AI Code For You

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

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  Altibase

  Altibase is a hybrid database, relational database management system manufactured by the Altibase Corporation. The software's hybrid architecture allows it to access both memory-resident and disk-resident tables using single interface. It supports both synchronous and asynchronous replication and offers real-time ACID compliance. Support is also offered for a variety of SQL standards and programming languages. Other important capabilities include data import and export, data encryption for security, multiple data access command sets, materialized view and temporary tables, and others. == History == From 1991 through 1997 the Mr. RT project was an in-memory database research project, conducted by the Electronics and Telecommunications Research Institute a government-funded research organization in South Korea. Altibase was incorporated in 1999. Altibase acquired an in-memory database engine from the Electronics and Telecommunications Research Institute in February 2000, and commercialized the database in October of the same year. In 2001, Altibase changed the name of the in-memory database product from "Spiner" to "Altibase" in 2001. In 2004, Altibase integrated the in-memory database with a disk-resident database to create a hybrid DBMS, released version 4.0 and renamed it as ALTIBASE HDB. Altibase released version 5.5.1 and 6.1.1 in 2012, version 6.3.1 in November 2013, and 6.5.1 in May 2015. Altibase claims that this is the world's first hybrid DBMS. Altibase released its open source edition version 7.1, however, closed the source in 2023. In August 2023, Altibase released its cloud-optimized version 7.3. === Awards === In 2006, Received the Presidential Award at the Korea Software Awards In 2007, Selected as World-Class Product by the Ministry of Commerce, Industry and Energy In 2009, Awarded the Outstanding Product Award in China's Telecommunications Industry In 2009, Received Outstanding Product Award at the China Billing China 2009 Telecommunication Industry Awards In 2010, Commendation from the Minister of Knowledge Economy for Technological Practicalization In 2011, Received the Grand Prize at the 10th Software Enterprise Competitiveness Award In 2011, Selected as Top 10 Emerging Technologies and received Special Award at the Korea Technology Grand Prize In 2012, Awarded for Contributions to Military Manpower Administration In 2014~2016, Included in Gartner Magic Quadrant for Operational DBMS In 2015, Selected as Outstanding BSS by China Fujian Mobile. In 2023, Awarded as the Excellent Research and Development Institution by the Korean Ministry Science and ICT In 2023, Won the Global Premium Commercial Software Presidential Award at the 9th Global Commercial Software Grand Exhibition in Korea === Release === The first version, called Spiner, was released in 2000 for commercial use. It took half of the in-memory DBMS market share in South Korea. In 2002 the second version was released renamed to Altibase v2.0. By 2003, Altibase v3.0 was released and it entered the Chinese market. Released version 4.0 with hybrid architecture, combining RAM and disk databases, was released in 2004. In 2005 Altibase began working with Chinese telecommunications providers for billing systems, and some financial companies in Taiwan, China, for home trading systems. The software was certified by the Telecommunications Technology Association. The Ministry of Government Administration and Home Affairs gave it an award in 2006. Offices in China and United States opened in 2009. In 2011, version 5.5.1 was renamed it to HDB (for "hybrid database"). The Altibase Data Stream product for complex event processing was renamed DSM. The product received a Korean technology award. Altibase introduced certification services. In 2012, HDB Zeta and Extreme were announced, and DSM renamed to CEP. In 2013, yet another variant called XDB was announced, and the company received ISO/IEC 20000 certification. In 2018, Altibase went open source. Altibase went open source in February, 2018. Altibase Corp has made the decision to discontinue the Altibase 7.1 open source edition, effective March 17, 2023. As a result, the open-source edition of Altibase 7.1 will no longer be available for download or use. Altibase released version 7.3 in September, 2023, its notable feature is the world’s first hybrid partition, allowing data to be stored in both memory and on disk at the partition level. Version 7.3 also added parallel processing capabilities for high-speed performance in both partitioned and non-partitioned scenarios. Improving potential bottlenecks associated with Commit and logging that impact transaction performance, version 7.3 has achieved an approximately 490% enhancement in performance compared to previous versions. === Release history === == Clients == According to marketing research, Altibase have over 700 customers and more than 8,000 of installations and deployments, including 22 Fortune Global 500 Companies. Altibase's clients in the telecommunications, financial services, manufacturing, and utilities sectors include Bloomberg, AT&T, LG, Intel, LGU+, ETRADE, HP, UAT Inc., POSCO, SK Telecom, KT Corporation, Samsung Electronics, Shinhan Bank, Woori Bank, Canon(Toshiba), Hanhwa, The South Korean Ministry of Defense, G-Market, CJ, and Chung-Ang University. === Global clients === Japan FX Prime, a foreign exchange services company Retela Crea Securities United States AT&T Implemented Altibase for its PS-LTE Safety network, where the Presence service plays a vital role. This service handles the reception and storage of user information, conducting real-time checks for online presence and location as needed. Canada Telus One of the major telecommunication companies. Utilizes Altibase for its operations involving real-time user management, processing high volumes of dedicated terminal data, and managing real-time location information (GIS) for terminals. Altibase contributes to the company's in-house solution for maintaining uninterrupted services during national disasters or similar situations, ensuring efficiency and reliability. China China Mobile, China Unicom, China Telecom The three major telecommunications companies. Utilize ALTIBASE HDB in 29 of 31 Chinese provinces. Turkish Ziraat Bank, Halk Bank, Deniz Bank, Garanti BBVA, TEB, Oyak Bank, QNB, Burgan Bank, and others. In 2018, Altibase entered the market through a partnership with ATP-Tradesoft, a subsidiary of Ata Holdings. Collaborating with ATP-Tradesoft. Altibase integrated into the Online Trading System XFront. This integration was well-received by major financial institutions and securities firms in Turkey. Altibase is currently implemented in the XFront Online Trading System, used by 13 significant financial institutions and banks in the Turkey. Thailand Bualuang Securities Altibase has been supplied its DBMS to support the construction of the online stock trading platform. Mongolia MobiCom The Mongolian telecommunication giant, has adopted Altibase’s 7.0 version for its mobile platform for storing the infrequently used data. Azerbaijan M1 highway Altibase has been supplied as the Database Management System (DBMS) for the electronic toll collection system. One of the most crucial transportation networks in the country. India State-owned Karur Vysya Bank In 2013, Altibase provided its hybrid database solution and was deployed for the online banking system === Industries === Telecommunications LGU+ SK Telecom KT Corporation AT&T Telus Financial services Shinhan Bank Woori Bank KakaoPay Securities Implemented Altibase in its stock trading system Leveraging Altibase's replication feature, along with offline replication through shared disk and adapter functionality, the system ensures a high level of availability and consistency, with a reliability rate of 99.999% even in the event of system failures. COREDAX Cryptocurrency market Altibase has entered into a strategic partnership by signing a database management system (DBMS) supply contract with the cryptocurrency exchange Bloomberg ETRADE Manufacturing Samsung Electronics LG POSCO Hanhwa Canon(Toshiba) Intel HP Utilities South Korean Ministry of Defense G-Market CJ UAT Inc. Chung-Ang University == Features == Altibase is a so-called "hybrid DBMS", meaning that it simultaneously supports access to both memory-resident and disk-resident tables via a single interface. It is compatible with Solaris, HP-UX, AIX, Linux, and Windows. It supports the complete SQL standard, features Multiversion concurrency control (MVCC), implements Fuzzy and Ping-Pong Checkpointing for periodically backing up memory-resident data, and ships with Replication and Database Link functionality. High performance, large -capacity service Fast real-time data processing and large amounts of data stable Provide parallel processing architecture for large data management Developed and provided Hybrid Partitioned Table function for efficiency according to data personality High stability

