Rapid application development (RAD), also called rapid application building (RAB), is both a general term for adaptive software development approaches, and the name for James Martin's method of rapid development. In general, RAD approaches to software development put less emphasis on planning and more emphasis on an adaptive process. Prototypes are often used in addition to or sometimes even instead of design specifications. RAD is especially well suited for (although not limited to) developing software that is driven by user interface requirements. Graphical user interface builders are often called rapid application development tools. Other approaches to rapid development include the adaptive, agile, spiral, and unified models. == History == Rapid application development was a response to plan-driven waterfall processes, developed in the 1970s and 1980s, such as the Structured Systems Analysis and Design Method (SSADM). One of the problems with these methods is that they were based on a traditional engineering model used to design and build things like bridges and buildings. Software is an inherently different kind of artifact. Software can change the process used to solve a problem. As a result, knowledge gained from the development process itself can feed back to the requirements and design of the solution. Plan-driven approaches attempt to define requirements, the solution, and the implementation plan, and have a process that discourages changes. RAD approaches, on the other hand, recognize that software development is a knowledge intensive process and provide flexible processes that help take advantage of knowledge gained during the project to improve or adapt the solution. The first such RAD alternative was developed by Barry Boehm and was known as the spiral model. Boehm and other subsequent RAD approaches emphasized developing prototypes as well as or instead of rigorous design specifications. Prototypes had several advantages over traditional specifications: Risk reduction. A prototype could test some of the most difficult potential parts of the system early on in the life-cycle. This can provide valuable information as to the feasibility of a design and can prevent the team from pursuing solutions that turn out to be too complex or time-consuming to implement. This benefit of finding problems earlier in the life-cycle rather than later was a key benefit of the RAD approach. The earlier a problem can be found the cheaper it is to address. Users are better at using and reacting than at creating specifications. In the waterfall model it was common for a user to sign off on a set of requirements but then when presented with an implemented system to suddenly realize that a given design lacked some critical features or was too complex. In general most users give much more useful feedback when they can experience a prototype of the running system rather than abstractly define what that system should be. Prototypes can be usable and can evolve into the completed product. One approach used in some RAD methods was to build the system as a series of prototypes that evolve from minimal functionality to moderately useful to the final completed system. The advantage of this besides the two advantages above was that the users could get useful business functionality much earlier in the process. Starting with the ideas of Barry Boehm and others, James Martin developed the rapid application development approach during the 1980s at IBM and finally formalized it by publishing a book in 1991, Rapid Application Development. This has resulted in some confusion over the term RAD even among IT professionals. It is important to distinguish between RAD as a general alternative to the waterfall model and RAD as the specific method created by Martin. The Martin method was tailored toward knowledge intensive and UI intensive business systems. These ideas were further developed and improved upon by RAD pioneers like James Kerr and Richard Hunter, who together wrote the seminal book on the subject, Inside RAD, which followed the journey of a RAD project manager as he drove and refined the RAD Methodology in real-time on an actual RAD project. These practitioners, and those like them, helped RAD gain popularity as an alternative to traditional systems project life cycle approaches. The RAD approach also matured during the period of peak interest in business re-engineering. The idea of business process re-engineering was to radically rethink core business processes such as sales and customer support with the new capabilities of Information Technology in mind. RAD was often an essential part of larger business re engineering programs. The rapid prototyping approach of RAD was a key tool to help users and analysts "think out of the box" about innovative ways that technology might radically reinvent a core business process. Much of James Martin's comfort with RAD stemmed from Dupont's Information Engineering division and its leader Scott Schultz and their respective relationships with John Underwood who headed up a bespoke RAD development company that pioneered many successful RAD projects in Australia and Hong Kong. Successful projects that included ANZ Bank, Lendlease, BHP, Coca-Cola Amatil, Alcan, Hong Kong Jockey Club and numerous others. Success that led to both Scott Shultz and James Martin both spending time in Australia with John Underwood to understand the methods and details of why Australia was disproportionately successful in implementing significant mission critical RAD projects. == James Martin approach == The James Martin