AI Detector Check

AI Detector Check — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Fully probabilistic design

    Fully probabilistic design

    Decision making (DM) can be seen as a purposeful choice of action sequences. It also covers control, a purposeful choice of input sequences. As a rule, it runs under randomness, uncertainty and incomplete knowledge. A range of prescriptive theories have been proposed how to make optimal decisions under these conditions. They optimise sequence of decision rules, mappings of the available knowledge on possible actions. This sequence is called strategy or policy. Among various theories, Bayesian DM is broadly accepted axiomatically based theory that solves the design of optimal decision strategy. It describes random, uncertain or incompletely known quantities as random variables, i.e. by their joint probability expressing belief in their possible values. The strategy that minimises expected loss (or equivalently maximises expected reward) expressing decision-maker's goals is then taken as the optimal strategy. While the probabilistic description of beliefs is uniquely and deductively driven by rules for joint probabilities, the composition and decomposition of the loss function have no such universally applicable formal machinery. Fully probabilistic design (of decision strategies or control, FPD) removes the mentioned drawback and expresses also the DM goals of by the "ideal" probability, which assigns high (small) values to desired (undesired) behaviours of the closed DM loop formed by the influenced world part and by the used strategy. FPD has axiomatic basis and has Bayesian DM as its restricted subpart. FPD has a range of theoretical consequences , and, importantly, has been successfully used to quite diverse application domains.

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  • Type–token distinction

    Type–token distinction

    The type–token distinction is the difference between a type of objects (analogous to a class) and the individual tokens of that type (analogous to instances). Since each type may be instantiated by multiple tokens, there are generally more tokens than types of an object. For example, the sentence "A rose is a rose is a rose" contains three word types: three word tokens of the type a, two word tokens of the type is, and three word tokens of the type rose. The distinction is important in disciplines such as logic, linguistics, metalogic, typography, and computer programming. == Overview == The type–token distinction separates types (abstract descriptive concepts) from tokens (objects that instantiate concepts). For example, in the sentence "the bicycle is becoming more popular" the word bicycle represents the abstract concept of bicycles and this abstract concept is a type, whereas in the sentence "the bicycle is in the garage", it represents a particular object and this particular object is a token. Similarly, the word type 'letter' uses only four letter types: L, E, T and R. Nevertheless, it uses both E and T twice. One can say that the word type 'letter' has six letter tokens, with two tokens each of the letter types E and T. Whenever a word type is inscribed, the number of letter tokens created equals the number of letter occurrences in the word type. Some logicians consider a word type to be the class of its tokens. Other logicians counter that the word type has a permanence and constancy not found in the class of its tokens. The type remains the same while the class of its tokens is continually gaining new members and losing old members. == Typography == In typography, the type–token distinction is used to determine the presence of a text printed by movable type: The defining criteria which a typographic print has to fulfill is that of the type identity of the various letter forms which make up the printed text. In other words: each letter form which appears in the text has to be shown as a particular instance ("token") of one and the same type which contains a reverse image of the printed letter. == Charles Sanders Peirce == The distinctions between using words as types or tokens were first made by American logician and philosopher Charles Sanders Peirce in 1906 using terminology that he established. Peirce's type–token distinction applies to words, sentences, paragraphs and so on: to anything in a universe of discourse of character-string theory, or concatenation theory. Peirce's original words are the following: A common mode of estimating the amount of matter in a ... printed book is to count the number of words. There will ordinarily be about twenty 'thes' on a page, and, of course, they count as twenty words. In another sense of the word 'word,' however, there is but one word 'the' in the English language; and it is impossible that this word should lie visibly on a page, or be heard in any voice .... Such a ... Form, I propose to term a Type. A Single ... Object ... such as this or that word on a single line of a single page of a single copy of a book, I will venture to call a Token. .... In order that a Type may be used, it has to be embodied in a Token which shall be a sign of the Type, and thereby of the object the Type signifies.

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  • Executive Order 14179

    Executive Order 14179

    Executive Order 14179, titled "Removing Barriers to American Leadership in Artificial Intelligence", is an executive order signed by Donald Trump, the 47th President of the United States, on January 23, 2025. The executive order aims to initiate the process of strengthening U.S. leadership in artificial intelligence, promote AI development free from ideological bias or social agendas, establish an action plan to maintain global AI dominance, and to revise or rescind policies that conflict with these goals. == Background == === Joe Biden === This executive order comes in response to the Executive Order 14110 titled Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (sometimes referred to as "Executive Order on Artificial Intelligence") signed by Joe Biden on October 30, 2023. === Donald Trump === Donald Trump rescinded Executive Order 14110 on his first day in office with the Initial Rescissions of Harmful Executive Orders and Actions executive order. On January 23, 2025, Trump signed the Removing Barriers to American Leadership in Artificial Intelligence executive order as the replacement executive order covering the development of artificial intelligence technologies. == Provisions == It revokes existing AI policies and directives that are seen as barriers to U.S. AI innovation. It mandates the creation of an action plan within 180 days to sustain U.S. AI leadership, focusing on human flourishing, economic competitiveness, and national security. It requires the review of policies, directives, and regulations related to Executive Order 14110 (from October 2023) to identify actions that may conflict with the new policy goals. Agencies are instructed to suspend, revise, or rescind actions from the previous executive order that may be inconsistent with the new policy. The Office of Management and Budget (OMB) must revise certain memoranda (M-24-10 and M-24-18) within 60 days to align with the new policy. The order specifies that it does not create new enforceable rights or benefits and should be implemented within the boundaries of existing law and appropriations. == Implementation == The NITRD program, on behalf of the Office of Science and Technology Policy (OSTP), requested public input on the development of an AI Action Plan by March 15. == Reactions == Over 10,000 public comments were submitted in response to the OSTP request for public input. OpenAI submitted comments proposing a five-point strategy focused on regulatory preemption, export controls, copyright protections, infrastructure investment, and government adoption to ensure AI innovation, promote democratic AI globally, and protect national security. They emphasized the ability to learn from copyrighted material to maintain America's lead against China's state-controlled AI efforts like DeepSeek. Google submitted comments advocating for a three-pronged plan that invests in domestic AI development through energy infrastructure reform, balanced export controls, continued research funding, and coherent federal policies, while modernizing government AI adoption and promoting innovation-friendly approaches internationally. Both OpenAI and Google urged White House opposition to foreign copyright and transparency obligations, for example in the UK Government's preferred option in their Copyright and AI consultation.

