AI Analytics Certification

AI Analytics Certification — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Cognition Network Technology

  Cognition Network Technology (CNT), also known as Definiens Cognition Network Technology, is an object-based image analysis method developed by Nobel laureate Gerd Binnig together with a team of researchers at Definiens AG in Munich, Germany. It serves for extracting information from images using a hierarchy of image objects (groups of pixels), as opposed to traditional pixel processing methods. To emulate the human mind's cognitive powers, Definiens used patented image segmentation and classification processes, and developed a method to render knowledge in a semantic network. CNT examines pixels not in isolation, but in context. It builds up a picture iteratively, recognizing groups of pixels as objects. It uses the color, shape, texture and size of objects as well as their context and relationships to draw conclusions and inferences, similar to human analysis. == History == In 1994 Professor Gerd Binnig founded Definiens. CNT was first available with the launch of the eCognition software in May 2000. In June 2010, Trimble Navigation Ltd (NASDAQ: TRMB) acquired Definiens business asset in earth sciences markets, including eCognition software, and also licensed Definiens' patented CNT. In 2014, Definiens was acquired by MedImmune, the global biologics research and development arm of AstraZeneca, for an initial consideration of $150 million. == Software == Definiens Tissue Studio Definiens Tissue Studio is a digital pathology image analysis software application based on CNT. The intended use of Definiens Tissue Studio is for biomarker translational research in formalin-fixed, paraffin-embedded tissue samples which have been treated with immunohistochemical staining assays, or hematoxylin and eosin (H&E). The central concept behind Definiens Tissue Studio is a user interface that facilitates machine learning from example digital histopathology images to derive an image analysis solution suitable for the measurement of biomarkers and/or histological features within pre-defined regions of interest on a cell-by-cell basis, and within sub-cellular compartments. The derived image analysis solution is then automatically applied to subsequent digital images to objectively measure defined sets of multiparametric image features. These data sets are used for further understanding the underlying biological processes that drive cancer and other diseases. Image processing and data analysis are performed either on a local desktop computer workstation, or on a server grid. eCognition The eCognition suite offers three components that can be used stand-alone or in combination to solve image analysis tasks. eCognition Developer is a development environment for object-based image analysis. It is used in earth sciences to develop rule sets (or applications) for the analysis of remote sensing data. eCognition Architect enables non-technical users to configure, calibrate and execute image analysis workflows created in eCognition Developer. eCognition Server software provides a processing environment for batch execution of image analysis jobs. eCognition software is utilized in numerous remote sensing and geospatial application scenarios and environments, using a variety of data types: Generic: Rapid Mapping, Change Detection, Object Recognition By environment: Diverse Landcover Mapping, Urban Analysis (i.e. impervious surface area analysis for taxation, property assessment for insurance, inventory of green infrastructure), Forestry (i.e. biomass measurement, species identification, firescar measurement), Agriculture (i.e. regional planning, precision farming, crisis response), Marine and Riparian (i.e. ecosystem evaluation, disaster management, harbor monitoring). Other: Defense, security, atmosphere and climate The online eCognition community was launched in July 2009 and had 2813 members as of July 9, 2010. Membership is distributed globally and user conferences are held regularly, the last having taken place in November 2009 in Munich, Germany. The bi-annual GEOBIA (Geographic Object-Based Image Analysis) conference is heavily attended by eCognition users, with the majority of presentations based on eCognition software.

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  Alexey Chervonenkis

  Alexey Yakovlevich Chervonenkis (Russian: Алексей Яковлевич Червоненкис; 7 September 1938 – 22 September 2014) was a Soviet and Russian mathematician. Along with Vladimir Vapnik, he was one of the main developers of the Vapnik–Chervonenkis theory, also known as the "fundamental theory of learning", an important part of computational learning theory. Chervonenkis held joint appointments with the Russian Academy of Sciences and Royal Holloway, University of London. Alexey Chervonenkis got lost in Losiny Ostrov National Park on 22 September 2014, and later during a search operation was found dead near Mytishchi, a suburb of Moscow. He had died of hypothermia.

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  AlphaFold

  AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. It was particularly successful at predicting the most accurate structures for targets rated as most difficult by the competition organizers, where no existing template structures were available from proteins with partially similar sequences. AlphaFold 2 (2020) repeated this placement in the CASP14 competition in November 2020. It achieved a level of accuracy much higher than any other entry. It scored above 90 on CASP's global distance test (GDT) for approximately two-thirds of the proteins, a test measuring the similarity between a computationally predicted structure and the experimentally determined structure, where 100 represents a complete match. The inclusion of metagenomic data has improved the quality of the prediction of multiple sequence alignments. One of the biggest sources of the training data was the custom-built Big Fantastic Database of 65,983,866 protein families, represented as multiple sequence alignments and Hidden Markov models, covering 2,204,359,010 protein sequences from reference databases, metagenomes, and metatranscriptomes. AlphaFold 2's results at CASP14 were described as "astounding" and "transformational". However, some researchers noted that the accuracy was insufficient for a third of its predictions, and that it did not reveal the underlying mechanism or rules of protein folding for the protein folding problem, which remains unsolved. Despite this, the technical achievement was widely recognized. On 15 July 2021, the AlphaFold 2 paper was published in Nature as an advance access publication alongside open source software and a searchable database of species proteomes. As of November 2025, the paper had been cited nearly 43,000 times. AlphaFold 3 was announced on 8 May 2024. It can predict the structure of complexes created by proteins with DNA, RNA, various ligands, and ions. The new prediction method shows a minimum 50% improvement in accuracy for protein interactions with other molecules compared to existing methods. Demis Hassabis and John Jumper shared one half of the 2024 Nobel Prize in Chemistry, awarded "for protein structure prediction," while the other half went to David Baker "for computational protein design." Hassabis and Jumper had previously won the Breakthrough Prize in Life Sciences and the Albert Lasker Award for Basic Medical Research in 2023 for their leadership of the AlphaFold project. == Background == Proteins consist of chains of amino acids which spontaneously fold to form the three dimensional (3-D) structures of the proteins. The 3-D structure is crucial to understanding the biological function of the protein. Protein structures can be determined experimentally through techniques such as X-ray crystallography, cryo-electron microscopy and nuclear magnetic resonance (NMR), which are all expensive and time-consuming. Such efforts, using the experimental methods, have identified the structures of about 170,000 proteins over the last 60 years, while there are over 200 million known proteins across all life forms. Over the years, researchers have applied numerous computational methods to predict the 3D structures of proteins from their amino acid sequences, accuracy of such methods in best possible scenario is close to experimental techniques (NMR) by the use of homology modeling based on molecular evolution. CASP, which was launched in 1994 to challenge the scientific community to produce their best protein structure predictions, found that GDT scores of only about 40 out of 100 can be achieved for the most difficult proteins by 2016. AlphaFold started competing in the 2018 CASP using an artificial intelligence (AI) deep learning technique. == Algorithm == DeepMind is known to have trained the program on over 170,000 protein structures from the Protein Data Bank, a public repository of protein sequences and structures. The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. The overall training was conducted on processing power between 100 and 200 GPUs. === AlphaFold 1 (2018) === AlphaFold 1 (2018) was built on work developed by various teams in the 2010s, work that looked at the large databases of related protein sequences now available from many different organisms (most without known 3D structures), to try to find changes at different residues (peptides) that appeared to be correlated, even though the residues were not consecutive in the main chain. Such correlations suggest that the residues may be close to each other physically, even though not close in the sequence, allowing a contact map to be estimated. Building on recent work prior to 2018, AlphaFold 1 extended this by estimating a probability distribution for the distances between residues, effectively transforming the contact map into a distance map. It also used more advanced learning methods than previously to develop the inference. The code was not made publicly available, except to run on sequences of proteins in the 2018 CASP competition. === AlphaFold 2 (2020) === The 2020 version of the program (AlphaFold 2, 2020) is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind. AlphaFold 1 used a number of separately trained modules to produce a guide potential, which was then combined with a physics-based energy potential. AlphaFold 2 replaced this with a system of interconnected sub-networks, forming a single, differentiable, end-to-end model based on pattern recognition. This model was trained in an integrated manner. After the neural network's prediction converges, a final refinement step applies local physical constraints using energy minimization based on the AMBER force field. This step only slightly adjusts the predicted structure. A key part of the 2020 system are two modules, believed to be based on a transformer design, which are used to progressively refine a vector of information for each relationship (or "edge" in graph-theory terminology) between an amino acid residue of the protein and another amino acid residue (these relationships are represented by the array shown in green); and between each amino acid position and each different sequences in the input sequence alignment (these relationships are represented by the array shown in red). Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learned from training data. These transformations are iterated, the updated information output by one step becoming the input of the next, with the sharpened residue/residue information feeding into the update of the residue/sequence information, and then the improved residue/sequence information feeding into the update of the residue/residue information. As the iteration progresses, according to one report, the "attention algorithm ... mimics the way a person might assemble a jigsaw puzzle: first connecting pieces in small clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole." The output of these iterations then informs the final structure prediction module, which also uses transformers, and is itself then iterated. In an example presented by DeepMind, the structure prediction module achieved a correct topology for the target protein on its first iteration, scored as having a GDT_TS of 78, but with a large number (90%) of stereochemical violations – i.e. unphysical bond angles or lengths. With subsequent iterations the number of stereochemical violations fell. By the third iteration the GDT_TS of the prediction was approaching 90, and by the eighth iteration the number of stereochemical violations was approaching zero. The training data was originally restricted to single peptide chains. However, the October 2021 update, named AlphaFold-Multimer, included protein complexes in its training data. DeepMind stated this update succeeded about 70% of the time at accurately predicting protein-protein interactions. === AlphaFold 3 (2024) === Announced on 8 May 2024, AlphaFold 3 was co-developed by Google DeepMind and Isomorphic Labs, both subsidiaries of Alphabet. AlphaFold 3 is not limited to proteins, as it can also predict the structures of protein complexes with DNA, RNA, post-translational modifications and selected ligands and ions. AlphaFold 3 introduces the "Pairformer," a deep learning architecture inspired by the transformer, which is considered similar to, but si

