AI Generator Letter

AI Generator Letter — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Token maxxing

  Token Maxxing or Token Maxing is a metric used in an attempt to track productivity in the workplace especially for those using Artificial Intelligence (AI) based services. AI services charge for each token which represent units of effort expended by an AI service to solve a problem. Some believe that token consumption equates to productivity and thus can be used as a metric to monitor an employee's work. Supporters believe that higher token usage indicates higher productivity and higher utilization of powerful AI services. This also suggests that those not consuming enough tokens may be less productive and underutilizing powerful AI services. This belief might lead to an environment that incentivizes higher token usage to predict increased productivity. Critics of token maxxing as a metric claim that prudent workers will maximize any metric that management wants increased to gain a workplace advantage. For example: Engineers in the tech industries pressed to consume as many tokens as possible might run several AI agents in tandem, enter longer input prompts, or automate their tasks to maximize their token consumption. To management, this higher token usage may indicate potential productivity, but in reality may cause additional token costs, worker burnout, or actually create more bloated code of lower quality. Another claim is AI service companies potentially benefit from such an emphasis on token consumption and actively encourage the trend. Some developers have publicly advocated the practice. Developer Sigrid Jin, who said he used 50 billion tokens in a single year, has argued that maximizing token consumption is the best way to understand the value of AI, advising others to spend as much on AI usage as they pay in rent to obtain a return on investment. == See Also == Goodhart's law Perverse incentive Jevons Paradox

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  Liveness test

  A liveness test, liveness check or liveness detection is an automated method for determining whether a subject is a real person or part of a spoofing attack. The technique is used as part of know your customer checks in financial services and during facial age estimation. Liveness detection is a cornerstone of digital safety. == Test process == The threat in face spoofing attacks is that "the attacker only needs to find a good face swap library on Github and understand how to inject the model into the camera feed during the KYC process". Fraudsters usually buy stolen IDs on the dark web to start a deepfake attack. An AI-powered generative adversarial network (GAN) can then generate the face swapping model that many online verification services fail to detect. Low level hackers may use face swapping apps such as SwapFace, DeepFaceLive, and Swapstream (increasing interest for those apps in 2023 according to Google Trends). In a video liveness test, users are typically asked to look into a camera and to move, smile or blink, and features of their moving face may then be compared to that of a still image. Artificial intelligence is used to counter presentation attacks such as deepfakes or users wearing hyperrealistic masks, or video injection attacks. Other forms of liveness test include checking for a pulse when using a fingerprint scanner or checking that a person's voice is not a recording or artificially generated during speaker recognition. == Adoption and certification == In a 2022 report published by the security firm Sensity, it was demonstrated that the liveness test of most US banks was easily cheated with new and publicly-available AI-powered techniques. Many of these banks disregarded the results of the report. In the first half of 2023, the security firm iProov detected a 704% increase in face-swap attacks. In 2023, in the UK, many customers of Ryanair were upset to have to go through many ID verification checks, including liveness tests, before boarding, as the airline was using it as a mean to deter customers to buy tickets through third-party websites. In the first half of 2024 iBeta Quality Assurance issued 18 new ISO/IEC 30107-3 Presentation Attack Detection certificates, raising the cumulative total to 85 since 2018. In January 2024, the Department of Homeland Security (DHS) opened applications from vendors to test their Liveness test. Identity frauds peaked during the COVID-19 lockdown, leading government agencies to take reinforced measures to secure their digital applications.

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  Predictive Model Markup Language