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  • Seeing AI

    Seeing AI

    Seeing AI is an artificial intelligence application developed by Microsoft for iOS. Seeing AI uses the device camera to identify people and objects, and then the app audibly describes those objects for visually impaired people. == Capabilities == Seeing AI is primarily used to describe short text, documents, products, people, currency scenery, colors, handwriting and light. The app can scan a barcode to describe a product and uses sounds to assist the user in focusing on the barcode. When the app describes people, it attempts to estimate the person's age, gender, and emotional status. Additionally, in a test run by German journalists in December 2019, Seeing AI apparently used some sort of facial recognition system to identify people on photographs by name. Some functions are performed on the device, however more complex functions such as describing a scene or recognizing handwriting require an Internet connection. In December 2017, Seeing AI introduced the ability for currency recognition for US and Canadian dollar, British pounds and Euros. In December 2019, Seeing AI added support for five more languages, Dutch, French, German, Japanese, Spanish. Seeing AI is available in 70 countries such as Brazil, Argentina, Australia, Canada, Egypt, Albania, Bhutan, etc. Supported on iPhone 5C, 5S and later best performance with iPhone 6S, SE and later models

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  • Haskins Laboratories

    Haskins Laboratories

    Haskins Laboratories, Inc. is an independent research laboratory, founded in 1935 and located in New Haven, Connecticut since 1970. Many current Haskins researchers are affiliated with Yale University's Child Study Center and/or the University of Connecticut. Haskins is a multidisciplinary and international community of researchers who conduct basic research on spoken and written language and global literacy. A guiding perspective of their research has been to view speech and language as emerging from biological processes, including those of adaptation, response to stimuli, and conspecific interaction. Haskins Laboratories has a long history of technological and theoretical innovation, from creating systems of rules for speech synthesis and development of an early working prototype of a reading machine for the blind to developing the landmark concept of phonemic awareness as the critical preparation for learning to read an alphabetic writing system. == Research tools and facilities == Haskins Laboratories is equipped, in-house, with a comprehensive suite of tools and capabilities to advance its mission of research into language and literacy. As of 2014, these included: Anechoic chamber Electroencephalography BioSemi 264 electrode, 24 bit Active Two System EGI 128 electrode, Geodesic EEG System 300 Electromagnetic articulography (EMMA) Carstens AG501 NDI WAVE Eye tracking: HL is equipped with 3 SR Research eye-trackers. 2 Model Eyelink 1000 systems. 1 Model Eyelink 1000plus system. Magnetic resonance imaging: Haskins has access to MRI scanners through agreements with the University of Connecticut and the Yale School of Medicine. On-site, HL has a Linux computer cluster dedicated to analysis of MRI data. Motion capture: HL is equipped with a Vicon motion capture system with one Basler high-speed digital camera, six Vicon MX T-20 cameras and a Vicon MX Giganet for synching camera data and connecting cameras to the data capture computer. Near infrared spectroscopy: HL has a TechEn CW6 8x8 system (four emitters; eight detectors). Ultrasound sonogram == History == Many researchers have contributed to scientific breakthroughs at Haskins Laboratories since its founding. All of them are indebted to the pioneering work and leadership of Caryl Parker Haskins, Franklin S. Cooper, Alvin Liberman, Seymour Hutner and Luigi Provasoli. The history presented here focuses on the research program of the division of Haskins Laboratories that, since the 1940s, has been most well known for its work in the areas of speech, language, and reading. === 1930s === Caryl Haskins and Franklin S. Cooper established Haskins Laboratories in 1935. It was originally affiliated with Harvard University, MIT, and Union College in Schenectady, NY. Caryl Haskins conducted research in microbiology, radiation physics, and other fields in Cambridge, MA and Schenectady. In 1939 Haskins Laboratories moved its center to New York City. Seymour Hutner joined the staff to set up a research program in microbiology, genetics, and nutrition. The descendant of the division led by Hutner program eventually became a department of Pace University in New York. The two identically named organizations are no longer formally affiliated. === 1940s === The U. S. Office of Scientific Research and Development, under Vannevar Bush asked Haskins Laboratories to evaluate and develop technologies for assisting blinded World War II veterans. Experimental psychologist Alvin Liberman joined Haskins Laboratories to assist in developing a "sound alphabet" to represent the letters in a text for use in a reading machine for the blind. Luigi Provasoli joined Haskins Laboratories to set up a research program in marine biology. The program in marine biology moved to Yale University in 1970 and disbanded with Provasoli's retirement in 1978. === 1950s === Franklin S. Cooper invented the pattern playback, a machine that converts pictures of the acoustic patterns of speech back into sound. With this device, Alvin Liberman, Cooper, and Pierre Delattre (and later joined by Katherine Safford Harris, Leigh Lisker, Arthur Abramson, and others), discovered the acoustic cues for the perception of phonetic segments (consonants and vowels). Liberman and colleagues proposed a motor theory of speech perception to resolve the acoustic complexity: they hypothesized that we perceive speech by tapping into a biological specialization, a speech module, that contains knowledge of the acoustic consequences of articulation. Liberman, aided by Frances Ingemann and others, organized the results of the work on speech cues into a groundbreaking set of rules for speech synthesis by the Pattern Playback. === 1960s === Franklin S. Cooper and Katherine Safford Harris, working with Peter MacNeilage, were the first researchers in the U.S. to use electromyographic techniques, pioneered at the University of Tokyo, to study the neuromuscular organization of speech. Leigh Lisker and Arthur Abramson looked for simplification at the level of articulatory action in the voicing of certain contrasting consonants. They showed that many acoustic properties of voicing contrasts arise from variations in voice onset time, the relative phasing of the onset of vocal cord vibration and the end of a consonant. Their work has been widely replicated and elaborated, here and abroad, over the following decades. Donald Shankweiler and Michael Studdert-Kennedy used a dichotic listening technique (presenting different nonsense syllables simultaneously to opposite ears) to demonstrate the dissociation of phonetic (speech) and auditory (nonspeech) perception by finding that phonetic structure devoid of meaning is an integral part of language, typically processed in the left cerebral hemisphere. Liberman, Cooper, Shankweiler, and Studdert-Kennedy summarized and interpreted fifteen years of research in "Perception of the Speech Code", still among the most cited papers in the speech literature. It set the agenda for many years of research at Haskins and elsewhere by describing speech as a code in which speakers overlap (or coarticulate) segments to form syllables. Researchers at Haskins connected their first computer to a speech synthesizer designed by Haskins Laboratories' engineers. Ignatius Mattingly, with British collaborators, John N. Holmes and J.N. Shearme, adapted the Pattern playback rules to write the first computer program for synthesizing continuous speech from a phonetically spelled input. A further step toward a reading machine for the blind combined Mattingly's program with an automatic look-up procedure for converting alphabetic text into strings of phonetic symbols. === 1970s === In 1970, Haskins Laboratories moved to New Haven, Connecticut, and entered into affiliation agreements with Yale University and the University of Connecticut; Haskins remains fully independent of both Yale and UConn, administratively and financially. The lab's original location in New Haven, at 270 Crown Street (from 1970 to 2005), was leased from Yale University. Isabelle Liberman, Donald Shankweiler, and Alvin Liberman teamed up with Ignatius Mattingly to study the relationship between speech perception and reading, a topic implicit in Haskins Laboratories' research program since its inception. They developed the concept of phonemic awareness, the knowledge that would-be readers must be aware of the phonemic structure of their language in order to be able to read. Leonard Katz related the work to contemporary cognitive theory and provided expertise in experimental design and data analysis. Under the broad rubric of the "alphabetic principle", this is the core of the lab's present program of reading pedagogy. Patrick Nye joined Haskins Laboratories to lead a team working on the reading machine for the blind. The project culminated when the addition of an optical character recognizer allowed investigators to assemble the first automatic text-to-speech reading machine. By the end of the decade this technology had advanced to the point where commercial concerns assumed the task of designing and manufacturing reading machines for the blind. In 1973, Franklin S. Cooper was selected to form a panel of six experts charged with investigating the famous 18-minute gap in the White House office tapes of President Richard Nixon related to the Watergate scandal. Building on earlier work, Philip Rubin developed the sinewave synthesis program, which was then used by Robert Remez, Rubin, and colleagues to show that listeners can perceive continuous speech without traditional speech cues from a pattern of sinewaves that track the changing resonances of the vocal tract. This paved the way for a view of speech as a dynamic pattern of trajectories through articulatory-acoustic space. Philip Rubin and colleagues developed Paul Mermelstein's anatomically simplified vocal tract model, originally worked on at Bell Laboratories, into the first articulatory synthesizer that can be controlled in a phy

