AI For Business Guide

AI For Business Guide — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

View synthesis

In computer graphics, view synthesis, or novel view synthesis, is a task which consists of generating images of a specific subject or scene from a specific point of view, when the only available information is pictures taken from different points of view. This task was only recently (late 2010s – early 2020s) tackled with significant success, mostly as a result of advances in machine learning. Notable successful methods are Neural radiance fields and 3D Gaussian Splatting. Applications of view synthesis are numerous, one of them being Free view point television. The technique has also been applied to real-estate marketing, where novel views of a listing's interior are generated from a limited set of photographs for use in virtual home staging.
Read more →
PauseAI

PauseAI is a global political movement founded in the Netherlands with the stated aim of achieving global coordination to stop the development of more powerful general artificial intelligence systems, at least until it is known how to build them safely, and keep them under democratic control. The movement was established in Utrecht in May 2023 by software entrepreneur Joep Meindertsma. == Proposal == PauseAI's stated goal is to "implement a temporary pause on the training of the most powerful general AI systems". Their website lists some proposed steps to achieve this goal: Set up an international AI safety agency, similar to the IAEA. Only allow training of general AI systems if their safety can be guaranteed. Only allow deployment of models after no dangerous capabilities are present. == Background == During the late 2010s and early 2020s, a rapid improvement in the capabilities of artificial intelligence models known as the AI boom was underway, which included the release of large language model GPT-3, its more powerful successor GPT-4, and image generation models Midjourney and DALL-E. This led to an increased concern about the risks of advanced AI, causing the Future of Life Institute to release an open letter calling for "all AI labs to immediately pause for at least six months the training of AI systems more powerful than GPT-4". The letter was signed by thousands of AI researchers and industry CEOs such as Yoshua Bengio, Stuart Russell, and Elon Musk. == History == Founder Joep Meindertsma first became worried about the existential risk from artificial intelligence after reading philosopher Nick Bostrom's 2014 book Superintelligence: Paths, Dangers, Strategies. He founded PauseAI in May 2023, putting his job as the CEO of a software firm on hold. Meindertsma claimed the rate of progress in AI alignment research is lagging behind the progress in AI capabilities, and said "there is a chance that we are facing extinction in a short frame of time". As such, he felt an urge to organise people to act. PauseAI's first public action was to protest in front of Microsoft's Brussels lobbying office in May 2023 during an event on artificial intelligence. In November of the same year, they protested outside the inaugural AI Safety Summit at Bletchley Park. The Bletchley Declaration that was signed at the summit, which acknowledged the potential for catastrophic risks stemming from AI, was perceived by Meindertsma to be a small first step. But, he argued "binding international treaties" are needed. He mentioned the Montreal Protocol and treaties banning blinding laser weapons as examples of previous successful global agreements. In February 2024, members of PauseAI gathered outside OpenAI's headquarters in San Francisco, in part due to OpenAI changing its usage policy that prohibited the use of its models for military purposes. On 13 May 2024, protests were held across thirteen countries before the AI Seoul Summit, including the United States, the United Kingdom, Brazil, Germany, Australia, and Norway. Meindertserma said that those attending the summit "need to realize that they are the only ones who have the power to stop this race". Protesters in San Francisco held signs reading "When in doubt, pause", and "Quit your job at OpenAI. Trust your conscience". Jan Leike, head of the "superalignment" team at OpenAI, resigned two days later due to his belief that "safety culture and processes [had] taken a backseat to shiny products".
Read more →
User modeling

