AI Chatbot Interface

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Pedagogical agent

A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion". == History == The history of Pedagogical Agents is closely aligned with the history of computer animation. As computer animation progressed, it was adopted by educators to enhance computerized learning by including a lifelike interface between the program and the learner. The first versions of a pedagogical agent were more cartoon than person, like Microsoft's Clippy which helped users of Microsoft Office load and use the program's features in 1997. However, with developments in computer animation, pedagogical agents can now look lifelike. By 2006 there was a call to develop modular, reusable agents to decrease the time and expertise required to create a pedagogical agent. There was also a call in 2009 to enact agent standards. The standardization and re-usability of pedagogical agents is less of an issue since the decrease in cost and widespread availability of animation tools. Individualized pedagogical agents can be found across disciplines including medicine, math, law, language learning, automotive, and armed forces. They are used in applications directed to every age, from preschool to adult. == Learning theories related to pedagogical agent design == === Distributed cognition theory === Distributed cognition theory is the method in which cognition progresses in the context of collaboration with others. Pedagogical agents can be designed to assist the cognitive transfer to the learner, operating as artifacts or partners with collaborative role in learning. To support the performance of an action by the user, the pedagogical agent can act as a cognitive tool as long as the agent is equipped with the knowledge that the user lacks. The interactions between the user and the pedagogical agent can facilitate a social relationship. The pedagogical agent may fulfill the role of a working partner. === Socio-cultural learning theory === Socio-cultural learning theory is how the user develops when they are involved in learning activities in which there is interaction with other agents. A pedagogical agent can: intervene when the user requests, provide support for tasks that the user cannot address, and potentially extend the learners cognitive reach. Interaction with the pedagogical agent may elicit a variety of emotions from the learner. The learner may become excited, confused, frustrated, and/or discouraged. These emotions affect the learners' motivation. === Extraneous Cognitive Load === Extraneous cognitive load is the extra effort being exerted by an individual's working memory due to the way information is being presented. A pedagogical agent can increase the user's cognitive load by distracting them and becoming the focus of their attention, causing split attention between the instructional material and the agent. Agents can reduce the perceived cognitive load by providing narration and personalization that can also promote a user's interest and motivation. While research on the reduction of cognitive load from pedagogical agents is minimal, more studies have shown that agents do not increase it. == Effectiveness == It has been suggested by researchers that pedagogical agents may take on different roles in the learning environment. Examples of these roles are: supplanting, scaffolding, coaching, testing, or demonstrating or modelling a procedure. A pedagogical agent as a tutor has not been demonstrated to add any benefit to an educational strategy in equivalent lessons with and without a pedagogical agent. According to Richard Mayer, there is some support in research for pedagogical agent increasing learning, but only as a presenter of social cues. A co-learner pedagogical agent is believed to increase the student's self-efficacy. By pointing out important features of instructional content, a pedagogical agent can fulfill the signaling function, which research on multimedia learning has shown to enhance learning. Research has demonstrated that human-human interaction may not be completely replaced by pedagogical agents, but learners may prefer the agents to non-agent multimedia systems. This finding is supported by social agency theory. Much like the varying effectiveness of the pedagogical agent roles in the learning environment, agents that take into account the user's affect have had mixed results. Research has shown pedagogical agents that make use of the users’ affect have been found to increase user knowledge retention, motivation, and perceived self-efficacy. However, with such a broad range of modalities in affective expressions, it is often difficult to utilize them. Additionally, having agents detect a user's affective state with precision remains challenging, as displays of affect are different across individuals. == Design == === Attractiveness === The appearance of a pedagogical agent can be manipulated to meet the learning requirements. The attractiveness of a pedagogical agent can enhance student's learning when the users were the opposite gender of the pedagogical agent. Male students prefer a sexy appearance of a female pedagogical agents and dislike the sexy appearance of male agents. Female students were not attracted by the sexy appearance of either male or female pedagogical agents. === Affective Response === Pedagogical agents have reached a point where they can convey and elicit emotion, but also reason about and respond to it. These agents are often designed to elicit and respond to affective actions from users through various modalities such as speech, facial expressions, and body gestures. They respond to the affective state of the given user, and make use of these modalities using a wide array of sensors incorporated into the design of the agent. Specifically in education and training applications, pedagogical agents are often designed to increasingly recognize when users or learners exhibit frustration, boredom, confusion, and states of flow. The added recognition in these agents is a step toward making them more emotionally intelligent, comforting and motivating the users as they interact. === Digital Representation === The design of a pedagogical agent often begins with its digital representation, whether it will be 2D or 3D and static or animated. Several studies have developed pedagogical agents that were both static and animated, then evaluated the relative benefits. Similar to other design considerations, the improved learning from static or animated agents remains questionable. One study showed that the appearance of an agent portrayed using a static image can impact a user's recall, based on the visual appearance. Other research found results that suggest static agent images improve learning outcomes. However, several other studies found user's learned more when the pedagogical agent was animated rather than static. Recently a meta-analysis of such research found a negligible improvement in learning via pedagogical agents, suggesting more work needs to be done in the area to support any claims.
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Outline of the Python programming language