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  • Information Coding Classification

    Information Coding Classification

    The Information Coding Classification (ICC) is a classification system covering almost all extant 6500 knowledge fields (knowledge domains). Its conceptualization goes beyond the scope of the well known library classification systems, such as Dewey Decimal Classification (DDC), Universal Decimal Classification (UDC), and Library of Congress Classification (LCC), by extending also to knowledge systems that so far have not afforded to classify literature. ICC actually presents a flexible universal ordering system for both literature and other kinds of information, set out as knowledge fields. From a methodological point of view, ICC differs from the above-mentioned systems along the following three lines: Its main classes are not based on disciplines but on nine live stages of development, so-called ontical levels. It breaks them roughly down into hierarchical steps by further nine categories which makes decimal number coding possible. The contents of a knowledge field is earmarked via a digital position scheme, which makes the first hierarchical step refer to the nine ontical levels (object areas as subject categories), and the second hierarchical step refer to nine functionally ordered form categories. Respective knowledge fields permit to step down by the same principle to a third and forth level, and even further to a fifth and sixth level. Finally, knowledge field subdivisions will have to conform to said digital position scheme. Hence, for a given knowledge field identical codes will mark identical categories under respective numbers of the coding system. This mnemotechnical aspect of the system helps memorizing and straightaway retrieving the whereabouts of respective interdisciplinary and transdisciplinary fields. The first two hierarchical levels may be regarded as a top- or upper ontology for ontologies and other applications. The terms of the first three hierarchical levels were set out in German and English in Wissensorganisation. Entwicklung, Aufgabe, Anwendung, Zukunft, on pp. 82 to 100. It was published in 2014 and available so far only in German. In the meantime, also the French terms of the knowledge fields have been collected. Competence for maintenance and further development rests with the German Chapter of the International Society for Knowledge Organization (ISKO) e.V. == Historical development == At the end of 1970, Prof. Alwin Diemer, Univ.of Düsseldorf proposed to Ingetraut Dahlberg to undertake a philosophical dissertation on The universal classification system of knowledge, its ontological, epistemological, and information theoretical foundations. Diemer had in mind an innovating ontological approach for such a system based on the whole spectrum of kinds of being and complying with epistemological requirements. The third requirement had already been taken up somehow in the Indian Colon Classification, yet it still called for explanations and additions. In 1974, the dissertation was published in German entitled Grundlagen universaler Wissensordnung. It started with conceptual clarifications, and why and how the term „universal“ was linked to knowledge, including knowledge fields, such as commodity science, artefacts, statistics, patents, standardization, communication, utility services et al. In chapter 3, six universal classification systems (DDC, UDC, LCC, BC, CC and BBK) were presented, analyzed and compared. While preparing the dissertation, Dahlberg started with elaborating the new universal system by first gleaning a lot of extant designations of knowledge fields from whatever available reference works. This was funded by the German Documentation Society (DGD) (1971-2) under the title of Order system of knowledge fields. In addition, the syllabuses of German universities and polytechniques were explored for relevant terms and documented (1975). Thereafter, it seemed necessary to add definitions from special dictionaries and encyclopediae; it soon appeared that the 12.500 terms included numerous synonyms, so that the whole collection boiled down to about 6.500 concept designations (Project Logstruktur, supported by the German Science Foundation (DFG) 1976-78). The outcome of this work was the formulation of 30 theses which ended up in 12 principles for the new system, published 40 years later under. These principles refer not only to theoretical foundations but also to structure and other organizational aspects of the whole array of knowledge fields. In 1974, the digital position scheme for field subdivision had already been developed to allow for classifying classification literature in the bibliographical section of the first issue of the Journal International Classification. In 1977, the entire ICC was ready for presentation at a seminar in Bangalore, India. A publication of the first three hierarchical levels appeared however only in 1982. It was applied to the bibliography of classification systems and thesauri in vol.1 of the International Classification and Indexing Bibliography; it has been updated. == Governing principles == These were published in full length in the book Wissensorganisation. Entwicklung, Aufgabe, Anwendung, Zukunft and the article Information Coding Classification. Geschichtliches, Prinzipien, Inhaltliches, hence it suffices to just mention their topics with some necessary additions. Principle 1: Concept theoretical approaches. Concepts are the contents of ICC, they are understood as being units of knowledge. The „birth“ of a concept. Where do the characteristics, the knowledge elements come from? How do conceptual relations arise? Principle 2: The four kinds of concept relations and their applications. Principle 3: Decimal numbers form the ICC codes as its universal language. Principle 4: The nine ontical