AI Generator Text To Human

AI Generator Text To Human — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Amália (LLM)

  Amália is a Portuguese large language model (LLM) announced in November 2024 by the Portuguese Prime-Minister Luís Montenegro. Its final version is expected to be launched in 2026. It is being developed by Center for Responsible AI (Centro para a AI Responsável) and by the research centers of NOVA School of Science and Technology and Instituto Superior Técnico. == History == In 2024 it was announced that the Portuguese Agency for Administrative Modernization (Agência para a Modernização Administrativa) transpose this LLM to Portuguese Public Administration. According to Paulo Dimas (CEO of the Center for Responsible AI) the three fundamental points of this LLM project are the linguistic variant (European Portuguese), cultural representation and data protection. In April 2025 it was announced that Amália had entered beta phase with an improved version being expected to be launched in September 2025. The beta version released in September is available only to the Public Administration, but the website launched in October reiterates the final version will be an open model.

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  Alec Radford

  Alec Radford is an American artificial intelligence researcher. == Biography == Radford grew up in Texas. He graduated from Cistercian Preparatory School in 2011, where he became an Eagle Scout, and dropped out of Olin College in August 2014, where he and fellow students Slater Victoroff, Diana Yuan, and Madison May had formed the startup Indico in their dorm room. In 2015, the quartet were joined by Luke Metz and the firm and the Facebook AI research lab in New York used generative adversarial networks to create realistic low pixel images. A demonstration of Indico's technology was used without proper attribution in an April 2016 demonstration by Nvidia chief executive Jensen Huang. Radford joined OpenAI around 2016, where he worked on natural-language processing. The following year, Radford trained a neural network on Amazon reviews. The model was fairly basic, with layers which allowed for human understanding. Upon exploring it, he saw that it had a special neuron linked to the sentiment of the reviews, which it had created on its own. This was a drastic improvement from previous neural networks that had analysed sentiment, because they had to be told to do so and specially trained on data that was explicitly labeled according to sentiment. This development made OpenAI chief scientist Ilya Sutskever consider that a future model, using more diverse language data, could map far more structures of meaning, eventually becoming a "learned core module" for superintelligence. In 2018, Radford was the lead author on OpenAI's seminal research paper on generative pre-trained transformers, which form the foundation of ChatGPT. At OpenAI, he worked on early GPT models, Whisper, a speech recognition model, and the image generator DALL-E. He left OpenAI in December 2024 to pursue independent research. Around March 2025, Radford joined Thinking Machines Lab as an advisor. He joined along with Bob McGrew who was previously the chief research officer of OpenAI. In April 2026, Radford, Nick Levine, and David Duvenaud released Talkie, an AI model trained on books, newspapers, scientific journals, patents, and case law published before December 31, 1930. When asked about the state of the world in 2026, it stated that one billion people would live in Europe, that London and New York would be connected by steamships that transit between the two in ten days, and "winter will be passed in Paris, and the summer in London."

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  Alec Radford

  Alec Radford is an American artificial intelligence researcher. == Biography == Radford grew up in Texas. He graduated from Cistercian Preparatory School in 2011, where he became an Eagle Scout, and dropped out of Olin College in August 2014, where he and fellow students Slater Victoroff, Diana Yuan, and Madison May had formed the startup Indico in their dorm room. In 2015, the quartet were joined by Luke Metz and the firm and the Facebook AI research lab in New York used generative adversarial networks to create realistic low pixel images. A demonstration of Indico's technology was used without proper attribution in an April 2016 demonstration by Nvidia chief executive Jensen Huang. Radford joined OpenAI around 2016, where he worked on natural-language processing. The following year, Radford trained a neural network on Amazon reviews. The model was fairly basic, with layers which allowed for human understanding. Upon exploring it, he saw that it had a special neuron linked to the sentiment of the reviews, which it had created on its own. This was a drastic improvement from previous neural networks that had analysed sentiment, because they had to be told to do so and specially trained on data that was explicitly labeled according to sentiment. This development made OpenAI chief scientist Ilya Sutskever consider that a future model, using more diverse language data, could map far more structures of meaning, eventually becoming a "learned core module" for superintelligence. In 2018, Radford was the lead author on OpenAI's seminal research paper on generative pre-trained transformers, which form the foundation of ChatGPT. At OpenAI, he worked on early GPT models, Whisper, a speech recognition model, and the image generator DALL-E. He left OpenAI in December 2024 to pursue independent research. Around March 2025, Radford joined Thinking Machines Lab as an advisor. He joined along with Bob McGrew who was previously the chief research officer of OpenAI. In April 2026, Radford, Nick Levine, and David Duvenaud released Talkie, an AI model trained on books, newspapers, scientific journals, patents, and case law published before December 31, 1930. When asked about the state of the world in 2026, it stated that one billion people would live in Europe, that London and New York would be connected by steamships that transit between the two in ten days, and "winter will be passed in Paris, and the summer in London."

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  LipNet

  LipNet is a deep neural network for audio-visual speech recognition (ASVR). It was created by University of Oxford researchers Yannis Assael, Brendan Shillingford, Shimon Whiteson, and Nando de Freitas. The researchers stated that could match mouth movements to text with 93 percent accuracy, though it was criticized for its test using a limited dataset of words and grammar. It was used in Nvidia's autonomous "backseat driver" prototype Co-Pilot.

