AI For Kids Dale Lane

AI For Kids Dale Lane — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Moving object detection

    Moving object detection

    Moving object detection is a technique used in computer vision and image processing. Multiple consecutive frames from a video are compared by various methods to determine if any moving object is detected. Moving objects detection has been used for wide range of applications like video surveillance, activity recognition, road condition monitoring, airport safety, monitoring of protection along marine border, etc. == Definition == Moving object detection is to recognize the physical movement of an object in a given place or region. By acting segmentation among moving objects and stationary area or region, the moving objects' motion can be tracked and thus analyzed later. To achieve this, consider a video is a structure built upon single frames, moving object detection is to find the foreground moving target(s), either in each video frame or only when the moving target shows the first appearance in the video. == Traditional methods == Among all the traditional moving object detection methods, we could categorize them into four major approaches: Background subtraction, Frame differencing, Temporal Differencing, and Optical Flow. === Frame differencing === Instead of using traditional approach, to use image subtraction operator by subtracting second and images afterwards, the frame differencing method makes comparisons between two successive frames to detect moving targets. === Temporal differencing === The temporal differencing method identifies the moving object by applying pixel-wise difference method with two or three consecutive frames.

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  • Distributed transaction

    Distributed transaction

    A distributed transaction operates within a distributed environment, typically involving multiple nodes across a network depending on the location of the data. A key aspect of distributed transactions is atomicity, which ensures that the transaction is completed in its entirety or not executed at all. It's essential to note that distributed transactions are not limited to databases. The Open Group, a vendor consortium, proposed the X/Open Distributed Transaction Processing Model (X/Open XA), which became a de facto standard for the behavior of transaction model components. Databases are common transactional resources and, often, transactions span a couple of such databases. In this case, a distributed transaction can be seen as a database transaction that must be synchronized (or provide ACID properties) among multiple participating databases which are distributed among different physical locations. The isolation property (the I of ACID) poses a special challenge for multi database transactions, since the (global) serializability property could be violated, even if each database provides it (see also global serializability). In practice most commercial database systems use strong strict two-phase locking (SS2PL) for concurrency control, which ensures global serializability, if all the participating databases employ it. A common algorithm for ensuring correct completion of a distributed transaction is the two-phase commit (2PC). This algorithm is usually applied for updates able to commit in a short period of time, ranging from couple of milliseconds to couple of minutes. There are also long-lived distributed transactions, for example a transaction to book a trip, which consists of booking a flight, a rental car and a hotel. Since booking the flight might take up to a day to get a confirmation, two-phase commit is not applicable here, it will lock the resources for this long. In this case more sophisticated techniques that involve multiple undo levels are used. The way you can undo the hotel booking by calling a desk and cancelling the reservation, a system can be designed to undo certain operations (unless they are irreversibly finished). In practice, long-lived distributed transactions are implemented in systems based on web services. Usually these transactions utilize principles of compensating transactions, Optimism and Isolation Without Locking. The X/Open standard does not cover long-lived distributed transactions. Several technologies, including Jakarta Enterprise Beans and Microsoft Transaction Server fully support distributed transaction standards. == Synchronization == In event-driven architectures, distributed transactions can be synchronized through using request–response paradigm and it can be implemented in two ways: Creating two separate queues: one for requests and the other for replies. The event producer must wait until it receives the response. Creating one dedicated ephemeral queue for each request.

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  • SciDB

    SciDB

    SciDB is a column-oriented database management system (DBMS) designed for multidimensional data management and analytics common to scientific, geospatial, financial, and industrial applications. It is developed by Paradigm4 and co-created by Michael Stonebraker. == History == Stonebraker claims that arrays are 100 times faster in SciDB than in a relational DBMS on a class of problems. It is swapping rows and columns for mathematical arrays that put fewer restrictions on the data and can work in any number of dimensions unlike the conventionally widely used relational database management system model, in which each relation supports only one dimension of records. A 2011 conference presentation on SciDB promoted it as "not Hadoop". Marilyn Matz became chief executive Paradigm4 in 2014.

