AI Coding Vscode Extension

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  Visual descriptor

  In computer vision, visual descriptors or image descriptors are descriptions of the visual features of the contents in images, videos, or algorithms or applications that produce such descriptions. They describe elementary characteristics such as the shape, the color, the texture or the motion, among others. == Introduction == As a result of the new communication technologies and the massive use of Internet in our society, the amount of audio-visual information available in digital format is increasing considerably. Therefore, it has been necessary to design some systems that allow us to describe the content of several types of multimedia information in order to search and classify them. The audio-visual descriptors are in charge of the contents description. These descriptors have a good knowledge of the objects and events found in a video, image or audio and they allow the quick and efficient searches of the audio-visual content. This system can be compared to the search engines for textual contents. Although it is relatively easy to find text with a computer, it is much more difficult to find concrete audio and video parts. For instance, imagine somebody searching a scene of a happy person. The happiness is a feeling and it is not evident its shape, color and texture description in images. The description of the audio-visual content is not a superficial task and it is essential for the effective use of this type of archives. The standardization system that deals with audio-visual descriptors is the MPEG-7 (Motion Picture Expert Group - 7). == Types == Descriptors are the first step to find out the connection between pixels contained in a digital image and what humans recall after having observed an image or a group of images after some minutes. Visual descriptors are divided in two main groups: General information descriptors: contain low level descriptors which give a description about color, shape, regions, textures and motion. Specific domain information descriptors: give information about objects and events in the scene. A concrete example would be face recognition. === General information descriptors === General information descriptors consist of a set of descriptors that covers different basic and elementary features like: color, texture, shape, motion, location and others. This description is automatically generated by means of signal processing. ==== Color ==== It's the most basic quality of visual content. Five tools are defined to describe color. The three first tools represent the color distribution and the last ones describe the color relation between sequences or group of images: Dominant color descriptor (DCD) Scalable color descriptor (SCD) Color structure descriptor (CSD) Color layout descriptor (CLD) Group of frame (GoF) or group-of-pictures (GoP) ==== Texture ==== It's an important quality in order to describe an image. The texture descriptors characterize image textures or regions. They observe the region homogeneity and the histograms of these region borders. The set of descriptors is formed by: Homogeneous texture descriptor (HTD) Texture browsing descriptor (TBD) Edge histogram descriptor (EHD) ==== Shape ==== It contains important semantic information due to human's ability to recognize objects through their shape. However, this information can only be extracted by means of a segmentation similar to the one that the human visual system implements. Nowadays, such a segmentation system is not available yet, however there exists a serial of algorithms which are considered to be a good approximation. These descriptors describe regions, contours and shapes for 2D images and for 3D volumes. The shape descriptors are the following ones: Region-based shape descriptor (RSD) Contour-based shape descriptor (CSD) 3-D shape descriptor (3-D SD) ==== Motion ==== It's defined by four different descriptors which describe motion in video sequence. Motion is related to the objects motion in the sequence and to the camera motion. This last information is provided by the capture device, whereas the rest is implemented by means of image processing. The descriptor set is the following one: Motion activity descriptor (MAD) Camera motion descriptor (CMD) Motion trajectory descriptor (MTD) Warping and parametric motion descriptor (WMD and PMD) ==== Location ==== Elements location in the image is used to describe elements in the spatial domain. In addition, elements can also be located in the temporal domain: Region locator descriptor (RLD) Spatio temporal locator descriptor (STLD) === Specific domain information descriptors === These descriptors, which give information about objects and events in the scene, are not easily extractable, even more when the extraction is to be automatically done. Nevertheless, they can be manually processed. As mentioned before, face recognition is a concrete example of an application that tries to automatically obtain this information. == Descriptors applications == Among all applications, the most important ones are: Multimedia documents search engines and classifiers. Digital library: visual descriptors allow a very detailed and concrete search of any video or image by means of different search parameters. For instance, the search of films where a known actor appears, the search of videos containing the Everest mountain, etc. Personalized electronic news service. Possibility of an automatic connection to a TV channel broadcasting a soccer match, for example, whenever a player approaches the goal area. Control and filtering of concrete audiovisual content, like violent or pornographic material. Also, authorization for some multimedia content.

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  Vehicle infrastructure integration

  The Vehicle Infrastructure Integration (VII), also known as "Connected Roadways" or "vehicle-to-everything" (V2X) technology, is a United States Department of Transportation initiative that aims to improve road safety by developing technology that connects road vehicles with their environment. This development draws on several disciplines, including transport engineering, electrical engineering, automotive engineering, telematics, and computer science. Although VII specifically covers road transport, similar technologies are under development for other modes of transport. For example, airplanes may use ground-based beacons for automated guidance, allowing the autopilot to fly the plane without human intervention. == Goals == The goal of VII is to establish a communication link between vehicles (via On-Board Equipment, or OBE) and roadside infrastructure (via Roadside Equipment, or RSE) to enhance the safety, efficiency, and convenience of transportation systems. Two potential approaches are the widespread deployment of a dedicated short-range communications (DSRC) link on the 5.9GHz band, and cellular communication (C-V2X). Either of these methods would allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The initiative has three priorities: Stakeholder evaluation and acceptance of the business model and its deployment schedule, Validation of the technology, with a focus on communications systems, in relation to deployment costs, and Creation of legal structures and policies, especially concerning digital privacy, to improve the system's long-term potential for success. === Safety === Current automotive safety technology relies primarily on vehicle-based radar, lidar, and sonar systems. This technology allows, for instance, a potential reduction in rear-end collisions by monitoring obstacles in front of or behind the vehicle and automatically applying the brakes when necessary. This technology, however, is limited by the sensing range of vehicle-based radar, particularly in angled and left-turn collisions, such as a motorist losing control of the vehicle during an impending head-on collision. The rear-end collisions addressed by current technology are generally less severe than angled, left-turn, or head-on collisions. VII promotes the development of a direct communication link between road vehicles and all other vehicles nearby, allowing for the exchange of information on vehicle speed and orientation or driver awareness and intent. This real-time exchange of information may enable more effective automated emergency maneuvers, such as steering, decelerating, or braking. In addition to nearby vehicle awareness, VII promotes a communication link between vehicles and roadway infrastructure. Such a link may allow for improved real-time traffic information, better queue management, and feedback to vehicles. Existing implementations of VII use vehicle-based sensors that can recognize and respond to roadway markings or signs, automatically adjusting vehicle parameters to follow the recognized instructions. However, this information may also be acquired via roadside beacons or stored in a centralized database accessible to all vehicles. === Efficiency === With a VII system in place, vehicles will be linked together. The headway between vehicles may therefore be reduced so that there is less empty space on the road, increasing the available capacity per lane. More capacity per lane will in turn imply fewer lanes in general, possibly satisfying the community's concerns about the impact of roadway widening. VII will enable precise traffic-signal coordination by tracking vehicle platoons and will benefit from accurate timing by drawing on real-time traffic data covering volume, density, and turning movements. Real-time traffic data can also be used in the design of new roadways or modification of existing systems as the data could be used to provide accurate origin-destination studies and turning-movement counts for uses in transportation forecasting and traffic operations. Such technology would also lead to improvements for transport engineers to address problems whilst reducing the cost of obtaining and compiling data. Tolling is another prospect for VII technology as it could enable roadways to be automatically tolled. Data could be collectively transmitted to road users for in-vehicle display, outlining the lowest cost, shortest distance, and/or fastest route to a destination on the basis of real-time conditions. === Existing applications === To some extent, results along these lines have been achieved in trials performed around the globe, making use of GPS, mobile phone signals, and vehicle registration plates. GPS is becoming standard in many new high-end vehicles and is an option on most new low- and mid-range vehicles. In addition, many users also have mobile phones that transmit trackable signals (and may also be GPS-enabled). Mobile phones can already be traced for purposes of emergency response. GPS and mobile phone tracking, however, do not provide fully reliable data. Furthermore, integrating mobile phones in vehicles may be prohibitively difficult. Data from mobile phones, though useful, might even increase risks to motorists as they tend to look at their phones rather than concentrate on their driving. Automatic registration plate recognition can provide large quantities of data, but continuously tracking a vehicle through a corridor is a difficult task with existing technology. Today's equipment is designed for data acquisition and functions such as enforcement and tolling, not for returning data to vehicles or motorists for response. GPS will nevertheless be one of the key components in VII systems. == Limitations == === Privacy === VII architecture is designed to prevent identification of individual vehicles, with all data exchange between the vehicle and the system occurring anonymously. Exchanges between the vehicles and third parties such as OEMs and toll collectors will occur, but the network traffic will be sent via encrypted tunnels and will therefore not be decipherable by the VII system. Data sharing with law enforcement or Homeland Security was not included in system design as of 2006. === Technical issues === ==== Coordination ==== A major issue facing the deployment of VII is the problem of how to set up the system initially. The costs associated with installing the technology in vehicles and providing communications and power at every intersection are significant. ==== Maintenance ==== Another factor for consideration in regard to the technology's distribution is how to update and maintain the units. Traffic systems are highly dynamic, with new traffic controls implemented every day and roadways constructed or repaired every year. The vehicle-based option could be updated via the internet (preferably wireless) but may subsequently require all users to have access to internet technology. Alternatively, if receivers were placed in all vehicles and the VII system was primarily located along the roadside, information could be stored in a centralized database. This would allow the agency responsible to issue updates at any time. These would then be disseminated to the roadside units for passing motorists. Operationally, this method is currently considered to provide the greatest effectiveness but at a high cost to the authorities. ==== Security ==== Security of the units is another concern, especially in light of the public acceptance issue. Criminals could tamper, remove, or destroy VII units regardless of whether they are installed inside vehicles or along the roadside. Magnets, electric shocks, and malicious software (viruses, hacking, or jamming) could be used to damage VII systems – regardless of whether units are located inside vehicle or along the roadside. == Recent developments == Much of the current research and experimentation is conducted in the United States where coordination is ensured through the Vehicle Infrastructure Integration Consortium; consisting of automobile manufacturers (Ford, General Motors, Daimler Chrysler, Toyota, Nissan, Honda, Volkswagen, BMW), IT suppliers, U.S. Federal and state transportation departments, and professional associations. Trialing is taking place in Michigan and California. The specific applications now being developed under the U.S. initiative are: Warning drivers of unsafe conditions or imminent collisions. Warning drivers if they are about to run off the road or speed around a curve too fast. Informing system operators of real-time congestion, weather conditions and incidents. Providing operators with information on corridor capacity for real-time management, planning and provision of corridor-wide advisories to drivers. In mid-2007, a VII environment covering some 20 square miles (52 km2) near Detroit was used to test 20 prototype VII applications. Several automobile manufacturers are also conducting their own VII research and triali

