AI Developer Tools

AI Developer Tools — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Digital video effect

Digital video effects (DVEs) are visual effects that provide comprehensive live video image manipulation, in the same form as optical printer effects in film. DVEs differ from standard video switcher effects (often referred to as analog effects) such as wipes or dissolves, in that they deal primarily with resizing, distortion or movement of the image. Modern video switchers often contain internal DVE functionality. Modern DVE devices are incorporated in high-end broadcast video switchers. Early examples of DVE devices found in the broadcast post-production industry include the Ampex Digital Optics (ADO), Quantel DPE-5000, Vital Squeezoom, NEC E-Flex and the Abekas A5x series of DVEs. By 1988, Grass Valley Group caught up with the competition with their Kaleidoscope, which integrated ADO-type effects with their widely used line of broadcast switching gear. DVEs are used by the broadcast television industry in live television production environments like television studios and outside broadcasts. They are commonly used in video post-production.
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OntoWiki

OntoWiki was a free and open-source semantic wiki application, meant to serve as an ontology editor and a knowledge acquisition system. It is a web-based application written in PHP and using either a MySQL database or a Virtuoso triple store. OntoWiki is form-based rather than syntax-based, and thus tries to hide as much of the complexity of knowledge representation formalisms from users as possible. OntoWiki is mainly being developed by the Agile Knowledge Engineering and Semantic Web (AKSW) research group at the University of Leipzig, a group also known for the DBpedia project among others, in collaboration with volunteers around the world. In 2009 the AKSW research group got a budget of €425,000 from the Federal Ministry of Education and Research of Germany for the development of the OntoWiki. In 2010 OntoWiki became part of the technology stack supporting the LOD2 (linked open data) project. Leipzig University is one of the consortium members of the project, which is funded by a €6.5m EU grant. The development ended in 2016 due to the lack of capacity migrating from PHP 5 to 7 including the required Zend Framework from version 1 to 2.
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IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence (sometimes abbreviated as IEEE PAMI or simply PAMI) is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. == Background == The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition. The editor-in-chief is Kyoung Mu Lee (Seoul National University). According to the Journal Citation Reports, the journal has a 2023 impact factor of 20.8.
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ProVisual Engine

The ProVisual Engine is an AI-powered imaging system developed by Samsung Electronics for mobile devices. It was introduced in 2024 with the Galaxy S24 series as a component of Samsung's Galaxy AI ecosystem, providing advanced image processing to enhance image quality in photography and videography. == Overview == The ProVisual Engine processes images using adaptive scene recognition, real-time optimization, and advanced image processing. It adjusts color accuracy, dynamic range, and noise levels, providing both automated and manual controls to accommodate various user preferences. == Features == The ProVisual Engine encompasses several features. === Quad Tele System === The Quad Tele System features 2x, 3x, 5x, and 10x optical zoom, supported by digital processing to enhance zoom clarity and detail. It incorporates Image Signal Processing (ISP) to refine detail retention, reduce noise, and enhance image clarity at different zoom levels while minimizing distortion. === Nightography === Nightography utilizes noise reduction techniques and advanced sensor technology to enhance low-light photography. By adjusting exposure and minimizing motion blur, the system helps produce more precise and more detailed images in dark environments for both photos and videos. === Generative Edit === Generative Edit allows for object removal, background expansion, and intelligent resizing. It reconstructs missing areas by filling backgrounds and completing cut-off objects, adjusting composition while preserving image integrity and refinement. === Expert RAW === Expert RAW allows users to capture RAW images directly from the camera app for advanced shooting and editing. It includes HDR (High Dynamic Range) support to enhance detail and dynamic range. The ProVisual Engine utilizes multi-frame processing to generate RAW images with increased clarity and depth for post-processing. === Enhance-X and Camera Shift === Enhance-X is an AI-based image processing tool that applies upscaling, noise reduction, and sharpening. Its Camera Shift feature adjusts the perceived camera height by modifying framing and proportions. A recent update extended support to human and pet images. == Compatible devices == As of 2025, the ProVisual Engine is available on the following devices: === Galaxy S series === Galaxy S26 Series (Galaxy S26, S26+. S26 Ultra) Galaxy S25 Series (Galaxy S25, S25+, S25 Edge, S25 Ultra, S25 FE) Galaxy S24 Series (Galaxy S24, S24+, S24 Ultra) === Galaxy Z series === Galaxy Z Fold 7 Galaxy Z Flip 7, Z Flip 7 FE Galaxy Z Fold 6 Galaxy Z Flip 6 === Galaxy Tab S series === Galaxy Tab S10 series (Tab S10+, Tab S10 Ultra) Galaxy Tab S9 series (Tab S9, Tab S9+, Tab S9 Ultra) === Galaxy Z series === Galaxy Z Fold 7, Z Flip 7, Z Flip 7 FE Galaxy Z Fold 6, Z Flip 6 === Galaxy Tab S series === Galaxy Tab S10 series (Tab S10+, Tab S10 Ultra) Galaxy Tab S9 series (Tab S9, Tab S9+, Tab S9 Ultra) Note: Quad Tele System refers to the multi-telephoto setup (2×, 3×, 5×, 10×) available only on the Ultra models (S24 Ultra and S25 Ultra). Note: On Galaxy Tab models, only Enhance-X editing features are supported; the Expert RAW camera app is not available.
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Cone tracing