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  • Basic Formal Ontology

    Basic Formal Ontology

    Basic Formal Ontology (BFO) is a top-level ontology developed by Barry Smith and colleagues to promote interoperability among domain ontologies. The BFO methodology accomplishes this through a process of downward population. BFO is a formal ontology. The structure of BFO is based on a division of entities into two disjoint categories of continuant and occurrent, the former consists of objects and spatial regions, the latter contains processes conceived as extended through (or spanning) time. BFO thereby seeks to consolidate both time and space within a single framework A guide to building BFO-conformant domain ontologies was published by MIT Press in 2015. In 2021, the standard ISO/IEC 21838-2:2021 Information Technology — Top-level Ontologies (TLO) — Part 2: Basic Formal Ontology (BFO) was published by the Joint Technical Committee of the International Standards Organization and the International Electrotechnical Commission. ISO/IEC 21838 is a multi-part standard. Part 1 of the standard specifies the requirements that must be met if an ontology is to be classified as a top-level ontology by the standard. == History == BFO arose against the background of research in ontologies in the domain of geospatial information science by David Mark, Pierre Grenon, Achille Varzi and others, with a special role for the study of vagueness and of the ways sharp boundaries in the geospatial and other domains are created by fiat. BFO has passed through four major releases. 2001: release of BFO 1 2007: release of BFO 1.1 2015: release of BFO 2.0 2020: release of BFO 2020 2021: release of BFO 2020 as an ISO/IEC Standard The current revision was released in 2020, and this forms the basis of the standard ISO/IEC 21838-2, which was released by the Joint Committee of the International Standards Organization and International Electrotechnical Commission in 2021. == Applications == BFO has been adopted as a foundational ontology by over 650 ontology projects, principally in the areas of biomedical ontology, security and defense (intelligence) ontology, and industry ontologies. Example applications of BFO can be seen in the Ontology for Biomedical Investigations (OBI). In January 2024, BFO and the Common Core Ontologies (CCO), a suite of BFO-extension ontologies, were adopted as the "baseline standards for formal DOD and IC ontology" development work in the DOD and Intelligence Community. A memorandum to this effect was signed by the chief data officers of the DOD, the Office of the Director of National Intelligence and the Chief Digital and Artificial Intelligence Office.

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  • Perusall

    Perusall

    Perusall is a social web annotation tool intended for use by students at schools and universities. It allows users to annotate the margins of a text in a virtual group setting that is similar to social media—with upvoting, emojis, chat functionality, and notification. It also includes automatic AI grading. == History == Perusall began as a research project at Harvard University. It later became an educational product for students and teachers. As of 2024, Perusall states more than 5 million students have used the tool at over 5,000 educational institutions in 112 countries." == Functionality == Perusall integrates with learning management systems such as Moodle, Canvas and Blackboard to aid with collaborative annotation. The tool supports annotation of a range of media including text, images, equations, videos, PDFs and snapshots of webpages.

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  • Collision problem

    Collision problem

    The r-to-1 collision problem is an important theoretical problem in complexity theory, quantum computing, and computational mathematics. The collision problem most often refers to the 2-to-1 version: given n {\displaystyle n} even and a function f : { 1 , … , n } → { 1 , … , n } {\displaystyle f:\,\{1,\ldots ,n\}\rightarrow \{1,\ldots ,n\}} , we are promised that f is either 1-to-1 or 2-to-1. We are only allowed to make queries about the value of f ( i ) {\displaystyle f(i)} for any i ∈ { 1 , … , n } {\displaystyle i\in \{1,\ldots ,n\}} . The problem then asks how many such queries we need to make to determine with certainty whether f is 1-to-1 or 2-to-1. == Classical solutions == === Deterministic === Solving the 2-to-1 version deterministically requires n 2 + 1 {\textstyle {\frac {n}{2}}+1} queries, and in general distinguishing r-to-1 functions from 1-to-1 functions requires n r + 1 {\textstyle {\frac {n}{r}}+1} queries. This is a straightforward application of the pigeonhole principle: if a function is r-to-1, then after n r + 1 {\textstyle {\frac {n}{r}}+1} queries we are guaranteed to have found a collision. If a function is 1-to-1, then no collision exists. Thus, n r + 1 {\textstyle {\frac {n}{r}}+1} queries suffice. If we are unlucky, then the first n / r {\displaystyle n/r} queries could return distinct answers, so n r + 1 {\textstyle {\frac {n}{r}}+1} queries is also necessary. === Randomized === If we allow randomness, the problem is easier. By the birthday paradox, if we choose (distinct) queries at random, then with high probability we find a collision in any fixed 2-to-1 function after Θ ( n ) {\displaystyle \Theta ({\sqrt {n}})} queries. == Quantum solution == The BHT algorithm, which uses Grover's algorithm, solves this problem optimally by only making O ( n 1 / 3 ) {\displaystyle O(n^{1/3})} queries to f. The matching lower bound of Ω ( n 1 / 3 ) {\displaystyle \Omega (n^{1/3})} was proved by Aaronson and Shi using the polynomial method.