User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing. == Background == A user model is the collection and categorization of personal data associated with a specific user. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and a user profile is the actual representation in a given user model. The process of obtaining the user profile is called user modeling. Therefore, it is the basis for any adaptive changes to the system's behavior. Which data is included in the model depends on the purpose of the application. It can include personal information such as users' names and ages, their interests, their skills and knowledge, their goals and plans, their preferences and their dislikes or data about their behavior and their interactions with the system. There are different design patterns for user models, though often a mixture of them is used. Static user models Static user models are the most basic kinds of user models. Once the main data is gathered they are normally not changed again, they are static. Shifts in users' preferences are not registered and no learning algorithms are used to alter the model. Dynamic user models Dynamic user models allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models Stereotype based user models are based on demographic statistics. Based on the gathered information users are classified into common stereotypes. The system then adapts to this stereotype. The application therefore can make assumptions about a user even though there might be no data about that specific area, because demographic studies have shown that other users in this stereotype have the same characteristics. Thus, stereotype based user models mainly rely on statistics and do not take into account that personal attributes might not match the stereotype. However, they allow predictions about a user even if there is rather little information about him or her. Highly adaptive user models Highly adaptive user models try to represent one particular user and therefore allow a very high adaptivity of the system. In contrast to stereotype based user models they do not rely on demographic statistics but aim to find a specific solution for each user. Although users can take great benefit from this high adaptivity, this kind of model needs to gather a lot of information first. == Data gathering == Information about users can be gathered in several ways. There are three main methods: Asking for specific facts while (first) interacting with the system Mostly this kind of data gathering is linked with the registration process. While registering users are asked for specific facts, their likes and dislikes and their needs. Often the given answers can be altered afterwards. Learning users' preferences by observing and interpreting their interactions with the system In this case users are not asked directly for their personal data and preferences, but this information is derived from their behavior while interacting with the system. The ways they choose to accomplish a tasks, the combination of things they takes interest in, these observations allow inferences about a specific user. The application dynamically learns from observing these interactions. Different machine learning algorithms may be used to accomplish this task. A hybrid approach which asks for explicit feedback and alters the user model by adaptive learning This approach is a mixture of the ones above. Users have to answer specific questions and give explicit feedback. Furthermore, their interactions with the system are observed and the derived information are used to automatically adjust the user models. Though the first method is a good way to quickly collect main data it lacks the ability to automatically adapt to shifts in users' interests. It depends on the users' readiness to give information and it is unlikely that they are going to edit their answers once the registration process is finished. Therefore, there is a high likelihood that the user models are not up to date. However, this first method allows the users to have full control over the collected data about them. It is their decision which information they are willing to provide. This possibility is missing in the second method. Adaptive changes in a system that learns users' preferences and needs only by interpreting their behavior might appear a bit opaque to the users, because they cannot fully understand and reconstruct why the system behaves the way it does. Moreover, the system is forced to collect a certain amount of data before it is able to predict the users' needs with the required accuracy. Therefore, it takes a certain learning time before a user can benefit from adaptive changes. However, afterwards these automatically adjusted user models allow a quite accurate adaptivity of the system. The hybrid approach tries to combine the advantages of both methods. Through collecting data by directly asking its users it gathers a first stock of information which can be used for adaptive changes. By learning from the users' interactions it can adjust the user models and reach more accuracy. Yet, the designer of the system has to decide, which of these information should have which amount of influence and what to do with learned data that contradicts some of the information given by a user. == System adaptation == Once a system has gathered information about a user it can evaluate that data by preset analytical algorithm and then start to adapt to the user's needs. These adaptations may concern every aspect of the system's behavior and depend on the system's purpose. Information and functions can be presented according to the user's interests, knowledge or goals by displaying only relevant features, hiding information the user does not need, making proposals what to do next and so on. One has to distinguish between adaptive and adaptable systems. In an adaptable system the user can manually change the system's appearance, behavior or functionality by actively selecting the corresponding options. Afterwards the system will stick to these choices. In an adaptive system a dynamic adaption to the user is automatically performed by the system itself, based on the built user model. Thus, an adaptive system needs ways to interpret information about the user in order to make these adaptations. One way to accomplish this task is implementing rule-based filtering. In this case a set of IF... THEN... rules is established that covers the knowledge base of the system. The IF-conditions can check for specific user-information and if they match the THEN-branch is performed which is responsible for the adaptive changes. Another approach is based on collaborative filtering. In this case information about a user is compared to that of other users of the same systems. Thus, if characteristics of the current user match those of another, the system can make assumptions about the current user by presuming that he or she is likely to have similar characteristics in areas where the model of the current user is lacking data. Based on these assumption the system then can perform adaptive changes. == Usages == Adaptive hypermedia: In an adaptive hypermedia system the displayed content and the offered hyperlinks are chosen on basis of users' specific characteristics, taking their goals, interests, knowledge and abilities into account. Thus, an adaptive hypermedia system aims to reduce the "lost in hyperspace" syndrome by presenting only relevant information. Adaptive educational hypermedia: Being a subdivision of adaptive hypermedia the main focus of adaptive educational hypermedia lies on education, displaying content and hyperlinks corresponding to the user's knowledge on the field of study. Intelligent tutoring system: Unlike adaptive educational hypermedia systems intelligent tutoring systems are stand-alone systems. Their aim is to help students in a specific field of study. To do so, they build up a user model where they store information about abilities, knowledge and needs of the user. The system can now adapt to this user by presenting approp
Read more →
Blockhead (thought experiment)

Blockhead is a theoretical computer system invented as part of a thought experiment by philosopher Ned Block, which appeared in a paper titled "Psychologism and Behaviorism". Block did not personally name the computer in the paper. == Overview == In "Psychologism and Behaviorism", Block argues that the internal mechanism of a system is important in determining whether that system is intelligent and claims to show that a non-intelligent system could pass the Turing test. Block asks the reader to imagine a conversation lasting any given amount of time. He states that given the nature of language, there are a finite number of syntactically and grammatically correct sentences that can be used to start a conversation. Consequently, there is a limit to how many "sensible" responses can be made to the first sentence, then to the second sentence, and so on until the conversation ends. Block then asks the reader to imagine a computer which had been programmed with all the sentences in theory, if not in practice. Block argues that such a machine could continue a conversation with a person on any topic because the computer would be programmed with every sentence that it was possible to use so the computer would be able to pass the Turing test despite the fact that—according to Block—it was not intelligent. Block says that this does not show that there is only one correct internal structure for generating intelligence but simply that some internal structures do not generate intelligence. The argument is related to John Searle's Chinese room.
Read more →
Optical granulometry

