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Automated attendant

In telephony, an automated attendant (also auto attendant, auto-attendant, autoattendant, automatic phone menus, AA, or virtual receptionist) allows callers to be automatically transferred to an extension without the intervention of an operator/receptionist. Many AAs will also offer a simple menu system ("for sales, press 1, for service, press 2," etc.). An auto attendant may also allow a caller to reach a live operator by dialing a number, usually "0". Typically the auto attendant is included in a business's phone system such as a PBX, but some services allow businesses to use an AA without such a system. Modern AA services (which now overlap with more complicated interactive voice response or IVR systems) can route calls to mobile phones, VoIP virtual phones, other AAs/IVRs, or other locations using traditional land-line phones or voice message machines. == Feature description == Telephone callers will recognize an automated attendant system as one that greets calls incoming to an organization with a recorded greeting of the form, "Thank you for calling .... If you know your party's extension, you may dial it any time during this message." Callers who have a touch-tone (DTMF) phone can dial an extension number or, in most cases, wait for operator ("attendant") assistance. Since the telephone network does not transmit the DC signals from rotary dial telephones (except for audible clicks), callers who have rotary dial phones have to wait for assistance. On a purely technical level it could be argued that an automated attendant is a very simple kind of IVR however, in the telecom industry the terms IVR and auto attendant are generally considered distinct. An automated attendant serves a very specific purpose (replace live operator and route calls), whereas an IVR can perform all sorts of functions (telephone banking, account inquiries, etc.). An AA will often include a directory which will allow a caller to dial by name in order to find a user on a system. There is no standard format to these directories, and they can use combinations of first name, last name, or both. The following lists common routing steps that are components of an automated attendant: Transfer to extension Transfer to voicemail Play message (i.e., "our address is ...") Go to a sub-menu Repeat choices In addition, an automated attendant would be expected to have values for the following: '0' – where to go when the caller dials '0' Timeout – what to do if the caller does nothing (usually go to the same place as '0') Default mailbox – where to send calls if '0' is not answered (or is not pointing to a live person) == Background == PBXs (private branch exchanges) or PABXs (private automatic branch exchanges) are telephone systems that serve an organization that has many telephone extensions but fewer telephone lines (sometimes called "trunks") that connect that organization to the rest of the global telecommunications network. While persons within an enterprise served by a PBX can call each other by dialing their extension numbers, incoming calls, i.e., calls originating from a telephone not served by the PBX but intended for a party served by the PBX, required assistance from a switchboard operator (also called a "switchboard attendant") or a telephone service called DID ("direct inward dialing"). Direct inward dialing has advantages such as rapid connection to the destination party and disadvantages including cost, lack of identification of the called organization and use of ten-digit telephone numbers. Automated attendants provide, among many other things, a way for an external caller to be directed to an extension or department served by a PBX system without using direct inward dialing or without switchboard attendant assistance. == History == Automated attendants are not part of voicemail systems. Voice messaging (or voicemail or VM) technology has existed since the late 1970s; in the early 1980s companies provided voice-prompting systems that allowed callers to reach (route the call) to an intended party, not necessarily to leave a message. Automated attendant systems are also referred to as automated menu systems and much early work in this field was done by Michael J. Freeman, Ph.D. == Time-based routing == Many auto attendants will have options to allow for time-of-day routing, as well as weekend and holiday routing. The specifics of these features will depend entirely on the particular automated attendant, but typically there would be a normal greeting and routing steps that would take place during normal business hours, and a different greeting and routing for non-business hours.
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T-norm fuzzy logics

T-norm fuzzy logics are a family of non-classical logics, informally delimited by having a semantics that takes the real unit interval [0, 1] for the system of truth values and functions called t-norms for permissible interpretations of conjunction. They are mainly used in applied fuzzy logic and fuzzy set theory as a theoretical basis for approximate reasoning. T-norm fuzzy logics belong in broader classes of fuzzy logics and many-valued logics. In order to generate a well-behaved implication, the t-norms are usually required to be left-continuous; logics of left-continuous t-norms further belong in the class of substructural logics, among which they are marked with the validity of the law of prelinearity, (A → B) ∨ (B → A). Both propositional and first-order (or higher-order) t-norm fuzzy logics, as well as their expansions by modal and other operators, are studied. Logics that restrict the t-norm semantics to a subset of the real unit interval (for example, finitely valued Łukasiewicz logics) are usually included in the class as well. Important examples of t-norm fuzzy logics are monoidal t-norm logic (MTL) of all left-continuous t-norms, basic logic (BL) of all continuous t-norms, product fuzzy logic of the product t-norm, or the nilpotent minimum logic of the nilpotent minimum t-norm. Some independently motivated logics belong among t-norm fuzzy logics, too, for example Łukasiewicz logic (which is the logic of the Łukasiewicz t-norm) or Gödel–Dummett logic (which is the logic of the minimum t-norm). == Motivation == As members of the family of fuzzy logics, t-norm fuzzy logics primarily aim at generalizing classical two-valued logic by admitting intermediary truth values between 1 (truth) and 0 (falsity) representing degrees of truth of propositions. The degrees are assumed to be real numbers from the unit interval [0, 1]. In propositional t-norm fuzzy logics, propositional connectives are stipulated to be truth-functional, that is, the truth value of a complex proposition formed by a propositional connective from some constituent propositions is a function (called the truth function of the connective) of the truth values of the constituent propositions. The truth functions operate on the set of truth degrees (in the standard semantics, on the [0, 1] interval); thus the truth function of an n-ary propositional connective c is a function Fc: [0, 1]n → [0, 1]. Truth functions generalize truth tables of propositional connectives known from classical logic to operate on the larger system of truth values. T-norm fuzzy logics impose certain natural constraints on the truth function of conjunction. The truth function ∗ : [ 0 , 1 ] 2 → [ 0 , 1 ] {\displaystyle \colon [0,1]^{2}\to [0,1]} of conjunction is assumed to satisfy the following conditions: Commutativity, that is, x ∗ y = y ∗ x {\displaystyle xy=yx} for all x and y in [0, 1]. This expresses the assumption that the order of fuzzy propositions is immaterial in conjunction, even if intermediary truth degrees are admitted. Associativity, that is, ( x ∗ y ) ∗ z = x ∗ ( y ∗ z ) {\displaystyle (xy)z=x(yz)} for