AI Email Review

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

  • Digital Darkroom

    Digital Darkroom

    Digital Darkroom was a graphics program for editing gray-scale photos, published by Silicon Beach Software for the Macintosh in 1987. It was programmed by Ed Bomke and Don Cone. Digital Darkroom was the first Macintosh program to incorporate a plug-in architecture. Silicon Beach and Ed Bomke are credited with having coined the term "plug-in". Another innovation of Digital Darkroom was the Magic Wand tool, which also appeared later in Photoshop. When Silicon Beach Software was acquired by Aldus Corporation, Digital Darkroom continued to be published by the Aldus Consumer Division, but was never updated to include color. The trademark "Digital Darkroom" was acquired by MicroFrontier in 1997 and used for a completely new image-editing program that does work with color. The software was acquired by Digimage Arts in 2002 and was sold for both Windows and Mac systems.

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  • Computing Machinery and Intelligence

    Computing Machinery and Intelligence

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

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  • The Life and Times of Multivac

    The Life and Times of Multivac

    "The Life and Times of Multivac" is a science fiction short story by American writer Isaac Asimov. The story first appeared in the 5 January 1975 issue of The New York Times Magazine, and was reprinted in the collections The Bicentennial Man and Other Stories and The Best of Creative Computing in 1976. It is one of a loosely connected series of stories concerning a fictional supercomputer called Multivac. "The Life and Times of Multivac" was the first piece of fiction ever commissioned and published by The New York Times. Asimov's original title for the story was "Mathematical Games", but after the story appeared under the new title he decided he liked it. In his commentary on the story in The Bicentennial Man and Other Stories collection, Asimov stated, "More people came up to me over the next few weeks to tell me they had read that story than had ever been the case for any other story I had ever written." == Plot summary == When humanity begins to chafe under Multivac’s benevolent tyranny, one man takes matters into his own hands to destroy the great computer. By appearing to betray his fellow humans, he places himself in a position to permanently destroy Multivac. It is implied that it is not until completion of the act that he and his peers suddenly realize the enormity of their actions and the consequences it will have on humanity.

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  • AI-generated content in American politics

    AI-generated content in American politics

    In American politics since the 2020s, political figures have deployed AI-generated images, videos, and audio to attack opponents, create misleading narratives, or inflame emotions. The use of generative AI by American political figures has been subject to criticism from many sides of the political spectrum. Republican president Donald Trump has notably used generative AI in several posts to Truth Social during his second term, many of which have made headlines due to their inflammatory nature. == Background == Generative artificial intelligence is a subfield of artificial intelligence that uses generative models to generate text, images, videos, audio, software code or other forms of data. In the mid 2020s with the release of 15.ai, ChatGPT, DALL-E and other generative artificial intelligence applications there was an AI boom. There has been an increase of usage of generative-AI within the United States political field during this boon, with both Republican and Democratic party members using it. The Trump administration during his second term, have embraced the use of AI-generated images, causing some misinformation experts to raise concerns about the continued usage would cause the erosion of public perception of the truth. In response to some criticisms White House deputy communications director Kaelan Dorr posted on X that the "memes will continue" with White House deputy press secretary Abigail Jackson also mocking concerns. == History of usage == === 2023 === In April 2023, the Republican National Committee released an attack ad made entirely with AI-generated images depicting a dystopian future under Joe Biden's re-election. === 2024 === Generative AI has increased the efficiency with which political candidates were able to raise money by analyzing donor data and identifying possible donors and target audiences. In March 2024 Democratic consultant working for Dean Phillips has admitted to using AI to generate a robocall which used Joe Biden's voice to discourage voter participation. In August 2024, The Atlantic noted that AI slop was becoming associated with the political right in the United States, who were using it for shitposting and engagement farming on social media, with the technology offering "cheap, fast, on-demand fodder for content". AI slop is frequently used in political campaigns in an attempt at gaining attention through content farming. === 2025 === The initial version of the Make Our Children Healthy Again Assessment of children's health issues, released by a commission of cabinet members and officials of the Trump administration, and led by US Department of Health and Human Services Secretary Robert F. Kennedy Jr., reportedly cited nonexistent and garbled references generated using artificial intelligence. Democratic governor Gavin Newsom has used AI-generated images to criticize Trump. In the midst of disruptions to food stamp distribution during the 2025 US government shutdown, anonymous social media users began using OpenAI's Sora to post slop videos of welfare queens complaining, stealing, and rioting in supermarkets; many comments to the videos appeared unaware that they were AI-generated, or acknowledged that they were AI-generated but nonetheless useful in pushing a narrative of widespread welfare fraud. On September 6, 2025, Trump posted an image on Truth Social making a reference to "Chipocalypse Now". Trump's post consisted of an AI-generated image showing Trump frowning and wearing a U.S. Cavalry hat and sunglasses, in front of Lake Michigan with the city of Chicago behind him with a smoke and fire spread across the background with five U.S. Army helicopters in the sky. The words "Chipocalypse Now" are rendered in a font resembling that in which the title of the 1979 film Apocalypse Now was styled. === 2026 === On February 5, 2026, Donald Trump shared a video of Barack and Michelle Obama depicted as apes in a Truth Social post. The two-second AI-generated clip of the Obamas portrayed as apes set to "The Lion Sleeps Tonight" appeared at the end of a one-minute two second long video, the rest of which was about false claims of voter fraud during the 2020 presidential election. The post received at least 4,650 likes, 409 comments, and 1,470 reTruths before it was deleted the next morning. The short clip was part of a longer AI-generated video posted in October 2025. The post received widespread backlash and bipartisan condemnation of the video as racist. In April 2026, Trump posted a picture of himself depicted as Jesus, drawing widespread criticism from Evangelicals and Catholics, resulting in Trump deleting the post hours later and claiming he believed he was depicted as a doctor. == Examples of use == === Election campaigns === In 2023, while he was still running for re-election, the presidential campaign of Joe Biden prepared a task force to respond to AI images and videos. The campaign for the 2024 Republican nominee, Donald Trump, has used deepfake videos of political opponents in campaign ads and fake images showing Trump with black supporters. During the first five months of his second term in 2025, Trump posted several AI-generated images of himself on official government social media accounts, including him as the Pope, him as a Jedi, and him as a muscular man. In August 2024, Trump posted a series of AI-generated images on his social media platform, Truth Social, that portrayed fans of the singer Taylor Swift in "Swifties for Trump" T-shirts, as well as a photo of the singer herself appearing to endorse Trump's 2024 presidential campaign. The images originated from the conservative Twitter account @amuse, which posted numerous AI slop images leading up to the 2024 United States elections that were shared by other high-profile figures within the US Republican Party, such as Elon Musk, who has publicly endorsed the utilization of generative AI, furthering this association. In 2024, Michigan GOP candidate Anthony Hudson posted an AI-generated video showing Martin Luther King Jr. endorsing his campaign, later claiming it was uploaded by a volunteer. In his 2025 bid to be the Democratic nominee for governor of New Jersey, Rep. Josh Gottheimer drew attention and criticism when he released a TV ad that used AI to portray him as a shirtless boxer sparring with Donald Trump in a boxing ring. In November 2025, the campaign of Mike Collins, a GOP candidate in the 2026 United States Senate election in Georgia released a fake video, generated by artificial intelligence, that depicted Democrat Jon Ossoff defending his vote on the 2025 United States federal government shutdown by declaring he could never say no to Chuck Schumer and that SNAP recipients did not attend his out-of-state fundraisers. The Collins campaign also shared an AI-generated video featuring Collins as a shirtless blue jeans model, referencing an American Eagle Outfitters advertisement featuring Sydney Sweeney. During the 2026 Los Angeles mayoral election, candidate Spencer Pratt reposted an AI-generated video portraying Pratt as Batman and prominent California politicians such as Karen Bass, Gavin Newsom, and Kamala Harris, as unruly aristocrats. Former governor of Florida Jeb Bush described the ad as “maybe the best political ad of the year.” In response, a spokesperson for Bass's campaign said, he was "doing his best Trump impression." Bass further responded that the AI ads are "taking on a violent trend." === Protests === In response to the nation-wide No Kings protests in October 2025, Donald Trump posted a video depicting himself flying a fighter jet and releasing feces on crowds of demonstrators, including Democratic influencer Harry Sisson. === Foreign interference === Officials from the ODNI and FBI have stated that Russia, Iran, and China used generative artificial intelligence tools to create fake and divisive text, photos, video, and audio content to foster anti-Americanism and engage in covert influence campaigns. The use of artificial intelligence was described as an accelerant rather than a revolutionary change to influence efforts. Regulation of AI with regard to elections was unlikely to see a resolution for most of the 2024 United States general election season. === Disasters and wars === In the aftermath of Hurricane Helene in the United States, members of the Republican Party circulated an AI-generated image of a young girl holding a puppy in a flood, and used it as evidence of the failure of President Joe Biden to respond to the disaster. Some, like Trump supporter Amy Kremer, shared the image on social media but acknowledged that it was not genuine. In February 2025, Donald Trump shared an AI-generated video on Truth Social depicting a hypothetical Gaza after a Trump takeover. The video's creator claimed it was made as political satire. == Reception == Ramesh Srinivasan, a professor at UCLA raised concerns about the use of AI-generative images stating that many people are questioning where they can find trustab

