospace Profession Award, AIAA San Diego Section 2012 – Medal for Significant Achievement, San Diego Chapter of Sigma Xi 2012 – Fellow of the Institute of Electrical and Electronics Engineers for contributions to computational intelligence and its application to biology, chemistry, and medicine. == Aeromodeling == Gary Fogel has established national and world records for model aircraft. He helped establish the National Model Aviation Heritage program for the Academy of Model Aeronautics. He is a leader member, contest director, and fellow of the Academy of Model Aeronautics, and was inducted into the Academy of Model Aeronautics Hall of Fame in 2025.

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  • Thomas G. Dietterich

    Thomas G. Dietterich

    Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research. In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal. Among his research contributions were the invention of error-correcting output coding to multi-class classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models. == Biography and education == Thomas Dietterich was born in South Weymouth, Massachusetts, in 1954. His family later moved to New Jersey and then again to Illinois, where Tom graduated from Naperville Central High School. Dietterich then entered Oberlin College and began his undergraduate studies. In 1977, Dietterich graduated from Oberlin with a degree in mathematics, focusing on probability and statistics. Dietterich spent the following two years at the University of Illinois, Urbana-Champaign. After those two years, he began his doctoral studies in the Department of Computer Science at Stanford University. Dietterich received his Ph.D. in 1984 and moved to Corvallis, Oregon, where he was hired as an assistant professor in computer science. in 2013, he was named "Distinguished Professor". In 2016, Dietterich retired from his position at Oregon State University. Throughout his career, Dietterich has worked to promote scientific publication and conference presentations. For many years, he was the editor of the MIT Press series on Adaptive Computation and Machine Learning. He also held the position of co-editor of the Morgan Claypool Synthesis Series on Artificial Intelligence and Machine Learning. He has organized several conferences and workshops including serving as Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90), Technical Program Chair of the Neural Information Processing Systems (NIPS-2000) and General Chair of NIPS-2001. He served as founding President of the International Machine Learning Society and he has been a member of the IMLS Board since its founding. He is currently also a member of the Steering Committee of the Asian Conference on Machine Learning. == Research interests == Professor Dietterich is interested in all aspects of machine learning. There are three major strands of his research. First, he is interested in the fundamental questions of artificial intelligence and how machine learning can provide the basis for building integrated intelligent systems. Second, he is interested in ways that people and computers can collaborate to solve challenging problems. And third, he is interested in applying machine learning to problems in the ecological sciences and ecosystem management as part of the emerging field of computational sustainability. Over his career, he has worked on a wide variety of problems ranging from drug design to user interfaces to computer security. His current focus is on ways that computer science methods can help advance ecological science and improve our management of the Earth's ecosystems. This passion has led to several projects including research in wildfire management, invasive vegetation and understanding the distribution and migration of birds. For example, Dietterich's research is helping scientists at the Cornell Lab of Ornithology answer questions like: How do birds decide to migrate north? How do they know when to land and stopover for a few days? How do they choose where to make a nest? Tens of thousands of volunteer birdwatchers (citizen scientists) all over the world contribute data to the study by submitting their bird sightings to the eBird website. The amount of data is overwhelming – in March 2012 they had over 3.1 million bird observations. Machine learning can uncover patterns in data to model the migration of species. But there are many other applications for the same techniques which will allow organizations to better manage our forests, oceans, and endangered species, as well