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  • Construction robots

    Construction robots

    Construction robots are a subset of industrial robots used for building and infrastructure construction on site, or in the production of materials and components offsite. A 2021 survey said 55% of construction companies in the United States, Europe, and China used robots in some form. This figure, however, reflects reported use across the construction value chain rather than widespread deployment of robots on active construction sites. Real-world adoption remains limited, with many robotic systems confined to pilot projects, controlled environments, or specific task applications rather than continuous on-site construction use. One of the main challenges in deploying robots on construction sites is the unstructured and variable nature of the environment, which differs fundamentally from controlled factory settings where industrial robots have traditionally operated. Some robots currently deployed on job sites assist with physically demanding or repetitive tasks: excavating, lifting heavy materials, surveying, laying out markers, tying rebar, and installing drywall. More advanced systems are being developed for exterior finishing, steel placement, masonry, and reinforced concrete work. In practice, rather than autonomous systems performing core building tasks, the most widely adopted robot applications on construction sites involve technologies such as aerial drones (or, less frequently, robot 'dogs' - for example, Boston Dynamics' Spot - or humanoid robots) used for surveying, inspection, and progress monitoring (the robots typically carry video and/or 360-degree cameras, LiDar scanners or other data capture devices, with data analysed using artificial intelligence and machine learning). Some emerging systems are designed as multifunctional construction robots, integrating multiple tools and capabilities within a single robotic platform to perform different stages of the construction process. These systems aim to improve operational flexibility and increase automation in complex construction environments. Experimental projects using robotic construction technologies and additive manufacturing have been demonstrated in several countries as part of broader efforts to industrialize the construction sector and improve productivity through automation and digitalization. == Features == Construction robots are generally required to meet the following criteria: Mobility: the ability to navigate around a construction site, including uneven terrain and confined spaces. Adaptability: the ability to handle components of variable size, weight, and shape. Environmental awareness: the ability to sense and respond to changing on-site conditions. Interactivity: the ability to operate alongside human workers and other equipment. Multitasking: the ability to perform several different operations within a single deployment. == Capabilities == Construction robots have been developed and tested for a range of on-site tasks, including: Progress monitoring — robots equipped with cameras and sensors can track construction progress and identify deviations from plans. Inspection — robots are used to investigate infrastructure at dangerous or inaccessible locations, reducing risk to human workers and eliminating human error. Wall construction — robotic systems can lay bricks and blocks with greater speed and consistency than manual labour. Earthmoving and material handling — autonomous excavators and haul trucks use GPS, lidar, and motion sensors to perform digging, trenching, and loading tasks with minimal human input. Grading and dozing — autonomous bulldozers use GPS, gyroscopes, and laser sensors to control blade angle and depth, improving surface finish accuracy and reducing material overuse. 3D printing — additive manufacturing systems can construct walls and structural elements directly from digital models. == Notable construction-related activities undertaken by robots == The distribution of robotic applications in construction varies across the project lifecycle. Most applications are concentrated in structural construction tasks such as masonry, concrete work, and assembly, while other phases, including planning, maintenance, and demolition, remain less represented. === Automated building systems === The Nisseki Yokohama Building (also known as Rail City Yokohama), a 30-storey office building in Yokohama, Japan, was constructed between 1994 and 1997 using the SMART system (Shimizu