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  ASR-complete

  ASR-complete is, by analogy to "NP-completeness" in complexity theory, a term to indicate that the difficulty of a computational problem is equivalent to solving the central automatic speech recognition problem, i.e. recognize and understanding spoken language. Unlike "NP-completeness", this term is typically used informally. Such problems are hypothesised to include: Spoken natural language understanding Understanding speech from far-field microphones, i.e. handling the reverbation and background noise These problems are easy for humans to do (in fact, they are described directly in terms of imitating humans). Some systems can solve very simple restricted versions of these problems, but none can solve them in their full generality.

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  Intelligent Robotics Group

  The Intelligent Robotics Group (IRG) is a research organization within the Intelligent Systems Division at the NASA Ames Research Center in California's Silicon Valley. IRG conducts applied research in the area of robotics and autonomy and is one of the principal organizations at NASA responsible for robotics expertise, along with groups at the Jet Propulsion Laboratory and Johnson Space Center. The group's portfolio includes robotics in support of human exploration, perception and navigation, user interfaces, software architectures, and simulation. IRG developed the Astrobee free-flying robots on the International Space Station and was a primary contributor to the VIPER lunar rover in the areas of flight software, navigation, simulation, and mission operations. IRG has also conducted many robotic field test campaigns in support of spaceflight mission concept developments. These experiences led to the commercialization of the GigaPan system in collaboration with Carnegie Mellon University.

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  Rule-based system

  In computer science, a rule-based system is a computer system in which domain-specific knowledge is represented in the form of rules and general-purpose reasoning is used to solve problems in the domain. Two different kinds of rule-based systems emerged within the field of artificial intelligence in the 1970s: Production systems, which use if-then rules to derive actions from conditions. Logic programming systems, which use conclusion if conditions rules to derive conclusions from conditions. The differences and relationships between these two kinds of rule-based system has been a major source of misunderstanding and confusion. Both kinds of rule-based systems use either forward or backward chaining, in contrast with imperative programs, which execute commands listed sequentially. However, logic programming systems have a logical interpretation, whereas production systems do not. == Production system rules == A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game. Rule-based systems can be used to perform lexical analysis to compile or interpret computer programs, or in natural language processing. Rule-based programming attempts to derive execution instructions from a starting set of data and rules. This is a more indirect method than that employed by an imperative programming language, which lists execution steps sequentially. === Construction === A typical rule-based system has four basic components: A list of rules or rule base, which is a specific type of knowledge base. An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base. The interpreter executes a production system program by performing the following match-resolve-act cycle: Match: In this first phase, the condition sides of all productions are matched against the contents of working memory. As a result a set (the conflict set) is obtained, which consists of instantiations of all satisfied productions. An instantiation of a production is an ordered list of working memory elements that satisfies the condition side of the production. Conflict-resolution: In this second phase, one of the production instantiations in the conflict set is chosen for execution. If no productions are satisfied, the interpreter halts. Act: In this third phase, the actions of the production selected in the conflict-resolution phase are executed. These actions may change the contents of working memory. At the end of this phase, execution returns to the first phase. Temporary working memory, which is a database of facts. A user interface or other connection to the outside world through which input and output signals are received and sent. Whereas the matching phase of the inference engine has a logical interpretation, the conflict resolution and action phases do not. Instead, "their semantics is usually described as a series of applications of various state-changing operators, which often gets quite involved (depending on the choices made in deciding which ECA rules fire, when, and so forth), and they can hardly be regarded as declarative". == Logic programming rules == The logic programming family of computer systems includes the programming language Prolog, the database language Datalog and the knowledge representation and problem-solving language Answer Set Programming (ASP). In all of these languages, rules are written in the form of clauses: A :- B1, ..., Bn. and are read as declarative sentences in logical form: A if B1 and ... and Bn. In the simplest case of Horn clauses (or "definite" clauses), which are a subset of first-order logic, all of the A, B1, ..., Bn are atomic formulae. Although Horn clause logic programs are Turing complete, for many practical applications, it is useful to extend Horn clause programs by allowing negative conditions, implemented by negation as failure. Such extended logic programs have the knowledge representation capabilities of a non-monotonic logic. == Differences and relationships between production rules and logic programming rules == The most obvious difference between the two kinds of systems is that production rules are typically written in the forward direction, if A then B, and logic programming rules are typically written in the backward direction, B if A. In the case of logic programming rules, this difference is superficial and purely syntactic. It does not affect the semantics of the rules. Nor does it affect whether the rules are used to reason backwards, Prolog style, to reduce the goal B to the subgoals A, or whether they are used, Datalog style, to derive B from A. In the case of production rules, the forward direction of the syntax reflects the stimulus-response character of most production rules, with the stimulus A coming before the response B. Moreover, even in cases when the response is simply to draw a conclusion B from an assumption A, as in modus ponens, the match-resolve-act cycle is restricted to reasoning forwards from A to B. Reasoning backwards in a production system would require the use of an entirely different kind of inference engine. In his Introduction to Cognitive Science, Paul Thagard includes logic and rules as alternative approaches to modelling human thinking. He does not consider logic programs in general, but he considers Prolog to be, not a rule-based system, but "a programming language that uses logic representations and deductive techniques" (page 40). He argues that rules, which have the form IF condition THEN action, are "very similar" to logical conditionals, but they are simpler and have greater psychological plausibility (page 51). Among other differences between logic and rules, he argues that logic uses deduction, but rules use search (page 45) and can be used to reason either forward or backward (page 47). Sentences in logic "have to be interpreted as universally true", but rules can be defaults, which admit exceptions (page 44). He does not observe that all of these features of rules apply to logic programming systems.