all x, y, and z in [0, 1]. This expresses the assumption that the order of performing conjunction is immaterial, even if intermediary truth degrees are admitted. Monotony, that is, if x ≤ y {\displaystyle x\leq y} then x ∗ z ≤ y ∗ z {\displaystyle xz\leq yz} for all x, y, and z in [0, 1]. This expresses the assumption that increasing the truth degree of a conjunct should not decrease the truth degree of the conjunction. Neutrality of 1, that is, 1 ∗ x = x {\displaystyle 1x=x} for all x in [0, 1]. This assumption corresponds to regarding the truth degree 1 as full truth, conjunction with which does not decrease the truth value of the other conjunct. Together with the previous conditions this condition ensures that also 0 ∗ x = 0 {\displaystyle 0x=0} for all x in [0, 1], which corresponds to regarding the truth degree 0 as full falsity, conjunction with which is always fully false. Continuity of the function ∗ {\displaystyle } (the previous conditions reduce this requirement to the continuity in either argument). Informally this expresses the assumption that microscopic changes of the truth degrees of conjuncts should not result in a macroscopic change of the truth degree of their conjunction. This condition, among other things, ensures a good behavior of (residual) implication derived from conjunction; to ensure the good behavior, however, left-continuity (in either argument) of the function ∗ {\displaystyle } is sufficient. In general t-norm fuzzy logics, therefore, only left-continuity of ∗ {\displaystyle } is required, which expresses the assumption that a microscopic decrease of the truth degree of a conjunct should not macroscopically decrease the truth degree of conjunction. These assumptions make the truth function of conjunction a left-continuous t-norm, which explains the name of the family of fuzzy logics (t-norm based). Particular logics of the family can make further assumptions about the behavior of conjunction (for example, Gödel–Dummett logic requires its idempotence) or other connectives (for example, the logic IMTL (involutive monoidal t-norm logic) requires the involutiveness of negation). All left-continuous t-norms ∗ {\displaystyle } have a unique residuum, that is, a binary function ⇒ {\displaystyle \Rightarrow } such that for all x, y, and z in [0, 1], x ∗ y ≤ z {\displaystyle xy\leq z} if and only if x ≤ y ⇒ z . {\displaystyle x\leq y\Rightarrow z.} The residuum of a left-continuous t-norm can explicitly be defined as ( x ⇒ y ) = sup { z ∣ z ∗ x ≤ y } . {\displaystyle (x\Rightarrow y)=\sup\{z\mid zx\leq y\}.} This ensures that the residuum is the pointwise largest function such that for all x and y, x ∗ ( x ⇒ y ) ≤ y . {\displaystyle x(x\Rightarrow y)\leq y.} The latter can be interpreted as a fuzzy version of the modus ponens rule of inference. The residuum of a left-continuous t-norm thus can be characterized as the weakest function that makes the fuzzy modus ponens valid, which makes it a suitable truth function for implication in fuzzy logic. Left-continuity of the t-norm is the necessary and sufficient condition for this relationship between a t-norm conjunction and its residual implication to hold. Truth functions of further propositional connectives can be defined by means of the t-norm and its residuum, for instance the residual negation ¬ x = ( x ⇒ 0 ) {\displaystyle \neg x=(x\Rightarrow 0)} or bi-residual equivalence x ⇔ y = ( x ⇒ y ) ∗ ( y ⇒ x ) . {\displaystyle x\Leftrightarrow y=(x\Rightarrow y)(y\Rightarrow x).} Truth functions of propositional connectives may also be introduced by additional definitions: the most usual ones are the minimum (which plays a role of another conjunctive connective), the maximum (which plays a role of a disjunctive connective), or the Baaz Delta operator, defined in [0, 1] as Δ x = 1 {\displaystyle \Delta x=1} if x = 1 {\displaystyle x=1} and Δ x = 0 {\displaystyle \Delta x=0} otherwise. In this way, a left-continuous t-norm, its residuum, and the truth functions of additional propositional connectives determine the truth values of complex propositional formulae in [0, 1]. Formulae that always evaluate to 1 are called tautologies with respect to the given left-continuous t-norm ∗ , {\displaystyle ,} or ∗ - {\displaystyle {\mbox{-}}} tautologies. The set of all ∗ - {\displaystyle {\mbox{-}}} tautologies is called the logic of the t-norm ∗ , {\displaystyle ,} as these formulae represent the laws of fuzzy logic (determined by the t-norm) that hold (to degree 1) regardless of the truth degrees of atomic formulae. Some formulae are tautologies with respect to a larger class of left-continuous t-norms; the set of such formulae is called the logic of the class. Important t-norm logics are the logics of particular t-norms or classes of t-norms, for example: Łukasiewicz logic is the logic of the Łukasiewicz t-norm x ∗ y = max ( x + y − 1 , 0 ) {\displaystyle xy=\max(x+y-1,0)} Gödel–Dummett logic is the logic of the minimum t-norm x ∗ y = min ( x , y ) {\displaystyle xy=\min(x,y)} Product fuzzy logic is the logic of the product t-norm x ∗ y = x ⋅ y {\displaystyle xy=x\cdot y} Monoidal t-norm logic MTL is the logic of (the class of) all left-continuous t-norms Basic fuzzy logic BL is the logic of (the class of) all continuous t-norms It turns out that many logics of particular t-norms and classes of t-norms are axiomatizable. The completeness theorem of the axiomatic system with respect to the corresponding t-norm semantics on [0, 1] is then called the standard completeness of the logic. Besides the standard real-valued semantics on [0, 1], the logics are sound and complete with respect to general algebraic semantics, formed by suitable classes of prelinear commutative bounded integral residuated lattices. == History == Some particular t-norm fuzzy logics have been introduced and investigated long before the family was re
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Computer-automated design

Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD) are more concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems. The concept of CAutoD perhaps first appeared in 1963, in the IBM Journal of Research and Development, where a computer program was written. to search for logic circuits having certain constraints on hardware design to evaluate these logics in terms of their discriminating ability over samples of the character set they are expected to recognize. More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologically-inspired machine learning, including heuristic search techniques such as evolutionary computation, and swarm intelligence algorithms. == Guiding designs by performance improvements == To meet the ever-growing demand of quality and competitiveness, iterative physical prototyping is now often replaced by 'digital prototyping' of a 'good design', which aims to meet multiple objectives such as maximised output, energy efficiency, highest speed and cost-effectiveness. The design problem concerns both finding the best design within a known range (i.e., through 'learning' or 'optimisation') and finding a new and better design beyond the existing ones (i.e., through creation and invention). This is equivalent to a search problem in an almost certainly, multidimensional (multivariate), multi-modal space with a single (or weighted) objective or multiple objectives. == Normalized objective function: cost vs. fitness == Using single-objective CAutoD as an example, if the objective function, either as a cost function J ∈ [ 0 , ∞ ) {\displaystyle J\in [0,\infty )} , or inversely, as a fitness function f ∈ ( 0 , 1 ] {\displaystyle f\in (0,1]} , where f = J 1 + J {\displaystyle f={\tfrac {J}{1+J}}} , is differentiable under practical constraints in the multidimensional space, the design problem may be solved analytically. Finding the parameter sets that result in a zero