AI Generator Song Maker

AI Generator Song Maker — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Eager learning

    Eager learning

    In artificial intelligence, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. The main advantage gained in employing an eager learning method, such as an artificial neural network, is that the target function will be approximated globally during training, thus requiring much less space than using a lazy learning system. Eager learning systems also deal much better with noise in the training data. Eager learning is an example of offline learning, in which post-training queries to the system have no effect on the system itself, and thus the same query to the system will always produce the same result. The main disadvantage with eager learning is that it is generally unable to provide good local approximations in the target function.

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  • Minne Atairu

    Minne Atairu

    Minne Atairu is a Nigerian interdisciplinary artist, a recipient of the 2021 Global South Award Lumen Prize for Art and Technology. She generates synthetic Benin Bronzes through recombination of historical fragments, sculptures, texts, images, and sounds. == Early life and education == Atairu was born in Benin, Nigeria. She holds a bachelor's degree in art history from the University of Maiduguri in Maiduguri, Nigeria; a master's degree in museum studies from the George Washington University in Washington, D.C.; and a doctorate in art education from Teachers College, Columbia University in New York City. Her academic research integrates artificial intelligence, art/museum education and hip-hop based education. == Works == Atairu's artmaking involves using artificial intelligence (AI; such as StyleGAN, GPT-3) to make artwork. She uses tools such as Midjourney and Blender software to develop her works. === Mami Wata === Her first work is a Yoruba goddess called Mami Wata where she used Midjourney in generating the images. === To the Hand === For her 2023 installation To the Hand at The Shed arts center, she worked with Blender to convert text into 3D-printed sculptures made of corn starch or sugarcane infused with bronze. The rings of ground terra-cotta that surround the sculpture represent the walls and deep moats of Benin. == Publications == Atairu, Minne (February 1, 2024). "Reimagining Benin Bronzes using generative adversarial networks". AI & Society. 39 (1): 91–102. doi:10.1007/s00146-023-01761-7. ISSN 1435-5655.