The following outline is provided as an overview of and topical guide to Python: Python is a general-purpose, interpreted, object-oriented, functional, multi-paradigm, and dynamically typed programming language known for its emphasis on code readability and broad standard library. Python was created by Guido van Rossum and first released in 1991. It emphasizes code readability and developer productivity. == What type of language is Python? == Programming language — artificial language designed to communicate instructions to a machine. Object-oriented programming — built primarily around objects and classes. Functional programming — supports functions as first-class objects. Scripting language — often used for automation and small programs. General-purpose programming language — designed for a wide variety of application domains. Dynamically typed — type checking occurs at runtime. Interpreted language — code is executed by an interpreter. Multi-paradigm — supports procedural, object-oriented, and functional programming. == History of Python == ABC (programming language) – precursor to Python Python was started by Guido van Rossum in 1989 and first released in 1991. Python 2 — major version released in 2000, officially retired in 2020. Python 3 — released in 2008 == General Python concepts == == Issues and limitations == Performance — generally slower than many compiled languages such as C or Java can be mitigated by C extensions or JIT compilers (PyPy). Global interpreter lock — limits parallel CPU-bound threads in CPython Memory consumption — high memory use compared to some lower-level languages Version compatibility — Python 2 vs Python 3 differences caused migration issues == Python implementations == CPython — reference implementation in C IronPython — Python for .NET Jython — Python for the JVM MicroPython — Python for microcontrollers and embedded systems Nuitka — compiler that packages user code with CPython into a static binary PyPy — JIT-compiled Python interpreter for speed PythonAnywhere — freemium hosted Python installation that runs in the browser Stackless Python — Python with lightweight concurrency features == Python toolchain == List of Python software Comparison of Python IDEs Comparison of server-side web frameworks for Python List of Python frameworks List of Python libraries List of unit testing frameworks for Python Python Package Index == Notable projects using Python == YouTube (backend) Instagram (backend) Dropbox Reddit OpenStack Blender (scripting and plugins) SageMath NumPy Pandas TensorFlow == Python development communities == ActiveState — commercial Python distributions and support Anaconda, Inc. — Python data science ecosystem GitHub Python Software Foundation Python Package Index (PyPI) — third-party software repository for Python == Example source code == Articles with example Python code == Python publications == === Books about Python === Automate the Boring Stuff with Python – Creative Commons Python book Alex Martelli — Python in a Nutshell and Python Cookbook Mark Pilgrim – Dive into Python Naomi Ceder — The Quick Python Book Wes McKinney — Python for Data Analysis Zed Shaw – Learn Python the Hard Way === Textbooks === Core Python Programming == Python programmers == == Python conferences == EuroPython – annual Python conference in Europe PyCon – the largest annual convention for the Python community PyData – conference series focused on data analysis, machine learning, and scientific computing with Python SciPy Conferences – focused on the use of Python in scientific computing and research DjangoCon – a conference dedicated to the Django web framework PyOhio – a free regional Python conference held in Ohio == Python learning resources == Codecademy – interactive Python programming lessons GeeksforGeeks – tutorials, coding examples, and interactive programming for Python concepts and data structures. Kaggle – free Python courses focused on data science and machine learning. Python.org Tutorial – the official Python tutorial from the Python Software Foundation. Real Python – articles, tutorials, and courses for Python developers. W3Schools – beginner-friendly Python tutorials. Wikibooks Python Programming – free open-content textbook on Python. === Competitive programming === Codeforces – an online platform for programming contests that supports Python submissions Codewars – gamified coding challenges supporting Python HackerRank – competitive programming and interview preparation site with Python challenges Kaggle – while focused on data science competitions, it also includes Python-based problem solving. LeetCode – online judge and problem-solving platform where Python is widely used
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Model-based clustering