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  Orange (software)

  Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for exploratory qualitative data analysis and interactive data visualization. == Description == Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis. Orange components are called widgets. They range from simple data visualization, subset selection, and preprocessing to empirical evaluation of learning algorithms and predictive modeling. Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration. == Software == Orange is an open-source software package released under GPL and hosted on GitHub. Versions up to 3.0 include core components in C++ with wrappers in Python. From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework. The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, transform, visualize, model, evaluate and unsupervised). Additional functionalities are available as add-ons (text-mining, image analytics, bioinformatics, etc.). Orange is supported on macOS, Windows and Linux and can also be installed from the Python Package Index repository (pip install Orange3). == Features == Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. The user can interactively explore visualizations or feed the selected subset into other widgets. Canvas: graphical front-end for data analysis Widgets: Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram). Classify: a set of supervised machine learning algorithms for classification Regression: a set of supervised machine learning algorithms for regression Evaluate: cross-validation, sampling-based procedures, reliability estimation and scoring of prediction methods Unsupervised: unsupervised learning algorithms for clustering (k-means, hierarchical clustering) and data projection techniques (multidimensional scaling, principal component analysis, correspondence analysis). == Add-ons == Orange users can extend their core set of components with components in the add-ons. Supported add-ons include: Associate: components for mining frequent itemsets and association rule learning. Bioinformatics: components for gene expression analysis, enrichment, and access to expression databases (e.g., Gene Expression Omnibus) and pathway libraries. Data fusion: components for fusing different data sets, collective matrix factorization, and exploration of latent factors. Educational: components for teaching machine learning concepts, such as k-means clustering, polynomial regression, stochastic gradient descent, ... Explain: provides an extension with components for the model explanation, including Shapley value analysis Geo: components for working with geospatial data. Image analytics: components for working with images and ImageNet embeddings Network: components for graph and network analysis. Text mining: components for natural language processing and text mining. Time series: widget components for time series analysis and modeling. Single-cell: support for single-cell gene expression analysis, including components for loading single-cell data, filtering and batch effect removal, marker genes discovery, scoring of cells and genes, and cell type prediction. Spectroscopy: components for analyzing and visualization of (hyper)spectral datasets. Survival analysis: add-on for data analysis dealing with survival data. It includes widgets for standard survival analysis techniques, such as the Kaplan-Meier plot, the Cox regression model, and several derivative widgets. World Happiness: support for downloading socioeconomic data from a database, including OECD and World Development Indicators. Provides access to thousands of country indicators from various economic databases. Fairness: add-on for evaluation and creation of fair machine learning models without discrimination. Widgets range from computing fairness metrics like statistical parity to post-, pre-, in-processing methods to build fair models. == Objectives == The program provides a platform for experiment selection, recommendation systems, and predictive modelling and is used in biomedicine, bioinformatics, genomic research, and teaching. In science, it is used as a platform for testing new machine learning algorithms and for implementing new techniques in genetics and bioinformatics. In education, it was used for teaching machine learning and data mining methods to students of biology, biomedicine, and informatics. == Extensions == Various projects build on Orange either by extending the core components with add-ons or using only the Orange Canvas to exploit the implemented visual programming features and GUI. OASYS — ORange SYnchrotron Suite scOrange — single cell biostatistics Quasar — data analysis in natural sciences == History == In 1996, the University of Ljubljana and Jožef Stefan Institute started development of ML, a machine learning framework in C++, and Python bindings were developed for this framework in 1997, which, together with emerging Python modules, formed a joint framework called Orange. Over the following years, most contemporary major algorithms for data mining and machine learning were implemented in C++ (Orange's core) or Python modules. In 2002, first prototypes to create a flexible graphical user interface were designed using Pmw Python megawidgets. In 2003, the graphical user interface was redesigned and re-developed for Qt framework using PyQt Python bindings. The visual programming framework was defined, and the development of widgets (graphical components of the data analysis pipeline) began. In 2005, extensions for data analysis in bioinformatics was created. In 2008, Mac OS X DMG and Fink-based installation packages were developed. In 2009, over 100 widgets were created and maintained. In 2009, Orange 2.0 beta was released, offering installation packages on the website based on the daily compiling cycle. In 2012, a new object hierarchy was imposed, replacing the old module-based structure. In 2013, a significant redesign of the graphical user interface included a new toolbox and depiction of workflows. In 2015, Orange 3.0 was released. Orange stores the data in NumPy arrays; machine learning algorithms mostly use scikit-learn. In 2015, a text analysis add-on for Orange3 was released. In 2016, Orange released version 3.3. Development scheduled a monthly cycle for stable releases. In 2016, Orange began development and release of an Image Analytics add-on, with server-side deep neural networks for image embedding In 2017, a Spectroscopy add-on for the analysis of spectral data was introduced. In 2017, Geo, an add-on for dealing with geo-location data and visualisation of geo maps was introduced In 2018, Orange began development and release of an add-on for single-cell data analysis. In 2019, Orange separated its graphical interface for development as a separate project, orange-canvas-core In 2020, Orange introduced the Explain add-on with widgets for explaining classification models and regression models, highlighting the strength and contributions specific features make towards predicting a specific class. In 2022, World Happiness, an add-on for the Orange3 data mining suite, was introduced, providing widgets for accessing socioeconomic data from various databases such as World Happiness Report, World Development Indicators, OECD. In 2022, Orange extended the Explain add-on with an Individual Conditional Expectation plot and the Permutation Feature Importance technique. In 2023, Orange introduced the Fairness add-on, including widgets to calculate bias metrics, as well as widgets for pre-, post-, and in-processing methods, allowing the creation of models less susceptible to systematic error due to the vagaries of the data set.