first-order derivative and that satisfy the second-order derivative conditions would reveal all local optima. Then comparing the values of the performance index of all the local optima, together with those of all boundary parameter sets, would lead to the global optimum, whose corresponding 'parameter' set will thus represent the best design. However, in practice, the optimization usually involves multiple objectives and the matters involving derivatives are a lot more complex. == Dealing with practical objectives == In practice, the objective value may be noisy or even non-numerical, and hence its gradient information may be unreliable or unavailable. This is particularly true when the problem is multi-objective. At present, many designs and refinements are mainly made through a manual trial-and-error process with the help of a CAD simulation package. Usually, such a posteriori learning or adjustments need to be repeated many times until a ‘satisfactory’ or ‘optimal’ design emerges. == Exhaustive search == In theory, this adjustment process can be automated by computerised search, such as exhaustive search. As this is an exponential algorithm, it may not deliver solutions in practice within a limited period of time. == Search in polynomial time == One approach to virtual engineering and automated design is evolutionary computation such as evolutionary algorithms. === Evolutionary algorithms === To reduce the search time, the biologically-inspired evolutionary algorithm (EA) can be used instead, which is a (non-deterministic) polynomial algorithm. The EA based multi-objective "search team" can be interfaced with an existing CAD simulation package in a batch mode. The EA encodes the design parameters (encoding being necessary if some parameters are non-numerical) to refine multiple candidates through parallel and interactive search. In the search process, 'selection' is performed using 'survival of the fittest' a posteriori learning. To obtain the next 'generation' of possible solutions, some parameter values are exchanged between two candidates (by an operation called 'crossover') and new values introduced (by an operation called 'mutation'). This way, the evolutionary technique makes use of past trial information in a similarly intelligent manner to the human designer. The EA based optimal designs can start from the designer's existing design database, or from an initial generation of candidate designs obtained randomly. A number of finely evolved top-performing candidates will represent several automatically optimized digital prototypes. There are websites that demonstrate interactive evolutionary algorithms for design. allows you to evolve 3D objects online and have them 3D printed. allows you to do the same for 2D images.
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Paranoia (role-playing game)

Paranoia is a dystopian science-fiction tabletop role-playing game originally designed and written by Greg Costikyan, Dan Gelber, and Eric Goldberg, and first published in 1984 by West End Games. Since 2004 the game has been published under license by Mongoose Publishing. The game won the Origins Award for Best Roleplaying Rules of 1984 and was inducted into the Origins Awards Hall of Fame in 2007. Paranoia is notable among tabletop games for being more competitive than co-operative, with players encouraged to betray one another for their own interests, as well as for keeping a light-hearted, tongue in cheek tone despite its dystopian setting. Several editions of the game have been published since the original version, and the franchise has spawned several spin-offs, novels and comic books based on the game. == Premise == The game is set in a dystopian future city controlled by the Computer (also known as "Friend Computer"), and where information (including the game rules) are restricted by color-coded "security clearance". Player characters are initially enforcers of the Computer's authority known as Troubleshooters, and are given missions to seek out and eliminate threats to the Computer's control. They are also part of prohibited underground movements, and have secret objectives including theft from and murder of other player characters. == Tone == Paranoia is a humorous role-playing game set in a dystopian future along the lines of Nineteen Eighty-Four, Brave New World, Logan's Run, and THX 1138; however, the tone of the game is rife with black humor, frequently tongue-in-cheek rather than dark and heavy. Most of the game's humor is derived from the players' (usually futile) attempts to complete their assignment while simultaneously adhering to the Computer's arbitrary, contradictory and often nonsensical security directives. The Paranoia rulebook is unusual in a number of ways; demonstrating any knowledge of the rules is forbidden, and most of the rulebook is written in an easy, conversational tone that often makes fun of the players and their characters, while occasionally taking digs at other notable role-playing games. === Setting === The game's main setting is an immense, futuristic city called Alpha Complex. Alpha Complex is controlled by the Computer, a civil service AI construct (a literal realization of the "Influencing Machine" that some schizophrenics fear). The Computer serves as the game's principal antagonist, and fears a number of threats to its 'perfect' society, such as the Outdoors, mutants, and secret societies (especially Communists). To deal with these threats, the Computer employs Troubleshooters, whose job is to go out, find trouble, and shoot it. Player characters are usually Troubleshooters, although later game supplements have allowed the players to take on other roles, such as High-Programmers of Alpha Complex. The player characters frequently receive mission instructions from the Computer that are incomprehensible, self-contradictory, or obviously fatal if adhered to, and side-missions (such as Mandatory Bonus Duties) that conflict with the main mission. Failing a mission generally results in termination of the player character, but succeeding can just as often result in the same fate, after being rewarded for successfully concluding the mission. They are issued equipment that is uniformly dangerous, faulty, or "experimental" (i.e., almost certainly dangerous and faulty). Additionally, each player character is generally an unregistered mutant and a secret society member (which are both termination offenses in Alpha Complex), and has a hidden agenda separate from the group's goals, often involving stealing from or killing teammates. Thus, missions often turn into a comedy of errors, as everyone on the team seeks to double-cross everyone else while keeping their own secrets. The game's manual encourages suspicion between players, offering several tips on how to make the gameplay as paranoid as possible. Every player's character is assigned six clones, known as a six-pack, which are used to replace the preceding clone upon his or her death. The game lacks a conventional health system; most wounds the player characters can suffer are assumed to be fatal. As a result, Paranoia allows characters to be routinely killed, yet the player can continue instead of leaving the game. This easy spending of clones tends to lead to frequent firefights, gruesome slapstick, and the horrible yet humorous demise of most if not all of the player character's clone family. Additional clones can be purchased if one gains sufficient favour with the Computer. === Security clearances === Paranoia features a security clearance system based on colors of the visible spectrum which heavily restricts what the players can and cannot legally do; everything from corridors to food and equipment have security restrictions. The lowest rating is Infrared, but the lowest playable security clearance is Red; the game usually begins with the characters having just