Cone tracing and beam tracing are a derivative of the ray tracing algorithm that replaces rays, which have no thickness, with thick rays. == Principles == In ray tracing, rays are often modeled as geometric ray with no thickness to perform efficient geometric queries such as a ray-triangle intersection. From a physics of light transport point of view, however, this is an inaccurate model provided the pixel on the sensor plane has non-zero area. In the simplified pinhole camera optics model, the energy reaching the pixel comes from the integral of radiance from the solid angle by which the sensor pixel sees the scene through the pinhole at the focal plane. This yields the key notion of pixel footprint on surfaces or in the texture space, which is the back projection of the pixel on to the scene. Note that this approach can also represent a lens-based camera and thus depth of field effects, using a cone whose cross-section decreases from the lens size to zero at the focal plane, and then increases. Real optical system do not focus on exact points because of diffraction and imperfections. This can be modeled with a point spread function (PSF) weighted within a solid angle larger than the pixel. From a signal processing point of view, ignoring the point spread function and approximating the integral of radiance with a single, central sample (through a ray with no thickness) can lead to strong aliasing because the "projected geometric signal" has very high frequencies exceeding the Nyquist-Shannon maximal frequency that can be represented using the uniform pixel sampling rate. The physically based image formation model can be approximated by the convolution with the point spread function assuming the function is shift-invariant and linear. In practice, techniques such as multisample anti-aliasing estimate this cone-based model by oversampling the signal and then performing a convolution (the reconstruction filter). The backprojected cone footprint onto the scene can also be used to directly pre-filter the geometry and textures of the scene. Note that contrary to intuition, the reconstruction filter should not be the pixel footprint (as the pinhole camera model would suggest), since a box filter has poor spectral properties. Conversely, the ideal sinc function is not practical, having infinite support with possibly negative values which often creates ringing artifacts due to the Gibbs phenomenon. A Gaussian or a Lanczos filter are considered good compromises. == Computer graphics models == Cone and Beam early papers rely on different simplifications: the first considers a circular section and treats the intersection with various possible shapes. The second treats an accurate pyramidal beam through the pixel and along a complex path, but it only works for polyhedrical shapes. Cone tracing solves certain problems related to sampling and aliasing, which can plague conventional ray tracing. However, cone tracing creates a host of problems of its own. For example, just intersecting a cone with scene geometry leads to an enormous variety of possible results. For this reason, cone tracing has remained mostly unpopular. In recent years, increases in computer speed have made Monte Carlo algorithms like distributed ray tracing - i.e. stochastic explicit integration of the pixel - much more used than cone tracing because the results are exact provided enough samples are used. But the convergence is so slow that even in the context of off-line rendering a huge amount of time can be required to avoid noise. Differential cone-tracing, considering a differential angular neighborhood around a ray, avoids the complexity of exact geometry intersection but requires a LOD representation of the geometry and appearance of the objects. MIPmapping is an approximation of it limited to the integration of the surface texture within a cone footprint. Differential ray-tracing extends it to textured surfaces viewed through complex paths of cones reflected or refracted by curved surfaces. Raymarching methods over signed distance fields (SDFs) naturally allow easy use of cone-like tracing, at zero additional cost to the tracing, and both speeds up tracing and improves quality. Voxel cone tracing is a real-time algorithm that uses a hierarchical voxel representation of scene geometry, such as a sparse voxel octree, to support fast cone tracing for indirect illumination. This approach allows for the approximation of effects like glossy reflections and ambient occlusion at interactive framerates without the need for precomputation.
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Social History and Industrial Classification