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  GOFAI

  In the philosophy of artificial intelligence, GOFAI (good old-fashioned artificial intelligence) is classical symbolic AI, as opposed to other approaches, such as neural networks, situated robotics, narrow symbolic AI or neuro-symbolic AI. The term was coined by philosopher John Haugeland in his 1985 book Artificial Intelligence: The Very Idea. Haugeland coined the term to address two questions: Can GOFAI produce human-level artificial intelligence in a machine? Is GOFAI the primary method that brains use to display intelligence? AI founder Herbert A. Simon speculated in 1963 that the answers to both these questions was "yes". His evidence was the performance of programs he had co-written, such as Logic Theorist and the General Problem Solver, and his psychological research on human problem solving. AI research in the 1950s and 60s had an enormous influence on intellectual history: it inspired the cognitive revolution, led to the founding of the academic field of cognitive science, and was the essential example in the philosophical theories of computationalism, functionalism and cognitivism in ethics and the psychological theories of cognitivism and cognitive psychology. The specific aspect of AI research that led to this revolution was what Haugeland called "GOFAI". In AI development and technology, GOFAI is used to refer to programs that are built with deliberate, explicit instructions for a single task. This is in contrast to approaches that use machine learning. Examples of GOFAI applications include AlphaGo and Apple's initial Siri design. == Western rationalism == Haugeland places GOFAI within the rationalist tradition in western philosophy, which holds that abstract reason is the "highest" faculty, that it is what separates man from the animals, and that it is the most essential part of our intelligence. This assumption is present in Plato and Aristotle, in Shakespeare, Hobbes, Hume and Locke, it was central to the Enlightenment, to the logical positivists of the 1930s, and to the computationalists and cognitivists of the 1960s. As Shakespeare wrote: What a piece of work is a man, How noble in reason, how infinite in faculty ... In apprehension how like a god, The beauty of the world, The paragon of animals. Symbolic AI in the 1960s was able to successfully simulate the process of high-level reasoning, including logical deduction, algebra, geometry, spatial reasoning and means-ends analysis, all of them in precise English sentences, just like the ones humans used when they reasoned. Many observers, including philosophers, psychologists and the AI researchers themselves became convinced that they had captured the essential features of intelligence. This was not just hubris or speculation -- this was entailed by rationalism. If it was not true, then it brings into question a large part of the entire Western philosophical tradition. Continental philosophy, which included Nietzsche, Husserl, Heidegger and others, rejected rationalism and argued that our high-level reasoning was limited and prone to error, and that most of our abilities come from our intuitions, culture, and instinctive feel for the situation. Philosophers who were familiar with this tradition were the first to criticize GOFAI and the assertion that it was sufficient for intelligence, such as Hubert Dreyfus and Haugeland. == Haugeland's GOFAI == Critics and supporters of Haugeland's position, from philosophy, psychology, or AI research have found it difficult to define "GOFAI" precisely, and thus the literature contains a variety of interpretations. Drew McDermott, for example, finds Haugeland's description of GOFAI "incoherent" and argues that GOFAI is a "myth". Haugeland coined the term GOFAI in order to examine the philosophical implications of “the claims essential to all GOFAI theories”, which he listed as: 1. our ability to deal with things intelligently is due to our capacity to think about them reasonably (including sub-conscious thinking); and 2. our capacity to think about things reasonably amounts to a faculty for internal “automatic” symbol manipulation This is very similar to the sufficient side of the physical symbol systems hypothesis proposed by Herbert A. Simon and Allen Newell in 1963: "A physical symbol system has the necessary and sufficient means for general intelligent action." It is also similar to Hubert Dreyfus' "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules. " Haugeland's description of GOFAI refers to symbol manipulation governed by a set of instructions for manipulating the symbols. The "symbols" he refers to are discrete physical things that are assigned a definite semantics -- like and . They do not refer to signals, or unidentified numbers, or matrixes of unidentified numbers, or the zeros and ones of digital machinery. Thus, Haugeland's GOFAI does not include "good old fashioned" techniques such as cybernetics, perceptrons, dynamic programming or control theory or modern techniques such as neural networks or support vector machines. These questions ask if GOFAI is sufficient for general intelligence -- they ask if there is nothing else required to create fully intelligent machines. Thus GOFAI, for Haugeland, does not include systems that combine symbolic AI with other techniques, such as neuro-symbolic AI, and also does not include narrow symbolic AI systems that are designed only to solve a specific problem and are not expected to exhibit general intelligence. == Replies == === Replies from AI scientists === Russell and Norvig wrote, in reference to Dreyfus and Haugeland:The technology they criticized came to be called Good Old-Fashioned AI (GOFAI). GOFAI corresponds to the simplest logical agent design ... and we saw ... that it is indeed difficult to capture every contingency of appropriate behavior in a set of necessary and sufficient logical rules; we called that the qualification problem. Later symbolic AI work after the 1980's incorporated more robust approaches to open-ended domains such as probabilistic reasoning, non-monotonic reasoning, and machine learning. Currently, most AI researchers believe deep learning, and more likely, a synthesis of neural and symbolic approaches (neuro-symbolic AI), will be required for general intelligence.