Manufacturing system by Advanced Robotics Technology), developed by Shimizu Corporation and a consortium of seven other Japanese companies. The system used automated horizontal hoists and vertical lifts to position steel beams, columns, precast concrete floor slabs, and prefabricated facade panels, with welding robots connecting structural elements under laser-guided precision. Each component was tracked by barcode to monitor progress and coordinate just-in-time delivery of materials. Obayashi Corporation developed the Advanced Building Construction System (ABCS), a similar automated platform used in several high-rise projects in Japan in the 1990s, including the NEC Head Office in Kanagawa (1997–2000). === Progress monitoring, inspection === Boston Dynamics' Spot was used in February 2024 to inspect sections of the M5 motorway in England for National Highways. A £15,000 humanoid robot (a G1 model from Chinese manufacturer Unitree) was deployed to capture 360-degree imagery and progress reports to support health and safety monitoring and reporting for UK contractor Tilbury Douglas in April 2026. In the US, Virginia Tech's ARCADE research lab is developing MARIO (Multi-Agent Robotic system for Inspection On-site), a heterogenous robotic system deploying multiple robots capable of different locomotion to perform remote real-time construction progress monitoring in complex construction sites. === Earthmoving === === Concrete works === Obayashi Corporation developed and deployed a robotic system for placing concrete layers in dam construction in Japan. A concrete floor finishing robot was deployed by Kajima and Tokimec in Japan. The MARK series were designed in 1984 to automate the levelling and trowelling of concrete slabs on construction sites, providing consistent finishing accuracy, improved efficiency, and reduced dependence on skilled labour === Masonry === SAM100 (Semi-Automated Mason), developed by Construction Robotics, is one of the first commercially available bricklaying robots for on-site masonry construction. In 2018, it was used in the construction of the University Arts Building at the University of Nevada, Reno — a $35.5 million facility — where it laid over 60,000 of the 100,000 bricks required, reducing the brick veneer installation time by approximately 50%. Hadrian X, developed by the Australian company Fastbrick Robotics, is a fully autonomous mobile bricklaying robot. In November 2022, it completed its first commercial project — five four-bedroom houses in Wellard, Western Australia. In February 2025, PulteGroup, one of the largest homebuilders in the United States, piloted Hadrian X on a site in Florida, constructing an entire house in a single day. === 3D printing === In May 2025, a residential building in Arinaga, Gran Canaria, Spain, was completed using 3D printing construction technology, as part of broader efforts to demonstrate robotic and additive manufacturing methods in the housing sector. In 2026, a three-storey apartment block in France was constructed using concrete 3D printing technology, three months faster than conventional building methods. Finland's Hyperion Robotics has opened a UK factory and used 3D printing with concrete to produce foundations for pipelines and for electricity substation bases, reducing time-consuming and weather-dependent onsite construction processes. == Social impact == The adoption of construction robots varies significantly by region and is shaped by labour market conditions, cultural attitudes, and regulatory frameworks. In Japan, construction robots have been embraced as a response to an ageing workforce and chronic labour shortages, and are generally viewed positively by the industry. In the United States, adoption has historically been slower, partly due to resistance from labour unions concerned about job displacement. Research suggests that the impact of automation on workers is uneven: while robots can create a productivity effect that benefits some workers, displacement effects are most pronounced among younger, less-educated workers in manufacturing-heavy regions. More than 60% of construction firms now report difficulty finding skilled operators, which has increased openness to automation as a practical solution to workforce shortages rather than a replacement for workers. In the UK, during onsite deployment of a humanoid robot for monitoring purposes, there were concerns that staff might think they were being watched ("It's not there to spy on people.... So, we insist that everyone is blurred out. N