Social History and Industrial Classification (SHIC) is a classification system used by many British museums for social history and industrial collections. It was first published in 1983. == Purpose == SHIC classifies materials (books, objects, recordings etc.) by their interaction with the people who used them. For example, a carpenter's hammer is classified with other tools of the carpenter, and not with a blacksmith's hammer. In contrast other classification systems, for example the Dewey Decimal Classification, might class all hammers together and close to the classification for other percussive tools. The specialist subject network, Social History Curator's Group (SHCG), obtained funding in 2012 to develop an on-line version, now on their website http://www.shcg.org.uk/ == Scheme == Materials are classified under four major category numbers: Community life Domestic and family life Personal life Working life Further classification within a category is by the use of further numbers after the decimal point. It is permissible to assign more than one classification in cases where the object had more than one use.
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Attribute–value system

An attribute–value system is a basic knowledge representation framework comprising a table with columns designating "attributes" (also known as "properties", "predicates", "features", "dimensions", "characteristics", "fields", "headers" or "independent variables" depending on the context) and "rows" designating "objects" (also known as "entities", "instances", "exemplars", "elements", "records" or "dependent variables"). Each table cell therefore designates the value (also known as "state") of a particular attribute of a particular object. == Example of attribute–value system == Below is a sample attribute–value system. It represents 10 objects (rows) and five features (columns). In this example, the table contains only integer values. In general, an attribute–value system may contain any kind of data, numeric or otherwise. An attribute–value system is distinguished from a simple "feature list" representation in that each feature in an attribute–value system may possess a range of values (e.g., feature P1 below, which has domain of {0,1,2}), rather than simply being present or absent (Barsalou & Hale 1993). == Other terms used for "attribute–value system" == Attribute–value systems are pervasive throughout many different literatures, and have been discussed under many different names: Flat data Spreadsheet Attribute–value system (Ziarko & Shan 1996) Information system (Pawlak 1981) Classification system (Ziarko 1998) Knowledge representation system (Wong & Ziarko 1986) Information table (Yao & Yao 2002)
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VoID

The Vocabulary of Interlinked Datasets (VoID) is a vocabulary for providing concise summaries (metadata) of Resource Description Framework (RDF) datasets—meaningful collections of semantic triples—using the syntax of RDF Schema. It can be used for general metadata (such as information about the license of the dataset), access metadata (information about how to access the dataset), structural metadata (information about how the dataset is structured), and linking metadata (information about links between datasets). A linked dataset is a collection of data, published and maintained by a single provider, available as RDF on the Web, where at least some of the resources in the dataset are identified by dereferencable Uniform Resource Identifiers (URIs). VoID is used to provide metadata on RDF datasets to facilitate query processing on a graph of interlinked datasets in the Semantic Web.
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Cuboid (computer vision)

In computer vision, the term cuboid is used to describe a small spatiotemporal volume extracted for purposes of behavior recognition. The cuboid is regarded as a basic geometric primitive type and is used to depict three-dimensional objects within a three dimensional representation of a flat, two dimensional image. == Production == Cuboids can be produced from both two-dimensional and three-dimensional images. One method used to produce cuboids utilizes scene understanding (SUN) primitive databases, which are collections of pictures that already contain cuboids. By sorting through SUN primitive databases with machine learning tools, computers observe the conditions in which cuboids are produced in images from SUN primitive databases and can learn to produce cuboids from other images. RGB-D images, which are RGB images that also record the depth of each pixel, are occasionally used to produce cuboids because computers no longer need to determine the depth of an object, as they typically do because depth is already recorded. Cuboid production is sensitive to changes in color and illumination, blockage, and background clutter. This means that it is difficult for computers to produce cuboids of objects that are multicolored, irregularly illuminated, or partially covered, or if there are many objects in the background. This is partially due to the fact that algorithms for producing cuboids are still relatively simple. == Usage == Cuboids are created for point cloud-based three-dimensional maps and can be utilized in various situations such as augmented reality, the automated control of cars, drones, and robots, and object detection. Cuboids allow for software to identify a scene through geometric descriptions in an “object-agnostic” fashion. Interest points, locations within images that are identified by a computer as essential to identifying the image, created from two-dimensional images can be used with cuboids for image matching, identifying a room or scene, and instance recognition. Interest points created from three dimensional images can be used with cuboids to recognize activities. This is possible because interest points aid software to focus on only the most important aspects of the images. RGB-D images and SLAM systems are used together in RGB-D SLAM systems, which are employed by Computer-aided design systems to generate point cloud-based three-dimensional maps. Most industrial multi-axis machining tools use computer-aided manufacturing and subsequently work in cuboid work spaces.
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Social History and Industrial Classification