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  DreamLab

  DreamLab was a volunteer computing Android and iOS app launched in 2015 by Imperial College London and the Vodafone Foundation. It was discontinued on 2nd April 2025. == Description == The app helped to research cancer, COVID-19, new drugs and tropical cyclones. To do this, DreamLab accessed part of the device's processing power, with the user's consent, while the owner charged their smartphone, to speed up the calculations of the algorithms from Imperial College London. The aim of the tropical cyclone project was to prepare for climate change risks. Other projects aimed to find existing drugs and food molecules that could help people with COVID-19 and other diseases. The performance of 100,000 smartphones would reach the annual output of all research computers at Imperial College in just three months, with a nightly runtime of six hours. The app was developed in 2015 by the Garvan Institute of Medical Research in Sydney and the Vodafone Foundation. In May 2020, the project had over 490,000 registered users.

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  ComfyUI

  ComfyUI is an open source, node-based program that allows users to generate images from a series of text prompts. It uses free diffusion models such as Stable Diffusion as the base model for its image capabilities combined with other tools such as ControlNet and LCM Low-rank adaptation with each tool being represented by a node in the program. == History == ComfyUI was released on GitHub in January 2023. According to comfyanonymous, the creator, a major goal of the project was to improve on existing software designs in terms of the user interface. The creator had been involved with Stability AI but by 3 June 2024 that involvement had ended and an organization called Comfy Org had been created along with the core developers. In July 2024, Nvidia announced support for ComfyUI within its RTX Remix modding software. In August 2024, support was added for the Flux diffusion model developed by Black Forest Labs, and Comfy Org joined the Open Model Initiative created by the Linux Foundation. As of Sept 2025, the project has 89.2k stars on GitHub. ComfyUI is one of the most popular user interfaces for Stable Diffusion, along with Automatic1111. == Features == ComfyUI's main feature is that it is node based. Each node has a function such as "load a model" or "write a prompt". The nodes are connected to form a control-flow graph called a workflow. When a prompt is queued, a highlighted frame appears around the currently executing node, starting from "load checkpoint" and ending with the final image and its save location. Workflows commonly consist of tens of nodes, forming a complex directed acyclic graph. Node types include loading a model, specifying prompts, samplers, schedulers, VAE decoders, face restoration and upscaling models, LoRAs, embeddings, and ControlNets. Several samplers are supported, such as Euler, Euler_a, dpmpp_2m_sde and dpmpp_3m_sde. Workflows can be saved to a file, allowing users to re-use node workflows and share them with other users. The file format for the workflows is in JSON and can be embedded in the generated images. Users have also created custom extensions to the base system which are exposed as new nodes, such as the extension for AnimateDiff, which aims to create videos. ComfyUI has been described as more complex compared to other diffusion UIs such as Automatic1111. A default node group is also included with the program. As of December 2024, 1,674 nodes were supported. ComfyUI Supports multiple text-to-image models including, Stable Diffusion, Flux and Tencent's Hunyuan-DiT, as well as custom models from Civitai like Pony. == LLMVision extension compromise == In June 2024, a hacker group called "Nullbulge" compromised an extension of ComfyUI to add malicious code to it. The compromised extension, called ComfyUI_LLMVISION, was used for integrating the interface with AI language models GPT-4 and Claude 3, and was hosted on GitHub. Nullbulge hosted a list of hundreds of ComfyUI users' login details across multiple services on its website, while users of the extension reported receiving numerous login notifications. vpnMentor conducted security research on the extension and claimed it could "steal crypto wallets, screenshot the user’s screen, expose device information and IP addresses, and steal files that contain certain keywords or extensions". Nullbulge's website claims they targeted users who committed "one of our sins", which included AI-art generation, art theft, promoting cryptocurrency, and any other kind of theft from artists such as from Patreon. They claimed that they were "a collective of individuals who believe in the importance of protecting artists' rights and ensuring fair compensation for their work" and that they believed that "AI-generated artwork is detrimental to the creative industry and should be discouraged".