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  Parents & Kids Safe AI Coalition

  The Parents & Kids Safe AI Coalition is a political action committee that advocates for regulation of artificial intelligence on child safety. As of April 2026, the group is funded solely by the artificial intelligence company OpenAI, which pledged $10 million to the effort. == History == In October 2025, California Gov. Gavin Newsom vetoed Assembly Bill 1064. Sponsored by Common Sense Media, the bill would have introduced stronger child safety protections for AI chatbots. The following month, Common Sense Media founder Jim Steyer filed a ballot initiative intended to restore the "guardrails" lost in the veto. In response, OpenAI introduced a competing initiative. In January 2026, Common Sense Media and OpenAI announced that they would be working together on a compromise ballot initiative, the Parents & Kids Safe AI Act. Reporting indicated that initial outreach emails to child safety organizations failed to disclose OpenAI's involvement. Several advocacy groups signed an open letter claiming the initiative would shield AI companies from liability and undermine age verification, among other concerns. After Common Sense Media met with opposing groups in February, the ballot initiative was put on hold and the organizations involved sought to negotiate with the Legislature instead. The Parents & Kids Safe AI Coalition was founded to support this effort. In March 2026, the group reached out to some of the same groups contacted earlier, asking them to endorse its list of policy priorities. Again, some organizations reported being unaware of OpenAI's level of involvement. At least two groups withdrew from the coalition after learning about the financial ties. The priorities themselves were described as "vague but fairly uncontroversial" by The San Francisco Standard.

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  Digital image processing

  Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more), digital image processing may be modeled in the form of multidimensional systems. The generation and development of digital image processing are mainly affected by three factors: first, the development of computers; second, the development of mathematics (especially the creation and improvement of discrete mathematics theory); and third, the demand for a wide range of applications in environment, agriculture, military, industry and medical science has increased. == History == Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s, at Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement. The purpose of early image processing was to improve the quality of the image. In image processing, the input is a low-quality image, and the output is an image with improved quality. Common image processing includes image enhancement, restoration, encoding, and compression. The first successful application was the American Jet Propulsion Laboratory (JPL). They used image processing techniques such as geometric correction, gradation transformation, noise removal, etc. on the thousands of lunar photos sent back by the Space Detector Ranger 7 in 1964, taking into account the position of the Sun and the environment of the Moon. The impact of the successful mapping of the Moon's surface map by the computer has been a success. Later, more complex image processing was performed on the nearly 100,000 photos sent back by the spacecraft, so that the topographic map, color map and panoramic mosaic of the Moon were obtained, which achieved extraordinary results and laid a solid foundation for human landing on the Moon. The cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. This led to images being processed in real-time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and computer-intensive operations. With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing, and is generally used because it is not only the most versatile method, but also the cheapest. === Image sensors === The basis for modern image sensors is metal–oxide–semiconductor (MOS) technology, invented at Bell Labs between 1955 and 1960, This led to the development of digital semiconductor image sensors, including the charge-coupled device (CCD) and later the CMOS sensor. The charge-coupled device was invented by Willard S. Boyle and George E. Smith at Bell Labs in 1969. While researching MOS technology, they realized that an electric charge was the analogy of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. The CCD is a semiconductor circuit that was later used in the first digital video cameras for television broadcasting. The NMOS active-pixel sensor (APS) was invented by Olympus in Japan during the mid-1980s. This was enabled by advances in MOS semiconductor device fabrication, with MOSFET scaling reaching smaller micron and then sub-micron levels. The NMOS APS was fabricated by Tsutomu Nakamura's team at Olympus in 1985. The CMOS active-pixel sensor (CMOS sensor) was later developed by Eric Fossum's team at the NASA Jet Propulsion Laboratory in 1993. By 2007, sales of CMOS sensors had surpassed CCD sensors. MOS image sensors are widely used in optical mouse technology. The first optical mouse, invented by Richard F. Lyon at Xerox in 1980, used a 5 μm NMOS integrated circuit sensor chip. Since the first commercial optical mouse, the IntelliMouse introduced in 1999, most optical mouse devices use CMOS sensors. === Image compression === An important development in digital image compression technology was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed in 1972. DCT compression became the basis for JPEG, which was introduced by the Joint Photographic Experts Group in 1992. JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format on the Internet. Its highly efficient DCT compression algorithm was largely responsible for the wide proliferation of digital images and digital photos, with several billion JPEG images produced every day as of 2015. Medical imaging techniques produce very large amounts of data, especially from CT, MRI and PET modalities. As a result, storage and communications of electronic image data are prohibitive without the use of compression. JPEG 2000 image compression is used by the DICOM standard for storage and transmission of medical images. The cost and feasibility of accessing large image data sets over low or various bandwidths are further addressed by use of another DICOM standard, called JPIP, to enable efficient streaming of the JPEG 2000 compressed image data. === Digital signal processor (DSP) === Electronic signal processing was revolutionized by the wide adoption of MOS technology in the 1970s. MOS integrated circuit technology was the basis for the first single-chip microprocessors and microcontrollers in the early 1970s, and then the first single-chip digital signal processor (DSP) chips in the late 1970s. DSP chips have since been widely used in digital image processing. The discrete cosine transform (DCT) image compression algorithm has been widely implemented in DSP chips, with many companies developing DSP chips based on DCT technology. DCTs are widely used for encoding, decoding, video coding, audio coding, multiplexing, control signals, signaling, analog-to-digital conversion, formatting luminance and color differences, and color formats such as YUV444 and YUV411. DCTs are also used for encoding operations such as motion estimation, motion compensation, inter-frame prediction, quantization, perceptual weighting, entropy encoding, variable encoding, and motion vectors, and decoding operations such as the inverse operation between different color formats (YIQ, YUV and RGB) for display purposes. DCTs are also commonly used for high-definition television (HDTV) encoder/decoder chips. == Tasks == Digital image processing allows the use of much more complex algorithms, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analogue means. In particular, digital image processing is a concrete application of, and a practical technology based on: Classification Feature extraction Multi-scale signal analysis Pattern recognition Projection Some techniques that are used in digital image processing include: Anisotropic diffusion Hidden Markov models Image editing Image restoration Independent component analysis Linear filtering Neural networks Partial differential equations Pixelation Point feature matching Principal components analysis Self-organizing maps Wavelets == Digital image transformations == === Filtering === Digital filters are used to blur and sharpen digital images. Filtering can be performed by: convolution with specifically designed kernels (filter array) in the spatial domain masking specific frequency regions in the frequency (Fourier) domain The following examples show both methods: ==== Image padding in Fourier domain filtering ==== Images are typically padded before being transformed to the Fourier space, the highpass filtered images below illustrate the consequences of different padding techniques: Notice that the highpass filter shows extra edges when zero padded compared to the repeated edge padding. ==== Filtering code examples ==== MATLAB example for spatial domain highpass filtering. === Affine transformations === Affine transformations enable basic image transformations including scale, rotate, translate, mirror and shear as is shown in the following examples: To apply the affine