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  Computational creativity

  Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts (e.g., computational art as part of computational culture). Is the application of computer systems to emulate human-like creative processes, facilitating the generation of artistic and design outputs that mimic innovation and originality. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends: To construct a program or computer capable of human-level creativity. To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans. To design programs that can enhance human creativity without necessarily being creative themselves. The field of computational creativity concerns itself with theoretical and practical issues in the study of creativity. Theoretical work on the nature and proper definition of creativity is performed in parallel with practical work on the implementation of systems that exhibit creativity, with one strand of work informing the other. The applied form of computational creativity is known as media synthesis. == Theoretical issues == Theoretical approaches concern the essence of creativity. Especially, under what circumstances it is possible to call the model a "creative" if eminent creativity is about rule-breaking or the disavowal of convention. This is a variant of Ada Lovelace's objection to machine intelligence, as recapitulated by modern theorists such as Teresa Amabile. If a machine can do only what it was programmed to do, how can its behavior ever be called creative? Indeed, not all computer theorists would agree with the premise that computers can only do what they are programmed to do—a key point in favor of computational creativity. == Defining creativity in computational terms == Because no single perspective or definition seems to offer a complete picture of creativity, the AI researchers Newell, Shaw and Simon developed the combination of novelty and usefulness into the cornerstone of a multi-pronged view of creativity, one that uses the following four criteria to categorize a given answer or solution as creative: The answer is novel and useful (either for the individual or for society) The answer demands that we reject ideas we had previously accepted The answer results from intense motivation and persistence The answer comes from clarifying a problem that was originally vague Margaret Boden focused on the first two of these criteria, arguing instead that creativity (at least when asking whether computers could be creative) should be defined as "the ability to come up with ideas or artifacts that are new, surprising, and valuable". Mihaly Csikszentmihalyi argued that creativity had to be considered instead in a social context, and his DIFI (Domain-Individual-Field Interaction) framework has since strongly influenced the field. In DIFI, an individual produces works whose novelty and value are assessed by the field—other people in society—providing feedback and ultimately adding the work, now deemed creative, to the domain of societal works from which an individual might be later influenced. Whereas the above reflects a top-down approach to computational creativity, an alternative thread has developed among bottom-up computational psychologists involved in artificial neural network research. During the late 1980s and early 1990s, for example, such generative neural systems were driven by genetic algorithms. Experiments involving recurrent nets were successful in hybridizing simple musical melodies and predicting listener expectations. == Historical evolution of computational creativity == The use computational processes to generate creative artifacts has been present from early times in history. During the late 1800's, methods for composing music combinatorily were explored, involving prominent figures like Mozart, Bach, Haydn, and Kiernberger. This approach extended to analytical endeavors as early as 1934, where simple mechanical models were built to explore mathematical problem solving. Professional interest in the creative aspect of computation also was commonly addressed in early discussions of artificial intelligence. The 1956 Dartmouth Conference, listed creativity, invention, and discovery as key goals for artificial intelligence. As the development of computers allowed systems of greater complexity, the 1970's and 1980's saw invention of early systems that modelled creativity using symbolic or rule-based approaches. The field of creative storytelling investigated several such models. Meehan's TALE-SPIN (1977) generated narratives through simulation of character goals and decision trees. Dehn's AUTHOR (1981) approached generation by simulating an author's process for crafting a story. Beyond narrative generation, computational creativity expanded into artistic and scientific domains. Artistic image generation was one of the disciplines that saw early potential in generated artifacts through computational creativity. One of the most prominent examples was Harold Cohen's AARON, which produced art through composition and adaptation of figures based on a large set of symbolic rules and heuristics for visual composition. Some systems also tackled creativity in scientific endeavors. BACON was said to rediscover natural laws like Boyle's Law and Kepler's law through hypothesis testing in constrained spaces. By the 1990's the modeling techniques became more adaptive, attempting to implement cognitive creative rules for generation. Turner's MINSTREL (1993) introduced TRAMs (Transform Recall Adapt Methods) to simulate creative re-use of prior material for generative storytelling. Meanwhile, Pérez y Pérez's MEXICA (1999) modeled the creative writing process using cycles of engagement and reflection. As systems increasingly incorporated models of internal evaluation, another approach that emerged was that of combining symbolic generation with domain-specific evaluation metrics, modeling generative and selective steps to creativity In the field of generational humor, the JAPE system (1994) generated pun-based riddles using Prolog and WordNet, applying symbolic pattern-matching rules and a large lexical database (WordNet) to compose riddles involving wordplay. WordNet is a system developed by George Miller and his team at Princeton, its platform and inspired word-mapping structures have been used as the backbone of several syntactic and semantic AI programs. A notable system for music generation was David Cope's EMI (Experiments in Musical Intelligence) or Emmy, which was trained in the styles of artists like Bach, Beethoven, or Chopin and generated novel pieces in their style through pattern abstraction and recomposition. In the 2000s and beyond, machine learning began influencing creative system design. Researchers such as Mihalcea and Strapparava trained classifiers to distinguish humorous from non-humorous text, using stylistic and semantic features. Meanwhile custom computational approaches led to chess systems like Deep Blue generating quasi-creative gameplay strategies through search algorithms and parallel processing constrained by specific rules and patterns for evaluation. The institutional development of computational creativity grew along its technical advances. Dedicated workshops such as the IJWCC emerged in the 1990s, growing out of interdisciplinary conferences focused on AI and creativity. By the early 2000s, the field coalesced around annual conferences like the International Conference on Computational Creativity (ICCC). Recently, with the advent of Deep Learning, Transformers, and further refinement in Machine Learning structures, computational creativity's implementation space has new tools for development. == Machine learning for computational creativity == While traditional computational approaches to creativity rely on the explicit formulation of prescriptions by developers and a certain degree of randomness in computer programs, machine learning methods allow computer programs to learn on heuristics from input data enabling creative capacities within the computer programs. Especially, deep artificial neural networks allow to learn patterns from input data that allow for the non-linear generation of creative artefacts. Before 1989, artificial neural networks have been used to model certain aspects of creativity. Peter Todd (1989) first trained a neural network to reproduce musical melodies from a training set of musical pieces. Then he used a change algorithm to modify the network's input parameters. The network was able to randomly generate new music in a highly uncontrolled manner. In 1992, Todd extended this work, using the so-called distal teacher approach that had been d

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  ITools Resourceome

  iTools is a distributed infrastructure for managing, discovery, comparison and integration of computational biology resources. iTools employs Biositemap technology to retrieve and service meta-data about diverse bioinformatics data services, tools, and web-services. iTools is developed by the National Centers for Biomedical Computing as part of the NIH Road Map Initiative.

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  Mind map

  A mind map is a diagram used to visually organize information into a hierarchy, showing relationships among pieces of the whole. It is often based on a single concept, drawn as an image in the center of a blank page, to which associated representations of ideas such as images, words and parts of words are added. Major ideas are connected directly to the central concept, and other ideas branch out from those major ideas. Mind maps can also be drawn by hand, either as "notes" during a lecture, meeting or planning session, for example, or as higher quality pictures when more time is available. Mind maps are considered to be a type of spider diagram. == Origin == Although the term "mind map" was first popularized by British popular psychology author and television personality Tony Buzan, the use of diagrams that visually "map" information using branching and radial maps traces back centuries. These pictorial methods record knowledge and model systems, and have a long history in learning, brainstorming, memory, visual thinking, and problem solving by educators, engineers, psychologists, and others. Some of the earliest examples of such graphical records were developed by Porphyry of Tyros, a noted thinker of the 3rd century, as he graphically visualized the concept categories of Aristotle. Philosopher Ramon Llull (1235–1315) also used such techniques. Buzan's specific approach, and the introduction of the term "mind map", started with a 1974 BBC TV series he hosted, called Use Your Head. In this show, and companion book series, Buzan promoted his conception of radial tree, diagramming key words in a colorful, radiant, tree-like structure. == Differences from other visualizations == Concept maps: Mind maps differ from concept maps in that mind maps are based on a radial hierarchy (tree structure) denoting relationships with a central concept, whereas concept maps can be more free-form, based on connections between concepts in more diverse patterns. Also, concept maps typically have text labels on the links between nodes. However, either can be part of a larger personal knowledge base system. Modeling graphs or graphical modeling languages: There is no rigorous right or wrong with mind maps, which rely on the arbitrariness of mnemonic associations to aid people's information organization and memory. In contrast, a modeling graph such as a UML diagram structures elements using a precise standardized iconography to aid the design of systems. == Research == === Effectiveness === Cunningham (2005) conducted a user study in which 80% of the students thought "mindmapping helped them understand concepts and ideas in science". Other studies also report some subjective positive effects of the use of mind maps. Positive opinions on their effectiveness, however, were much more prominent among students of art and design than in students of computer and information technology, with 62.5% vs 34% (respectively) agreeing that they were able to understand concepts better with mind mapping software. Farrand, Hussain, and Hennessy (2002) found that spider diagrams (similar to concept maps) had limited, but significant, impact on memory recall in undergraduate students (a 10% increase over baseline for a 600-word text only) as compared to preferred study methods (a 6% increase over baseline). This improvement was only robust after a week for those in the diagram group and there was a significant decrease in motivation compared to the subjects' preferred methods of note taking. A meta study about concept mapping concluded that concept mapping is more effective than "reading text passages, attending lectures, and participating in class discussions". The same study also concluded that concept mapping is slightly more effective "than other constructive activities such as writing summaries and outlines". However, results were inconsistent, with the authors noting "significant heterogeneity was found in most subsets". In addition, they concluded that low-ability students may benefit more from mind mapping than high-ability students. === Features === Joeran Beel and Stefan Langer conducted a comprehensive analysis of the content of mind maps. They analysed 19,379 mind maps from 11,179 users of the mind mapping applications SciPlore MindMapping (now Docear) and MindMeister. Results include that average users create only a few mind maps (mean=2.7), average mind maps are rather small (31 nodes) with each node containing about three words (median). However, there were exceptions. One user created more than 200 mind maps, the largest mind map consisted of more than 50,000 nodes and the largest node contained ~7,500 words. The study also showed that between different mind mapping applications (Docear vs MindMeister) significant differences exist related to how users create mind maps. === Automatic creation === There have been some attempts to create mind maps automatically. Brucks & Schommer created mind maps automatically from full-text streams. Rothenberger et al. extracted the main story of a text and presented it as mind map. There is also a patent application about automatically creating sub-topics in mind maps. == Tools == Mind-mapping software can be used to organize large amounts of information, combining spatial organization, dynamic hierarchical structuring and node folding.Software packages can extend the concept of mind-mapping by allowing individuals to map more than thoughts and ideas with information on their computers and the Internet, like spreadsheets, documents, Internet sites, images and videos. It has been suggested that mind-mapping can improve learning/study efficiency up to 15% over conventional note-taking. == Gallery == The following dozen examples of mind maps show the range of styles that a mind map may take, from hand-drawn to computer-generated and from mostly text to highly illustrated. Despite their stylistic differences, all of the examples share a tree structure that hierarchically connects sub-topics to a main topic.