Optical granulometry is the process of measuring the different grain sizes in a granular material, based on a photograph. Technology has been created to analyze a photograph and create statistics based on what the picture portrays. This information is vital in maintaining machinery in various trades worldwide. Mining companies can use optical granulometry to analyze inactive or moving rock to quantify the size of these fragments. Forestry companies can zero in on wood chip sizes without stopping the production process, and minimize sizing errors. With more photoanalysis technologies being produced, mining companies have shown an increased interest in these types of systems because of their ability to maintain efficiency throughout the mining process. Companies are saving millions of dollars annually because of this new technology, and are cutting back on maintenance costs on equipment. In order for optical granulometry to be completely successful, an accurate photo must be taken – under sufficient lighting, and using proper technology – to obtain quantified results. If these requirements are met, an image analysis system can be implemented. == The process == Software uses four basic steps in determining the average size of material: See the Wikipedia article on Photoanalysis to see how mining, forestry and agricultural companies are using this technology to improve quality control techniques. == Smartphone-based, segmentation-free estimation of grain size distribution == Recently, a methodology has emerged by which soil grain size distribution can be inferred from optical images acquired with commodity smartphones by training convolutional neural networks to predict parameters of the distribution curve directly from the image, without explicit image segmentation . In this approach, a standardized image of a soil surface is captured under controlled conditions, preprocessed to reduce device-specific variability, and passed to a regression model that outputs the parameters of a cumulative distribution function e.g., a two-parameter Weibull curve. The resulting distribution can be used to derive geotechnical descriptors and class boundaries.
Read more →
Reification (computer science)

In computer science, reification is the process by which an abstract idea about a program is turned into an explicit data model or other object created in a programming language. A computable/addressable object—a resource—is created in a system as a proxy for a non computable/addressable object. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explicitly formulated and made available to conceptual (logical or computational) manipulation. Informally, reification is often referred to as "making something a first-class citizen" within the scope of a particular system. Some aspect of a system can be reified at language design time, which is related to reflection in programming languages. It can be applied as a stepwise refinement at system design time. Reification is one of the most frequently used techniques of conceptual analysis and knowledge representation. == Reflective programming languages == In the context of programming languages, reification is the process by which a user program or any aspect of a programming language that was implicit in the translated program and the run-time system, are expressed in the language itself. This process makes it available to the program, which can inspect all these aspects as ordinary data. In reflective languages, reification data is causally connected to the related reified aspect such that a modification to one of them affects the other. Therefore, the reification data is always a faithful representation of the related reified aspect . Reification data is often said to be made a first class object. Reification, at least partially, has been experienced in many languages to date: in early Lisp dialects and in current Prolog dialects, programs have been treated as data, although the causal connection has often been left to the responsibility of the programmer. In Smalltalk-80, the compiler from the source text to bytecode has been part of the run-time system since the very first implementations of the language. The C programming language reifies the low-level detail of memory addresses.Many programming language designs encapsulate the details of memory allocation in the compiler and the run-time system. In the design of the C programming language, the memory address is reified and is available for direct manipulation by other language constructs. For example, the following code may be used when implementing a memory-mapped device driver. The buffer pointer is a proxy for the memory address 0xB8000000. Functional programming languages based on lambda-calculus reify the concept of a procedure abstraction and procedure application in the form of the Lambda expression. The Scheme programming language reifies continuations (approximately, the call stack). In C#, reification is used to make parametric polymorphism implemented in the form of generics as a first-class feature of the language. In the Java programming language, there exist "reifiable types" that are "completely available at run time" (i.e. their information is not erased during compilation). REBOL reifies code as data and vice versa. Many languages, such as Lisp, JavaScript, and Curl, provide an eval or evaluate procedure that effectively reifies the language interpreter. Smalltalk and Actor languages permit the reification of blocks and messages, which