as improve traffic flow, water systems, the electrical power grid, and more. I realized I wanted to have an impact on something that really mattered – and certainly the whole Earth's ecosystem, of which we are a part, is under threat in so many ways. And so if there's some way that I can use my technical skills to improve both the science base and the tools needed for policy and management decisions, then I would like to do that. I am passionate about that. == Dangers of AI: an academic perspective == Dietterich has argued that the most realistic risks about the dangers of artificial intelligence are basic mistakes, breakdowns and cyberattacks, and the fact that it simply may not always work, rather than machines that become super powerful or destroy the human race. Dietterich considers machines becoming self-aware and trying to exterminate humans to be more science fiction than scientific fact. But to the extent that computer systems are given increasingly dangerous tasks, and asked to learn from and interpret their experiences, he said they may simply make mistakes. Instead, much of the work done in the AI safety community does indeed focus around accidents and design flaws. == Positions held == 2014–2016: President, Association for the Advancement of Artificial Intelligence (AAAI). 2013–present: Distinguished Professor of computer science, Oregon State University. 2011–present: Chief Scientist, BigML, Corvallis, OR. 2005–present: Director of Intelligent Systems Research, School of Electrical Engineering and Computer Science, Oregon State University. 2006–2008: Chief Scientist, Smart Desktop, Inc., Seattle, WA. 2004–2005: Chief Scientist, MyStrands, Inc., Corvallis, OR. 1995-2013: Professor of computer science, Oregon State University. 1998–1999: Visiting Senior Scientist, Institute for the Investigation of Artificial Intelligence, Barcelona, Spain. (Sabbatical leave position) 1988–1995: Associate Professor of computer science, Oregon State University. 1991–1993: Senior Scientist, Arris Pharmaceutical Corporation, S. San Francisco, CA. 1985–1988: Assistant Professor of computer science, Oregon State University. 1979–1984: Research Assistant, Heuristic Programming Project, Department of Computer Science, Stanford University. 1979 (Summer): Member of Technical Staff, Bell Telephone Laboratories, Naperville, Illinois. Computer-to-computer file transfer and micro-code distribution to remote switching systems. 1977 (Summer): Assistant to the Director of Planning and Research, Oberlin College, Oberlin, Ohio. Developed institutional planning database. == Awards and honors == Thomas Dietterich was honored by Oregon State University in the spring of 2013 as a "Distinguished Professor" for his work as a pioneer in the field of machine learning and being one of the mostly highly cited scientists in his field. He has also earned exclusive "Fellow" status in the Association for the Advancement of Artificial Intelligence, the American Association for the Advancement of Science and the Association for Computing Machinery. Over his career, he obtained more than $30 million in research grants, helped build a world-class research group at Oregon State, and created three software companies. He also co-founded two of the field's leading journals and was elected first president of the International Machine Learning Society. His other awards and honors include: ACM Distinguished Lecturer, 2012-2013 Fellow, American Association for the Advancement of Science, 2007 Oregon State University, College of Engineering Collaboration Award, 2004 Winner, JAIR Award for Best Paper in Previous Five Years, 2003 Fellow, Association for Computing Machinery, elected 2003 Oregon State University, College of Engineering Research Award, 1998 Fellow, Association for the Advancement of Artificial Intelligence, elected 1994 NSF Presidential Young Investigator, 1987-92 Nominated for Carter Award for Graduate Teaching, 1987, 1988 IBM Graduate Fellow, 1982, 1983 Upsilon Pi Epsilon, 1996 Sigma Xi, 1979–present State Farm Companies Foundation Fellowship, 1978 Member, Board of Trustees, Oberlin College, 1977-1980 Graduation with Honors in Mathematics, Oberlin College, 1977 Phi Beta Kappa, 1977 National Merit Scholar, 1973 == Selected publications == Liping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou (2016). Transductive Optimization of Top k Precision. International Joint Conference on Artificial Intelligence (IJCAI-2016). pp. 1781–1787. New York, NY Md. Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Shubhomoy Da