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  • Driver scheduling problem

    Driver scheduling problem

    The driver scheduling problem (DSP) is type of problem in operations research and theoretical computer science. The DSP consists of selecting a set of duties (assignments) for the drivers or pilots of vehicles (e.g., buses, trains, boats, or planes) involved in the transportation of passengers or goods, within the constraints of various legislative and logistical criteria. == Criteria and modelling == This very complex problem involves several constraints related to labour and company rules and also different evaluation criteria and objectives. Being able to solve this problem efficiently can have a great impact on costs and quality of service for public transportation companies. There is a large number of different rules that a feasible duty might be required to satisfy, such as Minimum and maximum stretch duration Minimum and maximum break duration Minimum and maximum work duration Minimum and maximum total duration Maximum extra work duration Maximum number of vehicle changes Minimum driving duration of a particular vehicle Operations research has provided optimization models and algorithms that lead to efficient solutions for this problem. Among the most common models proposed to solve the DSP are the Set Covering and Set Partitioning Models (SPP/SCP). In the SPP model, each work piece (task) is covered by only one duty. In the SCP model, it is possible to have more than one duty covering a given work piece. In both models, the set of work pieces that needs to be covered is laid out in rows, and the set of previously defined feasible duties available for covering specific work pieces is arranged in columns. The DSP resolution, based on either of these models, is the selection of the set of feasible duties that guarantees that there is one (SPP) or more (SCP) duties covering each work piece while minimizing the total cost of the final schedule.

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  • Organizational information theory

    Organizational information theory

    Organizational Information Theory (OIT) is a communication theory, developed by Karl Weick, offering systemic insight into the processing and exchange of information within organizations and among its members. Unlike the past structure-centered theory, OIT focuses on the process of organizing in dynamic, information-rich environments. Given that, it contends that the main activity of organizations is the process of making sense of equivocal information. Organizational members are instrumental to reduce equivocality and achieve sensemaking through some strategies — enactment, selection, and retention of information. With a framework that is interdisciplinary in nature, organizational information theory's desire to eliminate both ambiguity and complexity from workplace messaging builds upon earlier findings from general systems theory and phenomenology. == Inspiration and influence of pre-existing theories == 1. General Systems Theory The General Systems Theory, on its most basic premise, describes the phenomenon of a cohesive group of interrelated parts. When one part of the system is changed or affected, it will affect the system as a whole. Weick uses this theoretical framework from 1950 to influence his organizational information theory. Likewise, organizations can be viewed as a system of related parts that work together towards a common goal or vision. Applying this to Weick's organizational information theory, organizations must work to reduce ambiguity and complexity in the workplace to maximize cohesiveness and efficiency. Weick uses the term, coupling, to describe how organizations, like a system, can be composed of interrelated and dependent parts. Coupling looks at the relationship between people and work. There are two types of coupling: 1. Loose coupling Loose coupling describes that while people within the organization or system are connected and often work together, they do not depend on one another to continue or fully complete individual work. The dependencies are weak and workflow is flexible. For example, "if the whole Science department completely shuts down because all of teachers are sick or for whatsoever reason, the school can still continue to operate because other departments are still present." 2. Tight coupling Tight coupling describes when connections within an organization are strong and dependent. If one part of the organization is not operating correctly, the organization as a whole cannot continue to their fullest potential. " For instance, the format and ink section completely shuts down hence the succeeding steps cannot be continued, so the whole process of the organization will be dropped. Thus, components of a system are directly dependent on one another." 2. Theory of evolution The theory of evolution, by Charles Darwin, is a framework for survival of the fittest. According to Darwin, organisms attempt to adapt and live in an unforgiving environment. Those that are unsuccessful in adaptation do not survive, while the strong organisms continue to thrive and reproduce. Weick invokes inspiration from Darwin, to incorporate a biological perspective to his theory. It is natural for organizations to have to adapt to incoming information that often interfere with the preexisting environment. Organizations that are able to plan and alter strategies in accordance with their constant need of organizing and sense making, will survive and be the most successful. However, there is a notable difference between animal evolution and survival of the fittest in organizations, "A given animal is what it is; variation comes through mutation. But the nature of an organization can change when its members alter their behavior." == Assumptions == 1. Human organizations exist in an information environment Unlike senders and receivers models, OIT stands on the situational perspective. Karl Weick views a human organization as an open social system. People in that system develop a mechanism to establish goals, obtain and process information, or perceive the environment. In this process, people and the environment come to conclusions on "what's going on here?". Colville believes that this attributional process is retrospective. Take an education institution as an example. A university can obtain information regarding students' needs in numerous ways. It might create feedback section in its website. It could organize alumni panels or academic affairs to attract prospective students and collect concrete questions they are interested in. It may also conduct the survey or host focus group to get the information. After that, the staff of the university have to decide how to deal with these information, based on which, it has to set and accomplish its goals for current and prospective students. 2. The information an organization receives differs in terms of equivocality Weick posits that numerous feasible interpretations of reality exist when organizations process information. Their varying levels of understandability lead to different outcomes of information inputs. In other academic works, scholars tend to say that messages are uncertain or ambiguous. While according to OIT, messages are described to be equivocal. believes that people proactively exclude a number of possibilities to perceive what is going on in the environment. Due to OIT's situational perspective, the meanings of messages consist of the messages, the interpretations of receivers, and the interactional context. However, ambiguity and uncertainty can mean that a standard answer - the only one true objective interpretation - exists. Also, Weick emphasizes that "the equivocality is the engine that motivates people to organize". Maitlis and Christianson states that the equivocality trigger sensemaking for three reasons: environment jolts and organizational crises, threats to identity, and planned change interventions. 3. Human organizations engage in information processing to reduce equivocality of information Based upon the first two assumption, OIT proposes that information processing within organizations is a social activity. Sharing is the key feature of organizational information processing. In that particular context, members jointly make sense the reality by reducing equivocality. It other words, the sensemaking is a joint responsibility which includes numerous interdependent people to accomplish. In this process, organizations and its members combine actions and attributions together in order to find the balance between the complexity of thoughts and the simplicity of actions. Weick also proposes that people create their own environment though enactment, which is the action of making sense. This is because people have different perceptual schemas and selective perception, so people create different information environments. In creating different information environments, people can arrive at the same or close to the same understanding or solution through different thought processes and overall understanding. == Key concepts == === The organization === In order to place Weick's vision regarding Organizational Information Theory into proper working context, exploring his view regarding what constitutes the organization and how its individuals embody that construct might yield significant insights. From a fundamental standpoint, he shared a belief that organizational validation is derived---not through bricks and mortar, or locale—but from a series of events which enable entities to "collect, manage and use the information they receive." In elaborating further on what constitutes an organization during early writings outlining OIT, Weick said, "The word organization is a noun and it is also a myth. if one looks for an organization, one will not find it. What will be found is that there are events linked together, that transpire within concrete walls and these sequences, their pathways, their timing, are the forms we erroneously make into substances when we talk about an organization". When viewed in this modular fashion, the organization meets Weick's theoretical vision by encompassing parameters that are less bound by concrete, wood, and structural restraints and more by an ability to serve as a repository where information can be consistently and effectively channeled. Taking these defining characteristics into account, proper channel execution relies on maximization of messaging clarity, context, delivery and evolution through any system. One example as to how these interactions might unfold on a more granular level within these confines can be gleaned through Weick's double interact loop, which he considers the "building blocks of every organization". Simply put, double interacts describe interpersonal exchanges that, inherently, occur across the organizational chain of command and in life, itself. Thus: "An act occurs when you say something (Can I have a Popsicle?). An interact occurs when you say something and I respond ("No, it will spoil your dinner