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  • Proof assistant

    Proof assistant

    In computer science and mathematical logic, a proof assistant or interactive theorem prover is a software tool to assist with the development of formal proofs by human–machine collaboration. This involves some sort of interactive proof editor, or other interface, with which a human can guide the search for proofs, the details of which are stored in, and some steps provided by, a computer. A recent effort within this field is making these tools use artificial intelligence to automate the formalization of ordinary mathematics. == Automated proof checking == Automated proof checking is the process of using software for checking proofs for correctness. It is one of the most developed fields in automated reasoning. Automated proof checking differs from automated theorem proving in that automated proof checking simply mechanically checks the formal workings of an existing proof, instead of trying to develop new proofs or theorems itself. Because of this, the task of automated proof verification is much simpler than that of automated theorem proving, allowing automated proof checking software to be much simpler than automated theorem proving software. Because of this small size, some automated proof checking systems can have less than a thousand lines of core code, and are thus themselves amenable to both hand-checking and automated software verification. The Mizar system, HOL Light, and Metamath are examples of automated proof checking systems. Automated proof checking can be done either as a batch operation, or interactively, as part of an interactive theorem proving system. == History == Automath, which was developed by Nicolaas Govert de Bruijn starting in 1967, is often considered the first proof checker and the first system to utilize the Curry–Howard correspondence between programs and proofs. Automath was used by L.S. van Benthem Jutting in 1977 to formalize Landau's Foundations of Analysis, which was the first formalization of the real numbers. In 1973, Robert Boyer and J Moore published Proving Theorems about LISP Functions which aimed to verify programs, not mathematics. Their theorem prover is now known as ACL2. In the 1970s, Edinburgh LCF introduced the idea of using a functional programming language as the metalanguage for a theorem prover, and led to the HOL family of proof assistants. The 1990s saw the rise of Rocq, (then known as Coq), which has been used for many large-scale formalization projects. Since the late 2010s, Lean, a proof assistant strongly influenced by Rocq, has become another popular choice, especially for formalizing mathematics. == System comparison == ACL2 – a programming language, a first-order logical theory, and a theorem prover (with both interactive and automatic modes) in the Boyer–Moore tradition. HOL theorem provers – A family of tools ultimately derived from the LCF theorem prover. In these systems, the logical core is a library of their programming language. Theorems represent new elements of the language and can only be introduced via "strategies" which guarantee logical correctness. Strategy composition gives users the ability to produce significant proofs with relatively few interactions with the system. Members of the family include: HOL4 – The "primary descendant", still under active development. Support for both Moscow ML and Poly/ML. Has a BSD-style license. HOL Light – A thriving "minimalist fork". OCaml based. ProofPower – Went proprietary, then returned to open source. Based on Standard ML. IMPS, An Interactive Mathematical Proof System. Isabelle is an interactive theorem prover where other systems can be encoded. Isabelle/HOL is its most popular instance, whose foundation is close to that of the HOL prover. Other instances include Isabelle/ZF and Isabelle/FOL. The main code-base is BSD-licensed, but the Isabelle distribution bundles many add-on tools with different licenses. Jape – Java based. Lean is both an interactive theorem prover and a functional, dependently-typed programming language. It is based on the calculus of inductive constructions with non-cumulative universes. Since version 4 (released in 2023), it is self-hosting. It can be used to formalise mathematics (and has a large, coherent library for formal mathematics), but also for software and hardware verification. LEGO Matita – A light system based on the calculus of inductive constructions. MINLOG – A proof assistant based on first-order minimal logic. Mizar – A proof assistant based on first-order logic, in a natural deduction style, and Tarski–Grothendieck set theory. PhoX – A proof assistant based on higher-order logic which is eXtensible. Prototype Verification System (PVS) – a proof language and system based on higher-order logic. Rocq (formerly named Coq) – A popular interactive theorem prover based on the calculus of inductive constructions. Theorem Proving System (TPS) and ETPS – Interactive theorem provers also based on simply typed lambda calculus, but based on an independent formulation of the logical theory and independent implementation. == User interfaces == A commonly used front-end for proof assistants was the Emacs-based Proof General, developed at the University of Edinburgh. Nowadays, many provers include their own editor. Rocq includes RocqIDE, which is based on OCaml/Gtk. Isabelle includes Isabelle/jEdit, which is based on jEdit and the Isabelle/Scala infrastructure for document-oriented proof processing. More recently, Visual Studio Code extensions have been developed for Rocq, Isabelle by Makarius Wenzel, and for Lean 4 by the leanprover developers. == Formalization extent == Freek Wiedijk has been keeping a ranking of proof assistants by the amount of formalized theorems out of a list of 100 well-known theorems. As of September 2025, only six systems have formalized proofs of more than 70% of the theorems, namely Isabelle, HOL Light, Lean, Rocq, Metamath and Mizar. == Notable formalized proofs == The following is a list of notable proofs that have been formalized within proof assistants.

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  • Sinewave synthesis

    Sinewave synthesis

    Sinewave synthesis, or sine wave speech, is a technique for synthesizing speech by replacing the formants (main bands of energy) with pure tone whistles. The first sinewave synthesis program (SWS) for the automatic creation of stimuli for perceptual experiments was developed by Philip Rubin at Haskins Laboratories in the 1970s. This program was subsequently used by Robert Remez, Philip Rubin, David Pisoni, and other colleagues to show that listeners can perceive continuous speech without traditional speech cues, i.e., pitch, stress, and intonation. This work paved the way for a view of speech as a dynamic pattern of trajectories through articulatory-acoustic space.