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

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

    Comparing the best AI text-to-image tool? An AI text-to-image tool is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI text-to-image tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • The Best Free AI Writing Assistant for Beginners

    The Best Free AI Writing Assistant for Beginners

    Shopping for the best AI writing assistant? An AI writing assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI writing assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Dan Hendrycks

    Dan Hendrycks

    Dan Hendrycks (born 1994 or 1995) is an American machine learning researcher. He serves as the director of the Center for AI Safety, a nonprofit research organization based in San Francisco, California. == Early life and education == Hendrycks was raised in a Christian evangelical household in Marshfield, Missouri. He received a B.S. from the University of Chicago in 2018 and a Ph.D. from the University of California, Berkeley in Computer Science in 2022. == Career and research == Hendrycks' research focuses on topics that include machine learning safety, machine ethics, and robustness. He credits his participation in the effective altruism (EA) movement-linked 80,000 Hours program for his career focus towards AI safety, though denies being an advocate for EA. Hendrycks is the main author of the research paper that introduced the activation function GELU in 2016, and of the paper that introduced the language model benchmark MMLU (Massive Multitask Language Understanding) in 2020. In February 2022, Hendrycks co-authored recommendations for the US National Institute of Standards and Technology (NIST) to inform the management of risks from artificial intelligence. In September 2022, Hendrycks wrote a paper providing a framework for analyzing the impact of AI research on societal risks. He later published a paper in March 2023 examining how natural selection and competitive pressures could shape the goals of artificial agents. This was followed by "An Overview of Catastrophic AI Risks", which discusses four categories of risks: malicious use, AI race dynamics, organizational risks, and rogue AI agents. Hendrycks is the safety adviser of xAI, an AI startup company founded by Elon Musk in 2023. To avoid any potential conflicts of interest, he receives a symbolic one-dollar salary and holds no company equity. In November 2024, he also joined Scale AI as an advisor collecting a one-dollar salary. Hendrycks is the creator of Humanity's Last Exam, a benchmark for evaluating the capabilities of large language models, which he developed in collaboration with Scale AI. In 2024, Hendrycks published the textbook Introduction to AI Safety, Ethics, and Society, based on courseware he had previously developed. == Selected publications == Hendrycks, Dan; Gimpel, Kevin (2020-07-08). "Gaussian Error Linear Units (GELUs)". arXiv:1606.08415 [cs.LG]. Hendrycks, Dan; Gimpel, Kevin (2018-10-03). "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks". International Conference on Learning Representations 2017. arXiv:1610.02136. Hendrycks, Dan; Mazeika, Mantas; Dietterich, Thomas (2019-01-28). "Deep Anomaly Detection with Outlier Exposure". International Conference on Learning Representations 2019. arXiv:1812.04606. Hendrycks, Dan; Mazeika, Mantas; Zou, Andy (2021-10-25). "What Would Jiminy Cricket Do? Towards Agents That Behave Morally". Conference on Neural Information Processing Systems 2021. arXiv:2110.13136.

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  • Semi-automation

    Semi-automation

    Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.

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  • AI Background Removers Reviews: What Actually Works in 2026

    AI Background Removers Reviews: What Actually Works in 2026

    Trying to pick the best AI background remover? An AI background remover 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 background remover 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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  • Best AI Presentation Makers in 2026

    Best AI Presentation Makers in 2026

    In search of the best AI presentation maker? An AI presentation maker 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 presentation maker 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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  • Nicholas Carlini

    Nicholas Carlini

