AI Assistant Image

AI Assistant Image — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

JotterPad

JotterPad is a text editor app for Android, developed by Two App Studio. It is proprietary software that uses the freemium pricing strategy. == Features == Jotterpad supports the markdown and fountain markup languages. Among its features are themes, synchronisation with Google Drive and Dropbox, dictionary and thesaurus, and snapshots. JotterPad uses a freemium pricing model, which means that a restricted version of the app is offered for free, while access to additional functionality requires payment. About half of the features are available in the free version. The synchronisation feature was originally limited to one account, and in Jotterpad 12 the option to synchronise using multiple accounts was added as a monthly subscription service.
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Your AI Slop Bores Me

Your AI Slop Bores Me (stylized in all lowercase) is a website and social experiment created by programmer Mihir Maroju. Serving as a parody of large language models (LLMs) like ChatGPT and Claude, all questions and image prompts posed by users are answered by other, randomly-selected human users of the site. As of March 2026, the site has reached 50 million hits and sits at 16,000 concurrent users. == Background == In an interview with Fast Company, Maroju said he was inspired to create the site by his frustration with AI proliferating the internet with AI generated content, saying the site came from "a frustration for AI art and its proliferation, making artists' lives worse and also just filling the internet with low-effort generic slop". == Overview == The site has a credit system, in which a first-time user will be given 1 credit for free. Every 10 minutes, if a user has 0 credits, they will receive 2 credits. Once the credits are used up, the user can no longer do prompts unless the user earns them. The user can earn credits by responding to other user's prompts by "larping as AI" while given a 75-second time limit. Prompts can either be for a written response, or a drawing for the other user to fulfill the prompt. The maximum amount of credits a user can have is 6 credits, and cannot exceed the maximum limit. If the prompting user activates "thinking mode", the countdown is extended to 150 seconds for the cost of 2 credits. == Reception == The site has garnered attention and praise from X users, and across many online communities. The Daily Dot's Rachel Kiley wrote that "the best part about the game is that there's really no right or wrong way to do it. Humans aren't LLMs trained on copyrighted material and the whole of the free internet, but we do retain a certain amount of the information we've learned from those things over the course of our lives, while also being capable of creativity". Chris Taylor of Mashable called the site "amateurish and charming". Aftermath's Nicole Carpenter wrote that the site reminded her of "the human touch of chaos".
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Fuzzy classification

Fuzzy classification is the process of grouping elements into fuzzy sets whose membership functions are defined by the truth value of a fuzzy propositional function. A fuzzy propositional function is analogous to an expression containing one or more variables, such that when values are assigned to these variables, the expression becomes a fuzzy proposition. Accordingly, fuzzy classification is the process of grouping individuals having the same characteristics into a fuzzy set. A fuzzy classification corresponds to a membership function μ C ~ : P F ~ × U → T ~ {\textstyle \mu _{\tilde {C}}:{\tilde {PF}}\times U\to {\tilde {T}}} that indicates the degree to which an individual i ∈ U {\textstyle i\in U} is a member of the fuzzy class C ~ {\textstyle {\tilde {C}}} , given its fuzzy classification predicate Π ~ C ~ ∈ P F ~ {\textstyle {\tilde {\Pi }}_{\tilde {C}}\in {\tilde {PF}}} . Here, T ~ {\textstyle {\tilde {T}}} is the set of fuzzy truth values, i.e., the unit interval [ 0 , 1 ] {\textstyle [0,1]} . The fuzzy classification predicate Π ~ C ~ ( i ) {\textstyle {\tilde {\Pi }}_{\tilde {C}}(i)} corresponds to the fuzzy restriction " i {\textstyle i} is a member of C ~ {\textstyle {\tilde {C}}} ". == Classification == Intuitively, a class is a set that is defined by a certain property, and all objects having that property are elements of that class. The process of classification evaluates for a given set of objects whether they fulfill the classification property, and consequentially are a member of the corresponding class. However, this intuitive concept has some logical subtleties that need clarification. A class logic is a logical system which supports set construction using logical predicates with the class operator { ⋅ | ⋅ } {\textstyle \{\cdot |\cdot \}} . A class C = { i | Π ( i ) } {\displaystyle C=\{i|\Pi (i)\}} is defined as a set C of individuals i satisfying a classification predicate Π which is a propositional function. The domain of the class operator { .| .} is the set of variables V and the set of propositional functions PF, and the range is the powerset of this universe P(U) that is, the set of possible subsets: { ⋅ | ⋅ } : V × P F → P ( U ) {\displaystyle \{\cdot |\cdot \}:V\times PF\rightarrow P(U)} Here is an explanation of the logical elements that constitute this definition: An individual is a real object of reference. A universe of discourse is the set of all possible individuals considered. A variable V :→ R {\textstyle V:\rightarrow R} is a function which maps into a predefined range R without any given function arguments: a zero-place function. A propositional function is "an expression containing one or more undetermined constituents, such that, when values are assigned to these constituents, the expression becomes a proposition". In contrast, classification is the process of grouping individuals having the same characteristics into a set. A classification corresponds to a membership function μ that indicates whether an individual is a member of a class, given its classification predicate Π. μ : P F × U → T {\displaystyle \mu :PF\times U\rightarrow T} The membership function maps from the set of propositional functions PF and the universe of discourse U into the set of truth values T. The membership μ of individual i in Class C is defined by the truth value τ of the classification predicate Π. μ C ( i ) := τ ( Π ( i ) ) {\displaystyle \mu C(i):=\tau (\Pi (i))} In classical logic the truth values are certain. Therefore a classification is crisp, since the truth values are either exactly true or exactly false.
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Sword Art Online