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  Software engineering professionalism

  Software engineering professionalism is a movement to make software engineering a profession, with aspects such as degree and certification programs, professional associations, professional ethics, and government licensing. The field is a licensed discipline in Texas in the United States (Texas Board of Professional Engineers, since 2013), Engineers Australia(Course Accreditation since 2001, not Licensing), and many provinces in Canada. == History == In 1993 the IEEE and ACM began a joint effort called JCESEP, which evolved into SWECC in 1998 to explore making software engineering into a profession. The ACM pulled out of SWECC in May 1999, objecting to its support for the Texas professionalization efforts, of having state licenses for software engineers. ACM determined that the state of knowledge and practice in software engineering was too immature to warrant licensing, and that licensing would give false assurances of competence even if the body of knowledge were mature. The IEEE continued to support making software engineering a branch of traditional engineering. In Canada the Canadian Information Processing Society established the Information Systems Professional certification process. Also, by the late 1990s (1999 in British Columbia) the discipline of software engineering as a professional engineering discipline was officially created. This has caused some disputes between the provincial engineering associations and companies who call their developers software engineers, even though these developers have not been licensed by any engineering association. In 1999, the Panel of Software Engineering was formed as part of the settlement between Engineering Canada and the Memorial University of Newfoundland over the school's use of the term "software engineering" in the name of a computer science program. Concerns were raised over the inappropriate use of the name "software engineering" to describe non-engineering programs could lead to student and public confusion, and ultimately threaten public safety. The Panel issued recommendations to create a Software Engineering Accreditation Board, but the task force created to carry out the recommendations was unable to get the various stakeholders to agree to concrete proposals, resulting in separate accreditation boards. == Ethics == Software engineering ethics is a large field. In some ways it began as an unrealistic attempt to define bugs as unethical. More recently it has been defined as the application of both computer science and engineering philosophy, principles, and practices to the design and development of software systems. Due to this engineering focus and the increased use of software in mission critical and human critical systems, where failure can result in large losses of capital but more importantly lives such as the Therac-25 system, many ethical codes have been developed by a number of societies, associations and organizations. These entities, such as the ACM, IEEE, EGBC and Institute for Certification of Computing Professionals (ICCP) have formal codes of ethics. Adherence to the code of ethics is required as a condition of membership or certification. According to the ICCP, violation of the code can result in revocation of the certificate. Also, all engineering societies require conformance to their ethical codes; violation of the code results in the revocation of the license to practice engineering in the society's jurisdiction. These codes of ethics usually have much in common. They typically relate the need to act consistently with the client's interest, employer's interest, and most importantly the public's interest. They also outline the need to act with professionalism and to promote an ethical approach to the profession. A Software Engineering Code of Ethics has been approved by the ACM and the IEEE-CS as the standard for teaching and practicing software engineering. === Examples of codes of conduct === The following are examples of codes of conduct for Professional Engineers. These 2 have been chosen because both jurisdictions have a designation for Professional Software Engineers. Engineers and Geoscientists of British Columbia (EGBC): All members in the association's code of Ethics must ensure that the government, the public can rely on BC's professional engineers and Geoscientists to act at all times with fairness, courtesy and good faith to their employers, employee and customers, and to uphold the truth, honesty and trustworthiness, and to safe guard human life and the environment. This is just one of the many ways in which BC's Professional Engineers and Professional Geoscientists maintain their competitive edge in today's global marketplace. Association of Professional Engineers and Geoscientists of Alberta (APEGA): Different with British Columbia, the Alberta Government granted self governance to engineers, Geoscientists and geophysicists. All members in the APEGA have to accept legal and ethical responsibility for the work and to hold the interest of the public and society. The APEGA is a standards guideline of professional practice to uphold the protection of public interest for engineering, Geoscientists and geophysics in Alberta. === Opinions on ethics === Bill Joy argued that "better software" can only enable its privileged end users, make reality more power-pointy as opposed to more humane, and ultimately run away with itself so that "the future doesn't need us." He openly questioned the goals of software engineering in this respect, asking why it isn't trying to be more ethical rather than more efficient. In his book Code and Other Laws of Cyberspace, Lawrence Lessig argues that computer code can regulate conduct in much the same way as the legal code. Lessig and Joy urge people to think about the consequences of the software being developed, not only in a functional way, but also in how it affects the public and society as a whole. Overall, due to the youth of software engineering, many of the ethical codes and values have been borrowed from other fields, such as mechanical and civil engineering. However, there are many ethical questions that even these, much older, disciplines have not encountered. Questions about the ethical impact of internet applications, which have a global reach, have never been encountered until recently and other ethical questions are still to be encountered. This means the ethical codes for software engineering are a work in progress, that will change and update as more questions arise. == Independent licensing and certification exams == Since 2002, the IEEE Computer Society offered the Certified Software Development Professional (CSDP) certification exam (in 2015 this was replaced by several similar certifications). A group of experts from industry and academia developed the exam and maintained it. Donald Bagert, and at a later period Stephen Tockey headed the certification committee. Contents of the exam centered around the SWEBOK (Software Engineering Body of Knowledge) guide, with an additional emphasis on Professional Practices and Software Engineering Economics knowledge areas (KAs). The motivation was to produce a structure at an international level for software engineering's knowledge areas. == Criticism of licensing == Professional licensing has been criticized for many reasons. The field of software engineering is too immature Licensing would give false assurances of competence even if the body of knowledge were mature Software engineers would have to study years of calculus, physics, and chemistry to pass the exams, which is irrelevant to most software practitioners. Many (most?) computer science majors don't earn degrees in engineering schools, so they are probably unqualified to pass engineering exams. == Licensing by country == === United States === The Bureau of Labor Statistics (BLS) classifies computer software engineers as a subcategory of "computer specialists", along with occupations such as computer scientist, Programmer, Database administrator and Network administrator. The BLS classifies all other engineering disciplines, including computer hardware engineers, as engineers. Many states prohibit unlicensed persons from calling themselves an Engineer, or from indicating branches or specialties not covered licensing acts. In many states, the title Engineer is reserved for individuals with a Professional Engineering license indicating that they have shown minimum level of competency through accredited engineering education, qualified engineering experience, and engineering board's examinations. In April 2013 the National Council of Examiners for Engineering and Surveying (NCEES) began offering a Professional Engineer (PE) exam for Software Engineering. The exam was developed in association with the IEEE Computer Society. NCEES ended the exam in April 2019 due to lack of participation. The American National Society of Professional Engineers provides a model law and lobbies legislatures to adopt occ

