AI Content Detection Tools

AI Content Detection Tools — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Brownout (software engineering)

  Brownout in software engineering is a technique that involves disabling certain features of an application. == Description == Brownout is used to increase the robustness of an application to computing capacity shortage. If too many users are simultaneously accessing an application hosted online, the underlying computing infrastructure may become overloaded, rendering the application unresponsive. Users are likely to abandon the application and switch to competing alternatives, hence incurring long-term revenue loss. To better deal with such a situation, the application can be given brownout capabilities: The application will disable certain features – e.g., an online shop will no longer display recommendations of related products – to avoid overload. Although reducing features generally has a negative impact on the short-term revenue of the application owner, long-term revenue loss can be avoided. The technique is inspired by brownouts in power grids, which consists in reducing the power grid's voltage in case electricity demand exceeds production. Some consumers, such as incandescent light bulbs, will dim – hence originating the term – and draw less power, thus helping match demand with production. Similarly, a brownout application helps match its computing capacity requirements to what is available on the target infrastructure. Brownout complements elasticity. The former can help the application withstand short-term capacity shortage, but does so without changing the capacity available to the application. In contrast, elasticity consists of adding (or removing) capacity to the application, preferably in advance, so as to avoid capacity shortage altogether. The two techniques can be combined; e.g., brownout is triggered when the number of users increases unexpectedly until elasticity can be triggered, the latter usually requiring minutes to show an effect. Brownout is relatively non-intrusive for the developer, for example, it can be implemented as an advice in aspect-oriented programming. However, surrounding components, such as load-balancers, need to be made brownout-aware to distinguish between cases where an application is running normally and cases where the application maintains a low response time by triggering brownout. == Usage in phased deprecation == A related use of the brownout concept in software engineering is the deliberate introduction of temporary outages to a system, API or feature that is being phased out. This is sometimes also called a "scream test" when it is used to discover unknown dependents of a system or API. The intention is to allow detection of downstream consumers of an API or service who may otherwise have missed deprecation announcements or to uncover hidden side-effects of the deprecation that may have been overlooked. The intention is that developers of dependent systems will notice their own system failures caused by the upstream brownout. Such brownouts are typically pre-announced scheduled outages or probabilistic in nature (such as artificially failing a percentage of requests). As a brownout is only a temporary or partial outage, it provides downstream consumers of an API or service time to remove any discovered dependencies on the deprecated API before it is fully retired. For consumers that have already prepared for the deprecation, a brownout provides valuable testing that the final removal of the service won't cause any unexpected problems.

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  Fuzzy mathematics

  Fuzzy mathematics is a branch of mathematics that extends classical set theory and logic to model reasoning under uncertainty. Initiated by Lotfi Asker Zadeh in 1965 with the introduction of fuzzy sets, the field has since evolved to include fuzzy set theory, fuzzy logic, and various fuzzy analogues of traditional mathematic structures. Unlike classical mathematics, which usually relies on binary membership (an element either belongs to a set or it does not), fuzzy mathematics allows elements to partially belong to a set, with degrees of membership represented by values in the interval [0, 1]. This framework enables more flexible modeling of imprecise or vague concepts. Fuzzy mathematics has found applications in numerous domains, including control theory, artificial intelligence, decision theory, pattern recognition, and linguistics, where the modeling of gradations and uncertainty is essential. == Definition == A fuzzy subset A of a set X is defined by a function A: X → L, where L is typically the interval [0, 1]. This function is called the membership function of the fuzzy subset and assigns to each element x in X a degree of membership A(x) in the fuzzy set A. In classical set theory, a subset of X can be represented by an indicator function (also known as a characteristic function), which maps elements to either 0 or 1, indicating non-membership or full membership, respectively. Fuzzy subsets generalize this concept by allowing any real value between 0 and 1, thereby enabling partial membership. More generally, the codomain L of the membership function can be replaced with any complete lattice, resulting in the broader framework of L-fuzzy sets. == Fuzzification == The development of fuzzification in mathematics can be broadly divided into three historical stages: Initial, straightforward fuzzifications (1960s–1970s), Expansion of generalization techniques (1980s), Standardization, axiomatization, and L-fuzzification (1990s). Fuzzification generally involves extending classical mathematical concepts from binary (crisp) logic, where membership is determined by characteristic functions, to fuzzy logic, where membership is expressed by values in the interval [0, 1] via membership functions. Let A and B be fuzzy subsets of a set X. The fuzzy versions of set-theoretic operations are commonly defined as: ( A ∩ B ) ( x ) = min ( A ( x ) , B ( x ) ) {\displaystyle (A\cap B)(x)=\min(A(x),B(x))} ( A ∪ B ) ( x ) = max ( A ( x ) , B ( x ) ) {\displaystyle (A\cup B)(x)=\max(A(x),B(x))} for all x ∈ X {\displaystyle x\in X} . These operations can be generalized using t-norms and t-conorms, respectively. For example, the minimum operation can be replaced by multiplication: ( A ∩ B ) ( x ) = A ( x ) ⋅ B ( x ) {\displaystyle (A\cap B)(x)=A(x)\cdot B(x)} Fuzzification of algebraic structures often relies on generalizing the closure property. Let ∗ {\displaystyle } be a binary operation on X, and let A be a fuzzy subset of X. Then A is said to satisfy fuzzy closure if: A ( x ∗ y ) ≥ min ( A ( x ) , A ( y ) ) {\displaystyle A(xy)\geq \min(A(x),A(y))} for all x , y ∈ X {\displaystyle x,y\in X} . If ( G , ∗ ) {\displaystyle (G,)} is a group, then a fuzzy subset A of G is a fuzzy subgroup if: A ( x ∗ y − 1 ) ≥ min ( A ( x ) , A ( y − 1 ) ) {\displaystyle A(xy^{-1})\geq \min(A(x),A(y^{-1}))} for all x , y ∈ G {\displaystyle x,y\in G} . Similar generalizations apply to relational properties. For example, for example, for fuzzification of the transitivity property, a fuzzy relation R {\displaystyle R} on X {\displaystyle X} (i.e., a fuzzy subset of X × X {\displaystyle X\times X} ) is said to be fuzzy transitive if: R ( x , z ) ≥ min ( R ( x , y ) , R ( y , z ) ) {\displaystyle R(x,z)\geq \min(R(x,y),R(y,z))} for all x , y , z ∈ X {\displaystyle x,y,z\in X} . == Fuzzy analogues == Fuzzy subgroupoids and fuzzy subgroups were introduced in 1971 by A. Rosenfeld. Analogues of other mathematical subjects have been translated to fuzzy mathematics, such as fuzzy field theory and fuzzy Galois theory, fuzzy topology, fuzzy geometry, fuzzy orderings, and fuzzy graphs.