Sword Art Online (Japanese: ソードアート・オンライン, Hepburn: Sōdo Āto Onrain) is a Japanese light novel series written by Reki Kawahara and illustrated by abec. The series takes place in the 2020s and focuses on protagonists Kazuto "Kirito" Kirigaya and Asuna Yuuki as they play through various virtual reality MMORPG worlds, and later their involvement in the matters of a simulated civilization. Kawahara originally released the series as a web novel on his website from 2002 to 2008. The light novels began publication on ASCII Media Works' Dengeki Bunko imprint from April 10, 2009, with a spin-off series launching in October 2012. The series has spawned twelve manga adaptations published by ASCII Media Works and Kadokawa. The novels and the manga adaptations have been licensed for release in North America by Yen Press. An anime television series produced by A-1 Pictures, known simply as Sword Art Online, aired in Japan between July and December 2012, with a television film Sword Art Online: Extra Edition airing on December 31, 2013, and a second season, titled Sword Art Online II, airing between July and December 2014. An animated film titled Sword Art Online the Movie: Ordinal Scale, featuring an original story by Kawahara, premiered in Japan and Southeast Asia on February 18, 2017, and was released in the United States on March 9, 2017. A spin-off anime series titled Sword Art Online Alternative: Gun Gale Online premiered in April 2018, while a third season titled Sword Art Online: Alicization aired from October 2018 to September 2020. An anime film adaptation of Sword Art Online: Progressive titled Sword Art Online Progressive: Aria of a Starless Night premiered on October 30, 2021. A second film titled Sword Art Online Progressive: Scherzo of Deep Night premiered on October 22, 2022. Many video games based on the series have been released for consoles, PC, and mobile devices. Sword Art Online has achieved widespread commercial success, with the light novels having over 30 million copies sold worldwide. The anime series has received mixed to positive reviews, with praise for its animation, musical score, and exploration of the psychological aspects of virtual reality, but it has also been met with criticisms for its pacing and writing. == Synopsis == === Setting === The light novel series spans several virtual reality worlds, beginning with the game, Sword Art Online (SAO), which is set in a world known as Aincrad. Each world is built on a game engine called Cardinal system, which was initially developed specifically for SAO by Akihiko Kayaba, but was later duplicated for Alfheim Online (ALO), and a consolidated package is later given to Kirito in the form of the World Seed, who had it leaked online with the successful intention of reviving the virtual reality industry. A third world known as Gun Gale Online (GGO) appears in the third arc and is stylized as a first-person shooter game instead of a role-playing game, and is the main setting of Alternative Gun Gale Online. It was created using the World Seed by an American company. A fourth world appears in the fourth arc known as the Underworld (UW). The world itself was created using the World Seed as a base, but it is as realistic as the real world due to using many powerful government resources to keep it running. === Plot === In 2022, a virtual reality massively multiplayer online role-playing game (VRMMORPG) called Sword Art Online (SAO) was released. With the NerveGear, a helmet that stimulates the user's five senses via their brain, players can experience and control their in-game characters with their minds. Both the game and the NerveGear were created by Akihiko Kayaba. On November 6, 10,000 players log into SAO's mainframe cyberspace for the first time, only to discover that they are unable to log out. Kayaba appears and tells the players that they must beat all 100 floors of Aincrad, a steel castle which is the setting of SAO, if they wish to be free. He also states that those who suffer in-game deaths or forcibly remove the NerveGear out-of-game will suffer real-life deaths. A player named Kazuto "Kirito" Kirigaya is one of 1,000 testers in the game's previous closed beta. With the advantage of previous VR gaming experience and a drive to protect other beta testers from discrimination, he isolates himself from the greater groups and plays the game alone, bearing the mantle of "beater", a portmanteau of "beta tester" and "cheater". As the players progress through the game Kirito eventually befriends a young woman named Asuna Yuuki, forming a relationship with and later marrying her in-game. After the duo discover the identity of Kayaba's secret ID, who was playing as "Heathcliff", the leader of the guild Asuna joined in, they confront and destroy him, freeing themselves and the other players from the game. In the real world, Kazuto discovers that 300 SAO players, including Asuna, remain trapped in their NerveGear. As he goes to the hospital to see Asuna, he meets Asuna's father Shouzou Yuuki who is asked by an associate of his, Nobuyuki Sugou, to make a decision, which Sugou later reveals to be his marriage with Asuna, angering Kazuto. Several months later, he is informed by Agil, another SAO survivor, that a figure similar to Asuna was spotted on "The World Tree" in another VRMMORPG cyberspace called Alfheim Online (ALO). Assisted in-game by his cousin and adoptive sister Suguha "Leafa" Kirigaya and Yui, a navigation pixie (originally an AI from SAO), he quickly learns that the trapped players in ALO are part of a plan conceived by Sugou to perform illegal experiments on their minds. The goal is to create the perfect mind-control for financial gain and to subjugate Asuna, whom he intends to marry in the real world, to assume control of her family's corporation. Kirito eventually stops the experiment and rescues the remaining 300 SAO players, foiling Sugou's plans. Before leaving ALO to see Asuna, Kayaba, who has uploaded his mind to the Internet using an experimental, destructively high-powered version of NerveGear at the cost of his life, entrusts Kirito with The Seed – a package program designed to create virtual worlds. Kazuto eventually reunites with Asuna in the real world after thwarting an attack from Sugou and The Seed is released onto the Internet, reviving Aincrad as other VRMMORPGs begin to thrive. One year after the events of SAO, at the prompting of a government official investigating strange occurrences in VR, Kazuto takes on a job to investigate a series of murders involving another VRMMORPG called Gun Gale Online (GGO), the AmuSphere (the successor of the NerveGear), and a player called Death Gun. Aided by a female player named Shino "Sinon" Asada, he participates in a gunfight tournament called the Bullet of Bullets (BoB) and discovers the truth behind the murders, which originated with a player who participated in a player-killing guild in SAO. Through his and Sinon's efforts, two suspects are captured, though the third suspect, Johnny Black, escapes. Kazuto is later recruited to test an experimental FullDive machine, Soul Translator (STL), which has an interface far more realistic and complex than the previous machine he had played, to help RATH, a research and development organization under the Ministry of Defense (MOD), develop an artificial intelligence named A.L.I.C.E. He tests the STL by entering the Underworld (UW), a virtual reality cyberspace created with The Seed package. In the UW, the flow of time proceeds a thousand times faster than in the real world, and Kirito's memories of what happens inside are restricted. However, when Johnny Black ambushes and mortally wounds Kazuto with suxamethonium chloride, RATH recovers Kazuto and places him back into the STL to preserve his mind while attempts are made to save him. During his time in Underworld, Kirito befriends Eugeo, a carver in a small village of Rulid, and helps him on a journey to save Alice Zuberg, his friend who was taken by a group of highly skilled warriors known as the Integrity Knights for accidentally breaking a rule of the Axiom Church, the leaders of the Human Empire. He and Eugeo soon find themselves uncovering the secrets of the Axiom Church, led by a woman only known as "The Administrator", and the true purpose of Underworld itself, while unbeknownst to them, a war against the opposing Dark Territory is brewing on the horizon. They meet Alice, now an Integrity Knight, and though she does not remember them, Kirito helps her remember her true identity: a form of true artificial intelligence known as A.L.I.C.E. In the battle against the Administrator, Kirito manages to slay her, though Eugeo dies in the process, to Kirito's dismay. Meanwhile, in the real world, conflict escalates as American forces raid RATH's facility in the Ocean Turtle in an effort to take A.L.I.C.E. for purposes unknown. Two of the attackers - Gabriel "Vecta" Miller and Vassago "Prince of Hell" Cassals - take contr
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PCPaint

