AI Chat Programs

AI Chat Programs — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  IWork

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

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  Combs method

  The Combs method is a rule base reduction method of writing fuzzy logic rules described by William E. Combs in 1997. It is designed to prevent combinatorial explosion in fuzzy logic rules. The Combs method takes advantage of the logical equality ( ( p ∧ q ) ⇒ r ) ⟺ ( ( p ⇒ r ) ∨ ( q ⇒ r ) ) {\displaystyle ((p\land q)\Rightarrow r)\iff ((p\Rightarrow r)\lor (q\Rightarrow r))} . == Equality proof == The simplest proof of given equality involves usage of truth tables: == Combinatorial explosion == Suppose we have a fuzzy system that considers N variables at a time, each of which can fit into at least one of S sets. The number of rules necessary to cover all the cases in a traditional fuzzy system is S N {\displaystyle S^{N}} , whereas the Combs method would need only S × N {\displaystyle S\times N} rules. For example, if we have five sets and five variables to consider to produce one output, covering all the cases would require 3125 rules in a traditional system, while the Combs method would require only 25 rules, taming the combinatorial explosion that occurs when more inputs or more sets are added to the system. This article will focus on the Combs method itself. To learn more about the way rules are traditionally formed, see fuzzy logic and fuzzy associative matrix. == Example == Suppose we were designing an artificial personality system that determined how friendly the personality is supposed to be towards a person in a strategic video game. The personality would consider its own fear, trust, and love in the other person. A set of rules in the Combs system might look like this: The table translates to: [IF Fear IS Unafraid THEN Friendship IS Enemies OR IF Fear IS ModerateFear THEN Friendship IS Neutral OR IF Fear IS Afraid THEN Friendship IS GoodFriends ] OR [IF Trust IS Distrusting THEN Friendship IS Enemies OR IF Trust IS ModerateTrust THEN Friendship IS Neutral OR IF Trust IS Trusting THEN Friendship IS GoodFriends] OR [IF Love IS Unloving THEN Friendship IS Enemies OR IF Love IS ModerateLove THEN Friendship IS Neutral OR IF Love IS Loving THEN Friendship IS GoodFriends] In this case, because the table follows a straightforward pattern in the output, it could be rewritten as: Each column of the table maps to the output provided in the last row. To obtain the output of the system, we just average the outputs of each rule for that output. For example, to calculate how much the computer is Enemies with the player, we take the average of how much the computer is Unafraid, Distrusting, and Unloving of the player. When all three averages are obtained, the result can then be defuzzified by any of the traditional means.

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  Void Trilogy

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

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  HYPO CBR

  HYPO is a computer program, an expert system, that models reasoning with cases and hypotheticals in the legal domain. It is the first of its kind and the most sophisticated of the case-based legal reasoners, which was designed by Kevin Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Edwina Rissland. HYPO's design represents a hybrid generalization/comparative evaluation method appropriate for a domain with a weak analytical theory and applies to tasks that rarely involve just one right answer. The domain covers US trade secret law, and is substantially a common law domain. Since Anglo-American common law operates under the doctrine of precedent, the definitive way of interpreting problems is of necessity and case-based. Thus, HYPO did not involve the analysis of a statute, as required by the Prolog program. Rissland and Ashley (1987) envisioned HYPO as employing the key tasks performed by lawyers when analyzing case law for precedence to generate arguments for the prosecution or the defence. HYPO was a successful example of a general category of legal expert systems (LESs), it applies artificial intelligence (A.I.) techniques to the domain of legal reasoning in patent law, implementing a case-based reasoning (CBR) system, in contrast to rule based systems like MYCIN, or mixed-paradigm systems integrating CBR with rule-based or model-based reasoning like IKBALS II. A legal case-based reasoning essentially reasons from prior tried cases, comparing the contextual information in the current input case with that of cases previously tried and entered into the system. As noted by Ashley and Rissland (1988) CBR is used to "... capture expertise in domains where rules are ill-defined, incomplete or inconsistent". The HYPO project set out to model the creation of hypotheticals in law, where no case matches well enough. HYPO uses hypotheticals for a variety of tasks necessary for good interpretation: "to redefine old situations in terms of new dimensions, to create new standard cases when an appropriate one doesn’t exist, to explore and test the limits of a concept, to refocus a case by excluding some issues and to organize or cluster cases". Hypotheticals can include facts that support two conflicting lines of reasoning. So, it makes and responds to arguments from competing viewpoints about who should win the dispute. HYPO use heuristics such as making a case weaker or stronger, making a case extreme, enabling a near-miss, disabling a near-hit to generate hypotheticals in the context of an argument by using the dimensions mechanism. Dimensions have a range of values, along which the supportive strength that may shift from one side to the other. What differentiated this expert system from others was its facility not only to return a primary to best-case response but to return near-best-fit responses also. == Components == Legal knowledge in HYPO is contained in: the case-knowledge-base (CKB) and the library of dimensions. The CKB contains HYPO's base of known cases that are highly structured objects and sub-objects both real and hypothetical in the area of trade secret law. Each case is represented as a hierarchical set of frames whose slots are important facets of the case (e.g. Plaintiff, defendant, secret knowledge, employer/employee data).Ashley’s HYPO system used a database of thirty cases in the area indexed by thirteen dimensions. A key mechanism in HYPO is a dimension i.e. a mechanism to allow retrieval from the CKB, in order to represent legal cases. Ashley's dimensions are composed of (i) prerequisites, which are a set of factual predicates that must be satisfied for the dimension to apply (ii) focal slots, which accommodate one or two of the dimension's prerequisites designated as being indicative of the case's strength along that dimension and (iii) range information, which tells how a change in focal slot value effects the strength of a party's case along a given dimension. Dimensions focus attention on important aspects of cases. In HYPO's domain of misappropriation of trade secrets the dimension called “secrets voluntary disclosed” captures the idea that the more disclosures the plaintiff has made of his/her putative secret, the less convincing is his/her argument that the defendant is responsible for letting the secret. HYPO, like any other CBR system has also the following components: Similarity/relevancy metrics: that is, standards by which to evaluate the closeness of cases, judge their relevancy to the instant case, and select “most on point” cases. Half-Order Theory of the Application Domain: that is, hierarchies and taxonomies of knowledge, especially regarding the application domain. Precedent-based argumentation abilities: that is, capabilities to generate and evaluate precedent-based arguments. Knowledge to generate hypotheticals: that is, the ability to generate hypothetical cases to deal with various circumstances, like testing the validity of an interpretation or argument by providing gedanken experiments such as test cases or to fill in a weak CKB. == Functions == HYPO's method of creating an argument and justifying a solution or position has several steps. HYPO begins its processing with the current fact situation (cfs) which is direct input by the user into HYPO's representation framework. Once the user inputs the case, HYPO begins its legal analysis. The cfc is analyzed for relevant factors. Based on these factors HYPO selects the relevant cases and produces a case-analysis-record that records which dimensions apply to the cfc and which nearly apply (i.e. are "near misses"). The combined list of applicable and near miss dimensions is called the D-list. At this point the fact gathered module may request additional information from the user in order to draw a legal conclusion. Once all the facts are in the case-positioner module it uses the case-analysis record to create the claim lattice. This is a technique that organizes the relevant retrieved cases from the point of view of the cfc and makes it easy for HYPO to ascertain the most-on point cases (mopc) and to least on-point-cases. HYPO's arguments are 3ply, leading to the construction of the skeleton of an argument: it makes a point for one side, drawing the analogy between the problem and the precedent, responds with an argument for the opponent side, endeavoring to differentiate the cited case and citing other cases as counterarguments. Then it makes a final rebuttal, attempting to differentiate the counterarguments. The claim lattice also enables the HYPO-generator module to produce legally hypotheticals. With its use of dimension-based heuristics, the HYPO-generator does a heuristic search of the space of all possible cases. Lastly, the Explanation module expands upon the argument skeleton and provides explanation and justification for the different lines of analysis and cases found by HYPO. == An intelligent legal tutoring system == Legal expert systems are specifically designed to teach an area of law and are useful for pedagogical purposes. Ashley's work was mainly concerned to build tools to help students understand legal reasoning. Explanation and argument are the bases of the case method used in many professional schools in the U.S., first introduced by the Dean of the Harvard Law School, Christopher Columbus Langdell in 1870. The case method focuses on close readings of cases and principles; it involves students in pointed Socratic dialogue and makes strong use of hypotheticals (hypos). Thus, CATO (Aleven 1997) was a research project to device and test an intelligent, case-based tutorial program for teaching law students how to argue with cases implementing the HYPO program. Within the tutor system, Ashley and Aleven (1991) proposed to leverage an understanding of legal reasoning against the standard case-based tutoring methodology. What makes this tutoring system stand out is the additional levels of abstraction involved in its results. The system presents exercises, including the facts of a problem and a set of on-line cases and instructions to make, or respond to, a legal argument about the problem. The student/user will have a set of tools to analyze the problem and fashion an answer comparing it to other cases. Instead of simply generating precedent cases, the system works to interpret student responses, comparing them against a list of possibilities and responding to student entries, for example, by citing counterexamples, and providing feedback on a student's problem solving activities with explanations of correctness or giving further hints as to what may be wrong with evaluating a student's ability to perform legal reasoning and argument, examples and follow-up assignments by employing HYPO's model of case-based structure. == HYPO’s progeny == The quality of HYPO's results speak for themselves, in that a number of sequent legal reasoning systems are either directly based upon H

