AI Code Vulnerability Scanner

AI Code Vulnerability Scanner — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Networked Help Desk

Networked Help Desk is an open standard initiative to provide a common API for sharing customer support tickets between separate instances of issue tracking, bug tracking, customer relationship management (CRM) and project management systems to improve customer service and reduce vendor lock-in. The initiative was created by Zendesk in June 2011 in collaboration with eight other founding member organizations including Atlassian, New Relic, OTRS, Pivotal Tracker, ServiceNow and SugarCRM. The first integration, between Zendesk and Atlassian's issue tracking product, Jira, was announced at the 2011 Atlassian Summit. By August 2011, 34 member companies had joined the initiative. A year after launching, over 50 organizations had joined. Within Zendesk instances this feature is branded as ticket sharing. == Basis == Support tools are generally built around a common paradigm that begins with a customer making a request or an incident report, these create a ticket. Each ticket has a progress status and is updated with annotations and attachments. These annotations and attachments may be visible to the customer (public), or only visible to analysts (private). Customers are notified of progress made on their ticket until it is complete. If the people necessary to complete a ticket are using separate support tools, additional overhead is introduced in maintaining the relevant information in the ticket in each tool while notifying the customer of progress made by each group in completing their ticket. For example, if a customer support issue is caused by a software bug and reported to a help desk using one system, and then the fix is documented by the developers in another, and analyzed in a customer relationship management tool, keeping the records in each system up-to-date and notifying the customer manually using a swivel chair approach is unnecessarily time-consuming and error-prone. If information is not transferred correctly, a customer may have to re-explain their problem each time their ticket is transferred. For systems with the Networked Help Desk API implemented, it is possible for several different applications related to a customer's support experience to synchronize data in one uniquely identified shared ticket. While many applications in these domains have implemented APIs that allow data to be imported, exported and modified, Network Help Desk provide a common standard for customer support information to automatically synchronize between several systems. Once implemented, two systems can quickly share tickets with just a configuration change as they both understand the same interface. Communication between two instances on a specific ticket occurs in three steps, an invitation agreement, sharing of ticket data and continued synchronization of tickets. The standard allows for "full delegation" (analysts in both systems each make public and private comments and synchronize status) as well as "partial delegation" where the instance receiving the ticket can only make private comments and status changes are not synchronized. Tickets may be shared with multiple instances. == Implementation list ==
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Shakey the robot

Shakey the Robot was the first general-purpose mobile robot able to reason about its own actions. While other robots would have to be instructed on each individual step of completing a larger task, Shakey could analyze commands and break them down into basic chunks by itself. Due to its nature, the project combined research in robotics, computer vision, and natural language processing. Because of this, it was the first project that melded logical reasoning and physical action. Shakey was developed at the Artificial Intelligence Center of Stanford Research Institute (now called SRI International). Some of the most notable results of the project include the A search algorithm, the Hough transform, and the visibility graph method. == History == Shakey was developed from approximately 1966 through 1972 with Charles Rosen, Nils Nilsson and Peter Hart as project managers. Other major contributors included Alfred Brain, Sven Wahlstrom, Bertram Raphael, Richard Duda, Richard Fikes, Thomas Garvey, Helen Chan Wolf and Michael Wilber. The project was funded by the Defense Advanced Research Projects Agency (DARPA) based on a SRI proposal submitted in April 1964 for research in "Intelligent Automata", later "Intelligent Automata to Reconnaissance". It was originally designed to have two retractable arms. Now retired from active duty, Shakey is currently on view in a glass display case at the Computer History Museum in Mountain View, California. The project inspired numerous other robotics projects, most notably the Centibots. == Software == The robot's programming was primarily done in LISP. The Stanford Research Institute Problem Solver (STRIPS) planner it used was conceived as the main planning component for the software it utilized. As the first robot that was a logical, goal-based agent, Shakey experienced a limited world. A version of Shakey's world could contain a number of rooms connected by corridors, with doors and light switches available for the robot to interact with. Shakey had a short list of available actions within its planner. These actions involved traveling from one location to another, turning the light switches on and off, opening and closing the doors, climbing up and down from rigid objects, and pushing movable objects around. The STRIPS automated planner could devise a plan to enact all the available actions, even though Shakey himself did not have the capability to execute all the actions within the plan personally. An example mission for Shakey might be something like, an operator types the command "push the block off the platform" at a computer console. Shakey looks around, identifies a platform with a block on it, and locates a ramp in order to reach the platform. Shakey then pushes the ramp over to the platform, rolls up the ramp onto the platform, and pushes the block off the platform. == Hardware == Physically, the robot was particularly tall, and had an antenna for a radio link, sonar range finders, a television camera, on-board processors, and collision detection sensors ("bump detectors"). The robot's tall stature and tendency to shake resulted in its name: We worked for a month trying to find a good name for it, ranging from Greek names to whatnot, and then one of us said, 'Hey, it shakes like hell and moves around, let’s just call it Shakey.' == Research results == The development of Shakey provided far-reaching impact on the fields of robotics and artificial intelligence, as well as computer science in general. Some of the more notable results include the development of the A search algorithm, which is widely used in pathfinding and graph traversal, the process of plotting an efficiently traversable path between points; the Hough transform, which is a feature extraction technique used in image analysis, computer vision, and digital image processing; and the visibility graph method for finding Euclidean shortest paths among obstacles in the plane. == Media and awards == In 1969 the SRI published "SHAKEY: Experimentation in Robot Learning and Planning", a 24-minute video. The project then received media attention. This included an article in the New York Times on April 10, 1969. In 1970, Life referred to Shakey as the "first electronic person"; and in November 1970 National Geographic Magazine covered Shakey and the future of computers. The Association for the Advancement of Artificial Intelligence's AI Video Competition's awards are named "Shakeys" because of the significant impact of the 1969 video. Shakey was inducted into Carnegie Mellon University's Robot Hall of Fame in 2004 alongside such notables as ASIMO and C-3PO. Shakey has been honored with an IEEE Milestone in Electrical Engineering and Computing. Shakey was showcased in the BBC's Towards Tomorrow: Robot (1967) documentary.
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Spark NLP