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  Project Maven

  Project Maven (officially Algorithmic Warfare Cross Functional Team) is a United States Department of Defense initiative launched in 2017 to accelerate the adoption of machine learning and data integration across U.S. military intelligence workflows, specifically in intelligence, surveillance, target acquisition, and reconnaissance as well as in geospatial intelligence. It initially focused on applying computer vision for processing images and videos for intelligence purposes. Currently, the program operates under the National Geospatial-Intelligence Agency (NGA) and encompasses multiple applications across the Department of Defense spanning military operation targeting support, data integration and visualization for analysts, and training machine learning models on labeled datasets of military assets and infrastructure. It integrates data from drones, satellites, and other sensors to flag potential targets, present findings to human analysts, and relay their decisions to operational systems. The program originated under Deputy Secretary Robert O. Work after he raised concerns about China's advances in defense applications of artificial intelligence. Project leaders, Colonel Drew Cukor, USMC, and Lt. Gen. Jack Shanahan, framed the program as human-in-the-loop decision support inside the Department of Defense rather than as an autonomous weapons platform. Contractors supporting Maven have included Google, which withdrew in 2018 after internal protests, and follow-on integrators such as Palantir, Anduril, Amazon Web Services, and Anthropic (withdrew in 2026). The Pentagon credits Maven with providing 2024 targeting support for U.S. airstrikes in Iraq, Syria, and Yemen, along with locating hostile maritime assets in the Red Sea. == Administrative history == Initially, the effort was led by Robert O. Work who was concerned about China's military use of the emerging technology. Reportedly, Pentagon development stops short of acting as an AI weapons system capable of firing on self-designated targets. The project was established in a memo by the U.S. Deputy Secretary of Defense on 26 April 2017 proposing an "Algorithmic Warfare Cross-Functional Team". With the help of Defense Innovation Unit, the project obtained the support of top talents in AI outside of the traditional defense contracting base. It was initially funded for $70 million. Jack Shanahan was the director of the project during April 2017 to December 2018. At the second Defense One Tech Summit in July 2017, Cukor said that the investment in a "deliberate workflow process" was funded by the Department [of Defense] through its "rapid acquisition authorities" for about "the next 36 months". In the defense industry, the standard procedure for the military to acquire hardware is by way of research, development, test, and evaluation (RDT&E), followed by production and sustainment. In 2017, acquiring software was done in the same way as hardware. This created a problem, since software is constantly updated. Project Maven procured software using Broad Agency Announcements, a flexible contracting vehicle that categorized software as consistently RDT&E, allowing constant updating. Another issue was that the government usually acquired the intellectual property (IP) for procured software, and with the project, only parts of the IP of the software was acquired. Cukor used the principle of "platform IP belongs to the vendor, configurations on top are the customer's". For example, Palantir retained IP to their core platform, while the government obtained the IP to Maven-specific logic configured on top of it. According to US Air Force Lt. Gen. Jack Shanahan in November 2017, it is "designed to be that pilot project, that pathfinder, that spark that kindles the flame front of artificial intelligence across the rest of the [Defense] Department". Its chief, U.S. Marine Corps Col. Drew Cukor, said: "People and computers will work symbiotically to increase the ability of weapon systems to detect objects." Project Maven has been noted by allies, such as Australia's Ian Langford, for the ability to identify adversaries by harvesting data from sensors on UAVs and satellites. As of 2017 December, 150,000 images had been manually labelled to establish the first training data sets, and it was projected to reach one million by January 2018. Project Maven was funded for $221 million in fiscal 2020. In 2020, the House and Senate conferees on the National Defense Authorization Act for Fiscal Year 2021, agreed to the Senate's recommendation to fund the Pentagon's $250 million request for Project Maven. At the GEOINT Symposium of 2022, it was announced that Project Maven was transferred from the Office of the Under Secretary of Defense for Intelligence and Security to the NGA, under President Biden’s proposed budget for Fiscal Year 2023. It became a Program of Record on 2023 November 7. Frank "Trey" Whitworth, vice admiral, was the director of NGA from June 2022 to November 2025. Whitworth was initially skeptical of the program, suspecting it was incautious about the targeting principles, but later regarded it as "important work". As of 2024, the project is jointly administered by the NGA and the CDAO, and its director is Rachel Martin. Before 2025, Biden appointees within CDAO had held back AI development for safety and reliability concerns, though as of 2025, this has stopped. As of 2024, Maven provided the cloud infrastructure, software capabilities, and AI for CDAO's Combined Joint All-Domain Command and Control initiatives. As of summer 2025, there were eight Maven initiatives. Of these, five were in the NGA, including analyzing drone feeds and satellite imagery. On 18 September 2025, the UK government announced a new partnership with Palantir to develop AI-powered military capabilities for decision-making and targeting, identifying opportunities worth up to £750 million over five years. On 25 March 2025, the NATO Communications and Information Agency and Palantir finalized the acquisition of the Palantir Maven Smart System NATO (MSS NATO) for employment within NATO's Allied Command Operations. It was planned to be used within 30 days of acquisition. In a letter to Pentagon on 9 March 2026, Steve Feinberg stated that Project Maven will become an official program of record by September 2026, the close of the current fiscal year. The project would transfer from the NGA to the CDAO within 30 days. Future contracting with Palantir would be handled by the US Army. In 2026-03, it was announced that the US Army Combined Arms Command would integrate Maven into its training. == Technology == Project Maven uses machine learning algorithms to analyze and fuse vast amounts of surveillance data from multiple sources made possible through data integration using Palantir Technologies. The data sources include photographs, satellite imagery, geolocation data (IP address, geotag, metadata, etc) from communications intercepts, infrared sensors, synthetic-aperture radar, and more. The system is mainly used for assisting analysts in intelligence, surveillance, target acquisition, and reconnaissance. Machine learning systems, including object recognition systems, process the data and identify potential targets, such as enemy tanks or location of new military facility. The training dataset included at least 4 million images of military objects such as warships, labelled by humans. The user interface is called Maven Smart System. It could display information such as aircraft movements, logistics, locations of key personnel, locations on the no-strike list, ships, etc. Yellow-outlined boxes show potential targets. Blue-outlined boxes show friendly forces or no-strike zones. It could also transmit, directly to weapons, a human decision to fire weapons. Internal documentation referred to "Maven ATR: automatic target recognition". Initially the project focused on applications of computer vision. The project's leaders were particularly impressed by model performance on ImageNet. As of 2018, the purpose of the system was AI-enabled analysis of full-motion video. In 2022 it expanded to combatant commands under the AI and Data Acceleration Initiative. In 2022, it was reported that the project expanded to non-image data, including captured enemy material, maritime intelligence, and publicly available information. In 2024, it was stated that Maven's key technical contribution was data management: Maven standardizes heterogeneous data through an ontology layer so data can be fused, exchanged across cloud and edge systems, and used by multiple applications. The system was presented as a broader data-centric warfighting system that feeds apps for planning, preparing, and executing operations. In 2024, the Broad Area Surveillance-Targeting (BAS-T) is a part of Maven. The system detects objects in images and uses data fusion to produce a common operational picture containing "priority based, in-depth assessment of the enemy systems pre

