AI Generator Canva

AI Generator Canva — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Bazaart

Bazaart is an AI-powered design platform with image and video editing capabilities for iOS, Android, MacOS, and the web. == History == Bazaart was founded in 2012 in Israel. In April 2012, Bazaart launched a Facebook app called Pinvolve, which converts Facebook Pages into Pinterest pinboards. From June to August 2012, it participated in the DreamIt startup accelerator in New York and raised $25,000 from the accelerator. In July 2012, it launched its first version as an iPad app connected to Pinterest. In December 2013, it pivoted and launched a major version of its app, a "social" photoshop that allowed users to edit images which could be pulled in from the camera roll, social networks, and other sources. In July 2014, Bazaart reached one million downloads and in December was selected by Apple as Best of 2014. In 2015, Bazaart added Photoshop integration in a partnership with Adobe. In September 2020, Bazaart launched an Android app. In December 2020, Bazaart was selected by Google as Best of 2020. In January 2022, Bazaart added video editing capabilities. In 2023, the platform added AI-powered backgrounds and video background removal features.
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Multiple buffering

In computer science, multiple buffering is the use of more than one buffer to hold a block of data, so that a "reader" will see a complete (though perhaps old) version of the data instead of a partially updated version of the data being created by a "writer". It is very commonly used for computer display images. It is also used to avoid the need to use dual-ported RAM (DPRAM) when the readers and writers are different devices. == Description == === Double buffering Petri net === The Petri net in the illustration shows double buffering. Transitions W1 and W2 represent writing to buffer 1 and 2 respectively while R1 and R2 represent reading from buffer 1 and 2 respectively. At the beginning, only the transition W1 is enabled. After W1 fires, R1 and W2 are both enabled and can proceed in parallel. When they finish, R2 and W1 proceed in parallel and so on. After the initial transient where W1 fires alone, this system is periodic and the transitions are enabled – always in pairs (R1 with W2 and R2 with W1 respectively). == Double buffering in computer graphics == In computer graphics, double buffering is a technique for drawing graphics that shows less stutter, tearing, and other artifacts. It is difficult for a program to draw a display so that pixels do not change more than once. For instance, when updating a page of text, it is much easier to clear the entire page and then draw the letters than to somehow erase only the pixels that are used in old letters but not in new ones. However, this intermediate image is seen by the user as flickering. In addition, computer monitors constantly redraw the visible video page (traditionally at around 60 times a second), so even a perfect update may be visible momentarily as a horizontal divider between the "new" image and the un-redrawn "old" image, known as tearing. === Software double buffering === A software implementation of double buffering has all drawing operations store their results in some region of system RAM; any such region is often called a "back buffer". When all drawing operations are considered complete, the whole region (or only the changed portion) is copied into the video RAM (the "front buffer"); this copying is usually synchronized with the monitor's raster beam in order to avoid tearing. Software implementations of double buffering necessarily require more memory and CPU time than single buffering because of the system memory allocated for the back buffer, the time for the copy operation, and the time waiting for synchronization. Compositing window managers often combine the "copying" operation with "compositing" used to position windows, transform them with scale or warping effects, and make portions transparent. Thus, the "front buffer" may contain only the composite image seen on the screen, while there is a different "back buffer" for every window containing the non-composited image of the entire window contents. === Page flipping === In the page-flip method, instead of copying the data, both buffers are capable of being displayed. At any one time, one buffer is actively being displayed by the monitor, while the other, background buffer is being drawn. When the background buffer is complete, the roles of the two are switched. The page-flip is typically accomplished by modifying a hardware register in the video display controller—the value of a pointer to the beginning of the display data in the video memory. The page-flip is much faster than copying the data and can guarantee that tearing will not be seen as long as the pages are switched over during the monitor's vertical blanking interval—the blank period when no video data is being drawn. The currently active and visible buffer is called the front buffer, while the background page is called the back buffer. == Triple buffering == In computer graphics, triple buffering is similar to double buffering but can provide improved performance. In double buffering, the program must wait until the finished drawing is copied or swapped before starting the next drawing. This waiting period could be several milliseconds during which neither buffer can be touched. In triple buffering, the program has two back buffers and can immediately start drawing in the one that is not involved in such copying. The third buffer, the front buffer, is read by the graphics card to display the image on the monitor. Once the image has been sent to the monitor, the front buffer is flipped with (or copied from) the back buffer holding the most recent complete image. Since one of the back buffers is always complete, the graphics card never has to wait for the software to complete. Consequently, the software and the graphics card are completely independent and can run at their own pace. Finally, the displayed image was started without waiting for synchronization and thus with minimum lag. Due to the software algorithm not polling the graphics hardware for monitor refresh events, the algorithm may continuously draw additional frames as fast as the hardware can render them. For frames that are completed much faster than interval between refreshes, it is possible to replace a back buffers' frames with newer iterations multiple times before copying. This means frames may be written to the back buffer that are never used at all before being overwritten by successive frames. Nvidia has implemented this method under the name "Fast Sync". An alternative method sometimes referred to as triple buffering is a swap chain three buffers long. After the program has drawn both back buffers, it waits until the first one is placed on the screen, before drawing another back buffer (i.e. it is a 3-long first in, first out queue). Most Windows games seem to refer to this method when enabling triple buffering. == Quad buffering == The term quad buffering is the use of double buffering for each of the left and right eye images in stereoscopic implementations, thus four buffers total (if triple buffering was used then there would be six buffers). The command to swap or copy the buffer typically applies to both pairs at once, so at no time does one eye see an older image than the other eye. Quad buffering requires special support in the graphics card drivers which is disabled for most consumer cards. AMD's Radeon HD 6000 Series and newer support it. 3D standards like OpenGL and Direct3D support quad buffering. == Double buffering for DMA == The term double buffering is used for copying data between two buffers for direct memory access (DMA) transfers, not for enhancing performance, but to meet specific addressing requirements of a device (particularly 32-bit devices on systems with wider addressing provided via Physical Address Extension). Windows device drivers are a place where the term "double buffering" is likely to be used. Linux and BSD source code calls these "bounce buffers". Some programmers try to avoid this kind of double buffering with zero-copy techniques. == Other uses == Double buffering is also used as a technique to facilitate interlacing or deinterlacing of video signals.
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Autonomous logistics