Spark NLP is an open-source text processing library for advanced natural language processing for the Python, Java and Scala programming languages. The library is built on top of Apache Spark and its Spark ML library. Its purpose is to provide an API for natural language processing pipelines that implement recent academic research results as production-grade, scalable, and trainable software. The library offers pre-trained neural network models, pipelines, and embeddings, as well as support for training custom models. == Features == The design of the library makes use of the concept of a pipeline which is an ordered set of text annotators. Out of the box annotators include, tokenizer, normalizer, stemming, lemmatizer, regular expression, TextMatcher, chunker, DateMatcher, SentenceDetector, DeepSentenceDetector, POS tagger, ViveknSentimentDetector, sentiment analysis, named entity recognition, conditional random field annotator, deep learning annotator, spell checking and correction, dependency parser, typed dependency parser, document classification, and language detection. The Models Hub is a platform for sharing open-source as well as licensed pre-trained models and pipelines. It includes pre-trained pipelines with tokenization, lemmatization, part-of-speech tagging, and named entity recognition that exist for more than thirteen languages; word embeddings including GloVe, ELMo, BERT, ALBERT, XLNet, Small BERT, and ELECTRA; sentence embeddings including Universal Sentence Embeddings (USE) and Language Agnostic BERT Sentence Embeddings (LaBSE). It also includes resources and pre-trained models for more than two hundred languages. Spark NLP base code includes support for East Asian languages such as tokenizers for Chinese, Japanese, Korean; for right-to-left languages such as Urdu, Farsi, Arabic, Hebrew and pre-trained multilingual word and sentence embeddings such as LaUSE and a translation annotator. == Usage in healthcare == Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction. The library offers access to several clinical and biomedical transformers: JSL-BERT-Clinical, BioBERT, ClinicalBERT, GloVe-Med, GloVe-ICD-O. It also includes over 50 pre-trained healthcare models, that can recognize the entities such as clinical, drugs, risk factors, anatomy, demographics, and sensitive data. == Spark OCR == Spark OCR is another commercial extension of Spark NLP for optical character recognition (OCR) from images, scanned PDF documents, and DICOM files. It is a software library built on top of Apache Spark. It provides several image pre-processing features for improving text recognition results such as adaptive thresholding and denoising, skew detection & correction, adaptive scaling, layout analysis and region detection, image cropping, removing background objects. Due to the tight coupling between Spark OCR and Spark NLP, users can combine NLP and OCR pipelines for tasks such as extracting text from images, extracting data from tables, recognizing and highlighting named entities in PDF documents or masking sensitive text in order to de-identify images. Several output formats are supported by Spark OCR such as PDF, images, or DICOM files with annotated or masked entities, digital text for downstream processing in Spark NLP or other libraries, structured data formats (JSON and CSV), as files or Spark data frames. Users can also distribute the OCR jobs across multiple nodes in a Spark cluster. == License and availability == Spark NLP is licensed under the Apache 2.0 license. The source code is publicly available on GitHub as well as documentation and a tutorial. Prebuilt versions of Spark NLP are available in PyPi and Anaconda Repository for Python development, in Maven Central for Java & Scala development, and in Spark Packages for Spark development. == Award == In March 2019, Spark NLP received Open Source Award for its contributions in natural language processing in Python, Java, and Scala.
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MIT Computer Science and Artificial Intelligence Laboratory