levels of ICC. They were grouped under three captions: Prolegomena (1-3), life sciences (4-6) and human output (7-9): Structure and form Matter and energy Cosmos and earth Biosphere Anthroposphere Sociosphere Material products (economics and technology) Intellectual products (knowledge and information) Spiritual products (products of mind and culture) Principle 5: Knowledge fields are structured by categories, based on the Aristotelian form-categories, under a digital position scheme, a kind of scaling rule for subdividing a given field as follows: General area: problems, theories, principles (axiom and structure) Object area: objects, kinds, parts, properties of objects Activity area: methods, processes, activities Field properties or first characterization Persons or secondary characterization Societies or tertiary characterization Influences from outside Applications of the field to other fields Field information and synthesizing tasks The digital position scheme, called Systematifier, has also been used for structuring the entire system via the categories figuring on the upper zero level. An example of its application is the structure of the classification system for knowledge organization literature Gliederung der Klassifikationsliteratur. (A simplified version with an additional introduction is given in, p. 71) Principle 6: The ontical levels outlined under principle 4 conform to the „integrative level theory“ which means that every level is integrated in the following one. In addition, each knowledge area presumes the following one. Principle 7: The combination potential of knowledge fields (interdisciplinarity and transdisciplinarity)is determined by the digital position scheme. (Examples are given in, p. 103-4) Principle 8: The categories of the zero-level are general concepts, their possible subdivisions could once be used for classificatory statements. (These subdivisions still need elaboration) Principle 9 and 10: These relate to the combination potential of classificatory statements with space and time concepts. (Still to be elaborated) Principle 11: The system's mnemotechnical aspect relies on the fixed system position codes and on the 3x3 form- and subject-categories. Principle 12: The combination potential of system position 1, 8 and 9 make ICC to a self-networking system which complies with the present scientific development. == In matrix form == The first two levels of ICC can be represented by following matrix. The first hierarchical level of the 9 subject categories results from the first vertical array under codes 1-9. The second hierarchical level of subject categories is structured by the 9 functionally ordered form categories, listed in the first horizontal line under codes 01-09. Some exceptions are mentioned in principle 7. == Research == === Exploration of automatic classification === For classifying web documents as conceived by Jens Hartmann, University of Karlsruhe, Prof.Walter Koch, University of Graz, has explored in his Institute for Applied Information Technology Research Society (AIT) the application of ICC to automatically classifying metadata of some 350.000 documents. This was facilitated by data generated within the framework of an E

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  • Ratio Club

    Ratio Club

    The Ratio Club was a small British informal dining club from 1949 to 1958 of young psychiatrists, psychologists, physiologists, mathematicians and engineers who met to discuss issues in cybernetics. == History == The idea of the club arose from a symposium on animal behaviour held in July 1949 by the Society of Experimental Biology in Cambridge. The club was founded by the neurologist John Bates, with other notable members such as W. Ross Ashby. The name Ratio was suggested by Albert Uttley, it being the Latin root meaning "computation or the faculty of mind which calculates, plans and reasons". He pointed out that it is also the root of rationarium, meaning a statistical account, and ratiocinatius, meaning argumentative. The use was probably inspired by an earlier suggestion by Donald Mackay of the 'MR club', from Machina ratiocinatrix, a term used by Norbert Wiener in the introduction to his then recently published book Cybernetics, or Control and Communication in the Animal and the Machine. Wiener used the term in reference to calculus ratiocinator, a calculating machine constructed by Leibniz. The initial membership was W. Ross Ashby, Horace Barlow, John Bates, George Dawson, Thomas Gold, W. E. Hick, Victor Little, Donald MacKay, Turner McLardy, P. A. Merton, John Pringle, Harold Shipton, Donald Sholl, Eliot Slater, Albert Uttley, W. Grey Walter and John Hugh Westcott. Alan Turing joined after the first meeting with I. J. Good, Philip Woodward and William Rushton added soon after. Giles Brindley attended several meetings as a guest. Warren McCulloch made presentations to the club twice, the first time at its inaugural meeting (a talk which the members found disappointing), and became a correspondent with and supporter of a number of its members. Others who attended at least one Ratio Club event as guests included Walter Pitts, Claude Shannon, J.Z. Young, C.H. Waddington, Peter Elias, J. C. R. Licklider, Oliver Selfridge, Benoît Mandelbrot, Colin Cherry and Anthony Oettinger. One one occasion I.J. Good brought along the then director of the USA's National Security Agency (presumably either Ralph Canine or John Samford given the dates). Several members admired the work of psychologist and philosopher Kenneth Craik and considered him an important influence; according to Husbands and Holland "there is no doubt Craik would have been a leading member of the club" had he not died young in 1945. The club has been considered the most influential cybernetics group in the UK, and many of its members went on to become prominent scientists.