are equivalent of lambda expressions in Lisp, and thisContext in Smalltalk, which is a reification of the current executing block. Homoiconic languages reify the syntax of the language as data that is understood by the language itself. This allows the user to write programs whose inputs and outputs are code (see macros, eval). Common representations of code include S-expressions (e.g. Clojure, Lisp), and abstract syntax trees (e.g. Rust). == Data reification vs. data refinement == Data reification (stepwise refinement) involves finding a more concrete representation of the abstract data types used in a formal specification. Data reification is the terminology of the Vienna Development Method (VDM) that most other people would call data refinement. An example is taking a step towards an implementation by replacing a data representation without a counterpart in the intended implementation language, such as sets, by one that does have a counterpart (such as maps with fixed domains that can be implemented by arrays), or at least one that is closer to having a counterpart, such as sequences. The VDM community prefers the word "reification" over "refinement", as the process has more to do with concretising an idea than with refining it. For similar usages, see Reification (linguistics). == In conceptual modeling == Reification is widely used in conceptual modeling. Reifying a relationship means viewing it as an entity. The purpose of reifying a relationship is to make it explicit, when additional information needs to be added to it. Consider the relationship type IsMemberOf(member:Person, Committee). An instance of IsMemberOf is a relationship that represents the fact that a person is a member of a committee. The figure below shows an example population of IsMemberOf relationship in tabular form. Person P1 is a member of committees C1 and C2. Person P2 is a member of committee C1 only. The same fact, however, could also be viewed as an entity. Viewing a relationship as an entity, one can say that the entity reifies the relationship. This is called reification of a relationship. Like any other entity, it must be an instance of an entity type. In the present example, the entity type has been named Membership. For each instance of IsMemberOf, there is one and only one instance of Membership, and vice versa. Now, it becomes possible to add more information to the original relationship. As an example, we can express the fact that "person p1 was nominated to be the member of committee c1 by person p2". Reified relationship Membership can be used as the source of a new relationship IsNominatedBy(Membership, Person). For related usages see Reification (knowledge representation). == In Unified Modeling Language (UML) == UML provides an association class construct for defining reified relationship types. The association class is a single model element that is both a kind of association and a kind of class. The association and the entity type that reifies are both the same model element. Note that attributes cannot be reified. == On Semantic Web == === RDF and OWL === In Semantic Web languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), a statement is a binary relation. It is used to link two individuals or an individual and a value. Applications sometimes need to describe other RDF statements, for instance, to record information like when statements were made, or who made them, which is sometimes called "provenance" information. As an example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. The example from the conceptual modeling section describes a particular person with URIref person:p1, who is a member of the committee:c1. The RDF triple from that description is Consider to store two further facts: (i) to record who nominated this particular person to this committee (a statement about the membership itself), and (ii) to record who added the fact to the database (a statement about the statement). The first case is a case of classical reification like above in UML: reify the membership and store its attributes and roles etc.: Additionally, RDF provides a built-in vocabulary intended for describing RDF statements. A description of a statement using this vocabulary is called a reification of the statement. The RDF reification vocabulary consists of the type rdf:Statement, and the properties rdf:subject, rdf:predicate, and rdf:object. Using the reification vocabulary, a reification of the statement about the person's membership would be given by assigning the statement a URIref such as committee:membership12345 so that describing statements can be written as follows: These statements say that the resource identified by the URIref committee:membership12345Stat is an RDF statement, that the subject of the statement refers to the resource identified by person:p1, the predicate of the statement refers to the resource identified by committee:isMemberOf, and the object of the statement refers to the resource committee:c1. Assuming that the original statement is actually identified by committee:membership12345, it should be clear by comparing the original statement with the reification that the reification actually does describe it. The conventional use of the RDF reification vocabulary always involves describing a statement using four statements in this pattern. Therefore, they are sometimes referred to as the "reification quad". Using reification according to this convention, we could record the fact that pe
Read more →
CuckooChess