Computer Science and Artificial Intelligence Laboratory (CSAIL) is a research institute at the Massachusetts Institute of Technology (MIT) formed by the 2003 merger of the Laboratory for Computer Science (LCS) and the Artificial Intelligence Laboratory (AI Lab). Housed within the Ray and Maria Stata Center, CSAIL is the largest on-campus laboratory as measured by research scope and membership. It is part of the Schwarzman College of Computing but is also overseen by the MIT Vice President of Research. == Research activities == CSAIL's research activities are organized around a number of semi-autonomous research groups, each of which is headed by one or more professors or research scientists. These groups are divided up into seven general areas of research: Artificial intelligence Computational biology Graphics and vision Language and learning Theory of computation Robotics Systems (includes computer architecture, databases, distributed systems, networks and networked systems, operating systems, programming methodology, and software engineering, among others) == History == Computing Research at MIT began with Vannevar Bush's research into a differential analyzer and Claude Shannon's electronic Boolean algebra in the 1930s, the wartime MIT Radiation Laboratory, the post-war Project Whirlwind and the Research Laboratory of Electronics (RLE), and MIT Lincoln Laboratory's SAGE in the early 1950s. At MIT, research in the field of artificial intelligence began in the late 1950s. === Project MAC === On July 1, 1963, Project MAC (the Project on Mathematics and Computation, later backronymed to Multiple Access Computer, Machine Aided Cognitions, or Man and Computer) was launched with a $2 million grant from the Defense Advanced Research Projects Agency (DARPA). Project MAC's original director was Robert Fano of MIT's Research Laboratory of Electronics (RLE). Fano decided to call MAC a "project" rather than a "laboratory" for reasons of internal MIT politics – if MAC had been called a laboratory, then it would have been more difficult to raid other MIT departments for research staff. The program manager responsible for the DARPA grant was J. C. R. Licklider, who had previously been at MIT conducting research in RLE, and would later succeed Fano as director of Project MAC. Project MAC would become famous for groundbreaking research in operating systems, artificial intelligence, and the theory of computation. Its contemporaries included Project Genie at Berkeley, the Stanford Artificial Intelligence Laboratory, and (somewhat later) University of Southern California's (USC's) Information Sciences Institute. An "AI Group" including Marvin Minsky (the director), John McCarthy (inventor of Lisp), and a talented community of computer programmers were incorporated into Project MAC. They were interested principally in the problems of vision, mechanical motion and manipulation, and language, which they view as the keys to more intelligent machines. In the 1960s and 1970s the AI Group developed a time-sharing operating system called Incompatible Timesharing System (ITS) which ran on PDP-6 and later PDP-10 computers. The early Project MAC community included Fano, Minsky, Licklider, Fernando J. Corbató, and a community of computer programmers and enthusiasts among others who drew their inspiration from former colleague John McCarthy. These founders envisioned the creation of a computer utility whose computational power would be as reliable as an electric utility. To this end, Corbató brought the first computer time-sharing system, Compatible Time-Sharing System (CTSS), with him from the MIT Computation Center, using the DARPA funding to purchase an IBM 7094 for research use. One of the early focuses of Project MAC would be the development of a successor to CTSS, Multics, which was to be the first high availability computer system, developed as a part of an industry consortium including General Electric and Bell Laboratories. In 1966, Scientific American featured Project MAC in the September thematic issue devoted to computer science, that was later published in book form. At the time, the system was described as having approximately 100 TTY terminals, mostly on campus but with a few in private homes. Only 30 users could be logged in at the same time. The project enlisted students in various classes to use the terminals simultaneously in problem solving, simulations, and multi-terminal communications as tests for the multi-access computing software being developed. === AI Lab and LCS === In the late 1960s, Minsky's artificial intelligence group was seeking more space, and was unable to get satisfaction from project director Licklider. Minsky found that although Project MAC as a single entity could not get the additional space he wanted, he could split off to form his own laboratory and then be entitled to more office space. As a result, the MIT AI Lab was formed in 1970, and many of Minsky's AI colleagues left Project MAC to join him in the new laboratory, while most of the remaining members went on to form the Laboratory for Computer Science. Talented programmers such as Richard Stallman, who used TECO to develop EMACS, flourished in the AI Lab during this time. Those researchers who did not join the smaller AI Lab formed the Laboratory for Computer Science and continued their research into operating systems, programming languages, distributed systems, and the theory of computation. Two professors, Hal Abelson and Gerald Jay Sussman, chose to remain neutral—their group was referred to variously as Switzerland and Project MAC for the next 30 years. Among much else, the AI Lab led to the invention of Lisp machines and their attempted commercialization by two companies in the 1980s: Symbolics and Lisp Machines Inc. === CSAIL === On the fortieth anniversary of Project MAC's establishment, July 1, 2003, LCS was merged with the AI Lab to form the MIT Computer Science and Artificial Intelligence Laboratory, or CSAIL. This merger created the largest laboratory (over 600 personnel) on the MIT campus. In 2018, CSAIL launched a five-year collaboration program with IFlytek, a company sanctioned the following year for allegedly using its technology for surveillance and human rights abuses in Xinjiang. In October 2019, MIT announced that it would review its partnerships with sanctioned firms such as iFlyTek and SenseTime. In April 2020, the agreement with iFlyTek was terminated. CSAIL moved from the School of Engineering to the newly formed Schwarzman College of Computing by February 2020. == Offices == From 1963 to 2004, Project MAC, LCS, the AI Lab, and CSAIL had their offices at 545 Technology Square, taking over more and more floors of the building over the years. In 2004, CSAIL moved to the new Ray and Maria Stata Center, which was built specifically to house it and other departments. == Outreach activities == The IMARA (from Swahili word for "power") group sponsors a variety of outreach programs that bridge the global digital divide. Its aim is to find and implement long-term, sustainable solutions which will increase the availability of educational technology and resources to domestic and international communities. These projects are run under the aegis of CSAIL and staffed by MIT volunteers who give training, install and donate computer setups in greater Boston, Massachusetts, Kenya, Native American Indian tribal reservations in the American Southwest such as the Navajo Nation, the Middle East, and Fiji Islands. The CommuniTech project strives to empower under-served communities through sustainable technology and education and does this through the MIT Used Computer Factory (UCF), providing refurbished computers to under-served families, and through the Families Accessing Computer Technology (FACT) classes, it trains those families to become familiar and comfortable with computer technology. == Notable researchers == (Including members and alumni of CSAIL's predecessor laboratories) MacArthur Fellows Tim Berners-Lee, Erik Demaine, Dina Katabi, Daniela L. Rus, Regina Barzilay, Peter Shor, Richard Stallman, and Joshua Tenenbaum Turing Award recipients Leonard M. Adleman, Fernando J. Corbató, Shafi Goldwasser, Butler W. Lampson, John McCarthy, Silvio Micali, Marvin Minsky, Ronald L. Rivest, Adi Shamir, Barbara Liskov, and Michael Stonebraker IJCAI Computers and Thought Award recipients Terry Winograd, Patrick Winston, David Marr, Gerald Jay Sussman, Rodney Brooks Rolf Nevanlinna Prize recipients Madhu Sudan, Peter Shor, Constantinos Daskalakis Gödel Prize recipients Shafi Goldwasser (two-time recipient), Silvio Micali, Maurice Herlihy, Charles Rackoff, Johan Håstad, Peter Shor, and Madhu Sudan Grace Murray Hopper Award recipients Robert Metcalfe, Shafi Goldwasser, Guy L. Steele, Jr., Richard Stallman, and W. Daniel Hillis Textbook authors Harold Abelson and Gerald Jay Sussman, Richard Stallman, Thomas H. Cormen, Charles E. Leiserson, Patrick Winston, Ronald L.
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CodeSandbox

CodeSandbox is a cloud-based online integrated development environment (IDE) focused on web application development. It supports popular web technologies such as JavaScript, TypeScript, React, Vue.js, and Node.js. CodeSandbox allows users to create, edit, and deploy web applications directly from the browser with zero setup. CodeSandbox is widely used for front-end development, rapid prototyping, sharing code snippets, and real-time collaborative coding. It provides GitHub integration, templates for common frameworks, and a cloud-based development container for full-stack projects. == Templates == == Limitations == Slower performance for larger tasks compared to native IDEs Some features require a paid subscription Performance and storage limits for free-tier users Limited offline capabilities
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Global call for AI red lines

The global call for AI red lines is a declaration made on 22 September 2025 calling on governments to define and internationally prohibit unacceptable AI uses and behaviors. The online declaration was announced by Nobel Peace Prize laureate Maria Ressa at the 80th United Nations General Assembly high-level week. The declaration was initially signed by 200 prominent politicians and scientists, including 10 Nobel Prize winners. The call does not specify which red lines to set, but suggests several, such as banning bioweapon design, mass surveillance or AI impersonation. == The declaration == The declaration was published online as an open letter on 22 September 2025. Nobel Peace Prize laureate Maria Ressa announced it in her opening speech at the 80th United Nations General Assembly high-level week in New York, urging governments to "define what AI should never be allowed to do" and "establish clear international boundaries to prevent universally unacceptable risks for A.I." The initiative was organized by three nonprofit organisations: the French Center for AI Safety (CeSIA), The Future Society, and the Center for Human-Compatible Artificial Intelligence (CHAI). The letter argues that humanity faces risks such as engineered pandemics, widespread disinformation, large-scale manipulation, unemployment and loss of control. Proponents argue that national laws are insufficient to address these risks and that "an international agreement on clear and verifiable red lines is necessary". They urge governments to reach an agreement by the end of 2026, and called for robust enforcement mechanisms and the creation of an independent organisation to implement it. The letter does not call for specific red lines, but suggests the possibility of banning lethal autonomous weapons, autonomous replication of AI systems and the use of AI in nuclear warfare. Other examples of possible red lines include social scoring, mass surveillance, bioweapon design, AI-generated child sexual abuse material and AI impersonation. A red line could prohibit either AI behaviors (what AI systems should be guaranteed to never do even if asked to) or AI uses. == Signatories == When published, the online declaration was signed by more than 200 prominent politicians and scientists, including 10 Nobel Prize winners. Signers include former president of Colombia Juan Manuel Santos and researchers Geoffrey Hinton and Yoshua Bengio. It also includes popular authors like Stephen Fry and Yuval Noah Harari. The letter received support from European lawmakers, including former Italian prime minister Enrico Letta, and former president of Ireland Mary Robinson. == Development of red lines == As of 2025, there is no global red line on AI. Some regional red lines exist, such as with the uses deemed "unacceptable" by the AI Act in Europe, and with the US-China agreement not to leave to AI the decision of whether to launch nuclear weapons. At the United Nations Security Council, days after the declaration, Michael Kratsios, Donald Trump's director of the White House Office of Science and Technology Policy, said "We totally reject all efforts by international bodies to assert centralized control and global governance of AI." The topic of AI red lines gained prominence in 2026 with the dispute between Anthropic and the Department of Defense (DoD), which resulted from the DoD requesting Anthropic to remove contractual red lines on fully autonomous weapons and mass domestic surveillance. The event led employees from Google and OpenAI as well as Senate Democrats to further call for red lines on military use of AI. Senator Adam Schiff proposed a bill to "codify" Anthropic's red lines.
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POSC Caesar