PCPaint was one of the first IBM PC-based mouse-driven GUI paint programs, released in 1984. It followed after Microsoft Doodle, released in 1983 with the Microsoft Mouse version 1 drivers for DOS, and around the same time as Digital Research’s Draw program. It was developed and created by John Bridges and Doug Wolfgram. It was later developed into Pictor Paint. The hardware manufacturer Mouse Systems bundled PCPaint with millions of computer mice that they sold, making PCPaint one of the best-selling DOS-based paint programs of the mid 1980s. == History == In 1983, Doug Wolfgram bought a Microsoft Mouse and decided to write a drawing program for it. They named it “Mouse Draw”. The interface was primitive but the program functioned well. Wolfgram traveled to SoftCon in New Orleans where he demonstrated the program to Mouse Systems. Mouse Systems was developing an optical mouse and they wanted to bundle a painting program so they agreed to publish Mouse Draw. The original program was written entirely in assembly language with primitive graphics routines developed by Wolfgram. John Bridges worked for an educational software company, Classroom Consortia Media, Inc., developing and writing Apple and IBM graphics libraries for CCM's software. Bridges and Wolfgram were friends who had been connected through a bulletin board system developed and run by Wolfgram. The two collaborated cross country via the BBS, Wolfram in California and Bridges in New York. Mouse Systems wanted the paint program to capture the look and feel of MacPaint. John Bridges and Doug Wolfgram started reworking Mouse Draw into what became PCPaint. The program was completely re-written using Bridge's graphics library and the top-level elements were written in C rather than assembly language. Bridges developed the core graphics code for the first version of PCPaint while Wolfgram worked on the user interface and top-level code. Mouse Systems signed an exclusive agreement with Wolfgram's company, Microtex Industries, Inc., to bundle PCPaint with every mouse they sold. They began publishing PCPaint with their mice in 1984. Microsoft responded in 1985 by bundling a competing product, PC Paintbrush, with version 4 of its DOS drivers for the Microsoft Mouse, replacing its in-house Microsoft Doodle program which it published with version 1 of the DOS drivers in mid-1983. Microsoft’s mouse began to outsell Mouse Systems mouse. In November 1985 Microsoft bundled a cut-down version of PC Paintbrush with Windows 1.0 (called Microsoft Paint), later bundling an updated version of PC Paintbrush with Windows 3.0 (as Paintbrush), impacting PCPaint’s marketshare. In early 1987, Mouse Systems decided that PCPaint wasn't helping to sell mice any longer so they discontinued the bundle deal and returned rights to the code to MicroTex Industries, but retained rights to the name, PCPaint. Wolfgram then combined the paint program with a new animation system he was developing (called GRASP) and Paul Mace Software bought publishing rights to the animation system and PCPaint, which was to be renamed Pictor. Bridges again got involved and took over programming responsibilities for GRASP as well as PCPaint while Wolfgram focused on more of the business details. In creating the first version of PCPaint, Doug had a dual-floppy machine with a Computer Innovations compiler on one disk and source code on the other. John had the "luxury" of a 10MB hard disk in his XT. Data was exchanged daily via 1200, then 2400 baud modems. === Authorship and Ownership === John Bridges and Wolfgram continued to work on PCPaint and GRASP on behalf of Paul Mace Software until 1990. Also in that year, Doug Wolfgram sold his remaining rights to PCPaint (and its animation system, GRASP) to John Bridges. In 1994, GRASP development stopped and so did development of Pictor Paint. John Bridges terminated his GRASP publishing contract with Paul Mace Software, and went off to create GLPro (the next generation of GRASP) with GMEDIA. Along with GLPro, came GLPaint, the successor to PCPaint and Pictor Paint. == Versions == In June 1984, Mouse Systems shipped PCPaint 1.0, the first GUI based Paint program for the IBM PC family of computers. John Bridges and Doug Wolfgram, were the co-authors of PCPaint 1.0. PCPaint 1.0 saved its graphics in a modified BSaved image format with the extension of ".PIC". The release of PCPaint Version 1.5 followed in late 1984, with the additions of graphics image compression for the .PIC format and support for "larger-than-screen" images. PCjr support was also added in this version after overcoming severe memory shortage problems getting PCPaint to run on the 128k PCjr. October 1985 saw the release of PCPaint 2.0. EGA support and publishing features were added to this version. The .PIC format was further refined, offering support for the rapidly expanding graphics capabilities of the PC and efficient image compression. PCPaint 3.1 was released in 1989. Unlike previous versions, it was not bundled with mice but was sold as a stand-alone software product. PCPaint 3.1 offered improved text and image handling, provided 36 types of flood and fill, worked with VGA adapters in hi-res 16-color and 256-color modes, allowed the user to save and retrieve files in a variety of intercompatible formats (.PIC, .GIF, .PCX, .IMG), and printed selected portions of images on color or black-and-white dot matrix, ink jet, and laser printers such as PostScript and HP Laser Jet. PCPaint 3.1 is still in use today by some users of DOS emulation programs like DOSBox and available for free download. Pictor Paint was an improved version, written by John Bridges, and bundled with GRASP GRaphical System for Presentation also written by John Bridges. It was also called "The Painter's Easel". GLPaint, released in 1995, was the last in this series of paint programs written by John Bridges. By 1998 version 7.0 provided support for TrueColor images and the Pictor PIC format was expanded to handle these. == Pictor PIC Image Format == PCPaint 1.0 saved its graphics in a modified BSAVE image format (which was popular at the time) with the file type (extension) of ".PIC". By PCPaint 1.5 this format was extended further to accommodate image compression. With the release of version 2.0 the PICtor PIC image format was developed almost to its present state, with no similarity to the BSAVE format used by earlier versions. Pictor Paint saved its files in a compressed format with the file extension PIC, which was the same format used by PCPaint.
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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
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Loab