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  Meta Content Framework

  Meta Content Framework (MCF) is a specification of a content format for structuring metadata about web sites and other data. == History == MCF was developed by Ramanathan V. Guha at Apple Computer's Advanced Technology Group between 1995 and 1997. Rooted in knowledge-representation systems such as CycL, KRL, and KIF, it sought to describe objects, their attributes, and the relationships between them. One application of MCF was HotSauce, also developed by Guha while at Apple. It generated a 3D visualization of a web site's table of contents, based on MCF descriptions. By late 1996, a few hundred sites were creating MCF files and Apple HotSauce allowed users to browse these MCF representations in 3D. When the research project was discontinued, Guha left Apple for Netscape, where, in collaboration with Tim Bray, he adapted MCF to use XML and created the first version of the Resource Description Framework (RDF). == MCF format == An MCF file consists of one or more blocks, each corresponding to an entity. A block looks like this:The identifier is a unique identifier for that entity (more on the scope of the identifier below) and is used to refer to that entity. The following lines each specify a property and one or more values, separated by commas. Each value can be a reference to another entity (via its identifier), a string (enclosed by double quotes) or a number. For example:NOTE: The identifier must not include a comma (,) and must not be enclosed within double quotes. A common parsing failure is due to odd number of unescaped double quotes in text. For instance, "foo bar" baz" needs to be "foo bar\" baz". Commas within double quotes are not considered as value separators. Every entity has at least one property: typeOf.

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  Richard S. Sutton

  Richard Stuart Sutton (born 1957 or 1958) is a Canadian computer scientist. He is a professor of computing science at the University of Alberta, fellow & Chief Scientific Advisor at the Alberta Machine Intelligence Institute, and a research scientist at Keen Technologies. Sutton is considered one of the founders of modern computational reinforcement learning. In particular, he contributed to temporal difference learning and policy gradient methods. He received the 2024 Turing Award with Andrew Barto. == Education and early life == Richard Sutton was born in either 1957 or 1958 in Toledo, Ohio, and grew up in Oak Brook, Illinois, a suburb of Chicago, United States. Sutton received his Bachelor of Arts (BA) degree in psychology from Stanford University in 1978 before taking a Master of Science (1980) and PhD (1984) in computer science from the University of Massachusetts Amherst supervised by Andrew Barto. His doctoral dissertation introduced actor-critic architectures and temporal credit assignment. He was influenced by Harry Klopf's work in the 1970s, which proposed that supervised learning is insufficient for AI or explaining intelligent behavior, and trial-and-error learning, driven by "hedonic aspects of behavior", is necessary. This focused his interest to reinforcement learning. == Career and research == Sutton held a postdoctoral research position at the University of Massachusetts Amherst in 1984. He worked at GTE Laboratories in Waltham, Massachusetts as principal member of technical staff from 1985 to 1994, then returned to the University of Massachusetts Amherst as a senior research scientist. He joined AT&T Labs Shannon Laboratory in Florham Park, New Jersey as principal technical staff member from 1998 to 2002. He has been a professor of computing science at the University of Alberta since 2003, where he helped establish the Reinforcement Learning and Artificial Intelligence Laboratory. In 2017 he became a distinguished research scientist with Google DeepMind and helped launch DeepMind Alberta in Edmonton, a research office operated in close collaboration with the University of Alberta. 1984: Postdoctoral researcher, University of Massachusetts Amherst (Amherst, Massachusetts) 1985–1994: Principal member of technical staff, Computer and Intelligent Systems Laboratory, GTE Laboratories (Waltham, Massachusetts) 1995–1998: Senior research scientist, University of Massachusetts Amherst (Amherst, Massachusetts) 1998–2002: Principal technical staff member, Artificial Intelligence Department, AT&T Labs Shannon Laboratory (Florham Park, New Jersey) 2003–present: Professor of computing science, University of Alberta (Edmonton, Alberta) 2017–2023: Distinguished research scientist, DeepMind Alberta, Google DeepMind (Edmonton, Alberta) 2024–Present: Research scientist, Keen Technologies === Reinforcement learning === Sutton joined Andrew Barto in the early 1980s at UMass, trying to explore the behavior of neurons in the human brain as the basis for human intelligence, a concept that had been advanced by computer scientist A. Harry Klopf. Sutton and Barto used mathematics toward furthering the concept and using it as the basis for artificial intelligence. This concept became known as reinforcement learning and went on to becoming a key part of artificial intelligence techniques. Barto and Sutton used Markov decision processes (MDP) as the mathematical foundation to explain how agents (algorithmic entities) made decisions when in a stochastic or random environment, receiving rewards at the end of every action. Traditional MDP theory assumed the agents knew all information about the MDPs in their attempt toward maximizing their cumulative rewards. Barto and Sutton's reinforcement learning techniques allowed for both the environment and the rewards to be unknown, and thus allowed for these category of algorithms to be applied to a wide array of problems. Sutton returned to Canada in the 2000s and continued working on the topic which continued to develop in academic circles until one of its first major real world applications saw Google's AlphaGo program built on this concept defeating the then prevailing human champion. Barto and Sutton have widely been credited and accepted as pioneers of modern reinforcement learning, with the technique itself being foundational to the AI boom. In a 2019 essay, Sutton proposed the "bitter lesson", which criticized the field of AI research for failing to learn that "building in how we think we think does not work in the long run", arguing that "70 years of AI research [had shown] that general methods that leverage computation are ultimately the most effective, and by a large margin", beating efforts building on human knowledge about specific fields like computer vision, speech recognition, chess or Go. Sutton argues that large language models aren’t capable of learning on-the-job, and so new model architectures are required to enable continual learning. Sutton further argues that a special training phase will be unnecessary — the agent will learn on-the-fly, rendering large language models obsolete. In 2023, Sutton and John Carmack announced a partnership for the development of artificial general intelligence (AGI). === Awards and honors === Sutton has been a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) since 2001; his nomination read: "For significant contributions to many topics in machine learning, including reinforcement learning, temporal difference techniques, and neural networks." In 2003, he received the President's Award from the International Neural Network Society and in 2013, the Outstanding Achievement in Research award from the University of Massachusetts Amherst. He received the 2024 Turing Award from the Association for Computing Machinery together with Andrew Barto; the citation of the award read: "For developing the conceptual and algorithmic foundations of reinforcement learning." In 2016, Sutton was elected Fellow of the Royal Society of Canada. In 2021, he was elected Fellow of the Royal Society (FRS) of London. === Research === Sutton introduced temporal-difference methods for prediction and control, establishing convergence properties and practical algorithms. He proposed integrated learning and planning through the Dyna architecture. He co-developed the options framework for temporal abstraction in reinforcement learning. He co-authored the first modern policy gradient formulation with function approximation. Sutton's essay The Bitter Lesson argued that general methods that scale with computation dominate domain-specific approaches in the long run. His former doctoral students include David Silver and Doina Precup. === Selected publications === His publications include: == Personal life == Sutton became a Canadian citizen in 2015, and his renunciation of US citizenship was reported in 2017.