In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not belong to any group. == Model-based clustering == Suppose that for each of n {\displaystyle n} observations we have data on d {\displaystyle d} variables, denoted by y i = ( y i , 1 , … , y i , d ) {\displaystyle y_{i}=(y_{i,1},\ldots ,y_{i,d})} for observation i {\displaystyle i} . Then model-based clustering expresses the probability density function of y i {\displaystyle y_{i}} as a finite mixture, or weighted average of G {\displaystyle G} component probability density functions: p ( y i ) = ∑ g = 1 G τ g f g ( y i ∣ θ g ) , {\displaystyle p(y_{i})=\sum _{g=1}^{G}\tau _{g}f_{g}(y_{i}\mid \theta _{g}),} where f g {\displaystyle f_{g}} is a probability density function with parameter θ g {\displaystyle \theta _{g}} , τ g {\displaystyle \tau _{g}} is the corresponding mixture probability where ∑ g = 1 G τ g = 1 {\displaystyle \sum _{g=1}^{G}\tau _{g}=1} . Then in its simplest form, model-based clustering views each component of the mixture model as a cluster, estimates the model parameters, and assigns each observation to cluster corresponding to its most likely mixture component. === Gaussian mixture model === The most common model for continuous data is that f g {\displaystyle f_{g}} is a multivariate normal distribution with mean vector μ g {\displaystyle \mu _{g}} and covariance matrix Σ g {\displaystyle \Sigma _{g}} , so that θ g = ( μ g , Σ g ) {\displaystyle \theta _{g}=(\mu _{g},\Sigma _{g})} . This defines a Gaussian mixture model. The parameters of the model, τ g {\displaystyle \tau _{g}} and θ g {\displaystyle \theta _{g}} for g = 1 , … , G {\displaystyle g=1,\ldots ,G} , are typically estimated by maximum likelihood estimation using the expectation-maximization algorithm (EM); see also EM algorithm and GMM model. Bayesian inference is also often used for inference about finite mixture models. The Bayesian approach also allows for the case where the number of components, G {\displaystyle G} , is infinite, using a Dirichlet process prior, yielding a Dirichlet process mixture model for clustering. === Choosing the number of clusters === An advantage of model-based clustering is that it provides statistically principled ways to choose the number of clusters. Each different choice of the number of groups G {\displaystyle G} corresponds to a different mixture model. Then standard statistical model selection criteria such as the Bayesian information criterion (BIC) can be used to choose G {\displaystyle G} . The integrated completed likelihood (ICL) is a different criterion designed to choose the number of clusters rather than the number of mixture components in the model; these will often be different if highly non-Gaussian clusters are present. === Parsimonious Gaussian mixture model === For data with high dimension, d {\displaystyle d} , using a full covariance matrix for each mixture component requires estimation of many parameters, which can result in a loss of precision, generalizabity and interpretability. Thus it is common to use more parsimonious component covariance matrices exploiting their geometric interpretation. Gaussian clusters are ellipsoidal, with their volume, shape and orientation determined by the covariance matrix. Consider the eigendecomposition of a matrix Σ g = λ g D g A g D g T , {\displaystyle \Sigma _{g}=\lambda _{g}D_{g}A_{g}D_{g}^{T},} where D g {\displaystyle D_{g}} is the matrix of eigenvectors of Σ g {\displaystyle \Sigma _{g}} , A g = diag { A 1 , g , … , A d , g } {\displaystyle A_{g}={\mbox{diag}}\{A_{1,g},\ldots ,A_{d,g}\}} is a diagonal matrix whose elements are proportional to the eigenvalues of Σ g {\displaystyle \Sigma _{g}} in descending order, and λ g {\displaystyle \lambda _{g}} is the associated constant of proportionality. Then λ g {\displaystyle \lambda _{g}} controls the volume of the ellipsoid, A g {\displaystyle A_{g}} its shape, and D g {\displaystyle D_{g}} its orientation. Each of the volume, shape and orientation of the clusters can be constrained to be equal (E) or allowed to vary (V); the orientation can also be spherical, with identical eigenvalues (I). This yields 14 possible clustering models, shown in this table: It can be seen that many of these models are more parsimonious, with far fewer parameters than the unconstrained model that has 90 parameters when G = 4 {\displaystyle G=4} and d = 9 {\displaystyle d=9} . Several of these models correspond to well-known heuristic clustering methods. For example, k-means clustering is equivalent to estimation of the EII clustering model using the classification EM algorithm. The Bayesian information criterion (BIC) can be used to choose the best clustering model as well as the number of clusters. It can also be used as the basis for a method to choose the variables in the clustering model, eliminating variables that are not useful for clustering. Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include the pgmm method, which is based on the mixture of factor analyzers model, and the HDclassif method, based on the idea of subspace clustering. The mixture-of-experts framework extends model-based clustering to include covariates. == Example == We illustrate the method with a dateset consisting of three measurements (glucose, insulin, sspg) on 145 subjects for the purpose of diagnosing diabetes and the type of diabetes present. The subjects were clinically classified into three groups: normal, chemical diabetes and overt diabetes, but we use this information only for evaluating clustering methods, not for classifying subjects. The BIC plot shows the BIC values for each combination of the number of clusters, G {\displaystyle G} , and the clustering model from the Table. Each curve corresponds to a different clustering model. The BIC favors 3 groups, which corresponds to the clinical assessment. It also favors the unconstrained covariance model, VVV. This fits the data well, because the normal patients have low values of both sspg and insulin, while the distributions of the chemical and overt diabetes groups are elongated, but in different directions. Thus the volumes, shapes and orientations of the three groups are clearly different, and so the unconstrained model is appropriate, as selected by the model-based clustering method. The classification plot shows the classification of the subjects by model-based clustering. The classification was quite accurate, with a 12% error rate as defined by the clinical classification. Other well-known clustering methods performed worse with higher error rates, such as single-linkage clustering with 46%, average link clustering with 30%, complete-linkage clustering also with 30%, and k-means clustering with 28%. == Outliers in clustering == An outlier in clustering is a data point that does not belong to any of the clusters. One way of modeling outliers in model-based clustering is to include an additional mixture component that is very dispersed, with for example a uniform distribution. Another approach is to replace the multivariate normal densities by t {\displaystyle t} -distributions, with the idea that the long tails of the t {\displaystyle t} -distribution would ensure robustness to outliers. However, this is not breakdown-robust. A third approach is the "tclust" or data trimming approach which excludes observations identified as outliers when estimating the model parameters. == Non-Gaussian clusters and merging == Sometimes one or more clusters deviate strongly from the Gaussian assumption. If a Gaussian mixture is fitted to such data, a strongly non-Gaussian cluster will often be represented by several mixture components rather than a single one. In that case, cluster merging can be used to find a better clustering. A different approach is to use mixtures of complex component densities to represent non-Gaussian clusters. == Non-continuous data == === Categorical data === Clustering multivariate categorical data is most often done using the latent class model. This assumes that the data arise from a finite mixture model, where within each cluster the variables are independent. === Mixed data === These arise when variables are of different types, such as continuous, categorical or ordinal data. A latent class model for mixed data assumes local independence between the variable. The location model relaxes the local independence assumption. The clustMD approach assumes that the observed variables are manifestations of underlying continuous Gaussian latent
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Peanut App