  The Predictive Model Markup Language (PMML) is an XML-based predictive model interchange format conceived by Robert Lee Grossman, then the director of the National Center for Data Mining at the University of Illinois at Chicago. PMML provides a way for analytic applications to describe and exchange predictive models produced by data mining and machine learning algorithms. It supports common models such as logistic regression and other feedforward neural networks. Version 0.9 was published in 1998. Subsequent versions have been developed by the Data Mining Group. Since PMML is an XML-based standard, the specification comes in the form of an XML schema. PMML itself is a mature standard with over 30 organizations having announced products supporting PMML. == PMML components == A PMML file can be described by the following components: Header: contains general information about the PMML document, such as copyright information for the model, its description, and information about the application used to generate the model such as name and version. It also contains an attribute for a timestamp which can be used to specify the date of model creation. Data Dictionary: contains definitions for all the possible fields used by the model. It is here that a field is defined as continuous, categorical, or ordinal (attribute optype). Depending on this definition, the appropriate value ranges are then defined as well as the data type (such as, string or double). Data Transformations: transformations allow for the mapping of user data into a more desirable form to be used by the mining model. PMML defines several kinds of simple data transformations. Normalization: map values to numbers, the input can be continuous or discrete. Discretization: map continuous values to discrete values. Value mapping: map discrete values to discrete values. Functions (custom and built-in): derive a value by applying a function to one or more parameters. Aggregation: used to summarize or collect groups of values. Model: contains the definition of the data mining model. E.g., A multi-layered feedforward neural network is represented in PMML by a "NeuralNetwork" element which contains attributes such as: Model Name (attribute modelName) Function Name (attribute functionName) Algorithm Name (attribute algorithmName) Activation Function (attribute activationFunction) Number of Layers (attribute numberOfLayers) This information is then followed by three kinds of neural layers which specify the architecture of the neural network model being represented in the PMML document. These attributes are NeuralInputs, NeuralLayer, and NeuralOutputs. Besides neural networks, PMML allows for the representation of many other types of models including support vector machines, association rules, Naive Bayes classifier, clustering models, text models, decision trees, and different regression models. Mining Schema: a list of all fields used in the model. This can be a subset of the fields as defined in the data dictionary. It contains specific information about each field, such as: Name (attribute name): must refer to a field in the data dictionary Usage type (attribute usageType): defines the way a field is to be used in the model. Typical values are: active, predicted, and supplementary. Predicted fields are those whose values are predicted by the model. Outlier Treatment (attribute outliers): defines the outlier treatment to be use. In PMML, outliers can be treated as missing values, as extreme values (based on the definition of high and low values for a particular field), or as is. Missing Value Replacement Policy (attribute missingValueReplacement): if this attribute is specified then a missing value is automatically replaced by the given values. Missing Value Treatment (attribute missingValueTreatment): indicates how the missing value replacement was derived (e.g. as value, mean or median). Targets: allows for post-processing of the predicted value in the format of scaling if the output of the model is continuous. Targets can also be used for classification tasks. In this case, the attribute priorProbability specifies a default probability for the corresponding target category. It is used if the prediction logic itself did not produce a result. This can happen, e.g., if an input value is missing and there is no other method for treating missing values. Output: this element can be used to name all the desired output fields expected from the model. These are features of the predicted field and so are typically the predicted value itself, the probability, cluster affinity (for clustering models), standard error, etc. The latest release of PMML, PMML 4.1, extended Output to allow for generic post-processing of model outputs. In PMML 4.1, all the built-in and custom functions that were originally available only for pre-processing became available for post-processing too. == PMML 4.0, 4.1, 4.2 and 4.3 == PMML 4.0 was released on June 16, 2009. Examples of new features included: Improved Pre-Processing Capabilities: Additions to built-in functions include a range of Boolean operations and an If-Then-Else function. Time Series Models: New exponential Smoothing models; also place holders for ARIMA, Seasonal Trend Decomposition, and Spectral density estimation, which are to be supported in the near future. Model Explanation: Saving of evaluation and model performance measures to the PMML file itself. Multiple Models: Capabilities for model composition, ensembles, and segmentation (e.g., combining of regression and decision trees). Extensions of Existing Elements: Addition of multi-class classification for Support Vector Machines, improved representation for Association Rules, and the addition of Cox Regression Models. PMML 4.1 was released on December 31, 2011. New features included: New model elements for representing Scorecards, k-Nearest Neighbors (KNN) and Baseline Models. Simplification of multiple models. In PMML 4.1, the same element is used to represent model segmentation, ensemble, and chaining. Overall definition of field scope and field names. A new attribute that identifies for each model element if the model is ready or not for production deployment. Enhanced post-processing capabilities (via the Output element). PMML 4.2 was released on February 28, 2014. New features include: Transformations: New elements for implementing text mining New built-in functions for implementing regular expressions: matches, concat, and replace Simplified outputs for post-processing Enhancements to Scorecard and Naive Bayes model elements PMML 4.3 was released on August 23, 2016. New features include: New Model Types: Gaussian Process Bayesian Network New built-in functions Usage clarifications Documentation improvements Version 4.4 was released in November 2019. == Release history == == Data Mining Group == The Data Mining Group is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008. The Data Mining Group also developed a standard called Portable Format for Analytics, or PFA, which is complementary to PMML.