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  Stockfish (chess)

  Stockfish is a free and open-source chess engine, available for various desktop and mobile platforms. It can be used in chess software through the Universal Chess Interface. Stockfish has been one of the strongest chess engines in the world for several years. It has won all main events of the Top Chess Engine Championship (TCEC) and the Chess.com Computer Chess Championship (CCC) since 2020 and, as of May 2026, is the strongest CPU chess engine in the world with an estimated Elo rating of 3653 in a time control of 40/15 (15 minutes to make 40 moves), according to CCRL. The Stockfish engine was developed by Tord Romstad, Marco Costalba, and Joona Kiiski, and was derived from Glaurung, an open-source engine by Tord Romstad released in 2004. It is now being developed and maintained by the Stockfish community. Stockfish historically used only a classical hand-crafted function to evaluate board positions, but with the introduction of the efficiently updatable neural network (NNUE) in August 2020, Stockfish 12 adopted a hybrid evaluation system that primarily used the neural network and occasionally relied on the hand-crafted evaluation. In July 2023, Stockfish removed the hand-crafted evaluation and transitioned to a fully neural network-based approach. == Features == Stockfish uses a tree-search algorithm based on alpha–beta search with several hand-designed heuristics. Stockfish represents positions using bitboards. Stockfish supports Chess960, a feature it inherited from Glaurung. Support for Syzygy tablebases, previously available in a fork maintained by Ronald de Man, was integrated into Stockfish in 2014. In 2018, support for the 7-man Syzygy was added, shortly after the tablebase was made available. Stockfish supports an unlimited number of CPU threads in multiprocessor systems, with a maximum transposition table size of 32 TB. Stockfish has been a very popular engine on various platforms. On desktop, it is the default chess engine bundled with the Internet Chess Club interface programs BlitzIn and Dasher. On mobile, it has been bundled with the Stockfish app, SmallFish and Droidfish. Other Stockfish-compatible graphical user interfaces (GUIs) include Fritz, Arena, Stockfish for Mac, and PyChess. Stockfish can be compiled to WebAssembly or JavaScript, allowing it to run in the browser. Both Chess.com and Lichess provide Stockfish in this form in addition to a server-side program. Release versions and development versions are available as C++ source code and as precompiled versions for Microsoft Windows, macOS, Linux 32-bit/64-bit and Android. == History == The program originated from Glaurung, an open-source chess engine created by Tord Romstad and first released in 2004. Four years later, Marco Costalba forked the project, naming it Stockfish because it was "produced in Norway and cooked in Italy" (Romstad is Norwegian and Costalba is Italian). The first version, Stockfish 1.0, was released in November 2008. For a while, new ideas and code changes were transferred between the two programs in both directions, until Romstad decided to discontinue Glaurung in favor of Stockfish, which was the stronger engine at the time. The last Glaurung version (2.2) was released in December 2008. Around 2011, Romstad decided to abandon his involvement with Stockfish in order to spend more time on his new iOS chess app. On 18 June 2014 Marco Costalba announced that he had "decided to step down as Stockfish maintainer" and asked that the community create a fork of the current version and continue its development. An official repository, managed by a volunteer group of core Stockfish developers, was created soon after and currently manages the development of the project. === Fishtest === Since 2013, Stockfish has been developed using a distributed testing framework named Fishtest, where volunteers can donate CPU time for testing improvements to the program. Changes to game-playing code are accepted or rejected based on results of playing of tens of thousands of games on the framework against an older "reference" version of the program, using sequential probability ratio testing. Tests on the framework are verified using the chi-squared test, and only if the results are statistically significant are they deemed reliable and used to revise the software code. After the inception of Fishtest, Stockfish gained 120 Elo points in 12 months, propelling it to the top of all major rating lists. As of May 2026, the framework has used a total of more than 20,100 years of CPU time to play over 10 billion chess games. === NNUE === In June 2020, Stockfish introduced the efficiently updatable neural network (NNUE) approach, based on earlier work by computer shogi programmers. Instead of using manually designed heuristics to evaluate the board, this approach introduced a neural network trained on millions of positions which could be evaluated quickly on CPU. On 2 September 2020, the twelfth version of Stockfish was released, incorporating NNUE, and reportedly winning ten times more game pairs than it loses when matched against version eleven. In July 2023, the classical evaluation was completely removed in favor of the NNUE evaluation. == Competition results == === Top Chess Engine Championship === Stockfish is a TCEC multiple-time champion and the current leader in trophy count. Ever since TCEC restarted in 2013, Stockfish has finished first or second in every season except one. Stockfish finished second in TCEC Season 4 and 5, with scores of 23–25 first against Houdini 3 and later against Komodo 1142 in the Superfinal event. Season 5 was notable for the winning Komodo team as they accepted the award posthumously for the program's creator Don Dailey, who succumbed to an illness during the final stage of the event. In his honor, the version of Stockfish that was released shortly after that season was named "Stockfish DD". On 30 May 2014, Stockfish 170514 (a development version of Stockfish 5 with tablebase support) convincingly won TCEC Season 6, scoring 35.5–28.5 against Komodo 7x in the Superfinal. Stockfish 5 was released the following day. In TCEC Season 7, Stockfish again made the Superfinal, but lost to Komodo with a score of 30.5–33.5. In TCEC Season 8, despite losses on time caused by buggy code, Stockfish nevertheless qualified once more for the Superfinal, but lost 46.5–53.5 to Komodo. In Season 9, Stockfish defeated Houdini 5 with a score of 54.5–45.5. Stockfish finished third during season 10 of TCEC, the only season since 2013 in which Stockfish had failed to qualify for the superfinal. It did not lose a game but was still eliminated because it was unable to score enough wins against lower-rated engines. After this technical elimination, Stockfish went on a long winning streak, winning seasons 11 (59–41 against Houdini 6.03), 12 (60–40 against Komodo 12.1.1), and 13 (55–45 against Komodo 2155.00) convincingly. In Season 14, Stockfish faced a new challenger in Leela Chess Zero, eking out a win by one point (50.5–49.5). Its winning streak was finally ended in Season 15, when Leela qualified again and won 53.5–46.5, but Stockfish promptly won Season 16, defeating AllieStein 54.5–45.5, after Leela failed to qualify for the Superfinal. In Season 17, Stockfish faced Leela again in the superfinal, losing 52.5–47.5. However, Stockfish has won every Superfinal since: beating Leela 53.5–46.5 in Season 18, 54.5–45.5 in Season 19, 53–47 in Season 20, and 56–44 in Season 21. In Season 22, Komodo Dragon beat out Leela to qualify for the Superfinal, losing to Stockfish by a large margin 59.5–40.5. Stockfish did not lose an opening pair in this match. Leela made the Superfinal in Seasons 23 and 24, but was crushed by Stockfish both times (58.5–41.5 and 58–42). In Season 25, Stockfish once again defeated Leela, but this time by a narrower margin of 52–48. Stockfish also took part in the TCEC cup, winning the first edition, but was surprisingly upset by Houdini in the semifinals of the second edition. Stockfish recovered to beat Komodo in the third-place playoff. In the third edition, Stockfish made it to the finals, but was defeated by Leela Chess Zero after blundering in a 7-man endgame tablebase draw. It turned this result around in the fourth edition, defeating Leela in the final 4.5–3.5. In TCEC Cup 6, Stockfish finished third after losing to AllieStein in the semifinals, the first time it had failed to make the finals. Since then, Stockfish has consistently won the tournament, with the exception of the 11th edition which Leela won 8.5–7.5. === Chess.com Computer Chess Championship === Ever since Chess.com hosted its first Chess.com Computer Chess Championship in 2018, Stockfish has been the most successful engine. It dominated the earlier championships, winning six consecutive titles before finishing second in CCC7. Since then, its dominance has come under threat from the neural-network engines Leelenstein and Leela Chess Zero, but it has continued to perform w

