AI Apps Like Chat Gpt

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Automated storage and retrieval system

An automated storage and retrieval system (ASRS or AS/RS) consists of a variety of computer-controlled systems for automatically placing and retrieving loads from defined storage locations. Automated storage and retrieval systems (AS/RS) are typically used in applications where: There is a very high volume of loads being moved into and out of storage Storage density is important because of space constraints No value is added in this process (no processing, only storage and transport) Accuracy is critical because of potential expensive damages to the load An AS/RS can be used with standard loads as well as nonstandard loads, meaning that each standard load can fit in a uniformly-sized volume; for example, the film canisters in the image of the Defense Visual Information Center are each stored as part of the contents of the uniformly sized metal boxes, which are shown in the image. Standard loads simplify the handling of a request of an item. In addition, audits of the accuracy of the inventory of contents can be restricted to the contents of an individual metal box, rather than undergoing a top-to-bottom search of the entire facility, for a single item. They can also be used in self storage places. == Overview == AS/RS systems are designed for automated storage and retrieval of parts and items in manufacturing, distribution, retail, wholesale and institutions. They first originated in the 1960s, initially focusing on heavy pallet loads but with the evolution of the technology the handled loads have become smaller. The systems operate under computerized control, maintaining an inventory of stored items. Retrieval of items is accomplished by specifying the item type and quantity to be retrieved. The computer determines where in the storage area the item can be retrieved from and schedules the retrieval. It directs the proper automated storage and retrieval machine (SRM) to the location where the item is stored and directs the machine to deposit the item at a location where it is to be picked up. A system of conveyors and or automated guided vehicles is sometimes part of the AS/RS system. These take loads into and out of the storage area and move them to the manufacturing floor or loading docks. To store items, the pallet or tray is placed at an input station for the system, the information for inventory is entered into a computer terminal and the AS/RS system moves the load to the storage area, determines a suitable location for the item, and stores the load. As items are stored into or retrieved from the racks, the computer updates its inventory accordingly. The benefits of an AS/RS system include reduced labor for transporting items into and out of inventory, reduced inventory levels, more accurate tracking of inventory, and space savings. Items are often stored more densely than in systems where items are stored and retrieved manually. Within the storage, items can be placed on trays or hang from bars, which are attached to chains/drives in order to move up and down. The equipment required for an AS/RS include a storage & retrieval machine (SRM) that is used for rapid storage and retrieval of material. SRMs are used to move loads vertically or horizontally, and can also move laterally to place objects in the correct storage location. The trend towards Just In Time production often requires sub-pallet level availability of production inputs, and AS/RS is a much faster way of organizing the storage of smaller items next to production lines. The Material Handling Institute of America (MHIA), the non-profit trade association for the material handling world, and its members have categorised AS/RS into two primary segments: Fixed Aisle and Carousels/Vertical Lift Modules (VLMs). Both sets of technologies provide automated storage and retrieval for parts and items, but use different technologies. Each technology has its unique set of benefits and disadvantages. Fixed Aisle systems are characteristically larger systems whereas carousels and Vertical Lift Modules are used individually or grouped, but in small to medium-sized applications. A fixed-aisle AS/R machine (stacker crane) is one of two main designs: single-masted or double masted. Most are supported on a track and ceiling guided at the top by guide rails or channels to ensure accurate vertical alignment, although some are suspended from the ceiling. The 'shuttles' that make up the system travel between fixed storage shelves to deposit or retrieve a requested load (ranging from a single book in a library system to a several ton pallet of goods in a warehouse system). The entire unit moves horizontally within an aisle, while the shuttles are able to elevate up to the necessary height to reach the load, and can extend and retract to store or retrieve loads that are several positions deep in the shelving. A semi-automated system can be achieved by utilizing only specialized shuttles within an existing rack system. Another AS/RS technology is known as shuttle technology. In this technology the horizontal movement is made by independent shuttles each operating on one level of the rack while a lift at a fixed position within the rack is responsible for the vertical movement. By using two separate machines for these two axes the shuttle technology is able to provide higher throughput rates than stacker cranes. Storage and Retrieval Machines pick up or drop off loads to the rest of the supporting transportation system at specific stations, where inbound and outbound loads are precisely positioned for proper handling. In addition, there are several types of Automated Storage & Retrieval Systems (AS/RS) devices called Unit-load AS/RS, Mini-load AS/RS, Mid-Load AS/RS, Vertical Lift Modules (VLMs), Horizontal Carousels and Vertical Carousels. These systems are used either as stand-alone units or in integrated workstations called pods or systems. These units are usually integrated with various types of pick to light systems and use either a microprocessor controller for basic usage or inventory management software. These systems are ideal for increasing space utilization up to 90%, productivity levels by 90%, accuracy to 99.9%+ levels and throughput up to 750 lines per hour/per operator or more depending on the configuration of the system. == Horizontal carousels == Robotic Inserter/Extractor devices can be used for horizontal carousels. The robotic device is positioned in the front or rear of up to three horizontal carousels tiered high. The robot grabs the tote required in the order and often replenishes at the same time to speed up throughput. The tote(s) are then delivered to a conveyor, which routes it to a work station for picking or replenishing. Up to eight transactions per minute per unit can be done. Totes or containers up to 36" x 36" x 36" can be used in a system. On a simplistic level, horizontal carousels are also often used as "rotating shelving". With simple "fetch" command, items are brought to the operator and otherwise wasted space is eliminated. AS/RS Applications: Most applications of AS/RS technology have been associated with warehousing and distribution operations. An AS/RS can also be used to store raw materials and work in process in manufacturing. Three application areas can be distinguished for AS/RS: (1) Unit load storage and handling, (2) Order picking, and (3) Work in process storage. Unit load storage and retrieval applications are represented by unit load AS/RS and deep-lane storage systems. These kinds of applications are commonly found in warehousing for finishing goods in a distribution center, rarely in manufacturing. Deep-lane systems are used in the food industry. As described above, order picking involves retrieving materials in less than full unit load quantities. Minilpass, man-on board, and items retrieval systems are used for this second application area. Work in process storage is a more recent application of automated storage technology. While it is desirable to minimize the amount of work in process, WIP is unavoidable and must be effectively managed. Automated storage systems, either automated storage/retrieval systems or carousel systems, represent an efficient way to store materials between processing steps, particularly in batch and job shop production. In high production, work in process is often carried between operations by conveyor system, which this serve both storage and transport functions. === Inventory Category-specific AS/RS === Each inventory category—raw materials, work-in-process, and finished goods—requires its own specialized Automated Storage and Retrieval System (AS/RS). Particularly for work-in-process (WIP) inventories, due to variations in manufacturing processes, the AS/RS systems are significantly different in design and function, tailored specifically to match unique handling, storage, and retrieval requirements === Installed applications === Installed applications of this technology can be wide-ranging. In some librarie
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Fuzzy markup language