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  Computer security

  Computer security (also cybersecurity, digital security, or information technology (IT) security) is a subdiscipline within the field of information security. It focuses on protecting computer software, systems, and networks from threats that can lead to unauthorized information disclosure, theft, or damage to hardware, software, or data, as well as to the disruption or misdirection of the services they provide. The growing significance of computer security reflects the increasing dependence on computer systems, the Internet, and evolving wireless network standards. This reliance has expanded with the proliferation of smart devices, including smartphones, televisions, and other components of the Internet of things (IoT). As digital infrastructure becomes more embedded in everyday life, cybersecurity has emerged as a critical concern. The complexity of modern information systems—and the societal functions they underpin—has introduced new vulnerabilities. Systems that manage essential services, such as power grids, electoral processes, and finance, are particularly sensitive to security breaches. Although many aspects of computer security involve digital security, such as electronic passwords and encryption, physical security measures, such as metal locks, are still used to prevent unauthorized tampering. IT security is not a perfect subset of information security and therefore does not completely align with the security convergence schema. == Vulnerabilities and attacks == A vulnerability refers to a flaw in the structure, execution, functioning, or internal oversight of a computer or system that compromises its security. Most of the vulnerabilities that have been discovered are documented in the Common Vulnerabilities and Exposures (CVE) database. An exploitable vulnerability is one for which at least one working exploit exists. Actors maliciously seeking vulnerabilities are known as threats. Vulnerabilities can be researched, reverse-engineered, hunted, or exploited using automated tools or customized scripts. Various people or parties are vulnerable to cyberattacks; however, different groups are likely to experience different types of attacks more than others. In April 2023, the United Kingdom Department for Science, Innovation & Technology released a report on cyberattacks over the previous 12 months. They surveyed 2,263 UK businesses, 1,174 UK registered charities, and 554 education institutions. The research found that "32% of businesses and 24% of charities overall recall any breaches or attacks from the last 12 months." These figures were much higher for "medium businesses (59%), large businesses (69%), and high-income charities with £500,000 or more in annual income (56%)." Yet, although medium or large businesses are more often the victims, since larger companies have generally improved their security over the last decade, small and midsize businesses (SMBs) have also become increasingly vulnerable as they often "do not have advanced tools to defend the business." SMBs are most likely to be affected by malware, ransomware, phishing, man-in-the-middle attacks, and Denial-of Service (DoS) Attacks. Normal internet users are most likely to be affected by untargeted cyberattacks. These are where attackers indiscriminately target as many devices, services, or users as possible. They do this using techniques that take advantage of the openness of the Internet. These strategies mostly include phishing, ransomware, water holing and scanning. To secure a computer system, it is important to understand the attacks that can be made against it, and these threats can typically be classified into one of the following categories: === Backdoor === A backdoor in a computer system, a cryptosystem or an algorithm, is any secret method of bypassing normal authentication or security controls. These weaknesses may exist for many reasons, including original design or poor configuration. Due to the nature of backdoors, they are of greater concern to companies and databases as opposed to individuals. Backdoors may be added by an authorized party to allow some legitimate access or by an attacker for malicious reasons. Criminals often use malware to install backdoors, giving them remote administrative access to a system. Once they have access, cybercriminals can "modify files, steal personal information, install unwanted software, and even take control of the entire computer." Backdoors can be difficult to detect, as they often remain hidden within source code or system firmware and may require intimate knowledge of the operating system to identify. === Denial-of-service attack === Denial-of-service attacks (DoS) are designed to make a machine or network resource unavailable to its intended users. Attackers can deny service to individual victims, such as by deliberately entering an incorrect password enough consecutive times to cause the victim's account to be locked, or they may overload the capabilities of a machine or network and block all users at once. While a network attack from a single IP address can be blocked by adding a new firewall rule, many forms of distributed denial-of-service (DDoS) attacks are possible, where the attack comes from a large number of points. In this case, defending against these attacks is much more difficult. Such attacks can originate from the zombie computers of a botnet or from a range of other possible techniques, including distributed reflective denial-of-service (DRDoS), where innocent systems are fooled into sending traffic to the victim. With such attacks, the amplification factor makes the attack easier for the attacker because they have to use little bandwidth themselves. To understand why attackers may carry out these attacks, see the 'attacker motivation' section. === Physical access attacks === A direct-access attack is when an unauthorized user (an attacker) gains physical access to a computer, typically to copy data from it or steal information. Attackers may also compromise security by making operating system modifications, installing software worms, keyloggers, covert listening devices or using wireless microphones. Even when the system is protected by standard security measures, these may be bypassed by booting another operating system or tool from a CD-ROM or other bootable media. Disk encryption and the Trusted Platform Module standard are designed to prevent these attacks. Direct service attackers are related in concept to direct memory attacks which allow an attacker to gain direct access to a computer's memory. The attacks "take advantage of a feature of modern computers that allows certain devices, such as external hard drives, graphics cards, or network cards, to access the computer's memory directly." === Eavesdropping === Eavesdropping is the act of surreptitiously listening to a private computer conversation (communication), usually between hosts on a network. It typically occurs when a user connects to a network where traffic is not secured or encrypted and sends sensitive business data to a colleague, which, when listened to by an attacker, could be exploited. Data transmitted across an open network can be intercepted by an attacker using various methods. Unlike malware, direct-access attacks, or other forms of cyberattacks, eavesdropping attacks are unlikely to negatively affect the performance of networks or devices, making them difficult to notice. In fact, "the attacker does not need to have any ongoing connection to the software at all. The attacker can insert the software onto a compromised device, perhaps by direct insertion or perhaps by a virus or other malware, and then come back some time later to retrieve any data that is found or trigger the software to send the data at some determined time." Using a virtual private network (VPN), which encrypts data between two points, is one of the most common forms of protection against eavesdropping. Using the best form of encryption possible for wireless networks is best practice, as well as using HTTPS instead of an unencrypted HTTP. Programs such as Carnivore and NarusInSight have been used by the Federal Bureau of Investigation (FBI) and the NSA to eavesdrop on the systems of internet service providers. Even machines that operate as a closed system (i.e., with no contact with the outside world) can be eavesdropped upon by monitoring the faint electromagnetic transmissions generated by the hardware. TEMPEST is a specification by the NSA referring to these attacks. === Malware === Malicious software (malware) is any software code or computer program "intentionally written to harm a computer system or its users." Once present on a computer, it can leak sensitive details such as personal information, business information and passwords, can give control of the system to the attacker, and can corrupt or delete data permanently. ==== Types of malware ==== Viruses are a specific type of malware, and are normally a malicious code that hijac

