AI Detector Reviews

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

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  Physicalization

  Physicalization of computer hardware (the opposite of virtualization), is a way to place multiple physical machines in a rack unit. It can be a way to reduce hardware costs, since in some cases, server processors cost more per core than energy efficient laptop processors, which may make up for added cost of board level integration. While Moore's law makes increasing integration less expensive, some jobs require much I/O bandwidth, which may be less expensive to provide using many less-integrated processors. Applications and services that are I/O bound are likely to benefit from such physicalized environments. This ensures that each operating system instance is running on a processor that has its own network interface card, host bus and I/O sub-system unlike in the case of a multi-core servers where a single I/O sub-system is shared between all the cores / VMs.

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  Google Nest

  Google Nest, formerly branded Google Home, is a line of smart home products including smart speakers, smart displays, streaming devices, thermostats, smoke detectors, routers and security systems including smart doorbells, cameras and smart locks. The Nest brand name was originally owned by Nest Labs, co-founded by former Apple engineers Tony Fadell and Matt Rogers in 2010. Its flagship product, which was the company's first offering, is the Nest Learning Thermostat, introduced in 2011. The product is programmable, self-learning, sensor-driven, and Wi-Fi-enabled: features that are often found in other Nest products. It was followed by the Nest Protect smoke and carbon monoxide detectors in October 2013. After its acquisition of Dropcam in 2014, the company introduced its Nest Cam branding of security cameras beginning in June 2015. The company quickly expanded to more than 130 employees by the end of 2012. Google acquired Nest Labs for US$3.2 billion in January 2014, when the company employed 280. As of late 2015, Nest employs more than 1,100 and added a primary engineering center in Seattle. After Google reorganized itself under the holding company Alphabet Inc., Nest operated independently of Google from 2015 to 2018. However, in 2018, Nest was merged into Google's home-devices unit led by Rishi Chandra, effectively ceasing to exist as a separate business. In July 2018, it was announced that all Google Home electronics products will henceforth be marketed under the brand Google Nest. == History == === Nest Labs before acquisition by Google === Nest Labs was founded in 2010 by former Apple engineers Tony Fadell and Matt Rogers. The idea came when Fadell was building a vacation home and found all of the available thermostats on the market to be inadequate, motivated to bring something better on the market. Early investors in Nest Labs included Shasta Ventures and Kleiner Perkins. === Acquisition by Google of Nest Labs, Dropcam, and Revolv === On January 13, 2014, Google announced plans to acquire Nest Labs for $3.2 billion in cash. Google completed the acquisition the next day, on January 14, 2014. The company would operate independently from Google's other businesses. In June 2014, it was announced that Nest would buy camera startup Dropcam for $555 million. With the purchase, Dropcam became integrated with other Nest products; if the Protect alarm is triggered, the Dropcam can automatically start recording, and the Thermostat can use Dropcam to sense for motion. In September 2014, the Nest Thermostat and Nest Protect (a smoke alarm) became available in Belgium, France, Ireland, and the Netherlands. Initially, they were sold in approximately 400 stores across Europe, with another 150 stores to be added by the end of the year. In June 2015, the new Nest Cam, replacing the Dropcam, was announced, together with the second generation of the Nest Protect; there were internal reports that sales of the rebranded camera fell. On October 24, 2014, Nest both acquired the hub service Revolv, and discontinued its product line, gaining the expertise of Revolv's staff. === Nest as a subsidiary of Alphabet Inc. === In August 2015, Google announced that it would restructure its operations under a new parent company, Alphabet Inc., with Nest being separated from Google as a subsidiary of the new holding company. In January 2016, some Nest thermostats stopped working, a fault attributed to a software update from two weeks earlier. There were no lawsuits, individual or class-action, due to an arbitration clause in the contract. All Revolv smart hubs, costing several hundred dollars, were deliberately remotely bricked on May 15, 2016; notice was posted on the company's website in February. The story became news on April 4. The "lifetime subscription" to Revolv's online service, which had been sold with the hub, was defined by Nest to be the lifetime of the device, which ended May 15. Nest's decision to brick the hubs, and its "acerbic" corporate culture, faced substantial criticism from within Google/Alphabet and in press coverage. Many of Nest's staffers came from Dropcam and Revolv, and by November 2015, about 70 of about 1000 staffers had quit, causing management concern. Some countermeasures had been taken in takeover deals, to financially discourage senior people from leaving before set dates. Of the ~100 Dropcam staffers, about half had left by March 2016, when former Dropcam CEO Greg Duffy (who left 8 months after the takeover) wrote a post openly regretting selling his company to Nest. He stated that about 500 people had left (of a 1200-person staff). On June 6, 2016, Tony Fadell, the Nest CEO, announced in a blog post that he was leaving the company he founded with Matt Rogers and stepping into an "advisory" role. At this point the Nest acquisition was described by some press as a "disaster" for Google. As of mid-June 2016, Nest's problems were considered symptomatic of the limited market for home automation. According to Frank Gillet of Forrester Research, only 6% of American households possessed internet-connected devices such as appliances, home-monitoring systems, speakers, or lighting. He also predicted this percentage would grow to only 15% by 2021. Furthermore, 72% of respondents in a 2016 British survey conducted by Pricewaterhouse Coopers did not foresee adopting smart-home technology over the next two to five years. === Nest as a part of Google hardware division === On February 7, 2018, it was announced by hardware head Rick Osterloh that Nest had been merged into Google's hardware division, directly alongside units such as Google Home and Chromecast. It would retain its separate Palo Alto headquarters, but Nest CEO Marwan Fawaz would now report to Osterloh, and there were plans for tighter integration with Google platforms and software such as Google Assistant in future products. Shortly after the announcement, co-founder and chief product officer Matt Rogers announced his plans to leave the company. On July 18, 2018, Nest CEO Marwan Fawaz stepped down. Nest was merged with Google's home devices team, led by Rishi Chandra. During the Google I/O keynote on May 7, 2019, it was announced that Google Nest will now serve as the blanket branding for all of Google's home products. The Google Home Hub was retroactively renamed Google Nest Hub, while a new and larger version of the product is now available called the Nest Hub Max with both a larger screen and an amplified speaker, for a greater low-end audio experience. Also, product lines such as Chromecast, Google Home, and Google Wifi will now be marketed under the Google Nest brand. In addition, Nest began to deprecate its own internal platforms, announcing the discontinuation of the existing "Works with Nest" program in favor of Google Assistant going forward, and pushing users to migrate themselves from Nest's account system to Google accounts. Google published Nest-specific privacy information outlining a commitment to transparency, not selling personal information, and giving users control of their data. In February 2019, a privacy incident affecting the Google Nest Guard system came about. The controversy stemmed from the fact that Nest Guard, a security device that was part of the Nest Secure system, contained a hidden microphone that was not disclosed in any product specifications. It resulted in a public relations failure. === Partnership with ADT === In August 2020 Google announced intent to invest $450 million in ADT Inc. for a 6.6% stake in the company. The companies intend to integrate Nest devices with ADT's security monitoring services and eventually make them the “cornerstone of ADT’s smart home offering”, according to Nest. Upon the announcement, the shares of ADT doubled in value and hit all-time high of $17.21. === Use with Amazon Alexa === As of mid-2022, Google's newer Nest cameras will now work with Amazon Alexa devices such as Amazon Echo Show, Fire TV, and Fire Tablet to view captured security camera footage. === End of support policies === On October 25, 2025, software support was ended for the 1st and 2nd generation Nest Learning Thermostats. In addition, most of the smart functionality including the Home Away features, notifications, and carbon monoxide sensor became inoperative as they were dependent on connection with Google servers. By mid-November, third-party software solutions became available to restore functionality to affected thermostats. == Products == === Nest Learning Thermostat === The Nest Learning Thermostat is an electronic, programmable, and self-learning Wi-Fi-enabled thermostat that optimizes heating and cooling of homes and businesses to conserve energy. It is based on a machine-learning algorithm: for the first weeks users have to regulate the thermostat in order to provide the reference data set. Nest can then learn people's schedules, at which temperature they are used to and when. Using built-in sensors and phones' locations it can

