Cleverbot is a chatterbot web application. It was created by British AI scientist Rollo Carpenter and launched in October 2008. It was preceded by Jabberwacky, a chatbot project that began in 1988 and went online in 1997. In its first decade, Cleverbot held several thousand conversations with Carpenter and his associates. Since launching on the web, the number of conversations held has exceeded 150 million. Besides the web application, Cleverbot is also available as an iOS, Android, and Windows Phone app. == Operation == Cleverbot's responses are not pre-programmed because it learns from human input: Humans type into the box below the Cleverbot logo and the system finds all keywords or an exact phrase matching the input. After searching through its saved conversations, it responds to the input by finding how a human responded to that input when it was asked, in part or in full, by Cleverbot. Cleverbot participated in a formal Turing test at the 2011 Techniche festival at the Indian Institute of Technology Guwahati on 3 September 2011. Out of the 1334 votes cast, Cleverbot was judged to be 59.3% human, compared to the rating of 63.3% human achieved by human participants. A score of 50.05% or higher is often considered to be a passing grade. The software running for the event had to handle just 1 or 2 simultaneous requests, whereas online Cleverbot is usually talking to around 10,000 to 50,000 people at once. == Developments == Cleverbot is constantly growing in data size at the rate of 4 to 7 million interactions per day. Updates to the software have been mostly behind the scenes. In 2014, Cleverbot was upgraded to use GPU serving techniques. Unlike Eliza, the program does not respond in a fixed way, instead choosing its responses heuristically using fuzzy logic, the whole of the conversation being compared to the millions that have taken place before. Cleverbot now uses over 279 million interactions, about 3-4% of the data it has already accumulated. The developers of Cleverbot are attempting to build a new version using machine learning techniques. An app that uses the Cleverscript engine to play a game of 20 Questions has been launched under the name Clevernator. Unlike other such games, the player asks the questions and it is the role of the AI to understand, and answer factually. An app that allows owners to create and talk to their own small Cleverbot-like AI has been launched, called Cleverme! for Apple products. == In popular culture == Cleverbot received media attention after being featured in the popular 2010 creepypasta ARG web serial Ben Drowned by Alexander D. Hall. In early 2017, a Twitch stream of two Google Home devices modified to talk to each other using Cleverbot garnered over 700,000 visitors and over 30,000 peak concurrent viewers.
Enonic XP
Enonic XP is a free and open-source content platform. Developed by the Norwegian software company Enonic, the platform can be used to build websites, progressive web applications, or web-based APIs. Enonic XP uses an application framework for coding server logic with JavaScript, and has no need for SQL as it ships with an integrated content repository. The CMS is fully decoupled, meaning developers can create traditional websites and landing pages, or use XP in headless mode, that is without the presentation layer, for loading editorial content onto any device or client. Enonic is used by major organizations in Norway, including the national postal service Norway Post, the insurance company Gjensidige, the Norwegian Labour and Welfare Administration, and all the top football clubs in the national football league for men, Eliteserien. == Overview == Enonic XP ships with the content management system (CMS) Content Studio. This includes a visual drag and drop editor, a landing page editor, support for multi-site and multi-language, media and structured content, advanced image editing, responsive user interface, permissions and roles management, revision and version control, and bulk publishing. Integrations and applications can be directly installed via the "Applications" section in XP, where the platform finds apps approved in the official Enonic Market. There are no third-party databases in Enonic XP. Instead, the developers have built a distributed storage repository, avoiding the need to index content. The system brings together capabilities from Filesystem, NoSQL, document stores, and search in the storage technology, which automatically indexes everything put into the storage. Enonic XP supports deployment of server side JavaScript. The open-source framework runs on top of a JVM (Java virtual machine), and allows developers to run the same code in the browser and on the server, thus enabling them to employ JavaScript. While running on the Java virtual machine, Enonic XP can be deployed on most infrastructures. The dependency on a third-party application server to deploy code has been removed, as the platform is an application server by default. A developer can for instance insert his own modules and code straight into the system while it is running. JavaScript unifies all the technical elements, and Enonic XP features a MVC framework where everything on the back-end can be coded with server-side JavaScript. The Enonic platform can use any template engine. === Progressive web apps === Another feature of Enonic XP is the possibility for developers to create