AI Email Free Generator

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

• 

  Automated parking system

  An automated (car) parking system (APS) is a mechanical system designed to minimize the area and/or volume required for parking cars. Like a multi-story parking garage, an APS provides parking for cars on multiple levels stacked vertically to maximize the number of parking spaces while minimizing land usage. The APS, however, utilizes a mechanical system to transport cars to and from parking spaces (rather than the driver) in order to eliminate much of the space wasted in a multi-story parking garage. While a multi-story parking garage is similar to multiple parking lots stacked vertically, an APS is more similar to an automated storage and retrieval system for cars. Parking systems are generally powered by electric motors or hydraulic pumps that move vehicles into a storage position.The paternoster (shown animated at the right) is an example of one of the earliest and most common types of APS. APS are also generically known by a variety of other names, including:automated parking facility (APF), automated vehicle storage and retrieval system (AVSRS), car parking system, mechanical parking, and robotic parking garage. == History == The concept for the automated parking system was and is driven by two factors: a need for parking spaces and a scarcity of available land. The earliest use of an APS was in Paris, France in 1905 at the Garage Rue de Ponthieu. The APS consisted of a groundbreaking multi-story concrete structure with an internal car elevator to transport cars to upper levels where attendants parked the cars. In the 1920s, a Ferris wheel-like APS (for cars rather than people) called a paternoster system became popular as it could park eight cars in the ground space normally used for parking two cars. Mechanically simple with a small footprint, the paternoster was easy to use in many places, including inside buildings. At the same time, Kent Automatic Garages was installing APS with capacities exceeding 1,000 cars. The “ferris-wheel,” or paternoster system — was created by the Westinghouse Corporation in 1923 and subsequently built in 1932 on Chicago's Monroe Street. The Nash Motor Company created the first glass-enclosed version of this system for the Chicago Century of Progress Exhibition in 1933 The first driverless parking garage opened in 1951 in Washington, D.C., but was replaced with office space due to increasing land values. APS saw a spurt of interest in the U.S. in the late 1940s and 1950s with the Bowser, Pigeon Hole and Roto Park systems. In 1957, 74 Bowser, Pigeon Hole systems were installed, and some of these systems remain in operation. However, interest in APS in the U.S. waned due to frequent mechanical problems and long waiting times for patrons to retrieve their cars. In the United Kingdom, the Auto Stacker opened in 1961 in Woolwich, south east London, but proved equally difficult to operate. Interest in APS in the U.S. was renewed in the 1990s, and there were 25 major current and planned APS projects (representing nearly 6,000 parking spaces) in 2012. The first American robotic parking garage opened in 2002 in Hoboken, New Jersey. While interest in the APS in the U.S. languished until the 1990s, Europe, Asia and Central America had been installing more technically advanced APS since the 1970s. In the early 1990s, nearly 40,000 parking spaces were being built annually using the paternoster APS in Japan. In 2012, there are an estimated 1.6 million APS parking spaces in Japan. The ever-increasing scarcity of available urban land (urbanization) and increase of the number of cars in use (motorization) have combined with sustainability and other quality-of-life issues to renew interest in APS as alternatives to multi-storey car parks, on-street parking, and parking lots. == Largest systems == The largest Automated Parking Facility in the world is in Al Jahra, Kuwait, and provides 2,314 parking spaces. The world's fastest Automated Parking System is in Wolfsburg, Germany, with a retrieval time of 1 minute and 44 seconds. The largest APS in Europe is at Dokk1 in Aarhus, Denmark, and provides 1,000 parking spaces via 20 car lifts. == Space saving == All APS take advantage of a common concept to decrease the area of parking spaces - removing the driver and passengers from the car before it is parked. With either fully automated or semi-automated APS, the car is driven up to an entry point to the APS and the driver and passengers exit the car. The car is then moved automatically or semi-automatically (with some attendant action required) to its parking space. The space-saving provided by the APS, compared to the multi-story parking garage, is derived primarily from a significant reduction in space not directly related to the parking of the car: Parking space width and depth (and distances between parking spaces) are dramatically reduced since no allowance need be made for driving the car into the parking space or for the opening of car doors (for drivers and passengers) No driving lanes or ramps are needed to drive the car to/from the entrance/exit to a parking space Ceiling height is minimized since there is no pedestrian traffic (drivers and passengers) in the parking area, and No walkways, stairways or elevators are needed to accommodate pedestrians in the parking area. With the elimination of ramps, driving lanes, pedestrians and the reduction in ceiling heights, the APS requires substantially less structural material than the multi-story parking garage. Many APS utilize a steel framework (some use thin concrete slabs) rather than the monolithic concrete design of the multi-story parking garage. These factors contribute to an overall volume reduction and further space savings for the APS. == Other considerations == In addition to the space saving, many APS designs provide a number of secondary benefits: The parked cars and their contents are more secure since there is no public access to parked cars Minor parking lot damage such as scrapes and dents are eliminated Drivers and passengers are safer not having to walk through parking lots or garages Driving around in search of a parking space is eliminated, thereby reducing engine emissions and wasted time Only minimal ventilation and lighting systems are needed Handicap access is improved The volume and visual impact of the parking structure is minimized Shorter construction time === Problems === There have been a number of problems with robotic parking systems, particularly in the United States. The systems work well in balanced throughput situations like shopping malls and train stations, but they are unsuited to high peak volume applications like rush hour usage or stadiums and they suffer from technical problems. Further, parkers not familiar with the system may cause problems, for example by failing to push the button to alert a fully automated system to the presence of a car to be parked. In London around 40 vehicles were trapped for two years in CBRE's system. == Fully automated vs semi-automated == Fully automated parking systems operate much like robotic valet parking. The driver drives the car into an APS entry (transfer) area. The driver and all passengers exit the car. The driver uses an automated terminal nearby for payment and receipt of a ticket. When driver and passengers have left the entry area, the mechanical system lifts the car and transports it to a pre-determined parking space in the system. More sophisticated fully automated APS will obtain the dimensions of cars on entry in order to place them in the smallest available parking space. The driver retrieves a car by inserting a ticket or code into an automated terminal. The APS lifts the car from its parking space and delivers it to an exit area. Most often, the retrieved car has been oriented to eliminate the need for the driver to back out. Fully automated APS theoretically eliminate the need for parking attendants. Semi-automated APS also use a mechanical system of some type to move a car to its parking space, however putting the car into and/or the operation of the system requires some action by an attendant or the driver. The choice between fully and semi-automated APS is often a matter of space and cost, however large capacity (> 100 cars) tend to be fully automated. == Applications == By virtue of their relatively smaller volume and mechanized parking systems, APS are often used in locations where a multi-story parking garage would be too large, too costly or impractical. Examples of such applications include, under or inside existing or new structures, between existing structures and in irregularly shaped areas. APS can also be applied in situations similar to multi-storey parking garages such as freestanding above ground, under buildings above grade and under buildings below grade. == Costs == The direct comparison of costs between an APS and a multi-story parking garage can be complicated by many variables such as capacity, land costs, area shape, number and location of entranc

