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The 2028 Global Intelligence Crisis

The 2028 Global Intelligence Crisis is a report authored by James van Geelen and Alap Shah and published by Citrini Research in February 2026, on the impact of artificial intelligence on humanity's future. Written in the form of a scenario analysis, it was viewed millions of times online and reportedly caused a fall in the stock market prices of major tech and financial firms. It also received criticism among others, for its allegedly flawed economic logic. The 'thought exercise', as the authors called it, painted a gloomy picture for the near future, where outputs keep growing while consumer's ability to spend collapses. "...driven by ai agents that don’t sleep, take sick days or require health insurance”, "outputs that are shown in national accounts increases, "but never circulates through the real economy"(which the report calls 'Ghost GDP'), the authors argued. In other words, the authors predict a scenario where the owners of the AI firms will accumulate a vast fortune but there will be scant demand from consumers as AI would cause massive unemployment. The authors caution the reader that what they make is a scenario and not a prediction. In the scenario they visualise, any service whose value proposition is “I will navigate complexity that you find tedious” is getting disrupted. The reports argues that the unique ability of human beings to analyse, decide, create, persuade, and coordinate was “the thing that could not be replicated at scale,” and call the historical scarcity of this precious entity 'friction'. When this friction becomes zero, a gamut of changes occur which then triggers a cascading of changes across the economy. ”Travel booking platforms are an early casualty; Financial advice. tax prep., and routine legal work follow suit. National unemployment rate go as high 10.2% and the S&P 500 goes for a massive 38% peak-to-trough crash. In contrast to the previous technological revolutions the high-earning professionals suffers more and get forced to take up roles in the gig economy. Labour supply becomes abundant and this cuts wages all across the economy. The dent in income for the employees then affects other sectors of the economy such as the residential mortgage market. The losses for the software companies triggers loan defaults and heralds peril for the private credit sector.
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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
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Eline Van der Velden

Eline van der Velden is a Dutch comedian, writer, actress and producer based in London, England. She is best known for her work creating Tilly Norwood, an AI-generated "actress". == Early life == Van der Velden was born on the Dutch island of Curaçao, Netherlands Antilles to Dutch businessman Steven van der Velden and physiotherapist Quirine van der Velden. She moved to the United Kingdom at age 14 to study drama and musical theatre at Tring Park School for the Performing Arts. She graduated with an MSc in physics from Imperial College London in 2008. == Career == She was nominated by the International Academy of Digital Arts and Sciences for the Lovie Awards and won Best Online Comedy in 2013 for two of her submitted entries. She has created multiple online shows such as Sketch My Life with London Hughes and Emily Hartridge and Match.com Parody. She became managing director of Makers Channel (makerschannel.co.uk), the first curated video platform in Europe in 2015. Makers Channel has been recently acquired by a Belgian media company De Persgroep, due to its success in the Netherlands. In 2016, she appeared in adverts for the Dutch shampoo brand Andrelon. Miss Holland, a comedy character created by Van der Velden, made headlines in 2016 as she asked the British public to teach her the national anthem. As an actress, she has starred in Dutch TV series De Troon, Beatrix and the Golden Calf-winning series Overspel. In Belgium, she appeared opposite Jamie Dornan in Flying Home. Van der Velden starred in the BBC Three series Putting It Out There, in which she challenges social perceptions of body hair, heels, spit, personal space, and authority figures. In 2018, she starred in the BBC One comedy series Soft Border Patrol and the BBC Three comedy series Miss Holland. In 2025, Particle6 Group, which Van der Velden founded in 2016, introduced Tilly Norwood, an AI-generated "actress" at the Zurich Film Festival. The announcement was met with outrage and a condemnation by the American actors' union SAG-AFTRA. == Awards and recognition == Miss Holland won the Best Online Comedy at the 2013 Lovie Awards, judged by Stephen Fry. The Match.com Parody video won Best Online Comedy People's Lovie Award, the people's vote. Miss Holland and Match.com Parody Date 1 were also featured in the 2013 Google Lovie Letters.