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  Fuzzy cognitive map

  A fuzzy cognitive map (FCM) is a cognitive map within which the relations between the elements (e.g. concepts, events, project resources) of a "mental landscape" can be used to compute the "strength of impact" of these elements. Fuzzy cognitive maps were introduced by Bart Kosko. Robert Axelrod introduced cognitive maps as a formal way of representing social scientific knowledge and modeling decision making in social and political systems, then brought in the computation. == Details == Fuzzy cognitive maps are signed fuzzy directed graphs. Spreadsheets or tables are used to map FCMs into matrices for further computation. FCM is a technique used for causal knowledge acquisition and representation, it supports causal knowledge reasoning process and belong to the neuro-fuzzy system that aim at solving decision making problems, modeling and simulate complex systems. Learning algorithms have been proposed for training and updating FCMs weights mostly based on ideas coming from the field of Artificial Neural Networks. Adaptation and learning methodologies used to adapt the FCM model and adjust its weights. Kosko and Dickerson (Dickerson & Kosko, 1994) suggested the Differential Hebbian Learning (DHL) to train FCM. There have been proposed algorithms based on the initial Hebbian algorithm; others algorithms come from the field of genetic algorithms, swarm intelligence and evolutionary computation. Learning algorithms are used to overcome the shortcomings that the traditional FCM present i.e. decreasing the human intervention by suggested automated FCM candidates; or by activating only the most relevant concepts every execution time; or by making models more transparent and dynamic. Fuzzy cognitive maps (FCMs) have gained considerable research interest due to their ability in representing structured knowledge and model complex systems in various fields. This growing interest led to the need for enhancement and making more reliable models that can better represent real situations. A first simple application of FCMs is described in a book of William R. Taylor, where the war in Afghanistan and Iraq is analyzed. In Bart Kosko's book Fuzzy Thinking, several Hasse diagrams illustrate the use of FCMs. As an example, one FCM quoted from Rod Taber describes 11 factors of the American cocaine market and the relations between these factors. For computations, Taylor uses pentavalent logic (scalar values out of {-1,-0.5,0,+0.5,+1}). That particular map of Taber uses trivalent logic (scalar values out of {-1,0,+1}). Taber et al. also illustrate the dynamics of map fusion and give a theorem on the convergence of combination in a related article. While applications in social sciences introduced FCMs to the public, they are used in a much wider range of applications, which all have to deal with creating and using models of uncertainty and complex processes and systems. Examples: In business FCMs can be used for product planning and decision support. In economics, FCMs support the use of game theory in more complex settings. In education for modeling Critical Success Factors of Learning Management Systems. In medical applications to model systems, provide diagnosis, develop decision support systems and medical assessment. In engineering for modeling and control mainly of complex systems and reliability engineering In project planning FCMs help to analyze the mutual dependencies between project resources. In robotics FCMs support machines to develop fuzzy models of their environments and to use these models to make crisp decisions. In computer assisted learning FCMs enable computers to check whether students understand their lessons. In expert systems a few or many FCMs can be aggregated into one FCM in order to process estimates of knowledgeable persons. In IT project management, a FCM-based methodology helps to success modelling, risk analysis and assessment, IT scenarios FCMappers is an international online community for the analysis and the visualization of fuzzy cognitive maps. FCMappers offer support for starting with FCM and also provide a Microsoft Excel-based tool that is able to check and analyse FCMs. The output is saved as Pajek file and can be visualized within third party software like Pajek, Visone, etc. They also offer to adapt the software to specific research needs. Additional FCM software tools, such as Mental Modeler, have recently been developed as a decision-support tool for use in social science research, collaborative decision-making, and natural resource planning.