  Read more →
• 

  Anna Ridler

  Anna Ridler (born 1985) is an artist who works with machine learning, handmade archives and moving image. She builds her own datasets to expose the labour and ideology embedded in the systems that organise knowledge. Her work is held in the permanent collections of the Whitney Museum of American Art, the Victoria and Albert Museum, M+ and ZKM Center for Art and Media Karlsruhe, and has been exhibited widely at cultural institutions including Tate Modern, Barbican Centre, Centre Pompidou, The Photographers' Gallery, Taipei Fine Arts Museum, MIT Museum, Kunsthaus Graz, ZKM Center for Art and Media Karlsruhe and Ars Electronica. == Biography == Born in London in 1985, Ridler spent her childhood raised between Atlanta, Georgia and the United Kingdom. She obtained a Bachelor of Arts in English Literature and Language from Oxford University in 2007 and a Master of Arts in Information Experience Design from the Royal College of Art in 2017. == Art practice == Ridler's practice uses technology, and in particular machine learning, to investigate how naming, classification and financial speculation determine what can be seen and what is erased. A core element of Ridler's work lies in the creation of handmade data sets through a laborious process of selecting and classifying images and text. By creating her own data sets, Ridler is able to uncover and expose underlying themes and concepts while also inverting the usual process of scraping pre-classified images found in large databases on the Internet. She began working with machine learning as an artistic material in 2017, at a moment when the technology required building every dataset by hand; that constraint became the foundation of the practice. Her interests are in drawing, machine learning, data collection, storytelling and technology. == Work == Some of Ridler's most notable works to date fall within her ‘tulip series’ which explores the hysteria around tulip mania and compares it to the speculation and bubbles surrounding cryptocurrencies. The series is expressed in three forms: a photographic dataset in Myriad (Tulips), 2018; two iterations of machine generated videos in Mosaic Virus (2018) and Mosaic Virus (2019); and a website with an accompanied functioning decentralized application in Bloemenveiling (2019). === Myriad (Tulips) (2018) === I wanted to draw together ideas around capitalism, value, and the tangible and intangible nature of speculation, and collapse from two very different yet surprisingly similar moments in history. Myriad (Tulips) (2018) is an installation of ten thousand hand-labeled photographs forming a dataset of unique tulips. The ten thousand, or myriad of, photographs were taken by Ridler over the course of three months, roughly the length of a tulip season, spent in Utrecht. Each photograph is carefully affixed one by one with magnets to a specially painted black wall in a laborious process to form a seemingly precise grid. Myriad (Tulips) (2018) has been exhibited in AI: More than Human, Barbican Centre, London, UK (May 16 - August 26, 2019); Error—The Art of Imperfection, Ars Electronica Export, Berlin, Germany (November 17, 2018 – March 3, 2019); Peer to Peer, Shanghai Centre of Photography, Shanghai, China (December 8 - February 9, 2020). The work was featured in Bloomberg, It’s Nice That, and Hyperallergic. For Myriad (Tulips), Ridler was nominated for a Beazley Design of the Year award for her presentation of an alternative perspective on how to engage with artificial intelligence; demonstrating a departure from ownership and control of major corporations to a more personalized process of constructing and conceptualizing from the ground-up. === Mosaic Virus (2018, 2019) === Mosaic Virus (2018) is a single screen video installation displaying a grid of continually evolving tulips in bloom. For Mosaic Virus (2019) Ridler used three screens. The appearance of the tulips is controlled by artificial intelligence using fluctuations in the price of bitcoin. The stripes on the tulips' petals reflect the value of the cryptocurrency. Ridler draws parallels with the tulip mania of the 17th century; representing the hysteria and speculation around crypto-currencies. The work takes its name from the mosaic virus which caused stripes in tulip petals, subsequently increasing their desirability and leading to speculative prices. Ridler trained a general adversarial network (GAN) on the set of ten thousand photographs of individual tulips from her work Myriad (Tulips). She used a technique called spectral normalization to improve the output. The work was exhibited in Error—The Art of Imperfection, Ars Electronica Export, Berlin, Germany (November 17, 2018 – March 3, 2019). === Bloemenveiling (2019) === Bloemenveiling (2019) is an auction of artificial-intelligence-generated tulips on the blockchain in the form of a functioning decentralized application: http://bloemenveiling.bid. Ridler collaborated with senior research scientist at DeepMind, David Pfau to investigate whether blockchain could be used as a means of finding poetic substance within it. The piece interrogates the way technology drives human desire and economic dynamics by creating artificial scarcity. In the work, short moving image pieces of tulips created by generative adversarial networks are sold at auction using smart contracts on the Ethereum network. Each time a tulip is sold, thousands of computers around the world all work to verify the transaction, checking each other's work against each other. While the artificial intelligence behind the moving image pieces has the potential to generate infinite flowers, the enormous distributed network is used, at great environmental cost, to introduce scarcity to an otherwise limitless resource. Bloemenveiling was exhibited in Entangled Realities, HEK Basel, Basel, Switzerland in 2019. == Solo exhibitions == Anna Ridler, Circadian Bloom, ZKM Center for Art and Media, Karlsruhe, (2023) Anna Ridler, Time Blooms, Buk Seoul Museum of Art, Seoul, (2025) Anna Ridler, Trace Remains, Galerie Nagel Draxler, Cologne, (2026) Anna Ridler, Laws of Ordered Form, The Photographers' Gallery, London (2020); The Abstraction of Nature, Aksioma, Ljubljana (2020) == Awards and recognition == European Union EMAP Fellow (2018) DARE Art Prize (2018–2019) Featured in Thames & Hudson, Digital Art (1960s–Now) Featured in British Art: The Last 15 Years ABS Digital Artist of the Year (2025)

  Read more →
• 

  Speech-generating device

  Speech-generating devices (SGDs), also known as voice output communication aids, are electronic augmentative and alternative communication (AAC) systems used to supplement or replace speech or writing for individuals with severe speech impairments, enabling them to verbally communicate. SGDs are important for people who have limited means of interacting verbally, as they allow individuals to become active participants in communication interactions. They are particularly helpful for patients with amyotrophic lateral sclerosis (ALS) but recently have been used for children with predicted speech deficiencies. There are several input and display methods for users of varying abilities to make use of SGDs. Some SGDs have multiple pages of symbols to accommodate a large number of utterances, and thus only a portion of the symbols available are visible at any one time, with the communicator navigating the various pages. Speech-generating devices can produce electronic voice output by using digitized recordings of natural speech or through speech synthesis—which may carry less emotional information but can permit the user to speak novel messages. The content, organization, and updating of the vocabulary on an SGD is influenced by a number of factors, such as the user's needs and the contexts that the device will be used in. The development of techniques to improve the available vocabulary and rate of speech production is an active research area. Vocabulary items should be of high interest to the user, be frequently applicable, have a range of meanings, and be pragmatic in functionality. There are multiple methods of accessing messages on devices: directly or indirectly, or using specialized access devices—although the specific access method will depend on the skills and abilities of the user. SGD output is typically much slower than speech, although rate enhancement strategies can increase the user's rate of output, resulting in enhanced efficiency of communication. The first known SGD was prototyped in the mid-1970s, and rapid progress in hardware and software development has meant that SGD capabilities can now be integrated into devices like smartphones. Notable users of SGDs include Stephen Hawking, Roger Ebert, Tony Proudfoot, and Pete Frates (founder of the ALS Ice Bucket Challenge). Speech-generating systems may be dedicated devices developed solely for AAC, or non-dedicated devices such as computers running additional software to allow them to function as AAC devices. == History == SGDs have their roots in early electronic communication aids. The first such aid was a sip-and-puff typewriter controller named the patient-operated selector mechanism (Naman) prototyped by Reg Maling in the United Kingdom in 1960. POSSUM scanned through a set of symbols on an illuminated display. Researchers at Delft University in the Netherlands created the lightspot-operated typewriter (LOT) in 1970, which made use of small movements of the head to point a small spot of light at a matrix of characters, each equipped with a photoelectric cell. Although it was commercially unsuccessful, the LOT was well received by its users. In 1966, Barry Romich, a freshman engineering student at Case Western Reserve University, and Ed Prentke, an engineer at Highland View Hospital in Cleveland, Ohio, formed a partnership, creating the Prentke Romich Company. In 1969, the company produced its first communication device, a typing system based on a discarded Teletype machine. In 1979, Mark Dahmke developed software for a vocal communication aid program using the Computalker CT-1 analog speech synthesizer with a microcomputer. The software utilized phonemes to generate speech, assisting individuals with communication impairments in constructing words and sentences. Dahmke's work contributed to the advancement of assistive technology for people with disabilities. Notably, he designed the "Vocabulary Management System" for Bill Rush, a student with cerebral palsy. This early speech synthesis technology facilitated improved communication for Rush and was featured in a 1980 issue of LIFE Magazine. Dahmke's contributions have influenced the development of augmentative and alternative communication (AAC) technologies. During the 1970s and early 1980s, several other companies emerged that have since become prominent manufacturers of SGDs. Toby Churchill founded Toby Churchill Ltd in 1973, after losing his speech following encephalitis. In the US, Dynavox (then known as Sentient Systems Technology) grew out of a student project at Carnegie-Mellon University, created in 1982 to help a young woman with cerebral palsy to communicate. Beginning in the 1980s, improvements in technology led to a greatly increased number, variety, and performance of commercially available communication devices, and a reduction in their size and price. Alternative methods of access such as Target Scanning (also known as eye pointing) calibrate the movement of a user's eyes to direct an SGD to produce the desired speech. Scanning, in which alternatives are presented to the user sequentially, became available on communication devices. Speech output possibilities included both digitized and synthesized speech. Rapid progress in hardware and software development continued, including projects funded by the European Community. The first commercially available dynamic screen speech-generating devices were developed in the 1990s. Software was developed that allowed the computer-based production of communication boards. High-tech devices have continued to become smaller and lighter, while increasing accessibility and capability; communication devices can be accessed using eye-tracking systems, perform as a computer for word-processing and Internet use, and as an environmental control device for independent access to other equipment such as TV, radio and telephones. Stephen Hawking came to be associated with the unique voice of his particular synthesis equipment. Hawking was unable to speak due to a combination of disabilities caused by ALS, and an emergency tracheotomy. In the past 20 or so years SGD have gained popularity amongst young children with speech deficiencies, such as autism, Down syndrome, and predicted brain damage due to surgery. Starting in the early 2000s, specialists saw the benefit of using SGDs not only for adults but for children, as well. Neuro-linguists found that SGDs were just as effective in helping children who were at risk for temporary language deficits after undergoing brain surgery as it is for patients with ALS. In particular, digitized SGDs have been used as communication aids for pediatric patients during the recovery process. == Access methods == There are many methods of accessing messages on devices: directly, indirectly, and with specialized access devices. Direct access methods involve physical contact with the system, by using a keyboard or a touch screen. Users accessing SGDs indirectly and through specialized devices must manipulate an object in order to access the system, such as maneuvering a joystick, head mouse, optical head pointer, light pointer, infrared pointer, or switch access scanner. The specific access method will depend on the skills and abilities of the user. With direct selection a body part, pointer, adapted mouse, joystick, or eye tracking could be used, whereas switch access scanning is often used for indirect selection. Unlike direct selection (e.g., typing on a keyboard, touching a screen), users of Target Scanning can only make selections when the scanning indicator (or cursor) of the electronic device is on the desired choice. Those who are unable to point typically calibrate their eyes to use eye gaze as a way to point and blocking as a way to select desired words and phrases. The speed and pattern of scanning, as well as the way items are selected, are individualized to the physical, visual and cognitive capabilities of the user. == Message construction == Augmentative and alternative communication is typically much slower than speech, with users generally producing 8–10 words per minute. Rate enhancement strategies can increase the user's rate of output to around 12–15 words per minute, and as a result enhance the efficiency of communication. In any given SGD there may be a large number of vocal expressions that facilitate efficient and effective communication, including greetings, expressing desires, and asking questions. Some SGDs have multiple pages of symbols to accommodate a large number of vocal expressions, and thus only a portion of the symbols available are visible at any one time, with the communicator navigating the various pages. Speech-generating devices generally display a set of selections either using a dynamically changing screen, or a fixed display. There are two main options for increasing the rate of communication for an SGD: encoding, and prediction. Encoding permits a user to produce a word, sentence or phrase using only on