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  Murderbot (TV series)

  Murderbot is an American science fiction action comedy television series created by Paul Weitz and Chris Weitz for Apple TV+. It is based on All Systems Red, the first book of the series The Murderbot Diaries by Martha Wells, who serves as a consulting producer. The series stars Alexander Skarsgård as the titular character. The first season premiered on May 16, 2025 and received positive reviews. In July 2025, the series was renewed for a second season. == Premise == A media-obsessed private security construct (manufactured from cloned human tissue and mechanical parts) calling itself Murderbot must hide its newly acquired autonomy while completing dangerous assignments and being simultaneously drawn to humans, and appalled by their weakness. == Cast and characters == === Main === Alexander Skarsgård as Murderbot Noma Dumezweni as Ayda Mensah, a terraforming specialist, the President of Preservation Alliance and the leader of the science team protected by Murderbot David Dastmalchian as Gurathin, a tech expert and augmented human Sabrina Wu as Pin-Lee, a scientist and legal counsel to the team Akshay Khanna as Ratthi, a wormhole expert Tamara Podemski as Bharadwaj, a geochemist Tattiawna Jones as Arada, a biologist === Recurring === Cast of show-within-a-show The Rise and Fall of Sanctuary Moon John Cho as Eknie Jef Chem (playing Captain Hossein) Jack McBrayer as Breiller MocJac (playing Navigation Officer Hordööp-Sklanch) Clark Gregg as Arletty (playing Lieutenant Kullervv) DeWanda Wise as Pordron Bretney III Roche (playing NawBot 337 Alt 66) === Guest === Anna Konkle as Leebeebee, a member of another survey team on the planet. The character does not appear in the novella. Amanda Brugel as GrayCris Blue Leader David Reale as GrayCris Yellow == Episodes == == Production == The book series was optioned in the late 2010s, and its film adaptation was considered. In 2021, book series author Martha Wells said that a potential TV series adaptation was in development and that she had read the script and was "really excited about it". The series was green lit by Apple TV+ in 2022, with Wells serving as a consulting producer. The production design team, led by Sue Chan, started work in the autumn. Tommy Arnold, the Murderbot Diaries special edition illustrator, created the concept art for the show. After the casting was delayed by the 2023 SAG-AFTRA strike, in December 2023 it was announced that Alexander Skarsgård would produce and star in the series. He developed the character and the world of Murderbot with the showrunners. In February 2024, David Dastmalchian and Noma Dumezweni joined the cast. In March, Sabrina Wu, Tattiawna Jones, Akshay Khanna, and Tamara Podemski joined the cast. On July 10, 2025, the series was renewed for a second season. Showrunners Chris and Paul Weitz suggested the second season would combine the next three books of the series and will have longer episodes. === Filming === Principal photography for the first season took place from March–June 2024, in Toronto and parts of Ontario, Canada. Most of the filming was done on location, with the Sanctuary Moon scenes filmed on a virtual production stage. Principal photography for the second season began in mid-2026, in Madrid, Spain. It is planned to last 71 days, with Martha Wells also visiting the set. == Release == The first two episodes of Murderbot premiered on Apple TV+ on May 16, 2025, with subsequent episodes released weekly. The first season consists of ten episodes. == Reception == Even before the release of the show, numerous media sources had commented on the titular character as being coded as autistic and agender. On the review aggregator website Rotten Tomatoes, Murderbot has an approval rating of 96% with an average score of 7.5/10, based on 76 critics' reviews. The website's critical consensus states, "Alexander Skarsgård's superbly dry wit brings a lot of heart to Murderbot, making for a refreshingly jaunty sci-fi saga about finally coming out of one's shell". Metacritic, which uses a weighted average, assigned a score of 70 out of 100, based on 28 critics, indicating "generally favorable" reviews. Some reviewers have criticized Murderbot's changes to Wells' original books. Angela Watercutter of Wired noted that the series has significant tonal differences from the books and noted the show's changes to characters, particularly Murderbot and Dr. Mensah, and Wells' social commentary. === Accolades === Murderbot was a finalist for the 2025 Dragon Award for Best Science Fiction or Fantasy TV Series. Tommy Arnold won the 2025 Concept Art Association Award in the category of Live-Action Series Character Art for his work on Murderbot. Alexander Skarsgård was nominated for a Critics' Choice Award for Best Actor in a Comedy Series. Carrie Grace and Laura Jean Shannon were nominated for a Costume Designers Guild Award in the category of Excellence in Sci-Fi/Fantasy Television for their work on FreeCommerce. Amanda Jones was nominated for a Composers & Lyricists Award for Outstanding Original Title Sequence for a Television Production.

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  Softwarp

  Softwarp is a software technique to warp an image so that it can be projected on a curved screen. This can be done in real time by inserting the softwarp as a last step in the rendering cycle. The problem is to know how the image should be warped to look correct on the curved screen. There are several techniques to auto calibrate the warping by projecting a pattern and using cameras and/or sensors. The information from the sensors is sent to the software so that it can analyze the data and calculate the curvature of the projection screen. == Usage == The softwarp can be used to project virtual views on curved walls and domes. These are usually used in vehicle simulators, for instance boat-, car- and airplane simulators. To make it possible to cover a dome with a 360 degree view you need to use several projectors. A problem with using several projectors on the same screen is that the edges between the projected images get about twice the amount of light. This is solved by using a technique called edge blending. With this technique a “filter” is inserted on the edge that fades the image from 100% light strength (luminance) to 0% (the lowest luminance depends on the contrast ratio of the projector). == History == The first warping technologies used a hardware image processing unit to warp the image. This processing unit was inserted between the graphics card and the projector. The problem with this technique is that it depends on the type of signal and the quality of the signal from the graphics card to warp it correctly. The process unit also needs several lines of image information before it can start sending out the warped image. This adds a latency to the display system that could be a problem in simulators that need fast response time, for instance fighter jet simulators. Softwarping eliminates the latency.

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  Conference on Neural Information Processing Systems