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  Niceaunties

  Niceaunties is the pseudonym of a Singapore-based artist and designer whose work incorporates generative artificial intelligence, video, and digital installation. Her practice centers around the figure of the "auntie", a common term for older women in Southeast Asian contexts, and explores themes such as aging, care, domesticity, and gender roles. Her work has been featured in exhibitions and media platforms including TED, Christie's Art + Tech, Expanded.Art, and publications such as The Guardian, The Straits Times. == Early life and career == Niceaunties was born in 1981 in Singapore. She attributes her inspiration for "auntie culture" to the matriarchal environment and older women of her household, including her grandmother, while growing up. She is also an architectural designer with Spark Architect. The Niceaunties project began in 2023 after she encountered AI-generated images in her work as an architect. It draws inspiration from women in the artist's family and broader Southeast Asian cultural dynamics. Her work often features AI-generated visuals created with tools such as DALL-E, Krea, RunwayML, and SORA. Her imagery and narratives center on the fictional "Auntieverse", which features older women in imagined settings involving community, ecology, and labor. Her notable works include 'Auntlantis', a five-part video series imagining older women engaged in ocean clean-up and collective ritual, and 'Goddess,' a video created with Sora, featuring a character who gradually forgets her divine identity through years of domestic labor. == Exhibitions == 2024 – Expanded.Art, Berlin – Auntiedote solo exhibition 2024 – TED (conference), Vancouver – Speaker and screening 2024 – Victoria and Albert Museum, London – Digital Art Weekend 2024 – Louisiana Museum of Modern Art, Denmark – Ocean exhibition 2025 – Christie's Augmented Intelligence Auction, New York == Reception == In 2024, Niceaunties gave a TED Talk titled The Weird and Wonderful Art of Niceaunties. Journalist Rebecca Ratcliffe, writing for The Guardian, described her work as combining AI with "the surreal and the political," noting her focus on older women as central characters. Her work has also received criticism for being reliant on generative AI, which many feel exploits and steals from traditional artists.

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  Artificial intelligence in pharmacy

  Artificial intelligence in pharmacy refers to the application of artificial intelligence (AI) techniques across pharmaceutical research and practice, including drug discovery, drug delivery, safety monitoring, clinical decision support, and pharmacy operations. Machine learning, deep learning, and natural language processing have been applied to tasks ranging from molecular design to patient adherence monitoring, with the aim of reducing development costs, improving accuracy, and personalizing treatment. Adoption has been uneven. Barriers include limited AI training among pharmacists, high infrastructure costs, and the risk of harm from models trained on unrepresentative data. Regulatory frameworks for AI-based pharmaceutical tools remain in active development across most jurisdictions. == Applications == === Drug discovery and development === Drug development is resource-intensive: bringing a single drug to market typically costs around $2.6 billion and takes 12–14 years. Machine learning algorithms have been applied to analyze molecular datasets to identify potential drug candidates, predict drug–target interactions, and optimize formulations. Artificial neural networks and generative adversarial networks have been used in drug discovery tasks including virtual screening, structure-activity relationship modeling, and de novo molecule generation. Peptides designed using AI methods have shown activity against multidrug-resistant bacteria, and transcriptomic data from human cell lines has been used to train deep learning models to classify drugs by therapeutic properties. Results in drug discovery have been mixed. AI models depend on the quality and diversity of their training data; those trained on narrow chemical libraries can fail to generalize to novel molecular scaffolds. The gap between high virtual screening hit rates and success in preclinical or clinical testing remains a persistent challenge, and the translation of computationally predicted candidates into approved drugs has been slower than early projections suggested. === Drug delivery systems === AI methods including neural networks, principal component analysis, and neuro-fuzzy logic have been applied to identifying biological targets for pharmaceuticals and analyzing genetic information relevant to drug design. Computational models can predict how a formulation will behave in biological systems, helping narrow the field before laboratory synthesis begins. Systems have been proposed that monitor patient response and adjust doses in real time based on individual physiology, with potential applications in chronic disease management. Research has also explored AI applications in targeted cancer treatments and oral vaccine delivery, areas where precise control over drug release kinetics is a design priority. === Drug safety === AI has been applied to predicting and detecting adverse drug reactions using techniques including knowledge graphs, logistic regression classifiers, and neural networks. A 2023 study developed a machine learning algorithm using knowledge graph analysis to classify known causes of adverse reactions. Natural language processing and deep learning models including long short-term memory (LSTM) networks have shown better performance than conventional methods for detecting opioid misuse, drawing on both structured data from electronic health records and unstructured sources such as clinical notes. AI-based pharmacovigilance systems can scan large volumes of electronic health records and social media for drug safety signals at a scale not feasible with manual review. Limitations include difficulty distinguishing drug-related adverse events from unrelated conditions in free-text data, and the need for validated benchmarks to measure model performance against existing safety monitoring standards. === Clinical decision support and personalized medicine === Machine learning systems trained on patient datasets can predict individual risk profiles, including potential allergies and drug–drug interactions, reducing the risk of harm in complex polypharmacy cases where the number of possible interactions exceeds what a clinician can readily assess. Personalized dosing models have been developed for drugs with narrow therapeutic windows — including anticoagulants and immunosuppressants — using patient-specific variables such as weight, renal function, and relevant genetic markers. Prospective clinical validation of these systems has lagged behind their technical development. Most published evaluations report performance on retrospective datasets, and the regulatory pathway for AI-based clinical decision support tools in pharmacy varies by jurisdiction. === Pharmacy operations and automation === Robotic and AI-driven systems have been applied to dispensing accuracy and pharmacy logistics. At the UCSF Medical Center, robotic technology produced 350,000 medication doses with no dispensing errors recorded. Robots such as TUG assist with preparing and transporting medications and laboratory samples within hospital settings. AI has also been applied to inventory management, with demand-forecasting systems predicting medicine requirements to reduce shortages and minimize waste from expired stock. In community pharmacy settings, AI tools have been used to flag potential prescription errors and alert pharmacists to drug–drug interactions before dispensing. === Medication adherence === Confirming that patients take prescribed medications as directed is a persistent challenge in healthcare. AI-enabled tools including smart pillboxes, RFID tags, ingestible sensors, and video check-ins have been applied to this problem. Smart pillboxes record when they are opened, providing real-time adherence data that can be reviewed remotely by care teams. Ingestible sensors transmit a signal after dissolution, offering direct confirmation of ingestion rather than proxy measures such as pill count or self-report. == Adoption challenges == === Barriers === Several barriers limit AI adoption in pharmacy practice. Many published evaluations report model performance on retrospective datasets rather than prospective clinical outcomes, making it difficult to assess real-world benefit. Pharmacists have reported limited AI training and knowledge, and research facilities often lack the computational infrastructure required for model development and validation. Models trained on biased or unrepresentative datasets can produce misleading results with direct patient safety consequences. === Regulatory frameworks === Regulatory frameworks for AI-based pharmaceutical tools are in active development. In the United States, the Food and Drug Administration (FDA) has issued guidance on AI and machine learning-based software as a medical device, addressing requirements for pre-market review and post-market performance monitoring. The European Medicines Agency has published discussion papers on the use of AI across the medicines development lifecycle, with particular attention to transparency in model training and validation. The absence of harmonized international standards creates compliance complexity for developers operating across multiple jurisdictions. === Ethical challenges === AI adoption raises data privacy and security concerns, including the risk of exposing sensitive patient information through data breaches. Algorithmic bias presents a related hazard: a model trained on an unrepresentative patient population may generate unsuitable treatment recommendations for patients not reflected in its training data, with potential for disparate outcomes across demographic groups. The opacity of some machine learning models, particularly deep neural networks, limits clinicians' ability to interpret or contest a recommendation, raising questions of accountability when a model-assisted decision results in patient harm. === Proposed solutions === Responses proposed in the literature include AI-focused education programs for pharmacists, increased public funding for healthcare AI research, encryption and governance frameworks for patient data, and regulatory requirements to prevent the use of biased training datasets. Greater transparency about training data provenance, model architecture, and validation methodology has also been recommended, including disclosure requirements in regulatory submissions. === Future directions === Research groups have called for tighter integration between AI systems and electronic health records to reduce healthcare costs and improve continuity of care across settings. International collaboration through shared AI frameworks and federated learning approaches has been proposed to address data scarcity in underrepresented patient populations and accelerate validation across institutions.