    Nicholas Carlini is an American researcher affiliated with Anthropic and previously with Google DeepMind who has published research in the fields of computer security and machine learning. He is known for his work on adversarial machine learning, particularly his work on the Carlini & Wagner attack in 2016. This attack was particularly useful in defeating defensive distillation, a method used to increase model robustness, and has since been effective against other defenses against adversarial input. In 2018, Carlini demonstrated an attack on Mozilla's DeepSpeech model, showing that hidden commands could be embedded in speech inputs, which the model would execute even if they were inaudible to humans. He also led a team at UC Berkeley that successfully broke seven out of nine defenses against adversarial attacks presented at the 2018 International Conference on Learning Representations. In addition to his work on adversarial attacks, Carlini has made significant contributions to understanding the privacy risks of machine learning models. In 2020, he revealed that large language models, like GPT-2, could memorize and output personally identifiable information. His research demonstrated that this issue worsened with larger models, and he later showed similar vulnerabilities in generative image models, such as Stable Diffusion. == Life and career == Nicholas Carlini obtained his Bachelor of Arts in Computer Science and Mathematics from the University of California, Berkeley, in 2013. He then continued his studies at the same university, where he pursued a PhD under the supervision of David Wagner, completing it in 2018. Carlini became known for his work on adversarial machine learning. In 2016, he worked alongside Wagner to develop the Carlini & Wagner attack, a method of generating adversarial examples against machine learning models. The attack was proved to be useful against defensive distillation, a popular mechanism where a student model is trained based on the features of a parent model to increase the robustness and generalizability of student models. The attack gained popularity when it was shown that the methodology was also effective against most other defenses, rendering them ineffective. In 2018, Carlini demonstrated an attack against Mozilla Foundation's DeepSpeech model where he showed that by hiding malicious commands inside normal speech input the speech model would respond to the hidden commands even when the commands were not discernible by humans. In the same year, Carlini and his team at UC Berkeley showed that out of the 11 papers presenting defenses to adversarial attacks accepted in that year's ICLR conference, seven of the defenses could be broken. Since 2021, he and his team have been working on large language models, creating a questionnaire where humans typically scored 35% whereas AI models scored in the 40%, with GPT-3 getting 38% which could be improved to 40% through few shot prompting. The best performer in the test was UnifiedQA, a model developed by Google specifically for answer questions and answer sets. Carlini has also developed methods to cause large language models like ChatGPT to answer harmful questions like how to construct bombs. He is also known for his work studying the privacy of machine learning models. In 2020, he showed for the first time that large language models would memorize some of the text data that they were trained on. For example, he found that GPT-2 could output personally identifiable information. He then led an analysis of larger models and studied how memorization increased with model size. Then, in 2022 he showed the same vulnerability in generative image models, and specifically diffusion models, by showing that Stable Diffusion could output images of people's faces that it was trained on. Following on this, Carlini then showed that ChatGPT would also sometimes output exact copies of webpages it was trained on, including personally identifiable information. Some of these studies have since been referenced by the courts in debating the copyright status of AI models. == Other work == Carlini received the Best of Show award at the 2020 IOCCC for implementing a tic-tac-toe game entirely with calls to printf, expanding on work from a research paper of his from 2015. The judges commented on his submission "This year's Best of Show (carlini) is such a novel way of obfuscation that it would be worth of a special mention in the (future) Best of IOCCC list!". [sic] == Awards == Best Student Paper Award, IEEE S&P 2017 ("Towards Evaluating the Robustness of Neural Networks") Best Paper Award, ICML 2018 ("Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples") Distinguished Paper Award, USENIX 2021 ("Poisoning the Unlabeled Dataset of Semi-Supervised Learning") Distinguished Paper Award, USENIX 2023 ("Tight Auditing of Differentially Private Machine Learning") Best Paper Award, ICML 2024 ("Stealing Part of a Production Language Model") Best Paper Award, ICML 2024 ("Considerations for Differentially Private Learning with Large-Scale Public Pretraining")