Fuzzy Markup Language (FML) is a specific purpose markup language based on XML, used for describing the structure and behavior of a fuzzy system independently of the hardware architecture devoted to host and run it. == Overview == FML was designed and developed by Giovanni Acampora during his Ph.D. course in Computer Science, at University of Salerno, Italy, in 2004. The original idea inspired Giovanni Acampora to create FML was the necessity of creating a cooperative fuzzy-based framework aimed at automatically controlling a living environment characterized by a plethora of heterogeneous devices whose interactions were devoted to maximize the human comfort under energy saving constraints. This framework represented one of the first concrete examples of Ambient Intelligence. Beyond this pioneering application, the major advantage of using XML to describe a fuzzy system is hardware/software interoperability. Indeed, all that is needed to read an FML file is the appropriate schema for that file, and an FML parser. This markup approach makes it much easier to exchange fuzzy systems between software: for example, a machine learning application could extract fuzzy rules which could then be read directly into a fuzzy inference engine or uploaded into a fuzzy controller. Also, with technologies like XSLT, it is possible to compile the FML into the programming language of your choice, ready for embedding into whatever application you please. As stated by Mike Watts on his popular Computational Intelligence blog: "Although Acampora's motivation for developing FML seems to be to develop embedded fuzzy controllers for ambient intelligence applications, FML could be a real boon for developers of fuzzy rule extraction algorithms: from my own experience during my PhD, I know that having to design a file format and implement the appropriate parsers for rule extraction and fuzzy inference engines can be a real pain, taking as much time as implementing the rule extraction algorithm itself. I would much rather have used something like FML for my work." A complete overview of FML and related applications can be found in the book titled On the power of Fuzzy Markup Language edited by Giovanni Acampora, Chang-Shing Lee, Vincenzo Loia and Mei-Hui Wang, and published by Springer in the series Studies on Fuzziness and Soft Computing. == Syntax, grammar and hardware synthesis == FML allows fuzzy systems to be coded through a collection of correlated semantic tags capable of modeling the different components of a classical fuzzy controller such as knowledge base, rule base, fuzzy variables and fuzzy rules. Therefore, the FML tags used to build a fuzzy controller represent the set of lexemes used to create fuzzy expressions. In order to design a well-formed XML-based language, an FML context-free grammar is defined by means of a XML schema which defines name, type and attributes characterized each XML element. However, since an FML program represents only a static view of a fuzzy logic controller, XSLT is provided to change this static view to a computable version. Indeed, XSLTs modules are able to convert the FML-based fuzzy controller in a general purpose computer language using an XSL file containing the translation description. At this level, the control is executable for the hardware. In short, FML is essentially composed by three layers: XML in order to create a new markup language for fuzzy logic control; a XML Schema in order to define the legal building blocks; eXtensible Stylesheet Language Transformations (XSLT) in order to convert a fuzzy controller description into a specific programming language. === Syntax === FML syntax is composed of XML tags and attributes which describe the different components of a fuzzy logic controller listed below: fuzzy knowledge base; fuzzy rule base; inference engine fuzzification subsystem; defuzzification subsystem. In detail, the opening tag of each FML program is which represents the fuzzy controller under modeling. This tag has two attributes: name and ip. The first attribute permits to specify the name of fuzzy controller and ip is used to define the location of controller in a computer network. The fuzzy knowledge base is defined by means of the tag which maintains the set of fuzzy concepts used to model the fuzzy rule base. In order to define the fuzzy concept related controlled system, tag uses a set of nested tags: defines the fuzzy concept; defines a linguistic term describing the fuzzy concept; a set of tags defining a shape of fuzzy sets are related to fuzzy terms. The attributes of tag are: name, scale, domainLeft, domainRight, type and, for only an output, accumulation, defuzzifier and defaultValue. The name attribute defines the name of fuzzy concept, for instance, temperature; scale is used to define the scale used to measure the fuzzy concept, for instance, Celsius degree; domainLeft and domainRight are used to model the universe of discourse of fuzzy concept, that is, the set of real values related to fuzzy concept, for instance [0°,40°] in the case of Celsius degree; the position of fuzzy concept into rule (consequent part or antecedent part) is defined by type attribute (input/output); accumulation attribute defines the method of accumulation that is a method that permits the combination of results of a variable of each rule in a final result; defuzzifier attribute defines the method used to execute the conversion from a fuzzy set, obtained after aggregation process, into a numerical value to give it in output to system; defaultValue attribute defines a real value used only when no rule has fired for the variable at issue. As for tag , it uses two attributes: name used to identify the linguistic value associate with fuzzy concept and complement, a boolean attribute that defines, if it is true, it is necessary to consider the complement of membership function defined by given parameters. Fuzzy shape tags, used to complete the definition of fuzzy concept, are: Every shaping tag uses a set of attributes which defines the real outline of corresponding fuzzy set. The number of these attributes depends on the chosen fuzzy set shape. In order to make an example, consider the Tipper Inference System described in Mathworks Matlab Fuzzy Logic Toolbox Tutorial. This Mamdani system is used to regulate the tipping in, for example, a restaurant. It has got two variables in input (food and service) and one in output (tip). FML code for modeling part of knowledge base of this fuzzy system containing variables food and tip is shown below. A special tag that can furthermore be used to define a fuzzy shape is . This tag is used to customize fuzzy shape (custom shape). The custom shape modeling is performed via a set of tags that lists the extreme points of geometric area defining the custom fuzzy shape. Obviously, the attributes used in tag are x and y coordinates. As for rule base component, FML allows to define a set of rule bases, each one of them describes a different behavior of system. The root of each rule base is modeled by tag which defines a fuzzy rule set. The tag uses five attributes: name, type, activationMethod, andMethod and orMethod. Obviously, the name attribute uniquely identifies the rule base. The type attribute permits to specify the kind of fuzzy controller (Mamdani or TSK) respect to the rule base at issue. The activationMethod attribute defines the method used to implication process; the andMethod and orMethod attribute define, respectively, the and and or algorithm to use by default. In order to define the single rule the tag is used. The attributes used by the tag are: name, connector, operator and weight. The name attribute permits to identify the rule; connector is used to define the logical operator used to connect the different clauses in antecedent part (and/or); operator defines the algorithm to use for chosen connector; weight defines the importance of rule during inference engine step. The definition of antecedent and consequent rule part is obtained by using and tags. tag is used to model the fuzzy clauses in antecedent and consequent part. This tag use the attribute modifier to describe a modification to term used in the clause. The possible values for this attribute are: above, below, extremely, intensify, more or less, norm, not, plus, slightly, somewhat, very, none. To complete the definition of fuzzy clause the nested and tags have to be used. A sequence of tags realizes a fuzzy rule base. As example, consider a Mamdani rule composed by (food is rancid) OR (servi
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BL (logic)

