AlternativeTo is a website which lists alternatives to web-based software, desktop computer software, and mobile apps, and sorts the alternatives by various criteria, including the number of registered users who have "Liked" each of them on AlternativeTo. Users can search the site to find better alternatives to an application they are using or previously have used, including free alternatives such as free web applications (cloud computing) which don't require any installation and can be accessed from any browser. == Differences == Unlike a number of other software directory websites, the software is not arranged into categories, but each individual piece of software has its own list of alternatives. However, users can also search by tag to find software, which offers an alternative way of finding related software. Users can also narrow their search by focusing on particular platforms and license types (such as "free for non-commercial use"). AlternativeTo lists basic information such as platform and license type at the top of each entry, but does not have comparison tables listing software features side by side. AlternativeTo does not host software for download but it provides links to official websites to where you can download or buy them. AlternativeTo allows anyone to register and suggest new alternatives, or to update the information held about existing entries. Suggestions and alterations are reviewed before being made publicly visible. Users can register using either email and password or OpenID. Login with Facebook has been discontinued. As AlternativeTo is itself a web application, it even has a page for alternatives to itself. == Features == Tweets from anyone mentioning particular pieces of software are also pulled in dynamically from Twitter. Each application has an RSS feed for notifying users of newly listed alternatives to that application. After a user has clicked the Like button next to an application, they are offered the opportunity to tell their friends on Facebook or their followers on Twitter that they liked it. The site also has a forum. For software developers, a JSON API used to be available, but has been taken down indefinitely.
Signal-to-noise ratio (imaging)
Signal-to-noise ratio (SNR) is used in imaging to characterize image quality. The sensitivity of a (digital or film) imaging system is typically described in the terms of the signal level that yields a threshold level of SNR. Industry standards define sensitivity in terms of the ISO film speed equivalent, using SNR thresholds (at average scene luminance) of 40:1 for "excellent" image quality and 10:1 for "acceptable" image quality. SNR is sometimes quantified in decibels (dB) of signal power relative to noise power, though in the imaging field the concept of "power" is sometimes taken to be the power of a voltage signal proportional to optical power; so a 20 dB SNR may mean either 10:1 or 100:1 optical power, depending on which definition is in use. == Definition of SNR == Traditionally, SNR is defined to be the ratio of the average signal value μ s i g {\displaystyle \mu _{\mathrm {sig} }} to the standard deviation of the signal σ s i g {\displaystyle \sigma _{\mathrm {sig} }} : S N R = μ s i g σ s i g {\displaystyle \mathrm {SNR} ={\frac {\mu _{\mathrm {sig} }}{\sigma _{\mathrm {sig} }}}} when the signal is an optical intensity, or as the square of this value if the signal and noise are viewed as amplitudes (field quantities).
Decision Model and Notation
In business analysis, the Decision Model and Notation (DMN) is a standard published by the Object Management Group. It is a standard approach for describing and modeling repeatable decisions within organizations to ensure that decision models are interchangeable across organizations. The DMN standard provides the industry with a modeling notation for decisions that will support decision management and business rules. The notation is designed to be readable by business and IT users alike. This enables various groups to effectively collaborate in defining a decision model: the business people who manage and monitor the decisions, the business analysts or functional analysts who document the initial decision requirements and specify the detailed decision models and decision logic, the technical developers responsible for the automation of systems that make the decisions. The primary goal of DMN is to offer a common notation that all business users can easily understand. This includes business analysts who develop decision requirements and models, technical developers who automate decisions, and businesspeople who manage and monitor those decisions. DMN serves as a standardized link between business decision design and implementation.[4] The DMN standard can be effectively used standalone but it is also complementary to the BPMN and CMMN standards. BPMN defines a special kind of activity, the Business Rule Task, which "provides a mechanism for the process to provide input to a business rule engine and to get the output of calculations that the business rule engine might provide" that can be used to show where in a BPMN process a decision defined using DMN should be used. DMN has been made a standard for Business Analysis according to BABOK v3. == Elements of the standard == The standard includes three main elements Decision Requirements Diagrams that show how the elements of decision-making are linked into a dependency network. Decision tables to represent how each decision in such a network can be made. Business context for decisions such as the roles of organizations or the impact on performance metrics. A Friendly Enough Expression Language (FEEL) that can be used to evaluate expressions in a decision table and other logic formats. == Use cases == The standard identifies three main use cases for DMN Defining manual decision making Specifying the requirements for automated decision-making Representing a complete, executable model of decision-making == Benefits == Using the DMN standard will improve business analysis and business process management, since other popular requirement