POSC Caesar Association (PCA) is an international, open and not-for-profit, member organization that promotes the development of open specifications to be used as standards for enabling the interoperability of data, software and related matters. PCA is the initiator of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities" and is committed to its maintenance and enhancement. Nils Sandsmark has been the General Manager of POSC Caesar Association since 1999 and Thore Langeland, Norwegian Oil Industry Association (Norwegian: Oljeindustriens Landsforening, OLF), is the chairman of the board. == History == === Caesar Offshore === The first predecessor of POSC Caesar Association, the Caesar Offshore program, started in 1993. The original focus was on standardizing technical data definitions for capital intensive projects at the handover from the EPC contractor to the owner/operators of onshore and offshore oil and gas production facilities. The program was sponsored by The Research Council of Norway, two EPC contractors (Aker Maritime and Kværner), three owners/operators (Norsk Hydro, Saga Petroleum and Statoil) and DNV as service provider and project owner. === POSC Caesar project === During the period 1994–96, Caesar Offshore Program was defined as a project of Petrotechnical Open Software Corporation (POSC) (now Energistics), and changed its name to the POSC Caesar Project. In 1995 the project was joined by BP, Brown and Root and Elf Aquitaine and in 1997 by Intergraph, IBM, Oracle, Lloyd's, Shell, ABB and UMOE Technologies. During that time, POSC Caesar also became a member of European Process Industries STEP Technical Liaison Executive (EPISTLE) where it collaborates with PISTEP (UK), and USPI-NL (The Netherlands) on the development of ISO 10303, also known as "Standard for the Exchange of Product model data (STEP)". === POSC Caesar Association === In 1997, POSC Caesar Association was founded as an independent, global, non-profit, member organization. POSC Caesar Association serves an international membership and collaborates with other international organizations. It has its main office in Norway. Albeit the name of POSC Caesar Association still hints to its past as a project within the Petrotechnical Open Software Corporation (POSC) (now Energistics), from 1997 onwards, the organization has been independent. Energistics and POSC Caesar Association do collaborate, and are formally member in each other's organization. == Membership == POSC Caesar Association has with its current 36 members from around the world and has established an international footprint (with a strong membership in Norway) that includes a variety of backgrounds, from academia and solution providers to engineering contractors and owners/operators. The members are (subdivided by organization type): Associations: Energistics (USA) and The Norwegian Oil Industry Association (OLF, Norway); Universities and Research Institutes: International Research Institute of Stavanger (IRIS, Norway), Norwegian University of Science and Technology (NTNU, Norway), Korea Advanced Institute of Science and Technology (KAIST, Korea), SINTEF (Norway), University of Bergen (Norway), University of Oslo (Norway), University of Stavanger (Norway), University of Tromsø (Norway) and Western Norway Research Institute (Norway); Oil and Gas Companies: BP (UK), Petronas (Malaysia) and Statoil (Norway); Engineering contractors and consultants: Akvaplan-niva (Norway), Aker Solutions (Norway), Asset Life Cycle Information Management (ALCIM, Malaysia), CAESAR systems (USA), Bechtel (USA), Det Norske Veritas (DNV, Norway), Information Logic (USA) and iXIT Engineering Technology (Germany), Phusion IM Ltd (UK); Solution providers: Aveva (UK), Bentley Systems (USA), Jotne EPM Technology (Norway), Epsis (Norway), Eurostep (Sweden), International Business Machines Corporation (IBM, USA), Siemens - Comos Industry Solutions (before Innotec) (Germany), Intergraph (USA), Invenia (Norway), Keel Solution (Denmark), Noumenon (UK), NRX (Canada), Octaga (Norway) and Tektonisk (Norway). In general, the organization holds three membership meetings a year; one in January / February in North-America (typically USA), one in April / May in Europe (typically Norway) and one in October in Asia (typically Malaysia). == Activities and services == === Initiator and custodian of ISO 15926 === In consultation with the other EPISTLE members and the International Organization for Standardization (ISO), it was decided in 2003 (some say already in 1997) that for modeling-technical reasons it was better to discontinue the development of ISO 10303 and to initiate the development of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities." Over the years, the scope of the standard has increased from the initial capital-intensive projects in the upstream oil and gas industry, to include also relevant terminology for downstream oil and gas industry applications and to deal with real-time data related to the actual oil and gas production. ISO 15926 has also over the years evolved from a dictionary (a list of terms with definitions), over a taxonomy (added hierarchy) to an ontology (a formal representation of a set of concepts within a domain and the relationships between those concepts). ISO 15926 is therefore sometimes nicknamed the "Oil and Gas Ontology", for some considered to be an essential prerequisite together with Semantic Web technologies to get to better interoperability, an optimal use of all available data across boundaries and an increase in efficiency. This is what some call the next generation of Integrated Operations. === Reference data services === Placeholders: Flow scheme of WIP - RDS - ISO and role of SIGs RDS Standards in database pilot (ISO) === Special interest groups === Placeholders: Overview of SIGs Drilling and Completion Reservoir and Production Operations and Maintenance == Projects == There are a number of projects (co-)organized by POSC Caesar Association working on the extension of the ISO 15926 standard in different application areas. === Capital intensive projects application domain === The following projects are running at the moment (August 2009): The ADI Project of FIATECH, to build the tools (which will then be made available in the public domain) The IDS Project of POSC Caesar Association, to define product models required for data sheets A joint collaboration project between FIATECH POSC Caesar Association is the ADI-IDS project is the ISO 15926 WIP === Upstream oil and gas industry application domain === The following projects are currently running (August 2009): The Integrated Operations in the High North (IOHN) project is working on extending ISO 15926 to handle real-time data transmission and (pre-)processing to enable the next generation of Integrated Operations. The Environment Web project to include environmental reporting terms and definitions as used in EPIM's EnvironmentWeb in ISO 15926. Finalised projects include: The Integrated Information Platform (IIP) project working on establishing a real-time information pipeline based on open standards. It worked among others on: Daily Drilling Report (DDR) to including all terms and definitions in ISO 15926. This standard became mandatory on February 1, 2008 for reporting on the Norwegian Continental Shelf by the Norwegian Petroleum Directorate (NPD) and Safety Authority Norway (PSA). NPD says that the quality of the reports has improved considerably since. Daily Production Report (DPR) to including all terms and definitions in ISO 15926. This standard was tested successfully on the Valhall (BP-operated) and Åsgard (StatoilHydro-operated) fields offshore Norway. The terminology and XML schemata developed have also been included in Energistics’ PRODML standard. == Conferences and events == === Semantic Days === === Sogndal academic network meeting === == Collaborations == POSC Caesar is collaborating with a number of standardization bodies, including: Mimosa: collaboration on open information standards for Operations and Maintenance mainly for the downstream oil and gas industry; FIATECH: collaboration on open information standards for life cycle data of capital projects; Energistics: collaboration on information standards for the upstream oil and gas industry, including WITSML and PRODML; OASIS: collaboration on e-business standards; ISO TC184/SC4: the host of the ISO 15926 standard.
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Leela Zero