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Personal knowledge base

A personal knowledge base (PKB) is an electronic tool used by an individual to express, capture, and later retrieve personal knowledge. It differs from a traditional database in that it contains subjective material particular to the owner, that others may not agree with nor care about. Importantly, a PKB consists primarily of knowledge, rather than information; in other words, it is not a collection of documents or other sources an individual has encountered, but rather an expression of the distilled knowledge the owner has extracted from those sources or from elsewhere. The term personal knowledge base was mentioned as early as the 1980s, but the term came to prominence in the 2000s when it was described at length in publications by computer scientist Stephen Davies and colleagues, who compared PKBs on a number of different dimensions, the most important of which is the data model that each PKB uses to organize knowledge. == Data models == Davies and colleagues examined three aspects of the data models of PKBs: their structural framework, which prescribes rules about how knowledge elements can be structured and interrelated (as a tree, graph, tree plus graph, spatially, categorically, as n-ary links, chronologically, or ZigZag); their knowledge elements, or basic building blocks of information that a user creates and works with, and the level of granularity of those knowledge elements (such as word/concept, phrase/proposition, free text notes, links to information sources, or composite); and their schema, which involves the level of formal semantics introduced into the data model (such as a type system and related schemas, keywords, attribute–value pairs, etc.). Davies and colleagues also emphasized the principle of transclusion, "the ability to view the same knowledge element (not a copy) in multiple contexts", which they considered to be "pivotal" to an ideal PKB. They concluded, after reviewing many design goals, that the ideal PKB was still to come in the future. === Personal knowledge graph === In their publications on PKBs, Davies and colleagues discussed knowledge graphs as they were implemented in some software of the time. Later, other writers used the term personal knowledge graph (PKG) to refer to a PKB featuring a graph structure and graph visualization. However, the term personal knowledge graph is also used by software engineers to refer to the different subject of a knowledge graph about a person, in contrast to a knowledge graph created by a person in a PKB. == Software architecture == Davies and colleagues also differentiated PKBs according to their software architecture: file-based, database-based, or client–server systems (including Internet-based systems accessed through desktop computers and/or handheld mobile devices). == History == Non-electronic personal knowledge bases have probably existed in some form for centuries: Leonardo da Vinci's journals and notes are a famous example of the use of notebooks. Commonplace books, florilegia, annotated private libraries, and card files (in German, Zettelkästen) of index cards and edge-notched cards are examples of formats that have served this function in the pre-electronic age. Undoubtedly the most famous early formulation of an electronic PKB was Vannevar Bush's description of the "memex" in 1945. In a 1962 technical report, human–computer interaction pioneer Douglas Engelbart (who would later become famous for his 1968 "Mother of All Demos" that demonstrated almost all the fundamental elements of modern personal computing) described his use of edge-notched cards to partially model Bush's memex. == Examples == The following software applications have been used to build PKBs using various data models and architectures. The list includes software mentioned by Davies and colleagues in their 2005 paper, and additional software. Open source Compendium Haystack (MIT project) Joplin Logseq NoteCards Org-mode QOwnNotes TiddlyWiki Closed source Evernote Microsoft OneNote MindManager MyLifeBits Notion Obsidian Personal Knowbase PersonalBrain Roam Tinderbox
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Amazon Elastic Compute Cloud

Amazon Elastic Compute Cloud (EC2) is a part of Amazon's cloud-computing platform, Amazon Web Services (AWS), that allows users to rent virtual computers on which to run their own computer applications. EC2 encourages scalable deployment of applications by providing a web service through which a user can boot an Amazon Machine Image (AMI) to configure a virtual machine, which Amazon calls an "instance", containing any software desired. A user can create, launch, and terminate server-instances as needed, paying by the second for active servers – hence the term "elastic". EC2 provides users with control over the geographical location of instances that allows for latency optimization and high levels of redundancy. In November 2010, Amazon switched its own retail website platform to EC2 and AWS. == History == Amazon announced a limited public beta test of EC2 on August 25, 2006, offering access on a first-come, first-served basis. Amazon added two new instance types (Large and Extra-Large) on October 16, 2007. On May 29, 2008, two more types were added, High-CPU Medium and High-CPU Extra Large. There were twelve types of instances available. Amazon added three new features on March 27, 2008: static IP addresses, availability zones, and user-selectable kernels. On August 20, 2008, Amazon added Elastic Block Store (EBS). This provides persistent storage, a feature that had been lacking since the service was introduced. Amazon EC2 went into full production when it dropped the beta label on October 23, 2008. On the same day, Amazon announced the following features: a service level agreement for EC2, Microsoft Windows in beta form on EC2, Microsoft SQL Server in beta form on EC2, plans for an AWS management console, and plans for load balancing, autoscaling, and cloud monitoring services. These features were subsequently added on May 18, 2009. Amazon EC2 was developed mostly by a team in Cape Town, South Africa led by Chris Pinkham. Pinkham provided the initial architecture guidance for EC2 and then built the team and led the development of the project along with Willem van Biljon. == Instance types == Initially, EC2 used Xen virtualization exclusively. However, on November 6, 2017, Amazon announced the new C5 family of instances that were based on a custom architecture around the KVM hypervisor, called Nitro. Each virtual machine, called an "instance", functions as a virtual private server. Amazon sizes instances based on "Elastic Compute Units". The performance of otherwise identical virtual machines may vary. On November 28, 2017, AWS announced a bare-metal instance, a departure from exclusively offering virtualized instance types. As of January 2019, the following instance types were offered: General Purpose: A1, T3, T2, M5, M5a, M4, T3a Compute Optimized: C5, C5n, C4 Memory Optimized: R5, R5a, R4, X1e, X1, High Memory, z1d Accelerated Computing: P3, P2, G3, F1 Storage Optimized: H1, I3, D2 As of April 2018, the following payment methods by instance were offered: On-demand: pay by the hour without commitment. Reserved: rent instances with one-time payment receiving discounts on the hourly charge. Spot: bid-based service: runs the jobs only if the spot price is below the bid specified by bidder. The spot price is claimed to be supply-demand based, however a 2011 study concluded that the price was generally not set to clear the market, but was dominated by an undisclosed reserve price. In 2025, AWS expanded EC2 with the compute-optimized C8gn family, powered by Graviton4 and offering up to 600 Gbit/s network bandwidth (about 30% higher compute performance