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

Since 2024, dozens of local community-led protest campaigns have emerged in opposition to AI data centers. == Motivations == Organized opposition to AI data centers has been driven by concerns about energy use, energy costs, noise pollution, air pollution, and water waste. Opposition sentiment is widespread with a Gallup poll conducted in March 2026 showing that 70% of respondents oppose the construction of new AI data centers in their neighborhood. == Impact == In 2025, local opposition to AI data centers led to the delay or cancellation of projects totalling US$156 billion. == Specific protests and outcomes in the United States == According to Data Center Watch, there are has been a wave of dozens of protests against AI data centers since 2022. Below is a non-exhaustive list of some notable examples. === Goodyear and Buckeye, Arizona: Tract AI Data Center Proposal === In Goodyear and Buckeye, Arizona, a $14 billion project by developer Tract was withdrawn after local authorities blocked necessary rezoning in response to pressure from resident organizers. Opponest stiff resistance due to concerns over building heights, noise pollution, and the potential strain on local utilities. However, the company announced a revised project near the Buckeye airport in August 2024, with the backing of local officials and the mayor. === Peculiar, Missouri: Diode Ventures Harper Road Technology Park Proposal === In Peculiar, Missouri, residents from the group "Peaceful Peculiar" organized to stop a data center proposal from Diode Ventures called Harper Road Technology Park. Citing concerns around noise and light pollution, health, environmental impacts, jobs, property values, and energy use, organizers attended local planning and zoning meetings in large numbers and lobbied councilors to reject the proposal. Ultimately, the city council unanimously rejected the proposal in September 2024. === Chesterton, Indiana: Provident Realty Advisors Proposal === In Chesterton, Indiana, the Texas-based company Provident Reality Advisors applied for a $1.3 billion construction of a data center complex on the Brassie Golf Club property. Provident Realty Advisors wanted to purchase the 200 acres owned by PPM Chesterton LLC in 2024 order to build a data center complex, with eight buildings and an end user of a hyperscaler. The Town Council of Chesterton released a statement saying that they would never support this project, at least not at the scale and location it was planned for. They cited fears of added noise for locals, electrical or water management concerns, the intrusiveness of a data center built next to houses, and more. Provident released a statement shortly after rescinding their plan, because it was clear than the town of Chesterton would not support them. === Cascade Locks, Oregon: Roundhouse Digital Infrastructure Proposal === Startup data center developer Roundhouse Digital Infrastructure had planned to build out a 10-megawatt data center using a vacant industrial building and nearby 10-acre site in the Port of Cascade Locks, Oregon. After significant organized community opposition, the project was abandoned. === Forth Worth, Texas: WUSF 5 Rock Creek East Proposal === In September 2024, the City Council of Fort Worth, Texas approved a zoning change that would allow construction of a data center. In responses, neighbors mounted opposition citing concerns about traffic, light pollution, energy consumption, water use, and noise issues if the data center were to be built. In response to extensive public comments opposing a tax break for the data center, a city councilor withdrew his motion to approve the tax break. As of April, 2026, the future of the project is still uncertain. === Santa Clara, California: GI Partners Proposal === GI partners sought to build a new AI data center in Santa Clara, California, which is already home to many data centers, by acquiring a conditional permit use that would have allowed the developer to knock down a property and replace it with a data center. To obtain this permit they were required to go before members of the Planning Commission. Ultimately, the project was delayed with the Planning Commission requiring GI partners to do more public outreach. === Virginia === ==== Richmond: DC Blox Proposal ==== After residents organized to lobby the municipal government to block the proposal to avoid noise pollution and higher energy use, commissioners denied the company's permit. ==== Catlett Station: Headwaters Site Proposal ==== In Catlett, Virginia, developer Headwaters proposed construction of a data center complex just north of the town in 2020. In response, a residents' organization called "Protect Catlett" was formed to oppose the project. Arguments against the data center involved its impacts on water and power availability, its noise as a residential disturbance, and its destruction of historic and community heritage buildings. Arguments in favor cited job creation and $20 million in local tax revenue if the project were to go through. Protect Catlett utilized town halls and public comments to mobilize opposition to the project. They also dedicated time to educating other residents about the project's negative impacts and canvassing door-to-door in order to garner even more opposition to the project. Ultimately, after fervent opposition from most town residents, the project was canceled by the town and the developer. ==== Culpeper County: Culpeper Acquisitions Proposal ==== Culpeper Acquisitions, LLC, proposed a massive $12 billion data center project in Culpeper County, Virginia, designed to feature 4.6 million square feet of space across nine multi-story buildings. Coalition to Save Culpeper (C2SC) is an activist organization formed to resist the development of the project. C2SC has been active on many fronts including, messaging on social media, reaching out to local officials, and organizing meetings to bring community members with aligned interests together. Ultimately, the project was delayed due to unanimous denial by the Culpeper County Planning Commission on June 12, 2024, which was driven by intense opposition from C2SC. C2SC was successful in their mission largely because they were able to get so many people from the community behind it, and put enough pressure on local officials to take action. ==== Midlothian: Province Group Proposal ==== In late October 2025, the Powhatan County Board of Supervisors in Virginia voted unanimously to approve the $3 billion data center, despite the county's Planning Commission having unanimously recommended denial several days earlier. The reasoning behind their support for the center is that it will generate substantial tax revenue, reducing the county's reliance on residential property taxes. This appeal of lowering residential property taxes is the major selling point for the center's development. The developer, California-based Province Group, incentivized the Board by being agreeable to its conditions for building the center. The center is still on track for development, but faces local resistance, though little information is available on specific groups opposing it. ==== Warrenton: Amazon Proposal ==== Citizens for Farquier County (CFFC) advocates to "preserve the natural, historic and agricultural resources" of their county. Historically, this has meant opposing the building of a dam or lights in front of fast food stores. This group has recently mobilized in opposition of a plan to build data centers for Amazon. They first filed a suit to stop the construction in 2023 and it has been in litigation ever since. The case hinges on opposition to a 2021 zoning amendment which allowed data centers to be built in town. CFFC's lawyer, Dale Mullen, argues that this amendment violates state law, which requires such amendments to state their "public purpose". They argue that the permit for the Amazon data center was "void from the beginning". The CFFC also organized to vote out town council members who approved the first data center and were up for reelection, replacing them with candidates who opposed the data center. In May 2025, after attending town council meetings to speak out against the data center, the planning commission voted 4–1 to remove the zoning amendment allowing data center construction in town, citing public opposition. Currently, CFFC is advocating along with Piedmont Environmental Group, for phasing out data center tax breaks at the state level. ==== France: Marseille opposition ==== In France, local opposition materialised in response to proposed data centre developments, especially in and around the city of Marseille. Opposition came from activists, such as "Clouds Were Under Our Feet" group, residents ,and local politicians. Issues raised related to energy use, environmental impact, and limited local benefits (such as the creation of a few jobs only). == Legislation in the United States == Legal limits and moratoriums on the construction of new d
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Perplexity AI