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  Conceptual dependency theory

  Conceptual dependency theory is a model of natural language understanding used in artificial intelligence systems. Roger Schank at Stanford University introduced the model in 1969, in the early days of artificial intelligence. This model was extensively used by Schank's students at Yale University such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner. Schank developed the model to represent knowledge for natural language input into computers. Partly influenced by the work of Sydney Lamb, his goal was to make the meaning independent of the words used in the input, i.e. two sentences identical in meaning would have a single representation. The system was also intended to draw logical inferences. The model uses the following basic representational tokens: real world objects, each with some attributes. real world actions, each with attributes times locations A set of conceptual transitions then act on this representation, e.g. an ATRANS is used to represent a transfer such as "give" or "take" while a PTRANS is used to act on locations such as "move" or "go". An MTRANS represents mental acts such as "tell", etc. A sentence such as "John gave a book to Mary" is then represented as the action of an ATRANS on two real world objects, John and Mary.

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  Adaptive neuro fuzzy inference system

  An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system, a class of fuzzy models introduced by Tomohiro Takagi and Michio Sugeno for system identification and control. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered to be a universal estimator. For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm. It has uses in intelligent situational aware energy management system. == ANFIS architecture == It is possible to identify two parts in the network structure, namely premise and consequence parts. In more details, the architecture is composed by five layers. The first layer takes the input values and determines the membership functions belonging to them. It is commonly called fuzzification layer. The membership degrees of each function are computed by using the premise parameter set, namely {a,b,c}. The second layer is responsible of generating the firing strengths for the rules. Due to its task, the second layer is denoted as "rule layer". The role of the third layer is to normalize the computed firing strengths, by dividing each value for the total firing strength. The fourth layer takes as input the normalized values and the consequence parameter set {p,q,r}. The values returned by this layer are the defuzzificated ones and those values are passed to the last layer to return the final output. === Fuzzification layer === The first layer of an ANFIS network describes the difference to a vanilla neural network. Neural networks in general are operating with a data pre-processing step, in which the features are converted into normalized values between 0 and 1. An ANFIS neural network doesn't need a sigmoid function, but it's doing the preprocessing step by converting numeric values into fuzzy values. Here is an example: Suppose, the network gets as input the distance between two points in the 2d space. The distance is measured in pixels and it can have values from 0 up to 500 pixels. Converting the numerical values into fuzzy numbers is done with the membership function which consists of semantic descriptions like near, middle and far. Each possible linguistic value is given by an individual neuron. The neuron “near” fires with a value from 0 until 1, if the distance is located within the category "near". While the neuron “middle” fires, if the distance in that category. The input value “distance in pixels” is split into three different neurons for near, middle and far.

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  Automate This

  Automate This: How Algorithms Came to Rule Our World is a book written by Christopher Steiner and published by Penguin Group. == Book == Steiner begins his study of algorithms on Wall Street in the 1980s but also provides examples from other industries. For example, he explains the history of Pandora Radio and the use of algorithms in music identification. He expresses concern that such use of algorithms may lead to the homogenization of music over time. Steiner also discusses the algorithms that eLoyalty (now owned by Mattersight Corporation following divestiture of the technology) was created by dissecting 2 million speech patterns and can now identify a caller's personality style and direct the caller with a compatible customer support representative. Steiner's book shares both the warning and the opportunity that algorithms bring to just about every industry in the world, and the pros and cons of the societal impact of automation (e.g. impact on employment).

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  Argument mining

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

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  Maschinen Krieger ZbV 3000