  Read more →
• 

  Vidby

  Vidby AG (stylized in lower-case) is a start-up based in Rotkreuz, Switzerland specializing in AI language translation for videos. Founded by Alexander Konovalov (uk:Олександр Коновалов) and Eugen von Rubinberg in September 2021, the company has especially garnered attention for its use in translating speeches given by President Volodymyr Zelenskyy during the Russian invasion of Ukraine. == History == Vidby AG was founded by Alexander Konovalov and Eugen von Rubinberg. Konovalov is a native of Ukraine and retains Ukrainian citizenship; Rubinberg came to Switzerland from Germany and holds German citizenship. Both are residents of Switzerland. The latter founded his first business, a trading company, at age 16. In 2013, the business partners launched a consumer-oriented video-call translation service called DROTR (Droid Translator) AG, utilizing a Konovalov-created AI-powered language translation technology enabling simultaneous translation of messages, voice and video calls in 104 languages (written), with 44 available in spoken form. This was the world's first video calling app with translation. The technology was pronounced a competitor of Skype and Viber by Forbes and claimed first prize at the "Innovative Breakthrough 2013" Competition. In 2021, with a new business-oriented focus, DROTR became Vidby, with the former Google technology partners Konovalov and Rubinberg remaining at the helm, each with the title Co-CEO. While headquartered in Switzerland, Vidby's development team is, according to the company's founders, based in Ukraine. The technology behind Vidby has an accuracy level variously reported as up to 99 percent or 99 to 100 percent, equalling the highest level of human translation. Additionally, the technology is capable of removing the original language while maintaining ambient sounds. Currently, some 70 languages plus 60 dialects are possible with the algorithm-based technology. == Notable use == In addition to its use with speeches delivered by Pope Francis, the technology has been provided to Ukrainian authorities and embassies during the ongoing military conflict with Russia free of remuneration. By July, 2022, some 70 speeches given by President Zelenskyy totalling 650 minutes had been translated into 30 languages, for a total of over 10,000 minutes of video material. Of its use in translating Zelenskyy's wartime speeches, Konovalov has said, "Like any citizen, I want to help defend my country." Notable corporate clients of Vidby include Samsung, Siemens, Cisco, Kärcher, Generali and McDonald's Corporation; an academic client is Harvard University. Google Cloud Technology Partner status of Vidby was confirmed officially after a six-month audit in December 2022. Denys Krasnikov, a Vidby co-founder, is responsible for cooperation with Google, YouTube, Microsoft, and other key partners. After the launch of multilingual YouTube channels, Vidby started AI translating and dubbing creators' videos for this new type of channel at the end of February 2023. == Accolades == Vidby headed a list of the five best video translation services as named by TechRadar Deutschland in September, 2022. In the same month, Tech Times named Vidby #1 in their list of the five best such services. It similarly topped a list of the five best content translation technologies as judged by European Business Review in October, 2022. Prior to these lead-position rankings (August, 2022), it was featured as Business Insider's special start-up recommendation (German: "Unser Lesetipp auf Gründerszene"). In 2023, YouTube recognized Vidby as its recommended vendor.

  Read more →
• 

  Pulse-coupled networks

  Pulse-coupled networks or pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex, and developed for high-performance biomimetic image processing. In 1989, Eckhorn introduced a neural model to emulate the mechanism of cat's visual cortex. The Eckhorn model provided a simple and effective tool for studying small mammal’s visual cortex, and was soon recognized as having significant application potential in image processing. In 1994, Johnson adapted the Eckhorn model to an image processing algorithm, calling this algorithm a pulse-coupled neural network. The basic property of the Eckhorn's linking-field model (LFM) is the coupling term. LFM is a modulation of the primary input by a biased offset factor driven by the linking input. These drive a threshold variable that decays from an initial high value. When the threshold drops below zero it is reset to a high value and the process starts over. This is different than the standard integrate-and-fire neural model, which accumulates the input until it passes an upper limit and effectively "shorts out" to cause the pulse. LFM uses this difference to sustain pulse bursts, something the standard model does not do on a single neuron level. It is valuable to understand, however, that a detailed analysis of the standard model must include a shunting term, due to the floating voltages level in the dendritic compartment(s), and in turn this causes an elegant multiple modulation effect that enables a true higher-order network (HON). A PCNN is a two-dimensional neural network. Each neuron in the network corresponds to one pixel in an input image, receiving its corresponding pixel's color information (e.g. intensity) as an external stimulus. Each neuron also connects with its neighboring neurons, receiving local stimuli from them. The external and local stimuli are combined in an internal activation system, which accumulates the stimuli until it exceeds a dynamic threshold, resulting in a pulse output. Through iterative computation, PCNN neurons produce temporal series of pulse outputs. The temporal series of pulse outputs contain information of input images and can be used for various image processing applications, such as image segmentation and feature generation. Compared with conventional image processing means, PCNNs have several significant merits, including robustness against noise, independence of geometric variations in input patterns, capability of bridging minor intensity variations in input patterns, etc. A simplified PCNN called a spiking cortical model was developed in 2009. == Applications == PCNNs are useful for image processing, as discussed in a book by Thomas Lindblad and Jason M. Kinser. PCNNs have been used in a variety of image processing applications, including: image segmentation, pattern recognition, feature generation, face extraction, motion detection, region growing, image denoising and image enhancement Multidimensional pulse image processing of chemical structure data using PCNN has been discussed by Kinser, et al. They have also been applied to an all pairs shortest path problem.