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  • Paprika (app)

    Paprika (app)

    Paprika is an app and website that helps users organize recipes, produce meal plans, and create grocery lists. The app is available for Android, iOS, macOS, and Windows devices. == Overview == The app allows users to import recipes from various sources, including websites and other apps. The app also allows users to automatically generate meal plans, which are also customizable, in order to achieve specific objectives such as weight loss, muscle gain, adherence to various dietary preferences, or personal taste. The app is also capable of generating grocery lists based on the daily or weekly meal plans chosen by the user. All the recipes, menus, and grocery lists of each user are accessible from smartphones, tablets, and computers. The app is part of a broader category of mobile apps focused on meal planning, recipe management, and shopping list automation, which have grown in popularity with the expansion of smartphone usage and digital cooking tools. == History == Paprika Recipe Manager for iPad version 1.0 was initially released in September 2010 by Hindsight LLC. Paprika 2.0 was released for iPhone and iPad in November 2013, and Paprika 3.0 was released for iOS and macOS in November 2017. == Reception == Paprika has been featured in technology and lifestyle publications as a recipe management and meal planning application. Coverage has noted features such as importing recipes from websites, ingredient scaling, and cross-platform synchronization. The app has also appeared in lists of cooking and meal planning tools published by outlets including The Verge and The Kitchn.

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  • They're Made Out of Meat

    They're Made Out of Meat

    "They're Made Out of Meat" is a short story by American writer Terry Bisson. It was originally published in OMNI. It consists entirely of dialogue between two characters. Bisson's website hosts a theatrical adaptation. A film adaptation won the Grand Prize at the Seattle Science Fiction Museum's 2006 film festival. The story was collected in the 1993 anthology Bears Discover Fire and Other Stories, and has circulated widely on the Internet, which Bisson found "flattering". It has been quoted in cognitive, cosmological, and philosophical scholarship. == Plot == The two characters are intelligent beings capable of traveling faster than light, on a mission to "contact, welcome and log in any and all sentient races or multibeings in this quadrant of the Universe." Bisson's stage directions represent them as "two lights moving like fireflies among the stars" on a projection screen. One of them tells the incredulous other about the recent discovery of carbon-based lifeforms "made up entirely of meat". After conversing briefly about it, they both deem such beings and communication with them too bizarre and agree to "erase the records and forget the whole thing", marking the Solar System "unoccupied". == Film adaptations == === They're Made out of Meat (2005) === In 2005, Stephen O'Regan wrote and directed a live film adaptation starring Tom Noonan and Ben Bailey. The film was made as a final project for the New York Film Academy. The main action takes place inside a diner full of teenagers in Staten Island, New York. The music for the film was scored by Bob Reynolds. === They're Made out of Meat (2010) === Jeff Frumess and Trevor Scott produced a version in 2010. They added the character of a homeless conspiracy theorist with an original score by musician Sam Belkin. The film was shot at Hartsdale station in Westchester County, New York. === Meat (2021) === Masha Maksimova developed a version in Cinemiracle format, a triple split-screen process, as a student project at the Berlin University of Applied Sciences in the communication design course. The dialogue is conducted by two telepathic humanoid aliens and the thoughts are visualised by found-footage collages.