Peanut, a product of Peanut App Ltd. is an online community for women who are planning to become pregnant, women who are pregnant, women who have had children, and women who are experiencing menopause. Profiles of potential friends are displayed to users who can swipe up to show intent to connect. Users can also connect via discussion threads, groups, and live audio conversations. The app allows users to select their stage of life (trying to conceive, pregnancy, motherhood, or menopause), so as to meet women at a similar life stage, and to discover relevant content. Peanut was founded by Michelle Kennedy shortly after she left Bumble, a female-first dating app. She has described Peanut as, "the app she wishes she had when she first became a mother". == History == Peanut was initially launched in 2017 for mothers and pregnant women. The app focuses on helping users find others with shared interests, such as spoken languages, occupations, and hobbies. It also displays a woman's life stage, such as the age of her children, or the stage of pregnancy. In 2018, it launched a community discussion feature that intended to give women an "alternative to other social platforms". In 2019, it started to serve women who are trying to conceive. In April 2021, it integrated live audio, in response to the COVID-19 pandemic, and the restrictions around in-person socializing. in September 2021, it started to include women who are navigating perimenopause, menopause, and postmenopausal. Although it had initially catered for younger women navigating into new families, a large number of users had undergone surgically or chemically induced menopause due to medical conditions. In July 2021, Peanut launched an investment micro fund, Peanut StartHER, focused on investing in women-owned businesses, as well as other historically excluded founders. == Operation == The Peanut app is a social network exclusively for women, focusing on topics of pregnancy, motherhood, fertility, and menopause. It is available on iOS and Android devices. Users must prove their identity, in keeping with the primary function of in-app safety, and then they can create a profile to interact with other users. For pregnant users, the “Bump Buddies” feature helps connect them with other Peanut users who have a similar due date, which aimed to help expecting mothers combat loneliness during the COVID-19 pandemic. Peanut users also have the option to join “Groups” ‒ sub-sections of users focused on specific topics, including (but not limited to) location, life stage, pregnancy due date, and interests or hobbies. The live voice chat feature “Pods”, enables Peanut users to socialize without the pressure of photos or video chat. It offers features such as a muted audience of listeners who need to virtually raise their hand to speak, emoji reactions, and hosts who can moderate the conversations and invite people to speak.
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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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Message queuing service

A message queueing service is a message-oriented middleware or MOM deployed in a compute cloud using software as a service model. Service subscribers access queues and or topics to exchange data using point-to-point or publish and subscribe patterns. It's important to differentiate between event-driven and message-driven (aka queue driven) services: Event-driven services (e.g. AWS SNS) are decoupled from their consumers. Whereas queue / message driven services (e.g. AWS SQS) are coupled with their consumers. Message queues can be a good buffer to handle spiky workloads but they have a finite capacity. According to Gregor Hohpe, message queues require proper mechanisms (aka flow controls) to avoid filling the queue beyond its manageable capacity and to keep the system stable. == Ordering Guarantees in Message Queues == Amazon SQS FIFO and Azure Service Bus sessions are queue-based messaging systems that provide ordering guarantees within a message group or session attempt but do not necessarily guarantee ordered delivery in cases of retries or failures. In SQS FIFO, messages in the same message group are processed in order, with subsequent messages held until the preceding message is successfully processed or moved to the dead-letter queue (DLQ). Once a message is placed in the DLQ, it is no longer retried, creating a gap in the sequence. However, the remaining messages continue to be delivered in order. Azure Service Bus sessions function similarly by maintaining ordering within a session, provided a single consumer processes messages sequentially. The implementation differs from SQS FIFO but follows the same fundamental ordering principle. In contrast, Apache Kafka is a distributed log-based messaging system that guarantees ordering within individual partitions rather than across the entire topic. Unlike queue-based systems, Kafka retains messages in a durable, append-only log, allowing multiple consumers to read at different offsets. Kafka uses manual offset management, giving consumers control over retries and failure handling. If a consumer fails to process a message, it can delay committing the offset, preventing further progress in that partition while other partitions remain unaffected. This partition-based design enables fault isolation and parallel processing while allowing ordering to be maintained within partitions, depending on consumer handling. == Vendors == Apache Kafka Apache Kafka is a distributed system consisting of servers that store and forward messages between producer client and consumer applications. IBM MQ IBM MQ offers a managed service that can be used on IBM Cloud and Amazon Web Services. Microsoft Azure Service Bus Service Bus offers queues, topics & subscriptions, and rules/actions in order to support publish-subscribe, temporal decoupling, and load balancing scenarios. Azure Service Bus is built on AMQP allowing any existing AMQP 1.0 client stack to interact with Service Bus directly or via existing .Net, Java, Node, and Python clients. Standard and Premium tiers allow for pay as you go or isolated resources at massive scale. Oracle Messaging Cloud Service This service provides a messaging solution for applications for asynchronous communication and is influenced by the Java Message Service (JMS) API specification. Any application platform that understands HTTP can also use Oracle Messaging Cloud Service through the REST interface. For Java applications, Oracle Messaging Cloud Service provides a Java library that implements and extends the JMS 1.1 interface. The Java library implements the JMS API by acting as a client of the REST API. Amazon Simple Queue Service Supports messages natively up to 256K, or up to 2GB by transmitting payload via S3. Highly scalable, durable and resilient. Provides loose-FIFO and 'at least once' delivery in order to provide massive scale. Supports REST API and optional Java Message Service client. Low latency. Utilizes Amazon Web Services. IronMQ Supports messages up to 64k; guarantees order; guarantees once only delivery; no delays retrieving messages. Supports REST API and beanstalkd open source protocol. Runs on multiple clouds including AWS and Rackspace. Scaling must be managed by user. RabbitMQ RabbitMQ is a reliable and mature messaging and streaming broker, which is easy to deploy on cloud environments, on-premises, and on your local machine. Supports AMQP, STOMP, MQTT StormMQ Open platform supports messages up to 50Mb. Uses AMQP to avoid vendor lock-in and provide language neutrality. Locate-It Option allows customers to audit the location of their data at all times and satisfy data protection principles. AnypointMQ An enterprise multi-tenant, cloud messaging service that performs advanced asynchronous messaging scenarios between applications. Anypoint MQ is fully integrated with Anypoint Platform, offering role based access control, client application management, and connectors.
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Pydio