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  Logic Theorist

  Logic Theorist is a computer program completed in 1956 by Allen Newell, Herbert A. Simon, and Cliff Shaw. It was the first program deliberately engineered to perform automated reasoning, and has been described as "the first artificial intelligence program". Logic Theorist proved 38 of the first 52 theorems in chapter two of Whitehead and Bertrand Russell's Principia Mathematica, and found new and shorter proofs for some of them. == History == In 1955, when Newell and Simon began to work on the Logic Theorist, the field of artificial intelligence did not yet exist; the term "artificial intelligence" would not be coined until the following summer. Simon was a political scientist who had previously studied the way bureaucracies function as well as developing his theory of bounded rationality (for which he would later win the Nobel Memorial Prize in Economic Sciences in 1978). He believed the study of business organizations requires, like artificial intelligence, an insight into the nature of human problem solving and decision making. Simon has stated that when consulting at RAND Corporation in the early 1950s, he saw a printer typing out a map, using ordinary letters and punctuation as symbols. This led him to think that a machine that could manipulate symbols could simulate decision making and possibly even the process of human thought. The program that printed the map had been written by Newell, a RAND scientist studying logistics and organization theory. For Newell, the decisive moment was in 1954 when Oliver Selfridge came to RAND to describe his work on pattern matching. Watching the presentation, Newell suddenly understood how the interaction of simple, programmable units could accomplish complex behavior, including the intelligent behavior of human beings. "It all happened in one afternoon," he would later say. It was a rare moment of scientific epiphany. "I had such a sense of clarity that this was a new path, and one I was going to go down. I haven't had that sensation very many times. I'm pretty skeptical, and so I don't normally go off on a toot, but I did on that one. Completely absorbed in it—without existing with the two or three levels consciousness so that you're working, and aware that you're working, and aware of the consequences and implications, the normal mode of thought. No. Completely absorbed for ten to twelve hours." Newell and Simon began to talk about the possibility of teaching machines to think. Their first project was a program that could prove mathematical theorems like the ones used in Bertrand Russell and Alfred North Whitehead's Principia Mathematica. They enlisted the help of computer programmer Cliff Shaw, also from RAND, to develop the program. (Newell says "Cliff was the genuine computer scientist of the three".) The first version was hand-simulated: they wrote the program onto 3x5 cards and, as Simon recalled:In January 1956, we assembled my wife and three children together with some graduate students. To each member of the group, we gave one of the cards, so that each one became, in effect, a component of the computer program ... Here was nature imitating art imitating nature. They succeeded in showing that the program could successfully prove theorems as well as a talented mathematician. Eventually Shaw was able to run the program on the computer at RAND's Santa Monica facility. In the summer of 1956, John McCarthy, Marvin Minsky, Claude Shannon and Nathan Rochester organized a conference on the subject of what they called "artificial intelligence" (a term coined by McCarthy for the occasion). Newell and Simon proudly presented the group with the Logic Theorist. It was met with a lukewarm reception. Pamela McCorduck writes "the evidence is that nobody save Newell and Simon themselves sensed the long-range significance of what they were doing." Simon confides that "we were probably fairly arrogant about it all" and adds: They didn't want to hear from us, and we sure didn't want to hear from them: we had something to show them! ... In a way it was ironic because we already had done the first example of what they were after; and second, they didn't pay much attention to it. Logic Theorist soon proved 38 of the first 52 theorems in chapter 2 of the Principia Mathematica. The proof of theorem 2.85 was actually more elegant than the proof produced laboriously by hand by Russell and Whitehead (2026-03-20: What is called here Theorem 2.85 is, in fact, numbered as 2.53 in the page 107 of the 1963 Cambridge University Press edition (https://www.uhu.es/francisco.moreno/gii_mac/docs/Principia_Mathematica_vol1.pdf) and which appears, under the same 2.53 number, on page 112 of the 1910 CUP Edition, according to the digitalization on wikibooks (https://en.wikisource.org/wiki/Russell_%26_Whitehead%27s_Principia_Mathematica/Part_1/Section_A#Discussion_2)). Simon was able to show the new proof to Russell himself who "responded with delight". They attempted to publish the new proof in The Journal of Symbolic Logic, but it was rejected on the grounds that a new proof of an elementary mathematical theorem was not notable, apparently overlooking the fact that one of the authors was a computer program. Newell and Simon formed a lasting partnership, founding one of the first AI laboratories at the Carnegie Institute of Technology and developing a series of influential artificial intelligence programs and ideas, including the General Problem Solver, Soar, and their unified theory of cognition. == Architecture == The Logic Theorist is a program that performs logical processes on logical expressions. The Logic Theorist operates on the following principles: === Expressions === An expression is made of elements. There are two kinds of memories: working and storage. Each working memory contains a single element. The Logic Theorist usually uses 1 to 3 working memories. Each storage memory is a list representing a full expression or a set of elements. In particular, it contains all the axioms and proven logical theorems. An expression is an abstract syntax tree, each node being an element with up to 11 attributes. For example, the logical expression ¬ P → ( Q ∧ ¬ P ) {\displaystyle \neg P\to (Q\wedge \neg P)} is represented as a tree with a root element representing → {\displaystyle \to } . Among the attributes of the root element are pointers to the two elements representing the subexpressions ¬ P {\displaystyle \neg P} and Q ∧ ¬ P {\displaystyle Q\wedge \neg P} . === Processes === There are four kinds of processes, from the lowest to the highest level. Instruction: These are similar to assembly code. They may either perform a primitive operation on an expression in working memory, or perform a conditional jump to another instruction. An example is "put the right sub-element of working-memory 1 to working-memory 2" Elementary process: These are similar to subroutines. A sequence of instructions that can be called. Method: A sequence of elementary processes. There are 4 methods: substitution: given an expression, it attempts to transform it to a proven theorem or axiom by substitutions of variables and logical connectives. detachment: given expression B {\displaystyle B} , it attempts to find a proven theorem or axiom of form A → B ′ {\displaystyle A\to B'} , where B ′ {\displaystyle B'} yields B {\displaystyle B} after substitution, then attempts to prove A {\displaystyle A} by substitution. chaining forward: given expression A → C {\displaystyle A\to C} , it attempts to find for a proven theorem or axiom of form A → B {\displaystyle A\to B} , then attempt to prove B → C {\displaystyle B\to C} by substitution. chaining backward: given expression A → C {\displaystyle A\to C} , it attempts to find for a proven theorem or axiom of form B → C {\displaystyle B\to C} , then attempt to prove A → B {\displaystyle A\to B} by substitution. executive control method: This method applies each of the 4 methods in sequence to each theorem to be proved. == Logic Theorist's influence on AI == Logic Theorist introduced several concepts that would be central to AI research: Reasoning as search Logic Theorist explored a search tree: the root was the initial hypothesis, each branch was a deduction based on the rules of logic. Somewhere in the tree was the goal: the proposition the program intended to prove. The pathway along the branches that led to the goal was a proof – a series of statements, each deduced using the rules of logic, that led from the hypothesis to the proposition to be proved. Heuristics Newell and Simon realized that the search tree would grow exponentially and that they needed to "trim" some branches, using "rules of thumb" to determine which pathways were unlikely to lead to a solution. They called these ad hoc rules "heuristics", using a term introduced by George Pólya in his classic book on mathematical proof, How to Solve It. (Newell had taken courses from Pólya at Stanford). Heuristics would become an important area o

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  IPO underpricing algorithm