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  TensorFlow

  TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks. It is one of the most popular deep learning frameworks, alongside others such as PyTorch. It is free and open-source software released under the Apache License 2.0. It was developed by the Google Brain team for Google's internal use in research and production. The initial version was released under the Apache License 2.0 in 2015. Google released an updated version, TensorFlow 2.0, in September 2019. TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java, facilitating its use in a range of applications in many sectors. == History == === DistBelief === Starting in 2011, Google Brain built DistBelief as a proprietary machine learning system based on deep learning neural networks. Its use grew rapidly across diverse Alphabet companies in both research and commercial applications. Google assigned multiple computer scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more robust application-grade library, which became TensorFlow. In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements, which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition. === TensorFlow === TensorFlow is Google Brain's second-generation system. Version 1.0.0 was released on February 11, 2017. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. TensorFlow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations that such neural networks perform on multidimensional data arrays, which are referred to as tensors. During the Google I/O Conference in June 2016, Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google. In March 2018, Google announced TensorFlow.js version 1.0 for machine learning in JavaScript. In Jan 2019, Google announced TensorFlow 2.0. It became officially available in September 2019. In May 2019, Google announced TensorFlow Graphics for deep learning in computer graphics. === Tensor processing unit (TPU) === In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and had found them to deliver an order of magnitude better-optimized performance per watt for machine learning. In May 2017, Google announced the second-generation, as well as the availability of the TPUs in Google Compute Engine. The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs, provide up to 11.5 petaflops. In May 2018, Google announced the third-generation TPUs delivering up to 420 teraflops of performance and 128 GB high bandwidth memory (HBM). Cloud TPU v3 Pods offer 100+ petaflops of performance and 32 TB HBM. In February 2018, Google announced that they were making TPUs available in beta on the Google Cloud Platform. === Edge TPU === In July 2018, the Edge TPU was announced. Edge TPU is Google's purpose-built ASIC chip designed to run TensorFlow Lite machine learning (ML) models on small client computing devices such as smartphones known as edge computing. === TensorFlow Lite === In May 2017, Google announced TensorFlow Lite as a software stack to support machine learning models for mobile and embedded devices, and in November 2017, provided the developer preview. In January 2019, the TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be merging. It was renamed as LiteRT in 2024. === TensorFlow 2.0 === As TensorFlow's market share among research papers was declining to the advantage of PyTorch, the TensorFlow Team announced a release of a new major version of the library in September 2019. TensorFlow 2.0 introduced many changes, the most significant being TensorFlow eager, which changed the automatic differentiation scheme from the static computational graph to the "Define-by-Run" scheme originally made popular by Chainer and later PyTorch. Other major changes included removal of old libraries, cross-compatibility between trained models on different versions of TensorFlow, and significant improvements to the performance on GPU. == Features == === AutoDifferentiation === AutoDifferentiation is the process of automatically calculating the gradient vector of a model with respect to each of its parameters. With this feature, TensorFlow can automatically compute the gradients for the parameters in a model, which is useful to algorithms such as backpropagation which require gradients to optimize performance. To do so, the framework must keep track of the order of operations done to the input Tensors in a model, and then compute the gradients with respect to the appropriate parameters. === Eager execution === TensorFlow includes an "eager execution" mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. This execution paradigm is considered to be easier to debug because of its step by step transparency. === Distribute === In both eager and graph executions, TensorFlow provides an API for distributing computation across multiple devices with various distribution strategies. This distributed computing can often speed up the execution of training and evaluating of TensorFlow models and is a common practice in the field of AI. === Losses === To train and assess models, TensorFlow provides a set of loss functions (also known as cost functions). Some popular examples include mean squared error (MSE) and binary cross entropy (BCE). === Metrics === In order to assess the performance of machine learning models, TensorFlow gives API access to commonly used metrics. Examples include various accuracy metrics (binary, categorical, sparse categorical) along with other metrics such as Precision, Recall, and Intersection-over-Union (IoU). === TF.nn === TensorFlow.nn is a module for executing primitive neural network operations on models. Some of these operations include variations of convolutions (1/2/3D, Atrous, depthwise), activation functions (Softmax, RELU, GELU, Sigmoid, etc.) and their variations, and other operations (max-pooling, bias-add, etc.). === Optimizers === TensorFlow offers a set of optimizers for training neural networks, including ADAM, ADAGRAD, and Stochastic Gradient Descent (SGD). When training a model, different optimizers offer different modes of parameter tuning, often affecting a model's convergence and performance. == Usage and extensions == === TensorFlow === TensorFlow serves as a core platform and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine-learning models. In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving. TensorFlow provides a stable Python Application Program Interface (API), as well as APIs without backwards compatibility guarantee for JavaScript, C++, and Java. Third-party language binding packages are also available for C#, Haskell, Julia, MATLAB, Object Pascal, R, Scala, Rust, OCaml, and Crystal. Bindings that are now archived and unsupported include Go and Swift. === TensorFlow.js === TensorFlow also has a library for machine learning in JavaScript. Using the provided JavaScript APIs, TensorFlow.js allows users to use either Tensorflow.js models or converted models from TensorFlow or TFLite, retrain the given models, and run on the web. === LiteRT === LiteRT, formerly known as Te