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  • Digital image

    Digital image

    A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with x, y on the x-axis and y-axis, respectively. An image can be vector or raster type. By itself, the term "digital image" usually refers to raster images or bitmapped images (as opposed to vector images). == Raster == Raster images have a finite set of digital values, called picture elements or pixels. The digital image contains a fixed number of rows and columns of pixels. Pixels are the smallest individual element in an image, holding quantized values that represent the brightness of a given color at any specific point. Typically, the pixels are stored in computer memory as a raster image or raster map, a two-dimensional array of small integers. These values are often transmitted or stored in a compressed form. Raster images can be created by a variety of input devices and techniques, such as digital cameras, scanners, coordinate-measuring machines, seismographic profiling, airborne radar, and more. They can also be synthesized from arbitrary non-image data, such as mathematical functions or three-dimensional geometric models; the latter being a major sub-area of computer graphics. The field of digital image processing is the study of algorithms for their transformation. === Raster file formats === Most users come into contact with raster images through digital cameras, which use any of several image file formats. Some digital cameras give access to almost all the data captured by the camera, using a raw image format. The Universal Photographic Imaging Guidelines (UPDIG) suggests these formats be used when possible since raw files produce the best quality images. These file formats allow the photographer and the processing agent the greatest level of control and accuracy for output. Their use is inhibited by the prevalence of proprietary information (trade secrets) for some camera makers, but there have been initiatives such as OpenRAW to influence manufacturers to release these records publicly. An alternative may be Digital Negative (DNG), a proprietary Adobe product described as "the public, archival format for digital camera raw data". Although this format is not yet universally accepted, support for the product is growing, and increasingly professional archivists and conservationists, working for respectable organizations, variously suggest or recommend DNG for archival purposes. == Vector == Vector images resulted from mathematical geometry (vector). In mathematical terms, a vector consists of both a magnitude, or length, and a direction. Often, both raster and vector elements will be combined in one image; for example, in the case of a billboard with text (vector) and photographs (raster). Example of vector file types are EPS, PDF, and AI. == Image viewing == Image viewer software displayed on images. Web browsers can display standard internet images formats including JPEG, GIF and PNG. Some can show SVG format which is a standard W3C format. In the past, when the Internet was still slow, it was common to provide "preview" images that would load and appear on the website before being replaced by the main image (to give a preliminary impression). Now Internet is fast enough and this preview image is seldom used. Some scientific images can be very large (for instance, the 46 gigapixel size image of the Milky Way, about 194 GB in size). Such images are difficult to download and are usually browsed online through more complex web interfaces. Some viewers offer a slideshow utility to display a sequence of images. == History == Early digital fax machines such as the Bartlane cable picture transmission system preceded digital cameras and computers by decades. The first picture to be scanned, stored, and recreated in digital pixels was displayed on the Standards Eastern Automatic Computer (SEAC) at NIST. The advancement of digital imagery continued in the early 1960s, alongside development of the space program and in medical research. Projects at the Jet Propulsion Laboratory, MIT, Bell Labs and the University of Maryland, among others, used digital images to advance satellite imagery, wirephoto standards conversion, medical imaging, videophone technology, character recognition, and photo enhancement. Rapid advances in digital imaging began with the introduction of MOS integrated circuits in the 1960s and microprocessors in the early 1970s, alongside progress in related computer memory storage, display technologies, and data compression algorithms. The invention of computerized axial tomography (CAT scanning), using x-rays to produce a digital image of a "slice" through a three-dimensional object, was of great importance to medical diagnostics. As well as origination of digital images, digitization of analog images allowed the enhancement and restoration of archaeological artifacts and began to be used in fields as diverse as nuclear medicine, astronomy, law enforcement, defence and industry. Advances in microprocessor technology paved the way for the development and marketing of charge-coupled devices (CCDs) for use in a wide range of image capture devices and gradually displaced the use of analog film and tape in photography and videography towards the end of the 20th century. The computing power necessary to process digital image capture also allowed computer-generated digital images to achieve a level of refinement close to photorealism. === Digital image sensors === The first semiconductor image sensor was the CCD, developed by Willard S. Boyle and George E. Smith at Bell Labs in 1969. While researching MOS technology, they realized that an electric charge was the analogy of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. The CCD is a semiconductor circuit that was later used in the first digital video cameras for television broadcasting. Early CCD sensors suffered from shutter lag. This was largely resolved with the invention of the pinned photodiode (PPD). It was invented by Nobukazu Teranishi, Hiromitsu Shiraki and Yasuo Ishihara at NEC in 1980. It was a photodetector structure with low lag, low noise, high quantum efficiency and low dark current. In 1987, the PPD began to be incorporated into most CCD devices, becoming a fixture in consumer electronic video cameras and then digital still cameras. Since then, the PPD has been used in nearly all CCD sensors and then CMOS sensors. The NMOS active-pixel sensor (APS) was invented by Olympus in Japan during the mid-1980s. This was enabled by advances in MOS semiconductor device fabrication, with MOSFET scaling reaching smaller micron and then sub-micron levels. The NMOS APS was fabricated by Tsutomu Nakamura's team at Olympus in 1985. The CMOS active-pixel sensor (CMOS sensor) was later developed by Eric Fossum's team at the NASA Jet Propulsion Laboratory in 1993. By 2007, sales of CMOS sensors had surpassed CCD sensors. === Digital image compression === An important development in digital image compression technology was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed in 1972. DCT compression is used in JPEG, which was introduced by the Joint Photographic Experts Group in 1992. JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format on the Internet. == Mosaic == In digital imaging, a mosaic is a combination of non-overlapping images, arranged in some tessellation. Gigapixel images are an example of such digital image mosaics. Satellite imagery are often mosaicked to cover Earth regions. Interactive viewing is provided by virtual-reality photography.