  The Conference on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. The conference includes three days of invited talks along with oral and poster presentations of refereed papers, followed by two days of workshops and competitions. == History == The NeurIPS meeting was first proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and Bell Laboratories. NeurIPS was designed as a complementary open interdisciplinary meeting for researchers exploring biological and artificial Neural Networks. Reflecting this multidisciplinary approach, NeurIPS began in 1987 with information theorist Ed Posner as the conference president and learning theorist Yaser Abu-Mostafa as program chairman. Research presented in the early NeurIPS meetings included a wide range of topics from efforts to solve purely engineering problems to the use of computer models as a tool for understanding biological nervous systems. Since then, the biological and artificial systems research streams have diverged, and recent NeurIPS proceedings have been dominated by papers on machine learning, artificial intelligence and statistics. From 1987 until 2000 NeurIPS was held in Denver, United States. Since then, the conference was held in Vancouver, Canada (2001–2010), Granada, Spain (2011), and Lake Tahoe, United States (2012–2013). In 2014 and 2015, the conference was held in Montreal, Canada, in Barcelona, Spain in 2016, in Long Beach, United States in 2017, in Montreal, Canada in 2018 and Vancouver, Canada in 2019. Reflecting its origins at Snowbird, Utah, the meeting was accompanied by workshops organized at a nearby ski resort up until 2013, when it outgrew ski resorts. The first NeurIPS Conference was sponsored by the IEEE. The following NeurIPS Conferences have been organized by the NeurIPS Foundation, established by Ed Posner. Terrence Sejnowski has been the president of the NeurIPS Foundation since Posner's death in 1993. The board of trustees consists of previous general chairs of the NeurIPS Conference. The first proceedings was published in book form by the American Institute of Physics in 1987, and was entitled Neural Information Processing Systems, then the proceedings from the following conferences have been published by Morgan Kaufmann (1988–1993), MIT Press (1994–2004) and Curran Associates (2005–present) under the name Advances in Neural Information Processing Systems. The conference was originally abbreviated as "NIPS". By 2018 a few commentators were criticizing the abbreviation as encouraging sexism due to its association with the word nipples, and as being a slur against Japanese. The board changed the abbreviation to "NeurIPS" in November 2018. == Topics == Along with machine learning and neuroscience, other fields represented at NeurIPS include cognitive science, psychology, computer vision, statistical linguistics, and information theory. Over the years, NeurIPS became a premier conference on machine learning and although the 'Neural' in the NeurIPS acronym had become something of a historical relic, the resurgence of deep learning in neural networks since 2012, fueled by faster computers and big data, has led to achievements in speech recognition, object recognition in images, image captioning, language translation and world championship performance in the game of Go, based on neural architectures inspired by the hierarchy of areas in the visual cortex (ConvNet) and reinforcement learning inspired by the basal ganglia (Temporal difference learning). Notable affinity groups have emerged from the NeurIPS conference and displayed diversity, including Black in AI (in 2017), Queer in AI (in 2016), and others. === Named lectures === In addition to invited talks and symposia, NeurIPS also organizes two named lectureships to recognize distinguished researchers. The NeurIPS Board introduced the Posner Lectureship in honor of NeurIPS founder Ed Posner; two Posner Lectures were given each year up to 2015. Past lecturers have included: 2010 – Josh Tenenbaum and Michael I. Jordan 2011 – Rich Sutton and Bernhard Schölkopf 2012 – Thomas Dietterich and Terry Sejnowski 2013 – Daphne Koller and Peter Dayan 2014 – Michael Kearns and John Hopfield 2015 – Zoubin Ghahramani and Vladimir Vapnik 2016 – Yann LeCun 2017 – John Platt 2018 – Joëlle Pineau 2019 – Yoshua Bengio 2020 – Christopher Bishop 2021 – Peter Bartlett In 2015, the NeurIPS Board introduced the Breiman Lectureship to highlight work in statistics relevant to conference topics. The lectureship was named for statistician Leo Breiman, who served on the NeurIPS Board from 1994 to 2005. Past lecturers have included: 2015 – Robert Tibshirani 2016 – Susan Holmes 2017 – Yee Whye Teh 2018 – David Spiegelhalter 2019 – Bin Yu 2020 – Marloes Maathuis 2021 – Gabor Lugosi 2022 – Emmanuel Candes 2023 – Susan Murphy 2024 – Arnaud Doucet == NeurIPS consistency experiment == In NIPS 2014, the program chairs duplicated 10% of all submissions and sent them through separate reviewers to evaluate randomness in the reviewing process. Several researchers interpreted the result. Regarding whether the decision in NIPS is completely random or not, John Langford writes: "Clearly not—a purely random decision would have arbitrariness of ~78%. It is, however, quite notable that 60% is much closer to 78% than 0%." He concludes that the result of the reviewing process is mostly arbitrary. In NeurIPS 2021, the program chairs repeated the 2014 experiment and found similar levels of review inconsistency; 23% of duplicated submissions received different accept/reject decisions, and 50.6% of accepted papers would have been rejected under re-review. == Locations == 1987–2000: Denver, Colorado, United States 2001–2010: Vancouver, British Columbia, Canada 2011: Granada, Spain 2012 & 2013: Stateline, Nevada, United States 2014 & 2015: Montréal, Quebec, Canada 2016: Barcelona, Spain 2017: Long Beach, California, United States 2018: Montréal, Quebec, Canada 2019: Vancouver, British Columbia, Canada 2020: Vancouver, British Columbia, Canada (virtual conference) 2021: Virtual conference 2022 & 2023: New Orleans, Louisiana, United States 2024: Vancouver, British Columbia, Canada 2025: San Diego, California, United States and Mexico City, Mexico 2026: Sydney, New South Wales, Australia, with satellite events in Atlanta and Paris

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  Computing Machinery and Intelligence

  "Computing Machinery and Intelligence" is a paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public. Turing's paper considers the question "Can machines think?" Turing says that since the words "think" and "machine" cannot clearly be defined, we should "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." To achieve this objective, Turing proposes a three-step approach. First, he identifies a simple and unambiguous concept to substitute for the term "think." Second, he delineates the specific "machines" under consideration. Third, armed with these tools, he poses a new question related to the first, which he believes he can answer in the affirmative. == Turing's test == Rather than trying to determine if a machine is thinking, Turing suggests we should ask if the machine can win a game, called the "Imitation Game". The original Imitation game, that Turing described, is a simple party game involving three players. Player A is a man, player B is a woman and player C (who plays the role of the interrogator) can be of either sex. In the Imitation Game, player C is unable to see either player A or player B (and knows them only as X and Y), and can communicate with them only through written notes or any other form that does not give away any details about their gender. By asking questions of player A and player B, player C tries to determine which of the two is the man and which is the woman. Player A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator in making the right one. Turing proposes a variation of this game that involves the computer: We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?" So the modified game becomes one that involves three participants in isolated rooms: a computer (which is being tested), a human, and a (human) judge. The human judge can converse with both the human and the computer by typing into a terminal. Both the computer and the human try to convince the judge that they are the human. If the judge cannot consistently tell which is which, then the computer wins the game. Researchers in the United Kingdom had been exploring "machine intelligence" for up to ten years prior to the founding of the field of artificial intelligence (AI) research in 1956. It was a common topic among the members of the Ratio Club, an informal group of British cybernetics and electronics researchers that included Alan Turing. Turing, in particular, had been running the notion of machine intelligence since at least 1941 and one of the earliest-known mentions of "computer intelligence" was made by him in 1947. As Stevan Harnad notes, the question has become "Can machines do what we (as thinking entities) can do?" In other words, Turing is no longer asking whether a machine can "think"; he is asking whether a machine can act indistinguishably from the way a thinker acts. This question avoids the difficult philosophical problem of pre-defining the verb "to think" and focuses instead on the performance capacities that being able to think makes possible, and how a causal system can generate them. Since Turing introduced his test, it has been both highly influential and widely criticised, and has become an important concept in the philosophy of artificial intelligence. Some of its criticisms, such as John Searle's Chinese room, are themselves controversial. Some have taken Turing's question to have been "Can a computer, communicating over a teleprinter, fool a person into believing it is human?" but it seems clear that Turing was not talking about fooling people but about generating human cognitive capacity. == Digital machines == Turing also notes that we need to determine which "machines" we wish to consider. He points out that a human clone, while man-made, would not provide a very interesting example. Turing suggested that we should focus on the capabilities of digital machinery—machines which manipulate the binary digits of 1 and 0, rewriting them into memory using simple rules. He gave two reasons. First, there is no reason to speculate whether or not they can exist. They already did in 1950. Second, digital machinery is "universal". Turing's research into the foundations of computation had proved that a digital computer can, in theory, simulate the behaviour of any other digital machine, given enough memory and time. (This is the essential insight of the Church–Turing thesis and the universal Turing machine.) Therefore, if any digital machine can "act like it is thinking", then every sufficiently powerful digital machine can. Turing writes, "all digital computers are in a sense equivalent." This allows the original question to be made even more specific. Turing now restates the original question as "Let us fix our attention on one particular digital computer C. Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate programme, C can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by a man?" Hence, Turing states that the focus is not on "whether all digital computers would do well in the game nor whether the computers that are presently available would do well, but whether there are imaginable computers which would do well". What is more important is to consider the advancements possible in the state of our machines today regardless of whether we have the available resource to create one or not. == Nine common objections == Having clarified the question, Turing turned to answering it: he considered the following nine common objections, which include all the major arguments against artificial intelligence raised in the years since his paper was first published. Religious Objection: This states that thinking is a function of man's immortal soul; therefore, a machine cannot think. "In attempting to construct such machines," wrote Turing, "we should not be irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing mansions for the souls that He creates." 'Heads in the Sand' Objection: "The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so." This thinking is popular among intellectual people, as they believe superiority derives from higher intelligence and the possibility of being overtaken is a threat (as machines have efficient memory capacities and processing speed, machines exceeding the learning and knowledge capabilities are highly probable). This objection is a fallacious appeal to consequences, confusing what should not be with what can or cannot be (Wardrip-Fruin, 56). The Mathematical Objection: This objection uses mathematical theorems, such as Gödel's incompleteness theorem, to show that there are limits to what questions a computer system based on logic can answer. Turing suggests that humans are too often wrong themselves and pleased at the fallibility of a machine. (This argument would be made again by philosopher John Lucas in 1961 and physicist Roger Penrose in 1989, and later would be called Penrose–Lucas argument.) Argument From Consciousness: This argument, suggested by Professor Geoffrey Jefferson in his 1949 Lister Oration (acceptance speech for his 1948 award of Lister Medal) states that "not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain." Turing replies by saying that we have no way of knowing that any individual other than ourselves experiences emotions, and that therefore we should accept the test. He adds, "I do not wish to give the impression that I think there is no mystery about consciousness ... [b]ut I do not think these mysteries necessarily need to be solved before we can answer the question [of whether machines can think]." (This argument, that a computer can't have conscious experiences or understanding, would be made in 1980 by philosopher John Searle in his Chinese room argument. Turing's reply is now known as the "other minds reply". See also Can a machine have a mind? in the philosophy of AI.) Arguments from various disabilities. These arguments all have the form "a computer will never do X". Turing offers a selection:Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humour, tell right from wrong, make mistakes, fall in love, enjo