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  Machine learning in video games

  Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control, procedural content generation (PCG) and deep learning-based content generation. Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models. This is in sharp contrast to traditional methods of artificial intelligence such as search trees and expert systems. Information on machine learning techniques in the field of games is mostly known to public through research projects as most gaming companies choose not to publish specific information about their intellectual property. The most publicly known application of machine learning in games is likely the use of deep learning agents that compete with professional human players in complex strategy games. There has been a significant application of machine learning on games such as Atari/ALE, Doom, Minecraft, StarCraft, and car racing. Other games that did not originally exists as video games, such as chess and Go have also been affected by the machine learning. == Overview of relevant machine learning techniques == === Deep learning === Deep learning is a subset of machine learning which focuses heavily on the use of artificial neural networks (ANN) that learn to solve complex tasks. Deep learning uses multiple layers of ANN and other techniques to progressively extract information from an input. Due to this complex layered approach, deep learning models often require powerful machines to train and run on. ==== Convolutional neural networks ==== Convolutional neural networks (CNN) are specialized ANNs that are often used to analyze image data. These types of networks are able to learn translation invariant patterns, which are patterns that are not dependent on location. CNNs are able to learn these patterns in a hierarchy, meaning that earlier convolutional layers will learn smaller local patterns while later layers will learn larger patterns based on the previous patterns. A CNN's ability to learn visual data has made it a commonly used tool for deep learning in games. === Recurrent neural network === Recurrent neural networks are a type of ANN that are designed to process sequences of data in order, one part at a time rather than all at once. An RNN runs over each part of a sequence, using the current part of the sequence along with memory of previous parts of the current sequence to produce an output. These types of ANN are highly effective at tasks such as speech recognition and other problems that depend heavily on temporal order. There are several types of RNNs with different internal configurations; the basic implementation suffers from a lack of long term memory due to the vanishing gradient problem, thus it is rarely used over newer implementations. ==== Long short-term memory ==== A long short-term memory (LSTM) network is a specific implementation of a RNN that is designed to deal with the vanishing gradient problem seen in simple RNNs, which would lead to them gradually "forgetting" about previous parts of an inputted sequence when calculating the output of a current part. LSTMs solve this problem with the addition of an elaborate system that uses an additional input/output to keep track of long term data. LSTMs have achieved very strong results across various fields, and were used by several monumental deep learning agents in games. === Reinforcement learning === Reinforcement learning is the process of training an agent using rewards and/or punishments. The way an agent is rewarded or punished depends heavily on the problem; such as giving an agent a positive reward for winning a game or a negative one for losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search, Deep Q-networks and others. It has seen strong performance in both the field of games and robotics. === Neuroevolution === Neuroevolution involves the use of both neural networks and evolutionary algorithms. Instead of using gradient descent like most neural networks, neuroevolution models make use of evolutionary algorithms to update neurons in the network. Researchers claim that this process is less likely to get stuck in a local minimum and is potentially faster than state of the art deep learning techniques. == Deep learning agents == Machine learning agents have been used to take the place of a human player rather than function as NPCs, which are deliberately added into video games as part of designed gameplay. Deep learning agents have achieved impressive results when used in competition with both humans and other artificial intelligence agents. === Chess === Chess is a turn-based strategy game that is considered a difficult AI problem due to the computational complexity of its board space. Similar strategy games are often solved with some form of a Minimax Tree Search. These types of AI agents have been known to beat professional human players, such as the historic 1997 Deep Blue versus Garry Kasparov match. Since then, machine learning agents have shown ever greater success than previous AI agents. === Go === Go is another turn-based strategy game which is considered an even more difficult AI problem than chess. The state space of is Go is around 10^170 possible board states compared to the 10^120 board states for Chess. Prior to recent deep learning models, AI Go agents were only able to play at the level of a human amateur. ==== AlphaGo ==== Google's 2015 AlphaGo was the first AI agent to beat a professional Go player. AlphaGo used a deep learning model to train the weights of a Monte Carlo tree search (MCTS). The deep learning model consisted of 2 ANN, a policy network to predict the probabilities of potential moves by opponents, and a value network to predict the win chance of a given state. The deep learning model allows the agent to explore potential game states more efficiently than a vanilla MCTS. The network were initially trained on games of humans players and then were further trained by games against itself. ==== AlphaGo Zero ==== AlphaGo Zero, another implementation of AlphaGo, was able to train entirely by playing against itself. It was able to quickly train up to the capabilities of the previous agent. === StarCraft series === StarCraft and its sequel StarCraft II are real-time strategy (RTS) video games that have become popular environments for AI research. Blizzard and DeepMind have worked together to release a public StarCraft 2 environment for AI research to be done on. Various deep learning methods have been tested on both games, though most agents usually have trouble outperforming the default AI with cheats enabled or skilled players of the game. ==== Alphastar ==== Alphastar was the first AI agent to beat professional StarCraft 2 players without any in-game advantages. The deep learning network of the agent initially received input from a simplified zoomed out version of the gamestate, but was later updated to play using a camera like other human players. The developers have not publicly released the code or architecture of their model, but have listed several state of the art machine learning techniques such as relational deep reinforcement learning, long short-term memory, auto-regressive policy heads, pointer networks, and centralized value baseline. Alphastar was initially trained with supervised learning, it watched replays of many human games in order to learn basic strategies. It then trained against different versions of itself and was improved through reinforcement learning. The final version was hugely successful, but only trained to play on a specific map in a protoss mirror matchup. === Dota 2 === Dota 2 is a multiplayer online battle arena (MOBA) game. Like other complex games, traditional AI agents have not been able to compete on the same level as professional human player. The only widely published information on AI agents attempted on Dota 2 is OpenAI's deep learning Five agent. ==== OpenAI Five ==== OpenAI Five utilized separate long short-term memory networks to learn each hero. It trained using a reinforcement learning technique known as Proximal Policy Learning running on a system containing 256 GPUs and 128,000 CPU cores. Five trained for months, accumulating 180 years of game experience each day, before facing off with professional players. It was eventually able to beat the 2018 Dota 2 esports champion team in a 2019 series of games. === Planetary Annihilation === Planetary Annihilation is a real-time strategy game which focuses on massive scale war. The developers use ANNs in their default AI agent. === Supreme Commander 2 === Supreme Commander 2 is a real-time strategy (RTS) video game. The game uses Multilayer Perceptrons (MLPs) to control a platoon’s reaction to encountered enemy units. Total of four MLPs are used, one for each platoon type: land, naval