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  • Tip and cue

    Tip and cue

    Tip and cue, sometimes referred to as tip and que, tipping and cueing, or tipping and queing, is a method for satellite imagery and reconnaissance satellites to automatically coordinate tracking of objects across different satellites in real or near real-time. This technique ensures continuous tracking of targets as they move across different regions by handing them off between satellites, sharing satellite imagery and collateral across discrete satellites. The coordination between various satellites and their complementary sensors allows for more accurate and efficient data collection. This system is particularly useful in scenarios requiring real-time monitoring and rapid response; the method significantly improves situational awareness and operational effectiveness. Tip and cue techniques involve integrating various sensor systems, each playing a specific role in the tracking process. As a target moves, it is handed off from one satellite to another, ensuring continuous monitoring. This coordination optimizes data collection and analysis, enhancing overall tracking accuracy. The real-time information gathered by these satellites is critical for decision-making in various applications, including defense and surveillance. By leveraging multiple satellites and their sensors, it provides broader coverage and more reliable tracking, and the continuous handoff between satellites ensures there are no gaps in monitoring, essential for high-stakes applications. The real-time data provided by this system allows for timely and informed decisions, improving response times and outcomes. Tip and cue methodologies are a part of geospatial intelligence, or GEOINT. Robert Cardillo, a former director of the National Geospatial-Intelligence Agency, highlighted the importance of tip and cue methods to their data collection efforts in 2015. == Historical Development == The concept of tip and cue in satellite monitoring has its origins in early military applications designed to enhance missile detection and tracking systems. During the Cold War, advancements in infrared sensing technologies laid the groundwork for more sophisticated tip and cue techniques. The integration of different sensor types, such as radar and optical sensors, in the 1990s expanded the capabilities of tip and cue systems beyond military applications. These advancements have made tip and cue techniques essential for various civilian uses, including disaster monitoring and environmental surveillance. Significant progress was made with the advent of high-speed data processing and communication technologies in the early 2000s, further refining the method. Advanced algorithms and data fusion techniques have been introduced to better integrate information from multiple sensors. Machine learning technologies now play a crucial role in improving detection and prediction capabilities, allowing for more adaptive and efficient tracking. Richmond and Brennan of Lockheed Martin, presenting to the annual technical conference of the Maui Space Surveillance Complex (formerly the Air Force Maui Optical Station (AMOS)), discussed the algorithms needed for 'tip and cue', to facilitate "multi-phenomenology data fusion." The Space Surveillance Telescope (SST) at Naval Communication Station Harold E. Holt in Australia, operated by the United States Space Force and designed by the Massachusetts Institute of Technology Lincoln Laboratory, was reported by the Defense Advanced Research Projects Agency (DARPA) to be a leader in creating and improving tip and cue techniques, from a large library of orbital object data. == Technical overview == Tip and cue systems utilize a network of at least two satellites equipped with complementary sensor technologies to track moving objects in real-time. The method involves detecting a target with a primary sensor, such as an infrared or photographic sensor, which then cues secondary sensors on the same or other satellites for more detailed monitoring. This handoff process between discrete systems ensures continuous tracking as the target moves across different areas, leveraging each systems strengths. Data collected by these systems and sensors are rapidly processed and shared among the network, enhancing situational awareness. This coordination optimizes resource usage and improves the accuracy of tracking moving objects over large areas. The primary sensors detect initial targets based on specific signatures, such as heat or movement, and then cue secondary sensors to gather more precise data. This ensures that each sensor operates within its optimal range, maintaining high tracking accuracy and reliability. The integration of various sensor types, including optical, radar, and infrared, allows the system to function effectively under different conditions and environments. Real-time data processing and communication between satellites and ground stations are crucial for timely and accurate target tracking. Satellites using tip and cue processes may use either passive or active scanning methodoloigies. These systems may also leverage both orbital and ground-based ELINT (electronic signals intelligence). == Known use cases == Tip and cue systems have been extensively utilized in military applications, particularly for missile detection and defense. These systems enable early detection of missile launches using infrared sensors, which then cue other sensors to track the missile's trajectory more accurately. In environmental monitoring, tip and cue techniques help track natural disasters such as wildfires and hurricanes by coordinating various satellite sensors for comprehensive data collection and analysis. Surveillance and reconnaissance operations also benefit from tip and cue systems, which provide continuous and precise tracking of moving objects, enhancing situational awareness. Additionally, these systems are used in maritime surveillance to monitor ship movements and detect illegal activities such as smuggling and piracy. Tip and cue systems are used in disaster management. For instance, during wildfires, infrared sensors can detect heat signatures, prompting other sensors to gather detailed imagery and data on fire spread and intensity. This coordinated approach allows for real-time monitoring and rapid response, crucial for mitigating damage and saving lives. Similarly, in hurricane tracking, satellites equipped with various sensors can monitor storm development and progression, providing timely information for emergency management agencies. The integration of multiple sensor types ensures accurate and comprehensive coverage of these dynamic and fast-changing events. In maritime surveillance, or maritime domain awareness (MDA), tip and cue systems enhance the detection and monitoring of vessel movements, contributing to maritime security. By coordinating satellite sensors, these systems can track ships over vast ocean areas, identifying potential threats or illegal activities such as smuggling, piracy, and illegal fishing. The ability to maintain continuous surveillance and share data in real-time with maritime authorities improves response times and enforcement capabilities. This application of tip and cue systems not only aids in law enforcement but also supports environmental conservation efforts by monitoring protected marine areas. Automatic Identification System (AIS) is one of the most important sources of data for the MDA agencies. AIS is used in order for ships to know each other's whereabouts, they transmit a signal from ship to ship and to shore. Lately, the system has been developed into satellite system, so called satellite AIS, which makes the system more effective. All ocean-going vessels above 300 tons, are supposed to use and transmit via AIS according to the International Maritime Organization. The satellite constellations help facilitate this with tip and cue methodologies.