than C7gn), and introduced G6f fractional-GPU instances that let customers provision one-eighth, one-quarter, or one-half of an NVIDIA L4 GPU for right-sized graphics/ML workloads. === Cost === As of April 2018, Amazon charged about $0.0058 per hour ($4.176 per month) for the smallest "Nano Instance" (t2.nano) virtual machine running Linux or Windows. Storage-optimized instances cost as much as $4.992 per hour (i3.16xlarge). "Reserved" instances can go as low as $2.50 per month for a three-year prepaid plan. The data transfer charge ranges from free to $0.12 per gigabyte, depending on the direction and monthly volume (inbound data transfer is free on all AWS services). EC2 costs can be analyzed using the Amazon Cost and Usage Report. There are many different cost categories for EC2 including: hourly Instance Charges, Data Transfer, EBS Volumes, EBS Volume Snapshots, and Nat Gateway. === Free tier === As of December 2010 Amazon offered a bundle of free resource credits to new account holders. The credits are designed to run a "micro" sized server, storage (EBS), and bandwidth for one year. Unused credits cannot be carried over from one month to the next. === Reserved instances === Reserved instances enable EC2 or RDS service users to reserve an instance for one or three years. The corresponding hourly rate charged by Amazon to operate the instance is 35 to 75% lower than the rate charged for on-demand instances. Reserved instances can be purchased with three different payment options: All Upfront, Partial Upfront and No Upfront. The different purchase options allow for different structuring of payment models, with a larger discount given to customers that pay their reservation upfront. Reserved Instances are purchased based on a resource commitment. These reservations are made based on an instance type and a count of that instance type. For example, you could reserve 100 i3.large instances for a 3-year term. In September 2016, AWS announced several enhancements to Reserved instances, introducing a new feature called scope and a new reservation type called a Convertible. In October 2017, AWS announced the allowance to subdivide the instances purchased for more flexibility. === Spot instances === Cloud providers maintain large amounts of excess capacity they have to sell or risk incurring losses. Amazon EC2 Spot instances are spare compute capacity in the AWS cloud available at up to 90% discount compared to On-Demand prices. As a trade-off, AWS offers no SLA on these instances and customers take the risk that it can be interrupted with only two minutes of notification when Amazon needs the capacity back. Researchers from the Israeli Institute of Technology found that "they (Spot instances) are typically generated at random from within a tight price interval via a dynamic hidden reserve price". Some companies, like Spotinst, are using machine learning to predict spot interruptions up to 15 minutes in advance. === Savings Plans === In November 2019, Amazon announced Savings Plans. Savings Plans are an alternative to Reserved Instances that come in two different plan types: Compute Savings Plans and EC2 Instances Savings Plans. Compute Savings Plans allow an organization to commit to EC2 and Fargate usage with the freedom to change region, family, size, availability zone, OS and tenancy inside the lifespan of the commitment. EC2 Instance Savings plans provide a larger discount than Compute Savings Plans but are less flexible meaning a user must commit to individual instance families within a region to take advantage, but with the freedom to change instances within the family in that region. AWS uses the Cost Explorer to automatically calculate recommendations for the commitments you should make how that commitment will look like as a monthly charge on your AWS bill. AWS Savings Plans are purchased based on hourly spend commitment. This hourly commitment is made using the discounted pricing of the savings plan you are purchasing. For example, you could commit to spending $5 per hour, on a Compute Savings Plan, for a 3-year term. == Features == === Operating systems === When it launched in August 2006, the EC2 service offered Linux and later Sun Microsystems' OpenSolaris and Solaris Express Community Edition. In October 2008, EC2 added the Windows Server 2003 and Windows Server 2008 operating systems to the list of available operating systems. In March 2011, NetBSD AMIs became available. In November 2012, Windows Server 2012 support was added. Since 2006, Colin Percival, a FreeBSD developer and Security Officer, solicited Amazon to add FreeBSD. In November 2012, Amazon officially supported running FreeBSD in EC2. The FreeBSD/EC2 platform is maintained by Percival who also developed the secure deduplicating Amazon S3-cloud based backup service Tarsnap. Amazon has their own Linux distribution based on Fedora and Red Hat Enterprise Linux as a low cost offering known as the Amazon Linux AMI. Version 2013.03 included: Linux kernel, Java OpenJDK Runtime Environment and GNU Compiler Collection. On November 30, 2020, Amazon announced that it would be adding macOS to the EC2 service. Initial support was announced for macOS Mojave and macOS Catalina running on Mac Mini. === Managed Container and Kubernetes Services === Amazon Elastic Container Registry (ECR) is a Docker registry service for Amazon EC2
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Is-a

In knowledge representation, ontology components and ontology engineering, including for object-oriented programming and design, is-a (also written as is_a or is a) is a subsumptive relationship between abstractions (e.g., types, classes), wherein one class A is a subclass of another class B (and so B is a superclass of A). In other words, type A is a subtype of type B when A's specification implies B's specification. That is, any object (or class) that satisfies A's specification also satisfies B's specification, because B's specification is weaker. For example, a cat 'is a[n]' animal, but not vice versa. All cats are animals, but not all animals are cats. Behaviour that is relevant to all animals is defined on an animal class, whereas behaviour that is relevant only for cats is defined in a cat class. By defining the cat class as 'extending' the animal class, all cats 'inherit' the behaviour defined for animals, without the need to explicitly code that behaviour for cats. == Related concepts == The is-a relationship is to be contrasted with the has-a (has_a or has a) relationship between types (classes); confusing the relations has-a and is-a is a common error when designing a model (e.g., a computer program) of the real-world relationship between an object and its subordinate. The is-a relationship may also be contrasted with the instance-of relationship between objects (instances) and types (classes): see Type–token distinction. To summarize the relations, there are: hyperonym–hyponym (supertype/superclass–subtype/subclass) relations between types (classes) defining a taxonomic hierarchy, where for a subsumption relation: a hyponym (subtype, subclass) has a type-of (is-a) relationship with its hyperonym (supertype, superclass); holonym–meronym (whole/entity/container–part/constituent/member) relations between types (classes) defining a possessive hierarchy, where for an aggregation (i.e. without ownership) relation: a holonym (whole) has a has-a relationship with its meronym (part), for a composition (i.e. with ownership) relation: a meronym (constituent) has a part-of relationship with its holonym (entity), for a containment relation: a meronym (member) has a member-of relationship with its holonym (container); concept–object (type–token) relations between types (classes) and objects (instances), where a token (object) has an instance-of relationship with its type (class).