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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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  Cognition Network Technology

  Cognition Network Technology (CNT), also known as Definiens Cognition Network Technology, is an object-based image analysis method developed by Nobel laureate Gerd Binnig together with a team of researchers at Definiens AG in Munich, Germany. It serves for extracting information from images using a hierarchy of image objects (groups of pixels), as opposed to traditional pixel processing methods. To emulate the human mind's cognitive powers, Definiens used patented image segmentation and classification processes, and developed a method to render knowledge in a semantic network. CNT examines pixels not in isolation, but in context. It builds up a picture iteratively, recognizing groups of pixels as objects. It uses the color, shape, texture and size of objects as well as their context and relationships to draw conclusions and inferences, similar to human analysis. == History == In 1994 Professor Gerd Binnig founded Definiens. CNT was first available with the launch of the eCognition software in May 2000. In June 2010, Trimble Navigation Ltd (NASDAQ: TRMB) acquired Definiens business asset in earth sciences markets, including eCognition software, and also licensed Definiens' patented CNT. In 2014, Definiens was acquired by MedImmune, the global biologics research and development arm of AstraZeneca, for an initial consideration of $150 million. == Software == Definiens Tissue Studio Definiens Tissue Studio is a digital pathology image analysis software application based on CNT. The intended use of Definiens Tissue Studio is for biomarker translational research in formalin-fixed, paraffin-embedded tissue samples which have been treated with immunohistochemical staining assays, or hematoxylin and eosin (H&E). The central concept behind Definiens Tissue Studio is a user interface that facilitates machine learning from example digital histopathology images to derive an image analysis solution suitable for the measurement of biomarkers and/or histological features within pre-defined regions of interest on a cell-by-cell basis, and within sub-cellular compartments. The derived image analysis solution is then automatically applied to subsequent digital images to objectively measure defined sets of multiparametric image features. These data sets are used for further understanding the underlying biological processes that drive cancer and other diseases. Image processing and data analysis are performed either on a local desktop computer workstation, or on a server grid. eCognition The eCognition suite offers three components that can be used stand-alone or in combination to solve image analysis tasks. eCognition Developer is a development environment for object-based image analysis. It is used in earth sciences to develop rule sets (or applications) for the analysis of remote sensing data. eCognition Architect enables non-technical users to configure, calibrate and execute image analysis workflows created in eCognition Developer. eCognition Server software provides a processing environment for batch execution of image analysis jobs. eCognition software is utilized in numerous remote sensing and geospatial application scenarios and environments, using a variety of data types: Generic: Rapid Mapping, Change Detection, Object Recognition By environment: Diverse Landcover Mapping, Urban Analysis (i.e. impervious surface area analysis for taxation, property assessment for insurance, inventory of green infrastructure), Forestry (i.e. biomass measurement, species identification, firescar measurement), Agriculture (i.e. regional planning, precision farming, crisis response), Marine and Riparian (i.e. ecosystem evaluation, disaster management, harbor monitoring). Other: Defense, security, atmosphere and climate The online eCognition community was launched in July 2009 and had 2813 members as of July 9, 2010. Membership is distributed globally and user conferences are held regularly, the last having taken place in November 2009 in Munich, Germany. The bi-annual GEOBIA (Geographic Object-Based Image Analysis) conference is heavily attended by eCognition users, with the majority of presentations based on eCognition software.