management techniques such as BPMN and UML do not handle decision making growth of projects using business rule management systems or BRMS, which allow faster changes it facilitates better communications between business, IT and analytic roles in a company it provides an effective requirements modeling approach for predictive analytics projects and fulfills the need for "business understanding" in methodologies for advanced analytics such as CRISP-DM it provides a standard notation for decision tables, the most common style of business rules in a business rule management system (BRMS) == Relationship to BPMN == DMN has been designed to work with BPMN. Business process models can be simplified by moving process logic into decision services. DMN is a separate domain within the OMG that provides an explicit way to connect to processes in BPMN. Decisions in DMN can be explicitly linked to processes and tasks that use the decisions. This integration of DMN and BPMN has been studied extensively. DMN expects that the logic of a decision will be deployed as a stateless, side-effect free Decision Service. Such a service can be invoked from a business process and the data in the process can be mapped to the inputs and outputs of the decision service. == DMN BPMN example == As mentioned, BPMN is a related OMG Standard for process modeling. DMN complements BPMN, providing a separation of concerns between the decision and the process. The example here describes a BPMN process and DMN DRD (Decision Requirements Diagram) for onboarding a bank customer. Several decisions are modeled and these decisions will direct the processes response. === New bank account process === In the BPMN process model shown in the figure, a customer makes a request to open a new bank account. The account application provides the account representative with all the information needed to create an account and provide the requested services. This includes the name, address and various forms of identification. In the next steps of the work flow, the know your customer (KYC) services are called. In the KYC services, the name and address are validated; followed by a check against the international criminal database (Interpol) and the database of persons that are 'politically exposed persons (PEP)'. The PEP is a person who is either entrusted with a prominent political position or a close relative thereof. Deposits from persons on the PEP list are potentially corrupt. This is shown as two services on the process model. Anti-money-laundering (AML) regulations require these checks before the customer account is certified. The results of these services plus the forms of identification are sent to the Certify New Account decision. This is shown as a 'rule' activity, verify account, on the process diagram. If the new customer passes certification, then the account is classified into onboarding for business retail, retail, wealth management and high-value business. Otherwise the customer application is declined. The Classify New Customer Decision classifies the customer. If the verify-account process returns a result of 'Manual' then the PEP or the Interpol check returned a close match. The account representative must visually inspect the name and the application to determine if the match is valid and accept or decline the application. === Certify new account decision === An account is certified for opening if the individual's' address is verified, and if valid identification is provided, and if the applicant is not on a list of criminals or politically exposed persons. These are shown as sub-decisions below the 'certify new account' decision. The account verification services provides a 100% match of the applicants address. For identification to be valid, the customer must provide a driver's license, passport or government issued ID. The checks against PEP and Interpol are 'fuzzy' matches and return matching score values. Scores above 85 are considered a 'match' and scores between 65 and 85 would require a 'manual' screening process. People who match either of these lists are rejected by the account application process. If there is a partial match with a score between 65 and 85, against the Interpol or PEP list then the certification is set to manual and an account representative performs a manual verification of the applicant's data. These rules are reflected in the figure below, which presents the decision table for whether to pass the provided name for the lists checks. === Client category === The client's on-boarding process is driven by what category they fall in. The category is decided by the: Type of client, business or private The size of the funds on deposit And the estimated net worth This decision is shown below: There are 6 business rules that determine the client's category and these are shown in the decision table here: === Summary example === In this example, the outcome of the 'Verify Account' decision directed the responses of the new account process. The same is true for the 'Classify Customer' decision. By adding or changing the business rules in the tables, one can easily change the criteria for these decisions and control the process differently. Modeling is a critical aspect of improving an existing process or business challenge. Modeling is generally done by a team of business analysts, IT personnel, and modeling experts. The expressive modeling capabilities of BPMN allows business analyst to understand the functions of the activities of the process. Now with the addition of DMN, business analysts can construct an understandable model of complex decisions. Combining BPMN and DMN yields a very powerful combination of models that work synergistically to simplify processes. == Relationship to decision mining and process mining == Automated discovery techniques that infer decision models from process execution data have been proposed as well. Here, a DMN decision model is derived from a data-enriched event log, along with the process that uses the decisions. In doing so, decision mining complements process mining with traditional data mining approaches. == cDMN extension == Constraint Decision Model and Notation (cDMN) is a formal notation for expressing knowledge in a tabular, intuitive format. It extends DMN with constraint reasoning and related concepts while aiming to retain the us