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  • ML.NET

    ML.NET

    ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions. == Machine learning == ML.NET brings model-based Machine Learning analytic and prediction capabilities to existing .NET developers. The framework is built upon .NET Core and .NET Standard inheriting the ability to run cross-platform on Linux, Windows and macOS. Although the ML.NET framework is new, its origins began in 2002 as a Microsoft Research project named TMSN (text mining search and navigation) for use internally within Microsoft products. It was later renamed to TLC (the learning code) around 2011. ML.NET was derived from the TLC library and has largely surpassed its parent says Dr. James McCaffrey, Microsoft Research. Developers can train a Machine Learning Model or reuse an existing Model by a 3rd party and run it on any environment offline. This means developers do not need to have a background in Data Science to use the framework. Support for the open-source Open Neural Network Exchange (ONNX) Deep Learning model format was introduced from build 0.3 in ML.NET. The release included other notable enhancements such as Factorization Machines, LightGBM, Ensembles, LightLDA transform and OVA. The ML.NET integration of TensorFlow is enabled from the 0.5 release. Support for x86 & x64 applications was added to build 0.7 including enhanced recommendation capabilities with Matrix Factorization. A full roadmap of planned features have been made available on the official GitHub repo. The first stable 1.0 release of the framework was announced at Build (developer conference) 2019. It included the addition of a Model Builder tool and AutoML (Automated Machine Learning) capabilities. Build 1.3.1 introduced a preview of Deep Neural Network training using C# bindings for Tensorflow and a Database loader which enables model training on databases. The 1.4.0 preview added ML.NET scoring on ARM processors and Deep Neural Network training with GPU's for Windows and Linux. === Performance === Microsoft's paper on machine learning with ML.NET demonstrated it is capable of training sentiment analysis models using large datasets while achieving high accuracy. Its results showed 95% accuracy on Amazon's 9GB review dataset. === Model builder === The ML.NET CLI is a Command-line interface which uses ML.NET AutoML to perform model training and pick the best algorithm for the data. The ML.NET Model Builder preview is an extension for Visual Studio that uses ML.NET CLI and ML.NET AutoML to output the best ML.NET Model using a GUI. === Model explainability === AI fairness and explainability has been an area of debate for AI Ethicists in recent years. A major issue for Machine Learning applications is the black box effect where end users and the developers of an application are unsure of how an algorithm came to a decision or whether the dataset contains bias. Build 0.8 included model explainability API's that had been used internally in Microsoft. It added the capability to understand the feature importance of models with the addition of 'Overall Feature Importance' and 'Generalized Additive Models'. When there are several variables that contribute to the overall score, it is possible to see a breakdown of each variable and which features had the most impact on the final score. The official documentation demonstrates that the scoring metrics can be output for debugging purposes. During training & debugging of a model, developers can preview and inspect live filtered data. This is possible using the Visual Studio DataView tools. === Infer.NET === Microsoft Research announced the popular Infer.NET model-based machine learning framework used for research in academic institutions since 2008 has been released open source and is now part of the ML.NET framework. The Infer.NET framework utilises probabilistic programming to describe probabilistic models which has the added advantage of interpretability. The Infer.NET namespace has since been changed to Microsoft.ML.Probabilistic consistent with ML.NET namespaces. === NimbusML Python support === Microsoft acknowledged that the Python programming language is popular with Data Scientists, so it has introduced NimbusML the experimental Python bindings for ML.NET. This enables users to train and use machine learning models in Python. It was made open source similar to Infer.NET. === Machine learning in the browser === ML.NET allows users to export trained models to the Open Neural Network Exchange (ONNX) format. This establishes an opportunity to use models in different environments that don't use ML.NET. It would be possible to run these models in the client side of a browser using ONNX.js, a JavaScript client-side framework for deep learning models created in the Onnx format. === AI School Machine Learning Course === Along with the rollout of the ML.NET preview, Microsoft rolled out free AI tutorials and courses to help developers understand techniques needed to work with the framework.

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  • WHATWG

    WHATWG

    The Web Hypertext Application Technology Working Group (WHATWG) was founded by representatives from Apple Inc., the Mozilla Foundation and Opera Software, leading web browser vendors in 2004. WHATWG is responsible for maintaining multiple web-related technical standards, including the specifications for the HyperText Markup Language (HTML) and the Document Object Model (DOM). The central organizational membership and control of WHATWG – its "Steering Group" – consists of Apple, Mozilla, Google, and Microsoft. WHATWG editors of the specifications ensure correct implementation, in consultation with participants, but ultimately in accordance with Steering Group member objectives. == History == The WHATWG was formed in response to the slow development of World Wide Web Consortium (W3C) Web standards and W3C's decision to abandon HTML in favor of XML-based technologies. The WHATWG mailing list was announced on 4 June 2004, two days after the initiatives of a joint Opera–Mozilla position paper had been voted down by the W3C members at the W3C Workshop on Web Applications and Compound Documents. On 10 April 2007, the Mozilla Foundation, Apple, and Opera Software proposed that the new HTML working group of the W3C adopt the WHATWG's HTML5 as the starting point of its work and name its future deliverable as "HTML5" (though the WHATWG specification was later renamed HTML Living Standard). On 9 May 2007, the new HTML working group of the W3C resolved to do that. An Internet Explorer platform architect from Microsoft was invited but did not join, citing the lack of a patent policy to ensure all specifications can be implemented on a royalty-free basis. Since then, the W3C and the WHATWG had been developing HTML independently, at times causing specifications to diverge. In 2017, the WHATWG established an intellectual property rights agreement that includes a patent policy. This spurred a renewed attempt to allow the W3C and the WHATWG to work together on specifications. In 2019, the W3C and WHATWG agreed to a memorandum of understanding where development of HTML and DOM specifications would be done principally in the WHATWG. The editor has significant control over the specification, but the community can influence the decisions of the editor. In one case, editor Ian Hickson proposed replacing the