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  • BL (logic)

    BL (logic)

    In mathematical logic, basic fuzzy logic (or shortly BL), the logic of the continuous t-norms, is one of the t-norm fuzzy logics. It belongs to the broader class of substructural logics, or logics of residuated lattices; it extends the logic MTL of all left-continuous t-norms. == Syntax == === Language === The language of the propositional logic BL consists of countably many propositional variables and the following primitive logical connectives: Implication → {\displaystyle \rightarrow } (binary) Strong conjunction ⊗ {\displaystyle \otimes } (binary). The sign & is a more traditional notation for strong conjunction in the literature on fuzzy logic, while the notation ⊗ {\displaystyle \otimes } follows the tradition of substructural logics. Bottom ⊥ {\displaystyle \bot } (nullary — a propositional constant); 0 {\displaystyle 0} or 0 ¯ {\displaystyle {\overline {0}}} are common alternative signs and zero a common alternative name for the propositional constant (as the constants bottom and zero of substructural logics coincide in MTL). The following are the most common defined logical connectives: Weak conjunction ∧ {\displaystyle \wedge } (binary), also called lattice conjunction (as it is always realized by the lattice operation of meet in algebraic semantics). Unlike MTL and weaker substructural logics, weak conjunction is definable in BL as A ∧ B ≡ A ⊗ ( A → B ) {\displaystyle A\wedge B\equiv A\otimes (A\rightarrow B)} Negation ¬ {\displaystyle \neg } (unary), defined as ¬ A ≡ A → ⊥ {\displaystyle \neg A\equiv A\rightarrow \bot } Equivalence ↔ {\displaystyle \leftrightarrow } (binary), defined as A ↔ B ≡ ( A → B ) ∧ ( B → A ) {\displaystyle A\leftrightarrow B\equiv (A\rightarrow B)\wedge (B\rightarrow A)} As in MTL, the definition is equivalent to ( A → B ) ⊗ ( B → A ) . {\displaystyle (A\rightarrow B)\otimes (B\rightarrow A).} (Weak) disjunction ∨ {\displaystyle \vee } (binary), also called lattice disjunction (as it is always realized by the lattice operation of join in algebraic semantics), defined as A ∨ B ≡ ( ( A → B ) → B ) ∧ ( ( B → A ) → A ) {\displaystyle A\vee B\equiv ((A\rightarrow B)\rightarrow B)\wedge ((B\rightarrow A)\rightarrow A)} Top ⊤ {\displaystyle \top } (nullary), also called one and denoted by 1 {\displaystyle 1} or 1 ¯ {\displaystyle {\overline {1}}} (as the constants top and zero of substructural logics coincide in MTL), defined as ⊤ ≡ ⊥ → ⊥ {\displaystyle \top \equiv \bot \rightarrow \bot } Well-formed formulae of BL are defined as usual in propositional logics. In order to save parentheses, it is common to use the following order of precedence: Unary connectives (bind most closely) Binary connectives other than implication and equivalence Implication and equivalence (bind most loosely) === Axioms === A Hilbert-style deduction system for BL has been introduced by Petr Hájek (1998). Its single derivation rule is modus ponens: from A {\displaystyle A} and A → B {\displaystyle A\rightarrow B} derive B . {\displaystyle B.} The following are its axiom schemata: ( B L 1 ) : ( A → B ) → ( ( B → C ) → ( A → C ) ) ( B L 2 ) : A ⊗ B → A ( B L 3 ) : A ⊗ B → B ⊗ A ( B L 4 ) : A ⊗ ( A → B ) → B ⊗ ( B → A ) ( B L 5 a ) : ( A → ( B → C ) ) → ( A ⊗ B → C ) ( B L 5 b ) : ( A ⊗ B → C ) → ( A → ( B → C ) ) ( B L 6 ) : ( ( A → B ) → C ) → ( ( ( B → A ) → C ) → C ) ( B L 7 ) : ⊥ → A {\displaystyle {\begin{array}{ll}{\rm {(BL1)}}\colon &(A\rightarrow B)\rightarrow ((B\rightarrow C)\rightarrow (A\rightarrow C))\\{\rm {(BL2)}}\colon &A\otimes B\rightarrow A\\{\rm {(BL3)}}\colon &A\otimes B\rightarrow B\otimes A\\{\rm {(BL4)}}\colon &A\otimes (A\rightarrow B)\rightarrow B\otimes (B\rightarrow A)\\{\rm {(BL5a)}}\colon &(A\rightarrow (B\rightarrow C))\rightarrow (A\otimes B\rightarrow C)\\{\rm {(BL5b)}}\colon &(A\otimes B\rightarrow C)\rightarrow (A\rightarrow (B\rightarrow C))\\{\rm {(BL6)}}\colon &((A\rightarrow B)\rightarrow C)\rightarrow (((B\rightarrow A)\rightarrow C)\rightarrow C)\\{\rm {(BL7)}}\colon &\bot \rightarrow A\end{array}}} The axioms (BL2) and (BL3) of the original axiomatic system were shown to be redundant (Chvalovský, 2012) and (Cintula, 2005). All the other axioms were shown to be independent (Chvalovský, 2012). == Semantics == Like in other propositional t-norm fuzzy logics, algebraic semantics is predominantly used for BL, with three main classes of algebras with respect to which the logic is complete: General semantics, formed of all BL-algebras — that is, all algebras for which the logic is sound Linear semantics, formed of all linear BL-algebras — that is, all BL-algebras whose lattice order is linear Standard semantics, formed of all standard BL-algebras — that is, all BL-algebras whose lattice reduct is the real unit interval [0, 1] with the usual order; they are uniquely determined by the function that interprets strong conjunction, which can be any continuous t-norm.