Autonomous logistics describes systems that provide unmanned, autonomous transfer of equipment, baggage, people, information or resources from point-to-point with minimal human intervention. Autonomous logistics is a new area being researched and currently there are few papers on the topic, with even fewer systems developed or deployed. With web enabled cloud software there are companies focused on developing and deploying such systems which will begin coming online in 2018. == Autonomous logistics vehicles == There are several subclasses of autonomous logistics vehicles: Ground autonomous logistics Based on Unmanned ground vehicle technology, a large autonomous logistics tracked carrier, which can be deployed in a tropical forest for day and night, has been developed. Another example is the TerraMax autonomous truck based on Oshkosh's Medium Tactical Vehicle Replacement (MTVR) military truck platform. Most recently, TerraMax competed in the 2007 Darpa Urban Challenge. The MTVR was designed for the U.S. Marine Corps with a 70% off-road mission profile. TerraMax's unmanned ground vehicle kit does not interfere with the conventional operation of the vehicle. A robust sensor suite allows for 360-degree situational awareness around TerraMax. Elements of the autonomous navigation kit could be used to enhance driver awareness. The complete kit could be used in applications such as snow removal on airport runways. Aerial autonomous logistics Based on unmanned aerial vehicle technology, aerial autonomous logistics (or logistics UAVs) provides transfer of resources and equipment in disaster relief situations, replenishment operations, reconnaissance operations where information is gathered, and general parcel or package delivery. Space autonomous logistics Describes the ability to provide logistics to and from space, be that orbital, lunar or beyond. Current space logistics vehicle examples are the Progress spacecraft, Russian expendable freighter uncrewed resupply spacecraft and the Automated Transfer Vehicle, expendable uncrewed resupply spacecraft developed by the European Space Agency. Above Water autonomous logistics Based on unmanned surface vehicle technology, this class of vehicles provides a range of surface fleet replenishment and equipment transfer capabilities. Subsea autonomous logistics Using autonomous underwater vehicle technology, these vehicles provide re-supply to underwater facilities, reconnaissance of underwater structures, emergency recovery capability, and so on. == Agent-based logistics == Shipping containers handle most of today's intercontinental transport of packaged goods. Managing them in terms of planning and scheduling is a challenging task due to the complexity and dynamics of the involved processes. Hence, recent developments show an increasing trend towards autonomous control with software agents acting on behalf of the logistic objects. Despite the high degree of autonomy it is still necessary to cooperate in order to achieve certain goals. The current trends and recent changes in logistics lead to new, complex and partially conflicting requirements for logistic planning and control systems. Due to the distributed nature of logistics, the usage of agent technology is promising. Due to the mobile nature of logistics, the usage of mobile agent technology is promising as well. Scenarios of usage of mobile agents in logistics has been envisioned.
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Canva