  Maschinen Krieger (Ma.K ZBV3000), often abbreviated as Ma.K., is a science fiction intellectual property created by Japanese artist and sculptor Kow Yokoyama in the 1980s. It consists of an illustrated series, a line of merchandise comprising display and action figures of mecha characters and a 1985 short film. == History == The franchise originally began as the science fiction series SF3D which ran as monthly installments in the Japanese hobby magazine Hobby Japan from 1982 to 1985. To develop the storyline, Kow Yokoyama collaborated with Hiroshi Ichimura as story editor and Kunitaka Imai as graphic designer. The three creators drew visual inspiration from their combined interest in World War I and World War II armor and aircraft, the American space program and films such as Star Wars, Blade Runner and The Road Warrior. Inspired by the ILM model builders who worked on Star Wars, Yokoyama built the original models from numerous kits including armor, aircraft, and automobiles. He mostly concentrated on powered armor suits, but later included bipedal walking tanks and aircraft with anti-gravity systems. In 1986, there was a dispute with Hobby Japan over the copyright of the series. The magazine dropped SF3D from its line-up of articles and Nitto ceased production of various kits of the series. The matter was tied up in the courts for years until Yokoyama was awarded the full copyright to the series in the 1990s. Yokoyama and Hobby Japan eventually reconciled and restarted their working relationship, ditching the old SF3D name in favor of Maschinen Krieger ZbV3000, otherwise known as Ma.K. == Story == A nuclear World War IV in 2807 kills most of Earth's population and renders the planet uninhabitable. Fifty-two years after the war, a research team from an interstellar union called the Galactic Federation is sent to Earth and discovers that the planet's natural environment has restored itself. The Federation decides to repopulate the planet and sends over colonists to the surface. Cities and towns are eventually reformed over the next 20 years, but this growth attracts the attention of criminals, military deserters, and other lawless elements who wanted to hide on Earth—away from the authorities. A few militias protect the colonists, but the new interlopers often defeat them. Fearing civil unrest and the colonists forming their own government, the Federation gives the Strahl Democratic Republic (SDR) the right to govern the planet in the late 2870s. The SDR sends three police battalions and three Foreign Legion corps to Earth and uses heavy-handed tactics such as travel restrictions and hard labor camps to restore order, which creates resentment amongst the colonists. In response, the colonists create the Earth Independent Provisional Government and declare independence from the SDR. The SDR immediately establishes a puppet government and attempts to quell the uprising. The wealthy colonists hire mercenaries who are descendants of WWIV veterans to form the Independent Mercenary Army (IMA), which is bolstered by the presence of SDR Foreign Legion defectors. They attack the SDR forces and the battle to control Earth begins in 2882. Over the next four years, the SDR and IMA fight each other at several locations worldwide while developing new technology along the way. The war turns up a notch in June 2883 when the IMA deploys a new weapon - the Armored Fighting Suit powered armor - to devastating effect. The SDR eventually builds their own AFS units. In the last SF3D installment published in the December 1986 issue of Hobby Japan, the IMA successfully defeats the new SDR Königs Kröte unmanned command-and-control mecha using a computer virus that also creates a new artificial intelligence system on the moon. == Merchandise == === Model kits === Fan interest from the installments in Hobby Japan resulted in a small Japanese model company, Nitto, securing the license and quickly released 21 injection molded kits from the series during its entire run in the magazine. Most of the Nitto model kits are in 1:20 scale, while others were made in 1:76 and 1:6 scale. Production of the kits stopped with the end of the Hobby Japan features in 1986, but Nitto reissued many of the original kits under the Maschinen Krieger name, albeit with new decals and box art. Some of the original Nitto kits such as the Krachenvogel are highly sought after by collectors. The Nitto models were also the basis for similar offerings from Japanese model companies Wave and ModelKasten. Wave, in particular, is currently producing original-tooled kits of various subjects in the franchise, such as the Armored Fighting Suits powered armor. Smaller companies such as Brick Works and Love Love Garden have made limited resin pilot figures to go with these model kits. At the 2008 Nuremberg Toy Fair in Germany, the Hasegawa company - known mostly for its line of military and civilian vehicles — announced plans to carry the Ma.K license, having successfully branched into pop culture franchises such as Macross. Hasegawa's venture into the franchise came with the release of the Pkf 85 Falke attack craft in March 2009. The company's Ma.K line has since expanded to at least ten kits either 1:35 or 1:20 scale, including a 1:35 Scale Nutrocker tank and the Mk44 humanoid mecha suit from Robot Battle V, a sidestory to the franchise. Wave corporation also has a line of 1/20 models. While Hasegawa largely maintained the yellow-box aesthetic from the older nitto kits, Wave has a more colorful box design. Certain garage kit manufacturers such as Rainbow-Egg are allowed to produce their own line of resin kits and accessories, upon securing special authorization from Yokoyama himself. === Toys === The franchise also contains a line of action and display figures. The Japanese hobby shop and toy company Yellow Submarine and garage kit maker Max Factory released several pre-finished figures in 1:35 and 1:16 scale. MediCom Toys included Chibi Ma.K. figures in their Kubrick line, plus two 1:6 SAFS figures with working lights and fully poseable pilot figures. === Books === Numerous sourcebooks and modeling guides that further flesh out the information in the series have been released. Hobby Japan published a compilation of the first 15 SF3D installments in 1983 and reprinted them in March 2010. Eventually, the magazine re-released all 43 installments in a slipcase compilation called "SF3D Chronicles" in August 2010, which organized the installments into two separate books: "Heaven" featuring articles on aerial models, and "Earth" for ground-based models. Model Graphix followed suit with their own line of sourcebooks, which provide tutorials from Yokoyama on how he makes his figures. Some sourcebooks also have custom decal sets. === Miniature wargaming === In 2019, Slave 2 Gaming gained the license to produce and sell 1:100 scale (15mm) metal and resin war gaming miniatures. This new range of Maschinen Krieger figures was given the name Ma.K in 15mm, so as to not complicate sales with customers, and rebrand the Ma.k name for the miniature wargaming world. The figures are designed and cast in Australia. They are sold exclusively through Slave 2 Gaming at this time due to the license agreement with Sensei Yokoyama. With the production of the miniatures, a set of gaming rules in the works, with the plan is to release all the current Maschinen Krieger models. == Short film == Yokoyama collaborated with Tsuburaya Productions to create a live-action SF3D film using miniatures in 1985. Directed by Shinichi Ohoka from a script penned by co-producer Hisao Ichikura, the 25-minute SF3D Original Video opens with wreckage left from a battle in the Wiltshire wastelands on Christmas Day 2884 before focusing on a badly damaged IMA SAFS unit. The pilot, Cpl Robert Bush (Tristan Hickey), who is still alive, seeks to get his armored suit back and running and leave the battle area, which is under heavy jamming. Seeing two of the SDR's new Nutrocker (Nutcracker) robot hovertanks arrive nearby, Bush tries to hide, but bodily functions give him away. One Nutcracker gives chase and the SAFS AI points out to Bush how to defeat it. He eventually clambers on to the tank, which passes through the rubble of a town and randomly shoots at high places to bring down objects that could snag him. With the SAFS' right arm sheared off by the Nutcracker's laser blasts and snow settling in, Bush is knocked unconscious all night long from the fall while the tank breaks down under the cold. The next day, the SAFS AI wakes up Bush because the Nutcracker is active again and is preparing to kill him. Bush gets up and faces the tank as it charges towards him. However, the Nutcracker gets too close to a cliff that buckles under its weight and Bush fires his laser into the tank's underbelly. The tank plunges into a ravine and explodes. Bush walks away and reestablishes radio contact with his base. It is revealed that the battle was a field test of th