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  • Caffe (software)

    Caffe (software)

    Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface. == History == Yangqing Jia created the Caffe project during his PhD at UC Berkeley, while working the lab of Trevor Darrell. The first version, called "DeCAF", made its first appearance in Spring 2013 when it was used for the ILSVRC challenge (later called ImageNet). The library was named Caffe and released to the public in December 2013. It reached end-of-support in 2018. It is hosted on GitHub. == Features == Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. == Applications == Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework. == Caffe2 == In April 2017, Facebook announced Caffe2, which included new features such as recurrent neural network (RNN). At the end of March 2018, Caffe2 was merged into PyTorch.

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  • UI data binding

    UI data binding

    UI data binding is a software design pattern to simplify development of GUI applications. UI data binding binds UI elements to an application domain model. Most frameworks employ the Observer pattern as the underlying binding mechanism. To work efficiently, UI data binding has to address input validation and data type mapping. A bound control is a widget whose value is tied or bound to a field in a recordset (e.g., a column in a row of a table). Changes made to data within the control are automatically saved to the database when the control's exit event triggers. == Example == == Data binding frameworks and tools == === Delphi === DSharp third-party data binding tool OpenWire Visual Live Binding - third-party visual data binding tool === Java === JFace Data Binding JavaFX Property === .NET === Windows Forms data binding overview WPF data binding overview Avalonia Unity 3D data binding framework (available in modifications for NGUI, iGUI and EZGUI libraries) === JavaScript === Angular AngularJS Backbone.js Ember.js Datum.js knockout.js Meteor, via its Blaze live update engine OpenUI5 React Vue.js

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  • Ballin' (Mustard and Roddy Ricch song)

    Ballin' (Mustard and Roddy Ricch song)