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  Jakub Pachocki

  Jakub Pachocki (born 1991) is a Polish computer scientist and former competitive programmer. He is best known as OpenAI's chief scientist and for his role in overseeing development of GPT-4. == Background == Pachocki was born in 1991 in Gdańsk, Poland. In high school, he was a six-time finalist of the Polish Olympiad in Informatics. In 2009, he qualified for the International Olympiad in Informatics, winning a silver medal. Pachocki obtained his undergraduate degree in Computer Science from the University of Warsaw. He represented his university at the International Collegiate Programming Contest with his team winning a gold medal and coming second place overall in 2012. In the same year he was also the champion of the Google Code Jam. From 2011 to 2012, Pachocki worked at Facebook as a software engineering intern. Pachocki attended graduate school at Carnegie Mellon University, where he obtained his PhD under the supervision of Gary Miller. == Career == After graduation, Pachocki did postdoc work at Harvard University and Simons Institute for the Theory of Computing. === OpenAI === In 2017, Pachocki joined OpenAI. In 2021, he became OpenAI's research director where he led the development of GPT-4 and OpenAI Five. In May 2024, he became chief scientist after his mentor Ilya Sutskever left the company. OpenAI CEO Sam Altman has called Pachocki "easily one of the greatest minds of our generation". == Competitive programming achievements == International Olympiad in Informatics: Silver medal (2009) International Collegiate Programming Contest World Finals: Gold medal (second place overall in 2012) Google Code Jam: Champion (2012), Third place (2011) Facebook Hacker Cup: Second place (2013) TopCoder Open Algorithm: Second place (2012) A more comprehensive list of achievements can be found at the Competitive Programming Hall Of Fame website.

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  Ratio Club

  The Ratio Club was a small British informal dining club from 1949 to 1958 of young psychiatrists, psychologists, physiologists, mathematicians and engineers who met to discuss issues in cybernetics. == History == The idea of the club arose from a symposium on animal behaviour held in July 1949 by the Society of Experimental Biology in Cambridge. The club was founded by the neurologist John Bates, with other notable members such as W. Ross Ashby. The name Ratio was suggested by Albert Uttley, it being the Latin root meaning "computation or the faculty of mind which calculates, plans and reasons". He pointed out that it is also the root of rationarium, meaning a statistical account, and ratiocinatius, meaning argumentative. The use was probably inspired by an earlier suggestion by Donald Mackay of the 'MR club', from Machina ratiocinatrix, a term used by Norbert Wiener in the introduction to his then recently published book Cybernetics, or Control and Communication in the Animal and the Machine. Wiener used the term in reference to calculus ratiocinator, a calculating machine constructed by Leibniz. The initial membership was W. Ross Ashby, Horace Barlow, John Bates, George Dawson, Thomas Gold, W. E. Hick, Victor Little, Donald MacKay, Turner McLardy, P. A. Merton, John Pringle, Harold Shipton, Donald Sholl, Eliot Slater, Albert Uttley, W. Grey Walter and John Hugh Westcott. Alan Turing joined after the first meeting with I. J. Good, Philip Woodward and William Rushton added soon after. Giles Brindley attended several meetings as a guest. Warren McCulloch made presentations to the club twice, the first time at its inaugural meeting (a talk which the members found disappointing), and became a correspondent with and supporter of a number of its members. Others who attended at least one Ratio Club event as guests included Walter Pitts, Claude Shannon, J.Z. Young, C.H. Waddington, Peter Elias, J. C. R. Licklider, Oliver Selfridge, Benoît Mandelbrot, Colin Cherry and Anthony Oettinger. One one occasion I.J. Good brought along the then director of the USA's National Security Agency (presumably either Ralph Canine or John Samford given the dates). Several members admired the work of psychologist and philosopher Kenneth Craik and considered him an important influence; according to Husbands and Holland "there is no doubt Craik would have been a leading member of the club" had he not died young in 1945. The club has been considered the most influential cybernetics group in the UK, and many of its members went on to become prominent scientists.