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  • DEAP (software)

    DEAP (software)

    Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic flow and estimation of distribution algorithm. It is developed at Université Laval since 2009. == Example == The following code gives a quick overview how the Onemax problem optimization with genetic algorithm can be implemented with DEAP.

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  • Argument mining

    Argument mining

    Argument mining, or argumentation mining, is a research area within the natural language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs. Such argumentative structures include the premise, conclusions, the argument scheme and the relationship between the main and subsidiary argument, or the main and counter-argument within discourse. The Argument Mining workshop series is the main research forum for argument mining related research. == Applications == Argument mining has been applied in many different genres including the qualitative assessment of social media content (e.g. Twitter, Facebook), where it provides a powerful tool for policy-makers and researchers in social and political sciences. Other domains include legal documents, product reviews, scientific articles, online debates, newspaper articles and dialogical domains. Transfer learning approaches have been successfully used to combine the different domains into a domain agnostic argumentation model. Argument mining has been used to provide students individual writing support by accessing and visualizing the argumentation discourse in their texts. The application of argument mining in a user-centered learning tool helped students to improve their argumentation skills significantly compared to traditional argumentation learning applications. == Challenges == Given the wide variety of text genres and the different research perspectives and approaches, it has been difficult to reach a common and objective evaluation scheme. Many annotated data sets have been proposed, with some gaining popularity, but a consensual data set is yet to be found. Annotating argumentative structures is a highly demanding task. There have been successful attempts to delegate such annotation tasks to the crowd but the process still requires a lot of effort and carries significant cost. Initial attempts to bypass this hurdle were made using the weak supervision approach.