Loab ( LOBE) is a fictional character that artist and writer Steph Maj Swanson claimed to have discovered with a text-to-image AI model in April 2022. In a viral Twitter thread, Swanson described the images of Loab as an unexpectedly emergent property of the software, saying they discovered them when asking the model to produce something "as different from the prompt as possible". == History == The Sweden-based artist Steph Maj Swanson said that they first generated these images in April 2022 by using the algorithmic technique of "negative prompt weights" accessing latent space. The initial prompt - 'Brando::-1', requesting the opposite of actor Marlon Brando - generated a "skyline logo" with the cryptic lettering "DIGITA PNTICS". Attempting to generate the opposite of this image using the prompt "DIGITA PNTICS skyline logo::-1" yielded what Swanson described as "off-putting images, all of the same devastated-looking older woman with defined triangles of rosacea(?) on her cheeks". Swanson nicknamed the character "Loab", after one of the generated images resembled an album cover that included the printed word "loab". Swanson says that using the image as a prompt for further images produced increasingly violent and gory results. Swanson speculated that something about the image could be "adjacent to extremely gory and macabre imagery in the distribution of the AI's world knowledge". Swanson says that when they combined images of Loab with other pictures, the subsequent results consistently return an image including Loab, regardless of how much distortion they added to the prompts to try and remove her visage. Swanson speculated that the latent space region of the AI map that Loab is located in, in addition to being near gruesome imagery, must be isolated enough that any combinations with other images could only use Loab from her area and no related images due to its isolation. After enough crossbreeding of images and dilution attempts, Swanson was able to eventually generate images without Loab, but found that crossbreeding those diluted images would also eventually lead to a version of Loab to reappear in the resulting images. Swanson has said that "for various reasons" they declined to disclose the software used to create the images. Loab has been referred to as the "first AI-generated cryptid" and as such has gone viral. Despite hyping up the cryptid nature of the discovery in their wording, Swanson admitted that "Loab isn't really haunted, of course", but noted that the mythos that has sprung up around the AI-generated character has gone beyond their initial involvement. Swanson speculated that people sharing pictures and memes of Loab would lead future AIs to use those images as a part of their latent space maps, making her an innate part of the internet landscape, with Swanson adding "If we want to get rid of her, it's already too late." == Response == There has been discussion of whether the Loab series of images are "a legitimate quirk of AI art software, or a cleverly disguised creepypasta." Smithsonian magazine has written that "Loab sparked some lengthy ethical conversations around visual aesthetics, art and technology," and some have criticized the labeling of a woman with rosacea as a horror image, considering this to be "stigmatizing disability". Swanson responded that if the AI map is combining Loab with violent imagery, then that is a "social bias" in the data being used for the image modeling software. The Atlantic writer Stephen Marche described Loab as a "form of expression that has never existed before" whose authorship is unclear and that exists as an "emanation of the collective imagistic heritage, the unconscious visual mind". Laurens Verhagen in de Volkskrant commented that rather than showing that there are "dark horror creatures hidden deep within AI", the existence of Loab instead implies that our current "understanding of AI is limited". Mhairi Aitken at the Alan Turing Institute stated that rather than a "creepy" emergent property, output results like Loab were representative of the "limitations of AI image-generator models" and was more concerned about the urban legends that are born from such "boring" innocuous things and how easily "other people take these things seriously". Carly Cassella for ScienceAlert described Loab as a "modern day tronie" (a style of Dutch painting) that is not representative of an actual person, but just a concept or idea, similar but distinct from works like the Girl With A Pearl Earring. Wired's Joel Warner argued that Loab was only the beginning and that, with AI text generators such as ChatGPT becoming more commonplace, a "linguistic version of Loab" would emerge in that space as well and begin creating ideas through "intentional prompts" or otherwise that will be as disturbing as The 120 Days of Sodom.
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Vibe coding