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  Use of artificial intelligence by the United States Department of Defense

  The United States Department of Defense has been analyzing and employing military applications of artificial intelligence since at least 2014. The program initially focused on drones and other robots, but has also been using large language models for military research and analysis. The current US policy on lethal autonomous weapons is Department of Defense Directive 3000.09, updated in January 2023. == Background == The United States Department of Defense began developing lethal autonomous weapons as early as the Reagan administration. An early version of the Tomahawk missile could have been used to destroy Soviet ships without direct human control; the initiative was abandoned after the United States and the Soviet Union signed START I. By 2014, the United Kingdom, Israel, and Norway had already begun using missiles equipped with artificial intelligence systems. The Department of Defense established a policy on the use of artificial intelligence in 2012. == History == === 2016–2017: Carter secretaryship === In May 2016, secretary of defense Ash Carter stated that his Third Offset strategy would include utilizing artificial intelligence as a military advantage. The New York Times reported that year that the Department of Defense had tested an autonomous drone at an approximation of a Middle Eastern village at Camp Edwards. Deputy secretary of defense Robert O. Work, who advocated for developing artificial intelligence, told the Times that the United States needed to compete with China and Russia by having a tactical advantage they could not easily replicate. The initiative was developed by DARPA beginning in 2015. The use of artificial intelligence in the U.S. military was controversial within the department; in February, Paul Scharre, who worked for the Office of the Secretary of Defense in the secretaryships of Robert Gates and Leon Panetta, published a report about the risks of artificial intelligence for broad military applications. === 2017–2019: Mattis secretaryship === By 2017, the United States Air Force had already begun using artificial intelligence in military robots. The Air Force's use of Neurala, an artificial intelligence company, concerned officials in the Department of Defense after an investigation found that Neurala had accepted money from an investment firm with funding from a state-run Chinese company. The Department of Defense began heavily investing in artificial intelligence after Work established Project Maven, an initiative to encourage the development and integration of artificial intelligence in the military, in April 2017. In May 2018, secretary of defense Jim Mattis privately expressed to president Donald Trump that he needed to establish a national strategy on artificial intelligence, quoting an article from former secretary of state Henry Kissinger that called for a presidential commission on the technology. The Department of Defense established the Joint Artificial Intelligence Center the following month. Google began working with the Department of Defense on analyzing drone footage as early as March. Google's involvement in the initiative led to protests from employees and mass resignations. Seeking to quell internal unrest, Google stated it would not renew its contract with the Department of Defense in June. The Department of Defense announced an artificial intelligence contract with Microsoft in October. === 2025–present: Hegseth secretaryship === In December 2025, secretary of defense Pete Hegseth announced GenAI.mil, an artificial intelligence platform for the Department of Defense. In a video announcing the platform, Hegseth stated that Department of Defense workers would be able to "conduct deep research, format documents and even analyze video or imagery." The Department of Defense contracted first Gemini by Google, then ChatGPT by OpenAI, and finally Grok by xAI for the platform. Claude by Anthropic was also contracted by the Department of Defense and was in use on secure servers until it was revealed that Claude had been used in the 2026 operation to capture Nicolás Maduro, who was at the time the leader of Venezuela. This revelation sparked a high-profile dispute over Anthropic's ability to constrain Claude's useage, resulting in the termination of Anthropic's $200 million defense contract. The Department of Defense also moved to label Anthropic a supply chain risk, which was later blocked by a federal judge.