Leela Zero is a free and open-source computer Go program released on 25 October 2017. It is developed by Belgian programmer Gian-Carlo Pascutto, the author of chess engine Sjeng and Go engine Leela. Leela Zero's algorithm is based on DeepMind's 2017 paper about AlphaGo Zero. Unlike the original Leela, which has a lot of human knowledge and heuristics programmed into it, the program code in Leela Zero only knows the basic rules and nothing more. The knowledge that makes Leela Zero a strong player is contained in a neural network, which is trained based on the results of previous games that the program played. Leela Zero is trained by a distributed effort, which is coordinated at the Leela Zero website. Members of the community provide computing resources by running the client, which generates self-play games and submits them to the server. The self-play games are used to train newer networks. Generally, over 500 clients have connected to the server to contribute resources. The community has provided high quality code contributions as well. == Version history == Leela Zero finished third at the BerryGenomics Cup World AI Go Tournament in Fuzhou, Fujian, China on 28 April 2018. The New Yorker at the end of 2018 characterized Leela and Leela Zero as "the world’s most successful open-source Go engines". In early 2018, another team branched Leela Chess Zero from the same code base, also to verify the methods in the AlphaZero paper as applied to the game of chess. AlphaZero's use of Google TPUs was replaced by a crowd-sourcing infrastructure and the ability to use graphics card GPUs via the OpenCL library. Even so, it is expected to take a year of crowd-sourced training to make up for the dozen hours that AlphaZero was allowed to train for its chess match in the paper. The distributed training server was shut down on 2021-02-15, marking the end of Leela Zero project. The page now directs visitors to KataGo and SAI. The model sizes increased steadily over time. The first released model has hash name d645af97, size 1x8 (1 layer, 8 channels), and released at 2017-11-10 13:04. The last released model has hash name 0e9ea880, size 40x256, and was released at 2021-02-15 09:04. == Technology == Leela Zero is an (almost) exact replication of AlphaGo Zero in both training process and architecture. The training process is Monte-Carlo Tree Search with self-play, exactly the same as AlphaGo Zero. The architecture is the same as AlphaGo Zero (with one difference). Consider the last released model, 0e9ea880. It has 47 million parameters, and the following architecture: The stem of the network takes as input a 18x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise. If the time step go before the beginning of the game, then 0 in all positions.) 8 channels are the positions of the other player's stones from the last eight time steps. 1 channel is all 1 if black is to move, and 0 otherwise. 1 channel is all 1 if white is to move, and 0 otherwise. (This channel is not present in the original AlphaGo Zero) The body is a ResNet with 40 residual blocks and 256 channels. There are two heads, a policy head and a value head. Policy head outputs a logit array of size 19 × 19 + 1 {\displaystyle 19\times 19+1} , representing the logit of making a move in one of the points, plus the logit of passing. Value head outputs a number in the range ( − 1 , + 1 ) {\displaystyle (-1,+1)} , representing the expected score for the current player. -1 represents current player losing, and +1 winning.
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Logistics automation

Logistics automation is the application of computer software or automated machinery to logistics operations in order to improve its efficiency. Typically this refers to operations within a warehouse or distribution center, with broader tasks undertaken by supply chain engineering systems and enterprise resource planning systems. Logistics automation systems can powerfully complement the facilities provided by these higher level computer systems. The focus on an individual node within a wider logistics network allows systems to be highly tailored to the requirements of that node. == Components == Logistics automation systems comprise a variety of hardware and software components: Fixed machinery Automated storage and retrieval systems, including: Cranes serve a rack of locations, allowing many levels of stock to be stacked vertically, and allowing for higher storage densities and better space utilization than alternatives. In systems produced by Amazon Robotics, automated guided vehicles move items to a human picker. Conveyors: Containers can enter automated conveyors in one area of the warehouse and, either through hard-coded rules or data input, be moved to a selected destination. Vertical carousels based on the paternoster lift system or using space optimization, similar to vending machines, but on a larger scale. Sortation systems: similar to conveyors but typically with higher capacity and able to divert containers more quickly. Typically used to distribute high volumes of small cartons to a large set of locations. Industrial robots: four- to six-axis industrial robots, e.g. palletizing robots, are used for palletizing, depalletizing, packaging, commissioning and order picking. Typically all of these will automatically identify and track containers using barcodes or, increasingly, RFID tags. Motion check weighers may be used to reject cases or individual products that are under or over their specified weight. They are often used in kitting conveyor lines to ensure all pieces belonging in the kit are present. Mobile technology Radio data terminals: these are handheld or truck-mounted terminals which connect by radio to logistics automation software and provide instructions to operators moving throughout the warehouse. Many also have barcode scanners to allow identification of containers more quickly and accurately than manual keyboard entry. Software Integration software: this provides overall control of the automation machinery and allows cranes to be connected to conveyors for seamless stock movements. Operational control software: provides low-level decision-making, such as where to store incoming containers, and where to retrieve them when requested. Business control software: provides higher-level functionality, such as identification of incoming deliveries/stock, scheduling order fulfillment, and assignment of stock to outgoing trailers. == Benefits == A typical warehouse or distribution center will receive stock of a variety of products from suppliers and store these until the receipt of orders from customers, whether individual buyers (e.g. mail order), retail branches (e.g. chain stores), or other companies (e.g. wholesalers). A logistics automation system may provide the following: Automated goods in processes: Incoming goods can be marked with barcodes and the automation system notified of the expected stock. On arrival, the goods can be scanned and thereby identified, and taken via conveyors, sortation systems, and automated cranes into an automatically assigned storage location. Automated goods retrieval for orders: On receipt of orders, the automation system is able to immediately locate goods and retrieve them to a pick-face location. Automated dispatch processing: Combining knowledge of all orders placed at the warehouse the automation system can assign picked goods into dispatch units and then into outbound loads. Sortation systems and conveyors can then move these onto the outgoing trailers. If needed, repackaging to ensure proper protection for further distribution or to change the package format for specific retailers/customers. A complete warehouse automation system can drastically reduce the workforce required to run a facility, with human input required only for a few tasks, such as picking units of product from a bulk packed case. Even here, assistance can be provided with equipment such as pick-to-light units. Smaller systems may only be required to handle part of the process. Examples include automated storage and retrieval systems, which simply use cranes to store and retrieve identified cases or pallets, typically into a high-bay storage system which would be unfeasible to access using fork-lift trucks or any other means. The use of Automatic Guided Vehicles maximizes the output compared to humans since they can do repetitive tasks for long hours and with least to no supervision. An AGV is built and programmed for precision and accuracy thereby reducing the chances of errors in a warehouse, especially when dealing with fragile goods. == Automation software == Software or cloud-based SaaS solutions are used for logistics automation which helps the supply chain industry in automating the workflow as well as management of the system. Knowledge @ Wharton staff writers noted in 2011 that some manufacturers and retailers were weathering the Great Recession "by signing up for pay-as-you-go logistics services available through the Internet 'cloud'". They identified the benefits and reduced costs which came from sharing information about shipments with suppliers, hauliers and end users. There is little generalized software available in this market. This is because there is no rule to generalize the system as well as work flow even though the practice is more or less the same. Most of the commercial companies do use one or the other of the custom solutions. But there are various software solutions that are being used within the departments of logistics. There are a few departments in Logistics, namely: Conventional Department, Container Department, Warehouse, Marine Engineering, Heavy Haulage, etc. Software used in these departments Conventional department : CVT software / CTMS software. Container Trucking: CTMS software Warehouse : WMS/WCS Improving Effectiveness of Logistics Management Logistical Network Information Transportation Sound Inventory Management Warehousing, Materials Handling & Packaging
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Infomax