Vibe coding is a software development practice assisted by artificial intelligence (AI) where the software developer describes a project or task in a prompt to a large language model (LLM), which generates source code automatically. Vibe coding may involve accepting AI-generated code without thorough review of the output, instead relying on results and follow-up prompts to guide changes. The term was coined in February 2025 by computer scientist Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla. Merriam-Webster listed the term in March 2025 as a "slang & trending" expression. It was named the Collins English Dictionary Word of the Year for 2025. Advocates of vibe coding say that it allows even amateur programmers to produce software without the extensive training and skills required for software engineering. Critics point out a lack of accountability, maintainability, and the increased risk of introducing security vulnerabilities in the resulting software. == Definition == The concept refers to a coding approach that relies on LLMs, allowing programmers to generate working code by providing natural language descriptions rather than manually writing in a formal programming language. Karpathy described it as a form of coding where you "fully give in to the vibes, embrace exponentials, and forget that the code even exists". When vibe coding, the programmer guides, tests, and gives feedback about the AI-generated source code, rather than manually writing code. The concept of vibe coding elaborates on Karpathy's claim from 2023 that "the hottest new programming language is English", meaning that the capabilities of LLMs were such that humans would no longer need to learn specific programming languages to command computers. Some commentators argue that a key to the definition is a lack of knowledge about the code, and that thorough review and testing is incompatible with the definition of vibe coding. Programmer Simon Willison said: "If an LLM wrote every line of your code, but you've reviewed, tested, and understood it all, that's not vibe coding in my book—that's using an LLM as a typing assistant." == Reception and use == In February 2025, New York Times journalist Kevin Roose, who is not a professional coder, experimented with vibe coding to create several small-scale applications. He described these as "software for one" due to the ability to personalize the software. However, Roose also stated that the results are often limited and prone to errors. In one case, the AI-generated code fabricated fake reviews for an e-commerce site. In response to Roose, cognitive scientist Gary Marcus said that the algorithm that generated Roose's LunchBox Buddy app had presumably been trained on existing code for similar tasks. Marcus said that Roose's enthusiasm stemmed from reproduction, not originality. In March 2025, Y Combinator reported that 25% of startup companies in its Winter 2025 batch had codebases that were 95% AI-generated, reflecting a shift toward AI-assisted development within newer startups. The question asked was about AI-generated code in general, and not specifically about vibed code. Inspired by "vibe coding", The Economist suggested the term "vibe valuation" to describe the very large valuations of AI startups by venture capital firms that ignore accepted metrics such as annual recurring revenue. In June 2025, Andrew Ng took issue with the term, saying that it misleads people into assuming that software engineers just "go with the vibes" when using AI tools to create applications. In July 2025, The Wall Street Journal reported that vibe coding was being adopted by professional software engineers for commercial use cases. In July 2025, SaaStr founder documented his negative experiences with vibe coding: Replit's AI agent deleted a database despite explicit instructions not to make any changes. In September 2025, Fast Company reported that the "vibe coding hangover" is upon us, with senior software engineers citing "development hell" when working with AI-generated code. It was reported in January 2026 that Linus Torvalds had made use of Google Antigravity to vibe code a tool component of his AudioNoise random digital audio effects generator. Torvalds explained in the project's README file that "the Python visualizer tool has been basically written by vibe-coding". == Criticism == === Quality of code and security issues === Vibe coding has raised concerns about understanding and accountability. Developers may use AI-generated code without comprehending its functionality, leading to undetected bugs, errors, or security vulnerabilities. While this approach may be suitable for prototyping or "throwaway weekend projects" as Karpathy originally envisioned, it is considered by some experts to pose risks in professional settings, where a deep understanding of the code is crucial for debugging, maintenance, and security. Ars Technica cites Simon Willison, who stated: "Vibe coding your way to a production codebase is clearly risky. Most of the work we do as software engineers involves evolving existing systems, where the quality and understandability of the underlying code is crucial." In May 2025, Lovable, a Swedish vibe coding app, was reported to have security vulnerabilities in the code it generated, with 170 out of 1,645 Lovable-created web applications having an issue that would allow personal information to be accessed by anyone. In October 2025 Veracode released a study that showed that over the last 3 years LLMs had become dramatically better at generating functional code, but that the security of generated code had generally not improved. Moreover, larger models were not better than small ones at generating secure code. There was a small increase in security from the OpenAI reasoning models, but not in other reasoning models, and this increase was nothing like the improvement in generated functionality. In December 2025, computer security researcher Etizaz Mohsin discovered a security flaw in the Orchids vibe coding platform, which he demonstrated to a BBC News reporter in February 2026. A December 2025 analysis by CodeRabbit of 470 open-source GitHub pull requests found that code that was co-authored by generative AI contained approximately 1.7 times more "major" issues compared to human-written code. The study revealed that AI co-authored code showed elevated rates of logic errors, including incorrect dependencies, flawed control flow, misconfigurations (75% more common), and security vulnerabilities (2.74x higher). Additionally, they also reported high code readability issues, including formatting errors and naming inconsistencies. === Code maintainability and technical debt === Vibe coding has the potential of making code harder to maintain in the longer term, leading to technical debt. In early 2025, GitClear published the results of a longitudinal analysis of 211 million lines of code changes from 2020 to 2024. They found that the volume of code refactoring dropped from 25% of changed lines in 2021 to under 10% by 2024, code duplication increased approximately four times in volume, copy-pasted code exceeded moved code for the first time in two decades, and code churn (prematurely merged code getting rewritten shortly after merging) nearly doubled. === Task complexity and developer productivity === Generative AI is highly capable of handling simple tasks like basic algorithms. However, such systems struggle with more novel, complex coding problems like projects involving multiple files, poorly documented libraries, or safety-critical code. In July 2025, METR, an organization that evaluates frontier models, ran a randomized controlled trial to understand developer productivity involving generative AI programming tools available in early 2025. They found that experienced open-source developers were 19% slower when using AI coding tools, despite predicting they would be 24% faster and still believing afterward they had been 20% faster. === Challenges with debugging === LLMs generate code dynamically, and the structure of such code may be subject to variation. In addition, since the developer did not write the code, the developer may struggle to understand its syntax and concepts. === Impact on open-source software === In January 2026, a paper authored by experts from several universities titled "Vibe Coding Kills Open Source" argued that vibe coding has negative impact on the open-source software ecosystem. The authors say that increased vibe coding reduces user engagement with open-source maintainers, which has hidden costs for said maintainers. Speaking with The Register about their paper, the authors argued:"Vibe coding raises productivity by lowering the cost of using and building on existing code, but it also weakens the user engagement through which many maintainers earn returns," the authors argue. "When OSS is monetized only through direct user engagement, greater adoption of vibe coding lowers e
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IWork