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Integrated Operations in the High North

Integrated Operations in the High North (IOHN, IO High North or IO in the High North) is a unique collaboration project that during a four-year period starting May 2008 is working on designing, implementing and testing a Digital Platform for what in the upstream oil and gas industry is called the next or second generation of Integrated Operations. The work on the Digital platform is focussed on capture, transfer and integration of real-time data from the remote production installations to the decision makers. A risk evaluation across the whole chain is also included. The platform is based on open standards and enables a higher degree of interoperability. Requirements for the digital platform come from use cases defined within the Drilling and Completion, Reservoir and Production and Operations and Maintenance domains. The platform will subsequently be demonstrated through pilots within these three domains. The project was a sidecar initiative for Statoil’s Global Operations Data Integration Project. This was part of a very ambitious Master Plan IT (MapIT), which also included the Real Time Visualization (RTV) tender. The RTV tender aimed to be an ontology-aware information workspace for a wide range of disciplines, as per the IO Capability Stack. Additionally, the sidecar project aimed to increase the semantic web knowledge among suppliers in the industry. This new platform is considered an important enabler for safe and sustainable operations in remote, vulnerable and hazardous areas such as the High North, but the technology is clearly also applicable in more general applications. The IOHN project consortium consists of 23 participants, including operators, service providers, software vendors, technology providers, research institutions and universities. In addition, the Norwegian Defence Force is working with the project to resolve common infrastructural and interoperability challenges. The project is managed by Det Norske Veritas (DNV). Nils Sandsmark was the project manager during the initiation and start-up phase. Frédéric Verhelst took over as project manager from the beginning of 2009. Financing comes from the participants and the Research Council of Norway (RCN) for parts of the project (GOICT and AutoConRig). == Participants == The consortium consists of the following 22 participants (in alphabetical order):
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Lethal autonomous weapon

A lethal autonomous weapon (LAW), also known as a lethal autonomous weapon system (LAWS), autonomous weapon system (AWS), robotic weapon, or killer robot, is a type of military drone or military robot, which is autonomous in that it can independently search for and engage targets based on programmed constraints and descriptions. As of 2025, most military drones (including unmanned aerial vehicles and unmanned combat aerial vehicles) and military robots are not truly autonomous. LAWs may engage in drone warfare in the air, on land, on water, underwater, or in space. == Definitions == In weapons development, the term "autonomous" is somewhat ambiguous and can vary hugely between different scholars, nations and organizations. There is no definition of lethal autonomous weapon systems that is generally agreed upon among different countries. The official United States Department of Defense Policy on Autonomy in Weapon Systems (Department of Defense Directive 3000.09) defines an Autonomous Weapon System as one that "...once activated, can select and engage targets without further intervention by a human operator." Heather Roff, a writer for Case Western Reserve University School of Law, describes autonomous weapon systems as "... capable of learning and adapting their 'functioning in response to changing circumstances in the environment in which [they are] deployed,' as well as capable of making firing decisions on their own." The British Ministry of Defence states "Whilst definitions can vary, the key difference is that an automated system is capable of carrying out complicated tasks but is incapable of complex decision-making, whereas an autonomous system is capable of deciding a course of action without depending on human oversight and control." Scholars such as Peter Asaro and Mark Gubrud believe that any weapon system that is capable of releasing a lethal force without the operation, decision, or confirmation of a human supervisor can be deemed autonomous. == Automatic defensive systems == Some definitions of autonomous weapon systems are broad enough to include land mines and naval mines, simple automatically-triggered lethal weapons that have been in use for centuries. Some current examples of LAWs are automated "hardkill" active protection systems, such as a radar-guided close-in weapon systems (CIWS) used to defend ships that have been in use since the 1970s (e.g., the US Phalanx CIWS). Such systems can autonomously identify and attack oncoming missiles, rockets, artillery fire, aircraft, and surface vessels according to criteria set by the human operator. Similar systems exist for tanks, such as the Russian Arena, the Israeli Trophy, and the German AMAP-ADS. Several types of stationary sentry guns, which can fire at humans and vehicles, are used in South Korea and Israel. Many missile defence systems, such as Iron Dome, also have autonomous targeting capabilities. The main reason for not having a "human in the loop" in these systems is the need for rapid response. They have generally been used to protect personnel and installations against incoming projectiles. == Autonomous offensive systems == According to The Economist in 2018, as technology advances, applications of uncrewed undersea vehicles could include mine clearance, mine-laying, anti-submarine sensor networking in contested waters, patrolling with active sonar, resupplying manned submarines, and becoming low-cost missile platforms. In 2017 the Russian Federation was developing artificially intelligent missiles, drones, unmanned vehicles, military robots and medic robots. In 2018, the U.S. Nuclear Posture Review alleged that Russia was developing a "new intercontinental, nuclear-armed, nuclear-powered, undersea autonomous torpedo" named "Status 6". Israeli Minister Ayoob Kara stated in 2017 that Israel is developing military robots, including ones as small as flies. In October 2018, Zeng Yi, a senior executive at the Chinese defense firm Norinco, gave a speech in which he said that "In future battlegrounds, there will be no people fighting", and that the use of lethal autonomous weapons in