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  The MANIAC

  The MANIAC is a 2023 novel by Chilean author Benjamín Labatut, written in English. It is a fictionalised biography of polymath John von Neumann, whom Labatut calls "the smartest human being of the 20th century". The book focuses on von Neumann, but is also about physicist Paul Ehrenfest, the history of artificial intelligence, and Lee Sedol's Go match against AlphaGo. The book received mostly positive reviews from critics. == Background == John von Neumann was a Jewish Hungarian-born polymath who was a prodigy from an early childhood. Von Neumann worked in multiple fields of science, theoretical (mathematical foundations of quantum mechanics, game theory, cellular automata) and applied (nuclear weapons research during the Manhattan Project in World War II, computer architecture later named after him, and many other subjects). Labatut calls him "the smartest human being of the 20th century". The title of the book is derived from an early computer based on von Neumann architecture, built after the war at Los Alamos laboratory, called MANIAC I. Benjamín Labatut is a Chilean author known for his 2020 book When We Cease to Understand the World, a collection of fictionalised stories about famous scientists that received positive reviews and was translated into multiple languages from Spanish. The MANIAC is Labatut's first book written in English. In an interview, Labatut said he prefers to write in English: English is my preferred form of thought. ... English is the language I do most if not all my reading it. And it is a far better language than Spanish, in so many ways. Writing "clean" prose in Spanish is almost impossible, because so many of its sounds clash. Borges said that he found English "a far finer language than Spanish" because it's both Germanic and Latin; because of its wonderful vocabulary ("Regal is not exactly the same thing as saying kingly," he explained); because of its physicality; and because you can do almost anything with verbs and prepositions. Labatut was inspired to write The MANIAC by George Dyson's book Turing's Cathedral. == Synopsis == The book has three chapters. The first chapter, "Paul or the Discovery of the Irrational", written in the third person, is about physicist Paul Ehrenfest. The chapter opens with Ehrenfest shooting dead his son Vassily, who suffered from Down syndrome, and then himself. It then recounts Ehrenfest's life story, describing his relationships with his wife Tatyana, his mistress Nelly Meyjes, and his eminent physicist colleagues. It chronicles his descent into despair and depression over his marriage's disintegration, the advent of quantum mechanics, and the direction Europe was heading in with the Nazi Party's rise to power in Germany, looping back to the initial scene of the chapter. The second chapter, "John or the Mad Dreams of Reason", is about John von Neumann, and is written as a series of interviews of his family members, wives, friends, and colleagues, each in a distinctive voice. It is divided into three parts. Part I, "The Limits of Logic", is about his early life, as told by von Neumann's childhood friend Eugene Wigner, mother Margrit Kann, brother Nicholas von Neumann, first wife Mariette Kövesi, and scientists Theodore von Karman, George Polya, and Gábor Szegő. It climaxes with von Neumann's participation in David Hilbert's program to create a logical basis for mathematics based on a consistent set of axioms, a quest ultimately scuppered by Kurt Gödel. Part II, "The Delicate Balance of Terror", discusses von Neumann's role in the Manhattan Project (as told by Richard Feynman); his development of game theory and the doctrine of mutual assured destruction (MAD) (as told by Oskar Morgenstern); and his creation of the MANIAC I computer and the von Neumann architecture (as told by Julian Bigelow). In Part III, "Ghosts in the Machine", Sydney Brenner discusses von Neumann's contributions to biology, his theoretical work on self-replicating and self-repairing machines, and his vision of Von Neumann probes exploring the universe. Nils Aall Barricelli talks about his ideas of digital life and his disagreements with von Neumann. Von Neumann's wife Klára Dán, daughter Marina, and Wigner talk about his final years, personal life, and death. The third chapter, "Lee or The Delusions of Artificial Intelligence", is about Lee Sedol's Go match against AlphaGo. The narrative reverts to the third person. The chapter also tells the story of Demis Hassabis, a chess prodigy in childhood who decided to work on artificial intelligence and founded DeepMind, the company behind AlphaGo. The way is pointed to the future, as artificial intelligence's growing capabilities outpace the human mind. The book ends with Lee Sedol's retirement from Go, and new version of DeepMind's program, AlphaZero, that did not train on human games but nevertheless became the strongest player in Go, chess, and Shogi. == Reception == The book received mostly positive reviews. In his review for The New York Times Tom McCarthy noted the ambiguity of genre: "At its best, as in the stunning opening sequence reconstructing the murder-suicide of the physicist Paul Ehrenfest and his disabled son, or in the final section's gripping account of a computer defeating the world's best human Go player, you just throw up your hands and think, Who cares what discourse label we assign this stuff? It's great." Becca Rothfeld of the Washington Post praised the book, writing that it is "Labatut's latest virtuosic effort, at once a historical novel and a philosophical foray": "The MANIAC is a work of dark, eerie and singular beauty." She noted that the book "can also be difficult to read" because of its unusual narrative structure: "The book is narrated by a cluttered polyphony of characters, among them both of von Neumann's wives and a number of his teachers and colleagues. ... Like von Neumann, The MANIAC strives to adopt the impartial standpoint of the universe." Killian Fox of The Guardian sees the book as "darkly fascinating novel", and notes Labatut's "impressive dexterity, unpicking complex ideas in long, elegant sentences that propel us forward at speed (this is his first book written in English). Even in the more feverish passages, when yet another great mind succumbs to madness, haunted by the spectres they've helped unleash on the world, he feels in full control of his material." Sam Byers of The Guardian praises the book and the author's style: "The opening chapter of Benjamín Labatut's second novel is such a perfect distillation of his technique that it could serve as a manifesto." and "Readers ... will recognise the sense of breathlessness his best writing can evoke. Seemingly loosened from the laws of physics they describe, his sentences range freely through time and space, connecting not only characters and events, but the delicate tissue of intellectual history, often with a lightness of touch that belies their underlying complexity." He writes on the narrative structure: "Through a cascade of staccato chapters, an ensemble of narrators offer their piecemeal insights." Byers adds that "a brilliant novel is not quite what we end up with" and sees the problem in the "diffusion": "Labatut simply spreads himself too thin. Too many years in too few pages; too many voices with far too little to distinguish them. Initially intriguing, the bite-size monologues quickly come to feel inadequate." Some reviewers did not see the book as a biography. In an essay for the Cleveland Review of Books, Ben Cosman juxtaposes the book with Christopher Nolan's biopic Oppenheimer, and writes that it "follows the development of artificial intelligence—first as an idea at the beginning of the twentieth century, and then as a practicality at the beginning of the twenty-first—through the lives of three men who faced it." He also compared the book's structure to "witness testimony". Another reviewer called the book "perfect for anyone thirsting for more nuclear anxiety after watching Oppenheimer". Garrett Biggs of the Chicago Review of Books writes of the book's style: "Labatut writes about scientists the way Roberto Bolaño writes about poets. They are near mythical figures, captured at the corner of the novel's eye. They become historical in the most fraught sense of the term: subject to rumor and speculation and, eventually, the novel's form inflates their personas into something so large they can only be understood as narrative, never known in any objective capacity." Biggs criticises the last chapter: "the story of artificial intelligence has yet to be written. And so when Labatut's narration editorializes about artificial intelligence as 'a future that inspires hope and horror,' The MANIAC disassembles as a novel and starts to sound like a stale thinkpiece. AlphaGo might represent the first glimmer of a true artificial intelligence, as Labatut suggests. It also could one day be considered nothing more than a souped-up cousin to IBM's DeepBlue.

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  Semantic compression

  In natural language processing, semantic compression is a process of compacting a lexicon used to build a textual document (or a set of documents) by reducing language heterogeneity, while maintaining text semantics. As a result, the same ideas can be represented using a smaller set of words. In most applications, semantic compression is a lossy compression. Increased prolixity does not compensate for the lexical compression and an original document cannot be reconstructed in a reverse process. == By generalization == Semantic compression is basically achieved in two steps, using frequency dictionaries and semantic network: determining cumulated term frequencies to identify target lexicon, replacing less frequent terms with their hypernyms (generalization) from target lexicon. Step 1 requires assembling word frequencies and information on semantic relationships, specifically hyponymy. Moving upwards in word hierarchy, a cumulative concept frequency is calculating by adding a sum of hyponyms' frequencies to frequency of their hypernym: c u m f ( k i ) = f ( k i ) + ∑ j c u m f ( k j ) {\displaystyle cumf(k_{i})=f(k_{i})+\sum _{j}cumf(k_{j})} where k i {\displaystyle k_{i}} is a hypernym of k j {\displaystyle k_{j}} . Then a desired number of words with top cumulated frequencies are chosen to build a target lexicon. In the second step, compression mapping rules are defined for the remaining words in order to handle every occurrence of a less frequent hyponym as its hypernym in output text. Example The below fragment of text has been processed by the semantic compression. Words in bold have been replaced by their hypernyms. They are both nest building social insects, but paper wasps and honey bees organize their colonies in very different ways. In a new study, researchers report that despite their differences, these insects rely on the same network of genes to guide their social behavior.The study appears in the Proceedings of the Royal Society B: Biological Sciences. Honey bees and paper wasps are separated by more than 100 million years of evolution, and there are striking differences in how they divvy up the work of maintaining a colony. The procedure outputs the following text: They are both facility building insect, but insects and honey insects arrange their biological groups in very different structure. In a new study, researchers report that despite their difference of opinions, these insects act the same network of genes to steer their party demeanor. The study appears in the proceeding of the institution bacteria Biological Sciences. Honey insects and insect are separated by more than hundred million years of organic processes, and there are impinging differences of opinions in how they divvy up the work of affirming a biological group. == Implicit semantic compression == A natural tendency to keep natural language expressions concise can be perceived as a form of implicit semantic compression, by omitting unmeaningful words or redundant meaningful words (especially to avoid pleonasms). == Applications and advantages == In the vector space model, compacting a lexicon leads to a reduction of dimensionality, which results in less computational complexity and a positive influence on efficiency. Semantic compression is advantageous in information retrieval tasks, improving their effectiveness (in terms of both precision and recall). This is due to more precise descriptors (reduced effect of language diversity – limited language redundancy, a step towards a controlled dictionary). As in the example above, it is possible to display the output as natural text (re-applying inflexion, adding stop words).