Social History and Industrial Classification (SHIC) is a classification system used by many British museums for social history and industrial collections. It was first published in 1983. == Purpose == SHIC classifies materials (books, objects, recordings etc.) by their interaction with the people who used them. For example, a carpenter's hammer is classified with other tools of the carpenter, and not with a blacksmith's hammer. In contrast other classification systems, for example the Dewey Decimal Classification, might class all hammers together and close to the classification for other percussive tools. The specialist subject network, Social History Curator's Group (SHCG), obtained funding in 2012 to develop an on-line version, now on their website http://www.shcg.org.uk/ == Scheme == Materials are classified under four major category numbers: Community life Domestic and family life Personal life Working life Further classification within a category is by the use of further numbers after the decimal point. It is permissible to assign more than one classification in cases where the object had more than one use.
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Artificial intelligence safety institute

An artificial intelligence safety institute is a type of state-backed organization aiming to evaluate and ensure the safety of advanced artificial intelligence (AI) models, also called frontier AI models. AI safety gained prominence in 2023, notably with public declarations about potential existential risks from AI. During the AI Safety Summit in November 2023, the United Kingdom and the United States both created their own AISI. During the AI Seoul Summit in May 2024, international leaders agreed to form a network of AI Safety Institutes, comprising institutes from the UK, the US, Japan, France, Germany, Italy, Singapore, South Korea, Australia, Canada and the European Union. In 2025, the UK's AI Safety Institute was renamed the "AI Security Institute", and its US counterpart became the Center for AI Standards and Innovation (CAISI). == Timeline == In 2023, Rishi Sunak, the Prime Minister of the United Kingdom, expressed his intention to "make the UK not just the intellectual home but the geographical home of global AI safety regulation" and unveiled plans for an AI Safety Summit. He emphasized the need for independent safety evaluations, stating that AI companies cannot "mark their own homework". During the summit in November 2023, the UK AISI was officially established as an evolution of the Frontier AI Taskforce, and the US AISI as part of the National Institute of Standards and Technology. Japan followed by launching an AI safety institute in February 2024. Politico reported in April 2024 that many AI companies had not shared pre-deployment access to their most advanced AI models for evaluation. Meta's president of global affairs Nick Clegg said that many AI companies were waiting for the UK and the US AI Safety Institutes to work out common evaluation rules and procedures. An agreement was indeed concluded between the UK and the US in April 2024 to collaborate on at least one joint safety test. Initially established in London, the UK AI Safety Institute announced in May 2024 that it would open an office in San Francisco, where many AI companies are located. This is part of a plan to "set new, international standards on AI safety", according to UK's technology minister Michele Donelan. == International network == At the AI Seoul Summit in May 2024, the European Union and other countries agreed to create their own AI safety institutes, forming an international network. In July 2025, the international network held an exercise to explore issues with evaluating AI agents, especially when it came to leaking sensitive information or cybersecurity. Network members also met at NeurIPS 2025 in the city of San Diego. == Specific institutes == === Australia === The Albanese government announced the creation of the Australian AI Safety Institute on 25 November 2025. === Canada === Canada announced in April 2024 that it would create an AI safety institute, and such an institute was officially founded in November 2024. The institute is housed under Innovation, Science and Economic Development Canada, though it also partners with the Canadian Institute for Advanced Research (CIFAR). It is supported by a budget of CA$50,000,000 for a five-year timespan. === European Union === The EU AI office, founded in May 2024, is a member of the international network of AI safety institutes. === France === On 31 January 2025, the government of France created the Institut national pour l'évaluation et la sécurité de l'intelligence artificielle (INESIA), or the National Institute for AI Evaluation and Security. === India === The Ministry of Electronics and Information Technology held consultations with Meta Platforms, Google, Microsoft, IBM, OpenAI, NASSCOM, Broadband India Forum, Software Alliance, Indian Institutes of Technology (IITs), The Quantum Hub, Digital Empowerment Foundation, and Access Now on October 7, 2024, in relation to the establishment of the AI Safety Institute. The decision was made to shift focus from regulation to standards-setting, risk identification, and damage detection—all of which require interoperable technologies. The AISI may spend the ₹20 crore allotted to the Safe and Trusted Pillar of the IndiaAI Mission for the initial budget. Future funding may come from other components of the IndiaAI Mission. UNESCO and MeitY began consulting on AI Readiness Assessment Methodology under Safety and Ethics in Artificial Intelligence from 2024. It is to encourage the ethical and responsible use of AI in industries. The study will find areas where government can become involved, especially in attempts to strengthen institutional and regulatory capabilities. Minister for Electronics & Information Technology Ashwini Vaishnaw announced the creation of an IndiaAI Safety Institute on January 30, 2025, to ensure the ethical and safe application of AI models. The institute will promote domestic R&D that is grounded in India's social, economic, cultural, and linguistic diversity and is based on Indian datasets. With the help of academic and research institutions, as well as private sector partners, the institute will follow the hub-and-spoke approach to carry out projects within Safe and Trusted Pillar of the IndiaAI Mission. It operates under a "hub-and-spoke" model with collaboration from academic institutions (e.g., IITs), tech firms, and international organizations like UNESCO. === Japan === The Japan AISI (or J-AISI) was founded in February 2024. Part of the Information Technology Promotion Agency, it employs about 23 people. The institute consists of the Council of AISI, the AISI Steering Committee, and a secretariat with six teams. Akiko Murakami (previously of IBM Japan and Sompo Japan) serves as the institute's executive director, and Kenji Hiramoto and Suguru Nishimura serve as the institute's two deputy executive directors. === Kenya === Kenya agreed to join the international network of AI safety institutes, but the country has not announced any details yet. It is the only African state in the network. === Singapore === The Digital Trust Centre was initially founded in June 2022. In May 2024, it was renamed to the Singapore AISI. Part of Nanyang Technological University, the institute partners with Infocomm Media Development Authority and is supported by an investment of S$10,000,000 per year. === South Korea === South Korea announced in May 2024 that it would create an AI safety institute under the umbrella of the Electronics and Telecommunications Research Institute. It will be supported by a tentative investment of somewhere between 10 and 20 million South Korean won per year, and employ at least 30 people. The institute was founded in November 2024 and is based in Bundang District within the city of Seongnam. === United Kingdom === The United Kingdom founded in April 2023 a safety organisation called Frontier AI Taskforce, with an initial budget of £100 million. In November 2023, it evolved into the AI Safety Institute, and continued to be led by Ian Hogarth. The AISI is part of the United Kingdom's Department for Science, Innovation and Technology. The United Kingdom's AI strategy aims to balance safety and innovation. Unlike the European Union which adopted the AI Act, the UK is reluctant to legislate early, considering that it may lower the sector's growth, and that laws might be rendered obsolete by technological progress. In May 2024, the institute open-sourced an AI safety tool called "Inspect", which evaluates AI model capabilities such as reasoning and their degree of autonomy. In February 2025, the UK body was renamed the AI Security Institute. Observers saw the name change as a signal that the institute will not focus on ethical issues such as algorithmic bias or freedom of speech in AI applications. === United States === The US AISI was founded in November 2023 as part of the National Institute of Standards and Technology (NIST). This happened the day after the signature of the Executive Order 14110. In February 2024, Joe Biden's former economic policy adviser Elizabeth Kelly was appointed to lead it. In February 2024, the US government created the US AI Safety Institute Consortium (AISIC), regrouping more than 200 organizations such as Google, Anthropic or Microsoft. In March 2024, a budget of $10 million was allocated. Observers noted that this investment is relatively small, especially considering the presence of many big AI companies in the US. The NIST itself, which hosts the AISI, is also known for its chronic lack of funding. Biden administration's request for additional funding was met with further budget cuts from congressional appropriators. Under President Trump, plans for members of the agency to attend the February 2025 AI Action Summit in Paris were scrapped. The US and the UK refused to sign the summit's final communique. US Vice President JD Vance said "pro-growth AI policies" should be prioritised over safety. The name of the agency was changed in June 2025 to the Center for AI Standards and Innovation
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Marco Camisani Calzolari