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  Trustworthy AI

  Trustworthy AI refers to artificial intelligence systems that are designed to have transparent reasoning, are explainable (XAI), accountable, robust, fair and honest, respectful of data privacy, and steerable or alignable with human goals. == Terminology == Recent work in AI ethics distinguishes trustworthiness and trustability as two different conditions relevant to trustworthy AI. Trustworthiness is concerned with whether an AI system or the institutions deploying it merit trust by being reliable, fair, and accountable. Trustability, on the other hand, is the prior question of whether a given entity is even the kind of thing to which interpersonal trust can coherently apply as opposed to mere instrumental reliance. Some philosophers argue that current AI systems are best understood as tools that are not genuine targets of interpersonal trust. They argue that trust should be directed toward the human and institutional arrangements that govern the systems' design, deployment, and oversight. This stance supports interpreting "trustworthy AI" as trustworthy governance and use of AI rather than trust in the artifacts themselves. Transparency in AI involves making the processes and decisions of such systems understandable to users and stakeholders. Accountability ensures that there are protocols for addressing adverse outcomes or biases that may arise, with designated responsibilities for oversight and remediation. Robustness and security aim to ensure that AI systems perform reliably under various conditions and are safeguarded against malicious attacks. Harmlessness can be achieved by refusal training: training the models to avoid problematic requests, and by adding filters to detect and prevent discussion on biased, unethical, or dangerous outputs. There is research on how to train AI so that it aligns with human goals. == Techniques and ITU standardization == Trustworthy AI creation is a goal of AI governance and policymaking. To achieve transparency and data privacy, several privacy-enhancing technologies (PETs) can be used. These include: Homomorphic encryption for computing with encrypted data without ever decrypting it. Federated learning and secure multi-party computation (MPC) for distributing the model training without sharing information between the learning centers and computing servers. Differential privacy for exposing statistical data while guaranteeing that no private information is exposed. Zero-knowledge proof - providing proven validity for statements without disclosing any extra information. A work programme for achieving Trustworthy AI was set up by the International Telecommunication Union, an agency of the United Nations, initiated under its AI for Good programme. Its origin lies with the ITU-WHO Focus Group on Artificial Intelligence for Health, where a strong need for both privacy and analytics created demand for a standard in these technologies. In 2020, AI for Good moved online, and the TrustworthyAI seminar series was established to initiate discussions on these topics. This eventually led to standardization activities. === Multi-party computation === Secure multi-party computation (MPC) is being standardized under "Question 5" (the incubator) of ITU-T Study Group 17. === Homomorphic encryption === Homomorphic encryption allows for computing on encrypted data, where the outcomes or result is still encrypted and unknown to those performing the computation, but can be deciphered by the original encryptor. It is often developed with the goal of enabling use in jurisdictions different from the data creation (under, for instance, GDPR). ITU has been collaborating since the early stage of the HomomorphicEncryption.org standardization meetings, which has developed a standard on homomorphic encryption. The fifth homomorphic encryption meeting was hosted at ITU HQ in Geneva. === Federated learning === Zero-sum masks as used by federated learning for privacy preservation are used extensively in the multimedia standards of ITU-T Study Group 16 (VCEG) such as JPEG, MP3, H.264, and H.265 (commonly known as MPEG). === Zero-knowledge proof === Previous pre-standardization work on the topic of zero-knowledge proof has been conducted in the ITU-T Focus Group on Digital Ledger Technologies. === Differential privacy === The application of differential privacy in the preservation of privacy was examined at several of the "Day 0" machine learning workshops at AI for Good Global Summits. == Mozilla "Rebel Alliance" == In January 2026, the Mozilla Foundation and its subsidiaries announced a strategic shift to deploy their entire $1.4 billion reserve into building what foundation president Mark Surman termed a "rebel alliance" for trustworthy AI. Framed by Surman as a mission-driven alternative to the market dominance of OpenAI and Anthropic, the initiative seeks to establish an open-source AI stack by 2028. The alliance includes several startups funded via Mozilla Ventures, specifically focusing on decentralized governance and transparency: Trail: A firm developing AI compliance frameworks for regulated industries. Transformer Lab: A developer of open-source tools for AI model management. Oumi: A platform for training and deploying open-source models. The "rebel alliance" terminology is a historical reference to Mozilla's efforts in 1998 to challenge Microsoft's browser monopoly. While the $1.4 billion in funding is significant, it has been contrasted with the tens of billions in capital raised by proprietary competitors like OpenAI.

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  Semi-automation

  Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.