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  2024 Abu Dhabi Autonomous Racing League

  On 27 April 2024, the inaugural race of the Abu Dhabi Autonomous Racing League was held at the Yas Marina Circuit in Abu Dhabi. The race, originally scheduled to last eight laps, was ultimately shortened to six laps due to various complications, including subpar performance. It involved four self-driving race cars, only two of which – German cars Hailey and Constructor AI – finished the race; the other two did not finish. == Background == === Abu Dhabi Autonomous Racing League (A2RL) === The A2RL is an autonomous racing championship based in Abu Dhabi and organized by ASPIRE, part of the Advanced Technology Research Council. It is one of two active autonomous car racing championships, the second being the US-based Indy Autonomous Challenge. Unlike the IAC, which primarily focuses on time trials, simulated races, and challenges for teams, the A2RL's car races are closer to a standard grand prix formula race format. Both use Dallara-supplied racecars; the IAC uses the AV-24 chassis derived from Indy NXT's IL-15, while the A2RL chassis is designated EAV-24 and is derived from the SF-23 chassis used in Japanese Super Formula races. === Entrants === In total, eight teams were part of the A2RL in 2024, but only four would compete in the race proper. The list of teams in 2024 is: Fly Eagle (China/UAE) Code19 Racing (United States) Constructor University (Germany) Kinetiz (Singapore/UAE) Humda Lab (Hungary) PoliMove (Italy) Unimore (Italy) Technical University of Munich (Germany) Most teams come from universities and many, such as PoliMove and TUM, already have experience with autonomous racing, primarily from competing in the IAC. All teams had two months to code and test their AIs. Unlike most international open-wheel racing tournaments, such as Formula 1 or Formula E, no free practice sessions were undertaken. === TII Pre-race demonstration === Prior to the race itself, a mock 1v1 duel between former F1 driver Danill Kvyat and a self-driving car from the non-competing TII Racing team took place; the autonomous car was green and had number 01, while Kvyat's car was red and had number 00. Kvyat spent most of the duel in the pits. Kvyat himself said: "I'm not racing autonomous cars here. It won't be a flat-out race". == Qualifying == === Qualifying report === As only four of the eight entrants would compete in the main event, qualifying time trials were held to determine the four main race competitors, as well as their positions in the grid. Only the cars with the four best lap times over three time trial sessions held on Friday and Saturday would qualify. Multiple errors and setbacks occurred during qualifying. In the first session, Maveric AI, Code19's car, left the track and stopped just after turn 14 due to connectivity issues. Fly Eagle's car, Feiying, had multiple upsets; at one point, Feiying ran into localization issues and began swerving left and right before stopping just before turn 10. Later, Feiying swerved again and nearly hit the wall at the back straight, near the support pits, due to further localization issues. Sparkz, the Kinetiz team's car, swerved and crashed into the wall near yacht berths 51-56 after turn 11, damaging the front right wheel's axle and partially detaching the forward wings. Sparkz would be the only car to not have a set time at the end of the time trials. PoliMove car Eva braked hard without warning at the straight, the LED status indicator turning off, suggesting the AI computer had a system crash or shut itself down. After the sun went down, during the second session, Hailey, the car from the TUM team, went off-track after turn 9 and stopped, its status indicator flashing red, meaning Hailey's AI disengaged itself. Eva had further issues, once again braking hard and spinning out into turn 1. Later, the same thing happened to Feiying; it later swerved left and right and stopped due to further localization issues. The morning after, during the third and final session, Hailey went off-track after turn 5, and were unable to regain the pole position. === Qualifying classification === == Attack/Defend challenge == === Attack/Defend challenge report === In this part of the event, cars would be put on a series of 1v1 duels to see how well they could defend their position or attack to gain one higher. During one such duel, an incident occurred where Hailey rear-ended Eva, sending both off the track and prematurely ending the duel. The challenge was otherwise uneventful. === Attack/Defend challenge results === == Main race == === Race report === Eventually, at around 20:30 Gulf Standard Time on the night of 27 April, the main event (termed the "Grand Final" on-stream) would begin. The starting order was Eva first, Gianna second, Hailey third, and Constructor AI last. The race began with a rolling start. As a safety measure, the first two laps were conducted under virtual safety car (VSC) to make sure the cars stayed together, making them de facto formation laps, even if they counted towards race distance. However, Hailey ended up stopping at the final turn and strayed too far from the cars ahead, and as a result, the VSC conditions were extended for another lap. According to the livestream's on-screen graphics, Hailey was upwards of one minute and 22.3 seconds behind Gianna after the former started moving again. On lap 4, halfway through the planned race, and with Hailey more than 30 seconds behind Gianna, the VSC was lifted, and the green flag finally dropped. At first, the two Italian cars were leading the pack, Eva was the race leader with Gianna 3.2 seconds behind, however, as it entered the chicane, Eva hit the brakes and spun out, with Gianna briefly stopping as it passed Eva. Eva's spin automatically triggered a full-course yellow flag. Normally, under yellow flag conditions, overtaking is not permitted, but with Eva stopped and being moved off the track, it was theoretically permitted to overtake Eva. However, presumably due to an oversight in the AI's code, the cars assumed overtaking Eva, despite being off the track, was not permitted. As a result, both Gianna and Constructor AI stopped as they did not want to overtake Eva due to the yellow flag, with Hailey following suit as it approached. Constructor AI's status indicator was solid red, suggesting the AI had disengaged; however, Gianna's status indicator remained solid purple, showing the AI was still in control. Eva's status indicator was also solid purple, but was soon flashing green, suggesting the AI had disengaged but was ready to take control again. With all cars stalled, and Eva being off the track, the race was effectively red-flagged and suspended. Hailey, Gianna, and Constructor AI drove themselves back to their team's pits; Eva did not, it was towed to the main pits on a flatbed truck. Constructor was the first to arrive at the pits, followed by Gianna and Hailey, in that order. This incident, combined with loss of internet connection, led to Eva retiring - it did not finish the race. Eventually, it was decided to resume the race. With Eva retired, the restart order was Gianna first, Hailey second, and Constructor AI third. The race was also shortened - from eight laps to six. With lap 5 under full-course yellow, this meant all three remaining teams would effectively restart the race on the sixth and final lap. The trio left the pits at 22:25 Gulf Standard Time, and the race resumed two minutes later. At first, Gianna was winning with Hailey 2.6 seconds behind, but then Gianna stopped on turn 5, giving Hailey the lead. Constructor AI also overtook Gianna, but not without briefly stopping. Gianna remained stopped, its status indicator solid red - it did not finish either. With both Italian teams out of the picture, Hailey finished first and won A2RL 2024, with Constructor AI finishing second, 27.2 seconds behind. === Final race classification ===