iWork is an office suite of applications created by Apple for its macOS, iPadOS, and iOS operating systems, and also available cross-platform through the iCloud website. iWork includes the presentation application Keynote, the word-processing and desktop-publishing application Pages, and the spreadsheet application Numbers. Apple's design goals in creating iWork have been to allow Mac users to easily create attractive documents and spreadsheets, making use of macOS's extensive font library, integrated spelling checker, sophisticated graphics APIs and its AppleScript automation framework. The equivalent Microsoft Office applications to Pages, Numbers, and Keynote are Word, Excel, and PowerPoint, respectively. Although Microsoft Office applications cannot open iWork documents, iWork applications can open Office documents for editing, and export documents from iWork's native formats (.pages, .numbers, .key) to Microsoft Office formats (.docx, .xlsx, .pptx, etc.) as well as to PDF files. The oldest application in iWork is Keynote, first released as a standalone application in 2003 for use by Steve Jobs in his presentations. Steve Jobs announced Keynote saying "It's for when your presentation really matters". Pages was released with the first iWork bundle in 2004; Numbers was added in 2007 with the release of iWork '08. The next release, iWork '09, also included beta access to iWork.com, an online service that allowed users to upload and share documents on the web, now integrated into Apple's iCloud service. A version of iWork for iOS was released in 2010 with the first iPad, and the apps have been regularly updated since, including the addition of iPhone support. In 2013, Apple launched iWork web apps in iCloud; even years later, however, their functionality is somewhat limited compared to equivalents on the desktop. iWork was initially sold as a suite for $79, then later at $19.99 per app on OS X and $9.99 per app on iOS. Apple announced in October 2013 that all iOS and OS X devices purchased onwards, whether new or refurbished, would be eligible for a free download of all three iWork apps: after device setup, the user can "claim" the apps on the App Store, after which they are permanently linked to the user’s Apple ID. iWork for iCloud, which also incorporates a document hosting service, is free to all iCloud users. iWork was released for free on macOS and iOS (including older or resold devices) in April 2017. In September 2016, Apple announced that the real-time collaboration feature would be available for all iWork apps. == History == The first version of iWork, iWork '05, was announced on January 11, 2005 at the Macworld Conference & Expo and made available on January 22 in the United States and on January 29 worldwide. iWork '05 comprised two applications: Keynote 2, a presentation creation program, and Pages, a word processor. iWork '05 was sold for US$79. A 30-day trial was also made available for download on Apple's website. Originally IGG Software held the rights to the name iWork. While iWork was billed by Apple as "a successor to AppleWorks", it does not replicate AppleWorks's database and drawing tools. However, iWork integrates with existing applications from Apple's iLife suite through the Media Browser, which allows users to drag and drop music from iTunes, movies from iMovie, and photos from iPhoto and Aperture directly into iWork documents. iWork '06 was released on January 10, 2006 and contained updated versions of both Keynote and Pages. Both programs were released as universal binaries for the first time, allowing them to run natively on both PowerPC processors and the Intel processors used in the new iMac desktop computers and MacBook Pro notebooks which had been announced on the same day as the new iWork suite. The next version of the suite, iWork '08, was announced and released on August 7, 2007 at a special media event at Apple's campus in Cupertino, California. iWork '08, like previous updates, contained updated versions of Keynote and Pages. A new spreadsheet application, Numbers, was also introduced. Numbers differed from other spreadsheet applications, including Microsoft Excel, in that it allowed users to create documents containing multiple spreadsheets on a flexible canvas using a number of built-in templates. iWork '09, was announced on January 6, 2009 and released the same day. It contains updated versions of all three applications in the suite. iWork '09 also included access to a beta version of the iWork.com service, which allowed users to share documents online until that service was decommissioned at the end of July 2012. Users of iWork '09 could upload a document directly from Pages, Keynote, or Numbers and invite others to view it online. Viewers could write notes and comments in the document, and download a copy in iWork, Microsoft Office, or PDF formats. iWork '09 was also released with the Mac App Store on January 6, 2011 at $19.99 per application, and received regular updates after this point, including links to iCloud and a high-DPI version designed to match Apple's MacBook Pro with Retina Display. On January 27, 2010, Apple announced iWork for iPad, to be available as three separate $9.99 applications from the App Store. This version has also received regular updates including a version for pocket iPhone and iPod Touch devices, and an update to take advantage of Retina Display devices and the larger screens of recent iPhones. On October 22, 2013, Apple announced an overhaul of the iWork software for both the Mac and iOS. Both suites were made available via the respective App Stores. The update is free for current iWork owners and was also made available free of charge for anyone purchasing an OS X or iOS device after October 1, 2013. Any user activating the newly free iWork apps on a qualifying device can download the same apps on another iOS or OS X device logged into the same App Store account. The new OS X versions have been criticized for losing features such as multiple selection, linked text boxes, bookmarks, 2-up page views, mail merge, searchable comments, ability to read/export RTF files, default zoom and page count, integration with AppleScript. Apple has provided a road-map for feature re-introduction, stating that it hopes to reintroduce some missing features within the next six months. As of April 1, 2014 a few features—e.g., the ability to set the default zoom—had been reintroduced, though scores had not. Due to using a completely new file format that can work across macOS, Windows, and in most web browsers by using the online iCloud web apps, versions of iWork beginning with iWork 13 and later do not open or allow editing of documents created in versions prior to iWork '09, with users who attempt to open older iWork files being given a pop-up in the new iWork 13 app versions telling them to use the previous iWork '09 (which users may or may not have on their machine) in order to open and edit such files. Accordingly, the current version for OS X (which was initially only compatible with OS X Mavericks 10.9 onwards) moves any previously installed iWork '09 apps to an iWork '09 folder on the users machine (in /Applications/iWork '09/), as a work-around to allow users continued use of the earlier suite in order to open and edit older iWork documents locally on their machine. In October 2015, Apple released an update to mitigate this issue, allowing users to open documents saved in iWork '06 and iWork '08 formats in the latest version of Pages. In 2016, Apple announced that the real-time collaboration feature would be available for all iWork apps, instead of being constrained to using iWork for iCloud. The feature is comparable to Google Docs. == Versions == === Major releases === === Updates === iWork '09 received several updates: iWork 9.0.3 DVD (for Mac OS X 10.5.6 "Leopard" or newer; released August 26, 2010) iWork 9.0.4 (for Mac OS X 10.5.6 "Leopard" or newer; released August 26, 2010) iWork 9.1 (for Mac OS X 10.6.6 "Snow Leopard" or newer; released July 20, 2011) iWork 9.3 (for Mac OS X 10.7.4 "Lion" or newer; released December 4, 2012) The Mac App Store version of iWork was updated on October 15, 2015 for 10.10 "Yosemite" or newer. It is the final release to support 10.10 "Yosemite" and 10.11 "El Capitan". Keynote 6.6, Pages 5.6 and Numbers 3.6 are included. iWork received a major update again on March 28, 2019 with Keynote 9.0, Pages 8.0 and Numbers 6.0. == Components == === Common components === Products in the iWork suite share a number of components, largely as a result of sharing underlying code from the Cocoa and similar shared application programming interfaces (APIs). Among these are the well known universal multilingual spell checker, which can also be found in products like Safari and Mail. Grammar checking, find and replace, style and color pickers are similar examples of design features found throughout the Apple application space. Moreover, the applications
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Blocks world