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  Open Mind Common Sense

  Open Mind Common Sense (OMCS) is an artificial intelligence project based at the Massachusetts Institute of Technology (MIT) Media Lab whose goal is to build and utilize a large commonsense knowledge base from the contributions of many thousands of people across the Web. It has been active from 1999 to 2016. Since its founding, it has accumulated more than a million English facts from over 15,000 contributors in addition to knowledge bases in other languages. Much of OMCS's software is built on three interconnected representations: the natural language corpus that people interact with directly, a semantic network built from this corpus called ConceptNet, and a matrix-based representation of ConceptNet called AnalogySpace that can infer new knowledge using dimensionality reduction. The knowledge collected by Open Mind Common Sense has enabled research projects at MIT and elsewhere. == History == The project was the brainchild of Marvin Minsky, Push Singh, Catherine Havasi, and others. Development work began in September 1999, and the project opened to the Internet a year later. Havasi described it in her dissertation as "an attempt to ... harness some of the distributed human computing power of the Internet, an idea which was then only in its early stages." The original OMCS was influenced by the website Everything2 and its predecessor, and presents a minimalist interface that is inspired by Google. Push Singh would have become a professor at the MIT Media Lab and lead the Common Sense Computing group in 2007, but committed suicide on February 28, 2006. The project is currently run by the Digital Intuition Group at the MIT Media Lab under Catherine Havasi. == Database and website == There are many different types of knowledge in OMCS. Some statements convey relationships between objects or events, expressed as simple phrases of natural language: some examples include "A coat is used for keeping warm", "The sun is very hot", and "The last thing you do when you cook dinner is wash your dishes". The database also contains information on the emotional content of situations, in such statements as "Spending time with friends causes happiness" and "Getting into a car wreck makes one angry". OMCS contains information on people's desires and goals, both large and small, such as "People want to be respected" and "People want good coffee". Originally, these statements could be entered into the Web site as unconstrained sentences of text, which had to be parsed later. The current version of the Web site collects knowledge only using more structured fill-in-the-blank templates. OMCS also makes use of data collected by the Game With a Purpose "Verbosity". In its native form, the OMCS database is simply a collection of these short sentences that convey some common knowledge. In order to use this knowledge computationally, it has to be transformed into a more structured representation. == ConceptNet == ConceptNet is a semantic network based on the information in the OMCS database. ConceptNet is expressed as a directed graph whose nodes are concepts, and whose edges are assertions of common sense about these concepts. Concepts represent sets of closely related natural language phrases, which could be noun phrases, verb phrases, adjective phrases, or clauses. ConceptNet is created from the natural-language assertions in OMCS by matching them against patterns using a shallow parser. Assertions are expressed as relations between two concepts, selected from a limited set of possible relations. The various relations represent common sentence patterns found in the OMCS corpus, and in particular, every "fill-in-the-blanks" template used on the knowledge-collection Web site is associated with a particular relation. The data structures that make up ConceptNet were significantly reorganized in 2007, and published as ConceptNet 3. The Software Agents group currently distributes a database and API for the new version 4.0. In 2010, OMCS co-founder and director Catherine Havasi, with Robyn Speer, Dennis Clark and Jason Alonso, created Luminoso, a text analytics software company that builds on ConceptNet. It uses ConceptNet as its primary lexical resource in order to help businesses make sense of and derive insight from vast amounts of qualitative data, including surveys, product reviews and social media. == Machine learning tools == The information in ConceptNet can be used as a basis for machine learning algorithms. One representation, called AnalogySpace, uses singular value decomposition to generalize and represent patterns in the knowledge in ConceptNet, in a way that can be used in AI applications. Its creators distribute a Python machine learning toolkit called Divisi for performing machine learning based on text corpora, structured knowledge bases such as ConceptNet, and combinations of the two. == Comparison to other projects == Other similar projects include Never-Ending Language Learning, Mindpixel (discontinued), Cyc, Learner, SenticNet, Freebase, YAGO, DBpedia, and Open Mind 1001 Questions, which have explored alternative approaches to collecting knowledge and providing incentive for participation. The Open Mind Common Sense project differs from Cyc because it has focused on representing the common sense knowledge it collected as English sentences, rather than using a formal logical structure. ConceptNet is described by one of its creators, Hugo Liu, as being structured more like WordNet than Cyc, due to its "emphasis on informal conceptual-connectedness over formal linguistic-rigor".