CuckooChess is an advanced free and open-source chess engine under the GNU General Public License written in Java by Peter Österlund. CuckooChess provides an own GUI, and optionally supports the Universal Chess Interface protocol for the use with external GUIs such as Arena. An Android port is available, where its GUI is also based on Peter Österlund's Stockfish port dubbed DroidFish. The program uses the Chess Cases chess font, created by Matthieu Leschemelle. The name CuckooChess comes due that the transposition table is based on Cuckoo hashing. Android app based chess gaming app Droidfish employs both CuckooChess and Stockfish chess engines. Similarly, Kickstarter funded AI based virtual reality chess game Square Off also uses CuckooChess engine. It has an ELO rating of 2583 (as of July 2018) and a rank of 135‑137 in the Computer Chess Rating List.
Read more →
Responsible AI Safety and Education Act

The Responsible AI Safety and Education Act (RAISE Act) is a New York State law that imposes transparency, safety, and reporting requirements on developers of large frontier artificial intelligence models. The law was signed by Governor Kathy Hochul on December 19, 2025. It was sponsored by State Senator Andrew Gounardes and Assemblymember Alex Bores. The RAISE Act is the second U.S. state law to regulate frontier AI model developers, following California's Transparency in Frontier Artificial Intelligence Act (TFAIA), which was signed in September 2025. Hochul signed the bill on the condition that the legislature would pass chapter amendments to bring the law closer to the California model. The amending bills (A9449/S8828) were introduced in January 2026; as of February 2026 they remain in committee, though the Governor's office and legal commentators treat the agreed-upon amendments as representing the final form of the law. == Provisions == The following describes the RAISE Act as it is expected to operate after the agreed-upon chapter amendments take effect. The law is expected to take effect on January 1, 2027. === Scope === The law applies to "large frontier developers," defined as companies with annual revenues exceeding $500 million that develop "frontier models," which are foundation models trained using more than 1026 floating-point operations (FLOPs). The version passed by the legislature in June 2025 had instead defined large developers based on having spent over $100 million in aggregate compute costs, and also included a provision prohibiting deployment of frontier models posing "unreasonable risk of critical harm"; both were removed as part of the negotiations between Hochul and the legislature. Accredited colleges and universities engaged in academic research are exempt, as is the state's Empire AI consortium. === Safety and transparency framework === Large frontier developers must write, implement, and publicly publish a "frontier AI framework" describing how they assess and mitigate catastrophic risks, secure unreleased model weights against unauthorized access, use third-party evaluators, govern internal use of frontier models, and respond to safety incidents. The framework must describe these measures "in detail," a requirement that goes beyond the California TFAIA's requirement to describe a developer's "approach." The framework must be reviewed at least annually, and material modifications must be published with justification within 30 days. Before or concurrently with deploying a new or substantially modified frontier model, developers must publish a transparency report including the model's release date, supported languages and output modalities, intended uses, and any restrictions on use. Large frontier developers must additionally include summaries of catastrophic risk assessments and the extent of third-party involvement. === Catastrophic risk and incident reporting === The law defines "catastrophic risk" as a foreseeable and material risk that a frontier model will contribute to the death of or serious injury to more than 50 people, or more than $1 billion in property damage, arising from a frontier model providing expert-level assistance in creating chemical, biological, radiological, or nuclear weapons; engaging in cyberattacks or conduct equivalent to crimes such as murder, assault, or theft without meaningful human oversight; or evading the control of its developer or user. Loss of equity value is explicitly excluded from the definition of property damage. "Critical safety incidents" include unauthorized access to model weights resulting in death or injury, materialization of a catastrophic risk, loss of control of a frontier model causing death or injury, and a model using deceptive techniques to subvert developer controls outside of an evaluation context in a manner that increases catastrophic risk. Frontier developers must report critical safety incidents within 72 hours, or within 24 hours if the incident poses an imminent risk of death or serious physical injury. === Enforcement === The chapter amendments establish a new office within the New York State Department of Financial Services to oversee compliance, receive incident reports, and publish annual reports on AI safety beginning in 2028. Large frontier developers must file disclosure statements with this office and pay pro rata assessments to fund its operations. The New York Attorney General may bring civil actions, with penalties of up to $1 million for a first violation and $3 million for subsequent violations. The version passed by the legislature in June 2025 had set penalties at up to $10 million and $30 million respectively. The law does not create a private right of action. == Legislative history == The bill was introduced in the Assembly on March 5, 2025, by Assemblymember Alex Bores, and in the Senate on March 27, 2025, by Senator Andrew Gounardes. After a series of amendments, the legislature passed the bill in June 2025. Governor Hochul did not immediately sign the bill, using nearly all the time available under New York law before acting; had she not signed by the end of 2025, the bill would have been pocket vetoed. The tech industry lobbied against the bill during this period, and Hochul initially proposed a near-complete rewrite modeled on California's TFAIA. Legislators resisted the extent of the changes, and the two sides ultimately agreed on a version that used the California law as a base but preserved several provisions that went beyond it, including the 72-hour incident reporting timeline and the creation of a dedicated enforcement office. Hochul signed the original bill (S6953-B/A6453-B) on December 19, 2025, with the legislature committing to pass chapter amendments formalizing the agreed changes in the January 2026 session. The amending bills (A9449 in the Assembly, S8828 in the Senate) were introduced on January 6 and January 8, 2026. OpenAI and Anthropic expressed support for the law. Anthropic's head of external affairs Sarah Heck said the two state laws "should inspire Congress to build on them." The super PAC network Leading the Future, backed by Andreessen Horowitz and OpenAI president Greg Brockman, subsequently announced plans to challenge Bores in a future election. == Federal preemption debate == Hochul signed the RAISE Act eight days after President Donald Trump issued an executive order on December 11, 2025, directing the Department of Justice to challenge state AI laws deemed to conflict with a "minimally burdensome" national AI policy. On January 9, 2026, the Department of Justice announced the establishment of an AI Litigation Task Force as called for by the executive order. The executive order also threatened states with loss of certain federal broadband funding if their AI laws were found to be onerous. Legal commentators have noted several potential avenues for federal challenge, including arguments that the law constitutes compelled speech, violates the dormant Commerce Clause by creating a patchwork of state regulations, or is preempted by federal AI policy. == Comparison with California's TFAIA == The RAISE Act was designed to align with California's Transparency in Frontier Artificial Intelligence Act, signed on September 29, 2025. Both laws use the same 1026 FLOP threshold to define frontier models and the same $500 million revenue threshold to define large developers. Both require public safety frameworks, transparency reports, and incident reporting. The RAISE Act's 72-hour incident reporting window is stricter than the TFAIA's 15-day window, though both require faster reporting for incidents posing imminent physical risk (24 hours under the RAISE Act, immediate under the TFAIA). The RAISE Act establishes a dedicated enforcement office within the Department of Financial Services, whereas California routes reports through the Office of Emergency Services. The RAISE Act requires developers to describe their safety measures "in detail" and how they "handle" various risks, whereas the TFAIA requires developers to describe their "approach."
Read more →
Natural Language Toolkit