Pydio Cells, previously known as just Pydio and formerly known as AjaXplorer, is an open-source file-sharing and synchronisation software that runs on the user's own server or in the cloud. == Presentation == The project was created by musician Charles Du Jeu (current CEO and CTO) in 2007 under the name AjaXplorer. The name was changed in 2013 and became Pydio (an acronym for Put Your Data in Orbit). In May 2018, Pydio switched from PHP to Go with the release of Pydio Cells. The PHP version reached end-of-life state on 31 December 2019. Pydio Cells runs on any server supporting a recent Go version. Windows/Linux/macOS on the Intel architecture are directly supported; a fully functional working ARM implementation is under active development. Pydio Cells has been developed from scratch using the Go programming language; release 4.0.0 introduced code refactoring to fully support the Go modular structure as well as grid computing. Nevertheless, the web-based interface of Cells is very similar to the one from Pydio 8 (in PHP), and it successfully replicates most of its features, while adding a few more. There is also a new synchronisation client (also written in Go). The PHP version has been phased out as the company's focus is moving to Pydio Cells, with community feedback on the new features. According to the company, the switch to the new environment was made "to overcome inherent PHP limitations and provide you with a future-proof and modern solution for collaborating on documents". From a technical point of view, Pydio differs from solutions such as Google Drive or Dropbox. Pydio is not based on a public cloud; instead, the software connects to the user's existing storage (such as SAN / Local FS, SAMBA / CIFS, (s)FTP, NFS, S3-compatible cloud storage, Azure Blob Storage, Google Cloud Storage) as well as to the existing user directories (LDAP / AD, OAuth2 / OIDC SSO, SAML / Azure ADFS SSO, RADIUS, Shibboleth...), which allows companies to keep their data inside their infrastructure, according to their data security policy and user rights management. The software is built in a modular perspective; up to Pydio 8, various plugins allowed administrators to implement extra features. On the server side, Pydio Cells is deployed as a collection of independent microservices communicating among themselves using gRPC and logging user actions via Activity Streams 2.0 (AS2). Pydio Cells microservices are built with the Go Micro framework (using an embedded NATS server). A standard installation will deploy all required services on the same physical server, but for the purposes of performance, reliability and high availability, these can now be spread across several different servers (even in geographically separate locations) according to the 12-factors architecture pattern. Pydio Cells is available either through a free and open-source community distribution (Pydio Cells Home), or a commercially-licensed enterprise distribution (in two variants, Pydio Cells Connect and Pydio Cells Enterprise), which add features not available in the community distribution as well as additional levels of support beyond the community forums. == Features == File sharing between different internal users and across other Pydio instances SSL/TLS Encryption WebDAV file server Creation of dedicated workspaces, for each line of business / project / client, with a dedicated user rights management for each workspace. File-sharing with external users (private links, public links, password protection, download limitation, etc.) Online viewing and editing of documents with Collabora Office (Pydio Cells Enterprise also offers OnlyOffice integration) Preview and editing of image files Integrated audio and video reader Activity stream ('timeline') for all actions taken by users Integrated chat platform Client applications are available for all major desktop and mobile platforms.
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Griffon (framework)