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  AirSim

  AirSim (Aerial Informatics and Robotics Simulation) is an open-source, cross-platform simulator for drones, ground vehicles such as cars and various other objects, built on Epic Games’ proprietary Unreal Engine 4 as a platform for AI research. It is developed by Microsoft and can be used to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. This allows testing of autonomous solutions without worrying about real-world damage. AirSim provides some 12 kilometers of roads with 20 city blocks and APIs to retrieve data and control vehicles in a platform independent way. The APIs are accessible via a variety of programming languages, including C++, C#, Python and Java. AirSim supports hardware-in-the-loop with driving wheels and flight controllers such as PX4 for physically and visually realistic simulations. The platform also supports common robotic platforms, such as Robot Operating System (ROS). It is developed as an Unreal plug-in that can be dropped into any Unreal environment. An experimental release for a Unity plug-in is also available. On December 15, 2023 Microsoft has shutdown the development of the project.

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  Information retrieval

  Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images, or sounds. Cross-modal retrieval implies retrieval across modalities. Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals, and other documents, as well as storing and managing those documents. Web search engines are the most visible IR applications. == Overview == An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval, a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevance. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query. == History == there is ... a machine called the Univac ... whereby letters and figures are coded as a pattern of magnetic spots on a long steel tape. By this means the text of a document, preceded by its subject code symbol, can be recorded ... the machine ... automatically selects and types out those references which have been coded in any desired way at a rate of 120 words a minute The idea of using computers to search for relevant pieces of information was popularized in the article As We May Think by Vannevar Bush in 1945. It would appear that Bush was inspired by patents for a 'statistical machine' – filed by Emanuel Goldberg in the 1920s and 1930s – that searched for documents stored on film. The first description of a computer searching for information was described by Holmstrom in 1948, detailing an early mention of the Univac computer. Automated information retrieval systems were introduced in the 1950s: one even featured in the 1957 romantic comedy Desk Set. In the 1960s, the first large information retrieval research group was formed by Gerard Salton at Cornell. By the 1970s several different retrieval techniques had been shown to perform well on small text corpora such as the Cranfield collection (several thousand documents). Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s. In 1992, the US Department of Defense along with the National Institute of Standards and Technology (NIST), cosponsored the Text Retrieval Conference (TREC) as part of the TIPSTER text program. The aim of this was to look into the information retrieval community by supplying the infrastructure that was needed for evaluation of text retrieval methodologies on a very large text collection. This catalyzed research on methods that scale to huge corpora. The introduction of web search engines has boosted the need for very large scale retrieval systems even further. By the late 1990s, the rise of the World Wide Web fundamentally transformed information retrieval. While early search engines such as AltaVista (1995) and Yahoo! (1994) offered keyword-based retrieval, they were limited in scale and ranking refinement. The breakthrough came in 1998 with the founding of Google, which introduced the PageRank algorithm, using the web's hyperlink structure to assess page importance and improve relevance ranking. During the 2000s, web search systems evolved rapidly with the integration of machine learning techniques. These systems began to incorporate user behavior data (e.g., click-through logs), query reformulation, and content-based signals to improve search accuracy and personalization. In 2009, Microsoft launched Bing, introducing features that would later incorporate semantic web technologies through the development of its Satori knowledge base. Academic analysis have highlighted Bing's semantic capabilities, including structured data use and entity recognition, as part of a broader industry shift toward improving search relevance and understanding user intent through natural language processing. A major leap occurred in 2018, when Google deployed BERT (Bidirectional Encoder Representations from Transformers) to better understand the contextual meaning of queries and documents. This marked one of the first times deep neural language models were used at scale in real-world retrieval systems. BERT's bidirectional training enabled a more refined comprehension of word relationships in context, improving the handling of natural language queries. Because of its success, transformer-based models gained traction in academic research and commercial search applications. Simultaneously, the research community began exploring neural ranking models that outperformed traditional lexical-based methods. Long-standing benchmarks such as the Text REtrieval Conference (TREC), initiated in 1992, and more recent evaluation frameworks Microsoft MARCO(MAchine Reading COmprehension) (2019) became central to training and evaluating retrieval systems across multiple tasks and domains. MS MARCO has also been adopted in the TREC Deep Learning Tracks, where it serves as a core dataset for evaluating advances in neural ranking models within a standardized benchmarking environment. As deep learning became integral to information retrieval systems, researchers began to categorize neural approaches into three broad classes: sparse, dense, and hybrid models. Sparse models, including traditional term-based methods and learned variants like SPLADE, rely on interpretable representations and inverted indexes to enable efficient exact term matching with added semantic signals. Dense models, such as dual-encoder architectures like ColBERT, use continuous vector embeddings to support semantic similarity beyond keyword overlap. Hybrid models aim to combine the advantages of both, balancing the lexical (token) precision of sparse methods with the semantic depth of dense models. This way of categorizing models balances scalability, relevance, and efficiency in retrieval systems. As IR systems increasingly rely on deep learning, concerns around bias, fairness, and explainability have also come to the picture. Research is now focused not just on relevance and efficiency, but on transparency, accountability, and user trust in retrieval algorithms. == Applications == Areas where information retrieval techniques are employed include (the entries are in alphabetical order within each category): === General applications === Digital libraries Information filtering Recommender systems Media search Blog search Image retrieval 3D retrieval Music retrieval News search Speech retrieval Video retrieval Search engines Site search Desktop search Enterprise search Federated search Mobile search Social search Web search === Domain-specific applications === Expert search finding Genomic information retrieval Geographic information retrieval Information retrieval for chemical structures Information retrieval in software engineering Legal information retrieval Vertical search === Other retrieval methods === Methods/Techniques in which information retrieval techniques are employed include: Cross-modal retrieval Adversarial information retrieval Automatic summarization Multi-document summarization Compound term processing Cross-lingual retrieval Document classification Spam filtering Question answering == Model types == In order to effectively retrieve relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Each retrieval strategy incorporates a specific model for its document representation purposes. The picture on the right illustrates the relationship of som