The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook. NLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics, cognitive science, artificial intelligence, information retrieval, and machine learning. NLTK has been used successfully as a teaching tool, as an individual study tool, and as a platform for prototyping and building research systems. == Library highlights == Discourse representation Lexical analysis: Word and text tokenizer n-gram and collocations Part-of-speech tagger Tree model and Text chunker for capturing Named-entity recognition
Read more →
Common Crawl

The Common Crawl Foundation (Common Crawl) is a nonprofit 501(c)(3) organization that crawls the web and freely provides its archives and datasets to the public. Common Crawl was founded by Gil Elbaz. The data had mostly been primarily used by researchers and some startups until the 2020s, when AI companies started training large language models using the data. In November 2025, an investigation by The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and it was not honoring requests from publishers to have their content removed from its databases. == History == Common Crawl was founded in 2007 in San Francisco. It began publishing its crawls in 2011. By 2013, sites like TinEye were building their products off of Common Crawl. The crawl reduces the reliance of companies and researchers on Google, which has the biggest dataset. Common Crawl was designed to have more and fresher data that was more efficient to analyze and utilize than the Wayback Machine created by the Internet Archive. By 2015, 1.8 billion webpages were on the Common Crawl, which started by crawling a list of URLs donated by the search engine Blekko. They use Amazon Web Services, which provides some of its services for free, allowing computing costs to average $2-4000/month. The Common Crawl website listed 30 studies based on Common Crawl data. Before 2023, Common Crawl was not very well known outside of academic researchers who utilize the data. Common Crawl received its first requests to redact information in 2023 and increasingly started seeing its crawler, CCBot, blocked. In 2023, it began receiving significant financial support from AI companies, including Anthropic and OpenAI, each of which donated $250,000. It was also used to train Google DeepMind's large language model Gemini. By April 2023, Common Crawl was capturing 3.1 billion webpages, with an estimated 5% of pages before 2021 containing hate speech or slurs. As of 2024, Common Crawl had been cited in more than 10,000 academic studies. By 2024, The Pile and Common Crawl had been the two main training datasets being used to train AI models. In November 2025, an investigation by technology journalist Alex Reisner for The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and when it said that it was honoring requests from publishers to have their content removed from its databases. It included misleading results in the public search function on its website that showed no entries for websites that had requested their archives be removed, when in fact those sites were still included in its scrapes used by AI companies. As of 2025, Reisner found that CCBot was the most widely-blocked bot by the top 1000 websites. A 2026 article in LWN.net discussed an advantage to services like Common Crawl being that it can limit the scraping costs to websites by allowing companies and researchers to download the data from Common Crawl instead of scraping it themselves. In April 2026, Common Crawl experimentally began to distribute its data through Hugging Face Storage Bucket, in addition to its standard storage on Amazon S3. == Organization == Peter Norvig and Joi Ito have served on the advisory board. Rich Skrenta is the executive director. It has received funding almost exclusively from the Elbaz Family Foundation Trust until 2023 when it started receiving donations from the AI industry. == Refined versions == A number of organizations take raw Common Crawl data and refine it into datasets that exclude edgy content or are otherwise higher-quality for their purposes, such as FineWeb, DCLM and C4. === Colossal Clean Crawled Corpus === Google version of the Common Crawl is called the Colossal Clean Crawled Corpus, or C4 for short. It was constructed for the training of the T5 language model series in 2019. As of 2023, there were some concerns over copyrighted content in the C4 as well as racist content. A 2024 study found that 45% of content was explicitly restricted by websites' terms of service to be used for purposes like AI training by for-profit companies.
Read more →
IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence (sometimes abbreviated as IEEE PAMI or simply PAMI) is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. == Background == The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition. The editor-in-chief is Kyoung Mu Lee (Seoul National University). According to the Journal Citation Reports, the journal has a 2023 impact factor of 20.8.
Read more →
Generative AI Copyright Disclosure Act

The Generative AI Copyright Disclosure Act is a piece of legislation introduced by California Representative Adam Schiff in the United States Congress on April 9, 2024. It concerns the transparency of companies regarding their use of copyrighted work to train their generative artificial intelligence (AI) models. The legislation requires the submission of a notice regarding the identity and the uniform resource locator (URL) address of the copyrighted works used in the training data to the Register of Copyrights at least 30 days before the public release of the new or updated version of the AI model; it does not ban the use of copyrighted works for AI training. The bill's requirements would apply retroactively to prior AI models. Violation penalties would start at US$5,000. The legislation does not have a maximum penalty assessment that can be charged. The bill by Schiff was introduced a few days after The New York Times published an article regarding the business activities of major tech firms, including Google and Meta, in the training of their generative AI platforms on April 6, 2024. The legislation is supported by the Professional Photographers of America (PPA), SAG-AFTRA, the Writers Guild of America, the International Alliance of Theatrical Stage Employees (IATSE), the Recording Industry Association of America (RIAA), and others.
Read more →
1 Second Everyday