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  • Web Intents

    Web Intents

    Web Intents was an experimental framework for web-based inter-application communication and service discovery. Web Intents consists of a discovery mechanism and a very light-weight RPC system between web applications, modelled after the Intents system in Android. In the context of the framework an Intent equals an action to be performed by a provider. Web Intents allow two web applications to communicate with each other, without either of them having to actually know what the other one is. == Support == === Client === Google Chrome versions 18 to 23 natively supported Web Intents. This support was disabled in version 24, citing the existence of a "number of areas for development in both the API and specific user experience in Chrome". There is a JavaScript shim with support for IE 8, IE 9, Opera, Safari, Firefox 3+ and Chrome 3+. === Server === There are some Web Intents proxy pages that make available some real services that don't yet support intents. AddThis supports Web Intents by their sharing tools regardless of browser support. == History == Paul Kinlan of Google announced the Web Intents project in December 2010. He soon released a prototype API to GitHub. In August 2011 Google announced that Chrome would support Web Intents. Google and Mozilla have started co-operating to unify Web Intents and Mozilla's Web Activities (which tries to solve the same problem) into one proposal. In November 2012, Greg Billock of Google announced that experimental support of Web Intents had been removed from Chrome.

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  • AI Chatbots: Free vs Paid (2026)

    AI Chatbots: Free vs Paid (2026)

    In search of the best AI chatbot? An AI chatbot 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 chatbot 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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  • Corpus manager

    Corpus manager

    A corpus manager (corpus browser or corpus query system) is a tool for multilingual corpus analysis, which allows effective searching in corpora. A corpus manager usually represents a complex tool that allows one to perform searches for language forms or sequences. It may provide information about the context or allow the user to search by positional attributes, such as lemma, tag, etc. These are called concordances. Other features include the ability to search for collocations, frequency statistics as well as metadata information about the processed text. The narrower meaning of corpus manager refers only to the server side or the corpus query engine, whereas the client side is simply called the user interface. A corpus manager can be software installed on a personal computer or it might be provided as a web service. == List of corpus managers == BNCweb – a web-based interface for the British National Corpus CQPweb - a web-based interface for the study of a large variety of corpora including the Spoken BNC2014 BYU-BNC – a website that allows searches of the British National Corpora and others created at Brigham Young University Coma – a tool extension of the system EXMARaLDA for working with oral corpora on a computer NoSketch Engine – a free open-source corpus management system combining Manatee (back-end) and Bonito (web interface) KonText – an extended and modified web interface to NoSketch Engine (a Bonito replacement) Sketch Engine – text corpus management and analysis software with more than 500 corpora in 90+ languages Spoco WordSmith Tools – a software package primarily for linguists

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  • Hartmut Neven

    Hartmut Neven

    Hartmut Neven (born 1964) is a German American scientist working in quantum computing, computer vision, robotics and computational neuroscience. He is best known for his work in face and object recognition and his contributions to quantum machine learning. He is currently Vice President of Engineering at Google where he leads the Quantum Artificial Intelligence Lab, which he founded in 2012. == Education == Hartmut Neven studied Physics and Economics in Brazil, Köln, Paris, Tübingen and Jerusalem. He wrote his Master thesis on a neuronal model of object recognition at the Max Planck Institute for Biological Cybernetics under Valentino Braitenberg. In 1996 he received his Ph.D. in Physics from the Institute for Neuroinformatics at the Ruhr University in Bochum, Germany, for a thesis on "Dynamics for vision-guided autonomous mobile robots" written under the tutelage of Christoph von der Malsburg. He received a scholarship from the Studienstiftung des Deutschen Volkes, Germany's most prestigious scholarship foundation. == Work == In 1998 Neven became research professor of computer science at the University of Southern California at the Laboratory for Biological and Computational Vision. In 2003 he returned as the head of the Laboratory for Human-Machine Interfaces at USC's Information Sciences Institute. === Face recognition, avatars and face filters === Neven co-founded two companies, Eyematic for which he served as CTO and Neven Vision which he initially led as CEO. At Eyematic he developed face recognition technology and real-time facial feature analysis for avatar animation. Teams led by Neven have repeatedly won top scores in government sponsored tests designed to determine the most accurate face recognition software. Face filters, now ubiquitous on mobile phones, were launched for the first time by Neven Vision on the networks of NTT DoCoMo and Vodafone Japan in 2003. Neven Vision also pioneered mobile visual search for camera phones. Neven Vision was acquired by Google in 2006. === Object recognition and adversarial images === At Google he managed teams responsible for advancing Google's visual search technologies. His team launched Google Goggles now Google Lens. The concept of adversarial patterns originated in his group when he tasked Christian Szegedy with a project to modify the pixel inputs of a deep neural network to lower the activity of select output nodes. The motivation was to use this technique for object localization which did not work out. But the idea gave rise to the fields of adversarial learning and DeepDream art. In 2013 his optical character recognition team won the ICDAR Robust Reading Competition by a wide margin and in 2014 the object recognition team won the ImageNet challenge. === Google Glass === Neven was a co-founder of the Google Glass project. His team completed the first prototype, codenamed Ant, in 2011. === Quantum Artificial Intelligence === In 2006 Neven started to explore the application of quantum computing to hard combinatorial problems arising in machine learning. In collaboration with D-Wave Systems he developed the first image recognition system based on quantum algorithms. It was demonstrated at SuperComputing07. At NIPS 2009 his team demonstrated the first binary classifier trained on a quantum processor. In 2012 together with Pete Worden at NASA Ames he founded the Quantum Artificial Intelligence Laboratory. In 2014 he invited John M. Martinis and his group at UC Santa Barbara to join the lab to start a fabrication facility for superconducting quantum processors. The Quantum Artificial Intelligence team performed the first experimental demonstration of a scalable simulation of a molecule. In 2016 the team formulated an experiment to demonstrate quantum supremacy. Quantum supremacy was then declared by Google in October 2019. In 2023 Quantum AI researchers demonstrated that quantum error correction works in practice by showing for the first time that the error of a logical qubit decreases when increasing the number of physical qubits it is composed of. Google's quantum processors have been used to study the physics of quantum many body states that otherwise are challenging to prepare in a laboratory such as time crystals, traversable wormholes and non-Abelian anyons. ==== Neven's law ==== Neven's law states that the performance of quantum computers improves at a doubly exponential rate.