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  • Uncertain inference

    Uncertain inference

    Uncertain inference was first described by C. J. van Rijsbergen as a way to formally define a query and document relationship in Information retrieval. This formalization is a logical implication with an attached measure of uncertainty. == Definitions == Rijsbergen proposes that the measure of uncertainty of a document d to a query q be the probability of its logical implication, i.e.: P ( d → q ) {\displaystyle P(d\to q)} A user's query can be interpreted as a set of assertions about the desired document. It is the system's task to infer, given a particular document, if the query assertions are true. If they are, the document is retrieved. In many cases the contents of documents are not sufficient to assert the queries. A knowledge base of facts and rules is needed, but some of them may be uncertain because there may be a probability associated to using them for inference. Therefore, we can also refer to this as plausible inference. The plausibility of an inference d → q {\displaystyle d\to q} is a function of the plausibility of each query assertion. Rather than retrieving a document that exactly matches the query we should rank the documents based on their plausibility in regards to that query. Since d and q are both generated by users, they are error prone; thus d → q {\displaystyle d\to q} is uncertain. This will affect the plausibility of a given query. By doing this it accomplishes two things: Separate the processes of revising probabilities from the logic Separate the treatment of relevance from the treatment of requests Multimedia documents, like images or videos, have different inference properties for each datatype. They are also different from text document properties. The framework of plausible inference allows us to measure and combine the probabilities coming from these different properties. Uncertain inference generalizes the notions of autoepistemic logic, where truth values are either known or unknown, and when known, they are true or false. == Example == If we have a query of the form: q = A ∧ B ∧ C {\displaystyle q=A\wedge B\wedge C} where A, B and C are query assertions, then for a document D we want the probability: P ( D → ( A ∧ B ∧ C ) ) {\displaystyle P(D\to (A\wedge B\wedge C))} If we transform this into the conditional probability P ( ( A ∧ B ∧ C ) | D ) {\displaystyle P((A\wedge B\wedge C)|D)} and if the query assertions are independent we can calculate the overall probability of the implication as the product of the individual assertions probabilities. == Further work == Croft and Krovetz applied uncertain inference to an information retrieval system for office documents they called OFFICER. In office documents the independence assumption is valid since the query will focus on their individual attributes. Besides analysing the content of documents one can also query about the author, size, topic or collection for example. They devised methods to compare document and query attributes, infer their plausibility and combine it into an overall rating for each document. Besides that uncertainty of document and query contents also had to be addressed. Probabilistic logic networks is a system for performing uncertain inference; crisp true/false truth values are replaced not only by a probability, but also by a confidence level, indicating the certitude of the probability. Markov logic networks allow uncertain inference to be performed; uncertainties are computed using the maximum entropy principle, in analogy to the way that Markov chains describe the uncertainty of finite-state machines.

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  • Quantexa

    Quantexa

    Quantexa is a UK-based software company that develops artificial intelligence-based applications for data analytics and decision-making. The company was founded in 2016 and is headquartered in London, with operations in North America, Europe, and the Asia-Pacific region. As of 2025, Quantexa reported a valuation of $2.6 billion and provides services to organizations in over 70 countries. Investors include Warburg Pincus, HSBC, and the Ontario Teachers’ Pension Plan. == History == Quantexa was founded in London in 2016 by several co-founders, including Jamie Hutton, Richard Seewald, Imam Hoque, Felix Hoddinott, and Vishal Marria, who also serves as the company's chief executive officer. The company was established to develop tools intended to address limitations in traditional data analysis methods, particularly those related to identifying hidden connections across large datasets. The name "Quantexa" is derived from the company's focus on quantitative methods and data analysis. In 2023, Quantexa acquired Dublin-based AI firm Aylien. In April 2023, the company completed a Series E funding round, raising $129 million at a valuation of approximately $1.8 billion, marking its entry into "unicorn" status. In October 2024, the company reported annual recurring revenue (ARR) exceeding $100 million. In early 2025, Quantexa participated in the World Economic Forum's Unicorn Program, which supports high-growth technology companies. In March 2025, Quantexa completed a Series F funding round of $175 million, led by Teachers' Venture Growth, the venture arm of the Ontario Teachers' Pension Plan. That August, the company was reported to be considering a 2026 IPO. The company formed a partnership with Zurich in October 2025, the first insurer to add its AI-based Decision Intelligence platform to enhance fraud detection.

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  • European Society for Fuzzy Logic and Technology