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  VoID

  The Vocabulary of Interlinked Datasets (VoID) is a vocabulary for providing concise summaries (metadata) of Resource Description Framework (RDF) datasets—meaningful collections of semantic triples—using the syntax of RDF Schema. It can be used for general metadata (such as information about the license of the dataset), access metadata (information about how to access the dataset), structural metadata (information about how the dataset is structured), and linking metadata (information about links between datasets). A linked dataset is a collection of data, published and maintained by a single provider, available as RDF on the Web, where at least some of the resources in the dataset are identified by dereferencable Uniform Resource Identifiers (URIs). VoID is used to provide metadata on RDF datasets to facilitate query processing on a graph of interlinked datasets in the Semantic Web.

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  Simultaneous localization and mapping

  Simultaneous localization and mapping (SLAM) is a process where a computer constructs or updates a map of an unknown environment while simultaneously keeping track of an entity's location within it. While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality. SLAM algorithms are tailored to the available resources and are not aimed at perfection but at operational compliance. Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body. == Mathematical description of the problem == Given a series of controls u t {\displaystyle u_{t}} and sensor observations o t {\displaystyle o_{t}} over discrete time steps t {\displaystyle t} , the SLAM problem is to compute an estimate of the agent's state x t {\displaystyle x_{t}} and a map of the environment m t {\displaystyle m_{t}} . All quantities are usually probabilistic, so the objective is to compute P ( m t + 1 , x t + 1 | o 1 : t + 1 , u 1 : t ) {\displaystyle P(m_{t+1},x_{t+1}|o_{1:t+1},u_{1:t})} Applying Bayes' rule gives a framework for sequentially updating the location posteriors, given a map and a transition function P ( x t | x t − 1 ) {\displaystyle P(x_{t}|x_{t-1})} , P ( x t | o 1 : t , u 1 : t , m t ) = ∑ m t − 1 P ( o t | x t , m t , u 1 : t ) ∑ x t − 1 P ( x t | x t − 1 ) P ( x t − 1 | m t , o 1 : t − 1 , u 1 : t ) / Z {\displaystyle P(x_{t}|o_{1:t},u_{1:t},m_{t})=\sum _{m_{t-1}}P(o_{t}|x_{t},m_{t},u_{1:t})\sum _{x_{t-1}}P(x_{t}|x_{t-1})P(x_{t-1}|m_{t},o_{1:t-1},u_{1:t})/Z} where Z {\displaystyle Z} is the normalization constant, which ensures all the probabilities sum up to 1. Similarly the map can be updated sequentially by P ( m t | x t , o 1 : t , u 1 : t ) = ∑ x t ∑ m t P ( m t | x t , m t − 1 , o t , u 1 : t ) P ( m t − 1 , x t | o 1 : t − 1 , m t − 1 , u 1 : t ) {\displaystyle P(m_{t}|x_{t},o_{1:t},u_{1:t})=\sum _{x_{t}}\sum _{m_{t}}P(m_{t}|x_{t},m_{t-1},o_{t},u_{1:t})P(m_{t-1},x_{t}|o_{1:t-1},m_{t-1},u_{1:t})} Like many inference problems, the solutions to inferring the two variables together can be found, to a local optimum solution, by alternating updates of the two beliefs in a form of an expectation–maximization algorithm. == Algorithms == Statistical techniques used to approximate the above equations include Kalman filters and particle filters (the algorithm behind Monte Carlo Localization). They provide an estimation of the posterior probability distribution for the pose of the robot and for the parameters of the map. Methods which conservatively approximate the above model using covariance intersection are able to avoid reliance on statistical independence assumptions to reduce algorithmic complexity for large-scale applications. Other approximation methods achieve improved computational efficiency by using simple bounded-region representations of uncertainty. Set-membership techniques are mainly based on interval constraint propagation. They provide a set which encloses the pose of the robot and a set approximation of the map. Bundle adjustment, and more generally maximum a posteriori estimation (MAP), is another popular technique for SLAM using image data, which jointly estimates poses and landmark positions, increasing map fidelity, and is used in commercialized SLAM systems such as Google's ARCore which replaces their prior augmented reality computing platform named Tango, formerly Project Tango. MAP estimators compute the most likely explanation of the robot poses and the map given the sensor data, rather than trying to estimate the entire posterior probability. New SLAM algorithms remain an active research area, and are often driven by differing requirements and assumptions about the types of maps, sensors and models as detailed below. Many SLAM systems can be viewed as