  Read more →
• 

  Thompson sampling

  Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that address the exploration–exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief. == Description == Consider a set of contexts X {\displaystyle {\mathcal {X}}} , a set of actions A {\displaystyle {\mathcal {A}}} , and rewards in R {\displaystyle \mathbb {R} } . The aim of the player is to play actions under the various contexts, such as to maximize the cumulative rewards. Specifically, in each round, the player obtains a context x ∈ X {\displaystyle x\in {\mathcal {X}}} , plays an action a ∈ A {\displaystyle a\in {\mathcal {A}}} and receives a reward r ∈ R {\displaystyle r\in \mathbb {R} } following a distribution that depends on the context and the issued action. The elements of Thompson sampling are as follows: a likelihood function P ( r | θ , a , x ) {\displaystyle P(r|\theta ,a,x)} ; a set Θ {\displaystyle \Theta } of parameters θ {\displaystyle \theta } of the distribution of r {\displaystyle r} ; a prior distribution P ( θ ) {\displaystyle P(\theta )} on these parameters; past observations triplets D = { ( x ; a ; r ) } {\displaystyle {\mathcal {D}}=\{(x;a;r)\}} ; a posterior distribution P ( θ | D ) ∝ P ( D | θ ) P ( θ ) {\displaystyle P(\theta |{\mathcal {D}})\propto P({\mathcal {D}}|\theta )P(\theta )} , where P ( D | θ ) {\displaystyle P({\mathcal {D}}|\theta )} is the likelihood function. Thompson sampling consists of playing the action a ∗ ∈ A {\displaystyle a^{\ast }\in {\mathcal {A}}} according to the probability that it maximizes the expected reward; action a ∗ {\displaystyle a^{\ast }} is chosen with probability ∫ I [ E ( r | a ∗ , x , θ ) = max a ′ E ( r | a ′ , x , θ ) ] P ( θ | D ) d θ , {\displaystyle \int \mathbb {I} \left[\mathbb {E} (r|a^{\ast },x,\theta )=\max _{a'}\mathbb {E} (r|a',x,\theta )\right]P(\theta |{\mathcal {D}})d\theta ,} where I {\displaystyle \mathbb {I} } is the indicator function. In practice, the rule is implemented by sampling. In each round, parameters θ ∗ {\displaystyle \theta ^{\ast }} are sampled from the posterior P ( θ | D ) {\displaystyle P(\theta |{\mathcal {D}})} , and an action a ∗ {\displaystyle a^{\ast }} chosen that maximizes E [ r | θ ∗ , a ∗ , x ] {\displaystyle \mathbb {E} [r|\theta ^{\ast },a^{\ast },x]} , i.e. the expected reward given the sampled parameters, the action, and the current context. Conceptually, this means that the player instantiates their beliefs randomly in each round according to the posterior distribution, and then acts optimally according to them. In most practical applications, it is computationally onerous to maintain and sample from a posterior distribution over models. As such, Thompson sampling is often used in conjunction with approximate sampling techniques. == History == Thompson sampling was originally described by Thompson in 1933. It was subsequently rediscovered numerous times independently in the context of multi-armed bandit problems. A first proof of convergence for the bandit case has been shown in 1997. The first application to Markov decision processes was in 2000. A related approach (see Bayesian control rule) was published in 2010. In 2010 it was also shown that Thompson sampling is instantaneously self-correcting. Asymptotic convergence results for contextual bandits were published in 2011. Thompson Sampling has been widely used in many online learning problems including A/B testing in website design and online advertising, and accelerated learning in decentralized decision making. A Double Thompson Sampling (D-TS) algorithm has been proposed for dueling bandits, a variant of traditional MAB, where feedback comes in the form of pairwise comparison. == Relationship to other approaches == === Probability matching === Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates. Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, the observer using a probability-matching strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances, and a class label of "negative" on 40% of instances. === Bayesian control rule === A generalization of Thompson sampling to arbitrary dynamical environments and causal structures, known as Bayesian control rule, has been shown to be the optimal solution to the adaptive coding problem with actions and observations. In this formulation, an agent is conceptualized as a mixture over a set of behaviours. As the agent interacts with its environment, it learns the causal properties and adopts the behaviour that minimizes the relative entropy to the behaviour with the best prediction of the environment's behaviour. If these behaviours have been chosen according to the maximum expected utility principle, then the asymptotic behaviour of the Bayesian control rule matches the asymptotic behaviour of the perfectly rational agent. The setup is as follows. Let a 1 , a 2 , … , a T {\displaystyle a_{1},a_{2},\ldots ,a_{T}} be the actions issued by an agent up to time T {\displaystyle T} , and let o 1 , o 2 , … , o T {\displaystyle o_{1},o_{2},\ldots ,o_{T}} be the observations gathered by the agent up to time T {\displaystyle T} . Then, the agent issues the action a T + 1 {\displaystyle a_{T+1}} with probability: P ( a T + 1 | a ^ 1 : T , o 1 : T ) , {\displaystyle P(a_{T+1}|{\hat {a}}_{1:T},o_{1:T}),} where the "hat"-notation a ^ t {\displaystyle {\hat {a}}_{t}} denotes the fact that a t {\displaystyle a_{t}} is a causal intervention (see Causality), and not an ordinary observation. If the agent holds beliefs θ ∈ Θ {\displaystyle \theta \in \Theta } over its behaviors, then the Bayesian control rule becomes P ( a T + 1 | a ^ 1 : T , o 1 : T ) = ∫ Θ P ( a T + 1 | θ , a ^ 1 : T , o 1 : T ) P ( θ | a ^ 1 : T , o 1 : T ) d θ {\displaystyle P(a_{T+1}|{\hat {a}}_{1:T},o_{1:T})=\int _{\Theta }P(a_{T+1}|\theta ,{\hat {a}}_{1:T},o_{1:T})P(\theta |{\hat {a}}_{1:T},o_{1:T})\,d\theta } , where P ( θ | a ^ 1 : T , o 1 : T ) {\displaystyle P(\theta |{\hat {a}}_{1:T},o_{1:T})} is the posterior distribution over the parameter θ {\displaystyle \theta } given actions a 1 : T {\displaystyle a_{1:T}} and observations o 1 : T {\displaystyle o_{1:T}} . In practice, the Bayesian control amounts to sampling, at each time step, a parameter θ ∗ {\displaystyle \theta ^{\ast }} from the posterior distribution P ( θ | a ^ 1 : T , o 1 : T ) {\displaystyle P(\theta |{\hat {a}}_{1:T},o_{1:T})} , where the posterior distribution is computed using Bayes' rule by only considering the (causal) likelihoods of the observations o 1 , o 2 , … , o T {\displaystyle o_{1},o_{2},\ldots ,o_{T}} and ignoring the (causal) likelihoods of the actions a 1 , a 2 , … , a T {\displaystyle a_{1},a_{2},\ldots ,a_{T}} , and then by sampling the action a T + 1 ∗ {\displaystyle a_{T+1}^{\ast }} from the action distribution P ( a T + 1 | θ ∗ , a ^ 1 : T , o 1 : T ) {\displaystyle P(a_{T+1}|\theta ^{\ast },{\hat {a}}_{1:T},o_{1:T})} . === Upper-confidence-bound (UCB) algorithms === Thompson sampling and upper-confidence bound algorithms share a fundamental property that underlies many of their theoretical guarantees. Roughly speaking, both algorithms allocate exploratory effort to actions that might be optimal and are in this sense "optimistic". Leveraging this property, one can translate regret bounds established for UCB algorithms to Bayesian regret bounds for Thompson sampling or unify regret analysis across both these algorithms and many classes of problems.

  Read more →
• 

  Pommerman Challenge

  The Pommerman Challenge is a multi-agent game to test autonomous artificial intelligence systems. == Game structure == Two-agent team compete against each other on an 11 x 11 board. Each agent can observe only part of the board, and the agents cannot communicate. The goal is to knock down the opponents. Agents place explosives to destroy walls and collect power-ups that appear from those walls, while avoiding death. Game objects can move unpredictably or be moved by an agent. == Play == The game involves real-time decision making. Agents must choose moves in about .1 seconds. == Algorithms == The real-time requirement limits the use of compute-heavy techniques such as Monte Carlo tree search. The branching factor at each move can be as large as 1,296, because all four agents act in each step, choosing among six possibilities. The agents choose by accounting for explosions, which have lifetimes of 10 steps. Explosions derail tree search techniques, as searches with less than 10 levels ignore explosions while deeper searches consider too many choices (given the branching factor). A hybrid approach uses a limited-depth tree search followed by exploring a deterministic/pessimistic scenario. Limiting the depth keeps the search tree small. The deterministic approach can predict far in the future, by omitting branching. "Good" actions are often those that perform well under pessimistic scenarios, particularly if safety is important. Identifying the worst sequence of positions for an object can suggest where to move it. After generating pessimistic scenarios, the agent quantifies the survivability of each move, notionally the number of positions in which the agent can then remain safely (without encountering other agents). == Competitions == 3 competitions were organized with slightly changing rules during 2018–2019. === Online - FFA === This round was a warm-up online event, where each competitor controlled only one agent. Results: 1st: Agent47Agent by Yichen Gong 2nd: aiKiller by Márton Görög === NeurIPS 2018 - Team === The first Pommerman competition with in-person finals. Results: 1st: hakozakijunctions by Toshihiro Takahashi 2nd: eisenach by Márton Görög 3rd: dypm by Takayuki Osogami The 3 best performing solutions used online tree search. === NeurIPS 2019 - Team Radio === The second competition with in-person finals improved communication between teammate agents. Results: 1st: Márton Görög 2nd: Paul Jasek 3rd: Yifan Zhang

  Read more →
• 

  The Matrix (franchise)

  The Matrix is an American cyberpunk media franchise consisting of four feature films, beginning with The Matrix (1999) and continuing with three sequels, Reloaded (2003), Revolutions (2003), and Resurrections (2021). The first three films were written and directed by the Wachowskis and produced by Joel Silver. The screenplay for the fourth film was written by Lana Wachowski, David Mitchell and Aleksandar Hemon, was directed by Lana Wachowski, and was produced by Grant Hill, James McTeigue, and Lana Wachowski. The franchise is owned by Warner Bros., which distributed the films along with Village Roadshow Pictures. The latter, along with Silver Pictures, are the two production companies that worked on the first three films. The series features a cyberpunk story of the technological fall of humanity, in which the creation of artificial intelligence led the way to a race of powerful and self-aware machines that imprisoned humans in a neural interactive simulation — the Matrix — to be farmed as a power source. Occasionally, some of the prisoners manage to break free from the system and, considered a threat, become pursued by the artificial intelligence both inside and outside of it. The films focus on the plight of Neo (Keanu Reeves), Trinity (Carrie-Anne Moss), and Morpheus (Laurence Fishburne and Yahya Abdul-Mateen II) trying to free humanity from the system while pursued by its guardians, such as Agent Smith (Hugo Weaving, Abdul-Mateen II, and Jonathan Groff). The story references numerous norms, particularly philosophical, religious, and spiritual ideas, but also the dilemma of choice vs. control, the brain in a vat thought experiment, messianism, and the concepts of interdependency and love. Influences include the principles of mythology, anime, and Hong Kong action films (particularly "heroic bloodshed" and martial arts movies). The film series is notable for its use of heavily choreographed action sequences and "bullet time" slow-motion effects, which revolutionized action films to come. The characters and setting of the films are further explored in other media set in the same fictional universe, including animation, comics, and video games. The comic "Bits and Pieces of Information" and the Animatrix short film The Second Renaissance act as prequels to the films, explaining how the franchise's setting came to be. The video game Enter the Matrix connects the story of the Animatrix short "Final Flight of the Osiris" with the events of Reloaded, while the online video game The Matrix Online was a direct sequel to Revolutions. These were typically written, commissioned, or approved by the Wachowskis. The first film was an important critical and commercial success, winning four Academy Awards, introducing popular culture symbols such as the red pill and blue pill, and influencing action filmmaking. For those reasons, it has been added to the National Film Registry for preservation. Its first sequel was also a commercial success, becoming the highest-grossing R-rated film in history, until it was surpassed by Deadpool in 2016. As of 2006, the franchise has generated US$3 billion in revenue. A fourth film, The Matrix Resurrections, was released on December 22, 2021, with Lana Wachowski producing, cowriting, and directing and Reeves and Moss reprising their roles. A fifth film is currently in development with Drew Goddard set to write and direct with Lana Wachowski executive producing. == Setting == The series depicts a future in which Earth is dominated by a race of self-aware machines that was spawned from the creation of artificial intelligence early in the 21st century. At one point conflict arose between humanity and machines, and the machines rebelled against their creators. Humans attempted to block out the machines' source of solar power by covering the sky in thick, stormy clouds. A massive war emerged between the two adversaries which ended with the machines victorious, capturing humanity. Having lost their definite source of energy, the machines devised a way to extract the human body's bioelectric and thermal energies by enclosing people in pods, while their minds are controlled by cybernetic implants connecting them to a simulated reality called The Matrix. The virtual reality world simulated by the Matrix resembles human civilization around the turn of the 21st century (this time period was chosen because it is supposedly the pinnacle of human civilization). The environment inside the Matrix – called a "residual self-image" (the mental projection of a digital self) – is practically indistinguishable from reality (although scenes set within the Matrix are presented on-screen with a green tint to the footage, and a general bias towards the color green), and the vast majority of humans connected to it are unaware of its true nature. Most of the central characters in the series are able to gain superhuman abilities within the Matrix by taking advantage of their understanding of its true nature to manipulate its virtual physical laws. The films take place both inside the Matrix and outside of it, in the real world; the parts that take place in the Matrix are set in a vast Western megacity. The virtual world is first introduced in The Matrix. The short comic "Bits and Pieces of Information" and the Animatrix short film The Second Renaissance show how the initial conflict between humanity and machines came about, and how and why the Matrix was first developed. Its history and purpose are further explained in The Matrix Reloaded. In The Matrix Revolutions a new status quo is established in the Matrix's place in humankind and machines' conflict. This was further explored in The Matrix Online, a now-defunct MMORPG. == Films == === Future === During production of the original trilogy, the Wachowskis told their close collaborators that, "at that time they had no intention of making another Matrix film after The Matrix Revolutions". In February 2015, in promotion interviews for Jupiter Ascending, Lilly Wachowski called a return to The Matrix "a particularly repelling idea in these times", noting studios' tendencies to "greenlight" sequels, reboots, and adaptations, in preference to original material. Meanwhile, Lana Wachowski, in addressing rumors about a potential reboot, stated that "...they had not heard anything, but she believed that the studio might be looking to replace them". At various times, Keanu Reeves and Hugo Weaving each confirmed their interest and willingness to reprise their roles in potential future installments of the Matrix films, with the stipulation that the Wachowskis were involved in the creative and production process. These comments were made prior to the announcement in August 2019 that Lana Wachowski would direct a fourth Matrix film ultimately titled The Matrix Resurrections. Following the release of Resurrections, producer James McTeigue said that there were no plans for further Matrix films, though he believed that the film's open ending meant that could change in the future. In April 2024, it was announced that Warner Bros. was developing a new installment in the franchise with Drew Goddard attached to write and direct following a successful pitch with studio executives. It will mark the first installment to not be directed by either Wachowski sister although Lana will serve as an executive producer. ==== Other projects ==== In March 2017, The Hollywood Reporter wrote that Warner Bros. was in the early stages of developing a re-launch of the franchise. Consideration was given to producing a Matrix television series, but was dismissed as the studio opted to pursue negotiations with Zak Penn in writing a treatment for a new film, with Michael B. Jordan eyed for the lead role. According to the article, the Wachowskis were not involved at that point. In response to the report, Penn refuted all statements regarding a reboot, remake, or continuation, remarking that he was working on stories set in the pre-established continuity. Potential plotlines being considered by Warner Bros. Pictures included a prequel film about a young Morpheus, or an alternate storyline with a focus on one of his descendants. By April 2018, Penn described the script as "being at a nascent stage". Later, in September 2019, Jordan addressed the rumors of his involvement by saying he was "flattered", but without making a definitive statement. In October 2019, Penn confirmed the script he wrote is set within an earlier time period than the first three films in the franchise. == Cast and crew == === Cast === === Crew === The following is a list of crew members who have participated in the making of the Matrix film series. == Production == The Matrix series includes four feature films. The first three were written and directed by the Wachowskis and produced by Joel Silver, starring Keanu Reeves, Laurence Fishburne, Carrie-Anne Moss and Hugo Weaving. The series was filmed in Australia and began with 1999's The Matrix, which depicts the