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  The Last Question

  "The Last Question" is a science fiction short story by American writer Isaac Asimov. It first appeared in the November 1956 issue of Science Fiction Quarterly; and in the anthologies in the collections Nine Tomorrows (1959), The Best of Isaac Asimov (1973), Robot Dreams (1986), The Best Science Fiction of Isaac Asimov (1986), the retrospective Opus 100 (1969), and Isaac Asimov: The Complete Stories, Vol. 1 (1990). While he also considered it one of his best works, "The Last Question" was Asimov's favorite short story of his own authorship, and is one of a loosely connected series of stories concerning a fictional computer called Multivac. Through successive generations, humanity questions Multivac on the subject of entropy. The story blends science fiction, theology, and philosophy. It has been recognized as a counterpoint to Fredric Brown's short short story "Answer", published two years earlier. == History == In conceiving Multivac, Asimov was extrapolating the trend towards centralization that characterized computation technology planning in the 1950s to an ultimate centrally managed global computer. After seeing a planetarium adaptation of his work, Asimov "privately" concluded that the story was his best science fiction yet written. He placed it just higher than "The Ugly Little Boy" (September 1958) and "The Bicentennial Man" (1976). The story asks the question of humanity's fate, and human existence as a whole, highlighting Asimov's focus on important aspects of our future like population growth and environmental issues. "The Last Question" ranks with "Nightfall" (1941) as one of Asimov's best-known and most acclaimed short stories. He wrote in 1973 that he appreciated how easy the story was to write after he had the idea. He was so often approached by fans who remembered the story but not the title, that in one instance he gave the answer, correctly, before the fan had even described the story. == Plot summary == By the year 2061, Multivac, a self-adjusting and self-correcting computer, has allowed mankind to reach beyond the planetary confines of Earth and harness solar energy. Two technicians, Adell and Lupov, celebrate Multivac's role in this development. Over drinks, they discuss that the sun will expire due to the second law of thermodynamics, which states that entropy inevitably increases. When Adell asks Multivac whether this can be reversed, the computer responds that it has insufficient data to answer. In several episodes over ten trillion years, increasingly advanced humans pose the same question to the computers of their time. Each time the computer gives the same response. At the heat death of the universe, the last disembodied consciousness of Man asks the question a final time of a computer that resides in hyperspace before merging with it. After collecting the last data from the dead universe, the computer continues to process it alone and finds an answer to the last question. Having no one to tell it to, it proceeds to demonstrate by saying "LET THERE BE LIGHT!" == Themes == === Philosophy === Although science and religion are frequently presented as having an oppositional relationship, "The Last Question" explores some biblical contexts ("Let there be light"). In Asimov's story, aspects like the great meaning of existence are culminated through both technology and human knowledge. The evolution from Multivac to AC also emulates a sort of cycle of existence. === Dystopian happy ending === Multivac's purpose was conceptualized with a desire for knowledge, promoting the idea that more knowledge will lead to a better and more fruitful future for humanity. However, the computer's answers regarding the future suggest an inevitable exhaustion of the Sun, and this thirst for knowledge becomes an obsession with the future. The story's end displays a dichotomy between annihilation and peace. == Dramatic adaptations == === Planetarium shows === "The Last Question" was first adapted for the Abrams Planetarium at Michigan State University (in 1966), featuring the voice of Leonard Nimoy, as Asimov wrote in his autobiography In Joy Still Felt (1980). It was adapted for the Strasenburgh Planetarium in Rochester, New York (in 1969), under the direction of Ian C. McLennan. It was adapted for the Edmonton Space Sciences Centre in Edmonton, Alberta (early 1970s), under the direction of John Hault. It was adapted for the Gates Planetarium at the Denver Museum of Natural History in 1973 under the direction of Mark B. Peterson It subsequently played at the: Fels Planetarium of the Franklin Institute in Philadelphia in 1973 Planetarium of the Reading School District in Reading, Pennsylvania in 1974 Buhl Planetarium, Pittsburgh in 1974 The Space Transit Planetarium of the Museum of Science in Miami during 1977 Vanderbilt Planetarium in Centerport New York, in 1978, read by singer-songwriter and Long Island resident Harry Chapin. Hansen Planetarium in Salt Lake City, Utah (in 1980 and 1989) A reading of the story was played on BBC Radio 7 in 2008 and 2009. Gates Planetarium in Denver, Colorado (in early 2020) In 1989 Asimov updated the star show adaptation to add in quasars and black holes. The story was adapted as a comic book by Don Thompson and drawn by John Estes in the third issue of ORBiT.

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  Fuzzy electronics

  Fuzzy electronics is an electronic technology that uses fuzzy logic, instead of the two-state Boolean logic more commonly used in digital electronics. Fuzzy electronics is fuzzy logic implemented on dedicated hardware. This is to be compared with fuzzy logic implemented in software running on a conventional processor. Fuzzy electronics has a wide range of applications, including control systems and artificial intelligence. == History == The first fuzzy electronic circuit was built by Takeshi Yamakawa et al. in 1980 using discrete bipolar transistors. The first industrial fuzzy application was in a cement kiln in Denmark in 1982. The first VLSI fuzzy electronics was by Masaki Togai and Hiroyuki Watanabe in 1984. In 1987, Yamakawa built the first analog fuzzy controller. The first digital fuzzy processors came in 1988 by Togai (Russo, pp. 2–6). In the early 1990s, the first fuzzy logic chips were presented to the public. Two companies which are Omron and NEC have announced the development of dedicated fuzzy electronic hardware in the year 1991. Two years later, the Japanese Omron Cooperation has shown a working fuzzy chip during a technical fair.