combinations of choices from each of these aspects. === Mapping === Topological maps are a method of environment representation which capture the connectivity (i.e., topology) of the environment rather than creating a geometrically accurate map. Topological SLAM approaches have been used to enforce global consistency in metric SLAM algorithms. In contrast, grid maps use arrays (typically square or hexagonal) of discretized cells to represent a topological world, and make inferences about which cells are occupied. Typically the cells are assumed to be statistically independent to simplify computation. Under such assumption, P ( m t | x t , m t − 1 , o t ) {\displaystyle P(m_{t}|x_{t},m_{t-1},o_{t})} are set to 1 if the new map's cells are consistent with the observation o t {\displaystyle o_{t}} at location x t {\displaystyle x_{t}} and 0 if inconsistent. Modern self driving cars mostly simplify the mapping problem to almost nothing, by making extensive use of highly detailed map data collected in advance. This can include map annotations to the level of marking locations of individual white line segments and curbs on the road. Location-tagged visual data such as Google's StreetView may also be used as part of maps. Essentially such systems simplify the SLAM problem to a simpler localization only task, perhaps allowing for moving objects such as cars and people only to be updated in the map at runtime. === Sensing === SLAM will always use several different types of sensors, and the powers and limits of various sensor types have been a major driver of new algorithms. Statistical independence is the mandatory requirement to cope with metric bias and with noise in measurements. Different types of sensors give rise to different SLAM algorithms which assumptions are most appropriate to the sensors. At one extreme, laser scans or visual features provide details of many points within an area, sometimes rendering SLAM inference unnecessary because shapes in these point clouds can be easily and unambiguously aligned at each step via image registration. At the opposite extreme, tactile sensors are extremely sparse as they contain only information about points very close to the agent, so they require strong prior models to compensate in purely tactile SLAM. Most practical SLAM tasks fall somewhere between these visual and tactile extremes. Sensor models divide broadly into landmark-based and raw-data approaches. Landmarks are uniquely identifiable objects in the world which location can be estimated by a sensor, such as Wi-Fi access points or radio beacons. Raw-data approaches make no assumption that landmarks can be identified, and instead model P ( o t | x t ) {\displaystyle P(o_{t}|x_{t})} directly as a function of the location. Optical sensors may be one-dimensional (single beam) or 2D- (sweeping) laser rangefinders, 3D high definition light detection and ranging (lidar), 3D flash lidar, 2D or 3D sonar sensors, and one or more 2D cameras. Since the invention of local features, such as SIFT, there has been intense research into visual SLAM (VSLAM) using primarily visual (camera) sensors, because of the increasing ubiquity of cameras such as those in mobile devices. Follow up research includes. Both visual and lidar sensors are informative enough to allow for landmark extraction in many cases. Other recent forms of SLAM include tactile SLAM (sensing by local touch only), radar SLAM, acoustic SLAM, and Wi-Fi-SLAM (sensing by strengths of nearby Wi-Fi access points). Recent approaches apply quasi-optical wireless ranging for multi-lateration (real-time locating system (RTLS)) or multi-angulation in conjunction with SLAM as a tribute to erratic wireless measures. A kind of SLAM for human pedestrians uses a shoe mounted inertial measurement unit as the main sensor and relies on the fact that pedestrians are able to avoid walls to automatically build floor plans of buildings by an indoor positioning system. For some outdoor applications, the need for SLAM has been almost entirely removed due to high precision differential GPS sensors. From a SLAM perspective, these may be viewed as location sensors which likelihoods are so sharp that they completely dominate the inference. However, GPS sensors may occasionally decline or go down entirely, e.g. during times of military conflict, which are of particular interest to some robotics applications. === Kinematics modeling === The P ( x t | x t − 1 ) {\displaystyle P(x_{t}|x_{t-1})} term represents the kinematics of the model, which usually include information about action commands given to a robot. As a part of the model, the kinematics of the robot is included, to improve estimates of sensing under con

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