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  • QF-Test

    QF-Test

    QF-Test from Quality First Software is a cross-platform software tool for automated testing of programs via the graphical user interface (GUI) test automation). The program is specialized on (Java/Swing, Standard Widget Toolkit (SWT), Eclipse plug-ins and rich client platform (RCP) applications, ULC and JavaFX) cross-web browser test automation of static and dynamic web applications (HTML and web frameworks like Angular, Ext JS, Fluent UI React, Google Web Toolkit (GWT), jQuery UI, jQueryEasyUI Remote Application Platform (RAP), Qooxdoo, RichFaces, Vaadin, React, Smart GWT, Vue.js, ICEfaces and ZK). Version 4.1 added support for macOS and the Apple Safari and Microsoft Edge browsers via the Selenium WebDriver. Representational State Transfer (RESTful) web service testing. From version 5.0, Windows applications can also be tested (classic Win32 applications, .NET framework applications (often developed in C#) based on Windows Presentation Foundation (WPF) or Windows Forms, Windows apps and Universal Windows Platform (UWP) applications using Extensible Application Markup Language (XAML) controls) and modern C++ applications (such as Qt applications). Version 5.3 added support for the Chrome DevTools protocol, which allows browsers to be controlled using CDP drivers. Since then, mobile testing for iOS and Android, accessibility testing of web applications and SmartID, a new approach for more flexible and robust component recognition, have been introduced. Powerful enhancements such as WebAPI testing and AI-assisted validation complement the test automation tool. == Overview == QF-Test (the successor of qftestJUI, available since 2001) enables regression and load testing and runs on Windows, Unix and macOS. It is mainly used commercially by testers, developers or business analysts (modelling, low code approaches) with or without programming knowledge as part of software Quality Assurance. Since December 2008, a webtest add-on is available which allows test automation of browser-based GUIs (such as Internet Explorer, Mozilla Firefox, Google Chrome, Apple Safari, and Microsoft Edge) along with extant Java GUI test functions, which was extended to include JavaFX in July 2014. From 2018, QF-Test version 4.2 can test PDF documents, from 2020 native desktop applications (QF-Test version 5) and in 2022, mobile application testing will be added. The basis for efficient use in test automation is stable component recognition (IDs, logical screen elements, labels, CustomWebResolver, SmartID, ...) with low maintenance effort. == Features == General – QF-Test's capture/replay function enables recording of tests for beginners, while modular programming (modularizing) allows creating large test suites in a concise arrangement. For the advanced user who requires even more control over his application, the tool offers access to internal program structures through the standard scripting languages Jython, the Java implementation of the popular Python language, JavaScript, and Groovy. The tool also offers a batch processing mode, allowing to run tests unattended and then generate XML, HTML and JUnit reports. Thus the tool can be integrated into existing build/test frameworks like Jenkins, Ant or Maven. Another mode is the so-called Daemon mode for distributed test execution. A specific integration with many test management tools exists. There is a test debugger (enabling arbitrary stepping and editing variables at runtime) and a fully automated dependency management that takes care of pre- and postconditions and helps isolating test cases. Data-driven testing with no need for scripting is possible. Web testing: cross-browser on Internet Explorer, Chrome, Firefox, Edge (including Chromium-based), Opera and Safari for static and dynamic websites (HTML5, Ajax, DOM). A headless browser can also be used for testing. QF-Test fully supports frameworks like Angular, React and Vue.js, but also many specific UI toolkits like Smart (GWT), GXT/ExtGWT, ExtJS, ICEfaces, jQuery UI, Kendo UI, PrimeFaces, Qooxdoo, RAP, RichFaces, Vaadin and ZK. Easy integration with Selenium makes it easy to balance development and functional testing. Electron applications can also be tested. Other (e.g., SAP UI5, Siebel Open UI, Salesforce) and future web toolkits can be integrated with little effort. Short-term and individual customisations (CustomWebResolver) are possible via an optimised interface JavaFX, Java Swing, SWT, Eclipse plug-ins and RCP applications and ULC. Support for testing when migrating from JavaSwing or JavaFX to web applications (e.g. via Webswing). Hybrid applications based on multiple technologies are also supported, e.g. applications that integrate HTML content into Java applications using JxBrowser. Windows-based applications (Win32, .NET, Windows Forms, WPF, Windows apps, Qt). Android applications can be tested on real devices and with the Android Studio emulator. iOS applications can also be tested on real devices and with the Xcode Simulator. Testing of PDF documents (document comparisons, checking content, texts, images/graphic objects, layouts, "invisible" or partially hidden objects). QF-Test 9 introduces web accessibility testing to automatically check compliance with WCAG and other standards. QF-Test 10 introduces powerful enhancements for WebAPI testing and AI-assisted validation.