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  Omar Al Olama

  Omar Sultan Al Olama (Arabic: عمر سلطان العلماء; born 16 February 1990) is Minister of State for Artificial Intelligence, Digital Economy, and Remote Work Applications in the United Arab Emirates. He was appointed in October 2017 by Vice President and Prime Minister of the UAE and Ruler of Dubai, Sheikh Mohammed bin Rashid Al Maktoum. The UAE was the first country to appoint a minister for artificial intelligence. == Early life and education == Al Olama was born on 16 February 1990 in Dubai. He has a bachelor's degree in Business and Administration and Management from the American University in Dubai, and a Diploma in Excellence and Project Management from the American University in Sharjah. == Career == Between February 2012 and May 2014, Al Olama was member of the corporate planning at the UAE's Prime Minister's Office. From November 2015 to November 2016, he was Deputy Head of Minister's Office at the UAE's Prime Minister's Office. Between December 2015 and October 2017, he was Secretary General of the World Organization of Racing Drones. In November 2017, he was appointed member of the Board of Trustees of Dubai Future Foundation and Deputy Managing Director of the Foundation. In July 2016, Al Olama was appointed the managing director, and later in 2021 appointed Vice-Chair of the World Government Summit. In 2021, Al Olama was appointed as the Chairman of the Dubai Chamber of Digital Economy, a sub-section of Dubai Chamber of Commerce and Industry. During the cabinet reshuffle in 2023, Al Olama was appointed as the Director General of the Prime Minister's Office, concurrently maintaining his role as the Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications. == Memberships == In November 2017, Al Olama was appointed as a member of the Future of Digital Economy and Society Council, part of the World Economic Forum (WEF). Later in 2023, the World Economic Forum selected Al Olama to join the steering committee of the AI Governance Alliance, a group comprising 10 global leaders in the digital and technological fields. In 2019, Al Olama was appointed as Chair of the Advisory Board of the Mohamed bin Zayed University of Artificial Intelligence. In 2022, Al Olama was appointed by the UAE Cabinet as Vice-Chair of the Higher Committee for Government Digital Transformation, and also appointed by the Government of Dubai as Vice-Chair of the Higher Committee for Future Technology. In 2022, Al Olama was appointed Chairman of the oversight committee of the Dubai Future District Fund. Since 2023, Al Olama has been on the High-Level Advisory Body on Artificial Intelligence. In 2023, Al Olama, recognized as the world's first minister for artificial intelligence, was included in Time Magazine's inaugural list of the 100 most influential people in AI.

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  ELVIS Act

  The ELVIS Act or Ensuring Likeness Voice and Image Security Act, signed into law by Tennessee Governor Bill Lee on March 21, 2024, marked a significant milestone in the area of regulation of artificial intelligence and public sector policies for artists in the era of artificial intelligence (AI) and AI alignment. It was noted as the first enacted legislation in the United States specifically designed to protect musicians from the unauthorized use of their voices through artificial intelligence technologies and against audio deepfakes and voice cloning. This legislation distinguishes itself by adding penalties for copying a performer's voice. == Origin and advocacy == The inception of the ELVIS Act has been attributed to Gebre Waddell, founder of Sound Credit, who initially conceptualized a framework in 2023 that later evolved into the legislation. Representative Justin J. Pearson acknowledged Waddell's pivotal role during the March 4 House Floor Session on the bill. Leading Tennessee musicians supported the ELVIS Act. Tennessee Governor Bill Lee endorsed it as a Governor's Bill, and it was introduced in the Tennessee Legislature as House Bill 2091 by William Lamberth (R-44) and Senate Bill 2096 by Jack Johnson (R-27). The ELVIS Act is an amendment to a 1984 law that was the result of the Elvis Presley estate litigation for controlling how his likeness could be used after death. == Lobbying from the recording industry == The legislative journey of the ELVIS Act included a broad coalition of music industry stakeholders, including: These organizations, led by the Recording Academy and the RIAA, played roles in drafting the legislation, advocating for passage, and rallying support among the industry and legislators. The act gained momentum through discussions that bridged industry concerns with legislative action. This collaborative process led to a proposal that specifically targets the use of AI to create unauthorized reproductions of artists' voices and images. == Opposition == The ELVIS Act saw industry opposition from the Motion Picture Association, including testimony in the House Banking & Consumer Affairs Subcommittee, including remarks that the law risks "interference with our members’ ability to portray real people and events." TechNet, representing companies such as OpenAI, Google and Amazon, expressed their opposition in the hearing to the bill as drafted, asserting that the language was too broadly written and could have unintended consequences. Other concerns included its potential application to cover bands, but lawmakers assured people that this was not the intention. The bill passed the Tennessee House and Senate with a unanimous, bi-partisan vote including 93 ayes and 0 Noes in the House, and 30 ayes and 0 noes in the Senate. == Passage == By explicitly addressing AI impersonation, the ELVIS Act originated a legal approach to safeguarding personal rights, in the context of digital and technological advancements. It extends protections to an artist's voice and likeness, areas vulnerable to exploitation with the proliferation of AI technologies that occurred in 2023. The legislation received widespread support from the music industry, signaling a significant step forward in the ongoing effort to balance innovation with the protection of individual rights and creative integrity. It was reported as underscoring Tennessee's commitment to its musical heritage and showed the state as a leader in adapting copyright and privacy protections to the modern technological landscape. Artists including Chris Janson and Luke Bryan appeared at the signing ceremony hosted at Robert's Western World to support the new law and commemorate its passing. == Legal precedent == The ELVIS Act was reported as representing a development in the discourse surrounding AI, intellectual property, and personal rights. It was hoped by proponents to set a precedent for future legislative efforts both within and beyond Tennessee, offering a model for how states and potentially the federal government could address similar challenges. As AI technology continues to evolve, the act represents a foundational framework for protecting the authenticity and rights of artists, ensuring contributions remain protected. The act prohibits usage of AI to clone the voice of an artist without consent and can be criminally enforced as a Class A misdemeanor. This legislation's success was hoped by its supporters to inspire similar actions in other states, contributing to a unified approach to copyright and privacy in the digital age. Such a national response would reinforce the importance of safeguarding artists' rights against unauthorized use of their voices and likenesses.