    European Society for Fuzzy Logic and Technology

    The European Society for Fuzzy Logic and Technology (EUSFLAT) is a scientific association with the aims to disseminate and promote fuzzy logic and related subjects (sometimes comprised under the collective terms soft computing or computational intelligence) and to provide a platform for exchange between scientists and engineers working in these fields. The society is both open for academic and industrial members. == History == EUSFLAT was founded in 1998 in Spain as the successor of the National Spanish Fuzzy Logic Society, ESTYLF, with the aim to open the society for members from other European countries. Since then, the society managed to attract a large share of members from outside Spain, and even beyond Europe, with the Spanish members still being the largest group inside EUSFLAT. For these historical reasons, the society is officially registered in Spain. == Conferences == Starting with 1999, EUSFLAT has been organizing its biannual conferences in odd years. Previous meetings: Palma de Mallorca, Balearic Islands, Spain, September 22–25, 1999 (jointly with National Spanish conference, ESTYLF) Leicester, United Kingdom, September 5–7, 2001 Zittau, Germany, September 10–12, 2003 Barcelona, Catalonia, Spain, September 7–9, 2005 (jointly with 11th Rencontres Francophones sur la Logique Floue et ses Applications) Ostrava, Czech Republic, September 11–14, 2007 Lisbon, Portugal, July 20–24, 2009 (jointly with 13th World Congress of the International Fuzzy Systems Association) Aix-les-Bains, France, July 18–22, 2011 (jointly with Les Rencontres Francophones sur la Logique Floue et ses Applications) Milan, Italy, September 11–13, 2013 Gijón, Spain, June, 30–3 July 2015 == Publications == EUSFLAT publishes the proceedings of its conferences in an open access manner. Until 2010, Mathware & Soft Computing was the official journal of EUSFLAT. On July 1, 2010, the International Journal of Computational Intelligence Systems (Atlantis Press, ISSN 1875-6891 (print) / ISSN 1875-6883 (on-line)) became the official journal of EUSFLAT. EUSFLAT publishes an electronic newsletter with three issues a year. == Presidents == EUSFLAT is led by the President, who is elected for a two-year period, and cannot serve for more than two consecutive periods. Francesc Esteva (1998–2011) Luis Magdalena (2001–2005) Ulrich Bodenhofer (2005–2009) Javier Montero (2009–2013) Gabriella Pasi (2013–present)

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  • Legal expert system

    Legal expert system

    A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. Legal expert systems employ a rule base or knowledge base and an inference engine to accumulate, reference and produce expert knowledge on specific subjects within the legal domain. == Purpose == It has been suggested that legal expert systems could help to manage the rapid expansion of legal information and decisions that began to intensify in the late 1960s. Many of the first legal expert systems were created in the 1970s and 1980s. Lawyers were originally identified as primary target users of legal expert systems. Potential motivations for this work included: quicker delivery of legal advice; reduced time spent in repetitive, labour-intensive legal tasks; development of knowledge management techniques that were not dependent on staff; reduced overhead and labour costs and higher profitability for law firms; and reduced fees for clients. Some early development work was oriented toward the creation of automated judges. One of the first use cases was the encoding of the British Nationality Act at Imperial College carried out under the supervision of Marek Sergot and Robert Kowalski. Lance Elliot wrote: "The British Nationality Act was passed in 1981 and shortly thereafter was used as a means of showcasing the efficacy of using Artificial Intelligence (AI) techniques and technologies, doing so to explore how the at-the-time newly enacted statutory law might be encoded into a computerized logic-based formalization." The authors’ seminal article, "The British Nationality Act as a Logic Program," published in 1986 in the Communications of the ACM journal, is one of the first and best-known works in computational law, and one of the most widely cited papers in the field. In 2021, the Inaugural CodeX Prize was awarded to Robert Kowalski, Fariba Sadri, and Marek Sergot in acknowledgment of their groundbreaking work on the application of logic programming to the formalization and analysis of the British Nationality Act. Later work on legal expert systems has identified potential benefits to non-lawyers as a means to increase access to legal knowledge. Legal expert systems can also support administrative processes, facilitate decision-making processes, automate rule-based analyses, and exchange information directly with citizen-users. == Types == === Architectural variations === Rule-based expert systems rely on a model of deductive reasoning that utilizes "If A, then B" rules. In a rule-based legal expert system, information is represented in the form of deductive rules within the knowledge base. In rule-based legal expert systems, logic programming has historically been applied to automate complex compliance paperwork. A notable early example designed for high-volume regulatory filings was the 1999 Intelligent Filing Manager (INTELLIFM), which utilized Prolog rules as its core inference engine to automate the generation, publishing, and population of structured forms via distributed COM interfaces. Case-based reasoning models, which store and manipulate examples or cases, hold the potential to emulate an analogical reasoning process thought to be well-suited for the legal domain. This model effectively draws on known experiences our outcomes for similar problems. A neural net relies on a computer model that mimics that structure of a human brain, and operates in a very similar way to the case-based reasoning model. This expert system model is capable of recognizing and classifying patterns within the realm of legal knowledge and dealing with imprecise inputs. Fuzzy logic models attempt to create 'fuzzy' concepts or objects that can then be converted into quantitative terms or rules that are indexed and retrieved by the system. In the legal domain, fuzzy logic can be used for rule-based and case-based reasoning models. === Theoretical variations === Some legal expert system architects have adopted a very practical approach, employing scientific modes of reasoning within a given set of rules or cases. Others have opted for a broader philosophical approach inspired by jurisprudential reasoning modes emanating from established legal theoreticians. === Functional variations === Some legal expert systems aim to arrive at a particular conclusion in law, while others are designed to predict a particular outcome. An example of a predictive system is one that predicts the outcome of judicial decisions, the value of a case, or the outcome of litigation. == Reception == Many forms of legal expert systems have become widely used and accepted by both the legal community and the users of legal services. == Challenges == === Domain-related problems === The inherent complexity of law as a discipline raises immediate challenges for legal expert system knowledge engineers. Legal matters often involve interrelated facts and issues, which further compound the complexity. Factual uncertainty may also arise when there are disputed versions of factual representations that must be input into an expert system to begin the reasoning process. === Computerized problem solving === The limitations of most computerized problem solving techniques inhibit the success of many expert systems in the legal domain. Expert systems typically rely on deductive reasoning models that have difficulty according degrees of weight to certain principles of law or importance to previously decided cases that may or may not influence a decision in an immediate case or context. === Representation of legal knowledge === Expert legal knowledge can be difficult to represent or formalize within the structure of an expert system. For knowledge engineers, challenges include: Open texture: Law is rarely applied in an exact way to specific facts, and exact outcomes are rarely a certainty. Statutes may be interpreted according to different linguistic interpretations, reliance on precedent cases or other contextual factors including a particular judge's conception of fairness. The balancing of reasons: Many arguments involve considerations or reasons that are not easily represented in a logical way. For instance, many constitutional legal issues are said to balance independently well-established considerations for state interests against individual rights. Such balancing may draw on extra-legal considerations that would be difficult to represent logically in an expert system. Indeterminacy of legal reasoning: In the adversarial arena of law, it is common to have two strong arguments on a single point. Determining the 'right' answer may depend on a majority vote among expert judges, as in the case of an appeal. === Time and cost effectiveness === Creating a functioning expert system requires significant investments in software architecture, subject matter expertise and knowledge engineering. Faced with these challenges, many system architects restrict the domain in terms of subject matter and jurisdiction. The consequence of this approach is the creation of narrowly focused and geographically restricted legal expert systems that are difficult to justify on a cost-benefit basis. Current applications of AI in the legal field utilize machines to review documents, particularly when a high level of completeness and confidence in the quality of document analysis is depended upon, such as in instances of litigation and where due diligence play a role. Among the numerically most quantifiable advantages of AI in the legal field are the time and money saving impact by freeing lawyers from having to spend inordinate amounts of their valuable time on routine tasks, aiding in setting free lawyers’ creative energy by reducing stress. This in turn increases the rate of case load reduction by accomplishing better results in less time, which unlocks potential additional revenue per unit of time spend on a case. The cost of setting up and maintaining AI systems in law is more than offset by the attained savings through increased efficacy; unbalanced cost can be assigned to clients. === Lack of correctness in results or decisions === Legal expert systems may lead non-expert users to incorrect or inaccurate results and decisions. This problem could be compounded by the fact that users may rely heavily on the correctness or trustworthiness of results or decisions generated by these systems. == Examples == ASHSD-II is a hybrid legal expert system that blends rule-based and case-based reasoning models in the area of matrimonial property disputes under English law. CHIRON is a hybrid legal expert system that blends rule-based and case-based reasoning models to support tax planning activities under United States tax law and codes. JUDGE is a rule-based legal expert system that deals with sentencing in the criminal legal domain for offences relating to murder, assault and manslaughter. Legislate is a knowledge graph powered contract management platform whi