    "Ballin'" is a song by American record producer Mustard featuring American rapper Roddy Ricch. The track was released as the third single from Mustard's third studio album, Perfect Ten, on August 20, 2019, though it was available as early as the end of June 2019. The song and its accompanying video received acclaim from music critics, with Complex magazine naming it the Best Song of 2019. It peaked at number 11 on the Billboard Hot 100, marking Mustard's highest charting song in the US. The song received a nomination for Best Rap/Sung Performance at the 2020 Grammy Awards, making it the first time Ricch has been nominated for a Grammy and Mustard's first nomination as an artist. Later in 2019, the two released another collaboration, "High Fashion". == Background == Roddy Ricch revealed in an interview that the song was composed in late 2018, but Mustard wanted to keep it for his album, Perfect Ten, which he was still working on. The song was later included on the album, released in June 2019. Ricch said he knew the song was "hard enough" the first time he heard it, while Mustard proclaimed "this is going to be the one". == Composition and lyrics == "Ballin'" has a "rags to riches" theme. In its intro, the song samples girl group 702's 1997 top ten hit "Get It Together". The song features a "smooth, bouncy beat", with Roddy Ricch rapping about his come-up and ascent in the music industry. In the first verse, Ricch salutes fellow Los Angeles rapper, the late Nipsey Hussle and his girlfriend Lauren London: "I run these racks up with my queen like London and Nip". The line simultaneously references Ricch and Hussle's collaboration "Racks in the Middle", released earlier in 2019 as Hussle's last single before his death. Billboard's Heran Mamo noted that "in typical Hussle fashion", Roddy Ricch "narrates his life's hardships before delving into his newfound treasures". == Critical reception == The song was widely acclaimed by music critics. Charles Holmes of Rolling Stone magazine called it "a song of the year contender", while Complex and Billboard both named it as a "standout track" on the album. Pitchfork magazine included "Ballin'" in its list of The Best Rap Songs of 2019 and called it "the centerpiece of Mustard's underappreciated album Perfect Ten". Complex later named it the Best Song of 2019, calling it "a feel-good anthem so infectious you'll need antibiotics just to stop running it back". == Chart performance == "Ballin'" was at the time Mustard's highest charting song in the US, peaking at number 11 on the Billboard Hot 100. It was also Roddy Ricch's highest charting song, until he surpassed it a week later, with the release of his album track "The Box", which eventually reached number 1 on the chart. It reached number one on Billboard's Rhythmic Songs chart, becoming Mustard's second number one following "Pure Water" and Ricch's first number one. The song also topped the Rap Airplay chart. == Music video == The music video for the track was teased by Mustard on his Instagram page on September 29, 2019. The music video for the track was eventually released on October 2, 2019 to critical acclaim. The video features Mustard and Roddy Ricch driving a Lamborghini Aventador in Los Angeles, where they both are from, playing poker in a casino, and going to a strip club. This is contrasted with scenes in which Mustard and Roddy Ricch as children play cards with Monopoly money and playing with miniature toy Lamborghinis together, aspiring for wealth and luxury, representing how they went from "rags to riches". The video also pays tribute to rapper Nipsey Hussle, who had been killed a few months ago. == Live performances == On December 16, 2019, Roddy Ricch performed the song live, alongside an 8-piece orchestra, at Peppermint Club in Los Angeles for Audiomack's Trap Symphony series. Along with Mustard, he performed it at The Pop Out: Ken & Friends on June 19, 2024. == Other uses == The song can be heard on "Elyse's Skit", track 10 off Roddy Ricch's debut album Please Excuse Me for Being Antisocial. In the skit, which is an actual voicenote recording, the mother of a woman named Elyse sends her daughter a voicenote, with "Ballin'" playing in the background, while the mother proceeds to say "I can't get that damn song out my head", jokingly calling it "inappropriate music". Ricch called the skit "something natural". In 2023, AI covers of the song using models based on pop culture characters and real-world celebrities gained viral popularity. == Awards and nominations == 62nd Annual Grammy Awards == Charts == == Certifications ==

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  • Outline of artificial intelligence