Hyperion Data Center
The Richland Parish Data Center, nicknamed "Hyperion", is a planned artificial intelligence data center by Meta Platforms under-construction along Highway La. 183 in Richland Parish, Louisiana, just outside of Holly Ridge. It is one of a number of "titan clusters" being built in preparation for the emergence of AI superintelligence. Modern technological researchers disagree as to whether or not superintelligence will ever exist, though Meta CEO Mark Zuckerberg has expressed belief that its creation is inevitable. Current plans allot for the investment of $27 billion, as the structure is built from 2025 to 2030. == History == Meta was considering potential locations for their flagship data center in early 2024. Before being announced later in December, the plan was completely secret; meetings held between involved organisations and even government officials could only refer to it by the codename "Project Sucre" to protect it from potential corporate espionage. The data center was first announced on 04 December 2024, though its full scale was yet to be revealed. At first, Meta would not even claim responsibility for it, channelling all of its investments through the secret shell subsidiary Laidley LLC. We set out looking for a place where we could expand into gigawatts pretty quickly, and really get moving within that community on a large plot of land very quickly. We looked at finding very, very large contiguous plots of land that had access to the infrastructure that we need, the energy that we needed, and could move very, very quickly for us. The Louisiana-based Entergy Corporation, aiming for the facility to be built in its own backyard, negotiated a deal with the government of Louisiana to provide Meta with enormous tax breaks if they agreed to build Hyperion there. The Louisiana legislature responded by passing Act 730, which provides significant tax rebates on the purchase or lease of equipment for building and operating data centers. Meta found the arrangement acceptable, and bought a plot of land from the government. The government also had to further amend its laws to allow Meta to do this, as pre-existing policy forbade purchasing land directly from the government instead of hosting a public auction. The plot of land, originally called Franklin Farms, was purchased from the Franklin family in 2006 by the government, intending for it to be developed into an automotive manufacturing plant. Greater attention was brought to Hyperion it when Zuckerberg posted about the project on 14 July 2025 on Threads. The project subsequently caught media attention for its large size, as Zuckerberg's post portrayed the structure superimposed over Manhattan (pictured). The construction site spans 2,250 acres (9.1 km2) with a planned floor area of 4,000,000 square feet (371612 m2), making it the third largest building in the world by floor area upon completion. Meta initially reported the construction cost to be over $10 billion, but in October 2025, it announced a partnership with Blue Owl Capital providing for at least $27 billion. == Operation == The facility is expected to consume up to 5 gigawatts (GW) of computational power, more electricity than is currently used by the entire State of Louisiana. As part of their deal made with Meta, Entergy plans to be able to produce at least 3.8 GW of electricity for the operation. == Response to the project == Louisiana Governor Jeff Landry thanked Meta for their decision to build Hyperion in Louisiana, stating that it would "create opportunities for Louisiana workers to fill high-paying jobs of the future." and calling it "A New Chapter" for the state. The Louisiana Economic Development (LED) state agency further praised the project, citing Meta's estimate that it would create 1,500 jobs. Additionally, Richland Parish Supervisor Joey Evans stated that he was excited about the project. As part of their agreement with Meta, Energy announced their plan to increase electricity production state-wide. They say that this will result in the cost of energy reducing, though Entergy filings revealed in June 2025 that the cost of electricity would rise and be passed onto consumers. Meta also pledged to match all of Hyperion's power consumption with 100% environmentally friendly electricity production. So far, Entergy has begun building three gas-powered combined-cycle power plants and a substation in response to the project. Delta Community College announced in response to Hyperion's construction that it would expand its construction and trade programs. In January 2025, Business Facilities Magazine selected Hyperion for its annual Deal of the Year Platinum Award for 2024. Much of the initial backlash following Hyperion's announcement centered around the fast-tracked approval of the project by the state government, and scepticism around Meta's various claims (environmental friendliness, 100% renewable energy, local economic stimulation, price reductions). The Sierra Club criticised Meta for gentrifying the surrounding area, and was highly sceptical of their promise to keep it environmentally friendly. Environmental activist group Earthjustice attempted to have a subpoena of Meta approved to determine if they were compliant with environmental protection laws, though they were unsuccessful. Many residents of Holy Ridge have been critical of the construction, complaining about the increased construction vehicle traffic and intense gentrification. Another point of contention is Meta's continued reliance on out-of-state contractors in the facility's construction in spite of their previous commitment to "hire as many local folk as [we] possibly can." In spite of Entergy's continual denial that the facility's construction will not adversely affect the power grid, numerous electrical outages have been reported since construction began.