approach to RAD divides the process into four distinct phases: Requirements planning phase – combines elements of the system planning and systems analysis phases of the systems development life cycle (SDLC). Users, managers, and IT staff members discuss and agree on business needs, project scope, constraints, and system requirements. It ends when the team agrees on the key issues and obtains management authorization to continue. User design phase – during this phase, users interact with systems analysts and develop models and prototypes that represent all system processes, inputs, and outputs. The RAD groups or subgroups typically use a combination of joint application design (JAD) techniques and CASE tools to translate user needs into working models. User design is a continuous interactive process that allows users to understand, modify, and eventually approve a working model of the system that meets their needs. Construction phase – focuses on program and application development task similar to the SDLC. In RAD, however, users continue to participate and can still suggest changes or improvements as actual screens or reports are developed. Its tasks are programming and application development, coding, unit-integration and system testing. Cutover phase – resembles the final tasks in the SDLC implementation phase, including data conversion, testing, changeover to the new system, and user training. Compared with traditional methods, the entire process is compressed. As a result, the new system is built, delivered, and placed in operation much sooner. == Advantages == In modern Information Technology environments, many systems are now built using some degree of Rapid Application Development (not necessarily the James Martin approach). In addition to Martin's method, agile methods and the Rational Unified Process are often used for RAD development. The purported advantages of RAD include: Better quality. By having users interact with evolving prototypes the business functionality from a RAD project can often be much higher than that achieved via a waterfall model. The software can be more usable and has a better chance to focus on business problems that are critical to end users rather than technical problems of interest to developers. However, this excludes other categories of what are usually known as Non-functional requirements (AKA constraints or quality attributes) including security and portability. Risk control. Although much of the literature on RAD focuses on speed and user involvement a critical feature of RAD done correctly is risk mitigation. It's worth remembering that Boehm initially characterized the spiral model as a risk based approach. A RAD approach can focus in early on the key risk factors and adjust to them based on empirical evidence collected in the early part of the process. E.g., the complexity of prototyping some of the most complex parts of the system. More projects completed on time and within budget. By focusing on the development of incremental units the chances for catastrophic failures that have dogged large waterfall projects is reduced. In the Waterfall model it was common to come to a realization after six months or more of analysis and development that required a radical rethinking of the entire system. With RAD this kind of information can be discovered and acted upon earlier in the proces
List of 3D rendering software
3D rendering software products are the dedicated engines used for rendering computer-generated imagery. This is not the same as 3D modeling software, which involves the creation of 3D models, for which the software listed below can produce realistically rendered visualisations.General-purpose packages which can have their own built-in rendering capabilities are not listed here; these can be found in the list of 3D computer graphics software and list of 3D animation software. See 3D computer graphics software for more discussion about the distinctions.
Yann LeCun
Yann André Le Cun ( lə-KUN; French: [ləkœ̃]; usually spelled LeCun; born 8 July 1960) is a French-American computer scientist working in the fields of artificial intelligence, machine learning, computer vision, robotics and image compression. He is the Jacob T. Schwartz Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. He served as Chief AI Scientist at Meta Platforms before co-founding Advanced Machine Intelligence Labs in December 2025. He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs). He is also one of the main creators of the DjVu image compression technology, alongside Léon Bottou and Patrick Haffner. He co-developed the Lush programming language with Léon Bottou. In 2018, LeCun, Yoshua Bengio, and Geoffrey Hinton received the Turing Award from the Association for Computing Machinery (ACM) for their work on deep learning. LeCun, Bengio, and Hinton, and occasionally Jürgen Schmidhuber, are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning". == Early life and education == Yann André Le Cun was born on 8 July 1960 at Soisy-sous-Montmorency, in the suburbs of Paris. His surname, Le Cun, derives from the old Breton form Le Cunff and originates from the region of Guingamp in northern Brittany. Yann is the Breton form of Jean, the French form of John. He received a Diplôme d'Ingénieur from the ESIEE Paris in 1983 and a PhD in computer science from Université Pierre et Marie Curie (now Sorbonne University) in 1987, during which he proposed an early form of backpropagation, an algorithm crucial for enabling neural networks to learn. Before joining AT&T, LeCun was a postdoctoral researcher