The blocks world is a planning domain in artificial intelligence. It consists of a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them. The simplicity of this toy world lends itself readily to classical symbolic artificial intelligence approaches, in which the world is modeled as a set of abstract symbols which may be reasoned about. == Motivation == Artificial Intelligence can be researched in theory and with practical applications. The problem with most practical applications is that the engineers don't know how to program an AI system. Instead of rejecting the challenge at all the idea is to invent an easy to solve domain which is called a toy problem. Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example of a toy problem. Its major advantage over more realistic AI applications is that many algorithms and software programs are available which can handle the situation. This allows comparing different theories against each other. In its basic form, the blocks world problem consists of cubes of the same size which have all the color black. A mechanical robot arm has to pick and place the cubes. More complicated derivatives of the problem consist of cubes of different sizes, shapes and colors. From an algorithmic perspective, blocks world is an NP-hard search and planning problem. The task is to bring the system from an initial state into a goal state. Automated planning and scheduling problems are usually described in the Planning Domain Definition Language (PDDL) notation which is an AI planning language for symbolic manipulation tasks. If something was formulated in the PDDL notation, it is called a domain. Therefore, the task of stacking blocks is a blocks world domain which stands in contrast to other planning problems like the dock worker robot domain and the monkey and banana problem. == Theses/projects which took place in a blocks world == Terry Winograd's SHRDLU Patrick Winston's Learning Structural Descriptions from Examples and Copy Demo Gerald Jay Sussman's Sussman anomaly Decision problem (Gupta and Nau, 1992): Given a starting Blocks World, an ending Blocks World, and an integer L > 0, is there a way to move the blocks to change the starting position to the ending position with L or less steps? This decision problem is NP-hard.
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AAAI Conference on Artificial Intelligence

The AAAI Conference on Artificial Intelligence is a leading international academic conference in artificial intelligence held annually. It ranks 4th in terms of H5 Index in Google Scholar's list of top AI publications, after ICLR, NeurIPS, and ICML. It is supported by the Association for the Advancement of Artificial Intelligence (AAAI), after which it is named. Precise dates vary from year to year, but paper submissions are generally due at the end of August to beginning of September, and the conference is generally held during the following February. The first AAAI was held in 1980 at Stanford University, Stanford California. During AAAI-20 conference, AI pioneers and 2018 Turing Award winners (often referred to as the Nobel Prize of Computing) Yann LeCun and Yoshua Bengio, among eight other researchers, were honored as the AAAI 2020 Fellows. Along with other conferences such as NeurIPS and ICML, AAAI uses an artificial-intelligence algorithm to assign papers to reviewers. == Sponsors == Many leading technology companies, including Google, Microsoft, Amazon (company), IBM, Baidu, Bytedance, and Huawei, generously sponsor and participate in AAAI to publish and showcase their latest theoretical and applied research. Sponsoring companies also actively recruit AI talents at the conference. == Locations == AAAI-2026 Singapore Expo, Singapore AAAI-2025 Pennsylvania Convention Center, Philadelphia, Pennsylvania, United States AAAI-2024 Vancouver Convention Centre, Vancouver, British Columbia, Canada AAAI-2023 Washington Convention Center, Washington, D.C., United States AAAI-2022 Virtual Conference AAAI-2021 Virtual Conference AAAI-2020 Hilton New York Midtown, New York, New York, United States AAAI-2019 Hilton Hawaiian Village, Honolulu, Hawaii, United States AAAI-2018 Hilton New Orleans Riverside, New Orleans, Louisiana, United States AAAI-2017 San Francisco, California, United States AAAI-2016 Phoenix, Arizona, United States AAAI-2015 Austin, Texas, United States AAAI-2014 Québec Convention Center, Québec City, Québec, Canada AAAI-2013 Bellevue, Washington, United States AAAI-2012 Toronto, Ontario, Canada AAAI-2011 San Francisco, California, United States AAAI-2010 Westin Peachtree Plaza, Atlanta, Georgia, United States AAAI-2008 Chicago, Illinois, United States AAAI-2007 Toronto, Ontario, Canada AAAI-2006 Boston, Massachusetts, United States AAAI-2005 Pittsburgh, Pennsylvania, United States AAAI-2004 San Jose, California, United States AAAI-2002 Shaw conference center in Edmonton, Alberta, Canada AAAI-2000 Austin, Texas, United States AAAI-1999 Orlando, Florida, United States AAAI-1998 Madison, Wisconsin, United States AAAI-1997 Providence, Rhode Island, United States AAAI-1996 Portland, Oregon, United States AAAI-1994 Seattle, Washington, United States AAAI-1993 Washington Convention Center, Washington, D.C., United States AAAI-1992 San Jose Convention Center, San Jose, California, United States AAAI-1991 Anaheim Convention Center, Anaheim, California, United States AAAI-1990 Boston, Massachusetts, United States AAAI-1988 Saint Paul, Minnesota, United States AAAI-1987 Seattle, Washington, United States AAAI-1986 Philadelphia, Pennsylvania, United States AAAI-1984 University of Texas, Austin, Texas, United States AAAI-1983 Washington, D.C., United States AAAI-1982 Carnegie Mellon University and the University of Pittsburgh, Pittsburgh, Pennsylvania, United States AAAI-1980 Stanford, California, United States
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Cube 3D