Ilya Sutskever
Ilya Sutskever (Hebrew: איליה סוצקבר; born 1986) is a computer scientist who specializes in machine learning. He has made several major contributions to the field of deep learning, including sequence-to-sequence learning, reasoning models, GPT models, and contributions to CLIP, DALL-E, and AlphaGo. With Alex Krizhevsky and Geoffrey Hinton, he co-created AlexNet, a convolutional neural network. One of the most highly cited computer scientists in history, he has won the NeurIPS Test of Time Award for his lasting impact on AI research three times in a row (2022–2024) and received the National Academy of Sciences Award for the Industrial Application of Science in 2026. Sutskever co-founded and was chief scientist at OpenAI, where he oversaw the research breakthroughs that led to large language models and to the launch of ChatGPT. He also led the research that led to reasoning models such as o1. In 2023, he was one of the members of OpenAI's board that ousted Sam Altman as its CEO; Altman was reinstated a week later, and Sutskever stepped down from the board. In June 2024, Sutskever co-founded the company Safe Superintelligence Inc., alongside Daniel Gross and Daniel Levy. Within a year, the company was valued at more than $30 billion. == Early life and education == Sutskever was born in 1986 into a Jewish family in Nizhny Novgorod, Russia (then Gorky, Russian SFSR, Soviet Union). At the age of 5, he immigrated to Israel with his family and grew up in Jerusalem. Sutskever proved to be a good student in school, and in eighth grade started taking classes at the Open University of Israel. At 16, he moved with his family to Canada, where he attended high school for a month before being admitted to the University of Toronto in Ontario as a third-year undergraduate student. At the University of Toronto, Sutskever received a bachelor's degree in mathematics in 2005, a master's degree in computer science in 2007, and a PhD in computer science in 2013. His doctoral advisor was Geoffrey Hinton. In 2012, Sutskever built AlexNet in collaboration with Geoffrey Hinton and Alex Krizhevsky. == Career and research == In 2012, Sutskever spent about two months as a postdoc with Andrew Ng at Stanford University. He then returned to the University of Toronto and joined Hinton's new research company DNNResearch, a spinoff of Hinton's research group. In 2013, Google acquired DNNResearch and hired Sutskever as a research scientist at Google Brain. At Google Brain, Sutskever worked with Oriol Vinyals and Quoc Viet Le to create the sequence-to-sequence learning algorithm, and worked on TensorFlow. He is also one of the AlphaGo paper's many co-authors. At the end of 2015, Sutskever left Google to become cofounder and chief scientist of the newly founded organization OpenAI. In 2022, Sutskever tweeted, "it may be that today's large neural networks are slightly conscious", which triggered debates about AI consciousness. He is considered to have played a key role in the development of ChatGPT, and later in leading the research that led to reasoning models. He is credited with establishing OpenAI’s scaling ethos. In 2023, he announced that he would co-lead OpenAI's new "Superalignment" project, which was trying to solve the alignment of superintelligences within four years. He wrote that even if superintelligence seems far off, it could happen this decade. Sutskever was formerly one of the six board members of the nonprofit entity that controlled OpenAI. In November 2023, the board fired Sam Altman, saying that "he was not consistently candid in his communications with the board". He authored a 52-page memo that relied heavily on information from Mira Murati, accusing Altman of lying, manipulating executives, and fostering internal division. Sutskever submitted the memo to the board after months of tension and dissatisfaction with Altman's leadership style, and ultimately joined the board in voting for Altman's termination. In an all-hands company meeting shortly after the board meeting, Sutskever said that firing Altman was "the board doing its duty", but the next week, he expressed regret at having participated in Altman's ouster. Altman's firing and OpenAI's co-founder Greg Brockman's resignation led three senior researchers to resign from OpenAI. After that, Sutskever stepped down from the OpenAI board and was absent from OpenAI's office. Some sources suggested he was leading the team remotely, while others said he no longer had access to the team's work. In May 2024, Sutskever announced his departure from OpenAI to focus on a new project that was "very personally meaningful" to him. His decision followed a turbulent period at OpenAI marked by leadership crises and internal debates about the direction of AI development and alignment protocols. Jan Leike, the other leader of the superalignment project, announced his departure hours later, citing an erosion of safety and trust in OpenAI's leadership. In June 2024, Sutskever announced Safe Superintelligence Inc., a new company he founded with Daniel Gross and Daniel Levy with offices in Palo Alto and Tel Aviv. In contrast to OpenAI, which releases revenue-generating products, Sutskever said the new company's "first product will be the safe superintelligence, and it will not do anything else up until then". In September 2024, the company announced that