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  Eigenmoments

  EigenMoments is a set of orthogonal, noise robust, invariant to rotation, scaling and translation and distribution sensitive moments. Their application can be found in signal processing and computer vision as descriptors of the signal or image. The descriptors can later be used for classification purposes. It is obtained by performing orthogonalization, via eigen analysis on geometric moments. == Framework summary == EigenMoments are computed by performing eigen analysis on the moment space of an image by maximizing signal-to-noise ratio in the feature space in form of Rayleigh quotient. This approach has several benefits in Image processing applications: Dependency of moments in the moment space on the distribution of the images being transformed, ensures decorrelation of the final feature space after eigen analysis on the moment space. The ability of EigenMoments to take into account distribution of the image makes it more versatile and adaptable for different genres. Generated moment kernels are orthogonal and therefore analysis on the moment space becomes easier. Transformation with orthogonal moment kernels into moment space is analogous to projection of the image onto a number of orthogonal axes. Nosiy components can be removed. This makes EigenMoments robust for classification applications. Optimal information compaction can be obtained and therefore a few number of moments are needed to characterize the images. == Problem formulation == Assume that a signal vector s ∈ R n {\displaystyle s\in {\mathcal {R}}^{n}} is taken from a certain distribution having correlation C ∈ R n × n {\displaystyle C\in {\mathcal {R}}^{n\times n}} , i.e. C = E [ s s T ] {\displaystyle C=E[ss^{T}]} where E[.] denotes expected value. Dimension of signal space, n, is often too large to be useful for practical application such as pattern classification, we need to transform the signal space into a space with lower dimensionality. This is performed by a two-step linear transformation: q = W T X T s , {\displaystyle q=W^{T}X^{T}s,} where q = [ q 1 , . . . , q n ] T ∈ R k {\displaystyle q=[q_{1},...,q_{n}]^{T}\in {\mathcal {R}}^{k}} is the transformed signal, X = [ x 1 , . . . , x n ] T ∈ R n × m {\displaystyle X=[x_{1},...,x_{n}]^{T}\in {\mathcal {R}}^{n\times m}} a fixed transformation matrix which transforms the signal into the moment space, and W = [ w 1 , . . . , w n ] T ∈ R m × k {\displaystyle W=[w_{1},...,w_{n}]^{T}\in {\mathcal {R}}^{m\times k}} the transformation matrix which we are going to determine by maximizing the SNR of the feature space resided by q {\displaystyle q} . For the case of Geometric Moments, X would be the monomials. If m = k = n {\displaystyle m=k=n} , a full rank transformation would result, however usually we have m ≤ n {\displaystyle m\leq n} and k ≤ m {\displaystyle k\leq m} . This is specially the case when n {\displaystyle n} is of high dimensions. Finding W {\displaystyle W} that maximizes the SNR of the feature space: S N R t r a n s f o r m = w T X T C X w w T X T N X w , {\displaystyle SNR_{transform}={\frac {w^{T}X^{T}CXw}{w^{T}X^{T}NXw}},} where N is the correlation matrix of the noise signal. The problem can thus be formulated as w 1 , . . . , w k = a r g m a x w w T X T C X w w T X T N X w {\displaystyle {w_{1},...,w_{k}}=argmax_{w}{\frac {w^{T}X^{T}CXw}{w^{T}X^{T}NXw}}} subject to constraints: w i T X T N X w j = δ i j , {\displaystyle w_{i}^{T}X^{T}NXw_{j}=\delta _{ij},} where δ i j {\displaystyle \delta _{ij}} is the Kronecker delta. It can be observed that this maximization is Rayleigh quotient by letting A = X T C X {\displaystyle A=X^{T}CX} and B = X T N X {\displaystyle B=X^{T}NX} and therefore can be written as: w 1 , . . . , w k = a r g m a x x w T A w w T B w {\displaystyle {w_{1},...,w_{k}}={\underset {x}{\operatorname {arg\,max} }}{\frac {w^{T}Aw}{w^{T}Bw}}} , w i T B w j = δ i j {\displaystyle w_{i}^{T}Bw_{j}=\delta _{ij}} === Rayleigh quotient === Optimization of Rayleigh quotient has the form: max w R ( w ) = max w w T A w w T B w {\displaystyle \max _{w}R(w)=\max _{w}{\frac {w^{T}Aw}{w^{T}Bw}}} and A {\displaystyle A} and B {\displaystyle B} , both are symmetric and B {\displaystyle B} is positive definite and therefore invertible. Scaling w {\displaystyle w} does not change the value of the object function and hence and additional scalar constraint w T B w = 1 {\displaystyle w^{T}Bw=1} can be imposed on w {\displaystyle w} and no solution would be lost when the objective function is optimized. This constraint optimization problem can be solved using Lagrangian multiplier: max w w T A w {\displaystyle \max _{w}{w^{T}Aw}} subject to w T B w = 1 {\displaystyle {w^{T}Bw}=1} max w L ( w ) = max w ( w T A w − λ w T B w ) {\displaystyle \max _{w}{\mathcal {L}}(w)=\max _{w}(w{T}Aw-\lambda w^{T}Bw)} equating first derivative to zero and we will have: A w = λ B w {\displaystyle Aw=\lambda Bw} which is an instance of Generalized Eigenvalue Problem (GEP). The GEP has the form: A w = λ B w {\displaystyle Aw=\lambda Bw} for any pair ( w , λ ) {\displaystyle (w,\lambda )} that is a solution to above equation, w {\displaystyle w} is called a generalized eigenvector and λ {\displaystyle \lambda } is called a generalized eigenvalue. Finding w {\displaystyle w} and λ {\displaystyle \lambda } that satisfies this equations would produce the result which optimizes Rayleigh quotient. One way of maximizing Rayleigh quotient is through solving the Generalized Eigen Problem. Dimension reduction can be performed by simply choosing the first components w i {\displaystyle w_{i}} , i = 1 , . . . , k {\displaystyle i=1,...,k} , with the highest values for R ( w ) {\displaystyle R(w)} out of the m {\displaystyle m} components, and discard the rest. Interpretation of this transformation is rotating and scaling the moment space, transforming it into a feature space with maximized SNR and therefore, the first k {\displaystyle k} components are the components with highest k {\displaystyle k} SNR values. The other method to look at this solution is to use the concept of simultaneous diagonalization instead of Generalized Eigen Problem. === Simultaneous diagonalization === Let A = X T C X {\displaystyle A=X^{T}CX} and B = X T N X {\displaystyle B=X^{T}NX} as mentioned earlier. We can write W {\displaystyle W} as two separate transformation matrices: W = W 1 W 2 . {\displaystyle W=W_{1}W_{2}.