1 Second Everyday (1SE) is an application developed by Cesar Kuriyama. The application allows the user to record one second of video every day and then chronologically edits (mashes) them together into a single film. It is compatible with iOS and Android. The idea of the application was developed by Kuriyama's 1 Second Everyday — Age 30 video. The application was launched in January 2013. 1 Second Everyday played a part in the plot of Chef and also became the inspiration for the 2014 short animated clip Feast. == Background == === Kuriyama's video === In February 2011, when Cesar Kuriyama turned 30, after saving money, he quit his job in an advertising firm and took a year off to travel. During this time, he started working on a project he called 1 Second Everyday. As part of the project, every day he recorded one second of video – something that was supposed to help him remember that day. He started the project because he was frustrated with his memory. He planned to stockpile the 365 one-second clips into one film to serve as a memento of his year. While working on the project Kuriyama realized that recording one second every day impacted the decisions he made in a positive way. After a year he made a 365-second clip out of his recordings. The video called 1 Second Everyday – Age 30, went viral. According to Kuriyama, he was initially inspired to take a year off from work by a TED talk given by Stefan Sagmeister called "The Power of Time Off." Kuriyama also delivered a TED talk about 1 Second Everyday in 2012 at TED 2012 in Long Beach California. === Kickstarter campaign === After completing his own video, Kuriyama decided to develop an application that would allow the users to record one second every day and compile their own videos. He developed a prototype of the application and then in 2012, he launched a Kickstarter campaign to raise funds for completing the application. The campaign became one of the most backed app campaigns in the history of Kickstarter. It was backed by 11,281 backers who pledged a total of $56,959 on an initial goal of $20,000. Following the completion of the Kickstarter campaign, he partnered with an application design studio in Brooklyn to develop the application. 1 Second Everyday was released two weeks after the completion of its Kickstarter campaign. == Application == The application was released for iOS on 10 January 2013. An Android-compatible version of the application was developed later. Using it, the user can record the videos in the application or they can select one second portions from their libraries. 1 Second Everyday dates every snippet. The user can also set alarms to remember to record their daily video. In order to compile a video, the user selects the seconds they want and the application creates a compilation video. The user can keep multiple timelines. It also allows users to post directly on social networks. The main interface in 1 Second Everyday is a calendar, which shows the user which days have snippets and which they can still fill in. In the beginning, 1 Second Everyday restricted the recording to one second. However, the developers later released Super Seconds, which allowed users to record an additional half a second video. In 2014, 1 Second Everyday Crowds was launched, which is an area in the application featuring compilations of second clips from different users. == In the media == The Kickstarter campaign of 1 Second Everyday was featured in Entrepreneur's 3 Innovative Tech Startups on Kickstarter Right Now in 2012. The application was featured in The New York Times, The Washington Post, Gawker and other media outlets. By the end of the launch day, it was in Top 10 Free Apps on App Store. It was also selected as the App of the Week on GeekWire in 2013. Several other one-second compilation videos were also posted on the Internet after Kuriyama's video gained media attention. Sam Cornwell, an English photographer documented his son Indigo's growth using a montage of one-second iPhone clips. He shot these clips every single day from the moment of birth right up to the baby's first birthday. According to Cornwell, he was inspired by Kuriyama's project. The video of Cornwell's son gained considerable media attention after it was posted on YouTube. Save the Children also made a video commercial based on a similar format that showed a British girl oblivious of the Syrian war end up being a refugee. 1SE was a finalist for the Fast Company Innovation by Design Award in 2015, but lost to Google Maps. In 2015, Google Android created a gallery, Leap Second 2015, with the help of Droga5 and Kuriyama. The gallery showcased how people around the world enjoyed the one extra second of their lives. Through the 1 Second Everyday app available at Google Play, people were able to submit their extra second, which were then vetted and added to the gallery. The viewers were able to view other celebratory seconds from around the world as well as searching for them using different hashtags.
Read more →
Predictions of the end of Wikipedia