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  • Cerebellar model articulation controller

    Cerebellar model articulation controller

    The cerebellar model arithmetic computer (CMAC) is a type of neural network based on a model of the mammalian cerebellum. It is also known as the cerebellar model articulation controller. It is a type of associative memory. The CMAC was first proposed as a function modeler for robotic controllers by James Albus in 1975 (hence the name), but has been extensively used in reinforcement learning and also as for automated classification in the machine learning community. The CMAC is an extension of the perceptron model. It computes a function for n {\displaystyle n} input dimensions. The input space is divided up into hyper-rectangles, each of which is associated with a memory cell. The contents of the memory cells are the weights, which are adjusted during training. Usually, more than one quantisation of input space is used, so that any point in input space is associated with a number of hyper-rectangles, and therefore with a number of memory cells. The output of a CMAC is the algebraic sum of the weights in all the memory cells activated by the input point. A change of value of the input point results in a change in the set of activated hyper-rectangles, and therefore a change in the set of memory cells participating in the CMAC output. The CMAC output is therefore stored in a distributed fashion, such that the output corresponding to any point in input space is derived from the value stored in a number of memory cells (hence the name associative memory). This provides generalisation. == Building blocks == In the adjacent image, there are two inputs to the CMAC, represented as a 2D space. Two quantising functions have been used to divide this space with two overlapping grids (one shown in heavier lines). A single input is shown near the middle, and this has activated two memory cells, corresponding to the shaded area. If another point occurs close to the one shown, it will share some of the same memory cells, providing generalisation. The CMAC is trained by presenting pairs of input points and output values, and adjusting the weights in the activated cells by a proportion of the error observed at the output. This simple training algorithm has a proof of convergence. It is normal to add a kernel function to the hyper-rectangle, so that points falling towards the edge of a hyper-rectangle have a smaller activation than those falling near the centre. One of the major problems cited in practical use of CMAC is the memory size required, which is directly related to the number of cells used. This is usually ameliorated by using a hash function, and only providing memory storage for the actual cells that are activated by inputs. == One-step convergent algorithm == Initially least mean square (LMS) method is employed to update the weights of CMAC. The convergence of using LMS for training CMAC is sensitive to the learning rate and could lead to divergence. In 2004, a recursive least squares (RLS) algorithm was introduced to train CMAC online. It does not need to tune a learning rate. Its convergence has been proved theoretically and can be guaranteed to converge in one step. The computational complexity of this RLS algorithm is O(N3). == Hardware implementation infrastructure == Based on QR decomposition, an algorithm (QRLS) has been further simplified to have an O(N) complexity. Consequently, this reduces memory usage and time cost significantly. A parallel pipeline array structure on implementing this algorithm has been introduced. Overall by utilizing QRLS algorithm, the CMAC neural network convergence can be guaranteed, and the weights of the nodes can be updated using one step of training. Its parallel pipeline array structure offers its great potential to be implemented in hardware for large-scale industry usage. == Continuous CMAC == Since the rectangular shape of CMAC receptive field functions produce discontinuous staircase function approximation, by integrating CMAC with B-splines functions, continuous CMAC offers the capability of obtaining any order of derivatives of the approximate functions. == Deep CMAC == In recent years, numerous studies have confirmed that by stacking several shallow structures into a single deep structure, the overall system could achieve better data representation, and, thus, more effectively deal with nonlinear and high complexity tasks. In 2018, a deep CMAC (DCMAC) framework was proposed and a backpropagation algorithm was derived to estimate the DCMAC parameters. Experimental results of an adaptive noise cancellation task showed that the proposed DCMAC can achieve better noise cancellation performance when compared with that from the conventional single-layer CMAC. == Summary ==

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  • Computational creativity

    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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  • Lernmatrix

    Lernmatrix

    Lernmatrix (German for "learning matrix") is a special type of artificial neural network (ANN) architecture, similar to associative memory, invented around 1960 by Karl Steinbuch, a pioneer in computer science and ANNs. This model for learning systems could establish complex associations between certain sets of characteristics (e.g., letters of an alphabet) and their meanings. == Function == The Lernmatrix generally consists of n "characteristic lines" and m "meaning lines," where each characteristic line is connected to each meaning line, similar to how neurons in the brain are connected by synapses. (This can be realized in various ways – according to Steinbuch, this could be done by hardware or software). To train a Lernmatrix, values are specified on the corresponding characteristic and meaning lines (binary or real); then the connections between all pairs of characteristic and meaning lines are strengthened by the Hebb rule. A trained Lernmatrix, when given a specific input on the characteristic lines, activates the corresponding meaning lines. In modern language, it is a linear projection module. By appropriately interconnecting several Lernmatrices, a switching system can be built that, after completing certain training phases, is ultimately able to automatically determine the most probable associated meaning for an input sequence of features.