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  • Behavior informatics

    Behavior informatics

    Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights. BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inference of future possible behaviors. This occurs through pattern recognition. Different from applied behavior analysis from the psychological perspective, BI builds computational theories, systems and tools to qualitatively and quantitatively model, represent, analyze, and manage behaviors of individuals, groups and/or organizations. BI is built on classic study of behavioral science, including behavior modeling, applied behavior analysis, behavior analysis, behavioral economics, and organizational behavior. Typical BI tasks consist of individual and group behavior formation, representation, computational modeling, analysis, learning, simulation, and understanding of behavior impact, utility, non-occurring behaviors, etc. for behavior intervention and management. The Behavior Informatics approach to data utilizes cognitive as well as behavioral data. By combining the data, BI has the potential to effectively illustrate the big picture when it comes to behavioral decisions and patterns. One of the goals of BI is also to be able to study human behavior while eliminating issues like self-report bias. This creates more reliable and valid information for research studies. == Behavior == From an Informatics perspective, a behavior consists of three key elements: actors (behavioral subjects and objects), operations (actions, activities) and interactions (relationships), and their properties. A behavior can be represented as a behavior vector, all behaviors of an actor or an actor group can be represented as behavior sequences and multi-dimensional behavior matrix. The following table explains some of the elements of behavior. Behavior Informatics takes into account behavior when analyzing business patterns and intelligence. The inclusion of behavior in these analyses provides prominent information on social and driving factors of patterns. == Applications == Behavior Informatics is being used in a variety of settings, including but not limited to health care management, telecommunications, marketing, and security. Behavior Informatics provides a manner in which to analyze and organize the many aspects that go into a person's health care needs and decisions. When it comes to business models, behavior informatics may be utilized for a similar role. Organizations implement behavior informatics to enhance business structure and regime, where it helps moderate ideal business decisions and situations.

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  • The Raimones

    The Raimones

    The Raimones (stylized as THE RAiMONES) is a 2017 generative music project that utilized artificial intelligence to compose music in the style of the American punk rock band The Ramones. Developed by Matthias Frey, a researcher at Sony CSL Tokyo, the project was an early experiment in applying deep learning to high-energy, minimalist musical genres. == Technical Development == The project utilized Long short-term memory (LSTM) recurrent neural networks to generate musical structures and lyrics. The model was trained on a dataset consisting of 130 Ramones songs in MIDI format and the band's complete lyrical catalog. The technical framework was built using Python and Jupyter Notebook, drawing influence from the character-level RNN text generation models popularized by Andrej Karpathy. Unlike contemporary AI music projects that focused on the harmonic complexities of classical or pop music, THE RAiMONES sought to determine if neural networks could replicate the "1-2-3-4" rhythmic consistency and formulaic nature of early punk. == "I'm Alive" == The primary output of the project was the song "I'm Alive," released in 2017. The work is described as a form of "augmented intelligence," a hybrid approach where the AI provides the compositional foundation and human musicians handle the arrangement and performance. The song was recorded by the musician Mr. Ratboy (Gilbert Avondet). Avondet's involvement provided a stylistic link to the subject material, as he had previously served as a touring guitarist for Marky Ramone and the Intruders in 1996. The project's discography has since been made available on major streaming platforms, including Apple Music. == Reception and Significance == The project has been cited as a "proof of concept" for AI's ability to tackle "noisy" and aggressive aesthetics. In 2019, the Belgian magazine Knack Focus profiled the project alongside other AI pioneers such as Holly Herndon, noting the project's attempt to recreate the sound of "deceased legends" while maintaining a distinct, machine-like quality. It has also been featured in academic settings, such as at UC Santa Cruz, as a case study for AI-driven genre mimicry.