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  • Vicarious (company)

    Vicarious (company)

    Vicarious was an artificial intelligence company based in the San Francisco Bay Area, California. They use the theorized computational principles of the brain to attempt to build software that can think and learn like a human. Vicarious describes its technology as "a turnkey robotics solution integrator using artificial intelligence to automate tasks too complex and versatile for traditional automations". Alphabet Inc acquired the company in 2022 for an undisclosed amount. == Founders == The company was founded in 2010 by D. Scott Phoenix and Dileep George. Before co-founding Vicarious, Phoenix was Entrepreneur in Residence at Founders Fund and CEO of Frogmetrics, a touchscreen analytics company he co-founded through the Y Combinator incubator program. Previously, George was Chief Technology Officer at Numenta, a company he co-founded with Jeff Hawkins and Donna Dubinsky while completing his PhD at Stanford University. == Funding == The company launched in February 2011 with funding from Founders Fund, Dustin Moskovitz, Adam D’Angelo (former Facebook CTO and co-founder of Quora), Felicis Ventures, and Palantir co-founder Joe Lonsdale. In August 2012, in its Series A round of funding, it raised an additional $15 million. The round was led by Good Ventures; Founders Fund, Open Field Capital and Zarco Investment Group also participated. The company received $40 million in its Series B round of funding. The round was led by individuals including Mark Zuckerberg, Elon Musk, and others. An additional undisclosed amount was later contributed by Amazon.com CEO Jeff Bezos, Yahoo! co-founder Jerry Yang, Skype co-founder Janus Friis and Salesforce.com CEO Marc Benioff. == Recursive Cortical Network == Vicarious is developing machine learning software based on the computational principles of the human brain. One such software is a vision system known as the Recursive Cortical Network (RCN), it is a generative graphical visual perception system that interprets the contents of photographs and videos in a manner similar to humans. The system is powered by a balanced approach that takes sensory data, mathematics, and biological plausibility into consideration. On October 22, 2013, beating CAPTCHA, Vicarious announced its model was reliably able to solve modern CAPTCHAs, with character recognition rates of 90% or better when trained on one style. However, Luis von Ahn, a pioneer of early CAPTCHA and founder of reCAPTCHA, expressed skepticism, stating: "It's hard for me to be impressed since I see these every few months." He pointed out that 50 similar claims to that of Vicarious had been made since 2003. Vicarious later published their findings in peer-reviewed journal Science. Vicarious has indicated that its AI was not specifically designed to complete CAPTCHAs and its success at the task is a product of its advanced vision system. Because Vicarious's algorithms are based on insights from the human brain, it is also able to recognize photographs, videos, and other visual data.

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  • Luca Maria Gambardella

    Luca Maria Gambardella

    Luca Maria Gambardella (born 4 January 1962) is an Italian computer scientist and author. He is the former director of the Dalle Molle Institute for Artificial Intelligence Research in Lugano, in the Ticino canton of Switzerland. He is currently the prorector of Università della Svizzera italiana, where he directs the Master of Science in Artificial Intelligence degree course. Several of his papers have been extensively cited, with his collaborators including Marco Dorigo, with whom he has published papers on the application of ant colony optimization theory to the traveling salesman problem, and Jürgen Schmidhuber with whom he has published research on deep neural networks.. Beside working in research, Gambardella explores the potentials of AI applied for the generation of art. Some of his artistic installations received significant media coverage. As a novelist, the genres he approached broad from Bildungsroman of his first book "Sei vite" ("Six lives"), to romance of his second book "Il suono dell'alba" ("The sound of sunrise").

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  • Top 10 AI Customer-support Bots Compared (2026)

    Top 10 AI Customer-support Bots Compared (2026)

    Trying to pick the best AI customer-support bot? An AI customer-support bot 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 customer-support bot 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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