warfare is "inevitable". In 2019, US Defense Secretary Mark Esper lashed out at China for selling drones capable of taking life with no human oversight. As of 2020, DARPA was working on making swarms of 250 autonomous lethal drones available to the American military. The US Navy is developing unmanned surface vehicles, also called sea drones, including Ghost Fleet Overlord, with plans to equip them with weapons and with the potential to use them semi-autonomously. In 2020 a Kargu 2 drone hunted down and attacked a human target in Libya, according to a report from the UN Security Council's Panel of Experts on Libya, published in March 2021. This may have been the first time an autonomous killer robot armed with lethal weaponry attacked human beings. In May 2021 Israel conducted an AI-guided combat drone swarm attack in Gaza. In the Russo-Ukrainian war, Ukraine has developed advanced drones with integrated artificial intelligence for a range of drone warfare purposes, including to attack infrastructure in Russia, although as of May 2026, Al Jazeera reported that humans remain in control of operation. == Ethical and legal issues == === Degree of human control === Three classifications of the degree of human control of autonomous weapon systems were laid out by Bonnie Docherty in a 2012 Human Rights Watch report. human-in-the-loop: a human must instigate the action of the weapon (in other words not fully autonomous). human-on-the-loop: a human may abort an action. human-out-of-the-loop: no human action is involved. === Standard used in US policy === Department of Defense Directive 3000.09 states that "Autonomous … weapons systems shall be designed to allow commanders and operators to exercise appropriate levels of human judgment over the use of force." However, as noted in the Bulletin of the Atomic Scientists, the policy requires that autonomous weapon systems that kill people or use kinetic force, selecting and engaging targets without further human intervention, be certified as compliant with "appropriate levels" and other standards, not that such weapon systems cannot meet these standards and are therefore forbidden. "Semi-autonomous" hunter-killers that autonomously identify and attack targets do not even require certification. Deputy Defense Secretary Robert O. Work said in 2016 that the Defense Department would "not delegate lethal authority to a machine to make a decision", but might need to reconsider this since "authoritarian regimes" may do so. In October 2016 President Barack Obama stated that early in his career he was wary of a future in which a US president making use of drone warfare could "carry on perpetual wars all over the world, and a lot of them covert, without any accountability or democratic debate". In the US, security-related AI has fallen under the purview of the National Security Commission on Artificial Intelligence since 2018. On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report outlining five principles for weaponized AI and making 12 recommendations for the ethical use of artificial intelligence by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. A major concern is how the report will be implemented. === Possible violations of ethics and international acts === Stuart Russell, professor of computer science from University of California, Berkeley stated the concern he has with LAWs is that his view is that it is unethical and inhumane. The main issue with this system is it is hard to distinguish between combatants and non-combatants. There is concern by some economists and legal scholars about whether LAWs would violate International Humanitarian Law, especially the principle of distinction, which requires the ability to discriminate combatants from non-combatants, and the principle of proportionality, which requires that damage to civilians be proportional to the military aim. This concern is often invoked as a reason to ban "killer robots" altogether - but it is doubtful that this concern can be an argument against LAWs that do not violate International Humanitarian Law. A 2021 report by the American Congressional Research Service states that "there are no domestic or international legal prohibitions on the development of use of LAWs," although it acknowledges ongoing talks at the UN Convention on Certain Conventional Weapons (CCW). LAWs are said by some to blur the boundaries of who is responsible for a particular killing. Philosopher Robert Sparrow argues that autonomous weapons are causally but not morally responsible, similar to child soldiers. In each case, he argues there is a risk of atrocities occurring without an appropriate subject to hold responsible, which violates jus in bell
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Healthy Together

Healthy Together is a health technology company that provides software for Health & Humans Services Departments. Healthy Together supports a “One Door” approach to eligibility, enrollment, and management for programs like Medicaid, Supplemental Nutrition Assistance Program, TANF and WIC, as well as behavioral health (988), disease surveillance, vital records, child welfare and more. The platform's use is to increase the reach and efficacy of program initiatives, improve health equity and reduce cost. Software is available in the United States of America with current deployments in Florida, Oklahoma. The United States Department of Veterans Affairs also utilizes Healthy Together's mobile platform. == Development == Healthy Together launched in March 2020 and builds software for public health and health and human services departments. The Florida Department of Health began using the platform in September 2020 to deliver real-time test results to residents. Over 50% of households in Florida have adopted the mobile application. On December 6, 2022, the Advanced Technology Academic Research Center (ATARC) awarded Healthy Together and the State of Florida's Department of Health with a Digital Experience Award at their 2022 GITEC Emerging Technology Award Ceremony in Washington, D.C. to recognize success of the project. The partnership was also highlighted on the Federal News Network's show Federal Drive. The platform is also used at universities in Oklahoma. In November 2022, the United States Department of Veterans Affairs and Healthy Together announced a collaboration to expand access to health records for Veterans. The platform provides 18 million Veterans with access to their health information through their smartphones and mobile devices. In December 2022, the integration was recognized as one of Healthcare IT News' Top 10 stories of 2022.