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  Transaction logic

  Transaction Logic is an extension of predicate logic that accounts in a clean and declarative way for the phenomenon of state changes in logic programs and databases. This extension adds connectives specifically designed for combining simple actions into complex transactions and for providing control over their execution. The logic has a natural model theory and a sound and complete proof theory. Transaction Logic has a Horn clause subset, which has a procedural as well as a declarative semantics. The important features of the logic include hypothetical and committed updates, dynamic constraints on transaction execution, non-determinism, and bulk updates. In this way, Transaction Logic is able to declaratively capture a number of non-logical phenomena, including procedural knowledge in artificial intelligence, active databases, and methods with side effects in object databases. Transaction Logic was originally proposed in 1993 by Anthony Bonner and Michael Kifer and later described in more detail in An Overview of Transaction Logic and Logic Programming for Database Transactions. The most comprehensive description appears in Bonner & Kifer's technical report from 1995. In later years, Transaction Logic was extended in various ways, including concurrency, defeasible reasoning, partially defined actions, and other features. In 2013, the original paper on Transaction Logic has won the 20-year Test of Time Award of the Association for Logic Programming as the most influential paper from the proceedings of ICLP 1993 conference in the preceding 20 years. == Examples == === Graph coloring === Here tinsert denotes the elementary update operation of transactional insert. The connective ⊗ is called serial conjunction. === Pyramid stacking === The elementary update tdelete represents the transactional delete operation. === Hypothetical execution === Here <> is the modal operator of possibility: If both action1 and action2 are possible, execute action1. Otherwise, if only action2 is possible, then execute it. === Dining philosophers === Here | is the logical connective of parallel conjunction of Concurrent Transaction Logic. == Implementations == A number of implementations of Transaction Logic exist: The original implementation. An implementation of Concurrent Transaction Logic. Transaction Logic enhanced with tabling. An implementation of Transaction Logic has also been incorporated as part of the Flora-2 knowledge representation and reasoning system. All these implementations are open source.

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  Caspio

  Caspio, Inc. is an American software company providing a low-code platform for building cloud-based business applications. Founded in 2000 by Frank Zamani, the company is headquartered in Sunnyvale, California, with operations in Poland, the Philippines, and Spain. Caspio’s platform allows organizations to create online database applications and workflow tools without extensive coding. == History == Caspio was founded by Frank Zamani in 2000. The company initially focused on simplifying custom cloud applications and reducing development time and cost as compared to traditional software development. Caspio released the first version of its platform, Caspio Bridge, in 2001. In 2014, Caspio released a HIPAA-Compliant Edition of its low-code application development platform. Caspio also released an EU General Data Protection Regulation (GDPR) Compliance Edition of its low-code application development platform in 2016. Caspio's second European Software Development Center opened in Kraków, Poland in 2017. In 2019, Forrester Research listed Caspio and three other platforms in its highest of four ranked tiers of twelve low-code platforms for business developers based on rankings of offerings and strategy at that time. Caspio also opened data centers in Montreal, Canada and India in 2020.