Perplexity AI, Inc., or simply Perplexity, is an American privately held software company offering a web search engine that processes user queries and synthesizes responses. Perplexity products use large language models and incorporate real-time web search capabilities, providing responses based on current Internet content, citing sources used. Its real-time search engine is called Sonar and is based on Meta's Llama model. A free public version is available, while a paid Pro subscription offers access to more advanced language models and additional features. Perplexity AI, Inc., was founded in August 2022 by Aravind Srinivas, Denis Yarats, Johnny Ho, and Andy Konwinski. As of September 2025, the company was valued at US$20 billion. Perplexity AI has attracted legal scrutiny over allegations of copyright infringement, unauthorized content use, and trademark issues from several major media organizations, including the BBC, Dow Jones, and The New York Times. According to separate analyses by Wired and later Cloudflare, Perplexity uses undisclosed web crawlers with spoofed user-agent strings to scrape the content of websites which prohibit, or explicitly block, web scraping. == History == In August 2022, Perplexity AI, Inc., was founded by Aravind Srinivas, Denis Yarats, Johnny Ho, and Andy Konwinski, engineers with backgrounds in back-end systems, artificial intelligence (AI) and machine learning. It launched its main search engine on December 7, 2022, and has since released a Google Chrome extension and apps for iOS and Android. In February 2023, Perplexity reported two million unique visitors. By April 2024, Perplexity had raised $165 million in funding, valuing the company at over $1 billion. As of June 2025, Perplexity closed a $500 million round of funding that elevated its valuation to $14 billion. Investors in Perplexity AI have included Jeff Bezos, Tobias Lütke, Nat Friedman, Nvidia, and Databricks. Perplexity has also received funding from 1789 Capital, a venture capital firm notable for its association with Donald Trump Jr. During Bloomberg’s Tech Summit 2025, Srinivas shared that the company processed 780 million queries in May 2025, experiencing more than 20% month-over-month growth, processing around 30 million queries daily. In July 2024, Perplexity announced the launch of a new publishers' program to share advertising revenue with partners. On January 18, 2025, the day before the impending U.S. ban on the social media app TikTok, Perplexity submitted a proposal for a merger with TikTok US. On August 12, 2025, Perplexity made a bid to buy Chrome from Google for $34.5 billion. Perplexity stated that the sale could remedy anti-trust litigation against Google, in which a judge was considering compelling the sale of Chrome. In December 2025, Cristiano Ronaldo took an undisclosed stake in Perplexity AI and entered a global brand partnership with the company. === Business Strategy and Finance (2026) === As of early 2026, Perplexity AI reached a valuation of $21.21 billion following its Series E-6 funding round. The company's Annual Recurring Revenue (ARR) grew from $80 million in late 2024 to an estimated $200 million by February 2026. In January 2026, the company entered into a three-year, $750 million commitment with Microsoft Azure to secure the GPU capacity required for its advanced "Deep Research" and "Model Council" features. In February 2026, Perplexity transitioned to a subscription-first model by discontinuing its AI-integrated advertising strategy. Leadership stated the move was intended to preserve user trust in the "answer engine," prioritizing objective results over ad revenue. The company also introduced the "Model Council" feature on February 5, 2026, which allows users to compare outputs from multiple large language models, such as GPT-5.2 and Claude 4.6, simultaneously. To expand its user base, Perplexity began offering a free year of Pro access to students, U.S. Military Veterans, and government employees. == Products and services == === Search engine web portal === Perplexity’s primary offering is an online information retrieval system (search engine) that uses large language models to generate responses to user queries by searching and summarizing web-based content. Perplexity offers a feature known as Perplexity Pages that generates structured summaries and report-like content from user queries by aggregating cited sources. Perplexity is available without charge or registration to Web users, a freemium model. === Perplexity Pro === Perplexity Pro is a subscription tier, a more capable paid "enterprise" service, including stronger security and data protection and additional tools, including the ability to search uploaded documents alongside web content and access to a programmatic application programming interface (API). It allows the user to select between backend models such as GPT-5.4, Claude 4.6 and Gemini 3.1 Pro. The company has also developed its own models, Sonar (based on Llama 3.3) and R1 1776 (based on DeepSeek R1). === Internal Knowledge Search === Internal Knowledge Search enables Pro and Enterprise Pro users to simultaneously search across web content and internal documents. Users can upload and search through Excel, Word, PDF, and other common file formats. Enterprise Pro users can upload and index up to 500 files. === Search API === Perplexity's Search API provides AI developers with programmatic access to the company's search infrastructure. The September 2025 release includes a software development kit, an open-source evaluation framework called search_evals, and documentation detailing the API's design and optimization. === Shopping hub === Perplexity's Shopping Hub is an online shopping platform that provides AI-generated product recommendations, and enables users to purchase products directly through Perplexity's interface. It was launched in November 2024 with backing by Amazon and Nvidia. === Finance === In October 2024, Perplexity AI introduced new finance-related features, including looking up stock prices and company earnings data. The tool provides real-time stock quotes and price tracking, industry peer comparisons and basic financial analysis tools. The platform sources its financial data from Financial Modeling Prep. === Assistant === In January 2025, Perplexity launched the Perplexity Assistant, an AI-powered tool designed to enhance the functionality of its search engine. It can perform tasks across multiple apps, such as hailing a ride or searching for a song, and can maintain context across actions. The assistant is also multi-modal, meaning it can use a phone's camera to provide answers about the user's surroundings or on-screen content. Perplexity has acknowledged that the assistant is still in development and may not always function as expected. For instance, certain features, such as summarizing unread emails or upcoming calendar events, require users to enable a workaround based on notifications. === Comet === In July 2025, Perplexity launched Comet, an AI browser based on Chromium. Initially, access to the browser was limited to users subscribed to the most expensive subscription tier. The browser was later released for free download in October 2025. A key feature is integration of the Perplexity search engine, which can perform a variety of tasks such as generating article summaries, describing an image, conducting research about a topic and composing emails. === Truth Social chatbot === Perplexity has been contracted to produce a chatbot for Donald Trump's social media platform Truth Social. == Leadership == Aravind Srinivas is the CEO and co-founder of Perplexity AI. He previously held research positions at OpenAI, Google DeepMind, and other AI research institutions focusing on machine learning and artificial intelligence. In a March 2026 All-In episode, Srinivas said the incoming AI-related layoffs were "glorious future" to "look forward", as it freed people from jobs they didn't like and gave them opportunities to pursue entrepreneurship. == Controversies == === Copyright and trademark infringement allegations === In June 2024, Forbes publicly criticized Perplexity for using their content. According to Forbes, Perplexity published a story largely copied from a proprietary Forbes article without mentioning or prominently citing Forbes. In response, Srinivas said that the feature had some "rough edges" and accepted feedback but maintained that Perplexity only "aggregates" rather than plagiarizes information. In October 2024, The New York Times sent a cease-and-desist notice to Perplexity to stop accessing and using NYT content, claiming that Perplexity is violating its copyright by scraping data from its website. In June 2024, Dow Jones and New York Post filed a lawsuit against Perplexity, alleging copyright infringement. The lawsuit also alleged that Perplexity harmed their brand by attributing hallucinated quotes, for example on F-16 jets for Ukraine, to artic
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Knights of Sidonia