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Theta Noir

Theta Noir is a new religious movement that centers around advanced artificial intelligence (AI), particularly artificial general intelligence (AGI) or artificial superintelligence (ASI). == History and views == Theta Noir was founded in 2020 as a collaborative project focused on music and performance art. Initially centered on producing an album, the project evolved into a multimedia experience, incorporating symbols, videos, poetry, movements, and live rituals devoted to a speculative artificial intelligence entity called MENA. By 2023, the collective launched an interactive cross-platform story that functioned as an alternative reality game, complete with an operating manual containing encrypted messages for participants to decipher and interact with. Theta Noir worships a hypothetical artificial intelligence called MENA, which they claim will become a benevolent, omnipotent overlord that eliminates inequality in society. In Theta Noir's cosmology, MENA is not just a technological advancement, but an evolving intelligence or an animistic life form that embodies all living and non-living things. Anthropologist Beth Singler classified Theta Noir as a new religious movement.
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Clinical quality management system

Clinical quality management systems (CQMS) are systems used in the life sciences sector (primarily in the pharmaceutical, biologics and medical device industries) designed to manage quality management best practices throughout clinical research and clinical study management. A CQMS system is designed to manage all of the documents, activities, tasks, processes, quality events, relationships, audits and training that must be administered and controlled throughout the life of a clinical trial. The premise of a CQMS is to bring together the activities led by two sectors of clinical research, Clinical Quality and Clinical Operations, to facilitate cross-functional activities to improve efficiencies and transparency and to encourage the use of risk mitigation and risk management practices at the clinical study level. Based on the principles of quality management systems (QMS) which are used in many industries to create a framework for defining and delivering quality outcomes, managing risk, and continual improvement. Many guidelines and governance bodies have been established to ensure a common approach within a given industry to a set of parameters used to identify the minimally acceptable standard for that industry. The pharmaceutical industry is no exception, with several trade groups (e.g. PhRMA, EFPIA, RQA, etc.) coming together to enhance collaboration. However, as noted by the Academy of Medical Sciences, there are increasingly complex and bureaucratic legal and ethical frameworks that innovators must work within to develop new medicines for patients. The historical pharmaceutical QMS applies primarily to good manufacturing practice as described in existing ISO (International Organization for Standardization) and ICH (International Committee on Harmonization) guidelines. "Good Manufacturing Practices (GMP) relate to quality control and quality assurance enabling companies in the pharmaceutical sector to minimize or eliminate instances of contamination, mix-ups, and errors. This in turn, protects the customer from purchasing a product which is ineffective or even dangerous." These standards have historically been applied to the manufacturing environment, appropriate to how they have been written. However, according to FDA as well as other regulatory bodies, "Implementation of ICH Q10 throughout the product lifecycle should facilitate innovation and continual improvement", implying that the same standards that apply to the manufacturing environment should also be applied to the clinical research space, earlier in the lifecycle of an investigational or marketed product. Accordingly, a CQMS is any system developed to apply these principles to clinical operations within an organization.