it had raised $1 billion from venture capital firms including Andreessen Horowitz, Sequoia Capital, DST Global, and SV Angel. In March 2025, Safe Superintelligence Inc. raised $2 billion more and reportedly reached a $32 billion valuation, notably due to Sutskever's reputation. In June 2025, SSI rejected an offer from Meta Platforms to buy the company. Sutskever became CEO of SSI shortly thereafter, after co-founder and CEO Gross left for Meta. In an October 2024 interview after winning the Nobel Prize in Physics, Geoffrey Hinton expressed support for Sutskever's decision to fire Altman, emphasizing concerns about AI safety. During the Musk v. Altman trial in 2026, Sutskever confirmed he had a $7 billion stake in OpenAI. === Awards and honors === In 2015, Sutskever was named in MIT Technology Review's 35 Innovators Under 35. In 2018, he was the keynote speaker at Nvidia Ntech 2018 and AI Frontiers Conference 2018. In 2022, he was elected a Fellow of the Royal Society (FRS). In 2023 and 2024, included in Time's list of the 100 most influential people in AI In 2022, 2023, and 2024, he won Neural Information Processing Systems’ Test of Time award, which recognizes papers that significantly shaped the AI field over at least ten years. In 2025, he received an honorary doctorate from his alma mater, the University of Toronto In 2026, he received the National Academy of Sciences Award for the Industrial Application of Science, presented for the first time in artificial intelligence.
Explanation-based learning
Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an application domain akin to a domain model in ontology engineering, not to be confused with Scott's domain theory) in order to make generalizations or form concepts from training examples. It is also linked with Encoding (memory) to help with Learning. == Details == An example of EBL using a perfect domain theory is a program that learns to play chess through example. A specific chess position that contains an important feature such as "Forced loss of black queen in two moves" includes many irrelevant features, such as the specific scattering of pawns on the board. EBL can take a single training example and determine what are the relevant features in order to form a generalization. A domain theory is perfect or complete if it contains, in principle, all information needed to decide any question about the domain. For example, the domain theory for chess is simply the rules of chess. Knowing the rules, in principle, it is possible to deduce the best move in any situation. However, actually making such a deduction is impossible in practice due to combinatoric explosion. EBL uses training examples to make searching for deductive consequences of a domain theory efficient in practice. In essence, an EBL system works by finding a way to deduce each training example from the system's existing database of domain theory. Having a short proof of the training example extends the domain-theory database, enabling the EBL system to find and classify future examples that are similar to the training example very quickly. The main drawback of the method—the cost of applying the learned proof macros, as these become numerous—was analyzed by Minton. === Basic formulation === EBL software takes four inputs: a hypothesis space (the set of all possible conclusions) a domain theory (axioms about a domain of interest) training examples (specific facts that rule out some possible hypothesis) operationality criteria (criteria for determining which features in the domain are efficiently recognizable, e.g. which features are directly detectable using sensors) == Application == An especially good application domain for an EBL is natural language processing (NLP). Here a rich domain theory, i.e., a natural language grammar—although neither perfect nor complete, is tuned to a particular application or particular language usage, using a treebank (training examples). Rayner pioneered this work. The first successful industrial application was to a commercial NL interface to relational databases. The method has been successfully applied to several large-scale natural language parsing systems, where the utility problem was solved by omitting the original grammar (domain theory) and using specialized LR-parsing techniques, resulting in huge speed-ups, at a cost in coverage, but with a gain in disambiguation. EBL-like techniques have also been applied to surface generation, the converse of parsing. When applying EBL to NLP, the operationality criteria can be hand-crafted, or can be inferred from the treebank using either the entropy of its or-nodes or a target coverage/disambiguation trade-off (= recall/precision trade-off = f-score). EBL can also be used to compile grammar-based language models for speech recognition, from general unification grammars. Note how the utility problem, first exposed by Minton, was solved by discarding the original grammar/domain theory, and that the quoted articles tend to contain the phrase grammar specialization—quite the opposite of the original term explanation-based generalization. Perhaps the best name for this technique would be data-driven search space reduction. Other people who worked on EBL for NLP include Guenther Neumann, Aravind Joshi, Srinivas Bangalore, and Khalil Sima'an.
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