} W 1 {\displaystyle W_{1}} can be found by first diagonalize B: P T B P = D B {\displaystyle P^{T}BP=D_{B}} . Where D B {\displaystyle D_{B}} is a diagonal matrix sorted in increasing order. Since B {\displaystyle B} is positive definite, thus D B > 0 {\displaystyle D_{B}>0} . We can discard those eigenvalues that large and retain those close to 0, since this means the energy of the noise is close to 0 in this space, at this stage it is also possible to discard those eigenvectors that have large eigenvalues. Let P ^ {\displaystyle {\hat {P}}} be the first k {\displaystyle k} columns of P {\displaystyle P} , now P T ^ B P ^ = D B ^ {\displaystyle {\hat {P^{T}}}B{\hat {P}}={\hat {D_{B}}}} where D B ^ {\displaystyle {\hat {D_{B}}}} is the k × k {\displaystyle k\times k} principal submatrix of D B {\displaystyle D_{B}} . Let W 1 = P ^ D B ^ − 1 / 2 {\displaystyle W_{1}={\hat {P}}{\hat {D_{B}}}^{-1/2}} and hence: W 1 T B W 1 = ( P ^ D B ^ − 1 / 2 ) T B ( P ^ D B ^ − 1 / 2 ) = I {\displaystyle W_{1}^{T}BW_{1}=({\hat {P}}{\hat {D_{B}}}^{-1/2})^{T}B({\hat {P}}{\hat {D_{B}}}^{-1/2})=I} . W 1 {\displaystyle W_{1}} whiten B {\displaystyle B} and reduces the dimensionality from m {\displaystyle m} to k {\displaystyle k} . The transformed space resided by q ′ = W 1 T X T s {\displaystyle q'=W_{1}^{T}X^{T}s} is called the noise space. Then, we diagonalize W 1 T A W 1 {\displaystyle W_{1}^{T}AW_{1}} : W 2 T W 1 T A W 1 W 2 = D A {\displaystyle W_{2}^{T}W_{1}^{T}AW_{1}W_{2}=D_{A}} , where W 2 T W 2 = I {\displaystyle W_{2}^{T}W_{2}=I} . D A {\displaystyle D_{A}} is the matrix with eigenvalues of W 1 T A W 1 {\displaystyle W_{1}^{T}AW_{1}} on its diagonal. We may retain all the eigenvalues and their corresponding eigenvectors since most of the noise are already discarded in previous step. Finally the transformation is given by: W = W 1 W 2 {\displaystyle W=W_{1}W_{2}} where W {\displaystyle W} diagonalizes both the numerator and denominator of the SNR, W T A W = D A {\displaystyle W^{T}AW=D_{A}} , W T B W = I {\displaystyle W^{T}BW=I} and the transformation of signal s {\displaystyle s} is defined as q = W T X T s = W 2 T W 1 T X T s {\displaystyle q=W^{T}X^{T}s=W_{2}^{T}W_{1}^{T}X^{T}s} . === Information loss === To find the information loss when we discard some of the eigenvalues and eigenvectors we can perform following analysis: η = 1 − t r a c e ( W 1 T A W 1 ) t r a c e ( D B − 1 / 2 P T A P D B − 1 / 2 ) = 1 − t r a c e ( D B ^ − 1 / 2 P ^ T A P ^ D B ^ − 1 / 2 ) t r a c e ( D B − 1 / 2 P T A P D B − 1 / 2 ) {\displaystyle {\begin{array}{lll}\eta &=&

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  Lymphater's Formula

  "Lymphater's Formula" (Polish: "Formula Lymphatera") is a 1961 science fiction short story by Polish writer Stanisław Lem. It is a story of a "mad scientist", mathematician Ammon Lymphater, who invents an artificial intelligence, and then he realizes that it is capable of rendering the humankind obsolete. It was first published in the 1961 collection Księga robotów (Book of Robots) with the pre-annotation "from the memoirs of Ijon Tichy". The story was never republished with this pre-annotation, and nothing in the novel gives any indication at Ijon Tichy. Piotr Krywak tried to figure out possible explanations for this, apart from a typographical error. == Plot == Ammon Lymphater became interested in the emerging science of cybernetics and information theory, and started studying the works of an animal brain, the ant's brain in particular. He took note that the inherited knowledge is an evolutionary advantage somehow not exploited in full by the evolution. Eventually he came to a conclusion that only by pure biological restrictions that adaptive abilities of insects were stopped in their tracks by the evolution. He went on further wondering whether the ants have an ability to apriori knowledge, i.e., knowledge neither inherited nor learned. He decided to consult a famous myrmecologist, who told him about a rare ant species Acanthis Rubra Willinsoniana with an exceptionally high adaptability. Eventually Lymphater devised and constructed "It" capable of instant precognition of everything within "Its" rapidly expanding range of perception. From "It" Lymphater learns that the humanity is not the "crown of evolution", but rather evolution's tool to create "It", because the evolution could not create "It" directly (confirming Lymphater's reasoning about ants). Realizing that the Superentity "It" renders the human civilization redundant and obsolete, Lymphater destroys "It". "It" already knew Lymphater's intentions, but was not worried, knowing that sooner or later someone else will create "It" again and again. "It" was only the first variant of Lymphater's formula and the second variant is possible. Lyphater wonders whether the second one would be capable to create the third stage of the evolution which would amount to an artificial God. == Publication history == It was translated in Russian (as "Формула Лимфатера") in 1963, in Hungarian (as "Lymphater utolsó képlete") in 1966, and in Bulgarian (as "Формулата на Лимфатер" by Георги Димитров Георгиев) in 1969. In 1973 an audiobook was released in German (as "Die lymphatersche Formel"), narrated by Martin Held. It was also republished (and translated) in some other collections of Lem's short stories.