Infomax', or the principle of maximum information preservation, is an optimization principle for artificial neural networks and other information processing systems. It prescribes that a function that maps a set of input values x {\displaystyle x} to a set of output values z ( x ) {\displaystyle z(x)} should be chosen or learned so as to maximize the average Shannon mutual information between x {\displaystyle x} and z ( x ) {\displaystyle z(x)} , subject to a set of specified constraints and/or noise processes. Infomax algorithms are learning algorithms that perform this optimization process. The principle was described by Linsker in 1988. The objective function is called the InfoMax objective. As the InfoMax objective is difficult to compute exactly, a related notion uses two models giving two outputs z 1 ( x ) , z 2 ( x ) {\displaystyle z_{1}(x),z_{2}(x)} , and maximizes the mutual information between these. This contrastive InfoMax objective is a lower bound to the InfoMax objective. Infomax, in its zero-noise limit, is related to the principle of redundancy reduction proposed for biological sensory processing by Horace Barlow in 1961, and applied quantitatively to retinal processing by Atick and Redlich. == Applications == (Becker and Hinton, 1992) showed that the contrastive InfoMax objective allows a neural network to learn to identify surfaces in random dot stereograms (in one dimension). One of the applications of infomax has been to an independent component analysis algorithm that finds independent signals by maximizing entropy. Infomax-based ICA was described by (Bell and Sejnowski, 1995), and (Nadal and Parga, 1995).
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Radiant AI

The Radiant AI is a technology developed by Bethesda Softworks for The Elder Scrolls video games. It allows non-player characters (NPCs) to make choices and engage in behaviors more complex than in past titles. The technology was developed for The Elder Scrolls IV: Oblivion and expanded in The Elder Scrolls V: Skyrim; it is also used in Fallout 3, Fallout: New Vegas and Fallout 4, also published by Bethesda, with 3 and 4 being developed by them as well. == Technology == The Radiant AI technology, as it evolved in its iteration developed for Skyrim, comprises two parts: === Radiant AI === The Radiant AI system deals with NPC interactions and behavior. It allows non-player characters to dynamically react to and interact with the world around them. General goals, such as "Eat in this location at 2pm" are given to NPCs, and NPCs are left to determine how to achieve them. The absence of individual scripting for each character allows for the construction of a world on a much larger scale than other games had developed, and aids in the creation of what Todd Howard described as an "organic feel" for the game. === Radiant Story === The Radiant Story system deals with how the game itself reacts to the player behavior, such as the creation of new dynamic quests. Dynamically generated quests are placed by the game in locations the player hasn't visited yet and are related to earlier adventures.
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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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Symbolic regression