progressive web apps (PWA). A PWA is a web application that is a regular web page or website, but can appear to the user like a mobile application. === Headless CMS and integrations === Enonic XP is headless, which means it separates content and presentation. The platform supports GraphQL, provides several default APIs, and allows for building custom APIs through the Guillotine starter kit. Consequently, Enonic supports modern front-end frameworks, and offers integrations with e.g. Next.js and React. == History == Enonic AS was founded in 2000 by Morten Øien Eriksen and Thomas Sigdestad. The software company specialized in building services and solutions, including a content management system known as "Vertical Site", then "Enonic CMS". Being aware that they had application, database, and website teams working on separate silos toward the same goal, Enonic sought to combine the different elements into a single software. The resulting application platform Enonic XP, first released in 2015, includes a CMS as an optional surface layer. In March 2020, Enonic XP was ranked by SoftwareReviews, a division of Info-Tech Research Group, a Canadian IT research and analyst firm, as the "Leader" in Web Experience Management. The ranking is based on user reviews, and is featured in SoftwareReviews‘ Digital Experience Data Quadrant Report, a comprehensive evaluation and ranking of leading Web Experience Management vendors. Enonic was also ranked first in 2021 and 2022. === Release history === Enonic XP assumed the mantle from the previous content management system Enonic CMS, and thus began with "version 5.0.0." The following list only contains major releases. == Development and support == Enonic offers a user and developer community consisting of a forum, support system with tickets, documentation, codex, learning and training center with certifications, and various community groups. Writing about the support system, Mike Johnston of CMS Critic notes that "enterprise customers obviously get access to a higher level of personalized support, where the Enonic support team can respond as fast as two hours." The support system is divided in three levels: silver, gold and platinum—from next day business support to 24/7 support. As Enonic XP is open-source, known vulnerabilities, bugs and issues are listed on GitHub.
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 {
AI Futures Project
The AI Futures Project is a nonprofit research organization based in the United States that specializes in forecasting the development and societal impact of advanced artificial intelligence. The organization is best known for its 2025 scenario forecast, AI 2027, which examines the potential near-term emergence of artificial general intelligence (AGI) and its possible global consequences. == History == The AI Futures Project was founded in 2025 by Daniel Kokotajlo, a former researcher in the governance division of OpenAI. Kokotajlo resigned from OpenAI in April 2024, expressing concerns that the company prioritized rapid product development over AI safety and was advancing without sufficient safeguards. He founded the nonprofit to conduct independent forecasting and policy research. The organization is registered as a 501(c)(3) nonprofit in the United States and is funded through donations. It operates with a small research staff and network of advisors drawn from fields including AI policy, forecasting, and risk analysis. == Activities == The mission of the AI Futures Project is to develop detailed scenario forecasts of the trajectory of advanced AI systems to inform policymakers, researchers, and the public. In addition to written reports, the group has conducted tabletop exercises and workshops based on its scenarios, involving participants from academia, technology, and public policy. == AI 2027 == In April 2025, the AI Futures Project released AI 2027, a detailed scenario forecast describing possible developments in AI between 2025 and 2027. The report was authored by Daniel Kokotajlo along with Eli Lifland, Thomas Larsen, and Romeo Dean, with editing assistance from blogger Scott Alexander. The scenario depicts very rapid progress in AI capabilities, including the development of autonomous AI systems capable of recursive self-improvement. AI 2027 presents two alternative endings: one in which international competition over advanced AI leads to catastrophic loss of human control, and another in which coordinated global action slows down development and averts imminent disaster. The authors emphasize that the narratives are hypothetical and intended as planning tools rather than literal forecasts. == Reception == AI 2027 attracted attention from technology journalists and AI researchers. Some commentators praised the report for its level of detail and its usefulness as a strategic planning exercise, while others criticized the scenario as implausibly aggressive in its timelines. The report was cited in policy discussions about AI governance. U.S. Vice President JD Vance reportedly read AI 2027 and referenced its warnings in conversations about international AI coordination. More recent reporting noted that the authors of AI 2027 had publicly revised some of their timelines. According to Kokotajlo, developments since the report's original publication suggested a slower path toward fully autonomous AI research systems than initially forecasted.