Canva Pty Ltd. is an Australian multinational proprietary software company launched in 2013 based in Sydney, Australia. The platform provides a graphic design platform to create visual content for presentations, websites, and other digital products. Its uses include templates for presentations, posters, and social media content, as well as photo and video editing functionality. The platform uses a drag-and-drop interface designed for users without professional design training or experience. Canva operates on a freemium model and has added features such as print services and video editing tools over time. == History == === 2013–2020 === Canva was founded in Perth, Australia, by Melanie Perkins, Cliff Obrecht and Cameron Adams on 1 January 2013. One of the company's early investors was Susan Wu, an American entrepreneur. In its first year, Canva had more than 750,000 users. In 2017, the company reached profitability and had 294,000 paying customers. In January 2018, Perkins announced that the company had raised A$40 million from Sequoia Capital, Blackbird Ventures, and Felicis Ventures, and the company was valued at A$1 billion. It raised A$70 million in May 2019, followed by A$85 million in October 2019 and the launch of Canva for Enterprise. In December 2019, Canva announced Canva for Education, a free product for schools and other educational institutions intended to facilitate collaboration between students and teachers. === 2021–2025 === In June 2020, Canva announced a partnership with FedEx Office and with Office Depot the following month. As of June 2020, Canva's valuation had risen to A$6 billion, rising to A$40 billion by September 2021. In September 2021, Canva raised US$200 million, with its value peaking that year at US$40 billion. By September 2022, the valuation of the company had leveled at US$26 billion. While Canva's value declined from its 2021 peak by mid-2022, it remained one of Australia's most prominent technology companies, alongside Atlassian. In March 2022, Canva had over 75 million monthly active users. In 2023, the pair were named in the Australian Financial Review's AFR Rich List as among the 10 most wealthy people in Australia. On 7 December 2022, Canva launched Magic Write, which is the platform's AI-powered copywriting assistant. On 22 March 2023, Canva announced its new Assistant tool, which makes recommendations on graphics and styles that match the user's existing design. On 11 January 2024, Canva launched its own GPT in OpenAI's GPT Store. The company has announced it intends to compete with Google and Microsoft in the office software category with website and whiteboard products. In May 2024, the company announced the launch of Canva Enterprise, a plan designed for large organisations, alongside new tools including Work Kits, Courses and AI capabilities. In 2024, it announced a co-funded solar energy project to enhance its sustainability efforts. On 10 April 2025, Canva released Visual Suite 2. The new interface combines Canva's design and productivity tools. New features include a spreadsheets application (Canva Sheets), a generative AI coding assistant (Canva Code), a chatbot, and an updated photo editor that can modify or remove background objects. In August 2025, Canva launched a stock sale to employees, valuing the company at US$42 billion. == Acquisitions == In 2018, the company acquired presentations startup Zeetings for an undisclosed amount, as part of its expansion into the presentations space. In May 2019, the company announced the acquisitions of Pixabay and Pexels, two free stock photography sites based in Germany, which enabled Canva users to access their photos for designs. In February 2021, Canva acquired Austrian startup Kaleido.ai and the Czech-based Smartmockups. In 2022, Canva acquired Flourish, a London-based data visualization startup. In March 2024, Canva acquired UK-based Serif, the developers of the Affinity suite of graphic design software, for approximately $380 million. In August 2024, Canva acquired the AI image generation platform and startup, Leonardo AI, for an undisclosed amount. In June 2025, it was announced that Canva had acquired Australian AI marketing startup MagicBrief for an undisclosed amount. In February 2026, Canva acquired two startups: Cavalry, which specializes in animation software, and MangoAI, which focuses on improving advertising performance. In April 2026, Canva acquired Simtheory, an AI Workflow Tool, and Ortto, a marketing automation tool. == Philanthropy == Canva's co-founders, Melanie Perkins and Cliff Obrecht, have publicly stated their intention to donate a significant portion of their personal wealth to charity. In 2021, Canva started a partnership with GiveDirectly, a nonprofit organization operating in low income areas that makes unconditional cash transfers to families living in extreme poverty. Since then, the company has donated $50 million to support GiveDirectly's work across Malawi. In 2025, Canva announced an additional $100 million commitment to expand its GiveDirectly partnership. == Controversies == === Data breach === In May 2019, Canva experienced a data breach in which the data of roughly 139 million users was exposed. The exposed data included real names of users, usernames, email addresses, geographical information, and password hashes for some users. In January 2020, approximately 4 million user passwords were decrypted and shared online. Canva responded by resetting the passwords of every user who had not changed their password since the initial breach. === Russian operations === In May 2022 Canva was criticized for continuing to provide free access to its services in Russia, even after suspending payment processing in the country. Activists from the Ukrainian diaspora in Australia and others said this could be viewed as indirectly supporting Russia’s war effort. They noted the company was the only one of several major Australian firms to receive the lowest “digging in” rating on a tracker run by the Yale School of Management for failing to pull out of Russia. Canva responded that it had suspended financial transactions in Russia from March 2022 and maintained the free version to allow the continued creation and sharing of “pro-peace and anti-war” content for its 1.4 million Russian users.
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Operational image

An operational image, also known as operative image, is an image that serves a functional, rather than aesthetic, purpose. Operational images are not intended to be viewed by people as representations of the real world; they are created to be used as instruments in performing some task or operation, often by machine automation. Operational images are used in a wide variety of applications, such as weapons targeting and guidance systems, and assisting surgeons performing robot-assisted surgery. The term "operational image" was first coined in 2000 by German filmmaker Harun Farocki in the first part of his three-part audiovisual installation, Eye/Machine. Farocki's installation included operational images used by militaries, such as weapons guidance and targeting systems. Eye/Machine featured images shown to the public by the United States military from the cameras used by laser-guided missiles in the Gulf War. Farocki defined operational images as "Images without a social goal, not for edification, not for reflection," and that they "do not represent an object, but rather are part of an operation." According to Volker Pantenburg, operational images are more accurately characterized as "visualizations of data". He describes operational images as a "working image" or an image that "performs work". Operational images are ubiquitous in modern society, used for a variety of military and non-military applications, such as inspecting sewer piping, and assisting surgeons performing robotic surgery.
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Companion robot