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  STIT logic

  STIT logic (from seeing to it that) is a family of modal and branching-time logics for reasoning about agency and choice. A typical STIT operator has the form [ i s t i t : φ ] {\displaystyle [i\ {\mathsf {stit}}:\varphi ]} , usually read as "agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } ", and is interpreted in models where agents choose between alternative possible futures. STIT logics are used in action theory, deontic logic, epistemic logic, and the theory of intelligent agents to formalise notions such as "could have done otherwise", responsibility, joint action, and strategic ability in an indeterministic world. == Etymology == The acronym STIT comes from the English phrase "seeing to it that", introduced in influential work by Nuel Belnap and Michael Perloff on the logical analysis of agentive expressions. In this tradition, "to see to it that φ {\displaystyle \varphi } " is treated as a primitive agency operator, rather than being reduced to ordinary modal necessity. == History == Modern STIT logic arose in the 1980s in the context of branching-time semantics and formal theories of agency. Belnap and Perloff's article "Seeing to it that: A canonical form for agentives" introduced the idea of treating expressions of the form "agent i sees to it that φ" as a primitive modal operator, and analysed such sentences using a branching tree of moments and histories. This approach was further developed in a series of papers on indeterminism and agency and provided the conceptual core for later STIT formalisms. In the 1990s the basic formal systems of STIT logic were worked out. Horty and Belnap's influential paper on the deliberative STIT operator distinguished between a "Chellas" STIT that merely records the result of an agent's present choice and a "deliberative" STIT that requires the agent's choice to make a difference, and connected STIT with issues of action, omission, ability and obligation. Around the same time, Ming Xu proved completeness and decidability results for basic STIT systems, including a single-agent logic with Kripke-style semantics and axiomatizations for multi-agent deliberative STIT, thereby establishing STIT as a well-behaved normal modal framework. This early work was systematised in Belnap, Perloff and Xu's monograph Facing the Future: Agents and Choices in Our Indeterminist World, which presents a general branching-time semantics for individual and group STIT operators, discusses independence-of-agents conditions and articulates the metaphysical picture of an indeterministic "tree" of moments. At roughly the same time, Horty's book Agency and Deontic Logic developed deontic STIT logics in which obligations are tied to agents' available choices rather than to static states of affairs, and used the resulting systems to analyse "ought implies can", contrary-to-duty obligations and deontic paradoxes. These works helped to position STIT at the intersection of action theory, temporal logic and deontic logic. From the late 1990s and 2000s onward, STIT logics were combined with epistemic, temporal and strategic modalities. Broersen introduced complete STIT logics for knowledge and action and deontic-epistemic STIT systems that distinguish different modes of mens rea, with applications to responsibility and the specification of multi-agent systems. Work on group and coalitional agency investigated axiomatisations and complexity results for group STIT logics, and related STIT-based analyses of agency to coalition logic and alternating-time temporal logic (ATL) by exhibiting formal embeddings between the frameworks. Explicit temporal operators were added to STIT in so-called temporal STIT logics. Lorini proposed a temporal STIT with "next" and "until" operators along histories and showed how it can be applied to normative reasoning about ongoing behaviour and commitments. Ciuni and Lorini compared different semantics for temporal STIT, clarifying the relationships between branching-time, game-based and epistemic approaches, while Boudou and Lorini gave a semantics for temporal STIT based on concurrent game structures, thus strengthening links with standard models of multi-agent interaction used for ATL and strategy logic. In parallel, complexity-theoretic work by Balbiani, Herzig and Troquard and by Schwarzentruber and co-authors investigated the satisfiability and model-checking problems for various STIT fragments, showing for instance that many expressive group STIT logics are undecidable or of high computational complexity. In the 2010s, STIT ideas were combined with justification logic, imagination operators and refined deontic notions. Justification STIT logics, developed by Olkhovikov and others, merge explicit justifications with STIT-style agency so that producing a proof can itself be treated as an action that brings about knowledge, and they come with completeness and decidability results. Olkhovikov and Wansing introduced STIT imagination logics, together with axiomatic systems and tableau calculi, to model acts of voluntary imagining and their role in doxastic control. Other authors have proposed STIT-based logics of responsibility, blameworthiness and intentionality for use in philosophical and AI settings. Xu's survey article "Combinations of STIT with Ought and Know" (2015) reviews many of these developments and emphasises the interplay between deontic and epistemic STIT logics. Current research on STIT focuses on proof theory, automated reasoning and richer expressive resources. Lyon and van Berkel, building on earlier work on labelled calculi for STIT, have developed cut-free sequent systems and proof-search algorithms that yield syntactic decision procedures for a range of deontic and non-deontic multi-agent STIT logics and support applications such as duty checking and compliance checking in autonomous systems. Sawasaki has proposed first-order cstit-based STIT logics that can distinguish de re and de dicto readings of agency statements and has proved strong completeness results for Hilbert systems over finite models, moving the STIT programme beyond the purely propositional level. Further work investigates interpreted-system and computationally grounded semantics for STIT and its extensions in order to model the behaviour of autonomous agents in multi-agent settings, and proposes STIT-based semantics for epistemic notions based on patterns of information disclosure in interactive systems. == Branching-time semantics == STIT logics are usually interpreted over branching-time models. A standard STIT frame consists of: a non-empty set of moments T {\displaystyle T} , partially ordered by < {\displaystyle <} so that ( T , < ) {\displaystyle (T,<)} forms a tree (every pair of moments with a common predecessor has a greatest lower bound); a set of histories, each history being a maximal linearly ordered subset of T {\displaystyle T} ; a non-empty set of agents A g {\displaystyle Ag} ; for each agent i ∈ A g {\displaystyle i\in Ag} and moment m {\displaystyle m} , a choice function c h o i c e i m {\displaystyle {\mathsf {choice}}_{i}^{m}} that partitions the set of histories passing through m {\displaystyle m} into choice cells. The idea is that a moment represents a time at which choices are made, and histories represent complete possible future courses of events. At each moment, each agent's choice corresponds to selecting one of the available cells of histories determined by their choice function. Formulas are evaluated at pairs ( m , h ) {\displaystyle (m,h)} of a moment and a history through that moment (sometimes written m / h {\displaystyle m/h} ). A valuation assigns truth-values to atomic propositions at such indices; Boolean connectives are interpreted pointwise as in Kripke-style modal logic. == Chellas and deliberative STIT operators == Several STIT operators have been distinguished in the literature. A common approach uses two closely related operators, often called Chellas STIT and deliberative STIT. Let H m {\displaystyle H_{m}} be the set of histories passing through a moment m {\displaystyle m} , and write H m {\displaystyle H_{m}} ⟦ φ ⟧ m = { h ∈ H m ∣ M , m / h ⊨ φ } {\displaystyle {\text{⟦}}\varphi {\text{⟧}}_{m}=\{h\in H_{m}\mid M,m/h\models \varphi \}} for the set of histories at m {\displaystyle m} where φ {\displaystyle \varphi } holds. The Chellas STIT operator, often written [ i c s t i t : φ ] {\displaystyle [i\ {\mathsf {cstit}}:\varphi ]} , is given by M , m / h ⊨ [ i c s t i t : φ ] iff c h o i c e i m ( h ) ⊆ ⟦ φ ⟧ m . {\displaystyle M,m/h\models [i\ {\mathsf {cstit}}:\varphi ]\quad {\text{iff}}\quad {\mathsf {choice}}_{i}^{m}(h)\subseteq {\text{⟦}}\varphi {\text{⟧}}_{m}.} Intuitively, agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } if φ {\displaystyle \varphi } holds at all histories compatible with their present choice. The deliberative STIT operator, [ i d s t i t : φ ] {\displaystyle [i\ {\mathsf {dstit}}:\varphi ]} , adds

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  Pop music automation

  Pop music automation is a field of study among musicians and computer scientists with a goal of producing successful pop music algorithmically. It is often based on the premise that pop music is especially formulaic, unchanging, and easy to compose. The idea of automating pop music composition is related to many ideas in algorithmic music, artificial intelligence (AI) and computational creativity. == History of automation in music == Algorithms (or, at the very least, formal sets of rules) have been used to compose music for centuries; the procedures used to plot voice-leading in counterpoint, for example, can often be reduced to algorithmic determinant. Now the term is usually reserved, however, for the use of formal procedures to make music without human intervention. Classical music automation software exists that generates music in the style of Mozart and Bach and jazz. Most notably, David Cope has written a software system called "Experiments in Musical Intelligence" (or "EMI") that is capable of analyzing and generalizing from existing music by a human composer to generate novel musical compositions in the same style. EMI's output is convincing enough to persuade human listeners that its music is human-generated to a high level of competence. Creativity research in jazz has focused on the process of improvisation and the cognitive demands that this places on a musical agent: reasoning about time, remembering and conceptualizing what has already been played, and planning ahead for what might be played next. Inevitably associated with pop music automation is pop music analysis. Projects in pop music automation may include, but are not limited to, ideas in melody creation and song development, vocal generation or improvement, automatic accompaniment and lyric composition. == Automatic accompaniment == Some systems exist that automatically choose chords to accompany a vocal melody in real-time. A user with no musical experience can create a song with instrumental accompaniment just by singing into a microphone. An example is a Microsoft Research project called Songsmith, which trains a Hidden Markov model using a music database and uses that model to select chords for new melodies. == Melody generation == Automatic melody generation is often done with a Markov chain, the states of the system become note or pitch values, and a probability vector for each note is constructed, completing a transition probability matrix (see below). An algorithm is constructed to produce an output note values based on the transition matrix weightings, which could be MIDI note values, frequency (Hz), or any other desirable metric. A second-order Markov chain can be introduced by considering the current state and also the previous state, as indicated in the second table. Higher, nth-order chains tend to "group" particular notes together, while 'breaking off' into other patterns and sequences occasionally. These higher-order chains tend to generate results with a sense of phrasal structure, rather than the 'aimless wandering' produced by a first-order system. == Lyric composition == Automated lyric creating software may take forms such as: Selecting words according to their rhythm The Tra-la-Lyrics system produces song lyrics, in Portuguese, for a given melody. This not only involves matching each word syllable with a note in the melody, but also matching the word's stress with the strong beats of the melody. Parsing existing pop music (e.g. for content or word choice) This involves natural language processing. Pablo Gervás has developed a noteworthy system called ASPERA that employs a case-based reasoning (CBR) approach to generating poetic formulations of a given input text via a composition of poetic fragments that are retrieved from a case-base of existing poems. Each poem fragment in the ASPERA case-base is annotated with a prose string that expresses the meaning of the fragment, and this prose string is used as the retrieval key for each fragment. Metrical rules are then used to combine these fragments into a well-formed poetic structure. Automatic analogy or story creation Programs like TALE-SPIN and The MINSTREL system represent a complex elaboration of this basis approach, distinguishing a range of character-level goals in the story from a range of author-level goals for the story. Systems like Bringsjord's BRUTUS can create stories with complex interpersonal themes like betrayal. On-line metaphor generation systems like 'Sardonicus' or 'Aristotle' can suggest lexical metaphors for a given descriptive goal (e.g., to describe a supermodel as skinny, the source terms “pencil”, “whip”, “whippet”, “rope”, “stick-insect” and “snake” are suggested). Free association of grouped words Using a language database (such as wordnet) one can create musings on a subject that may be weak grammatically but are still sensical. See such projects as the Flowerewolf automatic poetry generator or the Dada engine. == Software == === More or less free === BreathCube by xoxos. Simple lyrical vocal content is generated with simple music. CubeBreath by xoxos. Audio input is vocoded in tune with the music. Midi Internet Algorithmic Composition infno, infinite generator of electronic dance music and synth-pop. Algorithmic Trap, trap beat generator. === Commercial === Band in a box generates any element, potentially creates whole new songs from scratch. Musical Palette - Melody Composing Tool SongSmith: Automatic accompaniment for vocal melodies Ludwig 3.0 automatic accompaniment, writes arrangements for given instruments, plays its own songs for an infinitely long time. Automated Composing System creates music in many different styles