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  • Speech synthesis

    Speech synthesis

    Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output. The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. The earliest computer operating system to have included a speech synthesizer was Unix in 1974, through the Unix speak utility. In 2000, Microsoft Sam was the default text-to-speech voice synthesizer used by the narrator accessibility feature, which shipped with all Windows 2000 operating systems, and subsequent Windows XP systems. A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech. == History == Long before the invention of electronic signal processing, some people tried to build machines to emulate human speech. There were also legends of the existence of "Brazen Heads", such as those involving Pope Silvester II (d. 1003 AD), Albertus Magnus (1198–1280), and Roger Bacon (1214–1294). In 1779, the German-Danish scientist Christian Gottlieb Kratzenstein won the first prize in a competition announced by the Russian Imperial Academy of Sciences and Arts for models he built of the human vocal tract that could produce the five long vowel sounds (in International Phonetic Alphabet notation: [aː], [eː], [iː], [oː] and [uː]). There followed the bellows-operated "acoustic-mechanical speech machine" of Wolfgang von Kempelen of Pressburg, Hungary, described in a 1791 paper. This machine added models of the tongue and lips, enabling it to produce consonants as well as vowels. In 1837, Charles Wheatstone produced a "speaking machine" based on von Kempelen's design, and in 1846, Joseph Faber exhibited the "Euphonia". In 1923, Paget resurrected Wheatstone's design. In the 1930s, Bell Labs developed the vocoder, which automatically analyzed speech into its fundamental tones and resonances. From his work on the vocoder, Homer Dudley developed a keyboard-operated voice-synthesizer called The Voder (Voice Demonstrator), which he exhibited at the 1939 New York World's Fair. Franklin S. Cooper and his colleagues at Haskins Laboratories built the pattern playback in the late 1940s and completed it in 1950. There were several different versions of this hardware device; only one currently survives. The machine converts pictures of the acoustic patterns of speech in the form of a spectrogram back into sound. Using this device, Alvin Liberman and colleagues discovered acoustic cues for the perception of phonetic segments (consonants and vowels). === Electronic devices === The first computer-based speech-synthesis systems originated in the late 1950s. Noriko Umeda et al. developed the first general English text-to-speech system in 1968, at the Electrotechnical Laboratory in Japan. In 1961, physicist John Larry Kelly, Jr and his colleague Louis Gerstman used an IBM 704 computer to synthesize speech, an event among the most prominent in the history of Bell Labs. Kelly's voice recorder synthesizer (vocoder) recreated the song "Daisy Bell", with musical accompaniment from Max Mathews. Coincidentally, Arthur C. Clarke was visiting his friend and colleague John Pierce at the Bell Labs Murray Hill facility. Clarke was so impressed by the demonstration that he used it in the climactic scene of his screenplay for his novel 2001: A Space Odyssey, where the HAL 9000 computer sings the same song as astronaut Dave Bowman puts it to sleep. Despite the success of purely electronic speech synthesis, research into mechanical speech-synthesizers continues. Linear predictive coding (LPC), a form of speech coding, began development with the work of Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone (NTT) in 1966. Further developments in LPC technology were made by Bishnu S. Atal and Manfred R. Schroeder at Bell Labs during the 1970s. LPC was later the basis for early speech synthesizer chips, such as the Texas Instruments LPC Speech Chips used in the Speak & Spell toys from 1978. In 1975, Fumitada Itakura developed the line spectral pairs (LSP) method for high-compression speech coding, while at NTT. From 1975 to 1981, Itakura studied problems in speech analysis and synthesis based on the LSP method. In 1980, his team developed an LSP-based speech synthesizer chip. LSP is an important technology for speech synthesis and coding, and in the 1990s was adopted by almost all international speech coding standards as an essential component, contributing to the enhancement of digital speech communication over mobile channels and the internet. In 1975, MUSA was released, and was one of the first Speech Synthesis systems. It consisted of a stand-alone computer hardware and a specialized software that enabled it to read Italian. A second version, released in 1978, was also able to sing Italian in an "a cappella" style. Dominant systems in the 1980s and 1990s were the DECtalk system, based largely on the work of Dennis Klatt at MIT, and the Bell Labs system; the latter was one of the first multilingual language-independent systems, making extensive use of natural language processing methods. Handheld electronics featuring speech synthesis began emerging in the 1970s. One of the first was the Telesensory Systems Inc. (TSI) Speech+ portable calculator for the blind in 1976. Other devices had primarily educational purposes, such as the Speak & Spell toy produced by Texas Instruments in 1978. Fidelity released a speaking version of its electronic chess computer in 1979. The first video game to feature speech synthesis was the 1980 shoot 'em up arcade game, Stratovox (known in Japan as Speak & Rescue), from Sun Electronics. The first personal computer game with speech synthesis was Manbiki Shoujo (Shoplifting Girl), released in 1980 for the PET 2001, for which the game's developer, Hiroshi Suzuki, developed a "zero cross" programming technique to produce a synthesized speech waveform. Another early example, the arcade version of Berzerk, also dates from 1980. The Milton Bradley Company produced the first multi-player electronic game using voice synthesis, Milton, in the same year. In 1976, Computalker Consultants released their CT-1 Speech Synthesizer. Designed by D. Lloyd Rice and Jim Cooper, it was an analog synthesizer built to work with microcomputers using the S-100 bus standard. Synthesized voices typically sounded male until 1990, when Ann Syrdal, at AT&T Bell Laboratories, created a female voice. Ray Kurzweil predicted in 2005 that as the cost-performance ratio caused speech synthesizers to become cheaper and more accessible, more people would benefit from the use of text-to-speech programs. === Artificial intelligence === In September 2016, DeepMind released WaveNet, which demonstrated that deep learning models are capable of modeling raw waveforms and generating speech from acoustic features like spectrograms or mel-spectrograms, starting the field of deep learning speech synthesis. Although WaveNet was initially considered to be computationally expensive and slow to be used in consumer products at the time, a year after its