  IPO underpricing is the increase in stock value from the initial offering price to the first-day closing price. Many believe that underpriced IPOs leave money on the table for corporations, but some believe that underpricing is inevitable. Investors state that underpricing signals high interest to the market which increases the demand. On the other hand, overpriced stocks will drop long-term as the price stabilizes so underpricing may keep the issuers safe from investor litigation. == IPO underpricing algorithms == Underwriters and investors and corporations going for an initial public offering (IPO), issuers, are interested in their market value. There is always tension that results since the underwriters want to keep the price low while the companies want a high IPO price. Underpricing may also be caused by investor over-reaction causing spikes on the initial days of trading. The IPO pricing process is similar to pricing new and unique products where there is sparse data on market demand, product acceptance, or competitive response. Thus it is difficult to determine a clear price which is compounded by the different goals issuers and investors have. The problem with developing algorithms to determine underpricing is dealing with noisy, complex, and unordered data sets. Additionally, people, environment, and various environmental conditions introduce irregularities in the data. To resolve these issues, researchers have found various techniques from artificial intelligence that normalizes the data. == Evolutionary models == Evolutionary programming is often paired with other algorithms e.g. artificial neural networks to improve the robustness, reliability, and adaptability. Evolutionary models reduce error rates by allowing the numerical values to change within the fixed structure of the program. Designers provide their algorithms the variables, they then provide training data to help the program generate rules defined in the input space that make a prediction in the output variable space. In this approach, the solution is made an individual and the population is made of alternatives. However, the outliers cause the individuals to act unexpectedly as they try to create rules to explain the whole set. === Rule-based system === For example, Quintana first abstracts a model with 7 major variables. The rules evolved from the Evolutionary Computation system developed at Michigan and Pittsburgh: Underwriter prestige – Is the underwriter prestigious in role of lead manager? 1 for true, 0 otherwise. Price range width – The width of the non-binding reference price range offered to potential customers during the roadshow. This width can be interpreted as a sign of uncertainty regarding the real value of the company and a therefore, as a factor that could influence the initial return. Price adjustment – The difference between the final offer price and the price range width. It can be viewed as uncertainty if the adjustment is outside the previous price range. Offering price – The final offer price of the IPO Retained stock – Ratio of number of shares sold at the IPO divided by post-offering number of shares minus the number of shares sold at the IPO. Offering size – Logarithm of the offering size in millions of dollars excluding the over-allotment option Technology – Is this a technology company? 1 for true, 0 otherwise. Quintana uses these factors as signals that investors focus on. The algorithm his team explains shows how a prediction with a high-degree of confidence is possible with just a subset of the data. === Two-layered evolutionary forecasting === Luque approaches the problem with outliers by performing linear regressions over the set of data points (input, output). The algorithm deals with the data by allocating regions for noisy data. The scheme has the advantage of isolating noisy patterns which reduces the effect outliers have on the rule-generation system. The algorithm can come back later to understand if the isolated data sets influence the general data. Finally, the worst results from the algorithm outperformed all other algorithms' predictive abilities. == Agent-based modelling == Currently, many of the algorithms assume homogeneous and rational behavior among investors. However, there's an approach alternative to financial modeling, and it's called agent-based modelling (ABM). ABM uses different autonomous agents whose behavior evolves endogenously which lead to complicated system dynamics that are sometimes impossible to predict from the properties of individual agents. ABM is starting to be applied to computational finance. Though, for ABM to be more accurate, better models for rule-generation need to be developed.

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  Model

  A model is an informative representation of an object, person, or system. The term originally denoted the plans of a building in 16th-century English, and derived via French and Italian ultimately from Latin modulus, 'a measure'. Models can be divided into physical models (e.g. a ship model) and abstract models (e.g. a set of mathematical equations describing the workings of the atmosphere for the purpose of weather forecasting). Abstract or conceptual models are central to philosophy of science. In scholarly research and applied science, a model should not be confused with a theory: while a model seeks only to represent reality with the purpose of better understanding or predicting the world, a theory is more ambitious in that it claims to be an explanation of reality. == Types of model == === Model in specific contexts === As a noun, model has specific meanings in certain fields, derived from its original meaning of "structural design or layout": Model (art), a person posing for an artist, e.g. a 15th-century criminal representing the biblical Judas in Leonardo da Vinci's painting The Last Supper Model (person), a person who serves as a template for others to copy, as in a role model, often in the context of advertising commercial products; e.g. the first fashion model, Marie Vernet Worth in 1853, wife of designer Charles Frederick Worth. Model (product), a particular design of a product as displayed in a catalogue or show room (e.g. Ford Model T, an early car model) Model (organism) a non-human species that is studied to understand biological phenomena in other organisms, e.g. a guinea pig starved of vitamin C to study scurvy, an experiment that would be immoral to conduct on a person Model (mimicry), a species that is mimicked by another species Model (logic), a structure (a set of items, such as natural numbers 1, 2, 3,..., along with mathematical operations such as addition and multiplication, and relations, such as < {\displaystyle <} ) that satisfies a given system of axioms (basic truisms), i.e. that satisfies the statements of a given theory Model (CGI), a mathematical representation of any surface of an object in three dimensions via specialized software Model (MVC), the information-representing internal component of a software, as distinct from its user interface === Physical model === A physical model (most commonly referred to simply as a model but in this context distinguished from a conceptual model) is a smaller or larger physical representation of an object, person or system. The object being modelled may be small (e.g., an atom) or large (e.g., the Solar System) or life-size (e.g., a fashion model displaying clothes for similarly-built potential customers). The geometry of the model and the object it represents are often similar in the sense that one is a rescaling of the other. However, in many cases the similarity is only approximate or even intentionally distorted. Sometimes the distortion is systematic, e.g., a fixed scale horizontally and a larger fixed scale vertically when modelling topography to enhance a region's mountains. An architectural model permits visualization of internal relationships within the structure or external relationships of the structure to the environment. Another use is as a toy. Instrumented physical models are an effective way of investigating fluid flows for engineering design. Physical models are often coupled with computational fluid dynamics models to optimize the design of equipment and processes. This includes external flow such as around buildings, vehicles, people, or hydraulic structures. Wind tunnel and water tunnel testing is often used for these design efforts. Instrumented physical models can also examine internal flows, for the design of ductwork systems, pollution control equipment, food processing machines, and mixing vessels. Transparent flow models are used in this case to observe the detailed flow phenomenon. These models are scaled in terms of both geometry and important forces, for example, using Froude number or Reynolds number scaling (see Similitude). In the pre-computer era, the UK economy was modelled with the hydraulic model MONIAC, to predict for example the effect of tax rises on employment. === Conceptual model === A conceptual model is a theoretical representation of a system, e.g. a set of mathematical equations attempting to describe the workings of the atmosphere for the purpose of weather forecasting. It consists of concepts used to help understand or simulate a subject the model represents. Abstract or conceptual models are central to philosophy of science, as almost every scientific theory effectively embeds some kind of model of the physical or human sphere. In some sense, a physical model "is always the reification of some conceptual model; the conceptual model is conceived ahead as the blueprint of the physical one", which is then constructed as conceived. Thus, the term refers to models that are formed after a conceptualization or generalization process. === Examples === Conceptual model (computer science), an agreed representation of entities and their relationships, to assist in developing software Economic model, a theoretical construct representing economic processes Language model, a probabilistic model of a natural language, used for speech recognition, language generation, and information retrieval Large language models are artificial neural networks used for generative artificial intelligence (AI), e.g. ChatGPT Mathematical model, a description of a system using mathematical concepts and language Statistical model, a mathematical model that usually specifies the relationship between one or more random variables and other non-random variables Model (CGI), a mathematical representation of any surface of an object in three dimensions via specialized software Medical model, a proposed "set of procedures in which all doctors are trained" Mental model, in psychology, an internal representation of external reality Model (logic), a set along with a collection of finitary operations, and relations that are defined on it, satisfying a given collection of axioms Model (MVC), information-representing component of a software, distinct from the user interface (the "view"), both linked by the "controller" component, in the context of the model–view–controller software design Model act, a law drafted centrally to be disseminated and proposed for enactment in multiple independent legislatures Standard model (disambiguation) == Properties of models, according to general model theory == According to Herbert Stachowiak, a model is characterized by at least three properties: 1. Mapping A model always is a model of something—it is an image or representation of some natural or artificial, existing or imagined original, where this original itself could be a model. 2. Reduction In general, a model will not include all attributes that describe the original but only those that appear relevant to the model's creator or user. 3. Pragmatism A model does not relate unambiguously to its original. It is intended to work as a replacement for the original a) for certain subjects (for whom?) b) within a certain time range (when?) c) restricted to certain conceptual or physical actions (what for?). For example, a street map is a model of the actual streets in a city (mapping), showing the course of the streets while leaving out, say, traffic signs and road markings (reduction), made for pedestrians and vehicle drivers for the purpose of finding one's way in the city (pragmatism). Additional properties have been proposed, like extension and distortion as well as validity. The American philosopher Michael Weisberg differentiates between concrete and mathematical models and proposes computer simulations (computational models) as their own class of models. == Uses of models == According to Bruce Edmonds, there are at least 5 general uses for models: Prediction: reliably anticipating unknown data, including data within the domain of the training data (interpolation), and outside the domain (extrapolation) Explanation: establishing plausible chains of causality by proposing mechanisms that can explain patterns seen in data Theoretical exposition: discovering or proposing new hypotheses, or refuting existing hypotheses about the behaviour of the system being modelled Description: representing important aspects of the system being modelled Illustration: communicating an idea or explanation