Various observers have predicted the end of Wikipedia since it rose to prominence, with potential pitfalls from lack of quality-control, artificial intelligence or inconsistencies among contributors. Alternative online encyclopedias have been proposed as replacements for Wikipedia, including WolframAlpha, as well as the both now-defunct Knol (from Google) and Owl (from AOL). A 2013 review raised alarms regarding Wikipedia's shortcomings on hoaxes, on vandalism, an imbalance of material, and inadequate quality control of articles. Earlier critiques lamented the vulgar content and absence of sufficient references in articles. Others suggest that the unwarranted deletion of useful articles from Wikipedia may portend its end, which itself inspired the creation of the now inactive Deletionpedia. Contrary to such predictions, Wikipedia has constantly grown in both size and influence. Recent developments with artificial intelligence in Wikimedia projects have prompted new predictions that AI applications, which consume free and open content, will replace Wikipedia. == Personnel == Wikipedia is crowdsourced by a few million volunteer editors. Of the millions of registered editors, only tens of thousands contribute the majority of its contents, and a few thousand do quality control and maintenance work. As the encyclopedia expanded in the 2010s, the number of active editors did not grow proportionately. Various sources predicted that Wikipedia will eventually have too few editors to be functional and collapse from lack of participation. English Wikipedia has 818 volunteer administrators who perform various functions, including functions similar to those carried out by a forum moderator. Critics have described their actions as harsh, bureaucratic, biased, unfair, or capricious and predicted that the resulting outrage would lead to the site's closure. Various 2012 articles reported that a decline in English Wikipedia's recruitment of new administrators could end Wikipedia. === Decline in editors (2014–2015) === A 2014 trend analysis published in The Economist stated that "The number of editors for the English-language version has fallen by a third in seven years." The attrition rate for active editors in English Wikipedia was described by The Economist as substantially higher than in other (non-English) Wikipedias. It reported that in other languages, the number of "active editors" (those with at least five edits per month) has been relatively constant since 2008: some 42,000 editors, with narrow seasonal variances of about 2,000 editors up or down. In the English Wikipedia, the number of active editors peaked in 2007 at about 50,000 editors, and fell to 30,000 editors in 2014. Given that the trend analysis published in The Economist presented the number of active editors for non-English Wikipedias as remaining relatively constant, sustaining their numbers at approximately 42,000 active editors, the contrast pointed to the effectiveness of Wikipedia in those languages to retain their active editors on a renewable and sustained basis. Though different language versions of Wikipedia have different policies, no comment identified a particular policy difference as potentially making a difference in the rate of editor attrition for English Wikipedia. Editor count showed a slight uptick a year later, and no clear trend after that. In a 2013 article, Tom Simonite of MIT Technology Review said that for several years running, the number of Wikipedia editors had been falling, and cited the bureaucratic structure and rules as a factor. Simonite alleged that some Wikipedians use the labyrinthine rules and guidelines to dominate others and have a vested interest in keeping the status quo. A January 2016 article in Time by Chris Wilson said Wikipedia might lose many editors because a collaboration of occasional editors and smart software will take the lead. Andrew Lih and Andrew Brown both maintain editing Wikipedia with smartphones is difficult and discourages new potential contributors. Lih alleges there is serious disagreement among existing contributors on how to resolve this. In 2015, Lih feared for Wikipedia's long-term future while Brown feared problems with Wikipedia would remain and rival encyclopedias would not replace it. == Viewers and fundraisers == As of 2015, with more viewing by smartphones, there had been a marked decline in persons who viewed Wikipedia from their computers, and according to The Washington Post "[people are] far less likely to donate". At the time, the Wikimedia Foundation reported reserves equivalent to one year's budgeted expenditures. On the other hand, the number of paid staff had ballooned, so those expenses increased. In 2021, Andreas Kolbe, a former co-editor-in-chief of The Signpost, wrote that the Wikimedia Foundation was reaching its 10-year goal of a US$100 million endowment, five years earlier than planned, which may surprise donors and users around the world who regularly see Wikipedia fundraising banners. He also said accounting methods disguise the size of operating surpluses, top managers earn $300,000 – 400,000 a year, and over 40 people work exclusively on fundraising. == Artificial intelligence == Wikipedia faces a decline in human visitors, raising concerns about its long-term sustainability and community participation. The Wikimedia Foundation (WMF), when reporting this decline, attributed this in part to the lack of clicks from users of large language models and search engines that are using content from Wikipedia. Data published in August 2025 showed that after the launch of ChatGPT and the rise of other AI-powered search summaries, some types of articles on Wikipedia — especially those that closely resemble the kind of content ChatGPT produces — experienced a noticeable drop in readership. Overall human pageviews reportedly fell by about 8% between 2024 and 2025, suggesting that AI-overviews and chatbots are increasingly being used in place of direct visits to Wikipedia. According to industry web analytics data, ChatGPT's estimated monthly web traffic surpassed that of Wikipedia since May 2025, as visits to ChatGPT continued to grow while Wikipedia’s total site traffic declined. == Timeline of predictions == On the eve of the 20th anniversary of Wikipedia, associate professor of the Department of Communication Studies at Northeastern University Joseph Reagle conducted a retrospective study of numerous "predictions of the ends of Wikipedia" over two decades, divided into chronological waves: "Early growth (2001–2002)", "Nascent identity (2001–2005)", "Production model (2005–2010)", "Contributor attrition (2009–2017)" and the current period "(2020–)". Each wave brought its distinctive fatal predictions, which never came true; as a result, Reagle concluded Wikipedia was not in danger. Concern grew in 2023 that the ubiquity and proliferation of artificial intelligence (AI) may adversely affect Wikipedia. Rapid improvements and widespread application of AI may render Wikipedia obsolete or reduce its importance. A 2023 study found that AI, when applied to Wikipedia, works most efficiently for error-correction, while Wikipedia still needs to be written by humans.
Read more →
LipNet

LipNet is a deep neural network for audio-visual speech recognition (ASVR). It was created by University of Oxford researchers Yannis Assael, Brendan Shillingford, Shimon Whiteson, and Nando de Freitas. The researchers stated that could match mouth movements to text with 93 percent accuracy, though it was criticized for its test using a limited dataset of words and grammar. It was used in Nvidia's autonomous "backseat driver" prototype Co-Pilot.
Read more →