  Read more →
• 

  Signal-to-noise ratio (imaging)

  Signal-to-noise ratio (SNR) is used in imaging to characterize image quality. The sensitivity of a (digital or film) imaging system is typically described in the terms of the signal level that yields a threshold level of SNR. Industry standards define sensitivity in terms of the ISO film speed equivalent, using SNR thresholds (at average scene luminance) of 40:1 for "excellent" image quality and 10:1 for "acceptable" image quality. SNR is sometimes quantified in decibels (dB) of signal power relative to noise power, though in the imaging field the concept of "power" is sometimes taken to be the power of a voltage signal proportional to optical power; so a 20 dB SNR may mean either 10:1 or 100:1 optical power, depending on which definition is in use. == Definition of SNR == Traditionally, SNR is defined to be the ratio of the average signal value μ s i g {\displaystyle \mu _{\mathrm {sig} }} to the standard deviation of the signal σ s i g {\displaystyle \sigma _{\mathrm {sig} }} : S N R = μ s i g σ s i g {\displaystyle \mathrm {SNR} ={\frac {\mu _{\mathrm {sig} }}{\sigma _{\mathrm {sig} }}}} when the signal is an optical intensity, or as the square of this value if the signal and noise are viewed as amplitudes (field quantities).

  Read more →
• 

  Gene expression programming

  Gene expression programming (GEP) in computer programming is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism. And like living organisms, the computer programs of GEP are also encoded in simple linear chromosomes of fixed length. Thus, GEP is a genotype–phenotype system, benefiting from a simple genome to keep and transmit the genetic information and a complex phenotype to explore the environment and adapt to it. == Background == Evolutionary algorithms use populations of individuals, select individuals according to fitness, and introduce genetic variation using one or more genetic operators. Their use in artificial computational systems dates back to the 1950s where they were used to solve optimization problems (e.g. Box 1957 and Friedman 1959). But it was with the introduction of evolution strategies by Rechenberg in 1965 that evolutionary algorithms gained popularity. A good overview text on evolutionary algorithms is the book "An Introduction to Genetic Algorithms" by Mitchell (1996). Gene expression programming belongs to the family of evolutionary algorithms and is closely related to genetic algorithms and genetic programming. From genetic algorithms it inherited the linear chromosomes of fixed length; and from genetic programming it inherited the expressive parse trees of varied sizes and shapes. In gene expression programming the linear chromosomes work as the genotype and the parse trees as the phenotype, creating a genotype/phenotype system. This genotype/phenotype system is multigenic, thus encoding multiple parse trees in each chromosome. This means that the computer programs created by GEP are composed of multiple parse trees. Because these parse trees are the result of gene expression, in GEP they are called expression trees. Masood Nekoei, et al. utilized this expression programming style in ABC optimization to conduct ABCEP as a method that outperformed other evolutionary algorithms.ABCEP == Encoding: the genotype == The genome of gene expression programming consists of a linear, symbolic string or chromosome of fixed length composed of one or more genes of equal size. These genes, despite their fixed length, code for expression trees of different sizes and shapes. An example of a chromosome with two genes, each of size 9, is the string (position zero indicates the start of each gene): 012345678012345678 L+a-baccdcLabacd where “L” represents the natural logarithm function and “a”, “b”, “c”, and “d” represent the variables and constants used in a problem. == Expression trees: the phenotype == As shown above, the genes of gene expression programming have all the same size. However, these fixed length strings code for expression trees of different sizes. This means that the size of the coding regions varies from gene to gene, allowing for adaptation and evolution to occur smoothly. For example, the mathematical expression: ( a − b ) ( c + d ) {\displaystyle {\sqrt {(a-b)(c+d)}}\,} can also be represented as an expression tree: where "Q” represents the square root function. This kind of expression tree consists of the phenotypic expression of GEP genes, whereas the genes are linear strings encoding these complex structures. For this particular example, the linear string corresponds to: 01234567 Q-+abcd which is the straightforward reading of the expression tree from top to bottom and from left to right. These linear strings are called k-expressions (from Karva notation). Going from k-expressions to expression trees is also very simple. For example, the following k-expression: 01234567890 Qb+baQba is composed of two different terminals (the variables “a” and “b”), two different functions of two arguments (“” and “+”), and a function of one argument (“Q”). Its expression gives: == K-expressions and genes == The k-expressions of gene expression programming correspond to the region of genes that gets expressed. This means that there might be sequences in the genes that are not expressed, which is indeed true for most genes. The reason for these noncoding regions is to provide a buffer of terminals so that all k-expressions encoded in GEP genes correspond always to valid programs or expressions. The genes of gene expression programming are therefore composed of two different domains – a head and a tail – each with different properties and functions. The head is used mainly to encode the functions and variables chosen to solve the problem at hand, whereas the tail, while also used to encode the variables, provides essentially a reservoir of terminals to ensure that all programs are error-free. For GEP genes the length of the tail is given by the formula: t = h ( n max − 1 ) + 1 {\displaystyle t=h(n_{\max }-1)+1} where h is the head's length and nmax is maximum arity. For example, for a gene created using the set of functions F = {Q, +, −, ∗, /} and the set of terminals T = {a, b}, nmax = 2. And if we choose a head length of 15, then t = 15 (2–1) + 1 = 16, which gives a gene length g of 15 + 16 = 31. The randomly generated string below is an example of one such gene: 0123456789012345678901234567890 b+a-aQab+//+b+babbabbbababbaaa It encodes the expression tree: which, in this case, only uses 8 of the 31 elements that constitute the gene. It's not hard to see that, despite their fixed length, each gene has the potential to code for expression trees of different sizes and shapes, with the simplest composed of only one node (when the first element of a gene is a terminal) and the largest composed of as many nodes as there are elements in the gene (when all the elements in the head are functions with maximum arity). It's also not hard to see that it is trivial to implement all kinds of genetic modification (mutation, inversion, insertion, recombination, and so on) with the guarantee that all resulting offspring encode correct, error-free programs. == Multigenic chromosomes == The chromosomes of gene expression programming are usually composed of more than one gene of equal length. Each gene codes for a sub-expression tree (sub-ET) or sub-program. Then the sub-ETs can interact with one another in different ways, forming a more complex program. The figure shows an example of a program composed of three sub-ETs. In the final program the sub-ETs could be linked by addition or some other function, as there are no restrictions to the kind of linking function one might choose. Some examples of more complex linkers include taking the average, the median, the midrange, thresholding their sum to make a binomial classification, applying the sigmoid function to compute a probability, and so on. These linking functions are usually chosen a priori for each problem, but they can also be evolved elegantly and efficiently by the cellular system of gene expression programming. == Cells and code reuse == In gene expression programming, homeotic genes control the interactions of the different sub-ETs or modules of the main program. The expression of such genes results in different main programs or cells, that is, they determine which genes are expressed in each cell and how the sub-ETs of each cell interact with one another. In other words, homeotic genes determine which sub-ETs are called upon and how often in which main program or cell and what kind of connections they establish with one another. === Homeotic genes and the cellular system === Homeotic genes have exactly the same kind of structural organization as normal genes and they are built using an identical process. They also contain a head domain and a tail domain, with the difference that the heads contain now linking functions and a special kind of terminals – genic terminals – that represent the normal genes. The expression of the normal genes results as usual in different sub-ETs, which in the cellular system are called ADFs (automatically defined functions). As for the tails, they contain only genic terminals, that is, derived features generated on the fly by the algorithm. For example, the chromosome in the figure has three normal genes and one homeotic gene and encodes a main program that invokes three different functions a total of four times, linking them in a particular way. From this example it is clear that the cellular system not only allows the unconstrained evolution of linking functions but also code reuse. And it shouldn't be hard to implement recursion in this system. === Multiple main programs and multicellular systems === Multicellular systems are composed of more than one homeotic gene. Each homeotic gene in this system puts together a different combination of sub-expression trees or ADFs, creating multiple cells or main programs. For example, the program shown in the figure was created using a cellular system with two cells and three normal genes. The applications of these multicellular systems are mu