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  Tandem Money

  Tandem is one of the UK's original challenger banks. Tandem is a digital bank with a mobile app, and no branches. The acquisition of Harrods Bank in 2017 allowed the company to provide services using the former's banking licence. Tandem Bank Limited is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority. Tandem has offices across the UK in Blackpool, Cardiff, Durham and London, employing over 500 people. == History == The company was founded by Ricky Knox, Matt Cooper and Michael Kent in 2014. In December 2016, Tandem announced that it had secured a £35 million investment from The Sanpower Group, the Chinese company that also owned the department store House of Fraser; however, £29 million of this investment was later revoked by Sanpower over concerns that the Chinese Government would object to the investment following increased restrictions on outbound investment in China. This resulted in a delay in the launch of Tandem's savings products, which, at the time of the revocation, was expected imminently and, more importantly, meant that Tandem volunteered the return of their banking license but retained all other permissions. In April 2018, Tandem launched fixed-term savings accounts, offering one-, two- and three-year terms through its app. === Acquisitions === In August 2017, it was announced that Tandem would fully acquire Harrods Bank, founded in 1893, in a deal that would bring a near-£200m loan book, over £300m of deposits and nearly £80 million of capital. Prior to its sale to Tandem Money, Harrods Bank catered for high-net-worth (HNW) individuals and operated from the Harrods store in Knightsbridge, London. It offered a variety of personal and business current and savings accounts, mortgages, foreign currency and gold bullion trading services. On 7 August 2017, Tandem Money Limited announced a deal to acquire 100% of Harrods Bank Limited shares. The purchase deal closed successfully on 11 January 2018. In March 2018, Tandem agreed to acquire Pariti Technologies Limited, developers of the Pariti money management application. In August 2020 Tandem acquired green home improvement loan specialists Allium Lending Group. It was announced on 8 February 2021 that Tandem had agreed to purchase the mortgage book from private bank Bank and Clients, consisting of 300 B&C customers for an undisclosed amount. In January 2022 Tandem Bank acquired consumer lender Oplo, creating a combined business with £1.2 billion of total assets. In April 2023, it was announced that Tandem had acquired money-sharing app Loop Money. At the time of the purchase, one of Loop's founders – Paul Pester – was also chairman at Tandem. == Features == Tandem Bank offers customers savings, mortgages, personal and secured loans, green home improvement loans and motor finance. In November 2022, the bank launched its new Tandem Marketplace, providing information and resources to help promote greener living.

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  Mistral AI

  Mistral AI SAS (French: [mistʁal]) is a French artificial intelligence (AI) company, headquartered in Paris. Founded in 2023, it has open-weight large language models (LLMs), with both open-source and proprietary AI models. As of 2025 the company has a valuation of more than US$14 billion. == Namesake == The company is named after the mistral, a powerful, cold wind in southern France, a term which originates from the Occitan language. == History == Mistral AI was established in April 2023 by three French AI researchers, Arthur Mensch, Guillaume Lample and Timothée Lacroix. Mensch, an expert in advanced AI systems, is a former employee of Google DeepMind; Lample and Lacroix, meanwhile, are large-scale AI models specialists who had worked for Meta Platforms. The trio originally met during their studies at École Polytechnique. == Company operation == === Funding === In June 2023, the start-up carried out a first fundraising of €105 million ($117 million) with investors including the American fund Lightspeed Venture Partners, Eric Schmidt, Xavier Niel and JCDecaux. The valuation was then estimated by the Financial Times at €240 million ($267 million). On 10 December 2023, Mistral AI announced that it had raised €385 million ($428 million) as part of its second fundraising. This round of financing involves the Californian fund Andreessen Horowitz, BNP Paribas and the software publisher Salesforce. It was valued at over €2 billion. On 26 February 2024, Microsoft announced an investment of $16 million in Mistral AI. On 16 April 2024, reporting revealed that Mistral was in talks to raise €500 million, a deal that would more than double its current valuation to at least €5 billion. In June 2024, Mistral AI secured a €600 million ($645 million) funding round, increasing its valuation to €5.8 billion ($6.2 billion). Based on valuation, as of June 2024, the company was ranked fourth globally in the AI industry, and first outside the San Francisco Bay Area. In April 2025, Mistral AI announced a €100 million partnership with the shipping company CMA CGM. In August 2025, the Financial Times reported that Mistral was in talks to raise $1 billion at a $10 billion valuation. In September 2025, Bloomberg announced that Mistral AI has secured a €2 billion investment valuing it at €12 billion ($14 billion). This comes after $1.5 billion investment from Dutch company ASML, which owns 11% of Mistral. In February 2026, Mistral acquired Koyeb, a Paris-based AI startup. Later that month, Mistral AI announced a multi-year strategic partnership with Accenture to help enterprises deploy sovereign AI solutions at scale. In March 2026 Mistral raised $830 million in order to build new datacenters near Paris and in Sweden. == Services == On 19 November, 2024, the company announced updates for Le Chat (pronounced /lə ʃa/ in French, like the French word for "cat"). It added the ability to create images, using Black Forest Labs' Flux Pro model. On 6 February 2025, Mistral AI released Le Chat on iOS and Android mobile devices. Mistral AI also introduced a Pro subscription tier, priced at $14.99 per month, which provides access to more advanced models, unlimited messaging, and web browsing. At the end of May 2026, Le Chat was renamed Vibe, and new features were introduced at the same time. == Models == The following table lists the main model versions of Mistral, describing the significant changes included with each version: === Mistral 7B === Mistral AI claimed in the Mistral 7B release blog post that the model outperforms LLaMA 2 13B on all benchmarks tested, and is on par with LLaMA 34B on many benchmarks tested, despite having only 7 billion parameters, a small size compared to its competitors. === Mixtral 8x7B === Mistral AI claimed in 2023 that its model beat both LLaMA 70B, and GPT-3.5 in most benchmarks. In March 2024, research conducted by Patronus AI comparing performance of LLMs on a 100-question test with prompts to generate text from books protected under U.S. copyright law found that OpenAI's GPT-4, Mixtral, Meta AI's LLaMA-2, and Anthropic's Claude 2 generated copyrighted text verbatim in 44%, 22%, 10%, and 8% of responses respectively. === Mistral Small 3.1 === On 17 March 2025, Mistral released Mistral Small 3.1 as a smaller, more efficient model. === Mistral Medium 3 === On 7 May 2025, Mistral AI released Mistral Medium 3. === Magistral Small and Magistral Medium === On 10 June 2025, Mistral AI released their first AI reasoning models: Magistral Small (open-source), and Magistral Medium, models which are purported to have chain-of-thought capabilities. === Mistral Large 3 and Ministral 3 === On 2 December 2025, Mistral AI released Mistral Large 3, a sparse, mixture-of-experts model with 41 billion active parameters and 675 billion total parameters, and Ministral 3, three small, dense models with 3 billion, 7 billion and 14 billion parameters. === Devstral 2 and Devstral Small 2 === On 10 December 2025, Mistral AI released Devstral 2 and Devstral Small 2. Devstral Small 2, a 24B parameter model is claimed to achieve better performance at coding than Qwen 3 Coder Flash model which is a 30B parameter model.