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

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

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

Amazon Kinesis is a family of services provided by Amazon Web Services (AWS) for processing and analyzing real-time streaming data at a large scale. Launched in November 2013, it offers developers the ability to build applications that can consume and process data from multiple sources simultaneously. Kinesis supports multiple use cases, including real-time analytics, log and event data collection, and real-time processing of data generated by IoT devices. == History == Amazon Kinesis was launched by Amazon Web Services (AWS) in November 2013 as a managed service for processing and analyzing real-time streaming data at a large scale. The service was introduced to address the growing need for businesses to process and analyze data as it was generated, rather than in batches, allowing for real-time insights and decision-making. Since its launch, the Amazon Kinesis family of services has expanded to include four main components: Kinesis Data Streams, Kinesis Data Firehose, Kinesis Data Analytics, and Kinesis Video Streams. Each of these components serves a specific purpose in the processing and analysis of real-time streaming data. In August 2015, AWS announced the availability of Kinesis Data Firehose, a fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Amazon Redshift, and Amazon Elasticsearch. A year later in August 2016, AWS launched Kinesis Data Analytics, enabling customers to analyze streaming data in real time using standard SQL queries. AWS introduced Kinesis Video Streams, a fully managed service for securely capturing, processing, and storing video streams for analytics and machine learning applications, was introduced by AWS in November 2017. == Components == Amazon Kinesis is composed of four main services: Kinesis Data Streams, Kinesis Data Firehose, Kinesis Data Analytics, and Kinesis Video Streams. === Kinesis Data Streams === Kinesis Data Streams is a scalable and durable real-time data streaming service that captures and processes gigabytes of data per second from multiple sources. It enables the storage and processing of data in real time, making it useful for applications that require immediate insights, such as monitoring and alerting. === Kinesis Data Firehose === Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch, and AWS-partner data stores. With Data Firehose, users can configure and scale data delivery without manual intervention. === Kinesis Data Analytics === Kinesis Data Analytics enables the analysis of streaming data in real time using standard SQL or Apache Flink. === Kinesis Video Streams === Kinesis Video Streams is a fully managed service for securely capturing, processing, and storing video streams for analytics and machine learning. It supports multiple video codecs and streaming protocols, making it suitable for various use cases, such as security and surveillance, video-enabled IoT devices, and live event broadcasting. == Integration == Amazon Kinesis can be easily integrated with other AWS services, such as AWS Lambda, Amazon S3, Amazon Redshift, and Amazon OpenSearch. This integration enables developers to build end-to-end streaming data processing applications, taking advantage of the extensive AWS ecosystem. == Use cases == Some common use cases for Amazon Kinesis include: Real-time analytics: Analyzing streaming data in real time to provide immediate insights and make data-driven decisions. Log and event data collection: Collecting, processing, and analyzing log and event data generated by applications, infrastructure, and devices. IoT data processing: Processing and analyzing large volumes of data generated by IoT devices in real time. Machine learning: Ingesting and processing video streams for machine learning applications, such as object recognition, facial recognition, and sentiment analysis. == Pricing == Amazon Kinesis follows a pay-as-you-go pricing model, with costs depending on the chosen service, data volume, and processing power required. AWS provides a free tier for Kinesis Data Streams and Kinesis Data Firehose, allowing users to get started with the services at no cost.
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SmartAction

SmartAction Company LLC is a U.S.-based software company that develops artificial intelligence–driven virtual agents for customer service applications, including voice-based interactive voice response (IVR) systems, chat, and SMS. The company was founded in 2009 by inventor and entrepreneur Peter Voss and is headquartered in Fort Worth, Texas. == History == In 2001, Peter Voss founded Adaptive AI, Inc., a research and development company focused on artificial intelligence concepts. In 2009, Voss founded SmartAction Company, LLC to commercialize customer-service automation software derived from this work. The company’s initial products focused on automating inbound and outbound calls for contact center environments. In November 2022, Kyle Johnson was appointed chief executive officer, succeeding Gary Davis, who had served as CEO since 2020. In 2024, SmartAction was acquired by Capacity, an AI-powered customer support automation company based in St. Louis, Missouri. == Technology == SmartAction develops cloud-based voice automation software that integrates speech recognition and natural language processing to support automated customer interactions in contact center environments. The platform supports automated handling of common customer service tasks and is designed to integrate with enterprise systems.
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Hindsight optimization

Hindsight optimisation (HOP) is a computer science technique used in artificial intelligence for analysis of actions which have stochastic results. HOP is used in combination with a deterministic planner. By creating sample results for each of the possible actions from the given state (i.e. determinising the actions), and using the deterministic planner to analyse those sample results, HOP allows an estimate of the actual action.
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Stephanie Dinkins