for a year, starting in 1987, supervised by Geoffrey Hinton at the University of Toronto. LeCun has three sons, and his brother is employed by Google. He has American citizenship. == Career and research == LeCun's career has been spent primarily at Bell Labs, New York University and Meta Platforms, Inc. === Bell Labs === In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel, where he developed a number of new machine learning methods, such as a biologically inspired model of image recognition called convolutional neural networks (LeNet), the "Optimal Brain Damage" regularization methods, and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and Optical character recognition (OCR). The bank check recognition system that he helped develop was widely deployed by NCR and other companies. In 1996, he joined AT&T Labs-Research as head of the Image Processing Research Department, which was part of Lawrence Rabiner's Speech and Image Processing Research Lab, and worked primarily on the DjVu image compression technology, a format designed for efficient distribution of scanned documents, and used by the Internet Archive to provide access to digitized texts. His collaborators at AT&T include Léon Bottou and Vladimir Vapnik. === New York University === After a brief tenure as a fellow of NEC Research Institute, LeCun joined New York University in 2003, where he is Jacob T. Schwartz Chaired Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. At NYU, he has worked primarily on energy-based models for supervised and unsupervised learning, feature learning for object recognition in computer vision, and mobile robotics. In 2012, he became the founding director of the NYU Center for Data Science. On 9 December 2013, LeCun became the first director of Meta AI Research in New York City and in early 2014 stepped down from the NYU–CDS directorship. In 2013, he and Yoshua Bengio co-founded the International Conference on Learning Representations, which adopted a post-publication open review process he previously advocated on his website. He was the chair and organiser of the "Learning Workshop" held every year between 1986 and 2012 in Snowbird, Utah. He is a member of the Science Advisory Board of the Institute for Pure and Applied Mathematics at UCLA. He is the co-director of the Learning in Machines and Brain research program (formerly Neural Computation & Adaptive Perception) of CIFAR. In 2016, he was the visiting professor of computer science on the Chaire Annuelle Informatique et Sciences Numériques at Collège de France in Paris, where he presented the leçon inaugurale (inaugural lecture). In 2023, he was named as the inaugural Jacob T. Schwartz Chaired Professor in Computer Science at NYU's Courant Institute. LeCun is also a scientific advisor to French research group Kyutai which is being funded by Xavier Niel, Rodolphe Saadé, Eric Schmidt, and others. === Meta Platforms === LeCun joined Facebook (now Meta Platforms) in 2013 as chief AI scientist and led the company's AI research laboratory, FAIR. === AMI Labs === On 19 November 2025, LeCun confirmed that he would be leaving Meta after ten years to found his own company focused on world-model architectures and human-like artificial intelligence he calls superintelligence. The company he founded, Advanced Machine Intelligence Labs (or AMI Labs), is run by CEO Alex LeBrun, with LeCun serving as Executive Chair. This venture is focused on building AI "world models": systems that learn to understand the physical world's structure and dynamics rather than just predict text like large language models. In March 2026, AMI announced it had raised $1.03 billion in funding at a $3.5 billion pre-money valuation. The funding round was co-led by investors including Cathay Innovation, Greycroft, Hiro Capital, HV Capital and Bezos Expeditions. In January 2026, LeCun became founding chair of the Technical Research Board of Logical Intelligence, an AI company developing energy-based (EBM) reasoning systems. == Honours and awards == LeCun is a member of the US National Academy of Sciences, National Academy of Engineering and the French Académie des Sciences. He has received honorary doctorates from Instituto Politécnico Nacional (IPN) in Mexico City in 2016, from EPFL in 2018, from Université Côte d'Azur in 2021, from Università di Siena in 2023, and from Hong Kong University of Science and Technology in 2023. In 2014, he received the IEEE Neural Network Pioneer Award and in 2015, the PAMI Distinguished Researcher Award. In 2018, LeCun was awarded the IRI Medal, established by the Industrial Research Institute (IRI), and the Harold Pender Award, given by the University of Pennsylvania. In 2019, he received the Golden Plate Award of the American Academy of Achievement. In March 2019, LeCun won the 2018 Turing Award, sharing it with Yoshua Bengio and Geoffrey Hinton. In 2022, he received the Princess of Asturias Award in the category "Scientific Research", along with Yoshua Bengio, Geoffrey Hinton and Demis Hassabis. In 2023, the President of France made him a Chevalier (Knight) of the French Legion of Honour. During the World Economic Forum (WEF) 2024 in Davos, he received the Global Swiss AI Award 2023. The same year, he received the grand prize of the VinFuture Prize alongside Yoshua Bengio, Jensen Huang, Geoffrey Hinton, and Fei-Fei Li for their groundbreaking contributions to neural networks and deep learning algorithms. In 2025 he was awarded the Queen Elizabeth Prize for Engineering jointly with Yoshua Bengio, Bill Dally, Geoffrey E. Hinton, John Hopfield, Jensen Huang and Fei-Fei Li.