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  Capsule neural network

  A capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization. The idea is to add structures called "capsules" to a convolutional neural network (CNN), and to reuse output from several of those capsules to form more stable (with respect to various perturbations) representations for higher capsules. The output is a vector consisting of the probability of an observation, and a pose for that observation. This vector is similar to what is done for example when doing classification with localization in CNNs. Among other benefits, capsnets address the "Picasso problem" in image recognition: images that have all the right parts but that are not in the correct spatial relationship (e.g., in a "face", the positions of the mouth and one eye are switched). For image recognition, capsnets exploit the fact that while viewpoint changes have nonlinear effects at the pixel level, they have linear effects at the part/object level. This can be compared to inverting the rendering of an object of multiple parts. == History == In 2000, Geoffrey Hinton et al. described an imaging system that combined segmentation and recognition into a single inference process using parse trees. So-called credibility networks described the joint distribution over the latent variables and over the possible parse trees. That system proved useful on the MNIST handwritten digit database. A dynamic routing mechanism for capsule networks was introduced by Hinton and his team in 2017. The approach was claimed to reduce error rates on MNIST and to reduce training set sizes. Results were claimed to be considerably better than a CNN on highly overlapped digits. In Hinton's original idea one minicolumn would represent and detect one multidimensional entity. == Transformations == An invariant is an object property that does not change as a result of some transformation. For example, the area of a circle does not change if the circle is shifted to the left. Informally, an equivariant is a property that changes predictably under transformation. For example, the center of a circle moves by the same amount as the circle when shifted. A nonequivariant is a property whose value does not change predictably under a transformation. For example, transforming a circle into an ellipse means that its perimeter can no longer be computed as π times the diameter. In computer vision, the class of an object is expected to be an invariant over many transformations. I.e., a cat is still a cat if it is shifted, turned upside down or shrunken in size. However, many other properties are instead equivariant. The volume of a cat changes when it is scaled. Equivariant properties such as a spatial relationship are captured in a pose, data that describes an object's translation, rotation, scale and reflection. Translation is a change in location in one or more dimensions. Rotation is a change in orientation. Scale is a change in size. Reflection is a mirror image. Unsupervised capsnets learn a global linear manifold between an object and its pose as a matrix of weights. In other words, capsnets can identify an object independent of its pose, rather than having to learn to recognize the object while including its spatial relationships as part of the object. In capsnets, the pose can incorporate properties other than spatial relationships, e.g., color (cats can be of various colors). Multiplying the object by the manifold poses the object (for an object, in space). == Pooling == Capsnets reject the pooling layer strategy of conventional CNNs that reduces the amount of detail to be processed at the next higher layer. Pooling allows a degree of translational invariance (it can recognize the same object in a somewhat different location) and allows a larger number of feature types to be represented. Capsnet proponents argue that pooling: violates biological shape perception in that it has no intrinsic coordinate frame; provides invariance (discarding positional information) instead of equivariance (disentangling that information); ignores the linear manifold that underlies many variations among images; routes statically instead of communicating a potential "find" to the feature that can appreciate it; damages nearby feature detectors, by deleting the information they rely upon. == Capsules == A capsule is a set of neurons that individually activate for various properties of a type of object, such as position, size and hue. Formally, a capsule is a set of neurons that collectively produce an activity vector with one element for each neuron to hold that neuron's instantiation value (e.g., hue). Graphics programs use instantiation value to draw an object. Capsnets attempt to derive these from their input. The probability of the entity's presence in a specific input is the vector's length, while the vector's orientation quantifies the capsule's properties. Artificial neurons traditionally output a scalar, real-valued activation that loosely represents the probability of an observation. Capsnets replace scalar-output feature detectors with vector-output capsules and max-pooling with routing-by-agreement. Because capsules are independent, when multiple capsules agree, the probability of correct detection is much higher. A minimal cluster of two capsules considering a six-dimensional entity would agree within 10% by chance only once in a million trials. As the number of dimensions increase, the likelihood of a chance agreement across a larger cluster with higher dimensions decreases exponentially. Capsules in higher layers take outputs from capsules at lower layers, and accept those whose outputs cluster. A cluster causes the higher capsule to output a high probability of observation that an entity is present and also output a high-dimensional (20-50+) pose. Higher-level capsules ignore outliers, concentrating on clusters. This is similar to the Hough transform, the RHT and RANSAC from classic digital image processing. == Routing by agreement == The outputs from one capsule (child) are routed to capsules in the next layer (parent) according to the child's ability to predict the parents' outputs. Over the course of a few iterations, each parents' outputs may converge with the predictions of some children and diverge from those of others, meaning that that parent is present or absent from the scene. For each possible parent, each child computes a prediction vector by multiplying its output by a weight matrix (trained by backpropagation). Next the output of the parent is computed as the scalar product of a prediction with a coefficient representing the probability that this child belongs to that parent. A child whose predictions are relatively close to the resulting output successively increases the coefficient between that parent and child and decreases it for parents that it matches less well. This increases the contribution that that child makes to that parent, thus increasing the scalar product of the capsule's prediction with the parent's output. After a few iterations, the coefficients strongly connect a parent to its most likely children, indicating that the presence of the children imply the presence of the parent in the scene. The more children whose predictions are close to a parent's output, the more quickly the coefficients grow, driving convergence. The pose of the parent (reflected in its output) progressively becomes compatible with that of its children. The coefficients' initial logits are the log prior probabilities that a child belongs to a parent. The priors can be trained discriminatively along with the weights. The priors depend on the location and type of the child and parent capsules, but not on the current input. At each iteration, the coefficients are adjusted via a "routing" softmax so that they continue to sum to 1 (to express the probability that a given capsule is the parent of a given child.) Softmax amplifies larger values and diminishes smaller values beyond their proportion of the total. Similarly, the probability that a feature is present in the input is exaggerated by a nonlinear "squashing" function that reduces values (smaller ones drastically and larger ones such that they are less than 1). This dynamic routing mechanism provides the necessary deprecation of alternatives ("explaining away") that is needed for segmenting overlapped objects. This learned routing of signals has no clear biological equivalent. Some operations can be found in cortical layers, but they do not seem to relate this technique. === Math/code === The pose vector u i {\textstyle \mathbf {u} _{i}} is rotated and translated by a matrix W i j {\textstyle \mathbf {W} _{ij}} into a vector u ^ j | i {\textstyle \mathbf {\hat {u}} _{j|i}} that predicts the output of the parent capsule. u ^ j | i = W i j u i {\displaystyle \mathbf {

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  Dataism

  Dataism is a term that has been used to describe the mindset or philosophy created by the emerging significance of big data. It was first used by David Brooks in The New York Times in 2013. The term has been expanded to describe what historian Yuval Noah Harari, in his book Homo Deus: A Brief History of Tomorrow from 2015, calls an emerging ideology or even a new form of religion, in which "information flow" is the "supreme value". In art, the term was used by Albert-Laszlo Barabasi to refer to an artist movement that uses data as its primary source of inspiration. == History == "If you asked me to describe the rising philosophy of the day, I'd say it is Data-ism", wrote David Brooks in The New York Times in February 2013. Brooks argued that in a world of increasing complexity, relying on data could reduce cognitive biases and "illuminate patterns of behavior we haven't yet noticed". In 2015, Steve Lohr's book Data-ism looked at how Big Data is transforming society, using the term to describe the Big Data revolution. In his 2016 book Homo Deus: A Brief History of Tomorrow, Yuval Noah Harari argues that all competing political or social structures can be seen as data processing systems: "Dataism declares that the universe consists of data flows, and the value of any phenomenon or entity is determined by its contribution to data processing" and "we may interpret the entire human species as a single data processing system, with individual humans serving as its chips." According to Harari, a Dataist should want to "maximise dataflow by connecting to more and more media". Harari predicts that the logical conclusion of this process is that, eventually, humans will give algorithms the authority to make the most important decisions in their lives, such as whom to marry and which career to pursue. Harari argues that Aaron Swartz could be called the "first martyr" of Dataism. In 2022, Albert-László Barabási coined the term "Dataism" to define an artistic movement that positions data as the central means of understanding nature, society, technology, and human essence. This movement underscores the necessity for art to integrate with data to stay relevant in contemporary society. Dataism responds to the intricacy and interconnectedness of modern social, economic, and technological realms, which exceed individual understanding. Advocating for the use of methodologies from various fields like science, business, and politics in art, Dataism sees this fusion as essential for art to retain its significance and influence. == Criticism == Commenting on Harari's characterisation of Dataism, security analyst Daniel Miessler believes that Dataism does not present the challenge to the ideology of liberal humanism that Harari claims, because humans will simultaneously be able to believe in their own importance and that of data. Harari himself raises some criticisms, such as the problem of consciousness, which Dataism is unlikely to illuminate. Humans may also find out that organisms are not algorithms, he suggests. Dataism implies that all data is public, even personal data, to make the system work as a whole, which is a factor that's already showing resistance today. Other analysts, such as Terry Ortleib, have looked at the extent to which Dataism poses a dystopian threat to humanity. The Facebook–Cambridge Analytica data scandal showed how political leaders manipulated Facebook's users' data to build specific psychological profiles that went on to manipulate the network. A team of data analysts reproduced the AI technology developed by Cambridge Analytica around Facebook's data and was able to define the following rules: 10 likes enables a machine to know a person like a coworker, 70 likes like a friend would, 150 likes like a parent would, 300 likes like a lover would, and beyond it may be possible to know a people better than they know themselves.