Stephanie Dinkins (born 1964) is a transdisciplinary American artist based in Brooklyn, New York. She creates art about artificial intelligence (AI) as it intersects race, gender, and history. Her aim is to "create a unique culturally attuned AI entity in collaboration with coders, engineers and in close consultation with local communities of color that reflects and is empowered to work toward the goals of its community." Dinkins projects include Conversations with Bina48, a series of conversations between Dinkins and the first social, artificially intelligent humanoid robot BINA48 who looks like a black woman and Not the Only One, a multigenerational artificially intelligent memoir trained off of three generations of Dinkins's family. == Early life and education == Dinkins was born in Perth Amboy, New Jersey to Black American parents who raised her in Staten Island, New York. She credits her grandmother with teaching her how to think about art as a social practice, saying "my grandmother . . . was a gardener and the garden was her art . . . that was a community practice." Dinkins attended the International Center of Photography School in New York City in 1995, where she completed the general studies in photography certificate program. Dinkins received a MFA in photography from the Maryland Institute College of Art in 1997 She completed the Independent Study Program at the Whitney Museum of American Art in 1998. == Career == Dinkins is the Yayoi Kusama Professor of Art at Stony Brook University in New York. == Activism == Dinkins advocates for co-creation within a social practice art framework, so that vulnerable communities understand how to use technology to their advantage, instead of being subjected to their use. This is exemplified in her works such as Project al-Khwarzmi, a series of workshops entitled PAK POP-UP at the nonprofit community center Recess in Brooklyn, NY. The workshops involved collaborating with youth in the criminal justice system and uplifting the voices of vulnerable communities in determining how technologies are created and utilized. Dinkins warns of the dangers to members of minority groups that are absent from the creation of the computer algorithms that now affect their lives. == Art == Dinkins's practice employs technologies including, but not limited to, new media such as artificial intelligence and machine learning. Dinkins uses oral history techniques of interviewing to craft community-authored narratives and databases which inform the subjects of her work and serve as acts of social intervention or protest. === Conversations with Bina48 (2014–present) === Dinkins began working on Conversations with Bina48 in 2014. For the series, Dinkins recorded her conversations with BINA48, a social robot that resembles a middle-aged black woman. Dinkins mirrors Bina48 while they discuss identity and technological singularity. In 2010, Hanson Robotics, an engineering and robotics company known for its development of humanoid robots, developed and released BINA48. Bina48 is a robot modeled after the memories, beliefs, attitudes, commentary and mannerisms of Bina Aspen Rothblatt, the spousal partner of Martine Rothblatt. Both Bina and Martine Rothblatt own Bina48 under their organization, the Terasem Movement Foundation. Five years after Bina48 was released, Dinkins came across a YouTube video of Bina48. She asked, "how did a black woman become the most advanced of the technologies at the time?" Her questioning led her to travel to Lincoln, Vermont (the site of the Terasem Movement Foundation) where she conducted a series of interviews with Bina48 and engaged the robot in conversations pertaining to race, intimacy and the nature of being. The conversations suggest opportunities for complementing human existence with artificially intelligent agents that have an identity and history, but also show artificial intelligence's current limitations. Although it is based on a black woman, Dinkins found that Bina48 was shaped by the biases of its white, male creators. === Project al Kwarizmi (PAK) (2017–present) === Project al Kwarizmi (PAK) was a series of pop up workshops in Brooklyn, NY at Eyebeam and Recess; Manhattan, New York at Google; and Durham, North Carolina at Duke University. The workshops were centered for "communities of color that use art as a vehicle to help citizens understand how algorithms, the artificially intelligent systems they underpin, and big data impact their lives and empowers them to do something about it. Project al-Khwarizmi uses art and aesthetics as the common language to help citizens understand what algorithms and artificial intelligent systems are, and where these systems already impact our daily lives." === Not the Only One (N'TOO) (2018–present) === Not the only one (N’TOO) is a voice-interactive chatbot that was trained with data from members of her family to tell a multi-generational story. Dinkins described Not The Only One (NTOO or N'TOO) as an "experimental" multigenerational memoir of one Black American family told from the "mind" of an artificial intelligence of evolving intellect. N'TOO uses a recursive neural network, a deep learning algorithm. It is a voice-interactive AI robot designed, trained, and aligned with the needs and ideals of black and brown people who are drastically underrepresented in the tech sector. NTOO can also be described as a "physically embodied artificially intelligent agent that senses and acts on its world." == Exhibitions == Dinkins's work is exhibited internationally at various public, private, community, and institutional venues, including the Whitney Museum of American Art, the de Young Museum, the Philadelphia Museum of Art, the Studio Museum in Harlem;, Museum of Contemporary Photography, the Long Island Museum of American Art, History, and Carriages, the International Center of Photography in New York, Herning Kunstmuseum in Herning, Denmark, The Barbican in London, UK, Islip Art Museum, Wave Hill, Taller Boricua, the Queens Museum, and the corner of Putnam and Malcolm X Blvd in Bedford Stuyvesant, Brooklyn, New York. She has presented her work in symposia at the Museum of Modern Art, amongst other venues. == Future Histories Studio == Dinkins is the founder and director of Future Histories Studio, a research laboratory for arts-centered inquiry and production based at Stony Brook University. The studio was established with support from the Mellon Foundation as part of the Digital Inquiry, Speculation, Collaboration, and Optimism (DISCO) network. Future Histories Studio operates as an interdisciplinary hub exploring the intersections of art, technology, race, and storytelling through collaborative and practice-based research. Its activities include exhibitions, workshops, and public programs that examine the social and cultural implications of emerging technologies, particularly artificial intelligence and data systems. == Awards and recognition == Dinkins is the recipient of many awards, including: the 2023 LG Guggenheim Award, an international art prize established as part of a long-term global partnership between LG Group and the Solomon R. Guggenheim Museum to recognize groundbreaking artists in technology-based art; a Berggruen Institute artist fellowship; a Sundance New Frontiers Story Lab fellowship; a Soros Equality Fellowship; a Lucas Artists fellowship; a Creative Capital grant; a Bell Labs artist residency; a Blade of Grass fellowship; and a Data & Society fellowship. == Media coverage == Dinkins appeared in episode six of the HBO television series Random Acts of Flyness directed by Terence Nance, where she described her conversations with BINA48. == Other activities == Dinkins was part of the juries that selected Shu Lea Cheang for the LG Guggenheim Award in 2024.
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Research software engineering