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  • Poop Map

    Poop Map

    Poop Map is a social app where users can track on a map where and when they defecate. In addition to logging location and time of each bowel movement, users can also add a photo, "like" other users' logs, and rate each account. The social elements of the app allow for groups of users to create a competitive league. Certain behaviors unlock achievements in-app. == Development == The app was created by app developer Nino Uzelac. It was launched in July 2013. == Popularity == The app charted at number one on the Apple App Store charts in 2021 after going viral on TikTok. As of September 2024, the app has a 4.8 rating on the App Store and more than 58,000 ratings. It also has more than one million downloads on the Google Play Store. Poop Map is notably popular among hikers, and has been written about in the outdoors magazine Outside.

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  • AI Now Institute

    AI Now Institute

    The AI Now Institute (AI Now) is an American research institute studying the social implications of artificial intelligence and policy research that addresses the concentration of power in the tech industry. AI Now has partnered with organizations such as the Distributed AI Research Institute (DAIR), Data & Society, Ada Lovelace Institute, New York University Tandon School of Engineering, New York University Center for Data Science, Partnership on AI, and the ACLU. AI Now has produced annual reports that examine the social implications of artificial intelligence. In 2021–22, AI Now's leadership served as a Senior Advisors on AI to Chair Lina Khan at the Federal Trade Commission. Its executive director is Amba Kak. == Founding and mission == AI Now grew out of a 2016 symposium organized by Obama's White House Office of Science and Technology Policy. The event was led by Meredith Whittaker, the founder of Google's Open Research Group, and Kate Crawford, a principal researcher at Microsoft Research. The event focused on near-term implications of AI in social domains: Inequality, Labor, Ethics, and Healthcare. In November 2017, AI Now held a second symposium on AI and social issues, and publicly launched the AI Now Institute in partnership with New York University. It is claimed to be the first university research institute focused on the social implications of AI, and the first AI institute founded and led by women. It is now a fully independent institute. In an interview with NPR, Crawford stated that the motivation for founding AI Now was that the application of AI into social domains - such as health care, education, and criminal justice - was being treated as a purely technical problem. The goal of AI Now's research is to treat these as social problems first, and bring in domain experts in areas like sociology, law, and history to study the implications of AI. == Research == AI Now publishes an annual report on the state of AI and its integration into society. Its 2017 report stated that "current framings of AI ethics are failing" and provided ten strategic recommendations for the field - including pre-release trials of AI systems, and increased research into bias and diversity in the field. The report was noted for calling for an end to "black box" systems in core social domains, such as those responsible for criminal justice, healthcare, welfare, and education. In April 2018, AI Now released a framework for algorithmic impact assessments, as a way for governments to assess the use of AI in public agencies. According to AI Now, an AIA would be similar to environmental impact assessment, in that it would require public disclosure and access for external experts to evaluate the effects of an AI system, and any unintended consequences. This would allow systems to be vetted for issues like biased outcomes or skewed training data, which researchers have already identified in algorithmic systems deployed across the country. Its 2023 Report argued that meaningful reform of the tech sector must focus on addressing concentrated power in the tech industry.