been promoted to Red grade. Interfering with anything which is above that player's clearance carries significant risk. The full order of clearances from lowest to highest is Infrared (visually represented by black), Red, Orange, Yellow, Green, Blue, Indigo, Violet, and Ultraviolet (visually represented by white). Within the game, Infrared-clearance citizens live dull lives of mindless drudgery and are heavily medicated, while higher clearance characters may be allowed to demote or even summarily execute those of a lower rank and those with Ultraviolet clearance are almost completely unrestricted and have a great deal of access to the Computer; they are the only citizens that may (legally) access and modify the Computer's programming, and thus Ultraviolet citizens are also referred to as "High Programmers". Security clearance is not related to competence but is instead the result of the Computer's often insane and unjustified calculus of trust concerning a citizen. It is suggested that it may in fact be the High Programmers' meddling with The Computer's programming that resulted in its insanity. === Secret societies === In the game, secret societies tend to be based on sketchy and spurious knowledge of historical matters. For example, previous editions included societies such as the "Seal Club" that idolizes the Outdoors but is unsure what plants and animals actually look like. Other societies include the Knights of the Circular Object (based on the Knights of the Round Table), the Trekkies, and the First Church of Christ Computer Programmer. In keeping with the theme of paranoia, many secret societies have spies or double agents in each other's organizations. The first edition also included secret societies such as Programs Groups (the personal agents and spies of the High Programmers at the apex of Alpha Complex society) and Spy For Another Alpha Complex. The actual societies which would be encountered in a game depends on the play style; some societies are more suited for more light-hearted games (Zap-style, or the lighter end of Classic), whereas others represent a more serious threat to Alpha Complex and are therefore more suitable for Straight or the more dark sort of Classic games. == Publication history == Six editions have been published. Three of these were published by West End Games — the first, second, and fifth editions — whereas the later three editions (Paranoia XP, the 25th Anniversary edition and the "Red Clearance" edition) were published by Mongoose Publishing. In addition to these six published editions, it is known that West End Games were working on a third edition — to replace the poorly received fifth edition — in the late 1990s, but their financial issues would prevent this edition from being published, except for being included in one tournament adventure. === First edition === The first edition, was written by Greg Costikyan, Dan Gelber, and Eric Goldberg, and published in 1984 by West End Games. In 1985, this edition of Paranoia won the Origins Award for Best Roleplaying Rules of 1984. This edition, while encouraging dark humour in-game, took a fairly serious dystopian tone; the supplements and adventures released to accompany it emphasised the lighter side, however, establishing the freewheeling mix of slapstick, intra-team backstabbing and satire that is classically associated with a game of Paranoia. === Second edition === The second edition, is credited to Costikyan, Gelber, Goldberg, Ken Rolston, and Paul Murphy, was published in 1987 by West End Games. This edition can be seen as a response to the natural development of the line towards a rules-light, fast and entertaining play style. Here, the humorous possibilities of life in a paranoid dystopia are emphasised, and the rules are simplified. ==== Metaplot and the second edition ==== Many of the supplements released for the second edition fall into a story arc set up by new writers and line editors
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Gcore

Gcore is an edge AI, cloud, network, and security company headquartered in Luxembourg. Founded in 2014, the company provides low-latency services to industries including finance, healthcare, manufacturing, gaming, media and telecommunications internationally. As of March 2024, its global network includes over 180 Points of Presence (PoPs) across six continents. == History == Gcore was founded in 2014 in Luxembourg. The company built its own content delivery network, originally designed for the needs of the gaming industry. In 2016, Gcore's infrastructure expanded to multiple regions that were underserved by hyperscale cloud providers. In 2020, the company formed partnerships with Intel and Equinix. In 2022, Gcore launched the European AI Cloud, providing access to infrastructure for machine learning tasks. In March 2024, Gcore announced the acquisition of a web application and API protection (WAAP) solution from StackPath. In April 2024, Gcore received a commendation in the Industry Innovation category at the NVIDIA Partner Network Awards EMEA for developing the first speech-to-text technology for Luxembourgish, using the LuxemBERT AI model. In May 2024, Philipp Rösler, former vice-chancellor of Germany and federal minister of health joined the Gcore board. In July 2024, Gcore raised $60 million in a Series A funding round, marking the company's first external investment since its founding. In August 2024, Gcore was recognized as a Major Player in the IDC MarketScape report for European public cloud Infrastructure (IaaS) 2024 by IDC, the global market intelligence firm. In May 2025, Feiyu Xu became a member of the Gcore advisory board. == Network infrastructure == According to the company's website, Gcore has network locations in six continents: Europe, North America, Asia, South America, Africa, and Australia with over 14,000 peering partners and a network capacity exceeding 200 Tbps. According to a 2025 review by Geekflare, Gcore's CDN achieved an average global response time of around 30 milliseconds. Gcore offers AI cloud clusters, including a generative AI cluster with Nvidia GPUs in Luxembourg and additional sites in the Netherlands and Wales, as part of its European AI infrastructure. == Products and services == Gcore offers a range of services, including content delivery network (CDN), cloud computing,virtual machines, bare-metal servers, object storage AI infrastructure and inference, Kubernetes, video streaming, DDoS mitigation, web application and API protection (WAAP), Domain Name System (DNS). Gcore provides AI services and GPU cloud infrastructure to support model development, training, fine-tuning, and inference. In January 2025, the company introduced Everywhere Inference, a serverless inference solution that enables AI model deployment. == Controversies == Correctiv and Tageszeitung reported that Gcore supported the distribution of the TV network RT until April 2023, which has been under sanctions by the EU since March 2022. However, Gcore denies these allegations. == Collaborations == In 2024, Gcore and Qareeb Data Centres, a data center provider in the Middle East, launched a collaboration to integrate Gcore's AI, cloud and edge services across data centers in multiple Middle Eastern countries. In June 2025, Gcore joined the SmartSpires initiative, a €3.1 million smart city project co-funded by the Connecting Europe Facility. The three-year programme is coordinated by a public–private consortium including 5SKYE, the Luxembourg Institute of Science and Technology (LIST), Orange Luxembourg, and Gcore. The project aims to transform the Belval campus into a smart city by deploying 5G-enabled smart towers that integrate edge computing, artificial intelligence and IoT services. Within the consortium, Gcore acts as project coordinator and is responsible for the deployment of the edge infrastructure.