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  • Spyglass (app)

    Spyglass (app)

    Spyglass is a navigation and orientation mobile application developed by Pavel Ahafonau. It combines data from a digital compass, GNSS positioning, motion sensors, maps, and the device camera to provide direction finding, waypoint navigation, and measurement tools. The application is designed for offline and off-road use and is used in outdoor navigation, orientation tasks, astronomy, and fieldwork. == History == Spyglass was created by independent software developer Pavel Ahafonau as a personal project in 2009, following the introduction of a digital compass sensor in the iPhone. It initially focused on combining compass, GPS, and camera data into an augmented-reality tool for navigation and orientation. In September 2009, a public prototype was demonstrated, showing a live camera view combined with a digital compass overlay aligned to device orientation, presenting an early augmented-reality, location-aware heads-up display. The application was released on the Apple App Store in October 2009. In February 2010, a major update introduced target-based navigation, allowing users to navigate to saved locations, bearings, and selected celestial objects. The update also added visual measurement tools, including an optical-style rangefinder, as well as a vertical speed indicator displaying ascent and descent rates derived from device sensor data. In December 2010, Spyglass was featured by Apple in iTunes Rewind 2010 under augmented-reality applications. The application expanded to Android on 28 October 2017. In May 2021, Spyglass expanded its offline mapping capabilities by adding support for additional map styles by Thunderforest, extending the range of available cartographic themes for offline use. Also in 2021, navigation satellite tracking was introduced, allowing visualization and tracking of major GPS/GNSS satellite constellations. In 2022, a searchable offline database of major locations was added, including airports, seaports, mountains, castles, and landmarks, along with nearest-airport tracking functionality. In July 2024, previously separate iOS editions (Spyglass, Commander Compass, and Commander Compass Go) were consolidated into a single Spyglass application. At the same time, the app transitioned to a freemium model. == Features == Spyglass provides navigation and orientation functions by combining sensor data from the device. Core functionality includes a digital compass, GNSS-based positioning, waypoint creation and tracking, and map-based navigation with offline support. The application includes an augmented-reality viewfinder mode that overlays navigation and sensor information onto the live camera view. Displayed data may include heading, bearing, distance to targets, pitch, roll, yaw, altitude, speed, and estimated time of arrival. Additional tools include an altimeter, speedometer, vertical speed indicator, inclinometer, artificial horizon, coordinate conversion utilities, optical rangefinding, and angular measurement tools. Spyglass also supports celestial navigation features, such as tracking of the Sun, Moon, stars, and global navigation satellite systems. Spyglass uses data from the device's GNSS receiver, digital compass, gyroscope, accelerometer, barometer (when available), and camera. Sensor data are combined to calculate position, orientation, movement, and measurement overlays. The application is designed to function without an internet connection. Navigation tools, sensor readings, waypoint tracking, augmented-reality features, celestial tracking, and the built-in location database operate offline. Internet access is required only for loading online map tiles; previously downloaded offline maps remain available without connectivity.

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  • Alex Krizhevsky

    Alex Krizhevsky

    Alex Krizhevsky is a Canadian computer scientist most noted for his work on artificial neural networks and deep learning. In 2012, Krizhevsky, Ilya Sutskever and their PhD advisor Geoffrey Hinton, at the University of Toronto, developed a powerful visual-recognition network AlexNet using only two GeForce-branded GPU cards. This revolutionized research in neural networks. Previously neural networks were trained on CPUs. The transition to GPUs opened the way to the development of advanced AI models. == AlexNet == Motivated by Sutskever and inspired by Hinton, Krizhevsky developed AlexNet to expand the limits in image recognition and classification. Building on Convolutional Neural Networks and Sutskever’s Deep Neural Network approach of deepening the neural layers far beyond the convention of the time—as well as adding Dropout for training resilience—AlexNet won the ImageNet challenge in 2012. The team presented their paper for AlexNet at NeurIPS (NIPS) 2012. Shortly after AlexNet’s debut, Krizhevsky and Sutskever sold their startup, DNN Research Inc., to Google. Krizhevsky left Google in September 2017 after losing interest in the work, to work at the company Dessa in support of new deep-learning techniques. Many of his numerous papers on machine learning and computer vision are frequently cited by other researchers. He is also the main author of the CIFAR-10 and CIFAR-100 datasets. == Legacy == AlexNet is widely credited with igniting the deep learning revolution. Its success demonstrated the effectiveness of deep neural networks trained on GPUs, leading to rapid progress across multiple domains of artificial intelligence beyond computer vision. The techniques and momentum generated by AlexNet helped shape the development of modern natural language processing models, including large-scale transformer-based models such as BERT and GPT, which power tools like ChatGPT.