Griffon is an open source rich client platform framework which uses the Java, Apache Groovy, and/or Kotlin programming languages. Griffon is intended to be a high-productivity framework by rewarding use of the Model-View-Controller paradigm, providing a stand-alone development environment and hiding much of the configuration detail from the developer. The first release is the fruit of the effort by the Groovy Swing team and an attempt to take the best of rapid application development, as indicated by its Grails-like structure, the agility of Groovy, and the availability of components for Swing. The framework was redesign from scratch for version 2, allowing different JVM programming languages to be used either in isolation or in conjunction. Supported UI toolkits are Java Swing JavaFX Apache Pivot Lanterna == Overview == Griffon aims to reduce the typical confusion that occurs with traditional Java UI development. Due to the MVC structure of Griffon, developers never have to go searching for files or be confused on how to start a new project. Everything begins with: lazybones create The generated project follows this structure: %PROJECT_HOME% + griffon-app + conf ---> location of configuration artifacts like builder configuration + controllers ---> location of controller classes + i18n ---> location of message bundles for i18n + lifecycle ---> location of lifecycle scripts + models ---> location of model classes + resources ---> location of non code resources (images, etc) + views ---> location of view classes + src + main ---> optional; location for Groovy and Java source files (of types other than those in griffon-app/) The builder infrastructure enables seamless integration of different widget libraries such as Swing, JIDE, and SwingX. In the first release, three sample applications are included : Greet, a Groovy Twitter client featured in the JavaOne 2009 Script Bowl, FontPicker, an application to view the available fonts on one's machine, SwingPad, a lightweight designer application for Griffon user interfaces. == Plugins == Griffon can be extended with the use of plugins. Plugins provide run-time access to testing libraries such as Easyb and FEST, and all widget libraries besides core Swing are provided as plugins. The plugin system allows for a wide range of additions, for example Polyglot Programming with Java, Apache Groovy, Kotlin. SQL and NoSQL datastores like Berkleydb, CouchDB, Db4O, Neo4j, NeoDatis, Memcached and Riak. == Publications == === Books === Features that would eventually become integral parts of Griffon (UI builders) were featured in these books: Groovy In Action (published by Manning) Beginning Groovy and Grails Books that cover Griffon: Griffon In Action (published by Manning) Beginning Groovy, Grails and Griffon === Magazine === GroovyMag for Groovy and Grails developers
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Celia (virtual assistant)

Celia is an artificially intelligent virtual assistant developed by Huawei for their latest HarmonyOS and Android-based EMUI smartphones that lack Google Services and a Google Assistant. The assistant can perform day-to-day tasks, which include making a phone call, setting a reminder and checking the weather. It was unveiled on 7 April 2020 and got publicly released on 27 April 2020 via an OTA update solely to selected devices that can update their software to EMUI 10.1. Huawei had initially referred to the new assistant in late 2019 by having announced that there would be an English version of their already 2018 Chinese speaker assistant—Xiaoyi—to be released into the European markets. Due to the on-going China–United States trade war, the company's newly released smartphones were left without any Google services, including the loss of Google Assistant. This subsequently led to the development and release of Celia. AI technology is integrated into the software of Celia, which allows it to translate text using a phones camera and to identify everyday objects — similar to that of Google Lens. == Features == Celia has many features that are similar to that of its rivals: the Google Assistant and Siri. It can be triggered by the words, 'Hey Celia' or be summoned by pressing and holding down on the power button. The default search engine for Celia is Bing, but this can be changed in settings. Celia can make calls, check the agenda, send a message, show the weather, set alarms and control home appliances. The assistant also has the ability to integrate itself with the stock apps of the EMUI software and toggle with the device's settings, such as by turning on the flashlight and playing multimedia content, but with the users command. With the AI that is installed in Celia, it can identify food, everyday objects and translate text using the phones camera. In China, Chinese Xiaoyi packs with an LLM model called PanGu-Σ 3.0 AI on HarmonyOS 4.0 major upgrade improvements from Celia, making the assistant smarter and more advanced compared to when it was launched in 2020 on EMUI handsets in China and internationally, surpassing Apple and Google by the being the first in the AI industry, with a dedicated AI system framework of APIs on the latest operating system that evolves to a complete large dedicated AI software stack called Harmony Intelligence of Pangu Embedded variant model and MindSpore AI framework with Neural Network Runtime on OpenHarmony-based HarmonyOS NEXT base system to replace the dual framework system with a single frame HarmonyOS 5.0 version by Q4 2024, first introduced on June 21, 2024, in Developer Beta 1 preview release at HDC 2024. == Availability by country and language == Currently, Celia is available only in German, English, French and Spanish, and has been released in Germany, the UK, France, Spain, Chile, Mexico and Colombia. Huawei has said, that there will be more regions and languages to come. == Compatible devices == Celia only became available with the EMUI 10.1 update that was released in April, which means that a limited number of devices are compatible with it. More devices will be added to the list throughout the coming months as Celia's availability increases. The current list is shown below: === Huawei P series === Huawei P50 (Pro) Huawei P40 (Lite, Pro & Pro+) Huawei P30 (Pro) === Huawei Mate series === Huawei Mate 40 Huawei Mate 30 (Lite, Pro & RS Porche Design) Huawei MatePad Pro Huawei Mate 20 (Pro, 20X 4G, 20X 5G and RS Porche Design) Huawei Mate X & Xs === Huawei Nova series === Huawei Nova 6 (Nova 6 5G & Nova 6 SE) Huawei Nova 5 (Nova 5 Pro, Nova 5i Pro & Nova 5Z) Huawei Nova Y60 === Huawei Enjoy series === Huawei Enjoy 10S == Issues == Technology news website Engadget has noted that when saying, 'Hey Celia', out aloud in the presence of an iPhone, Siri will respond along with Celia; this is apparently because 'Celia' sounds similar to 'Siri'.
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Software configuration management