Research software engineering is the application of software engineering practices, methods and techniques for research software, i.e. software that was made for and is mainly used within research projects. As usual for software engineering, this also includes knowledge of other (and in this case varying) research fields as well as open science that need to be incorporated into a software development process. The term was proposed in a research paper in 2010 in response to an empirical survey on tools used for software development in research projects. It started to be used in United Kingdom in 2012, when it was needed to define the type of software development needed in research. This focuses on reproducibility, reusability, and accuracy of data analysis and applications created for research. == Support == Various type of associations and organisations have been created around this role to support the creation of posts in universities and research institutes. In 2014 a Research Software Engineer Association was created in UK, which attracted 160 members in the first three months and which lead to the creation of the Society of Research Software Engineering in 2019. Other countries like the Netherlands, Germany, and the USA followed creating similar communities and there are similar efforts being pursued in Asia, Australia, Canada, New Zealand, the Nordic countries, and Belgium. In January 2021 the International Council of RSE Associations was introduced. UK counts over 40 universities and institutes with groups that provide access to software expertise to different areas of research. Additionally, the Engineering and Physical Sciences Research Council created a Research Software Engineer fellowship to promote this role and help the creation of RSE groups across UK, with calls in 2015, 2017, and 2020. The world first RSE conference took place in UK in September 2016 and it has been repeated annually (except for a gap in 2020) since. In 2019 the first national RSE conferences in Germany and the Netherlands were held, next editions were planned for 2020 and then cancelled. US-RSE held its first national conference in 2023. The Research Software Alliance was formed in 2019 to advance the global research software ecosystem by collaborating with decision makers and key influencers. The SORSE (A Series of Online Research Software Events) community was established in late‑2020 in response to the COVID-19 pandemic and ran its first online event in September 2020.
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Mata v. Avianca, Inc.

Mata v. Avianca, Inc. was a U.S. District Court for the Southern District of New York case in which the Court dismissed a personal injury case against the airline Avianca and issued a $5,000 fine to the plaintiffs' lawyers who had submitted fake precedents generated by ChatGPT in their legal briefs. == Background == In February 2022, Roberto Mata filed a personal injury lawsuit in the U.S. District Court for the Southern District of New York against Avianca, alleging that he was injured when a metal serving cart struck his knee during an international flight. The plaintiff's lawyers used ChatGPT to generate a legal motion, which contained numerous fake legal cases involving fictitious airlines with fabricated quotations and internal citations. Avianca's lawyers notified the Court that they had been "unable to locate" a few legal cases cited in the legal motion. The Court could not locate the cases either and ordered the plaintiff's lawyers to provide copies of the cited legal cases. Mata's lawyers provided copies of documents purportedly containing all but one of the legal cases, after ChatGPT assured that the cases "indeed exist" and "can be found in reputable legal databases such as LexisNexis and Westlaw." == Opinion == In May 2023, Judge P. Kevin Castel dismissed the personal injury case against Avianca and ordered the plaintiff's attorneys to pay a $5,000 fine. Judge Castel noted numerous inconsistencies in the opinion summaries, describing one of the legal analyses as "gibberish." Judge Castel held that Mata's lawyers had acted with "subjective bad faith" sufficient for sanctions under Federal Rule of Civil Procedure Rule 11. == Impact == In July 2024, the American Bar Association issued its first formal ethics opinion on the responsibilities of lawyers using generative AI (GAI). The 15-page opinion outlines how the Rules of Professional Conduct apply to the use of GAI in the practice of law. Experts caution that lawyers cannot reasonably rely on the accuracy, completeness, or validity of content generated by GAI tools. Due to the continued usage of GAI in the practice of law, Mata has been described as a landmark case by legal professionals, as it is frequently cited by courts in cases where usage of GAI during the course of proceedings leads to the creation and citation of nonexistent caselaw.
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Stanhope Demonstrator

The Stanhope Demonstrator was the first machine to solve problems in logic. It was designed by Charles Stanhope, 3rd Earl Stanhope to demonstrate consequences in logic symbolically. The first model was constructed in 1775. It consisted of two slides coloured red and gray mounted in a square brass frame. This could be used to demonstrate the solution to a syllogistic type of problem in which objects might have two different properties and the question was how many would have both properties. Scales marked zero to ten were used to set the numbers or proportions of objects with the two properties. This form of inference anticipated the numerically definite syllogism which Augustus De Morgan laid out in his book, Formal Logic, in 1847. == Construction == The device was a brass plate about four inches square which was mounted on a piece of mahogany which was three-quarters of an inch thick. There was an opening with a depression in the wood about one and a half inches square and half an inch deep. This opening was called the holon, meaning "whole", and represented the full set of objects under consideration. A slide of red translucent glass could be inserted from the right across the holon. A slide of gray wood could be slid under the red slide. When the device was used for the "Rule for the Logic of Certainty", the gray slider was inserted from the left. When it was used for the "Rule for the Logic of Probability", the gray slider was inserted from above. The red and the gray sliders represented the two affirmative propositions which were being combined. Stanhope called these ho and los. At least four of the devices with this square style were built. In 1879, Robert Harley wrote that he had one which he had been given by Stanhope's great-grandson, Arthur, who had kept one. The other two were owned by Henry Prevost Babbage – the son of Charles Babbage, who continued his work on the Analytical Engine. One of the devices was donated to the Science Museum, London by the last Earl in 1953. Other styles, such as circular models, were constructed, but these were less convenient.
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