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  • Lisp machine

    Lisp machine

    Lisp machines are general-purpose computers designed to efficiently run Lisp as their main software and programming language, usually via hardware support. They are an example of a high-level language computer architecture. In a sense, they were the first commercial single-user workstations. Despite being modest in number (perhaps 7,000 units total as of 1988) Lisp machines commercially pioneered some now-commonplace technologies, including networking innovations such as Chaosnet, and effective garbage collection. Several firms built and sold Lisp machines in the 1980s: Symbolics (3600, 3640, XL1200, MacIvory, and other models), Lisp Machines Incorporated (LMI Lambda), Texas Instruments (Explorer, MicroExplorer), and Xerox (Interlisp-D workstations). The operating systems were written in Lisp Machine Lisp, Interlisp (Xerox), and later partly in Common Lisp. == History == === Historical context === Artificial intelligence (AI) computer programs of the 1960s and 1970s intrinsically required what was then considered a huge amount of computer power, as measured in processor time and memory space. The power requirements of AI research were exacerbated by the Lisp symbolic programming language, when commercial hardware was designed and optimized for assembly- and Fortran-like programming languages. At first, the cost of such computer hardware meant that it had to be shared among many users. As integrated circuit technology shrank the size and cost of computers in the 1960s and early 1970s, and the memory needs of AI programs began to exceed the address space of the most common research computer, the Digital Equipment Corporation (DEC) PDP-10, researchers considered a new approach: a computer designed specifically to develop and run large artificial intelligence programs, and tailored to the semantics of the Lisp language. To provide consistent performance for interactive programs, these machines would often not be shared, but would be dedicated to a single user at a time. === Initial development === In 1973, Richard Greenblatt and Thomas Knight, programmers at Massachusetts Institute of Technology (MIT) Artificial Intelligence Laboratory (AI Lab), began what would become the MIT Lisp Machine Project when they first began building a computer hardwired to run certain basic Lisp operations, rather than run them in software, in a 24-bit tagged architecture. The machine also did incremental (or Arena) garbage collection. More specifically, since Lisp variables are typed at runtime rather than compile time, a simple addition of two variables could take five times as long on conventional hardware, due to test and branch instructions. Lisp Machines ran the tests in parallel with the more conventional single instruction additions. If the simultaneous tests failed, then the result was discarded and recomputed; this meant in many cases a speed increase by several factors. This simultaneous checking approach was used as well in testing the bounds of arrays when referenced, and other memory management necessities (not merely garbage collection or arrays). Type checking was further improved and automated when the conventional byte word of 32 bits was lengthened to 36 bits for Symbolics 3600-model Lisp machines and eventually to 40 bits or more (usually, the excess bits not accounted for by the following were used for error-correcting codes). The first group of extra bits were used to hold type data, making the machine a tagged architecture, and the remaining bits were used to implement compressed data representation (CDR) coding (wherein the usual linked list elements are compressed to occupy roughly half the space), aiding garbage collection by reportedly an order of magnitude. A further improvement was two microcode instructions which specifically supported Lisp functions, reducing the cost of calling a function to as little as 20 clock cycles, in some Symbolics implementations. The first machine was called the CONS machine (named after the list construction operator cons in Lisp). Often it was affectionately referred to as the Knight machine, perhaps since Knight wrote his master's thesis on the subject; it was extremely well received. It was subsequently improved into a version called CADR (a pun; in Lisp, the cadr function, which returns the second item of a list, is pronounced /ˈkeɪ.dəɹ/ or /ˈkɑ.dəɹ/, as some pronounce the word "cadre") which was based on essentially the same architecture. About 25 of what were essentially prototype CADRs were sold within and without MIT for ~$50,000; it quickly became the favorite machine for hacking – many of the most favored software tools were quickly ported to it (e.g. Emacs was ported from ITS in 1975). It was so well received at an AI conference held at MIT in 1978 that Defense Advanced Research Projects Agency (DARPA) began funding its development. === Commercializing MIT Lisp machine technology === In 1979, Russell Noftsker, being convinced that Lisp machines had a bright commercial future due to the strength of the Lisp language and the enabling factor of hardware acceleration, proposed to Greenblatt that they commercialize the technology. In a counter-intuitive move for an AI Lab hacker, Greenblatt acquiesced, hoping perhaps that he could recreate the informal and productive atmosphere of the Lab in a real business. These ideas and goals were considerably different from those of Noftsker. The two negotiated at length, but neither would compromise. As the proposed firm could succeed only with the full and undivided assistance of the AI Lab hackers as a group, Noftsker and Greenblatt decided that the fate of the enterprise was up to them, and so the choice should be left to the hackers. The ensuing discussions of the choice divided the lab into two factions. In February 1979, matters came to a head. The hackers sided with Noftsker, believing that a commercial venture-fund-backed firm had a better chance of surviving and commercializing Lisp machines than Greenblatt's proposed self-sustaining start-up. Greenblatt lost the battle. It was at this juncture that Symbolics, Noftsker's enterprise, slowly came together. While Noftsker was paying his staff a salary, he had no building or any equipment for the hackers to work on. He bargained with Patrick Winston that, in exchange for allowing Symbolics' staff to keep working out of MIT, Symbolics would let MIT use internally and freely all the software Symbolics developed. A consultant from CDC, who was trying to put together a natural language computer application with a group of West-coast programmers, came to Greenblatt, seeking a Lisp machine for his group to work with, about eight months after the disastrous conference with Noftsker. Greenblatt had decided to start his own rival Lisp machine firm, but he had done nothing. The consultant, Alexander Jacobson, decided that the only way Greenblatt was going to start the firm and build the Lisp machines that Jacobson desperately needed was if Jacobson pushed and otherwise helped Greenblatt launch the firm. Jacobson pulled together business plans, a board, a partner for Greenblatt (one F. Stephen Wyle). The newfound firm was named LISP Machine, Inc. (LMI), and was funded by CDC orders, via Jacobson. Around this time Symbolics (Noftsker's firm) began operating. It had been hindered by Noftsker's promise to give Greenblatt a year's head start, and by severe delays in procuring venture capital. Symbolics still had the major advantage that while 3 or 4 of the AI Lab hackers had gone to work for Greenblatt, 14 other hackers had signed onto Symbolics. Two AI Lab people were not hired by either: Richard Stallman and Marvin Minsky. Stallman, however, blamed Symbolics for the decline of the hacker community that had centered around the AI lab. For two years, from 1982 to the end of 1983, Stallman worked by himself to clone the output of the Symbolics programmers, with the aim of preventing them from gaining a monopoly on the lab's computers. Regardless, after a series of internal battles, Symbolics did get off the ground in 1980/1981, selling the CADR as the LM-2, while Lisp Machines, Inc. sold it as the LMI-CADR. Symbolics did not intend to produce many LM-2s, since the 3600 family of Lisp machines was supposed to ship quickly, but the 3600s were repeatedly delayed, and Symbolics ended up producing ~100 LM-2s, each of which sold for $70,000. Both firms developed second-generation products based on the CADR: the Symbolics 3600 and the LMI-LAMBDA (of which LMI managed to sell ~200). The 3600, which shipped a year late, expanded on the CADR by widening the machine word to 36-bits, expanding the address space to 28-bits, and adding hardware to accelerate certain common functions that were implemented in microcode on the CADR. The LMI-LAMBDA, which came out a year after the 3600, in 1983, was compatible with the CADR (it could run CADR microcode), but hardware differences existed. Texas Instruments (TI) joined the fray whe

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