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  IEEE Transactions on Pattern Analysis and Machine Intelligence

  IEEE Transactions on Pattern Analysis and Machine Intelligence (sometimes abbreviated as IEEE PAMI or simply PAMI) is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. == Background == The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition. The editor-in-chief is Kyoung Mu Lee (Seoul National University). According to the Journal Citation Reports, the journal has a 2023 impact factor of 20.8.

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  DevOps toolchain

  A DevOps toolchain is a set or combination of tools that aid in the delivery, development, and management of software applications throughout the systems development life cycle, as coordinated by an organization that uses DevOps practices. Generally, DevOps tools fit into one or more activities, which supports specific DevOps initiatives: Plan, Create, Verify, Package, Release, Configure, Monitor, and Version Control. == Toolchains == In software, a toolchain is the set of programming tools that is used to perform a complex software development task or to create a software product, which is typically another computer program or a set of related programs. In general, the tools forming a toolchain are executed consecutively so the output or resulting environment state of each tool becomes the input or starting environment for the next one, but the term is also used when referring to a set of related tools that are not necessarily executed consecutively. As DevOps is a set of practices that emphasizes the collaboration and communication of both software developers and other information technology (IT) professionals, while automating the process of software delivery and infrastructure changes, its implementation can include the definition of the series of tools used at various stages of the lifecycle; because DevOps is a cultural shift and collaboration between development and operations, there is no one product that can be considered a single DevOps tool. Instead a collection of tools, potentially from a variety of vendors, are used in one or more stages of the lifecycle. == Stages of DevOps == === Plan === Plan consists of two elements: "define" and "plan". This activity refers to the business value and application requirements. Specifically "Plan" activities include: Production metrics, objects and feedback Requirements Business metrics Update release metrics Release plan, timing and business case Security policy and requirement A combination of the IT personnel will be involved in these activities: business application owners, software development, software architects, continual release management, security officers and the organization responsible for managing the production of IT infrastructure. === Create === Create consists of the building, coding, and configuring of the software development process. The specific activities are: Design of the software and configuration Coding including code quality and performance Software build and build performance Release candidate Tools and vendors in this category often overlap with other categories. Because DevOps is about breaking down silos, this is reflective in the activities and product solutions. === Verify === Verify is directly associated with ensuring the quality of the software release; activities designed to ensure code quality is maintained and the highest quality is deployed to production. The main activities in this are: Acceptance testing Regression testing Security and vulnerability analysis Performance Configuration testing Solutions for verify-related activities generally fall under four main categories: Test automation, Static analysis, Test Lab, and Security. === Package === Package refers to the activities involved once the release is ready for deployment, often also referred to as staging or Preproduction / "preprod". This often includes tasks and activities such as: Approval/preapprovals Package configuration Triggered releases Release staging and holding === Release === Release related activities include schedule, orchestration, provisioning and deploying software into production and targeted environment. The specific Release activities include: Release coordination Deploying and promoting applications Fallbacks and recovery Scheduled/timed releases Solutions that cover this aspect of the toolchain include application release automation, deployment automation and release management. === Configure === Configure activities fall under the operation side of DevOps. Once software is deployed, there may be additional IT infrastructure provisioning and configuration activities required. Specific activities including: Infrastructure storage, database and network provisioning and configuring Application provision and configuration. The main types of solutions that facilitate these activities are continuous configuration automation, configuration management, and infrastructure as code tools. === Monitor === Monitoring is an important link in a DevOps toolchain. It allows IT organization to identify specific issues of specific releases and to understand the impact on end-users. A summary of Monitor related activities are: Performance of IT infrastructure End-user response and experience Production metrics and statistics Information from monitoring activities often impacts Plan activities required for changes and for new release cycles. === Version Control === Version Control is an important link in a DevOps toolchain and a component of software configuration management. Version Control is the management of changes to documents, computer programs, large web sites, and other collections of information. A summary of Version Control related activities are: Non-linear development Distributed development Compatibility with existent systems and protocols Toolkit-based design Information from Version Control often supports Release activities required for changes and for new release cycles.

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  Tree (abstract data type)