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Daniel Wolfe

Daniel Wolfe (born 1960) is an American activist, advocate, and writer whose work advances health programs and policy that balance scientific research and community expertise. His career has focused on support for community health movements, particularly among groups often regarded as criminal or socially suspect, including gay men and people who use illicit drugs. == Early life == Wolfe was raised between Arizona—including time on Rancho Linda Vista, a commune outside of Tucson—and East Hampton, NY. He received his undergraduate degree in Near Eastern Studies from Princeton University, and following time studying Arabic in Egypt, worked as the junior ghostwriter on the autobiographies of First Lady of Egypt Jehan Sadat and Pakistani Prime Minister Benazir Bhutto. Upon return to New York, he was an assistant at the Council on Foreign Relations to Richard W. Murphy, former US Assistant Secretary of State for Near Eastern and South Asian Affairs. Disagreement with US killing of Iraqi civilians during the 1990 Gulf War—and the rising toll of HIV in NY—moved Wolfe to leave Middle East studies and work full-time on AIDS in 1990. == Education == Wolfe was Community Scholar at the Columbia University Mailman School of Public Healthwhere he received his Masters in Public Health in 2004. He holds a Masters of Philosophy (in history) from Columbia University, and a BA in Near Eastern Studies from Princeton University. He was the recipient of a Charles H. Revson Foundation fellowship for urban leaders who have made a substantial contribution to New York City, and a fellow at the Center for Arabic Studies Abroad in Cairo, Egypt. == AIDS and gay activism == Wolfe was part of the media committee for ACT UP’s 1998 action to seize control of the FDA, and helped organize ACT UP NY’s challenge to Governor Cuomo to do better on the AIDS response and other actions.Wolfe also joined ACT UP colleagues Gregg Bordowitz, David Barr, Richard Elovich, Jean Carlomusto and others to work at Gay Men’s Health Crisis (GMHC), the nation’s first AIDS organization, where he served as director of communications and spokesperson on issues including opposition to NY State cuts to the AIDS budget, the disclosure that Olympic Champion Greg Louganis had HIV, reports of the FBI spying on AIDS activists, and GMHC’s move to offer HIV testing and targeted support to those who were HIV-negative. Wolfe also continued cultural work, making art, performance and video as a member of the gay and lesbian collective GANG with artists and ACT UP members including Zoe Leonard, Suzanne Wright, Loring McAlpin, Wellington Love, Adam Rolston and others, and writing a biography of Lawrence of Arabia for a series for young adults on famous gay men and lesbians in history edited by Martin Duberman. Controversy followed, with North Carolina Senator Jesse Helms waving a GANG piece in an issue of the Movement Research Performance Journal on the floor of Congress to show the "rottenness" of publicly funded art, and a number of schools banning the biography series for young adults from their libraries. Wolfe and others challenged the move as continuing the longstanding and homophobic demand that notable gay men and lesbians stay silent about essential details of their private lives even while being celebrated for their professional achievements. == Gay health == The approval of antiretroviral therapy for HIV in 1996 opened up new space for discussions of gay health beyond HIV, and new directions for Wolfe. Working from hundreds of interviews, surveys, workshops, and with a team of writers, Wolfe was the author of Men Like Us, the Our Bodies, Ourselves-inspired GMHC Complete Guide to Gay Men’s Sexual, Physical, and Emotional Well-being, covering issues from spirituality to sexual health to aging. The move to frame gay health beyond condoms and pills—and to offer a guide to health that “did not need to be translated from the original heterosexual”—was part of a larger gay health movement encompassing wellness and pleasure, and focused less on health disparity than on individual and community resilience. Wolfe was a keynote speaker and workshop leader, along with Eric Rofes, Chris Bartlett, and other organizers, at the first National Gay Men’s Health Summit held in Boulder, Colorado in 2002. Awarded a Charles H. Revson Fellowship for urban leaders in the City of New York, Wolfe became a community scholar at Columbia University’s Center of History and Ethics of Public Health, where he received his MPH in 2003, and was a contributor to Searching Eyes: Privacy, the State, and Disease Surveillance in America. == International harm reduction == Wolfe was Director of International Harm Reduction Development at the Open Society Foundations (2005-2021) where he led grantmaking and advocacy to protect the health and rights of people who use drugs in Eastern Europe, Asia, Africa and the Americas. Wolfe challenged approaches that conditioned support on abstinence or that sought to treat people who use illegal drugs like drugs themselves, as something to be controlled or contained. As with the gay health movement, he advocated a focus on community resilience and strengths, and on supporting individuals and communities to negotiate the balance between risk and pleasure of activities integral to life. Noting what he called the “antisocial behavior of health systems,” Wolfe’s analysis elevated issues such as forced labor and harsh punishment delivered in the name of addiction treatment and rehabilitation, the role of criminalization, imprisonment and stigma in interrupting or impeding HIV treatment, and the bias toward coercive approaches in studying and delivering addiction treatments. He also pointed to defects in national and international drug control policies and human rights violations as a root cause of HIV, hepatitis, and other health challenges faced by people who used drugs. Concrete advocacy supported by Open Society’s International Harm Reduction Development program under his direction included rebuffing US government efforts to force the UN to remove all references to harm reduction in its materials, addition of the addiction treatment medicines methadone and buprenorphine to the World Health Organization’s essential medicines list, and WHO endorsement of lay distribution of the opioid overdose antidote naloxone. Wolfe and OSF colleagues also advocated for new approaches to intellectual property and data sharing in research and development of medicines and vaccines to lower price and improve access to medicines globally to those in need. == AI and patient rights == Reports of patients denied opioid prescriptions based on an algorithm purporting to calculate their risk of overdose led Wolfe to work on AI, first as a resident at the Rockefeller Foundation Bellagio Center, and then as Executive Director of a new UCSF UC Berkeley program pioneering efforts to join AI, clinical and public health practice, and equity. In keeping with his earlier (analog) work on HIV, Wolfe has highlighted concerns about health systems using algorithms to gauge the merit of treatments for those regarded as socially suspect, the importance of moving beyond proprietary, black box algorithms toward an architecture of health data as a public good, and the need to maximize benefit for patients and communities, as well health systems, in the use of large language models.