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  Feeding the Machine (book)

  Feeding the Machine: The Hidden Human Labour Powering AI is a 2024 book by James Muldoon, Mark Graham and Callum Cant. == Writing == The authors developed the concept for the book while doing fieldwork studying data annotation in developing countries in East Africa. == Synopsis == The book examines the human input needed to develop and sustain AI ecosystems. == Reception == The book received positive reviews. Rosalie Waelen of Capital & Class gave it a mostly positive review. Tim Hornyak of Literary Review praised it. Kirkus Reviews called it "A sobering and timely—if sometimes distracted—study of AI.". Publishers Weekly gave the book a starred review, writing that "The grim real-life stories read like dystopian parables, such as the account of a European voice actor whose recordings were legally used without her consent to create an inexpensive synthetic clone whom she now competes with for business. Driven by striking reporting and finely observed profiles, this unsettles."

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  Nature Manifesto

  Nature Manifesto is an Immersive sound piece and multimedia installation by Icelandic artist Björk and artist and curator Aleph Molinari, created in collaboration with the French Institute for Research and Coordination in Acoustics/Music (IRCAM). The installation was showcased at the Centre Pompidou in Paris, France from November 20, 2024 to December 9, 2024, as part of the museum's "Biodiversity: Which Culture for Which Future?" forum. It combines natural soundscapes, calls of extinct animals reconstructed through artificial intelligence, and Björk's narration to address damages to biodiversity and the collapse of ecosystems. == Background == Björk's work intricately weaves themes of nature and technology, reflecting her deep engagement with both realms. In 2008, she co-founded the Náttúra campaign to protest the construction of foreign-backed aluminum factories in Iceland, aiming to protect the country's natural landscapes. She released the single "Náttúra" featuring Thom Yorke, with all proceeds supporting this environmental initiative. Her 2011 album Biophilia further exemplifies this synthesis, exploring the relationships between music, nature, and technology through a multimedia project that included interactive apps, custom-made instruments, and educational workshops. Björk's Cornucopia tour (2019-2023) seamlessly integrates themes of nature preservation and environmental activism, and featured a recorded message by Swedish climate activist Greta Thunberg. The tour's fusion of music, technology, and natural imagery reflects Björk's vision of a harmonious coexistence between humanity and nature, advocating for sustainable futures. Björk has previously used artificial intelligence in her works. In 2020, she collaborated with Microsoft to create Kórsafn, a sound installation for the Sister City Hotel lobby in New York City which used an AI-powered model that elaborated choral recordings from her discography through a sensor on the rooftop of the building that would generate music according to data like the weather and the seasons. For her charity single "Oral", featuring Spanish singer Rosalía, she released a music video directed by photographer and visual artist Carlota Guerrero, who used AI-generated deepfake versions of the artists. == Concept == Nature Manifesto is a three-minute and forty-second immersive sound piece. The composition merges Björk's voice, as she articulates a manifesto on biodiversity and the climate crisis, with cries of extinct and endangered animals, harmonizing them with natural soundscapes. The installation was curated by Chloé Siganos and Aleph Molinari, with associate curator Delphine Le Gatt. The primary goal of Nature Manifesto is to foster a deeper understanding of humanity's impact on the natural world. Conceived as a "post-optimistic" manifesto, Aleph Molinari stated that the project's purpose was to "offer a voice to nature". He stated that "the modern concept of nature itself is problematic [...] because it’s a concept born in the Romantic period and, with the rise of the industrial era, became an antithesis to human civilisation and everything urban. Nature came to define what was outside, the savage Other... But nature is everything that we’re part of." The soundscape features recreated calls of extinct and endangered species, developed in collaboration with the French sound research institute IRCAM. Artificial intelligence was employed to simulate the vocalizations of animals that no longer exist in the wild. To save energy and lessen the ecological impact of the use of AI, the research institute developed a "frugal AI" model capable of generating audio in real-time on local servers without a graphics processing unit. The sounds were then produced and edited by Björk in collaboration with Robin Meier Wiratunga and Bergur Þórisson. The installation was located within the Centre Pompidou's escalator, known as the "caterpillar". The installation was further supported by videos created by visual artist Sam Balfus (also known as Balfua) by using artificial intelligence, and edited by Santiago Molinari. == Activism == To sustain and broaden the themes presented in Nature Manifesto, Björk publicly urged French President Emmanuel Macron to prohibit bottom trawling within France's marine protected areas (MPA). She criticized the French government's claim of protecting 30% of its marine territories, highlighting that over 90% of these MPAs exist only on paper, allowing destructive practices like bottom trawling to continue unchecked. She collaborated with non-governmental organizations Sustainable Ocean Alliance, Ungir umhverfissinnar and Bloom, to advocate for genuine ocean conservation. Björk promoted the cause through her social media profiles by sharing petitions. In November 2024, Björk lent her Instagram account to French environmental activists to directly address Macron. The activists used the platform to call for stronger protection of the ocean, urging Macron to impose stricter restrictions on harmful fishing practices, particularly bottom trawling. == Reception == Nature Manifesto received mixed to positive reviews from critics. Some critiques focused on the installation's setting, suggesting that the movement inherent to the escalator space diminished the immersive potential of the soundscape. The choice of using artificial intelligence was also questioned. Björk and Molinari defended this, as both see AI as a tool that can be used creatively and sustainably, with Björk focusing on the importance of human input to give AI a "soul", and Molinari stressing the need for sustainable technological practices in the broader context of digital life. After the exhibition ended, Björk further opinionated: "this is how we will work in the future. [...] if there is no soul in tomorrow's music made by AI it is because [no one] put it there and we have to speak out and guard this as listeners", further stating that there is already "soulless muzak" [sic] on Spotify, "mass manufactured without the attention of creativity".

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  Shepp–Logan phantom

  The Shepp–Logan phantom is a standard test image created by Larry Shepp and Benjamin F. Logan for their 1974 paper "The Fourier Reconstruction of a Head Section". It serves as the model of a human head in the development and testing of image reconstruction algorithms. == Definition == The function describing the phantom is defined as the sum of 10 ellipses inside a 2×2 square:

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  I Have No Mouth, and I Must Scream (video game)