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  Elon Musk

  Elon Reeve Musk ( EE-lon; born June 28, 1971) is a businessman and former public official known for his leadership of Tesla and SpaceX. Musk has been the wealthiest person in the world since 2025; as of June 2026, Forbes estimates his net worth to be US$834 billion. Born into the wealthy Musk family in Pretoria, South Africa, Musk emigrated in 1989 to Canada; he has Canadian citizenship since his mother was born there. He received bachelor's degrees in 1997 from the University of Pennsylvania before moving to California to pursue business ventures. In 1995, Musk co-founded the software company Zip2. Following its sale in 1999, he co-founded X.com, an online payment company that later merged to form PayPal, which was acquired by eBay in 2002. Musk also became an American citizen in 2002. In 2002, Musk founded the space technology company SpaceX, becoming its CEO and chief engineer; the company has since led innovations in reusable rockets and commercial spaceflight. Musk joined the automaker Tesla as an early investor in 2004 and became its CEO and product architect in 2008; it has since become a leader in electric vehicles. In 2015, he co-founded OpenAI to advance artificial intelligence (AI) research, but later left; growing discontent with the organization's direction and leadership in the AI boom in the 2020s led him to establish xAI, which became a subsidiary of SpaceX in 2026. In 2022, he acquired the social network Twitter, implementing significant changes, and rebranding it as X in 2023. His other businesses include the neurotechnology company Neuralink, which he co-founded in 2016, and the tunneling company the Boring Company, which he founded in 2017. In November 2025, Tesla approved a pay package worth $1 trillion for Musk, which he is to receive over 10 years if he meets specific goals. Musk is a supporter of global far-right politics, figures, and political parties. He was the largest donor in the 2024 U.S. presidential election, where he supported Donald Trump. After Trump was inaugurated as president in January 2025, Musk served as Senior Advisor to the President and as the de facto head of the Department of Government Efficiency (DOGE). Shortly before a public feud with Trump, Musk left the Trump administration in May 2025 and returned to managing his companies. Musk's political activities, statements and views have made him a polarizing figure. He has been criticized for making unscientific and misleading statements, including spreading COVID-19 misinformation, promoting conspiracy theories, and affirming antisemitic, racist, and transphobic comments. His acquisition of Twitter was controversial due to a subsequent increase in hate speech and the spread of misinformation on the service, following his pledge to decrease censorship. His role in the second Trump administration attracted public backlash, particularly in response to DOGE. == Early life and education == Elon Reeve Musk was born on June 28, 1971, in Pretoria, South Africa's administrative capital. He is of British and Pennsylvania Dutch ancestry. His mother, Maye (née Haldeman), is a model and dietitian born in Saskatchewan, Canada, and raised in South Africa. Musk therefore holds both South African and Canadian citizenship from birth. His father, Errol Musk, is a South African electromechanical engineer, pilot, sailor, consultant, emerald dealer, and property developer, who partly owned a rental lodge at Timbavati Private Nature Reserve. His maternal grandfather, Joshua N. Haldeman, who died in a plane crash when Elon was a toddler, was an American-born Canadian chiropractor, aviator and political activist in the Technocracy movement who moved to South Africa in 1950. Haldeman's anti-government, anti-democratic and conspiracist views, which included the promotion of far-right antisemitic conspiracy theories, "fanatical" support of apartheid, and according to Errol Musk, support of Nazism, have been suggested as an influence on Elon. During his childhood, Elon was told stories by his grandmother of Haldeman's travels and exploits, and Elon has suggested that all of Haldeman's descendants have his "desire for adventure, exploration – doing crazy things". Elon has a younger brother, Kimbal, a younger sister, Tosca, and four paternal half-siblings. Musk was baptized as a child in the Anglican Church of Southern Africa. The Musk family was wealthy during Elon's youth. Despite both Elon and Errol previously stating that Errol was a part owner of a Zambian emerald mine, in 2023, Errol recounted that the deal he made was to receive "a portion of the emeralds produced at three small mines". Errol was elected to the Pretoria City Council as a representative of the anti-apartheid Progressive Party and has said that his children shared their father's dislike of apartheid. After his parents divorced in 1979, Elon, aged around 9, chose to live with his father because he had an Encyclopædia Britannica set and a computer. Elon later regretted his decision and became estranged from his father. Elon has recounted trips to a wilderness school that he described as a "paramilitary Lord of the Flies" where "bullying was a virtue" and children were encouraged to fight over rations. In one incident, after an altercation with a fellow pupil, Elon was thrown down concrete steps and beaten severely, leading to him being hospitalized for his injuries. Elon described his father berating him after he was discharged from the hospital. Errol denied berating Elon and claimed, "The [other] boy had just lost his father to suicide, and Elon had called him stupid. Elon had a tendency to call people stupid. How could I possibly blame that child?" Elon was an enthusiastic reader of books, and had attributed his success in part to having read The Lord of the Rings, the Foundation series, and The Hitchhiker's Guide to the Galaxy. At age ten, he developed an interest in computing and video games, teaching himself how to program from the VIC-20 user manual. At age twelve, Elon sold his BASIC-based game Blastar to PC and Office Technology magazine for approximately $500 (equivalent to $1,600 in 2025). === Education === Musk attended Waterkloof House Preparatory School, Bryanston High School, and then Pretoria Boys High School, where he graduated. Musk was a decent but unexceptional student, earning a 61/100 in Afrikaans and a B on his senior math certification. Musk applied for a Canadian passport through his Canadian-born mother to avoid South Africa's mandatory military service, which would have forced him to participate in the apartheid regime, as well as to ease his path to immigration to the United States. While waiting for his application to be processed, he attended the University of Pretoria for five months. Musk arrived in Canada in June 1989, connected with a second cousin in Saskatchewan, and worked odd jobs, including at a farm and a lumber mill. In 1990, he entered Queen's University in Kingston, Ontario. Two years later, he transferred to the University of Pennsylvania, where he studied until 1995. Although Musk has said that he earned his degrees in 1995, the University of Pennsylvania did not award them until 1997 – a Bachelor of Arts in physics and a Bachelor of Science in economics from the university's Wharton School. He reportedly hosted large, ticketed house parties to help pay for tuition, and wrote a business plan for an electronic book-scanning service similar to Google Books. In 1994, Musk held two internships in Silicon Valley: one at energy storage startup Pinnacle Research Institute, which investigated electrolytic supercapacitors for energy storage, and another at Palo Alto–based startup Rocket Science Games. In 1995, he was accepted to a graduate program in materials science at Stanford University, but did not enroll. Musk decided to join the Internet boom of the 1990s, applying for a job at Netscape, to which he reportedly never received a response. The Washington Post reported that Musk lacked legal authorization to remain and work in the United States after failing to enroll at Stanford. In response, Musk said he was allowed to work at that time and that his student visa transitioned to an H1-B. According to numerous former business associates and shareholders, Musk said he was on a student visa at the time. == Business career == === Zip2 === In 1995, Musk, his brother Kimbal, and Greg Kouri founded the web software company Zip2 with funding from a group of angel investors. They housed the venture at a small rented office in Palo Alto. Replying to Rolling Stone, Musk denounced the notion that they started their company with funds borrowed from Elon's father Errol Musk, but in a tweet, he recognized that his father contributed 10% of a later funding round. The company developed and marketed an Internet city guide for the newspaper publishing industry, with maps, directions, and yellow pages. According to Musk, "The website was up during the day and I was coding it