  In computer science, a tree is a widely used abstract data type that represents a hierarchical tree structure with a set of connected nodes. Each node in the tree can be connected to many children (depending on the type of tree), but must be connected to exactly one parent, except for the root node, which has no parent (i.e., the root node as the top-most node in the tree hierarchy). These constraints mean there are no cycles or "loops" (no node can be its own ancestor), and also that each child can be treated like the root node of its own subtree, making recursion a useful technique for tree traversal. In contrast to linear data structures, many trees cannot be represented by relationships between neighboring nodes (parent and children nodes of a node under consideration, if they exist) in a single straight line (called edge or link between two adjacent nodes). Binary trees are a commonly used type, which constrain the number of children for each parent to at most two. When the order of the children is specified, this data structure corresponds to an ordered tree in graph theory. A value or pointer to other data may be associated with every node in the tree, or sometimes only with the leaf nodes, which have no children nodes. The abstract data type (ADT) can be represented in a number of ways, including a list of parents with pointers to children, a list of children with pointers to parents, or a list of nodes and a separate list of parent-child relations (a specific type of adjacency list). Representations might also be more complicated, for example using indexes or ancestor lists for performance. Trees as used in computing are similar to but can be different from mathematical constructs of trees in graph theory, trees in set theory, and trees in descriptive set theory. == Terminology == A node is a structure which may contain data and connections to other nodes, sometimes called edges or links. Each node in a tree has zero or more child nodes, which are below it in the tree (by convention, trees are drawn with descendants going downwards). A node that has a child is called the child's parent node (or superior). All nodes have exactly one parent, except the topmost root node, which has none. A node might have many ancestor nodes, such as the parent's parent. Child nodes with the same parent are sibling nodes. Typically siblings have an order, with the first one conventionally drawn on the left. Some definitions allow a tree to have no nodes at all, in which case it is called empty. An internal node (also known as an inner node, inode for short, or branch node) is any node of a tree that has child nodes. Similarly, an external node (also known as an outer node, leaf node, or terminal node) is any node that does not have child nodes. The height of a node is the length of the longest downward path to a leaf from that node. The height of the root is the height of the tree. The depth of a node is the length of the path to its root (i.e., its root path). Thus the root node has depth zero, leaf nodes have height zero, and a tree with only a single node (hence both a root and leaf) has depth and height zero. Conventionally, an empty tree (tree with no nodes, if such are allowed) has height −1. Each non-root node can be treated as the root node of its own subtree, which includes that node and all its descendants. Other terms used with trees: Neighbor Parent or child. Ancestor A node reachable by repeated proceeding from child to parent. Descendant A node reachable by repeated proceeding from parent to child. Also known as subchild. Degree For a given node, its number of children. A leaf, by definition, has degree zero. Degree of tree The degree of a tree is the maximum degree of a node in the tree. Distance The number of edges along the shortest path between two nodes. Level The level of a node is the number of edges along the unique path between it and the root node. This is the same as depth. Width The number of nodes in a level. Breadth The number of leaves. Complete tree A tree with every level filled, except the last. Forest A set of one or more disjoint trees. Ordered tree A rooted tree in which an ordering is specified for the children of each vertex. Size of a tree Number of nodes in the tree. == Common operations == Enumerating all the items Enumerating a section of a tree Searching for an item Adding a new item at a certain position on the tree Deleting an item Pruning: Removing a whole section of a tree Grafting: Adding a whole section to a tree Finding the root for any node Finding the lowest common ancestor of two nodes === Traversal and search methods === Stepping through the items of a tree, by means of the connections between parents and children, is called walking the tree, and the action is a walk of the tree. Often, an operation might be performed when a pointer arrives at a particular node. A walk in which each parent node is traversed before its children is called a pre-order walk; a walk in which the children are traversed before their respective parents are traversed is called a post-order walk; a walk in which a node's left subtree, then the node itself, and finally its right subtree are traversed is called an in-order traversal. (This last scenario, referring to exactly two subtrees, a left subtree and a right subtree, assumes specifically a binary tree.) A level-order walk effectively performs a breadth-first search over the entirety of a tree; nodes are traversed level by level, where the root node is visited first, followed by its direct child nodes and their siblings, followed by its grandchild nodes and their siblings, etc., until all nodes in the tree have been traversed. == Representations == There are many different ways to represent trees. In working memory, nodes are typically dynamically allocated records with pointers to their children, their parents, or both, as well as any associated data. If of a fixed size, the nodes might be stored in a list. Nodes and relationships between nodes might be stored in a separate special type of adjacency list. In relational databases, nodes are typically represented as table rows, with indexed row IDs facilitating pointers between parents and children. Nodes can also be stored as items in an array, with relationships between them determined by their positions in the array (as in a binary heap). A binary tree can be implemented as a list of lists: the head of a list (the value of the first term) is the left child (subtree), while the tail (the list of second and subsequent terms) is the right child (subtree). This can be modified to allow values as well, as in Lisp S-expressions, where the head (value of first term) is the value of the node, the head of the tail (value of second term) is the left child, and the tail of the tail (list of third and subsequent terms) is the right child. Ordered trees can be naturally encoded by finite sequences, for example with natural numbers. == Examples of trees and non-trees == == Type theory == As an abstract data type, the abstract tree type T with values of some type E is defined, using the abstract forest type F (list of trees), by the functions: value: T → E children: T → F nil: () → F node: E × F → T with the axioms: value(node(e, f)) = e children(node(e, f)) = f In terms of type theory, a tree is an inductive type defined by the constructors nil (empty forest) and node (tree with root node with given value and children). == Mathematical terminology == Viewed as a whole, a tree data structure is an ordered tree, generally with values attached to each node. Concretely, it is (if required to be non-empty): A rooted tree with the "away from root" direction (a more narrow term is an "arborescence"), meaning: A directed graph, whose underlying undirected graph is a tree (any two vertices are connected by exactly one simple path), with a distinguished root (one vertex is designated as the root), which determines the direction on the edges (arrows point away from the root; given an edge, the node that the edge points from is called the parent and the node that the edge points to is called the child), together with: an ordering on the child nodes of a given node, and a value (of some data type) at each node. Often trees have a fixed (more properly, bounded) branching factor (outdegree), particularly always having two child nodes (possibly empty, hence at most two non-empty child nodes), hence a "binary tree". Allowing empty trees makes some definitions simpler, some more complicated: a rooted tree must be non-empty, hence if empty trees are allowed the above definition instead becomes "an empty tree or a rooted tree such that ...". On the other hand, empty trees simplify defining fixed branching factor: with empty trees allowed, a binary tree is a tree such that every node has exactly two children, each of which is a tree (possibly empty). == Applications == Trees are commonly used to represent or manipulate hierarchical data in ap

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  Xinhua–Sogou AI news anchor

  Xinhua News Agency and Sogou of China developed an artificial intelligence (AI) for news reporting purposes. The AI was unveiled in 2018. It is touted to be the "world's first AI news anchor". == History == The AI was unveiled at the 2018 World Internet Conference in Wuzhen, Zhejiang, China. The AI devises avatars patterned after real life Xinhua anchors. The AI patterned after Qiu Hao spoke in Chinese, while the one derived from the likeness of Zhang Zhao speaks in English. The unveiling of the AI raised concerns of its impact on employment. Xinhua and Sogou unveiled Xin Xiaomeng, an AI with a female avatar in 2019. People's Daily followed suit by unveiling its own AI newscaster in 2023.