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  • Bob Coecke

    Bob Coecke

    Bob Coecke (born 23 July 1968) is a Belgian theoretical physicist and logician. He was Professor of Quantum foundations, Logics, and Structures at Oxford University until 2020. He was Chief Scientist at quantum computing company Quantinuum, until 2025 and founded a startup called Relational Intelligence in 2026. He is also Distinguished Visiting Research Chair at the Perimeter Institute for Theoretical Physics, and Emeritus Fellow at Wolfson College, Oxford. He pioneered categorical quantum mechanics (entry 18M40 in Mathematics Subject Classification 2020), Quantum Picturalism, ZX-calculus, DisCoCat model for natural language,, quantum natural language processing (QNLP) and quantum education through the book Quantum in Pictures. He is a founder of the Quantum Physics and Logic community and the Applied Category Theory communities and conference series, and of the journal Compositionality. Coecke is also a composer and musician, who has been called a pioneer of industrial music, and is also one of the pioneers of employing quantum computers in music. == Education and career == Coecke obtained his doctorate in sciences at the Vrije Universiteit Brussel in 1996, and performed postdoctoral work in the Theoretical Physics Group of Imperial College, London in the Category Theory Group of the Mathematics and Statistics Department at McGill University in Montreal, in the Department of Pure Mathematics and Mathematical Statistics of Cambridge University, and in the Department of Computer Science, University of Oxford. He was an EPSRC Advanced Research Fellow at the Department of Computer Science, University of Oxford, where he became Lecturer in Quantum Computer Science in 2007, and jointly with Samson Abramsky built and headed the Quantum Group. In July 2011, he was nominated professor of Quantum Foundations, Logics and Structures at Oxford University, with retroactive effect as of October 2010. He was a Governing Body Fellow of Wolfson College, Oxford since 2007, where he now is an Emeritus Fellow. In January 2019, Coecke became Senior Scientific Advisor of Cambridge Quantum Computing, and in January 2021 he resigned from his Professorship at Oxford, to become Chief Scientist of Cambridge Quantum Computing. After the merger of Cambridge Quantum Computing with Honeywell Quantum Systems, he stayed on as Chief Scientist of the joint entity Quantinuum until 2025. In January 2023 he also became Distinguished Visiting Research Chair at the Perimeter Institute for Theoretical Physics. == Work == Coecke's research focuses on the foundations of physics, more particularly category theory, logic, and diagrammatic reasoning, with application to quantum informatics, quantum gravity, and NLP. He has pioneered categorical quantum mechanics together with Samson Abramsky, and spearheaded the development of a diagrammatic quantum formalism based on Penrose graphical notation, on which he wrote a textbook entitled Picturing Quantum Processes with Aleks Kissinger. With Ross Duncan he pioneered ZX-calculus. He pioneered the DisCoCat model for natural language, with Stephen Clark and Mehrnoosh Sadrzadeh. He also pioneered quantum natural language processing (QNLP), with Will Zeng, and colleagues at Cambridge Quantum Computing. == Music == Coecke is also a musician, performing and recording since the eighties. He retrospectively has been named a pioneer of industrial music. His band, Black Tish, "used cutting edge sampling techniques for the time, a host of synth and sound loops and metal-style guitars to create a heavy rock/electronica fusion unlike anything heard before", and "bridge the gap between the pure experimental nature of bands like Throbbing Gristle and Einstürzende Neubauten and the (comparatively) more radio accessible Ministry or Nine Inch Nails". Coecke is also one of the pioneers of employing quantum computers in music. == Selected publications == Textbooks Bob Coecke, Aleks Kissinger:Picturing Quantum Processes. A First Course in Quantum Theory and Diagrammatic Reasoning, Cambridge University Press, 2017, ISBN 978-1316219317 Bob Coecke, Stefano Gogioso:Quantum in Pictures, Quantinuum, 2022, ISBN 978-1-7392147-1-5 Books (as editor) Bob Coecke, David Moore, Alexander Wilce (eds.): Current Research in Operational Quantum Logic: Algebras, Categories, Languages, Fundamental Theories of Physics, Kluwer Academic, 2010, ISBN 978-9048154371 Bob Coecke (ed.): New Structures for Physics, Lecture Notes in Physics 813, Springer, 2011, ISBN 978-3642128202 Articles Bob Coecke: Kindergarten quantum mechanics, arXiv:quant-ph/0510032 Samson Abramsky, Bob Coecke: A categorical semantics of quantum protocols, Proceedings of the 19th Annual IEEE Symposium on Logic in Computer Science, 2004, pp. 415–425 Bob Coecke, Ross Duncan: Interacting quantum observables, Automata, Languages and Programming, pp. 298–310, 2008 Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, Bob Coecke: Grammar-Aware Question-Answering on Quantum Computers, arXiv:2012.03756 Bob Coecke: The Mathematics of Text Structure, arXiv:1904.03478 Will Zeng, Bob Coecke: Quantum Algorithms for Compositional Natural Language Processing, arXiv:1608.01406 Bob Coecke, Tobias Fritz, Robert Spekkens: A mathematical theory of resources, arXiv:1409.5531 Bob Coecke: An Alternative Gospel of structure: order, composition, processes, arxiv:1307.4038 Bob Coecke, Mehrnoosh Sadrzadeh, Steven Clark: Mathematical Foundations for a Compositional Distributional Model of Meaning, arXiv:1003.4394 Bob Coecke: Quantum Picturalism, arXiv:0908.1787 Software articles Eduardo Reck Miranda, Richie Yeung, Anna Pearson, Konstantinos Meichanetzidis, Bob Coecke: A quantum natural language processing approach to musical intelligence, arXiv:2111.06741 Dimitri Kartsaklis, Ian Fan, Richie Yeung, Anna Pearson, Robin Lorenz, Alexis Toumi, Giovanni de Felice, Konstantinos Meichanetzidis, Stephen Clark, Bob Coecke: lambeq: An efficient high-level python library for quantum NLP, arXiv:2110.04236 Giovanni de Felice, Alexis Toumi, Bob Coecke: Discopy: monoidal categories in Python, arXiv:2111.06741