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  • Evolutionary acquisition of neural topologies

    Evolutionary acquisition of neural topologies

    Evolutionary acquisition of neural topologies (EANT/EANT2) is an evolutionary reinforcement learning method that evolves both the topology and weights of artificial neural networks. It is closely related to the works of Angeline et al. and Stanley and Miikkulainen. Like the work of Angeline et al., the method uses a type of parametric mutation that comes from evolution strategies and evolutionary programming (now using the most advanced form of the evolution strategies CMA-ES in EANT2), in which adaptive step sizes are used for optimizing the weights of the neural networks. Similar to the work of Stanley (NEAT), the method starts with minimal structures which gain complexity along the evolution path. == Contribution of EANT to neuroevolution == Despite sharing these two properties, the method has the following important features which distinguish it from previous works in neuroevolution. It introduces a genetic encoding called common genetic encoding (CGE) that handles both direct and indirect encoding of neural networks within the same theoretical framework. The encoding has important properties that makes it suitable for evolving neural networks: It is complete in that it is able to represent all types of valid phenotype networks. It is closed, i.e. every valid genotype represents a valid phenotype. (Similarly, the encoding is closed under genetic operators such as structural mutation and crossover.) These properties have been formally proven. For evolving the structure and weights of neural networks, an evolutionary process is used, where the exploration of structures is executed at a larger timescale (structural exploration), and the exploitation of existing structures is done at a smaller timescale (structural exploitation). In the structural exploration phase, new neural structures are developed by gradually adding new structures to an initially minimal network that is used as a starting point. In the structural exploitation phase, the weights of the currently available structures are optimized using an evolution strategy. == Performance == EANT has been tested on some benchmark problems such as the double-pole balancing problem, and the RoboCup keepaway benchmark. In all the tests, EANT was found to perform very well. Moreover, a newer version of EANT, called EANT2, was tested on a visual servoing task and found to outperform NEAT and the traditional iterative Gauss–Newton method. Further experiments include results on a classification problem.

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  • Cruel World of Dreams and Fears

    Cruel World of Dreams and Fears

    Cruel World of Dreams and Fears is the debut album from Ukrainian-born Czech black metal artist Draugveil, released independently on 13 June 2025. The album became notable among metal fans due to its cover, featuring Draugveil in a suit of armour and corpse paint, and lying in a field of red roses. The cover was the subject of parodying internet memes, as well as accusations of using artificial intelligence (AI) to make it. These claims were later expanded to suggest that AI was used to make the album's music. == Memes and AI accusations == Upon the album being released on YouTube on the channel Black Metal Promotion, the album attracted attention due to its cover, depicting Draugveil lying in a field of roses, dressed in armour, wearing corpse paint and having a sword stuck in the ground. Some compared it to covers where other artists are lying on the ground, such as Michael Jackson's Thriller, Luther Vandross's Give Me the Reason, and the UK cover of Lionel Richie's You Are. Critics of the album, however, suggested that AI was used to make the cover. This was partly due to suggestions that the rose stems in the picture come out from the ground in an unrealistic way. This later resulted in claims from some fans that AI was also used to produce the music, and later the lyrics and vocals. These claims began on a Facebook page entitled "AI Generated Nonsense", which was later deleted. No definitive evidence, however, was produced to back these claims. Derek McArthur, a journalist for Glasgow-based newspaper The Herald, wrote: "The music is in line with what one would expect from a one-man black metal project in the vein of Judas Iscariot and Burzum, but then if AI was asked to create music in a black metal style, that is probably what it would decide to generically produce and spit out." Draugveil's reaction to the claims was: "Let people decide." The result of the claims of AI has led to some writers to claim that artists in the future will have to prove they are human to be taken seriously, and that members of the public will be increasing doubt as to whether creative works are produced by either humans or AI. == Track listing ==