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  Sasha Stiles

  Sasha Stiles (born 1980) is an American artist and poet. After discovering natural language processing, she created the 2021 poetry collection Technelegy through an eponymous AI model, before presenting the 2025–2026 installation A Living Poem at the Museum of Modern Art. In addition to artificial intelligence, binary code and non-fungible tokens have been important aspects of her work. == Biography == Stiles was born in 1980 in Pasadena, California, to documentary filmmaker parents whose work includes Cosmos: A Personal Voyage. She was interested in science fiction during her youth, particularly how they addressed human-machine collaboration and posthumanism. She graduated magna cum laude from Harvard University with a Bachelor of Arts in 2002 and she graduated with high honors from the University of Oxford with a Master of Studies in 2004. Originally, Stiles's poetry focused on technology. In 2017, she discovered natural language processing, piquing her interest in its ability to process thoughts and words comparably to its human counterparts. Despite lacking a technological background, she managed to channel people like Gwern Branwen, Ross Goodwin, and Allison Parrish as inspirations for her AI work, and in 2019, she started training an AI model named Technelegy. In 2021, Black Spring Press published her poetry collection Technelegy, where she combines AI-generated content produced by the titular AI model with her own traditionally-created work; the AI-generated content was produced by processing Stiles's own poetry onto GPT-2 and GPT-3. She and Technelegy later co-created A Living Poem, which ran at the Museum of Modern Art's Hyundai Card Digital Wall from September 2025 to March 2026. Stiles also has used non-fungible tokens as a platform for her poetry, having been inspired to go into blockchain by her experiences working with a metaverse exhibition curated by Jess Conatser. She has used Christie's and SuperRare to sell several of her poems as tokenized real-world assets, including Daughter of E.V.E. (Ex-Vivo Uterine Environment), a 2021 single-channel video using freeze-frame shots to hide poetry. In 2021, she co-founded TheVerseVerse (stylized as theVERSEverse), a non-fungible token gallery specializing in poetry. She later created Four Core Texts: Humanifesto and Other Poems, involving four NFT videos of poetry written in looping handwriting and powered by Technelegy. Stiles uses binary code as an inspiration for her work, citing in part its "quite antagonistic system of a binary 'EITHER / OR'", which she connected to several dichotomies pitting humanity and the present against technology and the future. In 2018, she started Analog Binary Code, where she creates sculptures by arranging objects in binary code ciphers. She also created Cursive Binary, where she combines binary with cursive handwriting, after writing zeros and ones on a steamed wall while showering. Stiles and the robot BINA48 co-created the 2020 ArtYard exhibition A Valentine for the Future. She was part of the 2021 group exhibition Computational Poetics at the Beall Center for Art and Technology. From February 24 to March 18, 2023, she held her solo show Binary Odes (stylized as B1NARY 0DES) at Annka Kultys Gallery. By 2024, her work had appeared in places such as Gucci storefronts and Times Square billboards. She designed Words Beyond Words, the official poster for Art Basel in Basel 2025. Stiles is based in Milford, New Jersey, where she lives with her husband, musician Kris Bones. She has also lived in Jersey City and Bucks County, Pennsylvania. She is Kalmyk-American on her mother's side, and she has also announced plans to create a version of Technelegy in her ancestral language Kalmyk.

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  Symbolic artificial intelligence

  In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic, and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to important ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the mid-1990s. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the ultimate goal of their field. An early boom, with early successes such as the Logic Theorist and Samuel's Checkers Playing Program, led to unrealistic expectations and promises and was followed by the first AI Winter as funding dried up. A second boom (1969–1986) occurred with the rise of expert systems, their promise of capturing corporate expertise, and an enthusiastic corporate embrace. That boom, and some early successes, e.g., with XCON at DEC, was followed again by later disappointment. Problems with difficulties in knowledge acquisition, maintaining large knowledge bases, and brittleness in handling out-of-domain problems arose. Another, second, AI Winter (1988–2011) followed. Subsequently, AI researchers focused on addressing underlying problems in handling uncertainty and in knowledge acquisition. Uncertainty was addressed with formal methods such as hidden Markov models, Bayesian reasoning, and statistical relational learning. Symbolic machine learning addressed the knowledge acquisition problem with contributions including Version Space, Valiant's PAC learning, Quinlan's ID3 decision-tree learning, case-based learning, and inductive logic programming to learn relations. Neural networks, a subsymbolic approach, had been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt's perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, and work in convolutional neural networks by LeCun et al. in 1989. However, neural networks were not viewed as successful until about 2012: "Until Big Data became commonplace, the general consensus in the Al community was that the so-called neural-network approach was hopeless. Systems just didn't work that well, compared to other methods. ... A revolution came in 2012, when a number of people, including a team of researchers working with Hinton, worked out a way to use the power of GPUs to enormously increase the power of neural networks." Over the next several years, deep learning had spectacular success in handling vision, speech recognition, speech synthesis, image generation, and machine translation, though symbolic approaches continue to be useful in a few domains such as computer algebra systems and proof assistants. == History == A short history of symbolic AI to the present day follows below. Time periods and titles are drawn from Henry Kautz's 2020 AAAI Robert S. Engelmore Memorial Lecture and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity. === The first AI summer: irrational exuberance, 1948–1966 === Success at early attempts in AI occurred in three main areas: artificial neural networks, knowledge representation, and heuristic search, contributing to high expectations. This section summarizes Kautz's reprise of early AI history. ==== Approaches inspired by human or animal cognition or behavior ==== Cybernetic approaches attempted to replicate the feedback loops between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and seven vacuum tubes for control, based on a preprogrammed neural net, was built as early as 1948. This work can be seen as an early precursor to later work in neural networks, reinforcement learning, and situated robotics. An important early symbolic AI program was the Logic theorist, written by Allen Newell, Herbert Simon and Cliff Shaw in 1955–56, as it was able to prove 38 elementary theorems from Whitehead and Russell's Principia Mathematica. Newell, Simon, and Shaw later generalized this work to create a domain-independent problem solver, GPS (General Problem Solver). GPS solved problems represented with formal operators via state-space search using means-ends analysis. During the 1960s, symbolic approaches achieved great success at simulating intelligent behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was concentrated in four institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one developed its own style of research. Earlier approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background. Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s. ==== Heuristic search ==== In addition to the highly specialized domain-specific kinds of knowledge that we will see later used in expert systems, early symbolic AI researchers discovered another more general application of knowledge. These were called heuristics, rules of thumb that guide a search in promising directions: "How can non-enumerative search be practical when the underlying problem is exponentially hard? The approach advocated by Simon and Newell is to employ heuristics: fast algorithms that may fail on some inputs or output suboptimal solutions." Another important advance was to find a way to apply these heuristics that guarantees a solution will be found, if there is one, not withstanding the occasional fallibility of heuristics: "The A algorithm provided a general frame for complete and optimal heuristically guided search. A is used as a subroutine within practically every AI algorithm today but is still no magic bullet; its guarantee of completeness is bought at the cost of worst-case exponential time. ==== Early work on knowledge representation and reasoning ==== Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner. ===== Modeling formal reasoning with logic: the "neats" ===== Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate the exact mechanisms of human thought, but could instead try to find the essence of abstract reasoning and problem-solving with logic, regardless of whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming. ===== Modeling implicit common-sense knowledge with frames and scripts: the "scruffies" ===== Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad hoc solutions—they argued that no simple and general principle (like logic) would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time. === The first AI winter: crushed dreams, 1967–1977 === The first AI winter was a shock: During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research to use AI to solve problems of national security; in particular, to automate the translation of Russian to English for inte