A companion robot is a robot created to create real or apparent companionship for human beings. Target markets for companion robots include the elderly and single children. Companions robots are expected to communicate with non-experts in a natural and intuitive way. They offer a variety of functions, such as monitoring the home remotely, communicating with people, or waking people up in the morning. Their aim is to perform a wide array of tasks including educational functions, home security, diary duties, entertainment and message delivery services, etc. The idea of companionship with robots has already existed on science fictions of 1970s, like R2-D2. Starting from the late 20th century, companion robots became a reality, mostly as robotic pets. Besides entertainment purposes, interactive robots were also introduced as a personal service robot for elderly care around 2000. == Characteristics == Companion robots try to interact with users. They gather information about users based on their interactions and yield feedback. This procedure varies slightly based on their specific roles. For example, social-companion robots make simple conversations, while pet-companion robots mimic being real pets. == Types == Companion robots can perform a variety of tasks and they are produced in a specialized manner according to their purpose or target audience in order to increase convenience and end user satisfaction. === Social companion robots === Social companion robots are designed to provide companionship and be a solution for unwanted solitude. They often mimic adult human, child or pet behaviours appealing to the user base. Robots which are specifically devised for simple conversations, conveying emotions and respond to user feelings fall under this category. === Assistive companion robots === Assistive companion robots are aimed at people who require constant care because of age, disability or rehabilitation purposes. Such robots can help disadvantaged users with their daily tasks, act as reminders (e.g., for regular medication) and facilitate mobility in everyday actions. Assistive companion robots reduce the intensity of labour that should be performed by caretakers, nurses and legal guardians. === Educational companion robots === Educational companion robots perform tutorship for students, regardless of their ages, and can teach desired subjects with activities tailored for the user such as interactive assignments and games. Rather than replacing teachers and instructors, educational companion robots are aides to them. === Therapeutic companion robots === Designed for individuals coping with stress (PTSD in severe cases), anxiety and loneliness; therapeutic companion robots support users' emotional and mental wellbeing. Such robots can be utilized in hospitals and care facilities as well as dwellings where the distressed user may need the most help. Therapeutic companion robots bear a vast resemblance to assistive companion robots to the extent of being a branch of them; the nuance between these two types of companion robots is that the former is for long-term/lifetime usage while the latter is mostly for the duration of the therapy received by the user. === Pet companion robots === Pet companion robots are for individuals who seek an alternative to live pets as live animals demand a considerable amount of care and may not be eligible for people with allergies. These robots aim to be perfect imitations of a pet while diminishing the chore aspect of having one. === Entertainment companion robots === Entertainment companion robots are designed solely for entertainment and can provide numerous ways of entertainment, ranging from dancing to playing games with the user. People who would appreciate an individual to have fun with are the main audience of such products. === Personal assistant robots === Personal assistant robots help people with daily tasks, management, scheduling, reminding etc. Their area of activity can be offices as well as homes and public spaces. === Sex robots === Sex robots are anthropomorphic robotic sex dolls that have human-like movement or behavior, and some degree of artificial intelligence. As of 2026, although elaborately instrumented sex dolls have been created by a number of inventors, no fully animated sex robots yet exist. Simple devices have been created which can speak, make facial expressions, or respond to touch. There is controversy as to whether developing them would be morally justifiable. In 2015, robot ethicist Kathleen Richardson called for a ban on the creation of anthropomorphic sex robots with concerns about normalizing relationships with machines and reinforcing female dehumanization. Questions about their ethics, effects, and possible legal regulations have been discussed since then. == Examples == There are several companion robot prototypes, and these include Paro, CompanionAble, and EmotiRob, among others. === Paro === Paro is a pet-type robot system developed by Japan's National Institute of Advanced Industrial Science and Technology (AIST). The robot, which looked like a small harp seal, was designed as a therapeutic tool for use in hospitals and nursing homes. The robot is programmed to cry for attention and respond to its name. Experiments showed that Paro facilitated elderly residents to communicate with each other, which led to psychological improvements. === CompanionAble === This robot is classified as an FP 7 EU project. It is built to "cooperate with Ambient Assistive Living environment". The autonomous device, which is also built to support the elderly, helps its owner interact with smart home environment as well as caregivers. The robot functions as a mobile friend, by which natural interaction is possible via speech and the touchscreen to detect and track people at home. === EmotiRob === EmotiRob is developed in a robotics project which is the continuity of the MAPH (Active Media For the Handicap) project in emotion synthesis. The aim of the project was to maintain emotional interaction with children. EmotiRob designed in a way that a child can hold it in a his/her arms and with which he/she could interact by talking to it, and then the robot would express itself through body postures or facial expressions. It has cognitive capabilities, which are further extended so that the robot can have a natural linguistic interaction with its owner through the DRAGON speech-recognition software developed by a company called NUANCE. Such interaction is expected to facilitate a child's cognitive development and develop new learning patterns. === LOVOT === Lovot is a Japanese company robot whose only purpose is "to make you happy". It features over 50 sensors that mimic the behavior of a human baby or small pet, a 360° camera with a microphone, the ability to distinguish humans from objects, neoteny eyes, and an internal warmth of 30° celsius. An interactive Lovot Café was opened in Japan October 3, 2020. === NICOBO === Nicobo was developed by Panasonic and was influenced by the loneliness of lockdowns created as a measure of the COVID-19 pandemic. It was designed to appear vulnerable, which creates empathy in its owners. Nicobo's name derives from the Japanese word for "smile". It wags its tail, engages in baby talk, and stays as a housemate. === Hyodol === Hyodol is an advanced care robot designed to support the elderly by reminding them to take their medications and monitoring their movements to keep their guardians informed. Additionally, this innovative robot can detect and respond to the emotional states of its elderly users, adding a layer of personalized care. Hyodol is designed with the appearance and speech style of a 7-year-old Korean grandchild, featuring a soft fabric exterior and user interaction methods such as striking the head or patting the back. It is equipped with various sensors and wireless communication technologies to collect and process data, supporting mobile apps and PC web monitoring systems for remote monitoring from anywhere. In South Korea, approximately 10,000 Hyodol robots are deployed to the homes of elderly individuals living alone, providing essential support and companionship. Local governments, including provincial and county offices, have embraced Hyodol as a solution to address social challenges stemming from the country's rapidly aging society.Furthermore, the robot is widely utilized in the treatment of dementia patients at a university hospital in Gangwon province. Hyodol was honored with the Mobile World Congress (MWC) Global Mobile Awards (GLOMO) in the "Best Mobile Innovation for Connected Health and Wellbeing" category on February 29, 2024. === Moxie === Moxie was a companion robot for autistic children developed by a company called Embodied. Although it had limited motion, it presented itself as a lifelike avatar. It was designed to help the children learn emotional cognition, using remotely hosted large language models to direct its respons
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VueScan