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  • Cloud load balancing

    Cloud load balancing

    Cloud load balancing is a type of load balancing that is performed in cloud computing. Cloud load balancing is the process of distributing workloads across multiple computing resources. Cloud load balancing reduces costs associated with document management systems and maximizes availability of resources. It is a type of load balancing and not to be confused with Domain Name System (DNS) load balancing. While DNS load balancing uses software or hardware to perform the function, cloud load balancing uses services offered by various computer network companies. == Comparison With DNS load balancing == Cloud load balancing has an advantage over DNS load balancing as it can transfer loads to servers globally as opposed to distributing it across local servers. In the event of a local server outage, cloud load balancing delivers users to the closest regional server without interruption for the user. Cloud load balancing addresses issues relating to TTL reliance present during DNS load balancing. DNS directives can only be enforced once in every TTL cycle and can take several hours if switching between servers during a lag or server failure. Incoming server traffic will continue to route to the original server until the TTL expires and can create an uneven performance as different internet service providers may reach the new server before other internet service providers. Another advantage is that cloud load balancing improves response time by routing remote sessions to the best performing data centers. == Importance of Load Balancing == Cloud computing brings advantages in "cost, flexibility and availability of service users." Those advantages drive the demand for Cloud services. The demand raises technical issues in Service Oriented Architectures and Internet of Services (IoS)-style applications, such as high availability and scalability. As a major concern in these issues, load balancing allows cloud computing to "scale up to increasing demands" by efficiently allocating dynamic local workload evenly across all nodes. == Load Balancing Techniques == === Scheduling Algorithms === Opportunistic Load Balancing (OLB) is the algorithm that assigns workloads to nodes in free order. It is simple but does not consider the expected execution time of each node. Load balance Min-Min (LBMM) assigns sub-tasks to the node which requires minimum execution time. === Load Balancing Policies === Workload and Client Aware Policy (WCAP) specifies the unique and special property (USP) of requests and computing nodes. With the information of USP, the schedule can decide the most suitable node to complete a request. WCAP makes the most of computing nodes by reducing their idle time. Also, it reduces performance time through searches based on content information. === A Comparative Study of Algorithms === Biased Random Sampling bases its job allocation on the network represented by a directed graph. For each execution node in this graph, in-degree means available resources and out-degree means allocated jobs. In-degree will decrease during job execution while out-degree will increase after job allocation. Active Clustering is a self-aggregation algorithm to rewire the network. The experiment result is that"Active Clustering and Random Sampling Walk predictably perform better as the number of processing nodes is increased" while the Honeyhive algorithm does not show the increasing pattern. == Client-side Load Balancer Using Cloud Computing == Load balancer forwards packets to web servers according to different workloads on servers. However, it is hard to implement a scalable load balancer because of both the "cloud's commodity business model and the limited infrastructure control allowed by cloud providers." Client-side Load Balancer (CLB) solve this problem by using a scalable cloud storage service. CLB allows clients to choose back-end web servers for dynamic content although it delivers static content.

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  • Legal Knowledge Interchange Format

    Legal Knowledge Interchange Format

    The Legal Knowledge Interchange Format (LKIF) was developed in the European ESTRELLA project and was designed with the goal of becoming a standard for representing and interchanging policy, legislation and cases, including their justificatory arguments, in the legal domain. LKIF builds on and uses the Web Ontology Language (OWL) for representing concepts and includes a reusable basic ontology of legal concepts. The core of LKIF consists of a combination of OWL-DL and SWRL. LKIF was designed with two main roles in mind: the translation of legal knowledge bases written in different representation formats and formalisms and to be a knowledge representation formalism which could be part of larger architectures for developing legal knowledge systems.

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  • Rumelhart Prize

    Rumelhart Prize

    The David E. Rumelhart Prize for Contributions to the Theoretical Foundations of Human Cognition was founded in 2001 in honor of the cognitive scientist David Rumelhart to introduce the equivalent of a Nobel Prize for cognitive science. It is awarded annually to "an individual or collaborative team making a significant contemporary contribution to the theoretical foundations of human cognition". The annual award is presented at the Cognitive Science Society meeting, where the recipient gives a lecture and receives a check for $100,000. At the conclusion of the ceremony, the next year's award winner is announced. The award is funded by the Robert J. Glushko and Pamela Samuelson Foundation. The Rumelhart Prize committee is independent of the Cognitive Science Society. However, the society provides a large and interested audience for the awards. == Selection Committee == As of 2022, the selection committee for the prize consisted of: Richard Cooper (chair) Dedre Gentner Robert J. Glushko Tania Lombrozo Steven T. Piantadosi Jesse Snedeker == Recipients ==

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