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Shyster (expert system)

SHYSTER is a legal expert system developed at the Australian National University in Canberra in 1993. It was written as the doctoral dissertation of James Popple under the supervision of Robin Stanton, Roger Clarke, Peter Drahos, and Malcolm Newey. A full technical report of the expert system, and a book further detailing its development and testing have also been published. SHYSTER emphasises its pragmatic approach, and posits that a legal expert system need not be based upon a complex model of legal reasoning in order to produce useful advice. Although SHYSTER attempts to model the way in which lawyers argue with cases, it does not attempt to model the way in which lawyers decide which cases to use in those arguments. SHYSTER is of a general design, permitting its operation in different legal domains. It was designed to provide advice in areas of case law that have been specified by a legal expert using a bespoke specification language. Its knowledge of the law is acquired, and represented, as information about cases. It produces its advice by examining, and arguing about, the similarities and differences between cases. It derives its name from Shyster: a slang word for someone who acts in a disreputable, unethical, or unscrupulous way, especially in the practice of law and politics. == Methods == SHYSTER is a specific example of a general category of legal expert systems, broadly defined as systems that make use of artificial intelligence (AI) techniques to solve legal problems. Legal AI systems can be divided into two categories: legal retrieval systems and legal analysis systems. SHYSTER belongs to the latter category of legal analysis systems. Legal analysis systems can be further subdivided into two categories: judgment machines and legal expert systems. SHYSTER again belongs to the latter category of legal expert systems. A legal expert system, as Popple uses the term, is a system capable of performing at a level expected of a lawyer: "AI systems which merely assist a lawyer in coming to legal conclusions or preparing legal arguments are not here considered to be legal expert systems; a legal expert system must exhibit some legal expertise itself." Designed to operate in more than one legal domain, and be of specific use to the common law of Australia, SHYSTER accounts for statute law, case law, and the doctrine of precedent in areas of private law. Whilst it accommodates statute law, it is primarily a case-based system, in contradistinction to rule-based systems like MYCIN. More specifically, it was designed in a manner enabling it to be linked with a rule-based system to form a hybrid system. Although case-based reasoning possesses an advantage over rule-based systems by the elimination of complex semantic networks, it suffers from intractable theoretical obstacles: without some further theory it cannot be predicted what features of a case will turn out to be relevant. Users of SHYSTER therefore require some legal expertise. Richard Susskind argues that "jurisprudence can and ought to supply the models of law and legal reasoning that are required for computerized [sic] implementation in the process of building all expert systems in law". Popple, however, believes jurisprudence is of limited value to developers of legal expert systems. He posits that a lawyer must have a model of the law (maybe unarticulated) which includes assumptions about the nature of law and legal reasoning, but that model need not rest on basic philosophical foundations. It may be a pragmatic model, developed through experience within the legal system. Many lawyers perform their work with little or no jurisprudential knowledge, and there is no evidence to suggest that they are worse, or better, at their jobs than lawyers well-versed in jurisprudence. The fact that many lawyers have mastered the process of legal reasoning, without having been immersed in jurisprudence, suggests that it may indeed be possible to develop legal expert systems of good quality without jurisprudential insight. As a pragmatic legal expert system SHYSTER is the embodiment of this belief. A further example of SHYSTER’s pragmatism is its simple knowledge representation structure. This structure was designed to facilitate specification of different areas of case law using a specification language. Areas of case law are specified in terms of the cases and attributes of importance in those areas. SHYSTER weights its attributes and checks for dependence between them. In order to choose cases upon which to construct its opinions, SHYSTER calculates distances between cases and uses these distances to determine which of the leading cases are nearest to the instant case. To this end SHYSTER can be seen to adopt and expand upon nearest neighbor search methods used in pattern recognition. These nearest cases are used to produce an argument (based on similarities and differences between the cases) about the likely outcome in the instant case. This argument relies on the doctrine of precedent; it assumes that the instant case will be decided the same way as was the nearest case. SHYSTER then uses information about these nearest cases to construct a report. The report that SHYSTER generates makes a prediction and justifies that prediction by reference only to cases and their similarities and differences: the calculations that SHYSTER performs in coming to its opinion do not appear in that opinion. Safeguards are employed to warn users if SHYSTER doubts the veracity of its advice. == Results == SHYSTER was tested in four different and disparate areas of case law. Four specifications were written, each representing an area of Australian law: an aspect of the law of trover; the meaning of "authorization [sic]" in copyright law of Australia; the categorisation of employment contracts; and the implication of natural justice in administrative decision-making. SHYSTER was evaluated under five headings: its usefulness, its generality, the quality of its advice, its limitations, and possible enhancements that could be made to it. Despite its simple knowledge representation structure, it has shown itself capable of producing good advice, and its simple structure has facilitated the specification of different areas of law. Appreciating the difficulties encountered by legal expert systems developers in adequately representing legal knowledge can assist in appreciating the shortcomings of digital rights management technologies. Some academics believe future digital rights management systems may become sophisticated enough to permit exceptions to copyright law. To this end SHYSTER's attempt to model "authorization [sic]" in the Copyright Act can be viewed as pioneering work in this field. The term "authorization [sic]" is undefined in the Copyright Act. Consequently, a number of cases have been before the courts seeking answers as to what conduct amounts to authorisation. The main contexts in which the issue has arisen are analogous to permitted exceptions to copyright currently prevented by most digital rights management technologies: "home taping of recorded materials, photocopying in educational institutions and performing works in public". When applied to one case concerning compact cassettes, SHYSTER successfully agreed that Amstrad did not authorise the infringement. 'shyster-myci'n Popple highlighted the most obvious avenue of future research using SHYSTER as the development of a rule-based system, and the linking together of that rule-based system with the existing case-based system to form a hybrid system. This intention was eventually realised by Thomas O’Callaghan, the creator of SHYSTER-MYCIN: a hybrid legal expert system first presented at ICAIL '03, 24–28 June 2003 in Edinburgh, Scotland. MYCIN is an existing medical expert system, which was adapted for use with SHYSTER. MYCIN’s controversial "certainty factor" is not used in SHYSTER-MYCIN. The reason for this is the difficulty in scientifically establishing how certain a fact is in a legal domain. The rule-based approach of the MYCIN part is used to reason with the provisions of an Act of Parliament only. This hybrid system enables the case-based system (SHYSTER) to determine open textured concepts when required by the rule-based system (MYCIN). The ultimate conclusion of this joint endeavour is that a hybrid approach is preferred in the creation of legal expert systems where "it is appropriate to use rule-based reasoning when dealing with statutes, and…case-based reasoning when dealing with cases".