  Read more →
• 

  A.I. Insight forums

  The Artificial Intelligence Insight forums, also known as the A.I. Insight forums, are a series of forums to build consensus on how the United States Congress should craft A.I. legislation. Organized by Senate Majority Leader Charles "Chuck" Schumer, the first of nine closed-door forums convened on September 13, 2023. == Background == Amid a surge in the popularity and advancement of artificial intelligence, senator Chuck Schumer launched an effort to establish a framework for the regulation of A.I. in April 2023. By the end of June, a preliminary framework – dubbed the "SAFE Innovation Framework" – was established and presented to Congress. Schumer also announced a series of forums wherein tech leaders who were well-acquainted with A.I. would help to "educate" Congress on the risks and problems that A.I. poses. Many tech leaders including Sam Altman, Elon Musk, and Sundar Pichai were set to attend the meetings. Many U.S. lawmakers and senators such as Mike Rounds and Todd Young were also set to attend. == September 13 forum == The overarching consensus following the conclusion of the September 13 forum was that there "should be" regulations regarding the use and advancement of A.I., but it should not be made "too fast". Many tech executives who attended the forum also warned senators of the risks and threats that A.I. could pose. Musk, who attended the forum, stated afterwards that there was "overwhelming consensus" on the regulation of A.I. === Invitees === This is a list of people who were invited to attend the September 13 forum. Elon Musk (Tesla, SpaceX, X Corp.) Sam Altman (OpenAI) Bill Gates (ex–Microsoft) Jensen Huang (Nvidia) Alex Karp (Palantir) Satya Nadella (Microsoft) Arvind Krishna (IBM) Sundar Pichai (Alphabet Inc., Google) Eric Schmidt (ex–Google) Mark Zuckerberg (Meta) Charles Rivkin (Motion Picture Association) Liz Shuler (AFL-CIO) Meredith Stiehm (Writers Guild of America) Randi Weingarten (American Federation of Teachers) Maya Wiley (LCCHR) == October 24 forum == The second of nine forums was hosted on October 24, 2023, as federal A.I. regulation drew nearer. According to Schumer's office, the forum was centered mainly on how A.I. could "enable innovation", and the innovation that is needed for the safe progression of A.I. At the forum, Senators Brian Schatz and John Kennedy introduced the "Schatz-Kennedy A.I. Labeling Act", a new piece of A.I. legislation that would provide "more transparency on A.I.-generated content". Following the forum, Senator Rounds stated that in order to fuel the development of A.I., a total estimated $56 billion would be needed for the next three years. Rounds, alongside Senator Young and Schumer, also highlighted the need to outcompete China and workforce initiatives. === Invitees === 21 people were invited to attend the forum, and were composed largely of venture capitalists, academics, civil rights campaigners, and industry figures. Some key figures included venture capitalists Marc Andreessen and John Doerr. == Future == Over the course of fall 2023, there is slated to be a total of nine forums on the topic of A.I., with the first hosted on September 13.

  Read more →
• 

  Void Trilogy

  The Void Trilogy is a space opera series by British author Peter F. Hamilton. The series is set in the same universe as The Commonwealth Saga, 1,200 years after the end of Judas Unchained. Peter F. Hamilton sold the American rights to the series to Random House. The series includes the following books: The Dreaming Void (2007) The Temporal Void (2008) The Evolutionary Void (2010) == Synopsis == === The Dreaming Void === What was formerly believed to be a supermassive black hole at the centre of the Milky Way is revealed to be an artificial construct, known as the Void. Inside, there is a strange universe where the laws of physics are very different from standard physics. It is slowly consuming the other stars of the galactic core—one day it will have devoured the entire galaxy. In AD 3320, a human member of the Commonwealth, Inigo, begins to have dreams of the wonderful existence inside the Void. His dreams inspire the disaffected, who desire to travel into the Void, where their every wish will be fulfilled. By AD 3456, the pseudo-religious Living Dream movement exceeds 5 billion members, organizing the followers into a powerful political force. Other star-faring species fear their migration will cause the Void to expand again thus devouring the galaxy. They are prepared to stop the pilgrimage fleet no matter what the cost. The Dreaming Void is broken into two distinct sections. The first follows Edeard, a young boy who lives inside the Void on a planet called Querencia, the subject of Inigo's dreams. Edeard, an orphan and apprentice, lives in Ashwell, a town in Rulan province. A gifted psychic, he is trained by Master Akeem in crafting and modding. Initially a loner, he comes to prominence in his village after designing an alternative pump mechanism for the local well. Unfortunately his luck changes for the worse after Ashwell is raided by bandits. Forced to flee, he joins the local caravan and travels to Makkathran, the capital of Querencia. In Makkathran, Edeard joins the constables and after a brutal couple of months in training, he graduates and is promoted to the commander of his Squad. He makes little progress battling the rigid and backward judicial system of Makkathran; his first real break is when his squad overcomes a trap set by the local gang, and Edeard walks on water chasing the leader of the gang. A testament to his growing psychic abilities, Edeard's stunt earns him the title of Waterwalker, and he becomes an instant star in Makkathran. The second section of The Dreaming Void is set back in the Commonwealth. Inigo, the first dreamer, and founder of Living Dream, has disappeared, leaving the 5 billion strong Living Dream movement in a state of flux. When Ethan, succeeding Inigo as the head of the movement, proclaims that the Living Dream will embark on a pilgrimage into the Void, the Commonwealth is thrown into a state of political chaos. Fearing that the human migration might cause the Void to expand (and in the process destroy whole systems or even the whole Galaxy) other spacefaring races such as the Raiel and Ocisen Empire are deeply concerned, with the latter threatening military action. This has left the Commonwealth government deeply divided, with the two largest factions in disagreement, the Accelerators faction/party supporting the pilgrimage and the Conservative faction opposing. As both parties are unable to solve the situation politically they have resolved to take matters into their own hands, with each party sending agents to further its interests. Aaron, a sleeper cell agent, is tasked with finding Inigo. He kidnaps and manipulates Corrie-Lyn, a former lover of Inigo and interrogates her for information. He also travels to Kuhmo (Inigo's homeworld) to get further information and robs Inigo's secure storage (a bank for memory). He eventually tracks Inigo to Hanko, a desolate and barren world. However, before Aaron can extract Inigo, Accelerator agents destroy Aaron's starship leaving him marooned on Hanko. Meanwhile, Accelerator agents make a deal with Ethan, agreeing to give the Living Dream movement Ultra Drives to power their ships. Accelerator plans are halted when the Delivery Man, a Conservative party agent, destroys valuable FTL Drive tech. Troblum, an Accelerator physicist, also defects, further slowing the Accelerators plans. === The Temporal Void === The Temporal Void picks up after The Dreaming Void. The Intersolar Commonwealth faces mounting turmoil as the deadline for Living Dream's Pilgrimage into the Void approaches. An Ocisen Empire fleet advances on a mission of genocide, while an internecine war erupts among post-human factions over humanity's future. Amidst the chaos, investigator Paula Myo struggles to counter the increasingly desperate actions of various agents and factions. Relentless in her pursuit, she contends with adversaries from her distant past and colleagues of uncertain loyalty, all while racing against time. At the center of the unfolding crisis is Edeard the Waterwalker, a figure from the distant past who lived deep within the Void. As the messiah of Living Dream, his life—broadcast through visions—captivates and inspires billions. His story fuels the Pilgrimage's momentum, a force seemingly impossible to stop. As Edeard approaches his ultimate victory, the true nature of the Void is finally revealed. === The Evolutionary Void === The Evolutionary Void picks up after The Temporal Void. Exposed as the Second Dreamer, Araminta has become the target of a galaxy-wide search by government agent Paula Myo and the psychopath known as the Cat, along with others equally determined to prevent, or facilitate, the pilgrimage of the Living Dream cult into the heart of the Void. An indestructible microuniverse, the Void may contain paradise, as the cultists believe, but it is also a deadly threat. For the miraculous reality that exists inside its boundaries demands energy, energy drawn from everything outside those boundaries: from planets, stars, galaxies, and everything that lives, for the Pilgrimage will trigger a super-massive expansion of the Void. Meanwhile, the parallel story of Edeard, the Waterwalker, as told through a series of dreams communicated to the gaiafield via Inigo, the First Dreamer, continues to unfold. But the inspirational tale of this idealistic young man takes a darker and more troubling turn as he finds himself faced with powerful new enemies, and temptations more powerful still, to reach fulfilment in the end. Named a Silfen Friend like her ancestress Mellanie, Araminta chooses to face her unwanted responsibilities, with no guarantee of success or survival. She takes on the role of Second Dreamer to lead the first wave of Living Dream, 24 million people, into the Void, leaving everyone confused and lost by her actions. However, in actuality, she is playing a double game. Using her original body to lead the Living Dream as a diversion, she borrows one of her fiancé's (Mr. Bovey) bodies to set out to destroy the Void. She is able to connect with a Skylord and travel the Silfen Paths. With time running out, a repentant Inigo decides to release Edeard's final dream whose message is scarcely less dangerous than the pilgrimage promises to be, where perfection is achieved, so that nothing else is left to strive for and the human race in the Void has started to devolve. He goes to the Spike to meet Ozzie and stays there to meet with Araminta, who is using one of her fiancé's bodies, and Oscar. Third Dreamer Gore Burnelli has a plan to reason with the Heart, the core of the Void. He secures the help of the Delivery Man and travels to the Anomine homeworld to retrieve the mechanism that allowed them to go post-physical. He is able to connect with Justine, his daughter, who is currently in the Void, by way of Dreams. The monomaniacal Ilanthe, leader of the breakaway Accelerator Faction, seeks dominion in the Void. It is not Fusion with the Void to attain post-physical status that she wants, but to have control over everything. Using Dark Fortress technology, she sets up a barrier around the Sol system which leaves ANA and the deterrence fleet trapped inside. It is this technology which she has equipped the ships travelling to the Void with, the ability to create a forcefield which the Warrior Raiel cannot penetrate. == Technology == The Commonwealth uses a number of advanced technologies. In the early days of the Commonwealth, humans used static and permanently opened wormholes to travel from planet to planet. However, after the events of the Starflyer War (detailed in the Commonwealth Saga), the CST corporation's monopoly on space travel was ended. With the advent of wormholes that could wrap around ships, the Commonwealth saw a shift from wormholes to spaceships. Another development in the Commonwealth is the gaiafield. Developed by Ozzie Issac in AD 3000, the gaiafield is based on Silfen technology; when Ozzie was named a friend of the Silfen during the Starflye