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  • AI Clip Makers: Free vs Paid (2026)

    AI Clip Makers: Free vs Paid (2026)

    Shopping for the best AI clip maker? An AI clip maker is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI clip maker 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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  • Conversational AI Platforms Reviews: What Actually Works in 2026

    Conversational AI Platforms Reviews: What Actually Works in 2026

    Shopping for the best conversational AI platform? An conversational AI platform is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right conversational AI platform 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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  • Medical data breach

    Medical data breach

    Medical data, including patients' identity information, health status, disease diagnosis and treatment, and biogenetic information, not only involve patients' privacy but also have a special sensitivity and important value, which may bring physical and mental distress and property loss to patients and even negatively affect social stability and national security once leaked. However, the development and application of medical AI must rely on a large amount of medical data for algorithm training, and the larger and more diverse the amount of data, the more accurate the results of its analysis and prediction will be. However, the application of big data technologies such as data collection, analysis and processing, cloud storage, and information sharing has increased the risk of data leakage. In the United States, the rate of such breaches has increased over time, with 176 million records breached by the end of 2017. By 2024, the U.S. Department of Health and Human Services reported 725 large healthcare data breaches affecting approximately 275 million individual records in a single year, marking a significant escalation in both the frequency and scale of incidents. == Black market for health data == In February 2015 an NPR report claimed that organized crime networks had ways of selling health data in the black market. In 2015 a Beazley employee estimated that medical records could sell on the black market for US$40-50. == How data is lost == Theft, data loss, hacking, and unauthorized account access are ways in which medical data breaches happen. Among reported breaches of medical information in the United States networked information systems accounted for the largest number of records breached. There are many data breaches happening in the US health care system, among business associates of the health care providers that continuously gain access to patients' data. == List of data breaches == In February 2024, a ransomware attack on Change Healthcare, a subsidiary of UnitedHealth Group, compromised the protected health information of approximately 100 million individuals, making it the largest healthcare data breach in United States history. The attack disrupted claims processing for healthcare providers nationwide for several weeks. In May 2024, MediSecure suffered a cyberattack involving ransomware in Australia. In May 2021, the Health Service Executive in the Republic of Ireland was the victim of a cyberattack involving ransomware, in the Health Service Executive cyberattack, with admission records and test results present in a sample of the data reviewed by the Financial Times. In October 2018, the Centers for Medicare and Medicaid Services in the US reported that around 75,000 individual records had been affected by a data breach that took place through the ACA Agent and Broker Portal. In 2018, Social Indicators Research published the scientific evidence of 173,398,820 (over 173 million) individuals affected in USA from October 2008 (when the data were collected) to September 2017 (when the statistical analysis took place). In 2015, Anthem Inc. lost data for 37 million people in the Anthem medical data breach In 2014 4.5 million people using Complete Health Systems had their data stolen In 2013-14 1 million people using Montana Department of Public Health and Human Services had their data stolen In 2013 4 million people using Advocate Health and Hospitals Corporation had their data stolen In 2011 4.9 million users of Tricare services had their data stolen due to an employee error by Science Applications International Corporation In 2011 1.9 million people using Health Net had their data stolen In 2011 1 million people using Nemours Foundation had their data stolen In 2010 6800 people using New York-Presbyterian Hospital and Columbia University Medical Center had their data breached. In response, those organizations agreed to pay the United States Department of Health and Human Services a US$4.8 million dollar fine. In 2009 1 million people using BlueCross BlueShield of Tennessee had their data stolen == Regulation == In the United States, the Health Insurance Portability and Accountability Act and Health Information Technology for Economic and Clinical Health Act require companies to report data breaches to affected individuals and the federal government. Under the HIPAA Breach Notification Rule, covered entities must notify affected individuals without unreasonable delay and no later than 60 days after discovering a breach of unsecured protected health information. Breaches affecting 500 or more individuals must also be reported to the HHS Secretary and to prominent media outlets serving the affected state or jurisdiction within the same timeframe; HHS publicly lists these larger breaches on its