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Your AI Slop Bores Me

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

Type-2 fuzzy sets and systems generalize standard type-1 fuzzy sets and systems so that more uncertainty can be handled. From the beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word fuzzy, since that word has the connotation of much uncertainty. So, what does one do when there is uncertainty about the value of the membership function? The answer to this question was provided in 1975 by the inventor of fuzzy sets, Lotfi A. Zadeh, when he proposed more sophisticated kinds of fuzzy sets, the first of which he called a "type-2 fuzzy set". A type-2 fuzzy set lets us incorporate uncertainty about the membership function into fuzzy set theory, and is a way to address the above criticism of type-1 fuzzy sets head-on. And, if there is no uncertainty, then a type-2 fuzzy set reduces to a type-1 fuzzy set, which is analogous to probability reducing to determinism when unpredictability vanishes. Type1 fuzzy systems are working with a fixed membership function, while in type-2 fuzzy systems the membership function is fluctuating. A fuzzy set determines how input values are converted into fuzzy variables. == Overview == In order to symbolically distinguish between a type-1 fuzzy set and a type-2 fuzzy set, a tilde symbol is put over the symbol for the fuzzy set; so, A denotes a type-1 fuzzy set, whereas Ã denotes the comparable type-2 fuzzy set. When the latter is done, the resulting type-2 fuzzy set is called a "general type-2 fuzzy set" (to distinguish it from the special interval type-2 fuzzy set). Zadeh didn't stop with type-2 fuzzy sets, because in that 1976 paper he also generalized all of this to type-n fuzzy sets. The present article focuses only on type-2 fuzzy sets because they are the next step in the logical progression from type-1 to type-n fuzzy sets, where n = 1, 2, ... . Although some researchers are beginning to explore higher than type-2 fuzzy sets, as of early 2009, this work is in its infancy. The membership function of a general type-2 fuzzy set, Ã, is three-dimensional (Fig. 1), where the third dimension is the value of the membership function at each point on its two-dimensional domain that is called its "footprint of uncertainty"(FOU). For an interval type-2 fuzzy set that third-dimension value is the same (e.g., 1) everywhere, which means that no new information is contained in the third dimension of an interval type-2 fuzzy set. So, for such a set, the third dimension is ignored, and only the FOU is used to describe it. It is for this reason that an interval type-2 fuzzy set is sometimes called a first-order uncertainty fuzzy set model, whereas a general type-2 fuzzy set (with its useful third-dimension) is sometimes referred to as a second-order uncertainty fuzzy set model. The FOU represents the blurring of a type-1 membership function, and is completely described by its two bounding functions (Fig. 2), a lower membership function (LMF) and an upper membership function (UMF), both of which are type-1 fuzzy sets! Consequently, it is possible to use type-1 fuzzy set mathematics to characterize and work with interval type-2 fuzzy sets. This means that engineers and scientists who already know type-1 fuzzy sets will not have to invest a lot of time learning about general type-2 fuzzy set mathematics in order to understand and use interval type-2 fuzzy sets. Work on type-2 fuzzy sets languished during the 1980s and early-to-mid 1990s, although a small number of articles were published about them. People were still trying to figure out what to do with type-1 fuzzy sets, so even though Zadeh proposed type-2 fuzzy sets in 1976, the time was not right for researchers to drop what they were doing with type-1 fuzzy sets to focus on type-2 fuzzy sets. This changed in the latter part of the 1990s as a result of Jerry Mendel and his student's works on type-2 fuzzy sets and systems. Since then, more researchers around the world are writing articles about type-2 fuzzy sets and systems. == Interval type-2 fuzzy sets == Interval type-2 fuzzy sets have received the most attention because the mathematics that is needed for such sets—primarily Interval arithmetic—is much simpler than the mathematics that is needed for general type-2 fuzzy sets. The literature about interval type-2 fuzzy sets is large, whereas the literature about general type-2 fuzzy sets is much smaller. Both kinds of fuzzy sets are being actively researched by an ever-growing number of researchers around the world and have resulted in successful employment in a variety of domains such as robot control. Formally, the following have already been worked out for interval type-2 fuzzy sets: Fuzzy set operations: union, intersection and complement Centroid (a very widely used operation by practitioners of such sets, and also an important uncertainty measure for them) Other uncertainty measures [fuzziness, cardinality, variance and skewness and uncertainty bounds Similarity Subsethood Embedded fuzzy sets Fuzzy set ranking Fuzzy rule ranking and selection Type-reduction methods Firing intervals for an interval type-2 fuzzy logic system Fuzzy weighted average Linguistic weighted average Synthesizing an FOU from data that are collected from a group of subject == Interval type-2 fuzzy logic systems == Type-2 fuzzy sets are finding very wide applicability in rule-based fuzzy logic systems (FLSs) because they let uncertainties be modeled by them whereas such uncertainties cannot be modeled by type-1 fuzzy sets. A block diagram of a type-2 FLS is depicted in Fig. 3. This kind of FLS is used in fuzzy logic control, fuzzy logic signal processing, rule-based classification, etc., and is sometimes referred to as a function approximation application of fuzzy sets, because the FLS is designed to minimize an error function. The following discussions, about the four components in Fig. 3 rule-based FLS, are given for an interval type-2 FLS, because to-date they are the most popular kind of type-2 FLS; however, most of the discussions are also applicable for a general type-2 FLS. Rules, that are either provided by subject experts or are extracted from numerical data, are expressed as a collection of IF-THEN statements, e.g., IF temperature is moderate and pressure is high, then rotate the valve a bit to the right. Fuzzy sets are associated with the terms that appear in the antecedents (IF-part) or consequents (THEN-part) of rules, and with the inputs to and the outputs of the FLS. Membership functions are used to describe these fuzzy sets, and in a type-1 FLS they are all type-1 fuzzy sets, whereas in an interval type-2 FLS at least one membership function is an interval type-2 fuzzy set. An interval type-2 FLS lets any one or all of the following kinds of uncertainties be quantified: Words that are used in antecedents and consequents of rules—because words can mean different things to different people. Uncertain consequents—because when rules are obtained from a group of experts, consequents will often be different for the same rule, i.e. the experts will not necessarily be in agreement. Membership function parameters—because when those parameters are optimized using uncertain (noisy) training data, the parameters become uncertain. Noisy measurements—because very often it is such measurements that activate the FLS. In Fig. 3, measured (crisp) inputs are first transformed into fuzzy sets in the Fuzzifier block because it is fuzzy sets and not numbers that activate the