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  Korean Decimal Classification

  The Korean Decimal Classification (KDC) is a system of library classification used in South Korea. The structure and main level classes of the KDC are based on the Dewey Decimal Classification. The KDC is maintained and published by the Classification Committee of the Korean Library Association. The first edition of the classification was published in 1964; the most recent edition is the sixth edition published in 2013. Almost all school and public libraries in South Korea use the KDC to organize their collections, as well as the National Library of Korea and some university libraries. == History == Multiple library classification systems had been developed for Korean libraries before the publication of the KDC. These included the Railway Bureau Library Classification(1920), the Korean Decimal Classification edited by Bong-Suk Park(known as KDCP, 1947), the Han-Un Decimal Classification(1954), and the Kuk-Yeon Decimal Classification(1958). After the disappearance of editor Bong-Suk Park in the 1950s, the KDCP system decreased in use. Korean librarians considered adopting the Dewey Decimal Classification (DDC), especially after it was implemented at Yonsei University in 1957, but struggled to apply it to East Asian and Korean-focused works in their collections. In February 1963, members of the Korean Library Association's Classification were appointed to create a national classification; they decided to make revisions to the order of the main classes of the DDC, for example bringing together the class Language(700) together with the class for Literature(800). Committee members prepared draft classes and indexes and the first edition of the KDC was published in May 1964. Both the text and the index were written in Korean Hangul characters and Chinese characters. The second edition was published just two years later, in 1966, correcting errors and omissions found in the first edition. The third edition was published in 1980, maintaining the basic framework of the previous editions while expanding significantly. The fourth edition, published in 1996, made considerable changes, including increasing the number of representatives on the Classification Committee. The committee sought feedback from the library community and implemented revisions included in the recently published edition 20 of the DDC and edition 9 of the Nippon Decimal Classification. New policies applied to the fourth edition included principles suggesting the main classes should remain as static as possible, with focus shown to expanding classes devoted to technology and science. Likewise, many subject specialists were consulted for the publication of the fifth edition in 2009. The publication of the 23rd edition of the DDC in 2011 provided opportunity for a new revision of the KDC, and the sixth edition was published in July 2013. Greater numbers of classes provided number building capacity in the sixth edition, allowing for more specificity. == Description == The KDC classifies resources primarily by discipline, though some classes are collocated by subject. There are eight auxiliary mnemonic tables used to expand class numbers. The main classes of the KDC are the same as the main classes of the Dewey Decimal Classification, but four of those main classes are in a different order: Natural sciences (400), Technology and engineering (500), Arts (600), and Language 700. Though the structure is heavily influenced by the DDC, aspects of multiple library classifications have been invoked in the creation of the KDC, including the Library of Congress Classification for the arrangement of the social sciences (300), the Universal Decimal Classification for medical sciences (510), the KDCP for Korean and Oriental subjects, the Nippon Decimal Classification for those of Japan and Oriental subjects. === Classes of the KDC 6th edition === 000 General works 000 General works 010 Books, Bibliography 020 Library & information science 030 General encyclopedias 040 General collected essays 050 General serial publications 060 General societies 070 Newspapers, journalism 080 General collected works 090 Materials of province 100 Philosophy 100 Philosophy 110 Metaphysics 120 Epistemology, etc. 130 Systems of philosophy 140 Chinese classics 150 Oriental philosophy and thought 160 Western philosophy 170 Logic 180 Psychology 190 Ethics, moral philosophy 200 Religion 200 Religion 210 Comparative religion 220 Buddhism 230 Christian religion 240 Taoism 250 Chondoism 260 [Unassigned] 270 Hinduism, Brahmanism 280 Islam, Mohammedianism 290 Other religions 300 Social sciences 300 Social sciences 310 Statistics 320 Economics 330 Sociology and social problems 340 Political sciences 350 Public administration 360 Law 370 Education 380 Customs, Etiquette, Folklore 390 Military science 400 Natural sciences 400 Natural sciences 410 Mathematics 420 Physics 430 Chemistry 440 Astronomy 450 Earth science 460 Mineralogy 470 Life science 480 Botany 490 Zoological science 500 Technology 500 Technology 510 Medical science 520 Agriculture 530 Engineering, technology, etc. 540 Construction and architecture 550 Mechanical engineering 560 Electrical, comm. & electric engineering 570 Chemical engineering 580 Manufactures 590 Human ecology 600 Arts 600 Arts 610 [Unassigned] 620 Sculpture, plastic art 630 Crafts 640 Calligraphy 650 Painting, design 660 Photography 670 Music 680 Stage performance, museum arts 690 Amusements, sports & physical training 700 Language 700 Language 710 Korean language 720 Chinese language 730 Japanese & other Asian languages 740 English 750 German 760 French languages 770 Spanish languages & Portuguese language 780 Italian languages 790 Other languages 800 Literature 800 Literature 810 Korean literature 820 Chinese literature 830 Japanese & other Asian literature 840 English & American literature 850 German literature 860 French literature 870 Spanish & Portuguese literature 880 Italian literature 890 Other literatures 900 History 900 History 910 Asia 920 Europe 930 Africa 940 North America 950 South America 960 Oceania and Polar regions 970 [Unassigned] 980 Geography 990 Biography === Expansion tables === Table 1. Standard subdivisions Table 2. Geographic Areas Table 3. Korean geographic areas Table 4. Korean historical period Table 5. Languages Table 6. Subdivisions of individual languages Table 7. Subdivisions of individual literatures Table 8. Subdivisions of individual religions == Usage == KDC is used by a wide range of libraries within Korea, including by the National Library of Korea and most school and public libraries in the country, along with some university libraries, such as the one at Keimyung University.

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  Logic Theorist

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

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