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  Mobile DevOps

  Mobile DevOps is a set of practices that applies the principles of DevOps specifically to the development of mobile applications. Traditional DevOps focuses on streamlining the software development process in general, but mobile development has its own unique challenges that require a tailored approach. Mobile DevOps is not simply as a branch of DevOps specific to mobile app development, instead an extension and reinterpretation of the DevOps philosophy due to very specific requirements of the mobile world. == Rationale == Traditional DevOps approach has been formed around 2007-2008, close to the dates when iOS and Android mobile operating systems were released to the public. The traditional DevOps approach primarily evolved to meet the changing needs of the software development world with the paradigm shift towards continuous and rapid development and deployment (such as in web development, where interpreted languages are more prevalent than compiled languages). While traditional DevOps embraced agility and flexibility, mobile operating system providers steered towards a walled-garden approach with compiled apps with tight controls over how they can be distributed and installed on a mobile device. This difference in the mobile development mindset compared to what the traditional DevOps approach is advocating, is augmented further with the mobile applications to be deployed on a high number of varying devices and operating systems. Eventually, the concept of Mobile DevOps took off as a trend around 2014-2015, in line with the fast growth of the number of applications in mobile app stores. As individuals and corporations alike are developing and publishing more and more mobile applications, the need for efficiency and shorter release cycles increased, which is addressed by the continuous feedback and continuous development approach within the concept of DevOps, while requiring a significant level of adaptation and extension of the traditional DevOps practices. == Mindset shift from traditional DevOps to mobile DevOps == Mobile DevOps has a unique set of challenges and constraints, which solidifies the fact that it needs to be approached as a separate discipline. These challenges can be outlined as follows: Platform-specific requirements and tight controls of mobile operating system providers, where for instance a macOS device is mandatory for iOS application development and release. The walled-garden approach of distributing mobile apps, specifically applying to iOS applications, which comes with app review and app release delays that would not be needed in web development, for instance. Code signing requirements that come with the walled-garden approach, which introduce additional processes in the mobile application build pipeline along with new security concerns. An entire deployment cycle is re-run even in the slightest code change due to how applications are compiled and delivered to the users. The final product is to be deployed to a wide variety of mobile devices worldwide, which requires extensive testing and user feedback. Monitoring mobile applications require additional tools and approaches to be able to get data from an application running on a mobile device while respecting user privacy. Frequent operating system updates by mobile platforms can require rapid adaptation of apps, introducing further complexity to the development and maintenance cycles. == Benefits of mobile DevOps == Mobile DevOps is not an abstract concept and offers a range of benefits that can help improve the efficiency and effectiveness of the mobile app development process. These benefits can even be quantified by collecting the data within the mobile application development lifecycle. The benefits can be categorized into the following areas: Faster Release Cycles: By automating tasks and streamlining the development process, mobile DevOps enables teams to deliver new features and updates more frequently. Improved Quality: Automated testing and continuous monitoring help to identify and fix bugs earlier in the development cycle, leading to higher quality apps. Optimized Resource Utilization: Mobile DevOps promotes optimized resource utilization by automating tasks and streamlining workflows. Furthermore, mobile DevOps practices like containerization can help to create more efficient and scalable development environments. Increased Agility: Mobile DevOps allows teams to be more responsive to changes in the market and user feedback. == List of Dedicated Mobile DevOps Platforms == Even though it is possible to run a mobile DevOps cycle with most of the CI/CD platforms, they may require significant effort compared to non-mobile CI/CD (e.g. you need to bring your own infrastructure or it may require "reinventing the wheel" for commonly used platforms like Jenkins). To overcome the mobile-specific challenges specified, there are certain platforms that are dedicated to the lifecycle of mobile applications. These platforms exclusively focus on DevOps processes for mobile app development and are also referred as mobile CI/CD platforms. Appcircle (Multiplatform | Cloud-based & On-premise) Visual Studio App Center (Multiplatform | Cloud-based) Xcode Cloud (Apple platforms only | Cloud-based)

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  Felix, Net i Nika

  Felix, Net i Nika ("Felix, Net and Nika") is a series of Polish language science fiction books for teenagers, written by Rafał Kosik. It tells the adventures of three friends - Felix Polon, Net Bielecki and Nika Mickiewicz - who attend fictional Professor Kuszmiński Middle School in Warsaw. As of 2024, eighteen books have been published. == Books == There are currently 18 books in the series: Felix, Net and Nika and the Gang of Invisible People - November 2004. Felix, Net and Nika and the Theoretically Possible Catastrophe - November 2005 Felix, Net and Nika and the Palace of Dreams - November 2006 Felix, Net and Nika and the Trap of Immortality - November 2007 Felix, Net and Nika and the Orbital Conspiracy - November 2008 Felix, Net and Nika and the Orbital Conspiracy 2: Small Army - May 2009 Felix, Net and Nika and the Third Cousin - November 2009 Felix, Net and Nika and the Rebellion of Machines - March 2011 Felix, Net and Nika and the World Zero - November 2011 Felix, Net and Nika and the World Zero 2. Alternauts - November 2012 Felix, Net and Nika and the Extracurricular Stories - April 2013 Felix, Net and Nika and the Secret of Czerwona Hańcza - November 2013 Felix, Net and Nika and Curse of McKillian's House - November 2014 Felix, Net and Nika and (un)Safe Growing up - November 2015 Felix, Net and Nika and The End of The World as We Know It - November 2018 Felix, Net and Nika and No Chance - November 2022 Felix, Net and Nika and No Chance 2: other tomorrrow - 2023 Felix, Net and Nika and Fantology - June 2024 == Film == A feature motion picture, Felix, Net i Nika oraz Teoretycznie Możliwa Katastrofa (Felix, Net and Nika and the Theoretically Possible Catastrophe) was released in Poland on September 28, 2012. == Main characters == Felix Polon - a foresighted, fair-haired boy with dark brown eyes. He inherited the talent of constructing various things, especially robots, from his father- it saved his friends many times. He can make anything from nothing, always finds a way out of a situation; almost always has a plan. Together with his parents Marlene and Peter, grandmother Lucy, his dog Caban (a Black Russian Terrier) and Golem Golem a robot he built, Felix lives on Serdeczna Street in a small family house. Net Bielecki is quite tall & slim, has blue eyes and a high IQ level. "Net" is his nickname; his true name is unknown. He is the most trendy and 'awesome' in his entire class. He is a human calculator and is excellent in mathematics. He hates dictations and spelling because he is dyslexic. He is also quite lazy, absent-minded and sometimes hysterical, or panicking. His dark blond hair looks like a heap of hay after a grenade explosion. He is best in ICT and writes many of his own programs. His love interest is Nika Mickiewicz. Together with his parents Lila and Mark, and their newborn twins nicknamed Pompek and Prumcia he lives on the top floor of a Penthouse apartment. Nika Mickiewicz is a girl with a character. She is very brave and mature. She likes reading books. She has curly, red hair, green eyes and a few freckles. She is not very rich; she wears second-hand clothes and her only pair of black Dr. Martens shoes. She lives in a tiny apartment. She is an orphan, but hides that fact from people for almost 3 years. However, Felix and Net, her best and possibly only friends, find out about it. She also has abnormal abilities. She can move distant objects using her powers, ski uphill and knows some things by intuition. In other words, she is telekinetic. Manfred is a friendly AI program started and never finished by Net's father, and mastered and programmed further by Net himself. He likes going on adventures and solving mysteries with the trio much more than his actual job, which is controlling the traffic lights. He helped out the three friends many times and is their reliable and faithful friend. Morten is also an AI program, but he is the antagonist of the trio. He appears in all 6 books of Felix Net and Nika. In the first book, the trio thinks they finished him off for good, but as we find out later, he comes back in the third book. In the fifth/sixth book, he was the mastermind of the Orbital Conspiracy. Also, Morten's logo, appears in all 6 books and it is still a mystery what he has to do with each event.