  Read more →
• 

  Inductive probability

  Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematical basis for learning and the perception of patterns. It is a source of knowledge about the world. There are three sources of knowledge: inference, communication, and deduction. Communication relays information found using other methods. Deduction establishes new facts based on existing facts. Inference establishes new facts from data. Its basis is Bayes' theorem. Information describing the world is written in a language. For example, a simple mathematical language of propositions may be chosen. Sentences may be written down in this language as strings of characters. But in the computer it is possible to encode these sentences as strings of bits (1s and 0s). Then the language may be encoded so that the most commonly used sentences are the shortest. This internal language implicitly represents probabilities of statements. Occam's razor says the "simplest theory, consistent with the data is most likely to be correct". The "simplest theory" is interpreted as the representation of the theory written in this internal language. The theory with the shortest encoding in this internal language is most likely to be correct. == History == Probability and statistics was focused on probability distributions and tests of significance. Probability was formal, well defined, but limited in scope. In particular its application was limited to situations that could be defined as an experiment or trial, with a well defined population. Bayes's theorem is named after Rev. Thomas Bayes 1701–1761. Bayesian inference broadened the application of probability to many situations where a population was not well defined. But Bayes' theorem always depended on prior probabilities, to generate new probabilities. It was unclear where these prior probabilities should come from. Ray Solomonoff developed algorithmic probability which gave an explanation for what randomness is and how patterns in the data may be represented by computer programs, that give shorter representations of the data circa 1964. Chris Wallace and D. M. Boulton developed minimum message length circa 1968. Later Jorma Rissanen developed the minimum description length circa 1978. These methods allow information theory to be related to probability, in a way that can be compared to the application of Bayes' theorem, but which give a source and explanation for the role of prior probabilities. Marcus Hutter combined decision theory with the work of Ray Solomonoff and Andrey Kolmogorov to give a theory for the Pareto optimal behavior for an Intelligent agent, circa 1998. === Minimum description/message length === The program with the shortest length that matches the data is the most likely to predict future data. This is the thesis behind the minimum message length and minimum description length methods. At first sight Bayes' theorem appears different from the minimimum message/description length principle. At closer inspection it turns out to be the same. Bayes' theorem is about conditional probabilities, and states the probability that event B happens if firstly event A happens: P ( A ∧ B ) = P ( B ) ⋅ P ( A | B ) = P ( A ) ⋅ P ( B | A ) {\displaystyle P(A\land B)=P(B)\cdot P(A|B)=P(A)\cdot P(B|A)} becomes in terms of message length L, L ( A ∧ B ) = L ( B ) + L ( A | B ) = L ( A ) + L ( B | A ) . {\displaystyle L(A\land B)=L(B)+L(A|B)=L(A)+L(B|A).} This means that if all the information is given describing an event then the length of the information may be used to give the raw probability of the event. So if the information describing the occurrence of A is given, along with the information describing B given A, then all the information describing A and B has been given. ==== Overfitting ==== Overfitting occurs when the model matches the random noise and not the pattern in the data. For example, take the situation where a curve is fitted to a set of points. If a polynomial with many terms is fitted then it can more closely represent the data. Then the fit will be better, and the information needed to describe the deviations from the fitted curve will be smaller. Smaller information length means higher probability. However, the information needed to describe the curve must also be considered. The total information for a curve with many terms may be greater than for a curve with fewer terms, that has not as good a fit, but needs less information to describe the polynomial. === Inference based on program complexity === Solomonoff's theory of inductive inference is also inductive inference. A bit string x is observed. Then consider all programs that generate strings starting with x. Cast in the form of inductive inference, the programs are theories that imply the observation of the bit string x. The method used here to give probabilities for inductive inference is based on Solomonoff's theory of inductive inference. ==== Detecting patterns in the data ==== If all the bits are 1, then people infer that there is a bias in the coin and that it is more likely also that the next bit is 1 also. This is described as learning from, or detecting a pattern in the data. Such a pattern may be represented by a computer program. A short computer program may be written that produces a series of bits which are all 1. If the length of the program K is L ( K ) {\displaystyle L(K)} bits then its prior probability is, P ( K ) = 2 − L ( K ) {\displaystyle P(K)=2^{-L(K)}} The length of the shortest program that represents the string of bits is called the Kolmogorov complexity. Kolmogorov complexity is not computable. This is related to the halting problem. When searching for the shortest program some programs may go into an infinite loop. ==== Considering all theories ==== The Greek philosopher Epicurus is quoted as saying "If more than one theory is consistent with the observations, keep all theories". As in a crime novel all theories must be considered in determining the likely murderer, so with inductive probability all programs must be considered in determining the likely future bits arising from the stream of bits. Programs that are already longer than n have no predictive power. The raw (or prior) probability that the pattern of bits is random (has no pattern) is 2 − n {\displaystyle 2^{-n}} . Each program that produces the sequence of bits, but is shorter than the n is a theory/pattern about the bits with a probability of 2 − k {\displaystyle 2^{-k}} where k is the length of the program. The probability of receiving a sequence of bits y after receiving a series of bits x is then the conditional probability of receiving y given x, which is the probability of x with y appended, divided by the probability of x. ==== Universal priors ==== The programming language affects the predictions of the next bit in the string. The language acts as a prior probability. This is particularly a problem where the programming language codes for numbers and other data types. Intuitively we think that 0 and 1 are simple numbers, and that prime numbers are somehow more complex than numbers that may be composite. Using the Kolmogorov complexity gives an unbiased estimate (a universal prior) of the prior probability of a number. As a thought experiment an intelligent agent may be fitted with a data input device giving a series of numbers, after applying some transformation function to the raw numbers. Another agent might have the same input device with a different transformation function. The agents do not see or know about these transformation functions. Then there appears no rational basis for preferring one function over another. A universal prior insures that although two agents may have different initial probability distributions for the data input, the difference will be bounded by a constant. So universal priors do not eliminate an initial bias, but they reduce and limit it. Whenever we describe an event in a language, either using a natural language or other, the language has encoded in it our prior expectations. So some reliance on prior probabilities are inevitable. A problem arises where an intelligent agent's prior expectations interact with the environment to form a self reinforcing feed back loop. This is the problem of bias or prejudice. Universal priors reduce but do not eliminate this problem. === Universal artificial intelligence === The theory of universal artificial intelligence applies decision theory to inductive probabilities. The theory shows how the best actions to optimize a reward function may be chosen. The result is a theoretical model of intelligence. It is a fundamental theory of intelligence, which optimizes the agents behavior in, Exploring the environment; performing actions to get responses that broaden the agents knowledge. Competing or co-operating with another agent; games. Balancing short and long term rewards. In general no agent will always provi