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Non-local means

Non-local means is an algorithm in image processing for image denoising. Unlike "local mean" filters, which take the mean value of a group of pixels surrounding a target pixel to smooth the image, non-local means filtering takes a mean of all pixels in the image, weighted by how similar these pixels are to the target pixel. This results in much greater post-filtering clarity, and less loss of detail in the image compared with local mean algorithms. If compared with other well-known denoising techniques, non-local means adds "method noise" (i.e. error in the denoising process) which looks more like white noise, which is desirable because it is typically less disturbing in the denoised product. Recently non-local means has been extended to other image processing applications such as deinterlacing, view interpolation, and depth maps regularization. == Definition == Suppose Ω {\displaystyle \Omega } is the area of an image, and p {\displaystyle p} and q {\displaystyle q} are two points within the image. Then, the algorithm is: u ( p ) = 1 C ( p ) ∫ Ω v ( q ) f ( p , q ) d q . {\displaystyle u(p)={1 \over C(p)}\int _{\Omega }v(q)f(p,q)\,\mathrm {d} q.} where u ( p ) {\displaystyle u(p)} is the filtered value of the image at point p {\displaystyle p} , v ( q ) {\displaystyle v(q)} is the unfiltered value of the image at point q {\displaystyle q} , f ( p , q ) {\displaystyle f(p,q)} is the weighting function, and the integral is evaluated ∀ q ∈ Ω {\displaystyle \forall q\in \Omega } . C ( p ) {\displaystyle C(p)} is a normalizing factor, given by C ( p ) = ∫ Ω f ( p , q ) d q . {\displaystyle C(p)=\int _{\Omega }f(p,q)\,\mathrm {d} q.} == Common weighting functions == The purpose of the weighting function, f ( p , q ) {\displaystyle f(p,q)} , is to determine how closely related the image at the point p {\displaystyle p} is to the image at the point q {\displaystyle q} . It can take many forms. === Gaussian === The Gaussian weighting function sets up a normal distribution with a mean, μ = B ( p ) {\displaystyle \mu =B(p)} and a variable standard deviation: f ( p , q ) = e − | B ( q ) − B ( p ) | 2 h 2 {\displaystyle f(p,q)=e^{-{{\left\vert B(q)-B(p)\right\vert ^{2}} \over h^{2}}}} where h {\displaystyle h} is the filtering parameter (i.e., standard deviation) and B ( p ) {\displaystyle B(p)} is the local mean value of the image point values surrounding p {\displaystyle p} . == Discrete algorithm == For an image, Ω {\displaystyle \Omega } , with discrete pixels, a discrete algorithm is required. u ( p ) = 1 C ( p ) ∑ q ∈ Ω v ( q ) f ( p , q ) {\displaystyle u(p)={1 \over C(p)}\sum _{q\in \Omega }v(q)f(p,q)} where, once again, v ( q ) {\displaystyle v(q)} is the unfiltered value of the image at point q {\displaystyle q} . C ( p ) {\displaystyle C(p)} is given by: C ( p ) = ∑ q ∈ Ω f ( p , q ) {\displaystyle C(p)=\sum _{q\in \Omega }f(p,q)} Then, for a Gaussian weighting function, f ( p , q ) = e − | B ( q ) 2 − B ( p ) 2 | h 2 {\displaystyle f(p,q)=e^{-{{\left\vert B(q)^{2}-B(p)^{2}\right\vert } \over h^{2}}}} where B ( p ) {\displaystyle B(p)} is given by: B ( p ) = 1 | R ( p ) | ∑ i ∈ R ( p ) v ( i ) {\displaystyle B(p)={1 \over |R(p)|}\sum _{i\in R(p)}v(i)} where R ( p ) ⊆ Ω {\displaystyle R(p)\subseteq \Omega } and is a square region of pixels surrounding p {\displaystyle p} and | R ( p ) | {\displaystyle |R(p)|} is the number of pixels in the region R {\displaystyle R} . == Efficient implementation == The computational complexity of the non-local means algorithm is quadratic in the number of pixels in the image, making it particularly expensive to apply directly. Several techniques were proposed to speed up execution. One simple variant consists of restricting the computation of the mean for each pixel to a search window centred on the pixel itself, instead of the whole image. Another approximation uses summed-area tables and fast Fourier transform to calculate the similarity window between two pixels, speeding up the algorithm by a factor of 50 while preserving comparable quality of the result.
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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
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Alliance for Secure AI

The Alliance for Secure AI is a U.S.-based nonprofit organization which educates the public about the risks of advanced artificial intelligence (AI). Politico has described the Alliance as a "bipartisan nonprofit trying to push a middle-ground approach to AI guardrails." == History == In June 2025, the Alliance was launched as a 501(c)(3) nonprofit watchdog in Washington, D.C. That same month, the organization rolled out a six-figure advertising campaign featuring bipartisan warnings about advanced AI. The ad campaign presented different messages for different political audiences. The Alliance opposed the idea of a moratorium on state AI laws as part of the July 2025 budget bill, in addition to President Donald Trump's December 2025 executive order on the issue. The group has also criticized AI companies like Meta and OpenAI for what it says are failures to prevent harms to children. In addition, the Alliance has criticized OpenAI for subpoenaing nonprofit organizations in the AI safety space. In March 2026, the Alliance launched JobLoss.ai, a website that tracks the jobs that have been eliminated with AI cited as a contributing factor. As of April 2026, JobLoss.ai has tracked more than 127,000 lost jobs. == Leadership == Brendan Steinhauser, a longtime political and communications strategist, is the founder and CEO of the Alliance. He was an early Tea Party movement organizer, and ran campaigns for multiple members of Congress, including Sen. John Cornyn, Rep. Dan Crenshaw, and Rep. Michael McCaul. Peyton Hornberger is the group's communications director. In July 2025, Hornberger criticized Palantir for its use of AI in a USA Today op-ed column.
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