Software configuration management (SCM), a.k.a. software change and configuration management (SCCM), is the software engineering practice of tracking and controlling changes to a software system. It is part of the larger cross-disciplinary field of configuration management (CM). SCM includes version control and the establishment of baselines. == Goals == The goals of SCM include: Configuration identification - Identifying configurations, configuration items and baselines. Configuration control - Implementing a controlled change process. This is usually achieved by setting up a change control board whose primary function is to approve or reject all change requests that are sent against any baseline. Configuration status accounting - Recording and reporting all the necessary information on the status of the development process. Configuration auditing - Ensuring that configurations contain all their intended parts and are sound with respect to their specifying documents, including requirements, architectural specifications and user manuals. Build management - Managing the process and tools used for builds. Process management - Ensuring adherence to the organization's development process. Environment management - Managing the software and hardware that host the system. Teamwork - Facilitate team interactions related to the process. Defect tracking - Making sure every defect has traceability back to the source. With the introduction of cloud computing and DevOps the purposes of SCM tools have become merged in some cases. The SCM tools themselves have become virtual appliances that can be instantiated as virtual machines and saved with state and version. The tools can model and manage cloud-based virtual resources, including virtual appliances, storage units, and software bundles. The roles and responsibilities of the actors have become merged as well with developers now being able to dynamically instantiate virtual servers and related resources. == History == == Examples == Ansible – Open-source software platform for remote configuring and managing computers CFEngine – Configuration management software Chef – Configuration management toolPages displaying short descriptions of redirect targets LCFG – Computer configuration management system NixOS – Linux distribution OpenMake Software – DevOps company Otter Puppet – Open source configuration management software Salt – Configuration management software Rex – Open source software
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ISPConfig

ISPConfig is an open source hosting control panel for Linux, licensed under BSD license and developed by the company ISPConfig UG. The ISPConfig project was started in autumn 2005 by Till Brehm from the German company projektfarm GmbH. == Overview == Using the dashboard, administrators have the ability to manage websites, email addresses, MySQL and MariaDB as well as PostgreSQL (since version 3.3) databases, FTP accounts, Shell accounts and DNS records through a web-based interface. The software has 4 login levels: administrator, reseller, client, and email-user, each with a different set of permissions. == Operating Systems == ISPConfig is only available on Linux, with CentOS, Debian, and Ubuntu being among the supported distributions. == Features == The following services and features are supported: Management of a single or multiple servers from one control panel. Web server management for Apache HTTP Server and Nginx. Mail server management (with virtual mail users) with spam and antivirus filter using Postfix (software) and Dovecot (software). DNS server management (BIND, Powerdns). Configuration mirroring and clusters. Administrator, reseller, client and mail-user login. Virtual server management for OpenVZ Servers. Website statistics using Webalizer and AWStats
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AIX Toolbox for Linux Applications

The AIX Toolbox for Linux Applications is a collection of GNU tools for IBM AIX. These tools are available for installation using Red Hat's RPM format. == Licensing == Each of these packages includes its own licensing information and while IBM has made the code available to AIX users, the code is provided as is and has not been thoroughly tested. The Toolbox is meant to provide a core set of some of the most common development tools and libraries along with the more popular GNU packages.
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Hardware for artificial intelligence

Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs. As of the 2020s, AI computation is dominated by graphics processing units (GPUs) and newer domain-specific accelerators such as Google's Tensor Processing Units (TPUs), AMD's Instinct MI300 series, and various on-device neural-processing units (NPUs) found in consumer hardware. == Scope == For the purposes of this article, AI hardware refers to computing components and systems specifically designed or optimized to accelerate artificial-intelligence workloads such as machine-learning training or inference. This includes general-purpose accelerators used for AI (for example, GPUs) and domain-specific accelerators (for example, TPUs, NPUs, and other AI ASICs). Event-based cameras are sometimes discussed in the context of neuromorphic computing, but they are input sensors rather than AI compute devices. Conversely, components such as memristors are basic circuit elements rather than specialized AI hardware when considered alone. == Lisp machines == Lisp machines were developed in the late 1970s and early 1980s to make artificial intelligence programs written in the programming language Lisp run faster. == Dataflow architecture == Dataflow architecture processors used for AI serve various purposes with varied implementations like the polymorphic dataflow Convolution Engine by Kinara (formerly Deep Vision), structure-driven dataflow by Hailo, and dataflow scheduling by Cerebras. == Component hardware == === AI accelerators === Since the 2010s, advances in computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced central processing units (CPUs) as the dominant means to train large-scale commercial cloud AI. OpenAI estimated the hardware compute used in the largest deep learning projects from Alex Net (2012) to Alpha Zero (2017), and found a 300,000-fold increase in the amount of compute needed, with a doubling-time trend of 3.4 months. === General-purpose GPUs for AI === Since the 2010s, graphics processing units (GPUs) have been widely used to train and deploy deep learning models because of their highly parallel architecture and high memory bandwidth. Modern data-center GPUs include dedicated tensor or matrix-math units that accelerate neural-network operations. In 2022, NVIDIA introduced the Hopper-generation H100 GPU, adding FP8 precision support and faster interconnects for large-scale model training. AMD and other vendors have also developed GPUs and accelerators aimed at AI and high-performance computing workloads. === Domain-specific accelerators (ASICs / NPUs) === Beyond general-purpose GPUs, several companies have developed application-specific integrated circuits (ASICs) and neural processing units (NPUs) tailored for AI workloads. Google introduced the Tensor Processing Unit (TPU) in 2016 for deep-learning inference, with later generations supporting large-scale training through dense systolic-array designs and optical interconnects. Other vendors have released similar devices—such as Apple's Neural Engine and various on-device NPUs—that emphasize energy-efficient inference in mobile or edge computing environments. === Memory and interconnects === AI accelerators rely on fast memory and inter-chip links to manage the large data volumes of training and inference. High-bandwidth memory (HBM) stacks, standardized as HBM3 in 2022, provide terabytes-per-second throughput on modern GPUs and ASICs. These accelerators are often connected through dedicated fabrics such as NVIDIA's NVLink and NVSwitch or optical interconnects used in TPU systems to scale performance across thousands of chips.
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FlowVella