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  Micah Xavier Johnson

  Micah Xavier Johnson (July 2, 1991 – July 8, 2016) was an American Army reserve Afghan war veteran, black nationalist, and mass murderer who perpetrated the 2016 shooting of Dallas police officers during a Black Lives Matter protest. He ambushed and killed five officers and wounded eleven others in Downtown, Dallas, Texas. He was killed by police during a standoff after expressing anger over police killings of black men. The shootings were the second-deadliest targeted attack on law enforcement officers in U.S. history, surpassed only by the September 11 attacks. == Early life == Micah Xavier Johnson was born in Magee, Mississippi, on July 2, 1991, and he was raised in Mesquite, Texas. When he was four years old, his parents divorced. At 17, Johnson enrolled at John Horn High School, where he joined the Junior Reserve Officers' Training Corps, as reported by the Mesquite Independent school district. He faced academic challenges, graduating in 2009 with a 1.98 GPA and ranking 430th out of 453 students in his class. In Spring 2011, Johnson registered for four courses at Richland college but did not complete any. Evidence suggests his enrollment at Richland gave him access to El Centro College, due to his pre-planned and coordinated movements throughout Building B during his standoff with police in 2016. == Military service == === Enlistment and early service === Micah Xavier Johnson enlisted in the U.S. Army Reserve in March 2009 at the age of 18, shortly after graduating high school in Mesquite, Texas. His initial service was primarily stateside, where he trained as a carpentry and masonry specialist (military occupational specialty 51B). This role involved engineering tasks such as construction and repair in support of military operations. During his reserve tenure, Johnson served part-time while living at home, and he was described by family and friends as initially idealistic about the military, even aspiring to become a police officer. === Deployment to Afghanistan === In September 2013, Johnson was activated for full-time duty and deployed to Afghanistan as part of the 420th Engineer Brigade, a unit based in Seagoville, Texas. His tour began in November 2013 and lasted approximately eight months, ending in July 2014. During this period, he performed non-combat engineering duties, though the stresses of serving in a combat zone were noted by those close to him. Associates from his service later suggested he experienced significant psychological strain, including the loss of friends and general disillusionment with military life, which contrasted with his pre-deployment enthusiasm. His mother later reflected that "the military was not what Micah thought it would be." === Sexual harassment allegation and early return === About six months into his deployment, in May 2014, Johnson faced a serious accusation of sexual harassment from a higher-ranking female soldier. She filed for a military protective order against him, prompting an investigation. As a result, his chain of command recommended an "other than honorable" discharge—the second (more severe is a dishonorable discharge, which does not require a court martial) most severe administrative separation short of a court-martial—and he was sent back to the United States ahead of schedule. Despite this, Johnson was not court-martialed, and the case did not lead to criminal charges. A military lawyer who represented him described the handling as unusual, noting that "someone really screwed up" in allowing him to avoid harsher consequences. === Post-deployment and discharge === Upon returning stateside in August 2014, Johnson resumed reserve duties with his engineering brigade until April 2015. He was honorably discharged at the rank of private first class (E-3), a relatively low junior enlisted rank after six years of service, which military sources attributed partly to the unresolved harassment allegation impacting his promotions and evaluations. Friends and family observed a marked change in his demeanor post-deployment: he became more reclusive, resentful toward the government, and withdrawn, with some speculating that the Afghanistan experience and the scandal contributed to a "small breakdown." In July 2016, following the Dallas shooting, the U.S. Army launched an internal review of his service record, including the harassment claims, to assess whether all misconduct allegations had been fully investigated. == Shootings == On July 7, 2016, a peaceful Black Lives Matter protest marched through downtown Dallas, Texas, drawing about 800 demonstrators. The event responded to the recent police killings of Alton Sterling in Baton Rouge, Louisiana, on July 5, and Philando Castile in Falcon Heights, Minnesota, on July 6—both black men shot during encounters captured on video. Around 100 officers monitored the march, which passed near El Centro College without incident until gunfire erupted around 8:45 p.m. Johnson arrived in a dark SUV, armed with an SKS semi-automatic rifle, a handgun, extra ammunition, and ballistic vests. He parked near the protest's end, chatted briefly with two officers, then opened fire on police from an elevated position on Lamar Street (now Botham Jean Boulevard). He shot from behind barriers, through windows, and while moving, targeting white officers specifically. The ambush killed five officers and wounded seven more, plus two civilians. Gunfire scattered protesters in panic as Johnson used military-style tactics, like quick position changes, to prolong the assault. === Standoff and Johnson's end === Johnson fled into El Centro College's Building C, then Building B, navigating pre-planned routes with familiarity from prior enrollment at nearby Richland College. He barricaded in a parking garage, wounding more officers in close-range fights. During two-hour negotiations, he taunted police via phone—laughing, singing, asking kill counts, and claiming planted bombs (none found). He admitted solo action, rage at White officers, and no group ties. At 2:30 a.m. on July 8, SWAT ended the standoff by detonating a bomb via remote-controlled robot in the garage, killing Johnson. This marked the first U.S. police use of such a tactic. === Victims and investigation findings === The slain officers were: Brent Thompson (Transit Authority, 36), Patrick Zamarripa (Dallas PD, 33), Michael Krol (Dallas PD, 40), Lorne Ahrens (Dallas PD, 48), and Michael Smith (Dallas PD, 55). Wounded officers included Sheik Smith, John Mitchell, and others; civilians She Tamara El-Sobky and Hillary Castro. Searches of Johnson's home revealed bomb-making materials, rifles, vests, and notes on tactics, suggesting plans for a larger attack. He had practiced explosions and honed skills post-discharge, including marksmanship. === Aftermath and impact === Dallas mourned with vigils and memorials, while national protests against police violence continued amid grief. President Barack Obama, the first African American president of the United States, called Johnson a "demented individual" and formed a task force on race and policing. The incident fueled debates on gun control, race relations, and veteran mental health—Johnson had sought VA treatment for stress and anxiety but showed no prior violent signs to friends. El Centro College canceled all classes on July 8. Police barricaded the perimeter and began canvassing the crime scene. The explosion that killed Johnson also destroyed the school's servers, further delaying reopening. The school partially reopened on July 20, with staff returning that day and students on the following day. Buildings A, B, and C remained closed pending the FBI investigation. == Motive == An investigation into his online activities uncovered his interest in black nationalist groups. The Southern Poverty Law Center (SPLC), and news outlets reported that Johnson "liked" the Facebook pages of black nationalist organizations such as the New Black Panther Party (NBPP), Nation of Islam, and Black Riders Liberation Party, three groups which are listed by the SPLC as hate groups. On Facebook, Johnson posted an angry and "disjointed" post against White people on July 2, several days before the attack. NBPP head Quanell X said after the shooting that Johnson had been a member of the NBPP's Houston chapter for about six months, several years before. Quanell X added that Johnson had been "asked to leave" the group for violating the organization's "chain of command" and espousing dangerous rhetoric, such as asking the NBPP why they had not purchased more weapons and ammunition, and expressing his desire to harm black church preachers because he believed they were more interested in money than God. Following the shooting, a national NBPP leader distanced the group from Johnson, saying that he "was not a member of" the party. Further investigation into his digital footprint showed that Johnson visited the sites of Marxist Leninist groups associated with "Revolutionary Black Nationalism",