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  Mathematical model

  A mathematical model is an abstract description of a concrete system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in many fields, including applied mathematics, natural sciences, social sciences and engineering. In particular, the field of operations research studies the use of mathematical modelling and related tools to solve problems in business or military operations. A model may help to characterize a system by studying the effects of different components, which may be used to make predictions about behavior or solve specific problems. == Elements of a mathematical model == Mathematical models can take many forms, including dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving a variety of abstract structures. In many cases, the quality of a scientific field depends on how well the mathematical models developed on the theoretical side agree with results of repeatable experiments. Lack of agreement between theoretical mathematical models and experimental measurements often leads to important advances as better theories are developed. In the physical sciences, a traditional mathematical model contains most of the following elements: Governing equations Supplementary sub-models Defining equations Constitutive equations Assumptions and constraints Initial and boundary conditions Classical constraints and kinematic equations == Classifications == Mathematical models are of different types: === Linear vs. nonlinear === If all the operators in a mathematical model exhibit linearity, the resulting mathematical model is defined as linear. All other models are considered nonlinear. The definition of linearity and nonlinearity is dependent on context, and linear models may have nonlinear expressions in them. For example, in a statistical linear model, it is assumed that a relationship is linear in the parameters, but it may be nonlinear in the predictor variables. Similarly, a differential equation is said to be linear if it can be written with linear differential operators, but it can still have nonlinear expressions in it. In a mathematical programming model, if the objective functions and constraints are represented entirely by linear equations, then the model is regarded as a linear model. If one or more of the objective functions or constraints are represented with a nonlinear equation, then the model is known as a nonlinear model. Linear structure implies that a problem can be decomposed into simpler parts that can be treated independently or analyzed at a different scale, and therefore that the results will remain valid if the initial is recomposed or rescaled. Nonlinearity, even in fairly simple systems, is often associated with phenomena such as chaos and irreversibility. Although there are exceptions, nonlinear systems and models tend to be more difficult to study than linear ones. A common approach to nonlinear problems is linearization, but this can be problematic if one is trying to study aspects such as irreversibility, which are strongly tied to nonlinearity. === Static vs. dynamic === A dynamic model accounts for time-dependent changes in the state of the system, while a static (or steady-state) model calculates the system in equilibrium, and thus is time-invariant. Dynamic models are typically represented by differential equations or difference equations. === Explicit vs. implicit === If all of the input parameters of the overall model are known, and the output parameters can be calculated by a finite series of computations, the model is said to be explicit. But sometimes it is the output parameters which are known, and the corresponding inputs must be solved for by an iterative procedure, such as Newton's method or Broyden's method. In such a case the model is said to be implicit. For example, a jet engine's physical properties such as turbine and nozzle throat areas can be explicitly calculated given a design thermodynamic cycle (air and fuel flow rates, pressures, and temperatures) at a specific flight condition and power setting, but the engine's operating cycles at other flight conditions and power settings cannot be explicitly calculated from the constant physical properties. === Discrete vs. continuous === A discrete model treats objects as discrete, such as the particles in a molecular model or the states in a statistical model; while a continuous model represents the objects in a continuous manner, such as the velocity field of fluid in pipe flows, temperatures and stresses in a solid, and electric field that applies continuously over the entire model due to a point charge. === Deterministic vs. probabilistic (stochastic) === A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables; therefore, a deterministic model always performs the same way for a given set of initial conditions. Conversely, in a stochastic model—usually called a "statistical model"—randomness is present, and variable states are not described by unique values, but rather by probability distributions. === Deductive, inductive, or floating === A deductive model is a logical structure based on a theory. An inductive model arises from empirical findings and generalization from them. If a model rests on neither theory nor observation, it may be described as a 'floating' model. Application of mathematics in social sciences outside of economics has been criticized for unfounded models. Application of catastrophe theory in science has been characterized as a floating model. === Strategic vs. non-strategic === Models used in game theory are distinct in the sense that they model agents with incompatible incentives, such as competing species or bidders in an auction. Strategic models assume that players are autonomous decision makers who rationally choose actions that maximize their objective function. A key challenge of using strategic models is defining and computing solution concepts such as the Nash equilibrium. An interesting property of strategic models is that they separate reasoning about rules of the game from reasoning about behavior of the players. == Construction == In business and engineering, mathematical models may be used to maximize a certain output. The system under consideration will require certain inputs. The system relating inputs to outputs depends on other variables too: decision variables, state variables, exogenous variables, and random variables. Decision variables are sometimes known as independent variables. Exogenous variables are sometimes known as parameters or constants. The variables are not independent of each other as the state variables are dependent on the decision, input, random, and exogenous variables. Furthermore, the output variables are dependent on the state of the system (represented by the state variables). Objectives and constraints of the system and its users can be represented as functions of the output variables or state variables. The objective functions will depend on the perspective of the model's user. Depending on the context, an objective function is also known as an index of performance, as it is some measure of interest to the user. Although there is no limit to the number of objective functions and constraints a model can have, using or optimizing the model becomes more involved (computationally) as the number increases. For example, economists often apply linear algebra when using input–output models. Complicated mathematical models that have many variables may be consolidated by use of vectors where one symbol represents several variables. === A priori information === Mathematical modeling problems are often classified into black box or white box models, according to how much a priori information on the system is available. A black-box model is a system of which there is no a priori information available. A white-box model (also called glass box or clear box) is a system where all necessary information is available. Practically all systems are somewhere between the black-box and white-box models, so this concept is useful only as an intuitive guide for deciding which approach to take. Usually, it is preferable to use as much a priori information as possible to make the model more accurate. Therefore, the white-box models are usually considered easier, because if you have used the information correctly, then the model will behave correctly. Often the a priori information comes in forms of knowing the type of functions relating different variables. For example, if we make a model of how a medicine works in a human system, we know that usually the amount of medicine in the blood is an exponentially decaying function, but we are still left with several unknown parameters; how

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  Fei-Fei Li

  Fei-Fei Li (Chinese: 李飞飞; pinyin: Lǐ Fēifēi; born July 3, 1976) is a Chinese-born American computer scientist best known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is a professor of computer science at Stanford University, with research expertise in artificial intelligence, machine learning, deep learning, computer vision, and cognitive neuroscience. Li is a co-director of the Stanford Institute for Human-Centered Artificial Intelligence and a co-director of the Stanford Vision and Learning Lab, and served as Chief Scientist of AI/ML at Google Cloud and the director of the Stanford Artificial Intelligence Laboratory from 2013 to 2018. In 2017, she co-founded AI4ALL, a nonprofit organization working to increase diversity in the field of artificial intelligence. In 2023, Li was named one of the Time 100 AI Most Influential People. Li received the Intel Lifetime Achievements Innovation Award in 2017 for her contributions to artificial intelligence, and was elected member of the National Academy of Engineering, the National Academy of Medicine in 2020 and the American Academy of Arts and Sciences in 2021. In 2025, she was named as one of the "Architects of AI" for Time's Person of the Year. On August 3, 2023, Li was appointed to the United Nations Scientific Advisory Board, established by Secretary-General Antonio Guterres. In 2024, Li was included on the Gold House's most influential Asian A100 list. In 2024, she raised $230 million for a startup called World Labs, which she and three colleagues founded to develop a "spatial intelligence" AI technology that can understand how the three-dimensional physical world works. In 2026, World Labs raised $1 Billion. == Early life and education == Li was born in Beijing, China, in 1976 and grew up in Chengdu, Sichuan. She studied at Sichuan Chengdu No.7 High School. When she was 12, her father immigrated to Parsippany, New Jersey. When she was 16, Li and her mother joined him in the United States. While attending Parsippany High School, Li worked weekends at her family's dry-cleaning shop. She graduated from Parsippany High School in 1995. She was inducted into the hall of fame at Parsippany High School in 2017. Li pursued undergraduate study at Princeton University, where she received a Bachelor of Arts with a major in physics in 1999. Li completed her senior thesis, "Auditory binaural correlogram difference: a new computational model for Huggins dichotic pitch", under the supervision of Bradley Dickinson, professor of electrical engineering. During her years at Princeton, Li returned home most weekends to help run her family's dry cleaning business and worked as a dishwasher to supplement the family income. Li pursued graduate study at the California Institute of Technology, where she received a Master of Science in electrical engineering in 2001 and a Doctor of Philosophy in electrical engineering in 2005. Li completed her dissertation, "Visual Recognition: Computational Models and Human Psychophysics", under the primary supervision of Pietro Perona and secondary supervision of Christof Koch. Her graduate studies were supported by the National Science Foundation Graduate Research Fellowship and The Paul & Daisy Soros Fellowships for New Americans. == Career and research == From 2005 to 2006, Li was an assistant professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign, and from 2007 to 2009, she was an assistant professor in the Computer Science Department at Princeton University. She joined Stanford in 2009 as an assistant professor, and was promoted to associate professor with tenure in 2012, and then full professor in 2018. At Stanford, Li served as the director of Stanford Artificial Intelligence Lab (SAIL) from 2013 to 2018. Her research has focused on computer vision, deep learning, and cognitive neuroscience, with over 300 peer-reviewed publications. She became the founding co-director of Stanford's University-level initiative - the Human-Centered AI Institute, along with co-director Dr. John Etchemendy, former provost of Stanford University. The institute aligns with Li's aims to advance AI research, education, policy, and practice to improve the human condition. While at Princeton in 2007, Li led the development of ImageNet, a massive visual database designed to advance object recognition in AI. The project involved labeling over 14 million images using Amazon Mechanical Turk and inspired the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which catalyzed progress in deep learning and led to dramatic improvements in image classification performance. The database addressed a key bottleneck in computer vision: the lack of large, annotated datasets for training machine learning models. Today, ImageNet is credited as a cornerstone innovation that underpins advancements in autonomous vehicles, facial recognition, and medical imaging. On her sabbatical from Stanford University from January 2017 to fall of 2018, Li joined Google Cloud as its Chief Scientist of AI/ML and Vice President. At Google, her team focused on democratizing AI technology and lowering the barrier for entrance to businesses and developers, including the developments of products like AutoML. In September 2017, Google secured a contract from the Department of Defense called Project Maven, which aimed to use AI techniques to interpret images captured by drone cameras. Google told employees who protested the company's work on Project Maven that their role was "specifically scoped to be for non-offensive purposes". In June 2018, Google told employees it would not seek renewal of the contract. In internal emails which were later leaked to reporters, Li expressed enthusiasm for the Google Cloud role in Project Maven, but warned against mentioning its AI component, saying that military AI is linked in the public mind with the danger of autonomous weapons. Asked about those leaked emails, Li told The New York Times, "I believe in human-centered AI to benefit people in positive and benevolent ways. It is deeply against my principles to work on any project that I think is to weaponize AI." In the fall of 2018, Li left Google and returned to Stanford University to continue her professorship. In 2023, Li co-led the launch of the RAISE-Health (Responsible AI for Safe and Equitable Health) initiative at Stanford University in collaboration with Stanford medicine. The initiative aims to develop frameworks for the responsible use of artificial intelligence in healthcare, including clinical care, biomedical research, and patient safety. According to her Stanford profile, she has been on partial academic leave from January 2024 through the end of 2025 to focus on entrepreneurial ventures. In 2024, Li said there was a disparity between private-sector investment in AI and support for academic and government research, and called for greater public funding for scientific uses of the technology and for studying its risks. Li is also known for her non-profit work as the co-founder and chairperson of nonprofit organization AI4ALL, whose mission is to educate the next generation of AI technologists, thinkers and leaders by promoting diversity and inclusion through human-centered AI principles. The program was created in collaboration with Melinda French Gates and Jensen Huang. Prior to establishing AI4ALL in 2017, Li and her former student Olga Russakovsky, currently an assistant professor in Princeton University, co-founded and co-directed the precursor program at Stanford called SAILORS (Stanford AI Lab OutReach Summers). SAILORS was an annual summer camp at Stanford dedicated to 9th grade high school girls in AI education and research, established in 2015 till it changed its name to AI4ALL @Stanford in 2017. In 2018, AI4ALL has successfully launched five more summer programs in addition to Stanford, including Princeton University, Carnegie Mellon University, Boston University, University of California Berkeley, and Canada's Simon Fraser University. We are at a turning point. AI's influence continues to grow, but representation and inclusion of a diversity of researchers in the field does not. It's critical that we seize this moment to create structures that will support long-term, positive changes. This won't happen via a single mechanism or quick fix. It starts with early education and extends to the existing structures of power within academia, work cultures among current AI researchers, and gatekeeping functions of research publishing, to name a few levers of change. Li has been described as a "researcher bringing humanity to AI". Li was elected as a member of the American Academy of Arts and Sciences in 2021, the National Academy of Engineering in 2020, and the National Academy of Medicine in 2020. In a November 2023 interview with The Guardian, Li said that while she would not refer to herself as the "godmother