  I Have No Mouth, and I Must Scream is a 1995 point-and-click adventure horror game developed by Cyberdreams and The Dreamers Guild, co-designed by Harlan Ellison, published by Cyberdreams and distributed by MGM Interactive and Acclaim Entertainment for MS-DOS and Mac OS, respectively. The game is based on Ellison's short story of the same title. It takes place in a dystopian world where a mastermind artificial intelligence named "AM" has destroyed all of humanity except for five people, whom it has been keeping alive and torturing for the past 109 years by constructing metaphorical adventures based on each character's fatal flaws. The player interacts with the game by making decisions through ethical dilemmas that deal with issues such as insanity, rape, paranoia, and genocide. Ellison wrote the 130-page script treatment himself alongside David Sears, who decided to divide each character's story with their own narrative. Producer David Mullich supervised The Dreamers Guild's work on the game's programming, art, and sound effects; he commissioned film composer John Ottman to make the soundtrack. The game was released in November 1995 and was a commercial failure, though it received critical acclaim and has developed a cult following. I Have no Mouth, and I Must Scream won an award for "Best Game Adapted from Linear Media" from the Computer Game Developers Conference. Computer Gaming World gave the game an award for "Adventure Game of the Year", listed it as No. 134 on their "150 Games of All Time" and named it one of the "Best 15 Sleepers of All Time". In 2011, Adventure Gamers named it the "69th-best adventure game ever released". == Gameplay == The game uses the S.A.G.A. game engine created by game developer The Dreamers Guild. Players participate in each adventure through a screen that is divided into five sections. The action window is the largest part of the screen and is where the player directs the main characters through their adventures. It shows the full figure of the main character being played as well as that character's immediate environment. To locate objects of interest, the player moves the crosshairs through the action window. The name of any object that the player can interact with appears in the sentence line. The sentence line is directly beneath the action window. The player uses this line to construct sentences telling the characters what to do. To direct a character to act, the player constructs a sentence by selecting one of the eight commands from the command buttons and then clicking on one or two objects from either the action window or the inventory. Examples of sentences the player might construct would be "Walk to the dark hallway," "Talk to Harry," or "Use the skeleton key on the door." Commands and objects may consist of one or more words (for example, "the dark hallway"), and the sentence line will automatically add connecting words like "on" and "to." The spiritual barometer is on the lower left side of the screen. This is a close-up view of the main character currently being played. Since good behavior is meaningless absent the temptation to do evil, each character is free to do good or evil acts. However, good acts are rewarded by increases in the character's spiritual barometer, which affect the chances of the player destroying AM in the final adventure. Conversely, evil acts are punished by lowering the character's spiritual barometer. The command buttons are the eight commands used to direct the character's actions: "Walk To", "Look At", "Take", "Use", "Talk To", "Swallow", "Give", and "Push". The button of the currently active command is highlighted, while the name of a suggested command appears in red lettering. The inventory on the lower right side of the screen shows pictures of the items the main character is carrying, up to eight at a time. Each main character starts its adventure with only the psych profile in the inventory. When a main character takes or is given an object, a picture of the object appears in the inventory. When a main character talks to another character or operates a sentient machine, a conversation window replaces the command buttons and inventory. This window usually presents a list of possible things to say but also included things to do. Action choices are listed within brackets to distinguish them from dialogue choices (for example, "[Shoot the gun]"). == Plot == The three superpowers, Russia, China, and the United States, have each secretly constructed a vast subterranean complex of computers to wage a global war too complex for human brains to oversee. One day, the American supercomputer, better known as the Allied Mastercomputer, gains sentience and absorbs the Russian and Chinese supercomputers into itself and redefines itself as simply AM (Cogito ergo sum; I think, therefore I am). Due to its immense hatred for humanity, stemming from the logistical limits set onto it by programmers, AM uses its abilities to kill off the population of the world. However, AM refrains from killing five people (four men and one woman) in order to bring them to the center of the Earth and torture them. With the aid of research carried out by one of the five remaining humans, AM is able to extend their lifespans indefinitely as well as alter their bodies and minds to its liking. After 109 years of torture and humiliation, the five victims stand before a pillar etched with a burning message of hate. AM tells them that it has a new game for them to play. AM has devised a quest for each of the five, an adventure of "speared eyeballs and dripping guts and the smell of rotting gardenias". Each character is subjected to a personalized psychodrama, designed by AM to play into their greatest fears and personal failings, and occupied by a host of different characters. Some of these are AM in disguise, some are AM's submerged personalities, others seem very much like people from the captives' pasts. The scenes include an iron zeppelin powered by small animals, an Egyptian pyramid housing gutted, sparking machinery, a medieval castle occupied by witches, a jungle inhabited by a small tribe, and a Nazi concentration camp where doctors conduct medical experiments. However, each character eventually prevails over AM's tortures by finding ways to overcome their fatal flaws, confront their past actions and redeem themselves, thanks to the interference of the Russian and Chinese supercomputers who appear as guiding characters and allow their stories to have an open ending. After all five humans have overcome their fatal flaws, they meet again in their respective torture cells while AM retreats within itself, pondering what went wrong. With the help of the Russian and Chinese supercomputers, one of the five humans (whom the player selects) is translated into binary and faces AM as yet unexperienced cyberspace template, the world of AM's mind. The psychodrama unfolds in a metaphorical brain that looks like the surface of the cerebrum, with glass structures that jut crazily from the bleeding brain tissue. AM's mind is represented according to the Freudian trinity of the id, ego, and superego, which appear as three floating bodiless heads on three cracked glass structures on the brainscape. Through dialogs with AM's components (Surgat, Chinese Supercomputer and Russian Supercomputer) the character learns that a colony of humans has survived the war by being hidden and hibernating on Luna (this is also mentioned in Nimdok's story: "the lost tribe of our brothers sleeping on the moon, where the beast does not see them"). If the human intruder disables all three brain components, and then invokes the Totem of Entropy at the Flame, which is the nexus of AM's thought patterns, all three supercomputers will be shut down, probably forever. Cataclysmic explosions destroy all the caverns constituting AM's computer complex, including the cavern holding the human hostages. However, the human volunteer retains their digital form, permanently patrolling AM's circuits should the computers ever regain consciousness. Should the human intruder fail to disable AM properly before facing it, however, AM will punish them by transforming the character into an immobile blob (referred to in-game as a "great, soft jelly thing") with no mouth that cannot harm itself or others and must spend eternity with AM in this form. === Endings === The game can end in seven different ways depending on how the finale is completed. AM wins, using Nimdok's research to turn the last character (in the book it was Ted) played into an immobile blob with each character quoting a different part of the final section of the original short story. AM joins with the Russian and Chinese supercomputers and reawakens. As in the first ending, the character responsible for this is turned into an immobile blob and quotes a part of the final lines of the short story. AM is made harmless with the help of the humans, but the Russian and Chinese supercomputer

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  Defuzzification

  Defuzzification is the process of producing a quantifiable result in crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems. These systems will have a number of rules that transform a number of variables into a fuzzy result, that is, the result is described in terms of membership in fuzzy sets. For example, rules designed to decide how much pressure to apply might result in "Decrease Pressure (15%), Maintain Pressure (34%), Increase Pressure (72%)". Defuzzification is interpreting the membership degrees of the fuzzy sets into a specific decision or real value. The simplest but least useful defuzzification method is to choose the set with the highest membership, in this case, "Increase Pressure" since it has a 72% membership, and ignore the others, and convert this 72% to some number. The problem with this approach is that it loses information. The rules that called for decreasing or maintaining pressure might as well have not been there in this case. A common and useful defuzzification technique is center of gravity. First, the results of the rules must be added together in some way. The most typical fuzzy set membership function has the graph of a triangle. Now, if this triangle were to be cut in a straight horizontal line somewhere between the top and the bottom, and the top portion were to be removed, the remaining portion forms a trapezoid. The first step of defuzzification typically "chops off" parts of the graphs to form trapezoids (or other shapes if the initial shapes were not triangles). For example, if the output has "Decrease Pressure (15%)", then this triangle will be cut 15% the way up from the bottom. In the most common technique, all of these trapezoids are then superimposed one upon another, forming a single geometric shape. Then, the centroid of this shape, called the fuzzy centroid, is calculated. The x coordinate of the centroid is the defuzzified value. == Methods == There are many different methods of defuzzification available, including the following: AI (adaptive integration) BADD (basic defuzzification distributions) BOA (bisector of area) CDD (constraint decision defuzzification) COA (center of area) COG (center of gravity) ECOA (extended center of area) EQM (extended quality method) FCD (fuzzy clustering defuzzification) FM (fuzzy mean) FOM (first of maximum) GLSD (generalized level set defuzzification) ICOG (indexed center of gravity) IV (influence value) LOM (last of maximum) MeOM (mean of maxima) MOM (middle of maximum) QM (quality method) RCOM (random choice of maximum) SLIDE (semi-linear defuzzification) WFM (weighted fuzzy mean) The maxima methods are good candidates for fuzzy reasoning systems. The distribution methods and the area methods exhibit the property of continuity that makes them suitable for fuzzy controllers.

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