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  • Knights of Sidonia

    Knights of Sidonia

    Knights of Sidonia (Japanese: シドニアの騎士, Hepburn: Shidonia no Kishi) is a Japanese manga series written and illustrated by Tsutomu Nihei. It was serialized by Kodansha's seinen manga magazine Monthly Afternoon between April 2009 and September 2015, with its chapters collected in 15 tankōbon volumes. It tells the story of Nagate Tanikaze, an "under-dweller" destined to become a Garde pilot, whose mission is to defend the generation ship Sidonia from a hostile alien species called Gauna. The manga was licensed for English release in North America by Vertical. An anime television series adaptation was produced by Polygon Pictures. The first season aired from April to June 2014; the second between April and June 2015. An anime film sequel titled Knights of Sidonia: Love Woven in the Stars premiered in June 2021. In 2015, Knights of Sidonia received the 39th Kodansha Manga Award in the general category, as well as the 47th Seiun Award in the Best Comic category in 2016. == Plot == === Setting === The story is set in the year 3394, a thousand years after mankind flees from Earth after it was destroyed by a race of shapeshifting aliens called the Gauna (奇居子(ガウナ)), aboard hundreds of colossal spacecraft created from the remains of the planet. One such ship is the Sidonia, which has developed its own human culture closely based on that of Japan where human cloning, asexual reproduction, and human genetic engineering, such as granting humans photosynthesis, are commonplace. It is also revealed that the top echelons of this society have secretly been granted immortality. With a population of over 500,000 people, Sidonia is possibly the last human settlement remaining, as the fates of the other ships are unknown. Little is known about the true nature of the Gauna or their motivation for attacking humanity. At any given time, a Gauna consists of a nearly impenetrable core protected by a dense layer of malleable flesh known as "placenta" (胞衣, ena). Once the ena is shed away and the core is destroyed, the Gauna's body disintegrates. While Sidonia itself is heavily armed with an arsenal of high-output beam cannons and mass cannons including slow but powerful planet-destroying warheads, it is primarily defended by large mechanized weapons called Gardes (衛人, Morito) whose weaponry and mobility is powered by "Higgs particles" (ヘイグス粒子, Heigusu Ryūshi), armed with a high-output beam cannon for long range assaults and a special spear known as "Kabizashi" for close combat. The tip of the kabizashi is made of a rare and little-understood material which has the unique property of being able to destroy a Gauna's core. Later the Gardes are also equipped with firearms whose ammunition have the same material of the Kabizashi after a means to artificially mass-produce it is discovered. Most people in the surviving human population are screened and drafted as Garde pilots at a young age, if they are shown to be capable of piloting them. === Story === The story follows the adventures of Garde pilot Nagate Tanikaze, who lived in the underground layer of Sidonia since birth and was raised by his grandfather. Never having met anyone else, he trains himself in an old Guardian pilot simulator every day, eventually mastering it. After his grandfather's death, he emerges to the surface and is selected as a Garde pilot, just as Sidonia is once again threatened by the Gauna. == Media == === Manga === Written and illustrated by Tsutomu Nihei, Knights of Sidonia was serialized in Kodansha's seinen manga magazine Monthly Afternoon from April 25, 2009, to September 25, 2015. It was compiled in 15 tankōbon volumes. The manga has been licensed in North America by Vertical, who released all 15 volumes in English between February 5, 2013, and April 26, 2016. === Anime === An anime television series adaptation, produced by Polygon Pictures, aired its first season from April 10 to June 26, 2014, on MBS and later on TBS, CBC and BS-TBS. The series was directed by Kōbun Shizuno, assisted by Hiroyuki Seshita, with scripts by Sadayuki Murai and character designs by Yuki Moriyama. The opening theme song is "Sidonia" (シドニア, Shidonia), performed by Angela, while the ending theme song is "Show" (掌 -show-, Shō), performed by Eri Kitamura. A second season aired from April 11 to June 26, 2015. For the second season, the opening theme song is "Kishi Kōshinkyoku" (騎士行進曲, Knight March), performed by Angela, while the ending theme song is "Requiem" (鎮魂歌 -レクイエム-, Rekuiemu), performed by CustomiZ. The series was localized and streamed by Netflix in all of its territories since July 4, 2014, becoming the service's first original anime, as well as the first anime series on Netflix available in Dolby Vision/HDR. The first season has been licensed for home video release by Sentai Filmworks. The second season was released on Netflix on July 3, 2015, and has been licensed by Sentai Filmworks for home video distribution. In July 2021, Funimation announced they acquired the streaming rights from Netflix to both seasons. === Films === A compilation film of the first season with additional scenes and re-edited sound effects was released on March 6, 2015. A new anime film, titled Knights of Sidonia: Love Woven in the Stars, was announced on July 3, 2020. Hiroyuki Seshita served as chief director, while Tadahiro Yoshihira served as director for the new film, with Polygon Pictures returning for production. Sadayuki Murai and Tetsuya Yamada returned to write scripts, while Shūji Katayama composed the music. The rest of the staff and cast returned to reprise their roles. The first four minutes of the film were shown on YouTube on April 28, 2021. The film was set to premiere on May 14, 2021, but was delayed to June 4, 2021, due to the COVID-19 pandemic. Funimation screened the film in international theaters starting on September 13, 2021. == Reception == === Manga === Knights of Sidonia won the 39th Kodansha Manga Award in the general category in 2015. The manga won the 47th Seiun Award in the Best Comic category in 2016. It also won the Best Seinen category at the 26th Salón del Manga de Barcelona in 2020. It was one of the Jury Recommended works in the Manga Division at the 17th Japan Media Arts Festival in 2013. The Young Adult Library Services Association listed Knights of Sidonia in its 2014 list of Top 10 Graphic Novels for Teens. Carlo Santos from Anime News Network gave the first manga volume a B, stating, "It is got a young man piloting a giant robot against alien enemies, but Knight of Sidonia is no Neon Genesis Evangelion. Yet it is not as bleak or incomprehensible as Tsutomu Nihei works like Blame! or Biomega, either—rather, it is the best of both worlds, bringing Nihei's hard sci-fi mentality into a more conventional space-adventure environment". === Anime === The anime series received positive reviews, even from famous members of the Japanese anime/game industry, like Hideo Kojima, creator of the Metal Gear series, who claims that "It's a kind of anime that we haven't seen for a while that has that sci-fi spirit. Using digital technology cultivated through games, it creates animation that encapsulates Japan's cultural assets like manga, cel animation, kanji, giant robots, etc. What's born is a unique made-in-Japan work that could never be cooked up in Hollywood. Japanese culture has lost its 'cool', and Knights of Sidonia will be the white knight that saves it". Other industry pros left acknowledgements as well, including Akiko Higashimura, Digitarou and Yoshinao Dao.

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