breach portal, commonly known as the "wall of shame." Breaches affecting fewer than 500 individuals are reported to HHS annually, no later than 60 days after the end of the calendar year in which they were discovered. Health Information Privacy Health Insurance Portability and Accountability Act of 1996 (HIPAA). - 45 CFR Parts 160 and 164, Standards for Privacy of Individually Identifiable Health Information and Security Standards for the Protection of Electronic Protected Health Information. HIPAA includes provisions designed to save health care businesses money by encouraging electronic transactions, as well as regulations to protect the security and confidentiality of patient information. The Privacy Rule became effective April 14, 2001, and most covered entities (health plans, health care clearinghouses, and health care providers that conduct certain financial and administrative transactions electronically) had until April 2003 to comply. This security provision became effective April 21, 2003. The Health Insurance Portability and Accountability Act (HIPAA) is the baseline set of federal regulations governing medical information. It does three things: i. i. i.Establish a structure for how personal health information is disclosed and establish the rights of individuals with respect to health information; ii.Specify security standards for the retention and transmission of electronic patient information; iii.Need a common format and data structure for the electronic exchange of health information. California-Specific Laws California’s medical privacy laws, primarily the Confidentiality of Medical Information Act (CMIA), the data breach sections of the Civil Code, and sections of the Health and Safety Code, provide HIPAA-like protections, although the terminology is different. HIPAA establishes a federal "minimum standard" that applies where there are gaps in California law, and HIPAA also specifies that stricter state laws will override or supersede HIPAA. California's health care privacy laws apply to providers who provide personal health records (PHR), while HIPAA only applies when the provider providing the PHR is a business associate of a covered entity. Federal law does not grant individuals the right to file a lawsuit in the event of a data breach (only the Attorney General can file a lawsuit), but California law does. This means that California law sets a higher standard for medical privacy, and that individuals in California enjoy stronger legal protections and more ways to hold entities that violate their medical privacy accountable. In the UK, the legal framework for how patient data is cared for and processed is the Data Protection Act 2018 (DPA), which incorporates the EU General Data Protection Regulation (GDPR) into law, and the common law duty of confidentiality (CLDC). The data protection legislation requires that the collection and processing of personal data be fair, lawful and transparent. This means that the collection and processing of data as defined by data protection legislation must always have a valid lawful basis and must also meet the requirements of the CLDC. In the China, Article 18 of the "National Health Care Big Data Standards, Security and Services Management Measures (for Trial Implementation)" (National Health Planning and Development (2018) No. 23) promulgated by the National Health Care Commission in 2018 states, "The responsible unit shall adopt measures such as data classification, important data backup, and encryption authentication to guarantee the security of health care big data." However, the scope and definition of important data are not covered. Although the "Information Security Technology-Healthcare Data Security Guide" (the "Guide") issued by the National Standardization Committee also proposes that important data should be evaluated and approved in accordance with the regulations, there is likewise no definition of the connotation and definition of important data.

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    Is an AI Virtual Assistant Worth It in 2026?

    Shopping for the best AI virtual assistant? An AI virtual assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI virtual assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Barbara Di Eugenio

    Barbara Di Eugenio

    Barbara Di Eugenio is an Italian-American computer scientist, the Collegiate Warren S. McCulloch Professor of Computer Science at the University of Illinois Chicago. Her research focuses on natural language processing and its applications to human–computer interaction, educational technology, and artificial intelligence in healthcare. == Education and career == Di Eugenio is originally from Turin. After an undergraduate education in Italy, she completed her Ph.D. in computer and information science in 1993 at the University of Pennsylvania. Her dissertation, Understanding Natural Language Instructions: A Computational Approach to Purpose Clauses, was supervised by Bonnie Webber. She became a faculty member at the University of Illinois Chicago in 1999, and at that time was the only woman faculty member in the Department of Electrical Engineering and Computer Science. == Recognition == In 2022, Di Eugenio received the Zenith Award of the Association for Women in Science. She was named as a Fellow of the Association for Computational Linguistics in 2023, "for outstanding contributions to natural language generation; intelligent tutoring systems; discourse; intercoder agreement; and applying multimodal interactive systems to health".

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