    Outline of artificial intelligence

    The following outline is provided as an overview of and topical guide to artificial intelligence: Artificial intelligence (AI) is intelligence exhibited by machines or software. It is also the name of the scientific field which studies how to create computers and computer software that are capable of intelligent behavior. == AI terminology == Glossary of artificial intelligence == Goals and applications == === General intelligence === Artificial general intelligence AI-complete === Reasoning and problem solving === Automated reasoning Mathematics Automated theorem prover Computer-assisted proof – Computer algebra General Problem Solver Expert system – Decision support system – Clinical decision support system – === Knowledge representation === Knowledge representation Knowledge management Cyc === Planning === Automated planning and scheduling Strategic planning Sussman anomaly – === Learning === Machine learning – Constrained Conditional Models – Deep learning – Neural modeling fields – Supervised learning – Weak supervision (semi-supervised learning) – Unsupervised learning – === Natural language processing === Natural language processing (outline) – Chatterbots – Language identification – Large language model – Retrieval-augmented generation – Natural language user interface – Natural language understanding – Machine translation – Statistical semantics – Question answering – Semantic translation – Concept mining – Data mining – Text mining – Process mining – E-mail spam filtering – Information extraction – Named-entity extraction – Coreference resolution – Named-entity recognition – Relationship extraction – Terminology extraction – === Perception === Machine perception Pattern recognition – Computer Audition – Speech recognition – Speaker recognition – Computer vision (outline) – Image processing Intelligent word recognition – Object recognition – Optical mark recognition – Handwriting recognition – Optical character recognition – Automatic number plate recognition – Information extraction – Image retrieval – Automatic image annotation – Facial recognition systems – Silent speech interface – Activity recognition – Percept (artificial intelligence) === Robotics === Robotics – Behavior-based robotics – Cognitive – Cybernetics – Developmental robotics – Evolutionary robotics – === Control === Intelligent control Self-management (computer science) – Autonomic Computing – Autonomic Networking – === Social intelligence === Affective computing Kismet === Game playing === Game artificial intelligence – Computer game bot – computer replacement for human players. Video game AI – Computer chess – Computer Go – General game playing – General video game playing – === Creativity, art and entertainment === Artificial creativity Artificial life Artificial intelligence art AI anthropomorphism AI agent AI web browser AI boom AI slop Creative computing Generative artificial intelligence Generative pre trained transformer Uncanny valley Music and artificial intelligence Computational humor Chatbot === Integrated AI systems === AIBO – Sony's robot dog. It integrates vision, hearing and motorskills. Asimo (2000 to present) – humanoid robot developed by Honda, capable of walking, running, negotiating through pedestrian traffic, climbing and descending stairs, recognizing speech commands and the faces of specific individuals, among a growing set of capabilities. MIRAGE – A.I. embodied humanoid in an augmented reality environment. Cog – M.I.T. humanoid robot project under the direction of Rodney Brooks. QRIO – Sony's version of a humanoid robot. TOPIO, TOSY's humanoid robot that can play ping-pong with humans. Watson (2011) – computer developed by IBM that played and won the game show Jeopardy! It is now being used to guide nurses in medical procedures. Purpose: Open domain question answering Technologies employed: Natural language processing Information retrieval Knowledge representation Automated reasoning Machine learning Project Debater (2018) – artificially intelligent computer system, designed to make coherent arguments, developed at IBM's lab in Haifa, Israel. === Intelligent personal assistants === Intelligent personal assistant – Amazon Alexa – Assistant – Braina – Cortana – Google Assistant – Google Now – Mycroft – Siri – Viv – === Other applications === Artificial life – simulation of natural life through the means of computers, robotics, or biochemistry. Automatic target recognition – Diagnosis (artificial intelligence) – Speech generating device – Vehicle infrastructure integration – Virtual Intelligence – == History == History of artificial intelligence Progress in artificial intelligence Timeline of artificial intelligence AI effect – as soon as AI successfully solves a problem, the problem is no longer considered by the public to be a part of AI. This phenomenon has occurred in relation to every AI application produced, so far, throughout the history of development of AI. AI winter – a period of disappointment and funding reductions occurring after a wave of high expectations and funding in AI. Such funding cuts occurred in the 1970s, for instance. Moore's law === History by period === 2017 in artificial intelligence 2018 in artificial intelligence 2019 in artificial intelligence 2020 in artificial intelligence 2021 in artificial intelligence 2022 in artificial intelligence 2023 in artificial intelligence 2024 in artificial intelligence 2025 in artificial intelligence 2026 in artificial intelligence 2027 in artificial intelligence 2028 in artificial intelligence 2029 in artificial intelligence === History by subject === History of logic (formal reasoning is an important precursor of AI) History of machine learning (timeline) History of machine translation (timeline) History of natural language processing History of optical character recognition (timeline) == AI algorithms and techniques == === Search === Discrete search algorithms Uninformed search Brute force search – Problem-solving technique and algorithmic paradigmPages displaying short descriptions of redirect targets Search tree – Data structure in tree form sorted for fast lookup Breadth-first search – Algorithm to search the nodes of a graph Depth-first search – Algorithm to search the nodes of a graph State space search – Class of search algorithmsPages displaying short descriptions of redirect targets Informed search Best-first search – Graph exploring search algorithm A search algorithm – Algorithm used for pathfinding and graph traversal Heuristics – Problem-solving methodPages displaying short descriptions of redirect targets Pruning (algorithm) – Data compression techniquePages displaying short descriptions of redirect targets Adversarial search Minmax algorithm – Decision rule used for minimizing the possible loss for a worst-case scenarioPages displaying short descriptions of redirect targets Logic as search Production system (computer science) – Computer program used to provide artificial intelligence Rule based system – Type of computer systemPages displaying short descriptions of redirect targets Production rule – Computer program used to provide artificial intelligence Inference rule – Method of deriving conclusionsPages displaying short descriptions of redirect targets Horn clause – Type of logical formula Forward chaining – Inference engine in an expert system Backward chaining – Method of forming inferences Planning as search State space search – Class of search algorithmsPages displaying short descriptions of redirect targets Means–ends analysis – Problem solving technique === Optimization search === Optimization (mathematics) algorithms Hill climbing – Optimization algorithm Simulated annealing – Probabilistic optimization technique and metaheuristic Beam search – Heuristic search algorithm Random optimization – Optimization technique in mathematics Evolutionary computation Genetic algorithms – Competitive algorithm for searching a problem spacePages displaying short descriptions of redirect targets Gene expression programming – Evolutionary algorithm Genetic programming – Evolving computer programs with techniques analogous to natural genetic processes Differential evolution – Method of mathematical optimization Society based learning algorithms. Swarm intelligence – Collective behavior of decentralized, self-organized systems Particle swarm optimization – Iterative simulation method Ant colony optimization – Optimization algorithmPages displaying short descriptions of redirect targets Metaheuristic – Optimization technique === Logic === Logic and automated reasoning Programming using logic Logic programming – Programming paradigm based on formal logic See "Logic as search" above. Forms of Logic Propositional logic First-order logic First-order logic with equality Constraint satisfaction – Process in artificial intelligence and operations research Fuzzy logic Fuzzy set theory – Sets whose elements have degrees of membershipPages displaying short descriptions