VueScan is a computer program for image scanning, especially of photographs, including negatives. It supports optical character recognition (OCR) of text documents. The software can be downloaded and used free of charge, but adds a watermark on scans until a license is purchased. == Purpose == VueScan is intended to work with a large number of image scanners, excluding specialised professional scanners such as drum scanners, on many computer operating systems (OS), even if drivers for the scanner are not available for the OS. These scanners are supplied with device drivers and software to operate them, included in their price. A 2014 review considered that the reasons to purchase VueScan are to allow older scanners not supported by drivers for newer operating systems to be used in more up-to-date systems and for better scanning and processing of photographs (prints; also slides and negatives when supported by scanners) than is afforded by manufacturers' software. The review did not report any advantages to VueScan's processing of documents over other software. The reviewer considered VueScan comparable to SilverFast, a similar program, with support for some specific scanners better in one or the other. Vuescan supports more scanners, with a single purchase giving access to the full range of both film and flatbed scanners, and costs less. The VueScan program can be used with its own drivers or with drivers supplied by the scanner manufacturer, if supported by the operating system. VueScan drivers can also be used without the VueScan program by application software that supports scanning directly, such as Adobe Photoshop, again enabling the use of scanners without current manufacturers' drivers. In 2019 when Apple released macOS Catalina, they removed support for running 32-bit programs, including 32-bit drivers for scanning equipment. In response, Hamrick released VueScan 9.7, effectively saving thousands of scanners from being rendered obsolete. == Overview == VueScan enables the user to modify and fine-tune the scanning parameters. The program uses its own independent method to interface with scanner hardware, and can support many older scanners under computer operating systems for which drivers are not available, allowing old scanners to be used with newer platforms that do not otherwise support them. VueScan supports an increasing number of scanners and digital cameras; 2,400 on Windows, 2,100 on Mac OS X and 1,900 on Linux in 2018. VueScan is supplied as one downloadable file for each operating system, which supports the full range of scanners. Without the purchase of a license, the program runs in fully functional demonstration mode, identical to Professional mode, except that watermarks are superimposed on saved and printed images. Purchase of a license removes the watermark. A standard license allows updates for one year; a professional license allows unlimited updates and provides some additional features. VueScan supports optical character recognition (OCR), with English included, and 32 additional language packages available on its website. In September 2011, VueScan co-developer Ed Hamrick said that he was selling US$3 million per year of VueScan licenses.
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BeeSafe

BeeSafe is a personal safety mobile app launched in 2015 as a Slovak startup. It is a location-based security service that notifies family members and friends in case the user of the app gets in danger. The app has received numerous awards. The app has more than 700 downloads and 250 active logins from more than 60 countries worldwide. == History == BeeSafe was founded on March 20, 2015 by Peter Stražovec and Michal Kačerík. The project was a winner of Žilina’s Startup Weekend 2013 and a StartupAwards.SK 2015 finalist. Later on, the app was released in the Android and iOS marketplace. The whole BeeSafe project was in The Spot booster and incubator in Bratislava for three months. BeeSafe entered into an agreement with the city of Piešťany in November 2015 to increase the security of its citizen by connecting the mobile app with the police platform. It is the first city that started using the BeeSafe platform. Further on, the application tries to help people in other Slovak cities. The cities can see the users only if they are in danger. == Awards == BeeSafe app received the Via Bona award, it is a winner of a Slovak startup and has other nominations too.
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Artificial intelligence of things

Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to create systems capable of sensing, learning, and acting on data without continuous human intervention. While IoT focuses on connectivity and sensor data collection, AI enables IoT devices to analyse data in real time and produce actionable outputs, including automated decisions at the edge. == Applications == === Manufacturing and predictive maintenance === Manufacturing accounts for the largest share of AIoT adoption by industry vertical. A common application is predictive maintenance, where sensors measuring vibration, temperature, current draw, and acoustic emissions feed machine learning models trained to detect signatures that precede equipment failure. These systems can flag developing faults weeks or months in advance, and in more advanced deployments can autonomously adjust machine parameters such as motor speed or cooling cycles to delay or prevent failure. === Other industries === In healthcare, AIoT enables remote patient monitoring through wearable devices that collect vital signs and apply AI models to detect anomalies or predict deterioration. In logistics, GPS and telematics sensors combined with AI models support real-time route optimisation, vehicle maintenance prediction, and fuel cost forecasting. Smart building systems use occupancy, temperature, and energy sensors with AI to dynamically adjust HVAC and lighting, reducing energy consumption. == Architecture == AIoT systems typically operate across three layers: a device layer of sensors and actuators that collect data, a connectivity layer that transmits data via protocols such as MQTT or HTTP, and a compute layer where AI models process the data either in the cloud or at the edge. The trend toward edge-based processing, where inference runs on low-cost processors near the data source rather than in a centralised cloud, has accelerated as hardware costs have fallen and applications increasingly require sub-second response times. == Market == Market sizing estimates for AIoT vary significantly depending on scope and definition. Fortune Business Insights valued the AIoT market at USD 35.65 billion in 2023, projecting growth to USD 253.86 billion by 2030 at a compound annual growth rate of 32.4%. Grand View Research estimated the broader market at USD 171.4 billion in 2024 with a CAGR of 31.7% through 2030, reflecting a wider definition that includes AI-integrated hardware components. North America accounted for approximately 40% of global market share in 2024, with the Asia-Pacific region projected as the fastest-growing market.
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PNGOUT