rules which are described in terms of fuzzy sets and not numbers. Three kinds of fuzzifiers are possible in an interval type-2 FLS. When measurements are: Perfect, they are modeled as a crisp set; Noisy, but the noise is stationary, they are modeled as a type-1 fuzzy set; and, Noisy, but the noise is non-stationary, they are modeled as an interval type-2 fuzzy set (this latter kind of fuzzification cannot be done in a type-1 FLS). In Fig. 3, after measurements are fuzzified, the resulting input fuzzy sets are mapped into fuzzy output sets by the Inference block. This is accomplished by first quantifying each rule using fuzzy set theory, and by then using the mathematics of fuzzy sets to establish the output of each rule, with the help of an inference mechanism. If there are M rules then the fuzzy input sets to the Inference block will activate only a subset of those rules, where the subset contains at least one rule and usually way fewer than M rules. The inference is done one rule at a time. So, at the output of the Inference block, there will be one or more fired-rule fuzzy output sets. In most engineering applications of an FLS, a number (and not a fuzzy set) is needed as its final output, e.g., the consequent of the rule given above is "Rotate the valve a bit to the right." No automatic valve will know what this means because "a bit to the right" is a linguistic expression, and a valv
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Esdat

ESdat is a data management, analysis and reporting software for environmental and groundwater data, developed by EarthScience Information Systems (EScIS). It is used to manage many types of environmental data including laboratory chemistry (analytical results, QA data, lab sample planning, and electronic Chain of Custody), field chemistry (water, gas, and soil), hydrogeological data (groundwater, borehole and well construction, lithological, geotechnical and stratigraphic, and LNAPL), meteorological data (rain, wind, and temperature), emission data (dust deposition, HiVol, air quality, and noise) and logger data. Data can be compared against environmental standards or site-specific trigger levels to generate exceedence tables, time series graphs, maps, statistics, and other outputs. ESdat integrates with Power BI and ArcGIS and data can also be exported in a range of other database formats, including USEPA Regions 2,4 & 5, and NYS DEC. ESdat is used by environmental consultants, government, mining and industry for validation, interrogation, and reporting of data derived from complex environmental programs, such as contaminated sites, groundwater investigations, and regulatory compliance for landfills or mining operations.
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ACM SIGEVO

The ACM SIGEVO is a Special Interest Group of the Association of Computing Machinery for members of that organization who are practitioners, academics, students or others with interests in evolutionary computation and related algorithms. == History == ACM SIGEVO was founded in 2005 when the International Society for Genetic and Evolutionary Computation (ISGEC) became an ACM Special Interest Group under its present title. The ISGEC had been formed in 1999 by the merger of the Genetic Programming conference organization with the International Conference on Genetic Algorithms (ICGA) leading to the first Genetic and Evolutionary Computation Conference (GECCO). == Membership == Members of this SIG pay a small fee in addition to the ACM membership fee. In return they have access to a quarterly online newsletter, but more importantly can obtain reduced registration rates at the two conferences organised by ACM SIGEVO: GECCO and the Foundations of Genetic Algorithms conference (FOGA). They can also access material on evolutionary computation and related topics in the ACM Digital Library. In addition they can subscribe to email mailing lists in order to keep informed about news over time. For students, ACM SIGEVO sponsors Travel Awards for attendance at the GECCO Conference and FOGA (the Foundations of Genetic Algorithms conference). ACM SIGEVO also sponsors a Graduate Student Workshop. ACM also sponsors Awards to be competed for by attendees at the conferences it organises. == Conferences == ACM SIGEVO organises two major conferences in the field of evolutionary computation. The Genetic and Evolutionary Conference (GECCO) is held annually, while the Foundations of Genetic Algorithms conference (FOGA) is held biennially. === GECCO === The first GECCO conference was held prior to the formation of ACM SIGEVO but since 2005 (see History above) it has been organised annually by ACM SIGEVO. The latest (2025) was held in Málaga, Spain. The next (2026) will be held in San José, Costa Rica. === FOGA === Foundations of Genetic Algorithms (FOGA) is a biennial peer-reviewed research conference focusing on the theoretical principles underlying genetic algorithms, other evolutionary algorithms and related heuristics. It is organized by ACM SIGEVO. Its relevance to the computer science research community has been reflected in an A-rating in the CORE computer science conference assessment system. The Foundations of Genetic Algorithms (FOGA) conference originated as a workshop in 1990 in order to create an opportunity for researchers on genetic algorithms and related areas of evolutionary computation to focus on the theoretical principles underlying their field. From the start its multi-day duration made it comparable to conferences in the field, and since 2015 its proceedings have used conference rather than workshop in their titles. In 2005 ACM SIGEVO the Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation was formed and every FOGA conference since then has been supported by SIGEVO. The table below shows FOGA conferences by year, location, websites (where available) and publisher of proceedings. A citation follows the reference to the publisher giving the full details of each FOGA proceedings. Papers accepted at recent conferences have been presented as digital or print posters in poster sessions at the conference, before being published in written form in the conference proceedings. FOGA is comparable in its multi-day duration to other conferences on evolutionary computation such as CEC, GECCO and PPSN. The main difference is that FOGA focuses on the theoretical basis of evolutionary computation and related subjects. While the above conferences devote some time to theory they also cover a wide range of other topics including competitions and applications. This focus on theoretical computer science was reflected in the CORE computer science conference assessment exercise, where FOGA was given an A-ranking in the 2023 assessment. GECCO and PPSN also obtained A-rankings, but many other conferences in the field of evolutionary computation obtained lower rankings. This suggests that FOGA is a relevant conference in its field, comparable with others including the much larger CEC or GECCO. Keynote speakers at past conferences have been: == Awards == ACM SIGEVO sponsors a number of awards. === SIGEVO Outstanding Contribution Award === The SIGEVO Outstanding Contribution Award commenced in 2023, and these awards are designed to recognise distinctive contributions to the field of evolutionary computation when evaluated over a period of at least 15 years. As a result many recipients to date are notable academics or industrial practitioners, and include Anne Auger, Kalyanmoy Deb, Stephanie Forrest, Emma Hart and Hans-Paul Schwefel. === SIGEVO