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  Kinect

  Kinect is a discontinued line of motion sensing input devices produced by Microsoft and first released in 2010. The devices generally contain RGB cameras, and infrared projectors and detectors that map depth through either structured light or time of flight calculations, which can in turn be used to perform real-time gesture recognition and body skeletal detection, among other capabilities. They also contain microphones that can be used for speech recognition and voice control. Kinect was originally developed as a motion controller peripheral for Xbox video game consoles, distinguished from competitors (such as Nintendo's Wii Remote and Sony's PlayStation Move) by not requiring physical controllers. The first-generation Kinect was based on technology from Israeli company PrimeSense, and unveiled at E3 2009 as a peripheral for Xbox 360 codenamed "Project Natal". It was first released on November 4, 2010, and would go on to sell eight million units in its first 60 days of availability. The majority of the games developed for Kinect were casual, family-oriented titles, which helped to attract new audiences to Xbox 360, but did not result in wide adoption by the console's existing, overall userbase. As part of the 2013 unveiling of Xbox 360's successor, Xbox One, Microsoft unveiled a second-generation version of Kinect with improved tracking capabilities. Microsoft also announced that Kinect would be a required component of the console, and that it would not function unless the peripheral is connected. The requirement proved controversial among users and critics due to privacy concerns, prompting Microsoft to backtrack on the decision. However, Microsoft still bundled the new Kinect with Xbox One consoles upon their launch in November 2013. A market for Kinect-based games still did not emerge after the Xbox One's launch; Microsoft would later offer Xbox One hardware bundles without Kinect included, and later revisions of the console removed the dedicated ports used to connect it (requiring a powered USB adapter instead). Microsoft ended production of Kinect for Xbox One in October 2017. Kinect has also been used as part of non-game applications in academic and commercial environments, as it was cheaper and more robust than other depth-sensing technologies at the time. While Microsoft initially objected to such applications, it later released software development kits (SDKs) for the development of Microsoft Windows applications that use Kinect. In 2020, Microsoft released Azure Kinect as a continuation of the technology integrated with the Microsoft Azure cloud computing platform. Part of the Kinect technology was also used within Microsoft's HoloLens project. Microsoft discontinued the Azure Kinect developer kits in October 2023. == History == === Development === The origins of the Kinect started around 2005, at a point where technology vendors were starting to develop depth-sensing cameras. Microsoft had been interested in a 3D camera for the Xbox line earlier but because the technology had not been refined, had placed it in the "Boneyard", a collection of possible technology they could not immediately work on. In 2005, Israeli company PrimeSense was founded by mathematicians and engineers to develop the "next big thing" for video games, incorporating cameras that were capable of mapping a human body in front of them and sensing hand motions. They showed off their system at the 2006 Game Developers Conference, where Microsoft's Alex Kipman, the general manager of hardware incubation, saw the potential in PrimeSense's technology for the Xbox system. Microsoft began discussions with PrimeSense about what would need to be done to make their product more consumer-friendly: not only improvements in the capabilities of depth-sensing cameras, but a reduction in size and cost, and a means to manufacture the units at scale was required. PrimeSense spent the next few years working at these improvements. Nintendo released the Wii in November 2006. The Wii's central feature was the Wii Remote, a handheld device that was detected by the Wii through a motion sensor bar mounted onto a television screen to enable motion controlled games. Microsoft felt pressure from the Wii, and began looking into depth-sensing in more detail with PrimeSense's hardware, but could not get to the level of motion tracking they desired. While they could determine hand gestures, and sense the general shape of a body, they could not do skeletal tracking. A separate path within Microsoft looked to create an equivalent of the Wii Remote, considering that this type of unit may become standardized similar to how two-thumbstick controllers became a standard feature. However, it was still ultimately Microsoft's goal to remove any device between the player and the Xbox. Kudo Tsunoda and Darren Bennett joined Microsoft in 2008, and began working with Kipman on a new approach to depth-sensing aided by machine learning to improve skeletal tracking. They internally demonstrated this and established where they believed the technology could be in a few years, which led to the strong interest to fund further development of the technology; this has also occurred at a time that Microsoft executives wanted to abandon the Wii-like motion tracking approach, and favored the depth-sensing solution to present a product that went beyond the Wii's capabilities. The project was greenlit by late 2008 with work started in 2009. The project was codenamed "Project Natal" after the Brazilian city Natal, Kipman's birthplace. Additionally, Kipman recognized the Latin origins of the word "natal" to mean "to be born", reflecting the new types of audiences they hoped to draw with the technology. Much of the initial work was related to ethnographic research to see how video game players' home environments were laid out, lit, and how those with Wiis used the system to plan how Kinect units would be used. The Microsoft team discovered from this research that the up-and-down angle of the depth-sensing camera would either need to be adjusted manually, or would require an expensive motor to move automatically. Upper management at Microsoft opted to include the motor despite the increased cost to avoid breaking game immersion. Kinect project work also involved packaging the system for mass production and optimizing its performance. Hardware development took around 22 months. During hardware development, Microsoft engaged with software developers to use Kinect. Microsoft wanted to make games that would be playable by families since Kinect could sense multiple bodies in front of it. One of the first internal titles developed for the device was the pack-in game Kinect Adventures developed by Good Science Studio that was part of Microsoft Studios. One of the game modes of Kinect Adventures was "Reflex Ridge", based on the Japanese Brain Wall game where players attempt to contort their bodies in a short time to match cutouts of a wall moving at them. This type of game was a key example of the type of interactivity they wanted with Kinect, and its development helped feed into the hardware improvements. Another development was Project Milo, a prototype game developed by Lionhead Studios led by Peter Molyneux where the player could interact with a virtual avatar through motion controls and voice recognition. Lionhead had developed the project based on original capabilities of the Kinect, but according to Molyneux, Microsoft had found that a consumer-grade version of the Kinect would cost thousands of dollars, so they scaled back the device and refocused the role of games for the Kinect to be more casual games as seen on the Wii. As a result, Project Milo no longer fit Microsoft's portfolio and was cancelled. Nearing the planned release, there was a problem of widespread testing of Kinect in various room types and different bodies accounting for age, gender, and race among other factors, while keeping the details of the unit confidential. Microsoft engaged in a company-wide program offering employees to take home Kinect units to test them. Microsoft also brought other non-gaming divisions, including its Microsoft Research, Microsoft Windows, and Bing teams to help complete the system. Microsoft established its own large-scale manufacturing facility to bulk product Kinect units and test them. === Introduction === Kinect was first announced to the public as "Project Natal" on June 1, 2009, during Microsoft's press conference at E3 2009; film director Steven Spielberg joined Microsoft's Don Mattrick to introduce the technology and its potential. Three demos were presented during the conference—Microsoft's Ricochet and Paint Party, and Lionhead Studios' Milo & Kate created by Peter Molyneux—while a Project Natal-enabled version of Criterion Games' Burnout Paradise was shown during the E3 exhibition. By E3 2009, the skeletal mapping technology was capable of simultaneously tracking four people, with a feature extraction of 4