PNGOUT is a freeware command line optimizer for PNG images written by Ken Silverman. The transformation is lossless, meaning that the resulting image is visually identical to the source image. According to its author, this program can often get higher compression than other optimizers by 5–10%. It is possible to compress some inflated PNGs to a size below 1% of the original file. PNGOUT was also available as a plug-in for the freeware image viewer IrfanView and can be enabled as an option when saving files. It allows editing of various PNGOUT settings via a dialog box. PNGOUT integration was removed in IrfanView version 4.58 in favour of OptiPNG. In 2006, a commercial version of PNGOUT with a graphical user interface, known as PNGOUTWin, was released by Ardfry Imaging, a small company Silverman co-founded in 2005. There is also a freeware GUI frontend to PNGOUT available, known as PNGGauntlet. == Main operation == The main function of PNGOUT is to reduce the size of image data contained in the IDAT chunk. This chunk is compressed using the deflate algorithm. Deflate algorithms can vary in speed and compression ratio, with higher compression ratios generally implying lower speed. Ken Silverman wrote a deflate compressor for PNGOUT that is slower than the ones used in most graphics software, but produces smaller files. PNGOUT also performs automatic bit depth, color, and palette reduction where appropriate.
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Optical sorting

Optical sorting (sometimes called digital sorting) is the automated process of sorting solid products using cameras and/or lasers. Depending on the types of sensors used and the software-driven intelligence of the image processing system, optical sorters can recognize an object's color, size, shape, structural properties and chemical composition. The sorter compares objects to user-defined accept/reject criteria to identify and remove defective products and foreign material (FM) from the production line, or to separate product of different grades or types of materials. Optical sorters are in widespread use in the food industry worldwide, with the highest adoption in processing harvested foods such as potatoes, fruits, vegetables and nuts where it achieves non-destructive, 100 percent inspection in-line at full production volumes. The technology is also used in pharmaceutical manufacturing and nutraceutical manufacturing, tobacco processing, waste recycling and other industries. Compared to manual sorting, which is subjective and inconsistent, optical sorting helps improve product quality, maximize throughput and increase yields while reducing labor costs. == History == Optical sorting is an idea that first came out of the desire to automate industrial sorting of agricultural goods like fruits and vegetables. Before automated optical sorting technology was conceived in the 1930s, companies like Unitec were producing wooden machinery to assist in the mechanical sorting of fruit processing. In 1931, a company known as “the Electric Sorting Company” was incorporated and began the creation of the world’s first color sorters, which were being installed and used in Michigan’s bean industry by 1932. In 1937, optical sorting technology had advanced to allow for systems based on a two-color principle of selection. The next few decades saw the installation of new and improved sorting mechanisms, like gravity feed systems and the implementation of optical sorting in more agricultural industries. In the late 1960s, optical sorting began to be implemented to new industries beyond agriculture, like the sorting of ferrous and non-ferrous metals. By the 1990s, optical sorting was being used heavily in the sorting of solid wastes. With the large technological revolution happening in the late 1990s and early 2000s, optical sorters were being made more efficient via the implementation of new optical sensors, like CCD, UV, and IR cameras. Today, optical sorting is used in a wide variety of industries and, as such, is implemented with a varying selection of mechanisms to assist in that specific sorter’s task. == The sorting system == In general, optical sorters feature four major components: the feed system, the optical system, image processing software, and the separation system. The objective of the feed system is to spread products into a uniform monolayer so products are presented to the optical system evenly, without clumps, at a constant velocity. The optical system includes lights and sensors housed above and/or below the flow of the objects being inspected. The image processing system compares objects to user-defined accept/reject thresholds to classify objects and actuate the separation system. The separation system — usually compressed air for small products and mechanical devices for larger products, like whole potatoes — pinpoints objects while in-air and deflects the objects to remove into a reject chute while the good product continues along its normal trajectory. The ideal sorter to use depends on the application. Therefore, the product's characteristics and the user's objectives determine the ideal sensors, software-driven capabilities and mechanical platform. == Sensors == Optical sorters require a combination of lights and sensors to illuminate and capture images of the objects so the images can be processed. The processed images will determine if the material should be accepted or rejected. There are camera sorters, laser sorters and sorters that feature a combination of the two on one platform. Lights, cameras, lasers and laser sensors can be designed to function within visible light wavelengths as well as the infrared (IR) and ultraviolet (UV) spectrums. The optimal wavelengths for each application maximize the contrast between the objects to be separated. Cameras and laser sensors can differ in spatial resolution, with higher resolutions enabling the sorter to detect and remove smaller defects. === Cameras === Monochromatic cameras detect shades of gray from black to white and can be effective when sorting products with high-contrast defects. Sophisticated color cameras with high color resolution are capable of detecting millions of colors to better distinguish more subtle color defects. Trichromatic color cameras (also called three-channel cameras) divide light into three bands, which can include red, green and/or blue within the visible spectrum as well as IR and UV. The interaction of different materials with parts of the electromagnetic spectrum make these contrasts more evident than how they appear to the naked human eye. Coupled with intelligent software, sorters that feature cameras are capable of recognizing each object's color, size and shape; as well as the color, size, shape and location of a defect on a product. Some intelligent sorters even allow the user to define a defective product based on the total defective surface area of any given object. === Lasers === While cameras capture product information based primarily on material reflectance, lasers and their sensors are able to distinguish a material's structural properties along with their color. This structural property inspection allows lasers to detect a wide range of organic and inorganic foreign material such as insects, glass, metal, sticks, rocks and plastic; even if they are the same color as the good product. Lasers can be designed to operate within specific wavelengths of light; whether on the visible spectrum or beyond. For example, lasers can detect chlorophyll by stimulating fluorescence using specific wavelengths; which is a process that is very effective for removing foreign material from green vegetables. === Camera/laser combinations === Sorters equipped with cameras and lasers on one platform are generally capable of identifying the widest variety of attributes. Cameras are often better at recognizing color, size and shape while laser sensors identify differences in structural properties to maximize foreign material detection and removal. === Hyperspectral Imaging === Driven by the need to solve previously impossible sorting challenges, a new generation of sorters that feature multispectral and hyperspectral imaging Optical Sorters. Like trichromatic cameras, multispectral and hyperspectral cameras collect data from the electromagnetic spectrum. Unlike trichromatic cameras, which divide light into three bands, hyperspectral systems can divide light into hundreds of narrow bands over a continuous range that covers a vast portion of the electromagnetic spectrum. This opens the door for more detailed analysis that leads to a more consistent product. Using IR alone might detect some defects, but combining it with a broader range of the spectrum makes it more effective. Compared to the three data points per pixel collected by trichromatic cameras, hyperspectral cameras can collect hundreds of data points per pixel, which are combined to create a unique spectral signature (also called a fingerprint) for each object. When complemented by capable software intelligence, a hyperspectral sorter processes those fingerprints to enable sorting on the chemical composition of the product. This is an emerging area of chemometrics. == Software-driven intelligence == Once the sensors capture the object's response to the energy source, image processing is used to manipulate the raw data. The image processing extracts and categorizes information about specific features. The user then defines accept/reject thresholds that are used to determine what is good and bad in the raw data flow. The art and science of image processing lies in developing algorithms that maximize the effectiveness of the sorter while presenting a simple user-interface to the operator. Object-based recognition is a classic example of software-driven intelligence. It allows the user to define a defective product based on where a defect lies on the product and/or the total defective surface area of an object. It offers more control in defining a wider range of defective products. When used to control the sorter's ejection system, it can improve the accuracy of ejecting defective products. This improves product quality and increases yields. New software-driven capabilities are constantly being developed to address the specific needs of various applications. As computing hardware becomes more powerful, new software-driven advancements become possible. Some of these advancements enhance the effectivene
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Straight-Through Quality