FlowVella (formerly Flowboard) is an interactive presentation platform that includes an iPad/iPhone app, a Mac app and web site for viewing presentations, built first for the iPad and web. FlowVella allows users to create, publish and share presentations through their cloud-based SaaS system. FlowVella allows embedding of text, images, PDFs, video and gallery objects in easy linkable screens, defining modern interactive presentations. FlowVella grew out of Treemo Labs. == History == FlowVella launched as 'Flowboard' on April 18, 2013 after being built for almost a year. FlowVella was incubated out of Treemo Labs, which had years of experience building native apps for iPhone, iPad and Android devices. FlowVella is an iPad app and Mac app where users create, view, publish and share interactive presentations. Presentations are viewable on flowvella.com through a web-based viewer on any device or through the FlowVella native iPad app or Mac app. On December 18, 2014, Flowboard rebranded as FlowVella after a trademark dispute. == Presentation format == FlowVella is an interactive presentation format where instead of single directional slides, presentations are made up of linkable screens with embeddable media and content objects. While 'Flows' can be exported to PDF, they all have a web address and are meant to be viewed via a web browser or the FlowVella native applications. == Revenue model == FlowVella uses the freemium model for its presentation apps. Free users can make 4 public presentations with limited number of screens/slides, but most features are available to try out the software. In 2016, FlowVella introduced a second paid plan called PRO which includes team sharing, tracking and newly introduced 'Kiosk Mode' that launched in March of 2017. == Features == FlowVella is a native iPad app and Mac app which has advantages over web based tools. All downloaded presentations can be viewed offline, without an Internet connection. This includes videos which are enabled by caching the video files into memory. For students, teachers, sales people and all users, this is extremely important because this prevents having a presentation fail because of lack of an Internet connection. Beyond the offline capabilities, there is a trend to build native applications versus HTML5 as noted by Facebook and LinkedIn both rebuilding their mobile apps as 100% native applications.
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Taskworld

Taskworld is a cloud-based collaboration platform created by Fred Mouawad. The SaaS (software as a service) is designed to facilitate project and task management, collaboration, delegation, communication, knowledge management, measure progress and provide performance metrics for evidence-based evaluations within teams. It allows team members to assign and receive tasks, add followers, record comments, share and store unlimited files and organize projects. == Background == An initial version of Taskworld was custom-built by the IT team working for Mouawad in 2006. This was done as a way to try and overcome internal issues regarding delegation, accountability and time-management. The application was constructed to prevent tasks from falling through the cracks and make it easy to follow up on-going projects where many individuals throughout various departments of the organization were involved. Mouawad’s Synergia One group of companies later implemented the application internally as the ‘Task Management System’ and found a general improvement in execution across international offices and departments. This successful implementation led Mouawad to found the ‘My Taskworld’ website which later evolved into ‘Taskworld.' The company was officially founded in February 2012, and in June later that year, Mouawad presented a prototype of the Taskworld website to an Executive Program at the Stanford Graduate School of Business. == Reception == The launch of the app was covered by the media as an addition to the Synergia One group of companies founded by CEO Fred Mouawad. The mobile app is currently available on both android and iOS platforms. Google Play gives the app 3.8 out of 5 stars while the Apple App Store gave it 2.9 out of 5 stars. Alex Williams in a 2014 article for Tech Crunch said, “Taskworld’s technology plays to human emotions. As the feedback is continuous, people are compelled to engage with the service. But in some respects, Taskworld is fairly simple and still needing more to make it a potent competitor in the market.” == Features == Taskworld's main features include, but are not limited to: Project &Task Management - Taskworld includes up to five levels of hierarchy including Project Group, Project, Tasklist, Task and Checklist. Some features in this group are assigning tasks, setting due dates, adding followers, task comments, set repeating tasks, tasks in multiple locations, project templates, copy project, archiving, smart notifications, drag and drop Kanban boards, image preview boards, file management, people page and personnel directory, customizable tags and colored labels. Enterprise Messaging - The app includes a native chat application with channels and groups, private and direct messaging capabilities. Other communication features inside of the app include project chat, drag and drop file attachments, an email bridge to send and receive messages and @mentions. Overview & Analytics - Taskworld includes several features under this section including a dashboard, workspace snapshot, workspace filter, interactive calendar, project analytics and health status, project burndown chart, project burn-up chart and interactive timeline. == Languages and customers == Taskworld is used by 4,000 companies in 80 countries. The app is currently available in eight languages: English, French, German, Spanish, Mandarin, Portuguese, Thai and Korean. == Customer support == The Taskworld User Guide offers details on how to use features of the application. Customer support is offered inside of the application for questions and feedback regarding the software, and also via email. The Taskworld customer support team has received a 98% Customer Satisfaction Rating, according to customer ratings on its support platform Zendesk. Free, live demos are also offered for those companies who need assistance.
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