Symbolic regression (SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is provided as a starting point for symbolic regression. Instead, initial expressions are formed by randomly combining mathematical building blocks such as mathematical operators, analytic functions, constants, and state variables. Usually, a subset of these primitives will be specified by the person operating it, but that's not a requirement of the technique. The symbolic regression problem for mathematical functions has been tackled with a variety of methods, including recombining equations most commonly using genetic programming, as well as more recent methods utilizing Bayesian methods and neural networks. Another non-classical alternative method to SR is called Universal Functions Originator (UFO), which has a different mechanism, search-space, and building strategy. Further methods such as Exact Learning attempt to transform the fitting problem into a moments problem in a natural function space, usually built around generalizations of the Meijer-G function. By not requiring a priori specification of a model, symbolic regression isn't affected by human bias, or unknown gaps in domain knowledge. It attempts to uncover the intrinsic relationships of the dataset, by letting the patterns in the data itself reveal the appropriate models, rather than imposing a model structure that is deemed mathematically tractable from a human perspective. The fitness function that drives the evolution of the models takes into account not only error metrics (to ensure the models accurately predict the data), but also special complexity measures, thus ensuring that the resulting models reveal the data's underlying structure in a way that's understandable from a human perspective. This facilitates reasoning and favors the odds of getting insights about the data-generating system, as well as improving generalisability and extrapolation behaviour by preventing overfitting. Accuracy and simplicity may be left as two separate objectives of the regression—in which case the optimum solutions form a Pareto front—or they may be combined into a single objective by means of a model selection principle such as minimum description length. It has been proven that symbolic regression is an NP-hard problem. Nevertheless, if the sought-for equation is not too complex it is possible to solve the symbolic regression problem exactly by generating every possible function (built from some predefined set of operators) and evaluating them on the dataset in question. == Difference from classical regression == While conventional regression techniques seek to optimize the parameters for a pre-specified model structure, symbolic regression avoids imposing prior assumptions, and instead infers the model from the data. In other words, it attempts to discover both model structures and model parameters. This approach has the disadvantage of having a much larger space to search, because not only the search space in symbolic regression is infinite, but there are an infinite number of models which will perfectly fit a finite data set (provided that the model complexity isn't artificially limited). This means that it will possibly take a symbolic regression algorithm longer to find an appropriate model and parametrization, than traditional regression techniques. This can be attenuated by limiting the set of building blocks provided to the algorithm, based on existing knowledge of the system that produced the data; but in the end, using symbolic regression is a decision that has to be balanced with how much is known about the underlying system. Nevertheless, this characteristic of symbolic regression also has advantages: because the evolutionary algorithm requires diversity in order to effectively explore the search space, the result is likely to be a selection of high-scoring models (and their corresponding set of parameters). Examining this collection could provide better insight into the underlying process, and allows the user to identify an approximation that better fits their needs in terms of accuracy and simplicity. == Benchmarking == === SRBench === In 2021, SRBench was proposed as a large benchmark for symbolic regression. In its inception, SRBench featured 14 symbolic regression methods, 7 other ML methods, and 252 datasets from PMLB. The benchmark intends to be a living project: it encourages the submission of improvements, new datasets, and new methods, to keep track of the state of the art in SR. === SRBench Competition 2022 === In 2022, SRBench announced the competition Interpretable Symbolic Regression for Data Science, which was held at the GECCO conference in Boston, MA. The competition pitted nine leading symbolic regression algorithms against each other on a novel set of data problems and considered different evaluation criteria. The competition was organized in two tracks, a synthetic track and a real-world data track. ==== Synthetic Track ==== In the synthetic track, methods were compared according to five properties: re-discovery of exact expressions; feature selection; resistance to local optima; extrapolation; and sensitivity to noise. Rankings of the methods were: QLattice PySR (Python Symbolic Regression) uDSR (Deep Symbolic Optimization) ==== Real-world Track ==== In the real-world track, methods were trained to build interpretable predictive models for 14-day forecast counts of COVID-19 cases, hospitalizations, and deaths in New York State. These models were reviewed by a subject expert and assigned trust ratings and evaluated for accuracy and simplicity. The ranking of the methods was: uDSR (Deep Symbolic Optimization) QLattice geneticengine (Genetic Engine) == Non-standard methods == Most symbolic regression algorithms prevent combinatorial explosion by implementing evolutionary algorithms that iteratively improve the best-fit expression over many generations. Recently, researchers have proposed algorithms utilizing other tactics in AI. Silviu-Marian Udrescu and Max Tegmark developed the "AI Feynman" algorithm, which attempts symbolic regression by training a neural network to represent the mystery function, then runs tests against the neural network to attempt to break up the problem into smaller parts. For example, if f ( x 1 , . . . , x i , x i + 1 , . . . , x n ) = g ( x 1 , . . . , x i ) + h ( x i + 1 , . . . , x n ) {\displaystyle f(x_{1},...,x_{i},x_{i+1},...,x_{n})=g(x_{1},...,x_{i})+h(x_{i+1},...,x_{n})} , tests against the neural network can recognize the separation and proceed to solve for g {\displaystyle g} and h {\displaystyle h} separately and with different variables as inputs. This is an example of divide and conquer, which reduces the size of the problem to be more manageable. AI Feynman also transforms the inputs and outputs of the mystery function in order to produce a new function which can be solved with other techniques, and performs dimensional analysis to reduce the number of independent variables involved. The algorithm was able to "discover" 100 equations from The Feynman Lectures on Physics, while a leading software using evolutionary algorithms, Eureqa, solved only 71. AI Feynman, in contrast to classic symbolic regression methods, requires a very large dataset in order to first train the neural network and is naturally biased towards equations that are common in elementary physics.
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MIT Computer Science and Artificial Intelligence Laboratory