Straight-Through Quality (STQ) are approaches and outputs of test automation that have quality and deliver business benefit. STQ takes its name from the business concept of straight-through processing (STP). Also acting as a tool and enabler for STP. Traditional techniques for testing and delivery have often required a great deal of manual support and intervention. These approaches are subject to human error, cost of delay and lack of reuse. These also have the negative side-effect of being unable to deliver 'fail-fast' approaches, which have proven popular with Agile practitioners. Previous traditional approaches have been typically expensive where whole silo'ed departments are created within commercial companies to deliver Quality and Deployment alone. Thus STQ as an approach hopes to resolve this problem. == Examples == Tangible examples of STQ approaches in the software industry are present and often known as continuous integration (CI) and continuous delivery (CD). These combined can ensure that software delivery is integrated, automatically tested and ready for automatic delivery at any time. Together CI/CD can enable STQ which can be used as Business output terminology for business users who do not understand the technical complexities of CI/CD.
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Confused deputy problem

In information security, a confused deputy is a computer program that is tricked by another program (with fewer privileges or less rights) into misusing its authority on the system. It is a specific type of privilege escalation. The confused deputy problem is often cited as an example of why capability-based security is important. Capability systems protect against the confused deputy problem, whereas access-control list–based systems do not. Such systems can mitigate the confused deputy problem by eliminating ambient authority, allowing programs to act only on resources for which they hold explicit capabilities, whereas access-control list–based systems are more susceptible to it. However, this protection depends on correct implementation; in formally verified capability systems such as seL4, it can be shown that the kernel enforces capability constraints correctly, preventing such behavior at the system level. == Example == In the original example of a confused deputy, there was a compiler program provided on a commercial timesharing service. Users could run the compiler and optionally specify a filename where it would write debugging output, and the compiler would be able to write to that file if the user had permission to write there. The compiler also collected statistics about language feature usage. Those statistics were stored in a file called "(SYSX)STAT", in the directory "SYSX". To make this possible, the compiler program was given permission to write to files in SYSX. But there were other files in SYSX: in particular, the system's billing information was stored in a file "(SYSX)BILL". A user ran the compiler and named "(SYSX)BILL" as the desired debugging output file. This produced a confused deputy problem. The compiler made a request to the operating system to open (SYSX)BILL. Even though the user did not have access to that file, the compiler did, so the open succeeded. The compiler wrote the compilation output to the file (here "(SYSX)BILL") as normal, overwriting it, and the billing information was destroyed. === The confused deputy === In this example, the compiler program is the deputy because it is acting at the request of the user. The program is seen as 'confused' because it was tricked into overwriting the system's billing file. Whenever a program tries to access a file, the operating system needs to know two things: which file the program is asking for, and whether the program has permission to access the file. In the example, the file is designated by its name, “(SYSX)BILL”. The program receives the file name from the user, but does not know whether the user had permission to write the file. When the program opens the file, the system uses the program's permission, not the user's. When the file name was passed from the user to the program, the permission did not go along with it; the permission was increased by the system silently and automatically. It is not essential to the attack that the billing file be designated by a name represented as a string. The essential points are that: the designator for the file does not carry the full authority needed to access the file; the program's own permission to access the file is used implicitly. == Other examples == A cross-site request forgery (CSRF) is an example of a confused deputy attack that uses the web browser to perform sensitive actions against a web application. A common form of this attack occurs when a web application uses a cookie to authenticate all requests transmitted by a browser. Using JavaScript, an attacker can force a browser into transmitting authenticated HTTP requests. The Samy computer worm used cross-site scripting (XSS) to turn the browser's authenticated MySpace session into a confused deputy. Using XSS the worm forced the browser into posting an executable copy of the worm as a MySpace message which was then viewed and executed by friends of the infected user. Clickjacking is an attack where the user acts as the confused deputy. In this attack a user thinks they are harmlessly browsing a website (an attacker-controlled website) but they are in fact tricked into performing sensitive actions on another website. An FTP bounce attack can allow an attacker to connect indirectly to TCP ports to which the attacker's machine has no access, using a remote FTP server as the confused deputy. Another example relates to personal firewall software. It can restrict Internet access for specific applications. Some applications circumvent this by starting