Linde–Buzo–Gray algorithm
The Linde–Buzo–Gray algorithm (named after its creators Yoseph Linde, Andrés Buzo and Robert M. Gray, who designed it in 1980) is an iterative vector quantization algorithm to improve a small set of vectors (codebook) to represent a larger set of vectors (training set), such that it will be locally optimal. It combines Lloyd's Algorithm with a splitting technique in which larger codebooks are built from smaller codebooks by splitting each code vector in two. The core idea of the algorithm is that by splitting the codebook such that all code vectors from the previous codebook are present, the new codebook must be as good as the previous one or better. == Description == The Linde–Buzo–Gray algorithm may be implemented as follows: algorithm linde-buzo-gray is input: set of training vectors training, codebook to improve old-codebook output: codebook that is twice the size and better or as good as old-codebook new-codebook ← {} for each old-codevector in old-codebook do insert old-codevector into new-codebook insert old-codevector + 𝜖 into new-codebook where 𝜖 is a small vector return lloyd(new-codebook, training) algorithm lloyd is input: codebook to improve, set of training vectors training output: improved codebook do previous-codebook ← codebook clusters ← divide training into |codebook| clusters, where each cluster contains all vectors in training who are best represented by the corresponding vector in codebook for each cluster cluster in clusters do the corresponding code vector in codebook ← the centroid of all training vectors in cluster while difference in error representing training between codebook and previous-codebook > 𝜖 return codebook
Lose It!
Lose It! is an American health and wellness mobile app developed by FitNow, Inc. The app generates calorie budgets for users by tracking weight, exercise, food and calorie intake, and personal goals, primarily to assist them in achieving weight loss. == History == Lose It! was developed in Boston and debuted in 2008. The app and its associated company were founded by J.J. Allaire, Charles Teague and Paul Dicristina. Prior to founding Lose It!, Teague and Allaire had founded the online research tool Onfolio, which was acquired by Microsoft in 2006. The Lose It! app was originally released as an iOS app before being released as a website in 2010 and an Android app in 2011. In 2015, Lose It! announced plans to release the app internationally. Lose It! was also available as an app for Apple Watch at its launch in 2015. The app’s “Snap It” feature, which allows users to approximate calorie counts by taking pictures of their daily meals and snacks, was released in beta in 2016. Snap It was named an Innovation Awards Honoree at the 2017 Consumer Electronics Show in Las Vegas. In 2020, Patrick Wetherille, one of the company’s earliest employees, was appointed chief executive officer. == App == Lose It! is weight loss app. The app allows users to set goals such as increasing strength, overall health/maintenance, and weight loss. It provides users recommended calorie budgets based on data such as their current weight and their desired weight. Lose It! also tracks data such as exercise/activity level and food consumption and allows users to track calories consumed by scanning barcodes for food products then retrieving calorie information for products. The app can also estimate the amount of calories in a food products. Lose It! has integration features connecting it to other apps such as Fitbit and Runkeeper. It also has social features such as joining groups and sharing progress with friends. The Premium version of the app allows users to track foods according to specific diets like keto, heart healthy or Mediterranean.
Instantaneously trained neural networks
Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample. The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization. This separation is done using the nearest hyperplane that can be written down instantaneously. In the two most important implementations the neighborhood of generalization either varies with the training sample (CC1 network) or remains constant (CC4 network). These networks use unary coding for an effective representation of the data sets. This type of network was first proposed in a 1993 paper of Subhash Kak. Since then, instantaneously trained neural networks have been proposed as models of short term learning and used in web search, and financial time series prediction applications. They have also been used in instant classification of documents and for deep learning and data mining. As in other neural networks, their normal use is as software, but they have also been implemented in hardware using FPGAs and by optical implementation. == CC4 network == In the CC4 network, which is a three-stage network, the number of input nodes is one more than the size of the training vector, with the extra node serving as the biasing node whose input is always 1. For binary input vectors, the weights from the input nodes to the hidden neuron (say of index j) corresponding to the trained vector is given by the following formula: w i j = { − 1 , for x i = 0 + 1 , for x i = 1 r − s + 1 , for i = n + 1 {\displaystyle w_{ij}={\begin{cases}-1,&{\mbox{for }}x_{i}=0\\+1,&{\mbox{for }}x_{i}=1\\r-s+1,&{\mbox{for }}i=n+1\end{cases}}} where r {\displaystyle r} is the radius of generalization and s {\displaystyle s} is the Hamming weight (the number of 1s) of the binary sequence. From the hidden layer to the output layer the weights are 1 or -1 depending on whether the vector belongs to a given output class or not. The neurons in the hidden and output layers output 1 if the weighted sum to the input is 0 or positive and 0, if the weighted sum to the input is negative: y = { 1 if ∑ x i ≥ 0 0 if ∑ x i < 0 {\displaystyle y=\left\{{\begin{matrix}1&{\mbox{if }}\sum x_{i}\geq 0\\0&{\mbox{if }}\sum x_{i}<0\end{matrix}}\right.} == Other networks == The CC4 network has also been modified to include non-binary input with varying radii of generalization so that it effectively provides a CC1 implementation. In feedback networks the Willshaw network as well as the Hopfield network are able to learn instantaneously.