Dissertation Award === The SIGEVO Dissertation Award recognises thesis research in the field of evolutionary computation completed at least by the year prior to a GECCO conference. Theses are submitted and reviewed by a panel that selects one winner and a maximum of two honourable mentions. Awards will be made to the winner and any others at the next GECCO conference. === SIGEVO Chair Award === The SIGEVO Chair Award, established in 2016 is a lecture sponsored by ACM SIGEVO, to take place on the last day of the GECCO conference. It recognizes through the lectures that the lecturers are influential researchers in the field of evolutionary computation. The more recent lectures are available online. The 2024 Award winner was Una-May O'Reilly. === SIGEVO Impact Award === The SIGEVO Impact Award looks back to the GECCO conference ten years earlier and recognizes up to three papers a year which are considered by the current ACM SIGEVO Executive Committee to have had significant impact over the period since their first publication at the GECCO conference. An example (originally published in GECCO 2010) received this award in 2020. === GECCO Best Paper Award === The ACM SIGEVO sponsors awards for the best papers presented at the GECCO conference. Because GECCO conferences have very many parallel tracks there are multiple awards recognising presentations in the different tracks. At GECCO 2025 Best Paper Awards were presented across 12 tracks. === FOGA Best Paper Award === The ACM SIGEVO sponsors awards for the best papers presented at the FOGA conference. Because FOGA operates on a single track, it is easier to compare papers. Since 2019 this Award has been made (suggesting only four awards up to the latest conference in 2025). ACM SIGEVO records the 2019 award. === Humie Award === The Humies Awards are rewards for the best form of human-competitive results using evolutionary computation or related algorithms and published in the wider literature (they do not need to be published at a conference or in a journal sponsored by ACM SIGEVO or even the ACM.) They were established through a gift from John Koza and have been in operation from 2004 to the present. The link with ACM SIGEVO is that the winners of the competition (submissions are evaluated in advance) are presented with Humie Awards at GECCO conferences. The Humie Awards website provides full details for the rules and how to submit entries to the competition. == Journals == ACM SIGEVO sponsors the main journal covering evolutionary computation published by the ACM: ACM Transactions on Evolutionary Learning and Optimization. ACM SIGEVO refers to the preceding ISGEC organisation (see History above) as sponsoring two other important journals in the field: The Evolutionary Computation journal. Genetic Programming and Evolvable Machines. While these journals continue to be important in the field, the wording on the website of ACM SIGEVO suggests that ACM SIGEVO is not involved in their publication. == References and notes ==
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Legal Knowledge Interchange Format

The Legal Knowledge Interchange Format (LKIF) was developed in the European ESTRELLA project and was designed with the goal of becoming a standard for representing and interchanging policy, legislation and cases, including their justificatory arguments, in the legal domain. LKIF builds on and uses the Web Ontology Language (OWL) for representing concepts and includes a reusable basic ontology of legal concepts. The core of LKIF consists of a combination of OWL-DL and SWRL. LKIF was designed with two main roles in mind: the translation of legal knowledge bases written in different representation formats and formalisms and to be a knowledge representation formalism which could be part of larger architectures for developing legal knowledge systems.
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GITEX AI Europe

GITEX AI Europe is an annual technology trade show and conference held in Berlin, Germany, as part of GITEX GLOBAL. The event focuses on the European technology market, specifically in the sectors of artificial intelligence (AI), cybersecurity, quantum computing, and digital infrastructure. The event is organized by Kaoun International GmbH, the international arm of the Dubai World Trade Centre (DWTC), in partnership with Messe Berlin. == History == The establishment of GITEX AI Europe was announced in 2023 as part of a strategic move to bring the GITEX brand to the European market. The inaugural edition took place from May 21 to 23, 2025, at the Messe Berlin exhibition grounds. The launch was supported by the Berlin Senate and the German Federal Ministry for Economic Affairs and Climate Action. The first edition of GITEX AI Europe in 2025 featured 21,650 attendees, 1,434 exhibiting companies, and 755 startups, with 513 speakers representing 125 countries. The next edition is scheduled for June 30 – July 1, 2026 in Berlin. == Program == The event consists of an exhibition floor for corporate displays, several conference stages for keynote speeches, and specialized sub-events. The conference program includes tracks such as "AI Stack Sovereignty," "Cyber Regulation & Trust Convergence," and "Institutional Growth Capital." GITEX AI Europe incorporates brands under its umbrella: AI Everything Europe: Focused on the development and application of generative AI and machine learning. North Star Europe: A dedicated program for startups and venture capital, featuring the "Supernova Challenge" pitch competition. GISEC Europe: A cybersecurity forum discussing regulation and infrastructure defense. GITEX Quantum Expo: Focused on the commercialization of quantum computing. Institutional partners for the event include the German Federal Ministry for Economic Affairs and Climate Action, the European Innovation Council (EIC), the International Telecommunication Union (ITU), Bitkom, and Digital Dubai.
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Zé Delivery

Zé Delivery is a startup developed by Brazilian drinks company AmBev which offers an app for delivering drinks. The app is available for Android and iOS. Created in 2016 by AmBev's ZX Ventures hub, the service has an international presence in Argentina, Paraguay, Bolivia, Panama and the Dominican Republic. It is also present in more than 300 Brazilian cities. Because it has an extensive category of alcoholic beverages, the service is only used by people over 18. It also offers soft drinks, juices, energy drinks and other non-alcoholic beverages.
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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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Monkey and banana problem

The monkey and banana problem is a famous toy problem in artificial intelligence, particularly in logic programming and planning. It has been framed as: A monkey is in a room containing a box and a bunch of bananas. The bananas are hanging from the ceiling out of reach of the monkey. How can the monkey obtain the bananas? The situation is used as a toy problem for computer science and can be solved with an expert system such as CLIPS. The example set of rules that CLIPS provides is somewhat fragile, in that, naive changes to the rulebase that might seem to a human of average intelligence to make common sense can cause the engine to fail to get the monkey to reach the banana. Other examples exist using Rules Based System (RBS), including a project implemented in Python.
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