Marco Camisani Calzolari (born March 1969) is an Italian British university professor, author, and television personality specializing in digital communications, transformation, and artificial intelligence. He advises the Italian government and police on ethical AI and digital safety and hosts the digital segment of the Italian news show Striscia la Notizia. His research gained international attention in 2012 after creating an algorithm claiming to identify real Twitter users from fake users of bots. Marco Camisani Calzolari was awarded as an Honorary Police Officer by the Italian State Police and the Knight of the Italian Republic. == Biography == Camisani Calzolari was born in Milan, Italy where he began his television career, hosting on local provider LA7 in (2001). In 2008 Camisani Calzolari moved to the UK where he founded multiple digital start-ups. He is now a naturalised British citizen and applied to become a "Freeman of the City" in June 2022. In 2024, Marco Camisani Calzolari began serving as the Chair and Adjunct Professor of the elective course Cyber-Humanities within the Degree Programme in Medicine and Surgery at Università Vita-Salute S.Raffaele in Milan. On the 14th of May 2024, Camisani Calzolari was awarded the Knight of the Italian Republic (Order of the Star of Italy). In 2024, Marco Camisani Calzolari was awarded the title of Honorary Police Officer by the Italian State Police for his commitment to combating cybercrime and promoting digital security. He also received the Keynes Sraffa Award 2024 from the Italian Chamber of Commerce and Industry for the UK. Additionally, he was honored with the University Seal by Università degli Studi della Tuscia (Viterbo) for his efforts in disseminating knowledge both in Italy and abroad. == Academic career == Camisani Calzolari began his academic career at the Università Statale di Milano in 2007, until chairing a course on Corporate Communication and Digital Languages at the IULM University of Milan between 2007 and 2010. During this time Camisani Calzolari published his first written work under the title 'Impresa 4.0'. After moving to London, Camisani Calzolari focussed on digital start-ups including 'Digitalevaluation ltd' where he would publish the results of his Twitter algorithm study. Following its publication, he accepted a role as Affiliate Practitioner at the Centre for Culture Media & Regulation (CCMR), University of Brunel London, and subsequently another role at a British University as Lecturer in Digital Communication at the LCA Business School. Camisani Calzolari returned to Italy to lecture on Interactive Digital Communication at the University of Milan. From 2017 to 2023, he held various roles at the European University of Rome, including Adjunct Professor and Chair in Digital Communication, and published The Fake News Bible in 2018. In 2024 he became the Scientific Coordinator for a Master's program at Università San Raffaele in Milan. === Twitter fake followers study === In 2012, Camisani Calzolari's research came into the focus of the public eye following the publication of his findings in a study analysing the followers of high-profile public figures and corporations. He developed a computer algorithm claiming to be able to distinguish real followers from computer-generated "bots". The algorithm compiled data correlative of human activity such as having a name, image, physical address, using punctuation and cross-account activity. Genuine Twitter users were considered to have written at least 50 posts and possessed over 30 followers themselves. The findings led to scrutiny of several individuals and corporations for allegedly purchasing followers. === Publications === Camisani Calzolari is best for known for his work in improving accessibility to digital and tech solutions for everyday business and personal use. His work in digital and communications has been included in several publications including: Cyberhumanism (2023) The Fake News Bible (2018), First Digital Aid for Business (2015), The Digital World (2013), Escape from Facebook (2012), Enterprise 4.0. Camisani Calzolari was also the subject of a University College London (UCL) case study titled Marco Camisani-Calzolari: the Digital Renaissance Man. == Government work == Since 2023, he is a member of the Coordination Committee on Artificial Intelligence at the Presidency of the Council of Ministers and an advisor in Digital Skills and Designer of initiatives for the Department for Digital Transformation. He also serves as the official spokesperson for the State Police, educating the public on preventing digital threats, avoiding digital scams, and explaining criminal case. Since August 2024, Marco Camisani Calzolari has served as an expert for the Italian Agency for the National Cybersecurity (ACN). In October of the same year, he also became a member of the General-Purpose AI Code of Practice working group for the European Commission. == Television work == Camisani Calzolari hosts a digital segment for Striscia la Notizia, an Italian satirical television program on the Mediaset-controlled Canale 5. He presented on weekly segments that include: RAI 1 – Digital First Aid (TV Program – 2014 to 2017) in the program "Uno Mattina" as a digital expert; RTL 102.5 – Technology Space (Radio Program – 2012 to 2017) in the morning news program as a digital expert (100 episodes from 2012 to 2017); DIGITALK Talkshow (2004) as host of Digitalk; Misterweb (TV Program – 2001 to 2002), he presented the TV program “MisterWeb”, on "LA7". Marco Camisani Calzolari was a testimonial for several institutional communication campaigns by the Italian Department of Digital Transformation. These include initiatives promoting the Punti Digitale Facile, raising awareness about the NIS2 Directive for cybersecurity, and advocating for the adoption of the Electronic Identity Card (CIE).
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Infogram