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  Ratio Club

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

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  Racter

  Racter is an artificial intelligence program that generates English language prose at random. It was published by Mindscape for IBM PC compatibles in 1984, then for the Apple II, Mac, and Amiga. An expanded version of the software, not the one released through Mindscape, was used to generate the text for the published book The Policeman's Beard Is Half Constructed. == History == Racter, short for raconteur, was written by William Chamberlain and Thomas Etter. Racter's initial creation was the short story Soft Ions, which appeared in the October 1981 issue of Omni (magazine). The publication's editors bought the story in January 1980, before it had even been written. In exchange for the rights, the editors offered financial support to Chamberlain and Etter so the two could refine Racter. In 1983, Racter produced a book called The Policeman's Beard Is Half Constructed (ISBN 0-446-38051-2). The program originally was written for an OSI which only supported file names at most six characters long, causing the name to be shorted to Racter and it was later adapted to run on a CP/M machine where it was written in "compiled ASIC on a Z80 microcomputer with 64K of RAM." This version, the program that allegedly wrote the book, was not released to the general public. The sophistication claimed for the program was likely exaggerated, as could be seen by investigation of the template system of text generation. In 1984, Mindscape released an interactive version of Racter, developed by Inrac Corporation, for IBM PC compatibles, and it was ported to the Apple II, Mac, and Amiga. The published Racter was similar to a chatterbot. The BASIC program that was released by Mindscape was far less sophisticated than anything that could have written the fairly sophisticated prose of The Policeman's Beard. The commercial version of Racter could be likened to a computerized version of Mad Libs, the game in which you fill in the blanks in advance and then plug them into a text template to produce a surrealistic tale. The commercial program attempted to parse text inputs, identifying significant nouns and verbs, which it would then regurgitate to create "conversations", plugging the input from the user into phrase templates which it then combined, along with modules that conjugated English verbs. By contrast, the text in The Policeman's Beard, apart from being edited from a large amount of output, would have been the product of Chamberlain's own specialized templates and modules, which were not included in the commercial release of the program. == Reception == The Boston Phoenix called the story Soft Ions "schematic nonsense. But the scheme is obvious enough and the nonsense accessible enough to an attentive reader that one can almost believe Chamberlain when he predicts that before long Racter will be ready to write for the pulp-reading public." PC Magazine described some of Policeman's Beard's scenes as "surprising for their frankness" and "reflective". It concluded that the book was "whimsical and wise and sometimes fun". Computer Gaming World described Racter as "a diversion into another dimension that might best be seen before paying the price of a ticket. (Try before you buy!)" A 1985 review of the program in The New York Times notes that, "As computers move ever closer to artificial intelligence, Racter is on the edge of artificial insanity." It also states that Racter's "always-changing sentences are grammatically correct, often funny and, for a computer, sometimes profound." The article includes examples showing interaction with Racter, most often Racter asking the user questions. == Reviews == Jeux & Stratégie #47

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  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

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  Harvey (software)

  Harvey is a generative artificial intelligence (AI) product developed by the Counsel AI Corporation for the legal industry. The product has been described as a provider of customised large language models (LLMs) for law firms and in-house legal teams. It is named after the lead character of the legal drama Suits, Harvey Specter. == History == Harvey was founded in the summer of 2022 by Winston Weinberg, who was a securities and antitrust litigator at O'Melveny & Myers, and Gabriel Pereyra, who was a research scientist at Google DeepMind and Meta. Pereyra and Weinberg were roommates in Los Angeles. Pereyra was brainstorming startup ideas with his research colleagues. He showed Weinberg OpenAI's GPT-3 text-generating system, and Weinberg realized that it could be used to improve legal workflows. They developed an early chain-of-thought prompt based on GPT-3, focused on California tenant law. They ran the model on 100 legal questions from a public forum and hired three attorneys to evaluate the answers and determine whether they could be sent to clients unchanged. Out of those 100 questions, 86 were approved. After that, Pereyra and Weinberg contacted Sam Altman and Jason Kwon, General Counsel at OpenAI, about their results. Shortly after, on July 4, 2022, they met with OpenAI's C-suite, and OpenAI became their seed investor. OpenAI also gave Pereyra and Weinberg early access to GPT-4. Gordon Moodie, a corporate partner at Wachtell, Lipton, Rosen & Katz, also joined Harvey in July 2023 as the company's chief product officer. In March 2024, Harvey had 82 employees and stated that it intended to double that figure by the end of 2024. The company has reportedly hired a large number of lawyers, including from White & Case, Latham & Watkins, Skadden, Gunderson Dettmer, Katten Muchin Rosenman, and Paul Weiss. Harvey CEO Weinberg explained that many members of the company's sales team were formerly attorneys at 'Big Law', i.e. large US law firms, and that the sales team's experience was useful in convincing attorneys to trial the company's software. The integration of former 'Big Law' attorneys into product and sales teams has been attributed as a major factor in Harvey's success. In February 2026, Harvey announced its first brand partnership with actor Gabriel Macht, who portrayed the character Harvey Specter in Suits, to launch the company's Instagram page. In May 2026, it was announced the company is sponsoring the Golden State Valkyries and the New York Liberty. == Funding == In November 2022, it was reported that Harvey raised US$5 million in funding led by the OpenAI Startup Fund, together with other investors such as Jeff Dean, the head of Google AI, Elad Gil, the founder of Mixer Labs, Sarah Guo, the founder of Conviction, and other angel investors. Harvey raised another $23 million in April 2023 in a funding round led by Sequoia Capital. Harvey announced in December 2023 that it had raised $80 million in a Series B funding round led by Elad Gil and Kleiner Perkins which valued the company at $715 million. Other investors in the round included Sequoia Capital and the OpenAI Startup Fund. In July 2024, Harvey announced that it had raised $100 million in a Series C funding round that valued the company at $1.5 billion. The round was led by venture capital firm GV, and other participants included OpenAI, Kleiner Perkins, Sequoia Capital, Elad Gil, and SV Angel. In February 2025, Harvey announced it had raised $300 million in a Series D funding round that valued the company at $3 billion. Just months later, in June 2025, Harvey closed a $300 million Series E co-led by Kleiner Perkins and Coatue, again with participation from Conviction, Elad Gil, OpenAI, and Sequoia, boosting its valuation to about $5 billion and supporting international growth and expanded legal product offerings. In December 2025, Harvey secured a $160 million Series F round led by Andreessen Horowitz, with continued participation from investors including EQT, WndrCo, Sequoia, Kleiner Perkins, Conviction, and Elad Gil, valuing the legal AI company at roughly $8 billion. In March 2026, Harvey raised $200 million at a valuation of $11 billion, in a round co-led by GIC and Sequoia Capital. == Features == In May 2024, Harvey launched its products on Microsoft Azure and stated that it would offer a Harvey on Azure version of its product going forward. It was also reported that Harvey would begin offering general commercial access to some of its products, such as its case law models, as well as product bundles that included its AI assistant, specialised models, and its Vault feature for running prompts on large document collections. == Applications == Various law firms around the world are customers of Harvey. US law firm Paul Weiss began testing Harvey within the firm in January 2023, and became a client of the company later that year. Gina Lynch, the firm's chief knowledge and innovation officer, explained that the firm was not using hard metrics, such as time saved, to assess productivity gains because the time and effort needed to carefully review the output made efficiency gains difficult to measure. In February 2023, the UK law firm, Allen & Overy (now A&O Shearman), announced that it had been trialing Harvey since November 2022 within its Markets Innovation Group. This was reported to be the first known use of a generative AI product within the UK magic circle law firms. According to Allen & Overy, during the trial, 3,500 lawyers had used Harvey for around 40,000 queries in the course of their day to day work. The firm's press release stated that "Whilst the output needs careful review by an A&O lawyer, Harvey can help generate insights, recommendations and predictions based on large volumes of data". David Wakeling, head of the Markets Innovation Group, also cautioned that "You must validate everything coming out of the system. You have to check everything". The Irish law firm, A&L Goodbody, announced in February 2024 that it would be working with Harvey to enhance its services in relation to document analysis, due diligence, litigation, and regulatory compliance. In June 2024, UK law firm Ashurst announced that it would partner with Harvey and roll out its services to its branches worldwide. In September 2024, PwC announced that it would be adopting Harvey to empower its lawyers in Singapore. Singapore law firm WongPartnership also announced that month that it had become the first Southeast Asian law firm to test Harvey's generative AI solutions.

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