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  • Play Integrity API

    Play Integrity API

    Play Integrity API (formerly known as SafetyNet) consists of several application programming interfaces (APIs) offered by the Google Play Services to support security sensitive applications and enforce DRM. Currently, these APIs include device integrity verification, app verification, recaptcha and web address verification. It uses an environment called DroidGuard to perform the attestation. == Attestation == The SafetyNet Attestation API, one of the APIs under the SafetyNet umbrella, provides verification that the integrity of the device is not compromised. In practice, non-official ROMs such as LineageOS fail the hardware attestation and thus prevent the user from using a non-compliant ROM with third-party apps (mainly banking) that require the API. Due to this, some consider this a monopolistic practice deterring the entrance of competing mobile operating systems in the market. It requires a network connection to Google servers and validates the hardware signatures. Amongst the checks, the API looks for bootloader unlock status, ROM signatures, kernel strings, it also uses AVB2.0 and dm-verity attestations. Upon successful checks, Google Play will mark the device as Certified. The attestation runs in an environment called DroidGuard (com.google.android.gms.unstable). The SafetyNet Attestation API (one of the four APIs under the SafetyNet umbrella) has been deprecated. As of 6 October 2023, Google planned to replace it with the Play Integrity API by the end of January 2025. The transition ended on 20 May 2025, breaking applications which hadn't been updated. These attestations are offered by Google Play Services and thus are not available on free Android environments, like AOSP. Therefore, developers can require the API to be available and may refuse to execute on AOSP builds. == Google Play Protect == Under the same umbrella, Play Protect is a mechanism to find and remove "vulnerable" apps from one's Android device as well as store apps. Although it's meant to scan for malware-containing apps, it also looks for non-DRM compliant apps. == Criticism == Multiple groups have criticised SafetyNet and the Play Integrity API. Criticisms include that it offers weaker protection compared to alternatives such as Android's hardware attestation API, which provides a stronger form of verification while having the ability to remain compatible with more secure Android operating systems like GrapheneOS. Critics argued it undermines competition by effectively requiring developers to rely on Google's proprietary services, strengthening its monopoly over the Android ecosystem and disadvantaging alternative, privacy-focused operating systems. Users have also developed tools, such as the Play Integrity Fix module for Magisk/KernelSU/APatch, which tricks the attestation using leaked fingerprints of vulnerable devices. Furthermore, some have questioned the effectiveness of the attestation, claiming it does not deliver the level of security promised by Google and instead serves more as a form of vendor lock-in than a meaningful security measure. Activists have also raised concerns that it may violate antitrust and competition laws, like the Digital Markets Act.