Infogram is a web-based data visualization and infographics platform, created in Riga, Latvia. It allows people to make and share digital charts, infographics and maps. Infogram offers an intuitive WYSIWYG editor that converts users’ data into infographics that can be published, embedded or shared. Users do not need coding skills to use this tool; users include newsrooms, marketing teams, governments, educators and students. The company that created Infogram, also called Infogram, was founded in 2012 in Riga, Latvia and has another office in San Francisco. As of October 2017, Infogram says it has 3 million users who have created charts and infographics that have been viewed more than 1.5 billion times. Infogram was bought by Prezi, a web-based presentation software company, in May 2017. == History == Infogram was founded in February 2012 in Riga, Latvia by Uldis Leiterts, Raimonds Kaže and Alise Dīrika. In January 2013, Infogram won the international Hy Berlin pitch contest. During his pitch, Infogram CEO Uldis Leiterts announced that the company had created more templates and was working with Microsoft to integrate its platform with the contemporaneous version of Microsoft Office. The company also won the 2013 Kantar Information Is Beautiful Award, which “celebrates excellence and beauty in data visualizations, infographics, interactives & information art.” In December 2014, Infogram acquired the Brazil-based data visualization blog, Visualoop. In an effort to expand sales and marketing in the U.S., Infogram secured $1.8 million in funding in February 2014. The announcement was made at TechChill, a startup conference for the Baltics in Riga, Latvia. At the time, the funding was believed to be the largest to date for the company. Infogram won the 2017 National Design Award of Latvia. == Acquisition by Prezi == Prezi, a web-based presentation software company, acquired Infogram in May 2017. Infogram is now a wholly owned subsidiary of Prezi. Infogram was rated #1 on Forbes’ list of “The Best Infographic Tools for 2017,” which was published in September 2017. In October 2017, Infogram announced a new version of its data visualization platform, including a drag-and-drop editor, over 40 new designer templates and social media support.
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Pretext