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  Transportation Economic Development Impact System

  Transportation Economic Development Impact System (TREDIS) is an economic analysis system sold by consulting firm Economic Development Research Group that is used in planning major transportation investments in the US and Canada. The role of economic impact analysis and TREDIS in the transportation planning process is explained in guidebooks of the US Department of Transportation and the American Association of State Highway and Transportation Officials. TREDIS has been most commonly used for assessing the expected economic impacts of statewide highway programs, regional multi-modal plans and public transport investment. Its history and theoretical foundation are explained in peer reviewed journal articles. == How It Works == TREDIS has a series of modules that calculate different forms of impacts and benefits. One module is an accounting framework that calculates user benefits, including impacts on cargo transportation and commuting costs, based on transportation forecasting results. A second module calculates wider economic development benefits, including impacts on business productivity, economic development and multiplier effects from the input-output analysis. It applies an economic model to estimate impacts on jobs, income, gross regional product and business output, by sector of the economy. A third module applies cost-benefit analysis from alternative perspectives.

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  Imageability

  Imageability is a measure of how easily a physical object, word or environment will evoke a clear mental image in the mind of any person observing it. It is used in architecture and city planning, in psycholinguistics, and in automated computer vision research. In automated image recognition, training models to connect images with concepts that have low imageability can lead to biased and harmful results. == History and components == Kevin A. Lynch first introduced the term, "imageability" in his 1960 book, The Image of the City. In the book, Lynch argues cities contain a key set of physical elements that people use to understand the environment, orient themselves inside of it, and assign it meaning. Lynch argues the five key elements that impact the imageability of a city are Paths, Edges, Districts, Nodes, and Landmarks. Paths: channels in which people travel. Examples: streets, sidewalks, trails, canals, railroads. Edges: objects that form boundaries around space. Examples: walls, buildings, shoreline, curbstone, streets, and overpasses. Districts: medium to large areas people can enter into and out of that have a common set of identifiable characteristics. Nodes: large areas people can enter, that serve as the foci of the city, neighborhood, district, etc. Landmarks: memorable points of reference people cannot enter into. Examples: signs, mountains and public art. In 1914, half a century before The Image of the City was published, Paul Stern discussed a concept similar to imageability in the context of art. Stern, in Susan Langer's Reflections on Art, names the attribute that describes how vividly and intensely an artistic object could be experienced apparency. == In computer vision == Automated image recognition was developed by using machine learning to find patterns in large, annotated datasets of photographs, like ImageNet. Images in ImageNet are labelled using concepts in WordNet. Concepts that are easily expressed verbally, like "early", are seen as less "imageable" than nouns referring to physical objects like "leaf". Training AI models to associate concepts with low imageability with specific images can lead to problematic bias in image recognition algorithms. This has particularly been critiqued as it relates to the "person" category of WordNet and therefore also ImageNet. Trevor Pagan and Kate Crawford demonstrated in their essay "Excavating AI" and their art project ImageNet Roulette how this leads to photos of ordinary people being labelled by AI systems as "terrorists" or "sex offenders". Images in datasets are often labelled as having a certain level of imageability. As described by Kaiyu Yang, Fei-Fei Li and co-authors, this is often done following criteria from Allan Paivio and collaborators' 1968 psycholinguistic study of nouns. Yang el.al. write that dataset annotators tasked with labelling imageability "see a list of words and rate each word on a 1-7 scale from 'low imagery' to 'high imagery'. To avoid biased or harmful image recognition and image generation, Yang et.al. recommend not training vision recognition models on concepts with low imageability, especially when the concepts are offensive (such as sexual or racial slurs) or sensitive (their examples for this category include "orphan", "separatist", "Anglo-Saxon" and "crossover voter"). Even "safe" concepts with low imageability, like "great-niece" or "vegetarian" can lead to misleading results and should be avoided.

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  Interim Measures for the Management of Anthropomorphic AI Interactive Services

  The Interim Measures for the Management of Anthropomorphic AI Interactive Services (Chinese: 人工智能拟人化互动服务管理暂行办法) is a document proposed by the Cyberspace Administration of China to regulate anthropomorphic artificial intelligence systems. The draft was released on December 27, 2026 for public comment period until January 25, 2026. The proposed document would prohibit AI companies and users of AI services from generating certain types of content deemed harmful to national interests or the social order, and impose various regulatory and safety requirements on providers of AI systems. The proposed regulation is motivated by concerns about the psychological and social effects of AI systems that are perceived as personalities by their users, including addiction, encouragement of self-harm, or generation of illegal content. == Description == === Scope === The regulation would apply to AI systems that are offered to the general public within China. They would not apply to company-internal or research use, or to products that are only available outside of China. For the purpose of the regulation, anthropomorphic Ai systems are defined as those that "simulate human personality traits, modes of thinking, and communication styles, and that engage in emotional interaction with humans through text, images, audio, video, or other means". === Requirements === The regulation would require AI providers to monitor users for signs of harmful use and to take various interventions when indications of harmful use are detected. It would also prohibit AI systems from certain types of behaviors and generation of certain types of content. In some circumstances where a user appears to be at risk of self harm, the system would be required to hand over control to a human operator who would manually intervene. The regulation would also require more rigorous practices for managing the provenance of training data used to develop these systems, and would require explicit opt-in consent from users before their interactions with an AI system were used as training data. Data used to train the regulated systems would be required to reflect core socialist values and traditional Chinese culture.

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