Computer Science and Artificial Intelligence Laboratory (CSAIL) is a research institute at the Massachusetts Institute of Technology (MIT) formed by the 2003 merger of the Laboratory for Computer Science (LCS) and the Artificial Intelligence Laboratory (AI Lab). Housed within the Ray and Maria Stata Center, CSAIL is the largest on-campus laboratory as measured by research scope and membership. It is part of the Schwarzman College of Computing but is also overseen by the MIT Vice President of Research. == Research activities == CSAIL's research activities are organized around a number of semi-autonomous research groups, each of which is headed by one or more professors or research scientists. These groups are divided up into seven general areas of research: Artificial intelligence Computational biology Graphics and vision Language and learning Theory of computation Robotics Systems (includes computer architecture, databases, distributed systems, networks and networked systems, operating systems, programming methodology, and software engineering, among others) == History == Computing Research at MIT began with Vannevar Bush's research into a differential analyzer and Claude Shannon's electronic Boolean algebra in the 1930s, the wartime MIT Radiation Laboratory, the post-war Project Whirlwind and the Research Laboratory of Electronics (RLE), and MIT Lincoln Laboratory's SAGE in the early 1950s. At MIT, research in the field of artificial intelligence began in the late 1950s. === Project MAC === On July 1, 1963, Project MAC (the Project on Mathematics and Computation, later backronymed to Multiple Access Computer, Machine Aided Cognitions, or Man and Computer) was launched with a $2 million grant from the Defense Advanced Research Projects Agency (DARPA). Project MAC's original director was Robert Fano of MIT's Research Laboratory of Electronics (RLE). Fano decided to call MAC a "project" rather than a "laboratory" for reasons of internal MIT politics – if MAC had been called a laboratory, then it would have been more difficult to raid other MIT departments for research staff. The program manager responsible for the DARPA grant was J. C. R. Licklider, who had previously been at MIT conducting research in RLE, and would later succeed Fano as director of Project MAC. Project MAC would become famous for groundbreaking research in operating systems, artificial intelligence, and the theory of computation. Its contemporaries included Project Genie at Berkeley, the Stanford Artificial Intelligence Laboratory, and (somewhat later) University of Southern California's (USC's) Information Sciences Institute. An "AI Group" including Marvin Minsky (the director), John McCarthy (inventor of Lisp), and a talented community of computer programmers were incorporated into Project MAC. They were interested principally in the problems of vision, mechanical motion and manipulation, and language, which they view as the keys to more intelligent machines. In the 1960s and 1970s the AI Group developed a time-sharing operating system called Incompatible Timesharing System (ITS) which ran on PDP-6 and later PDP-10 computers. The early Project MAC community included Fano, Minsky, Licklider, Fernando J. Corbató, and a community of computer programmers and enthusiasts among others who drew their inspiration from former colleague John McCarthy. These founders envisioned the creation of a computer utility whose computational power would be as reliable as an electric utility. To this end, Corbató brought the first computer time-sharing system, Compatible Time-Sharing System (CTSS), with him from the MIT Computation Center, using the DARPA funding to purchase an IBM 7094 for research use. One of the early focuses of Project MAC would be the development of a successor to CTSS, Multics, which was to be the first high availability computer system, developed as a part of an industry consortium including General Electric and Bell Laboratories. In 1966, Scientific American featured Project MAC in the September thematic issue devoted to computer science, that was later published in book form. At the time, the system was described as having approximately 100 TTY terminals, mostly on campus but with a few in private homes. Only 30 users could be logged in at the same time. The project enlisted students in various classes to use the terminals simultaneously in problem solving, simulations, and multi-terminal communications as tests for the multi-access computing software being developed. === AI Lab and LCS === In the late 1960s, Minsky's artificial intelligence group was seeking more space, and was unable to get satisfaction from project director Licklider. Minsky found that although Project MAC as a single entity could not get the additional space he wanted, he could split off to form his own laboratory and then be entitled to more office space. As a result, the MIT AI Lab was formed in 1970, and many of Minsky's AI colleagues left Project MAC to join him in the new laboratory, while most of the remaining members went on to form the Laboratory for Computer Science. Talented programmers such as Richard Stallman, who used TECO to develop EMACS, flourished in the AI Lab during this time. Those researchers who did not join the smaller AI Lab formed the Laboratory for Computer Science and continued their research into operating systems, programming languages, distributed systems, and the theory of computation. Two professors, Hal Abelson and Gerald Jay Sussman, chose to remain neutral—their group was referred to variously as Switzerland and Project MAC for the next 30 years. Among much else, the AI Lab led to the invention of Lisp machines and their attempted commercialization by two companies in the 1980s: Symbolics and Lisp Machines Inc. === CSAIL === On the fortieth anniversary of Project MAC's establishment, July 1, 2003, LCS was merged with the AI Lab to form the MIT Computer Science and Artificial Intelligence Laboratory, or CSAIL. This merger created the largest laboratory (over 600 personnel) on the MIT campus. In 2018, CSAIL launched a five-year collaboration program with IFlytek, a company sanctioned the following year for allegedly using its technology for surveillance and human rights abuses in Xinjiang. In October 2019, MIT announced that it would review its partnerships with sanctioned firms such as iFlyTek and SenseTime. In April 2020, the agreement with iFlyTek was terminated. CSAIL moved from the School of Engineering to the newly formed Schwarzman College of Computing by February 2020. == Offices == From 1963 to 2004, Project MAC, LCS, the AI Lab, and CSAIL had their offices at 545 Technology Square, taking over more and more floors of the building over the years. In 2004, CSAIL moved to the new Ray and Maria Stata Center, which was built specifically to house it and other departments. == Outreach activities == The IMARA (from Swahili word for "power") group sponsors a variety of outreach programs that bridge the global digital divide. Its aim is to find and implement long-term, sustainable solutions which will increase the availability of educational technology and resources to domestic and international communities. These projects are run under the aegis of CSAIL and staffed by MIT volunteers who give training, install and donate computer setups in greater Boston, Massachusetts, Kenya, Native American Indian tribal reservations in the American Southwest such as the Navajo Nation, the Middle East, and Fiji Islands. The CommuniTech project strives to empower under-served communities through sustainable technology and education and does this through the MIT Used Computer Factory (UCF), providing refurbished computers to under-served families, and through the Families Accessing Computer Technology (FACT) classes, it trains those families to become familiar and comfortable with computer technology. == Notable researchers == (Including members and alumni of CSAIL's predecessor laboratories) MacArthur Fellows Tim Berners-Lee, Erik Demaine, Dina Katabi, Daniela L. Rus, Regina Barzilay, Peter Shor, Richard Stallman, and Joshua Tenenbaum Turing Award recipients Leonard M. Adleman, Fernando J. Corbató, Shafi Goldwasser, Butler W. Lampson, John McCarthy, Silvio Micali, Marvin Minsky, Ronald L. Rivest, Adi Shamir, Barbara Liskov, and Michael Stonebraker IJCAI Computers and Thought Award recipients Terry Winograd, Patrick Winston, David Marr, Gerald Jay Sussman, Rodney Brooks Rolf Nevanlinna Prize recipients Madhu Sudan, Peter Shor, Constantinos Daskalakis Gödel Prize recipients Shafi Goldwasser (two-time recipient), Silvio Micali, Maurice Herlihy, Charles Rackoff, Johan Håstad, Peter Shor, and Madhu Sudan Grace Murray Hopper Award recipients Robert Metcalfe, Shafi Goldwasser, Guy L. Steele, Jr., Richard Stallman, and W. Daniel Hillis Textbook authors Harold Abelson and Gerald Jay Sussman, Richard Stallman, Thomas H. Cormen, Charles E. Leiserson, Patrick Winston, Ronald L.
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OpenVINO

OpenVINO is an open-source software toolkit developed by Intel for optimizing and deploying deep learning models. It supports several popular model formats and categories, such as large language models, computer vision, and generative AI. OpenVINO is optimized for Intel hardware, but offers support for ARM/ARM64 processors. It sees great use in AI Sound Processing drivers when tied with Intel's Gaussian & Neural Accelerator (GNA). Based in C++, it extends API support for C and Python, as well as Node.js (in early preview). OpenVINO is cross-platform and free for use under Apache License 2.0. == Workflow == The simplest OpenVINO usage involves obtaining a model and running it as is. Yet for the best results, a more complete workflow is suggested: obtain a model in one of supported frameworks, convert the model to OpenVINO IR using the OpenVINO Converter tool, optimize the model, using training-time or post-training options provided by OpenVINO's NNCF. execute inference, using OpenVINO Runtime by specifying one of several inference modes. == OpenVINO model format == OpenVINO IR is the default format used to run inference. It is saved as a set of two files, .bin and .xml, containing weights and topology, respectively. It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter. Models of the supported formats may also be used for inference directly, without prior conversion to OpenVINO IR. Such an approach is more convenient but offers fewer optimization options and lower performance, since the conversion is performed automatically before inference. Some pre-converted models can be found in the Hugging Face repository. The supported model formats are: PyTorch TensorFlow TensorFlow Lite ONNX (including formats that may be serialized to ONNX) PaddlePaddle JAX/Flax == OS support == OpenVINO runs on Windows, Linux and MacOS.
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