UMBEL
UMBEL (Upper Mapping and Binding Exchange Layer) is a logically organized knowledge graph of 34,000 concepts and entity types that can be used in information science for relating information from disparate sources to one another. It was retired at the end of 2019. UMBEL was first released in July 2008. Version 1.00 was released in February 2011. Its current release is version 1.50. The grounding of this information occurs by common reference to the permanent URIs for the UMBEL concepts; the connections within the UMBEL upper ontology enable concepts from sources at different levels of abstraction or specificity to be logically related. Since UMBEL is an open-source extract of the OpenCyc knowledge base, it can also take advantage of the reasoning capabilities within Cyc. UMBEL has two means to promote the semantic interoperability of information:. It is: An ontology of about 35,000 reference concepts, designed to provide common mapping points for relating different ontologies or schema to one another, and A vocabulary for aiding that ontology mapping, including expressions of likelihood relationships distinct from exact identity or equivalence. This vocabulary is also designed for interoperable domain ontologies. UMBEL is written in the Semantic Web languages of SKOS and OWL 2. It is a class structure used in Linked Data, along with OpenCyc, YAGO, and the DBpedia ontology. Besides data integration, UMBEL has been used to aid concept search, concept definitions, query ranking, ontology integration, and ontology consistency checking. It has also been used to build large ontologies and for online question answering systems. Including OpenCyc, UMBEL has about 65,000 formal mappings to DBpedia, PROTON, GeoNames, and schema.org, and provides linkages to more than 2 million Wikipedia pages (English version). All of its reference concepts and mappings are organized under a hierarchy of 31 different "super types", which are mostly disjoint from one another. Each of these "super types" has its own typology of entity classes to provide flexible tie-ins for external content. 90% of UMBEL is contained in these entity classes.
Project Joshua Blue
Joshua Blue is a project under development by IBM that focuses on advancing the artificial intelligence field by designing and programming computers to emulate human mental functions. == Goals == According to researchers at IBM's Thomas J. Watson Research Center, the main goal of Joshua Blue is "to achieve cognitive flexibility that approaches human functioning". In short, IBM is aiming to design Joshua Blue to 'think like a human', mainly in terms of emotional thought. == How it will work == A model of Joshua Blue's learning pattern has been created. Similar to how young children learn human traits through interacting with their surroundings, Joshua Blue will acquire knowledge through external stimuli present in its environment. IBM believes that if computers evolve to learn in this way and then comprehend and analyze the knowledge gained using reason, computers could begin to possess a "mind", of sorts, capable of demonstrating complex social behaviors similar to those of humans. Thus far, IBM has revealed that Joshua Blue will be a computer with a network of wires and input nodes that function as a computer nervous system. This nervous system will be used by Joshua Blue to perceive affect or personal emotional feelings. Not only will this network of input nodes help Joshua Blue discover things physically, but it will also allow Joshua Blue to interpret the significance of events. The input nodes, or proprioceptors, will enable Joshua Blue to be aware of things that happen around itself, as well as recognize and attach meaning to the emotional effect produced by interacting with an object in a certain way. In addition, Joshua Blue's proprioceptors will function as pain and pleasure sensors, allowing Joshua Blue to employ a similar "reward and punishment" system that humans use to form behaviors.