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  Blocks world

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

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  EasyA

  EasyA is a web3 technology company and education platform based in London (United Kingdom), founded in 2022 by Phil Kwok and Dom Kwok. EasyA was officially launched in 2022, focusing on web3 technologies. This community was influenced by the founders' experiences during the COVID-19 pandemic and early collaborations with universities and other educational institutions. Subsequently, the community was used as a foundation for developing Web3-related initiatives, including the organisation of EasyA's first Web3 hackathon in 2022. The EasyA app has over one million users and provides educational content on various blockchain technologies. EasyA Labs is a separate initiative focused on developing products intended to improve accessibility to cryptocurrency for a broader audience.

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  KitKat (cat)

  KitKat was a bodega cat from the Mission District of San Francisco who was killed by a Waymo car on October 27, 2025. Locals built altars and the death has raised comments about the safety of self-driving cars. == Life == Mike Zeidan, the owner of Randa's Market, adopted KitKat as a stray to help keep rodents out of his store. KitKat lived in Randa's Market for six years and was well-loved by the neighborhood, including an appearance on a shop cats map that went viral in 2022 as a "particularly friendly cat". After KitKat arrived at the bodega, customers were said to come more often, and regularly brought the cat food and gifts. == Death == At around 11:40 pm on October 27, 2025, witnesses saw KitKat sitting in front of a stopped Waymo car for seven seconds. He walked under the car as the car pulled out, and the right rear tire ran over the back half of his body. A bartender who was taking a cigarette break used a sandwich board sign as a stretcher and took KitKat to an emergency animal clinic. An hour later, KitKat was pronounced dead. Waymo confirmed that the cat was killed by one of its vehicles on October 30. Surveillance footage of the incident was released in December. From Waymo's report to the National Highway Traffic Safety Administration (NHTSA): The Waymo AV was stopped next to the curb for a passenger pickup facing east on 16th Street. As the passengers were boarding the Waymo AV, a cat approached the Waymo AV from the southern sidewalk of 16th Street and sat in the roadway partially under the front right corner of the Waymo AV. A pedestrian approached the Waymo AV from the east on the southern sidewalk of 16th Street and began crouching near the front of the Waymo AV, stepping partially into the roadway, appearing to reach for the cat. As they did so, the cat moved farther from the sidewalk under the Waymo AV and the pedestrian stepped back onto the sidewalk. The Waymo AV then departed the pickup location and the rear right tire made contact with the cat. At the time of impact, the Waymo AV's Level 4 ADS was engaged in autonomous mode. Waymo later received notice that the cat did not survive. The passengers in the Waymo AV did not have seatbelts fastened at the time, having just boarded the Waymo AV. Prior to KitKat's death, the NHTSA had logged 14 collisions between Waymo cars and animals, of which 5 were confirmed fatalities. == Aftermath == After KitKat's death, an altar was created outside Randa's Market. People left flowers, candles, cat food, written notes, and Kit Kat candy bars in the cat's honor. A city worker took down the memorial for fire safety reasons, but neighbors built it again. Local supervisor Jackie Fielder held a rally called "Justice for KitKat" in support of a non-binding San Francisco resolution to shift decision-making about the operation of self-driving cars from the state to individual counties. Critics say that the resolution is performative because it is non-binding, that local control would make autonomous vehicle operation impractical, and that Waymo is still far less dangerous to animals than human drivers. Elon Musk commented that "many pets will be saved by autonomy". There are multiple meme coins inspired by KitKat.

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  List of artificial intelligence artists

  Many notable artificial intelligence artists have created a wide variety of artificial intelligence art from the 1960s to today. These include: == 20th century == Harold Cohen, active from 1960s to 2010s. Cohen's work is primarily with AARON, a series of computer programs that autonomously create original images. Eric Millikin, active from 1980s to present. Millikin's work includes AI-generated virtual reality, video art, poetry, music, and performance art, on topics such as animal rights, climate change, anti-racism, witchcraft, and the occult. Karl Sims, active from 1980s to present. Sims is best known for using particle systems and artificial life in computer animation. == 21st century == Refik Anadol, active from 2010s to present. Anadol's work includes video installations based on generative algorithms with artificial intelligence. Sougwen Chung, active from 2010s to present. Chung's work includes performances with a robotic arm that uses AI to attempt to draw in a manner similar to Chung. Stephanie Dinkins, active from 2010s to present. Dinkins' work includes recordings of conversations with an artificially intelligent robot that resembles a black woman, discussing topics such as race and the nature of being. Jake Elwes, active from 2010s to present. Their practice is the exploration of artificial intelligence, queer theory and technical biases. Libby Heaney, active from 2010s to present. Heaney's practice includes work with chatbots. Mario Klingemann, active from 2010s to present. Klingemann's works examine creativity, culture, and perception through machine learning and artificial intelligence. Mauro Martino, active from 2010s to present. Martino's work includes design, data visualization and infographics. Trevor Paglen, active from 2000s to present. Paglen's practice includes work in photography and geography, on topics like mass surveillance and data collection. Anna Ridler, active from 2010s to present. Ridler works with collections of information, including self-generated data sets, often working with floral photography.

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