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  Clinical decision support system

  A clinical decision support system (CDSS) is a form of health information technology that provides clinicians, staff, patients, or other individuals with knowledge and person-specific information to enhance decision-making in clinical workflows. CDSS tools include alerts and reminders, clinical guidelines, condition-specific order sets, patient data summaries, diagnostic support, and context-aware reference information. They often leverage artificial intelligence to analyze clinical data and help improve care quality and safety. CDSSs constitute a major topic in artificial intelligence in medicine. == Characteristics == A clinical decision support system is an active knowledge system that uses variables of patient data to produce advice regarding health care. This implies that a CDSS is simply a decision support system focused on using knowledge management. === Purpose === The main purpose of modern CDSS is to assist clinicians at the point of care. This means that clinicians interact with a CDSS to help to analyze and reach a diagnosis based on patient data for different diseases. In the early days, CDSSs were conceived to make decisions for the clinician in a literal manner. The clinician would input the information and wait for the CDSS to output the "right" choice, and the clinician would simply act on that output. However, the modern methodology of using CDSSs to assist means that the clinician interacts with the CDSS, utilizing both their knowledge and the CDSS's, better to analyse the patient's data than either a human or a CDSS could do on their own. Typically, a CDSS makes suggestions for the clinician to review, and the clinician is expected to pick out useful information from the presented results and discount erroneous CDSS suggestions. The two main types of CDSS are knowledge-based systems and non-knowledge-based (machine learning–based) systems: An example of how a clinician might use a clinical decision support system is a diagnosis decision support system (DDSS). DDSS requests some of the patient's data and, in response, proposes a set of possible diagnoses. The physician then takes the output of the DDSS and determines which diagnoses are likely and which are not, and, if necessary, orders further tests to narrow down the diagnosis. Another example of a CDSS would be a case-based reasoning (CBR) system. A CBR system might use previous case data to help determine the appropriate amount of beams and the optimal beam angles for use in radiotherapy for brain cancer patients; medical physicists and oncologists would then review the recommended treatment plan to determine its viability. Another important classification of a CDSS is based on the timing of its use. Physicians use these systems at the point of care to help them as they are dealing with a patient, with the timing of use being either pre-diagnosis, during diagnosis, or post-diagnosis. Pre-diagnosis CDSS systems help the physician prepare the diagnoses. CDSSs help review and filter the physician's preliminary diagnostic choices to improve outcomes. Post-diagnosis CDSS systems are used to mine data to derive connections between patients and their past medical history and clinical research to predict future events. Early speculation that AI-based decision support would replace clinicians in common tasks has largely given way to a consensus around assistive models, in which AI augments rather than supplants clinical judgment. Contemporary deep learning-based systems, unlike earlier rule-based tools, can be trained directly on clinical data without manual rule authoring and integrated into electronic health record workflows at the point of care. Another approach, used by the National Health Service in England, is to use a CDSS to triage medical conditions out of hours by suggesting a suitable next step to the patient (e.g. call an ambulance, or see a general practitioner on the next working day). The suggestion, which may be disregarded by either the patient or the phone operative if common sense or caution suggests otherwise, is based on the known information and an implicit conclusion about what the worst-case diagnosis is likely to be; it is not always revealed to the patient because it might well be incorrect and is not based on a medically-trained person's opinion - it is only used for initial triage purposes. === Knowledge-based === Most CDSSs consist of three parts: the knowledge base, an inference engine, and a mechanism to communicate. The knowledge base contains the rules and associations of compiled data which most often take the form of IF-THEN rules. If this was a system for determining drug interactions, then a rule might be that IF drug X is taken AND drug Y is taken THEN alert the user. Using another interface, an advanced user could edit the knowledge base to keep it up to date with new drugs. The inference engine combines the rules from the knowledge base with the patient's data. The communication mechanism allows the system to show the results to the user as well as have input into the system. An expression language such as GELLO or CQL (Clinical Quality Language) is needed for expressing knowledge artefacts in a computable manner. For example: if a patient has diabetes mellitus, and if the last haemoglobin A1c test result was less than 7%, recommend re-testing if it has been over six months, but if the last test result was greater than or equal to 7%, then recommend re-testing if it has been over three months. The current focus of the HL7 CDS WG is to build on the Clinical Quality Language (CQL). The U.S. Centers for Medicare & Medicaid Services (CMS) has announced that it plans to use CQL for the specification of Electronic Clinical Quality Measures (eCQMs). === Non-knowledge-based === CDSSs which do not use a knowledge base use a form of artificial intelligence called machine learning, which allow computers to learn from past experiences and/or find patterns in clinical data. This eliminates the need for writing rules and expert input. However, since systems based on machine learning cannot explain the reasons for their conclusions, most clinicians do not use them directly for diagnoses, reliability and accountability reasons. Nevertheless, they can be useful as post-diagnostic systems, for suggesting patterns for clinicians to look into in more depth. As of 2012, three types of non-knowledge-based systems are support-vector machines, artificial neural networks and genetic algorithms. Artificial neural networks use nodes and weighted connections between them to analyse the patterns found in patient data to derive associations between symptoms and a diagnosis. Genetic algorithms are based on simplified evolutionary processes using directed selection to achieve optimal CDSS results. The selection algorithms evaluate components of random sets of solutions to a problem. The solutions that come out on top are then recombined and mutated and run through the process again. This happens over and over until the proper solution is discovered. They are functionally similar to neural networks in that they are also "black boxes" that attempt to derive knowledge from patient data. Non-knowledge-based networks often focus on a narrow list of symptoms, such as symptoms for a single disease, as opposed to the knowledge-based approach, which covers the diagnosis of many diseases. An example of a non-knowledge-based CDSS is a web server developed using a support vector machine for the prediction of gestational diabetes in Ireland. == Regulations == === History, United States === The IOM had published a report in 1999, To Err is Human, which focused on the patient safety crisis in the United States, pointing to the incredibly high number of deaths. This statistic attracted great attention to the quality of patient care. The Institute of Medicine (IOM) promoted the usage of health information technology, including clinical decision support systems, to advance the quality of patient care. With the enactment of the American Recovery and Reinvestment Act of 2009 (ARRA), there was a push for widespread adoption of health information technology through the Health Information Technology for Economic and Clinical Health Act (HITECH). Through these initiatives, more hospitals and clinics were integrating electronic medical records (EMRs) and computerized physician order entry (CPOE) within their health information processing and storage. Despite the absence of laws, the CDSS vendors would almost certainly be viewed as having a legal duty of care to both the patients who may adversely be affected due to CDSS usage and the clinicians who may use the technology for patient care. However, duties of care legal regulations are not explicitly defined yet. With the enactment of the HITECH Act included in the ARRA, encouraging the adoption of health IT, more detailed case laws for CDSS and EMRs were still being defined by the Office of National Coordinator for Health Informati

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