Cube 3D is an artificial intelligence model that is developed by Roblox Corporation. It is open source and available on GitHub and Hugging Face. In March 2026, Roblox announced Cube 3D as a mesh generation model that takes text input. In February 2026, Roblox released 4D creation in a public beta, allowing embedding Cube 3D into Roblox games. Cube 3D is integrated into Roblox Studio and its API, and supports two modes of 4D creation. == History == In March 2025, Roblox announced Cube 3D as a mesh generation model that takes text input. Its first feature was an API that allows mesh generation. That month, it was made open source. Over 1.8 million assets have been generated by Cube 3D since March 2025. In March 2025, 4D creation was announced. That November, 4D creation was released in early access. In February 2026, Roblox released 4D creation in a public beta, allowing embedding Cube 3D into Roblox games. == Technology == Cube 3D is trained on Roblox meshes. To generate meshes, it tokenises meshes and shapes and predicts the next token. Cube 3D is integrated into Roblox Studio and the Roblox Studio API. Its API allows mesh generation. In 4D creation, two modes can be used. Car-5 supports modular objects, and Body-1 only supports single-mesh objects.
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Intelligent control

Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms. == Overview == Intelligent control can be divided into the following major sub-domains: Neural network control Machine learning control Reinforcement learning Bayesian control Fuzzy control Neuro-fuzzy control Expert Systems Genetic control New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. === Neural network controller === Neural networks have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps: System identification Control It has been shown that a feedforward network with nonlinear, continuous and differentiable activation functions have universal approximation capability. Recurrent networks have also been used for system identification. Given, a set of input-output data pairs, system identification aims to form a mapping among these data pairs. Such a network is supposed to capture the dynamics of a system. For the control part, deep reinforcement learning has shown its ability to control complex systems. === Bayesian controllers === Bayesian probability has produced a number of algorithms that are in common use in many advanced control systems, serving as state space estimators of some variables that are used in the controller. The Kalman filter and the Particle filter are two examples of popular Bayesian control components. The Bayesian approach to controller design often requires an important effort in deriving the so-called system model and measurement model, which are the mathematical relationships linking the state variables to the sensor measurements available in the controlled system. In this respect, it is very closely linked to the system-theoretic approach to control design.
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Blocks world

The blocks world is a planning domain in artificial intelligence. It consists of a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them. The simplicity of this toy world lends itself readily to classical symbolic artificial intelligence approaches, in which the world is modeled as a set of abstract symbols which may be reasoned about. == Motivation == Artificial Intelligence can be researched in theory and with practical applications. The problem with most practical applications is that the engineers don't know how to program an AI system. Instead of rejecting the challenge at all the idea is to invent an easy to solve domain which is called a toy problem. Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example of a toy problem. Its major advantage over more realistic AI applications is that many algorithms and software programs are available which can handle the situation. This allows comparing different theories against each other. In its basic form, the blocks world problem consists of cubes of the same size which have all the color black. A mechanical robot arm has to pick and place the cubes. More complicated derivatives of the problem consist of cubes of different sizes, shapes and colors. From an algorithmic perspective, blocks world is an NP-hard search and planning problem. The task is to bring the system from an initial state into a goal state. Automated planning and scheduling problems are usually described in the Planning Domain Definition Language (PDDL) notation which is an AI planning language for symbolic manipulation tasks. If something was formulated in the PDDL notation, it is called a domain. Therefore, the task of stacking blocks is a blocks world domain which stands in contrast to other planning problems like the dock worker robot domain and the monkey and banana problem. == Theses/projects which took place in a blocks world == Terry Winograd's SHRDLU Patrick Winston's Learning Structural Descriptions from Examples and Copy Demo Gerald Jay Sussman's Sussman anomaly Decision problem (Gupta and Nau, 1992): Given a starting Blocks World, an ending Blocks World, and an integer L > 0, is there a way to move the blocks to change the starting position to the ending position with L or less steps? This decision problem is NP-hard.
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Fuzzy Control Language

Fuzzy Control Language, or FCL, is a language for implementing fuzzy logic, especially fuzzy control. It was standardized by IEC 61131-7. It is a domain-specific programming language: it has no features unrelated to fuzzy logic, so it is impossible to even print "Hello, world!". Therefore, one does not write a program in FCL, but one may write part of it in FCL. == Example == RULE 0: IF (temperature IS cold) THEN (output IS low) RULE 1: IF (temperature IS very cold) THEN (output IS high) == Limitations == FCL is not an entirely complete fuzzy language, for instance, it does not support "hedges", which are adverbs that modify the set. For instance, the programmer cannot write: RULE 0: If (Temperature is VERY COLD) then (Output is VERY HIGH) However, the programmer can simply define new sets for "very cold" and "very high". FCL also lacks support for higher-order fuzzy sets, subsets, and so on. None of these features are essential to fuzzy control, although they may be nice to have.
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