A pretext (adj.: pretextual) is an excuse to do something or say something that is not accurate. Pretexts may be based on a half-truth or developed in the context of a misleading fabrication. Pretexts have been used to conceal the true purpose or rationale behind actions and words. They are often heard in political speeches. In US law, a pretext usually describes false reasons that hide the true intentions or motivations for a legal action. If a party can establish a prima facie case for the proffered evidence, the opposing party must prove that these reasons were "pretextual" or false. This can be accomplished by directly demonstrating that the motivations behind the presentation of evidence is false, or indirectly by evidence that the motivations are not "credible". In Griffith v. Schnitzer, an employment discrimination case, a jury award was reversed by a Court of Appeals because the evidence was not sufficient that the defendant's reasons were "pretextual". That is, the defendant's evidence was either undisputed, or the plaintiff's was "irrelevant subjective assessments and opinions". A "pretextual" arrest by law enforcement officers is one carried out for illegal purposes such as to conduct an unjustified search and seizure. As one example of pretext, in the 1880s, the Chinese government raised money on the pretext of modernizing the Chinese navy. Instead, these funds were diverted to repair a ship-shaped, two-story pavilion which had been originally constructed for the mother of the Qianlong Emperor. This pretext and the Marble Barge are famously linked with Empress Dowager Cixi. This architectural folly, known today as the Marble Boat (Shifang), is "moored" on Lake Kunming in what the empress renamed the "Garden for Cultivating Harmony" (Yiheyuan). Another example of pretext was demonstrated in the speeches of the Roman orator Cato the Elder (234–149 BC). For Cato, every public speech became a pretext for a comment about Carthage. The Roman statesman had come to believe that the prosperity of ancient Carthage represented an eventual and inevitable danger to Rome. In the Senate, Cato famously ended every speech by proclaiming his opinion that Carthage had to be destroyed (Carthago delenda est). This oft-repeated phrase was the ultimate conclusion of all logical argument in every oration, regardless of the subject of the speech. This pattern persisted until his death in 149, which was the year in which the Third Punic War began. In other words, any subject became a pretext for reminding his fellow senators of the dangers Carthage represented. == Uses in warfare == The early years of Japan's Tokugawa shogunate were unsettled, with warring factions battling for power. The causes for the fighting were in part pretextual, but the outcome brought diminished armed conflicts after the Siege of Osaka in 1614–1615. The next two-and-a-half centuries of Japanese history were comparatively peaceful under the successors of Tokugawa Ieyasu and the bakufu government he established. === United States === During the War of 1812, US President James Madison was often accused of using impressment of American sailors by the Royal Navy as a pretext to invade Canada. The sinking of the USS Maine in 1898 was blamed on the Spanish, despite early reports of it having been an accident, contributing to U.S. entry into the Spanish–American War. The slogan "Remember the Maine! To hell with Spain!" was used as a rallying cry. Some have argued that United States President Franklin D. Roosevelt used the attack on Pearl Harbor by Japanese forces on December 7, 1941, as a pretext to enter World War II. American soldiers and supplies had been assisting British and Soviet operations for almost a year by this point, and the United States had thus "chosen a side", but due to the political climate in the States at the time and some campaign promises made by Roosevelt that he would not send American troops to fight in foreign wars, Roosevelt could not declare war for fear of public backlash. The attack on Pearl Harbor united the American people's resolve against the Axis powers and created the bellicose atmosphere in which to declare war. The 1964 Gulf of Tonkin incident, later revealed to have been partly provoked and partly not to have happened, was used to bring the United States fully into the Vietnam War. United States President George W. Bush used the September 11 attacks and faulty intelligence about the existence of weapons of mass destruction as a pretext for the war in Iraq. == Social engineering == A type of social engineering called pretexting uses a pretext to elicit information fraudulently from a target. The pretext in this case includes research into the identity of a certain authorized person or personality type in order to establish legitimacy in the mind of the target.
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Ishikawa diagram