Language Server Protocol
The Language Server Protocol (LSP) is an open, JSON-RPC-based protocol for use between source-code editors or integrated development environments (IDEs) and servers that provide "language intelligence tools": programming language-specific features like code completion, syntax highlighting and marking of warnings and errors, as well as refactoring routines. The goal of the protocol is to allow programming language support to be implemented and distributed independently of any given editor or IDE. In the early 2020s, LSP quickly became a "norm" for language intelligence tools providers. == History == LSP was originally developed for Microsoft Visual Studio Code and is now an open standard. On June 27, 2016, Microsoft announced a collaboration with Red Hat and Codenvy to standardize the protocol's specification. Its specification is hosted and developed on GitHub. == Background == Modern IDEs provide programmers with sophisticated features like code completion, refactoring, navigating to a symbol's definition, syntax highlighting, and error and warning markers. For example, in a text-based programming language, a programmer might want to rename a method read. The programmer could either manually edit the respective source code files and change the appropriate occurrences of the old method name into the new name, or instead use an IDE's refactoring capabilities to make all the necessary changes automatically. To be able to support this style of refactoring, an IDE needs a sophisticated understanding of the programming language that the program's source is written in. A programming tool without such an understanding—for example, one that performs a naive search-and-replace instead—could introduce errors. When renaming a read method, for example, the tool should not replace the partial match in a variable that might be called readyState, nor should it replace the portion of a code comment containing the word "already". Neither should renaming a local variable read, for example, end up altering identically-named variables in other scopes. Conventional compilers or interpreters for a specific programming language are typically unable to provide these language services, because they are written with the goal of either transforming the source code into object code or immediately executing the code. Additionally, language services must be able to handle source code that is not well-formed, e.g. because the programmer is in the middle of editing and has not yet finished typing a statement, procedure, or other construct. Additionally, small changes to a source code file which are done during typing usually change the semantics of the program. In order to provide instant feedback to the user, the editing tool must be able to very quickly evaluate the syntactical and semantical consequences of a specific modification. Compilers and interpreters therefore provide a poor candidate for producing the information needed for an editing tool to consume. Prior to the design and implementation of the Language Server Protocol for the development of Visual Studio Code, most language services were generally tied to a given IDE or other editor. In the absence of the Language Server Protocol, language services are typically implemented by using a tool-specific extension API. Providing the same language service to another editing tool requires effort to adapt the existing code so that the service may target the second editor's extension interfaces. The Language Server Protocol allows for decoupling language services from the editor so that the services may be contained within a general-purpose language server. Any editor can inherit sophisticated support for many different languages by making use of existing language servers. Similarly, a programmer involved with the development of a new programming language can make services for that language available to existing editing tools. Making use of language servers via the Language Server Protocol thus also reduces the burden on vendors of editing tools, because vendors do not need to develop language services of their own for the languages the vendor intends to support, as long as the language servers have already been implemented. The Language Server Protocol also enables the distribution and development of servers contributed by an interested third party, such as end users, without additional involvement by either the vendor of the compiler for the programming language in use or the vendor of the editor to which the language support is being added. LSP is not restricted to programming languages. It can be used for any kind of text-based language, like specifications or domain-specific languages (DSL). == Technical overview == When a user edits one or more source code files using a language server protocol-enabled tool, the tool acts as a client that consumes the language services provided by a language server. The tool may be a text editor or IDE and the language services could be refactoring, code completion, etc. The client informs the server about what the user is doing, e.g., opening a file or inserting a character at a specific text position. The client can also request the server to perform a language service, e.g. to format a specified range in the text document. The server answers a client's request with an appropriate response. For example, the formatting request is answered either by a response that transfers the formatted text to the client or by an error response containing details about the error. The Language Server Protocol defines the messages to be exchanged between client and language server. They are JSON-RPC preceded by headers similar to HTTP. Messages may originate from the server or client. The protocol does not make any provisions about how requests, responses and notifications are transferred between client and server. For example, client and server could be components within the same process exchanging JSON strings via method calls. They could also be different processes on the same or on different machines communicating via network sockets. == Registry == There are lists of LSP-compatible implementations, maintained by the community-driven Langserver.org or Microsoft.
Thomas Bolander
Thomas Bolander is a Danish professor at DTU Compute, Technical University of Denmark, where he studies logic and artificial intelligence. Most of his studies focus on the social aspect of artificial intelligence, and how we can make future AI able to navigate in social interactions. Thomas Bolander also sits in different commissions, expert panels and boards, among these he is a member of the Siri Commission, the TeckDK Commission, a member of the editorial board of the journal Studia Logica and co-organizer of Science and Cocktails. Bolander is known for his dissemination of science. In 2019 he was awarded the H. C. Ørsted Medal. Which he was the first to achieve after a break of three years.