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  • Maritime Informatics

    Maritime Informatics

    Maritime Informatics is a thematic topic within the broader discipline of informatics. It can be considered as both a field of study and domain of application. As an application domain, it is the outlet of innovations originating from data science and artificial intelligence; as a field of study, it is positioned between computer science and marine engineering. == Beginnings of maritime informatics == As a result of the increasing levels of digitalisation occurring in the maritime sector starting around 2010 and stimulated by the EU-endorsed MonaLisa project for sea traffic management (STM), a number of academics and shipping industry leaders recognised that the maritime transportation sector would benefit from a specific field of study and application to be known as Maritime Informatics - the use of information systems, data sharing and data analytics in the business and operations of maritime transportation. They considered that it would lead to improvements in efficiency, safety, resilience, and ecological sustainability - all of which are currently lacking for many aspects of sea transport. One of the first public airings of the concept of Maritime Informatics was a presentation delivered on 11 September 2014 in Gothenburg, Sweden. A proposal for an inaugural minitrack on Maritime Informatics was accepted for the 2015 Americas Conference on Information Systems in Puerto Rico where three papers were presented. Since then numerous publications has been brought forward captured at www.maritimeinformatics.org and in late 2020 the first reference book on Maritime Informatics was co-written by 81 expert contributors (47 practitioners and 34 researchers) from 20 countries. Most impactful authors and journals in the domain have been documented in a review paper. Dimitrios Zissis, Luca Cazzanti and Leonardo M. Millefiori are the top three authors; top journals and conferences include Ocean Engineering, Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, Sensors, the international Conference On Engineering, Technology And Innovation, Expert Systems With Applications, IEEE Access, and Journal of Navigation. == Background == The shipping industry has several particular organisational aspects that are recognised and taken into account in maritime informatics: It is predominantly a self-organising ecosystem Many activities are undertaken as part of episodic tight coupling There is a so-called maritime stack There is increasing pressure to balance capital productivity and energy efficiency There is the potential virtuous interplay between different types of systems == Data sharing == Digital data sharing is key to the all-important, arguably fundamental, data analytics aspects of maritime informatics because it opens the way for better access to relevant and reliable data. As in land-based commerce, digital data sharing is a growing phenomenon in maritime operations - though there is a way to go. It is enabling greater transparency for all those involved in the transportation of goods and passengers, not least being the end-customer. This leads to better and more informed decision-making and planning by all those involved. The push for digitalisation and data sharing is being pursued both by governments and the commercial sector. For example, the Member States of the IMO agreed a mandatory requirement for their governments to introduce electronic information exchange between ships and ports as from 8 April 2019. Meanwhile, commercial operators, particularly in the container lines are putting systems in place for sharing data for mutual benefit in their operations. Data sharing is an important aspect of the Port Collaborative Decision Making (PortCDM) and Port Call Optimization initiatives, both of which seek to improve the coordination, synchronization and efficiency of the port call process by enabling a common and shared situational awareness among all those involved. == Standardisation == The availability and sharing of relevant digital data underpins maritime informatics and is key to more effective and efficient coordination and synchronisation in the predominantly self-organising ecosystem that is maritime transportation. For this to occur, a high priority underpinning maritime informatics is the encouragement of standardised digital data exchange and data sharing, leading, in turn, to improvements in shipping analytics. Improved availability of data will support better historical analysis, now-casting and forecasting. The International Maritime Organization (IMO) FAL Committee is taking the lead in ensuring that the common terms used in the various standards being developed or in use in the maritime sector are compatible and therefore interoperable as far as is practicable, by creating and maintaining The IMO Compendium on Facilitation and Electronic Business. The IMO Compendium consists of an IMO Data Set and IMO Reference Data Model agreed by the main organisations involved in the development of standards for the electronic exchange of information related to the FAL Convention: the World Customs Organization (WCO), the United Nations Economic Commission for Europe (UNECE) and the International Organization for Standardization (ISO). There are several other prominent international governmental and non-governmental organisations actively contributing to the ongoing standardisation and harmonisation process including the UN Electronic Data Interchange for Administration, Commerce and Transport (UN EDIFACT), the Digital Container Shipping Association (DCSA), the International Harbour Masters Association (IHMA) and BIMCO - the world's largest direct-membership organisation for shipowners, charterers, shipbrokers and agents.

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  • Bibliometrician

    Bibliometrician

    A bibliometrician is a researcher or a specialist in bibliometrics. It is near-synonymous with an informetrican (who studies informetrics), a scientometrican (who study scientometrics) and a webometrician, who study webometrics. == Notable bibliometricians == Christine L. Borgman Samuel C. Bradford Blaise Cronin Margaret Elizabeth Egan Eugene Garfield (developer of the Science Citation Index and the Impact factor) Jorge E. Hirsch (developer of the h-index) Alfred J. Lotka Vasily Nalimov Derek J. de Solla Price Ronald Rousseau George Kingsley Zipf

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