Ishikawa diagrams (also called fishbone diagrams, herringbone diagrams, cause-and-effect diagrams) are causal diagrams created by Kaoru Ishikawa that show the potential causes of a specific event. Common uses of the Ishikawa diagram are product design and quality defect prevention to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify and classify these sources of variation. == Overview == The defect, or the problem to be solved, is shown as the fish's head, facing to the right, with the causes extending to the left as fishbones; the ribs branch off the backbone for major causes, with sub-branches for root-causes, to as many levels as required. Ishikawa diagrams were popularized in the 1960s by Kaoru Ishikawa, who pioneered quality management processes in the Kawasaki shipyards, and in the process became one of the founding fathers of modern management. The basic concept was first used in the 1920s, and is considered one of the seven basic tools of quality control. It is known as a fishbone diagram because of its shape, similar to the side view of a fish skeleton. Mazda Motors famously used an Ishikawa diagram in the development of the Miata (MX5) sports car. == Root causes == Root-cause analysis is intended to reveal key relationships among various variables, and the possible causes provide additional insight into process behavior. It shows high-level causes that lead to the problem encountered by providing a snapshot of the current situation. There can be confusion about the relationships between problems, causes, symptoms and effects. Smith highlights this and the common question “Is that a problem or a symptom?” which mistakenly presumes that problems and symptoms are mutually exclusive categories. A problem is a situation that bears improvement; a symptom is the effect of a cause: a situation can be both a problem and a symptom. At a practical level, a cause is whatever is responsible for, or explains, an effect - a factor "whose presence makes a critical difference to the occurrence of an outcome". The causes emerge by analysis, often through brainstorming sessions, and are grouped into categories on the main branches off the fishbone. To help structure the approach, the categories are often selected from one of the common models shown below, but may emerge as something unique to the application in a specific case. Each potential cause is traced back to find the root cause, often using the 5 Whys technique. Typical categories include: === The 5 Ms (used in manufacturing) === Originating with lean manufacturing and the Toyota Production System, the 5 Ms is one of the most common frameworks for root-cause analysis: Manpower / Mindpower (physical or knowledge work, includes: kaizens, suggestions) Machine (equipment, technology) Material (includes raw material, consumables, and information) Method (process) Measurement / medium (inspection, environment) These have been expanded by some to include an additional three, and are referred to as the 8 Ms: Mission / mother nature (purpose, environment) Management / money power (leadership) Maintenance === The 8 Ps (used in product marketing) === This common model for identifying crucial attributes for planning in product marketing is often also used in root-cause analysis as categories for the Ishikawa diagram: Product (or service) Price Place Promotion People (personnel) Process Physical evidence (proof) Performance === The 4 or 5 Ss (used in service industries) === An alternative used for service industries, uses four categories of possible cause: Surroundings: Refers to the environment in which the process occurs. Suppliers: Refers to external parties that provide inputs—raw materials, components, or services. Systems: Refers to the procedures, processes, and technologies used to perform the work. Skill: Refers to the human factor, particularly the knowledge and abilities of employees. Safety: Refers to physical and psychological well-being in the workplace. == Use in specific industries == The Ishikawa diagram has been widely adopted across various industries as an effective tool for root cause analysis in quality, efficiency, and safety-related issues. Its versatility allows it to be applied in both manufacturing and service contexts. In the manufacturing industry, particularly in the automotive and electronics sectors, the diagram is frequently used in continuous improvement initiatives such as Six Sigma and Lean Manufacturing. Quality teams use it to identify causes related to materials, methods, machinery, manpower, environment, and measurement, facilitating informed decision-making to reduce defects and optimize processes. In the food industry, the Ishikawa diagram is applied to analyze issues related to food safety, temperature control, cross-contamination, and regulatory compliance. Its use enables companies to identify improvement opportunities in production, packaging, and distribution stages. In the pharmaceutical sector, it is a key tool in process validation, quality control, and compliance with Good Manufacturing Practices (GMP). It helps visualize factors affecting product quality from formulation to storage. It has also been successfully implemented in sectors such as aerospace, pulp and paper, construction, education, and healthcare, where it supports structured problem-solving and promotes continuous improvement and a culture of quality.
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