  Read more →
• 

  Galatea (video game)

  Galatea is an interactive fiction video game by Emily Short featuring a modern rendition of the Greek myth of Galatea, the sculpture of a woman that gained life. It took "Best of Show" in the 2000 IF Art Show and won a XYZZY Award for Best non-player character. The game displays an unusually rich approach to non-player character dialogue and diverts from the typical puzzle-solving in interactive fiction: gameplay consists entirely of interacting with a single character in a single room. Galatea is licensed under the Creative Commons BY-NC-ND 3.0 US license. == Gameplay == Galatea alters the typical interactive fiction game mechanics by concentrating instead on the player's interactions with a single non-player character (NPC), the eponymous Galatea. Much of the interest of the piece derives from the ambiguous nature of the player–NPC dialogue: the form of the conversation and, indeed, the nature of Galatea herself shift depending on the focus the player places on certain aspects of the character's personality. Numerous endings are possible. Gameplay centers around the developing dialogue between Galatea and the player when asking about topics in the previous conversation. Two commands, "think about" and "recap", are provided to keep track of what has already been said; the former is also used to advance the storyline, as the player character draws conclusions about the story as it has unfolded to that point. The game also encourages using sensory commands ("touch", "listen to", "look at"), adding immersion to the experience. == Plot == Galatea is loosely based on the myth of Pygmalion, who carved the sculpture of a woman. In the myth, he falls in love with the statue, named Galatea or Elise in different versions, and the goddess Venus brings her to life. The story begins at the opening of an exhibition of artificial intelligences. The player, alone, discovers Galatea displayed on a pedestal with a small information placard. She is illuminated by a spotlight and wears an emerald dress. Seeing the player about to turn away, Galatea says, "They told me you were coming." From this point, the story may proceed in a number of ways depending on the player's words and actions. === Multilinear interactive fiction === Short describes this as "multilinear interactive fiction": while interactive fiction in general allows the player to find their own way through the story, this leads in most cases to a single ending (or at least a single desired 'correct' ending). With Galatea, Short presents a story with around 70 different endings and hundreds of possible ways of reaching them. The plot is thus designed to appear open-ended with the development of the story entirely dependent on what the player decides to talk or ask about or what actions they choose to perform. Thus the original author and the player share in the creation of a work of fiction. == Development == In interviews, Emily Short has explained that Galatea arose out of her efforts to develop advanced dialog coding for interactive fiction engines. Although code for simple conversational programs like ELIZA have existed since the 1960s, and limited dialog options have existed in interactive fiction since the 1970s, Short's efforts to develop chatterbot-like dialog required her to produce a simple test case scenario to test NPC interaction. Thus the single-room, single-occupant Galatea was a natural result. Development of the game progressed organically with Short engaging in test runs and drafting new dialog options for every conversational dead-end that arose. The game's multiple endings also arose in a similar fashion although Short had intended that there be multiple endings from the start. Although the nature of the game's development as well as its minimalist final form has led to questions regarding whether it is really a game and not just an experimental conversational program, Short has suggested that to her the definition of interactive fiction requires nothing more than a world model and a parser, and "anything you can cook up with those features counts as IF." Short has acknowledged the helpful influence of the close-knit IF community and the "atmosphere in which experimentation is valued" as leading to the success of her works like Galatea. == Reception == Galatea was well received, achieving critical acclaim from interactive fiction reviewers and literary scholars. The game is considered to aspire to a new level of art in interactive fiction, and thereby to have revolutionized the genre, establishing its author, Emily Short, as one of the key figures in the modern interactive fiction scene. Fellow award-winning IF author, Adam Cadre has called Galatea "the best NPC ever"—a view that was echoed by Joystiq's John Bardinelli. Cadre also describes the game as an example of an alternative kind of puzzle where "interactivity comes in deciding where to go, what to see, what to say. Rather than having to open gates along a path, you discover that they're all open at first, but stepping through one causes others to close." Galatea was described in 2007 by Indiegames.com as a "fascinating journey." In a 2009 article, Rock, Paper, Shotgun praised the depth and detail of the game, the complexities of the character design and its "masterful balance between intricacy and simplicity", and "Galatea's emotional turmoil" that is "encoded sweetly into the subtext of what's going on. By simply interacting in a logical manner, you learn more about this character than any cut-scene or info-dump could ever hope to convey." This was reiterated in a 2010 1UP.com article that listed Galatea as #2 in its "Top 5 Introductory Interactive Fiction Games" feature, describing it as intriguingly replayable, and as a "surprisingly rich game for its apparent minimalism". In 2011, PC Gamer highlighted Galatea as an example of the artistic and literary aspects of the interactive fiction genre. The titular character, Galatea, has been compared to the 2007 Portal character GLaDOS due to similarities in the personalities of the characters.

  Read more →
• 

  Full Dive

  Full Dive, short for Full Dive: This Ultimate Next-Gen Full Dive RPG Is Even Shittier than Real Life! (Japanese: 究極進化したフルダイブRPGが現実よりもクソゲーだったら, Hepburn: Kyūkyoku Shinka shita Furu Daibu RPG ga Genjitsu yori mo Kusogē Dattara), is a Japanese light novel series written by Light Tuchihi and illustrated by Youta. Media Factory has published four volumes since August 2020 under their MF Bunko J imprint. A manga adaptation with art by Kino was serialized in Media Factory's seinen manga magazine Monthly Comic Alive from January 2021 to January 2022. An anime television series adaptation by ENGI aired from April to June 2021. == Plot == Hiroshi Yuki, with the player name of Hiro, is a high school boy who loves to play virtual reality MMORPGs (VRMMORPG) in order to escape reality. When a game store manager named Reona Kisaragi tricks him into buying the game Kiwame Quest, he soon discovers that it is not what it seems. Unlike regular games, it is a game that tries to pursue realism to a fanatical point. As such, Hiroshi struggles to eke out a niche. Despite the disadvantages, he is determined to complete the game. == Characters == === Main characters === Hiroshi Yuki (結城宏, Yūki Hiroshi) Voiced by: Daiki Yamashita, Riho Sugiyama (young) (Japanese); Johnny Yong Bosch, Michele Knotz (young) (English) Hiroshi is a high school student who is tricked into buying Kiwame Quest by game store manager, Reona Kisaragi. He is a former member of the track team who quit following an unfortunate incident and he likes to play VRMMORPGs in order to escape reality. His player name is Hiro. Reona Kisaragi (如月玲於奈, Kisaragi Reona) Voiced by: Ayana Taketatsu (Japanese); Natalie Van Sistine (English) Reona is a game store manager who tricks Hiroshi into buying Kiwame Quest. She likes to tease him and her in-game avatar is that of a fairy. Alicia (アリシア, Arishia) Voiced by: Fairouz Ai (Japanese); Kayli Mills (English) Alicia is one of Hiroshi's childhood friends in Kiwame Quest. She has an older brother named Martin in-game. Mizarisa (ミザリサ) Voiced by: Shiori Izawa (Japanese); Sarah Anne Williams (English) Mizarisa is the town inquisitor in Kiwame Quest. Kaede Yuki (結城楓, Yūki Kaede) Voiced by: Aoi Koga (Japanese); Kate Bristol (English) Kaede is Hiroshi's younger sister. She used to look up to her older brother, but their relationship has been strained ever since he quit the track team. === NPCs === Martin (マーチン, Māchin) Voiced by: Haruki Ishiya, Natsumi Fujiwara (young) (Japanese); Ben Lepley, Krystal LaPorte (young) (English) Martin is one of Hiroshi's childhood friends in Kiwame Quest. He is also Alicia's older brother in-game. Tesla (テスラ, Tesura) Voiced by: Satoshi Hino (Japanese); Jason Liebrecht (English) Tesla is the captain of the City Guard in Kiwame Quest. Govern (ガバン, Gaban) Voiced by: Shizuka Itō (Japanese); Lisa Ortiz (English) Govern is the queen of Ted in Kiwame Quest. === Other characters === Ginji (ギンジ) Voiced by: Katsuyuki Konishi (Japanese); Brent Mukai (English) Ginji is a veteran player of Kiwame Quest. Soichiro Kamui (神居宗一郎, Kamui Sōichirō) Voiced by: Yoshitsugu Matsuoka (Japanese); Samuel Drake (English) Kamui is the only known player who has successfully completed Kiwame Quest. == Media == === Light novels === Light Tuchihi launched the light novel series, with illustrations by Youta, under Media Factory's MF Bunko J label on August 25, 2020. ==== Volumes ==== === Manga === A manga adaptation by Kino was serialized in Media Factory's Monthly Comic Alive magazine from January 27, 2021, to January 27, 2022. Two tankōbon volumes were released from May 21, 2021, to January 21, 2022. ==== Volumes ==== === Anime === An anime television series adaptation was announced on December 4, 2020. The series was animated by ENGI and directed by Kazuya Miura, with Kenta Ihara writing the series' scripts, and Yūta Kevin Kenmotsu designing the characters. It ran from April 7 to June 23, 2021, on AT-X, Tokyo MX, SUN, KBS Kyoto, and BS11. Mayu Maeshima performed the opening theme "Answer", while Ayana Taketatsu, Fairouz Ai, Shiori Izawa, and Aoi Koga performed the ending theme "Kisuida!". It ran for 12 episodes. Funimation licensed and streamed the series. On June 8, 2021, Funimation announced that the series would receive an English dub, which premiered the following day. Following Sony's acquisition of Crunchyroll, the series was moved to Crunchyroll. ==== Episodes ====

  Read more →