a browser with instructions to access a specific URL. The browser has authority to open a network connection, even though the application does not. Firewall software can attempt to address this by prompting the user in cases where one program starts another which then accesses the network. However, the user frequently does not have sufficient information to determine whether such an access is legitimate—false positives are common, and there is a substantial risk that even sophisticated users will become habituated to clicking "OK" to these prompts. Not every program that misuses authority is a confused deputy. Sometimes misuse of authority is simply a result of a program error. The confused deputy problem occurs when the designation of an object is passed from one program to another, and the associated permission changes unintentionally, without any explicit action by either party. It is insidious because neither party did anything explicit to change the authority. Another example is when an administrator authorizes an AI agent to act on their behalf, and that AI subsequently delegates authority to another AI agent neither vetted nor authorized by the original administrator. The unvetted AI can then act without permissions or oversight from the original developer. == Solutions == In some systems it is possible to ask the operating system to open a file using the permissions of another client. This solution has some drawbacks: It requires explicit attention to security by the server. A naive or careless server might not take this extra step. It becomes more difficult to identify the correct permission if the server is in turn the client of another service and wants to pass along access to the file. It requires the client to trust the server to not abuse the borrowed permissions. Note that intersecting the server and client's permissions does not solve the problem either, because the server may then have to be given very wide permissions (all of the time, rather than those needed for a given request) in order to act for arbitrary clients. The simplest way to solve the confused deputy problem is to bundle together the designation of an object and the permission to access that object. This is exactly what a capability is. Using capability security in the compiler example, the client would pass to the server a capability to the output file, such as a file descriptor, rather than the name of the file. Since it lacks a capability to the billing file, it cannot designate that file for output. In the cross-site request forgery example, a URL supplied "cross"-site would include its own authority independent of that of the client of the web browser.
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Microsoft Fresh Paint

Fresh Paint is a painting app developed by Microsoft and released on May 25, 2012. == History == Fresh Paint originated from a Microsoft Research project known as Project Gustav, an endeavor to reproduce the behavior of physical oil paint on a digital medium. To push the boundaries of simulating oil on a digital medium, the research team created a physics model that precisely replicated on a screen what would happen in the real world if you combined oil, a surface and a tool such as a paint brush. Two publications, Detail-Preserving Paint Modeling for 3D Brushes and Simple Data-Driven Modeling of Brushes, were released as a result of the team’s findings. After a variety of internal testing Project, Gustav was codenamed Digital Art. Partnering with The Museum of Modern Art, Digital Art was tested for a year by 60,000 people. With feedback culled from MoMA, developers expanded the existing physics model, experimenting with how real oil paint blended and reacted to the texture of a canvas. After final adjustments were made, Digital Art was rebranded as Fresh Paint. It was released to the public on 25 May 2012.
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Manual override

A manual override (MO) or manual analog override (MAO) is a mechanism where control is taken from an automated system and given to the user. For example, a manual override in photography refers to the ability for the human photographer to turn off the automatic aperture sizing, automatic focusing, or any other automated system on the camera. Some manual overrides can be used to veto an automated system's judgment when the system is in error. An example of this is a printer's ink level detection: in one case, a researcher found that when he overrode the system, up to 38% more pages could be printed at good quality by the printer than the automated system would have allowed. Automated systems are becoming increasingly common and integrated into everyday objects such as automobiles and domestic appliances. This development of ubiquitous computing raises general issues of policy and law about the need for manual overrides for matters of great importance such as life-threatening situations and major economic decisions. The loyalty of such autonomous devices then becomes an issue. If they follow rules installed by the manufacturer or required by law and refuse to cede control in some situations then the owners of the devices may feel disempowered, alienated and lacking true ownership. == Major incidents == China Airlines Flight 140 crashed, causing many deaths, due to a misunderstanding about the manual overrides for the autopilot. The Take-Off/Go Around system had been activated to abort a landing. It was programmed to ignore manual controls in this situation but the human pilots tried to continue the landing. The conflicting control signals from the pilots and autopilot then resulted in the aircraft stalling and crashing. The autopilot for this aircraft type was then reprogrammed so that it would never ignore a manual override.
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