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  Open-source robotics

  Open-source robotics is a branch of robotics where robots are developed with open-source hardware and free and open-source software, publicly sharing blueprints, schematics, and source code. It is thus closely related to the open design movement, the maker movement and open science. == Requirements == Open source robotics means that information about the hardware is easily discerned, so that others can easily rebuild it. In turn, this requires design to use only easily available standard subcomponents and tools, and for the build process to be documented in detail including a bill of materials and detailed ('Ikea style') step-by-step building and testing instructions. (A CAD file alone is not sufficient, as it does not show the steps for performing or testing the build). These requirements are standard to open source hardware in general, and are formalised by various licences, certifications, especially those defined by the peer-reviewed journals Journal of Open Hardware and HardwareX. Licensing requirements for software are the same as for any open source software. But in addition, for software components to be of practical use in real robot systems, they need to be compatible with other software, usually as defined by some robotics middleware community standard. == Hardware systems == Applications to date include: Robot arms, e.g. PARA or Thor Wheeled mobile robots. e.g. OpenScout Four-legged robots such as the Open Dynamic Robot Initiative UAV quadcopters (drones) such as Agilicious Humanoid robots, e.g. iCub, Berkeley Humanoid Lite Self-driving cars, e.g. OpenPodcar (→ Personal rapid transit) Submersible robots, eg. OpenFish Laboratory robotics such as chemical liquid handling Vertical farming Swarm robots, e.g. HeRoSwarm Domestic tasks: vacuum cleaning, floor washing and grass mowing Robot sports including robot combat and autonomous racing Education == Hardware subcomponents == Most open source hardware definitions allow non-open subcomponents to be used in modular design, as long as they are easily available. However many designs try to push openness down into as many subcomponents as possible, with the aim of ultimately reaching fully open designs. Open hardware manual-drive vehicles and their subcomponents, such as from Open Source Ecology, are often used as starting points and extended with automation systems. Open subcomponents can include open-source computing hardware as subcomponents, such as Arduino and RISC-V, as well as open source motors and drivers such as the Open Source Motor Controller and ODrive. Open hardware robotics interface boards can simplify interfacing between middleware software and physical hardware. == Software subcomponents == === Middleware === Robotics middleware is software which links multiple other software components together. In robotics, this specifically means real-time communication systems with standardized message passing protocols. The predominant open source middleware is ROS2, the robot operating system, now as version 2. Other alternatives include ROS1, YARP — used in the iCub, URBI, and Orca. Open source middleware is usually run on an open source operating system, especially the Ubuntu distribution of Linux. === Driver software === Most robot sensors and actuators require software drivers. There is little standardization of open source software at this level, because each hardware device is different. Creating open drivers for closed hardware is difficult as it requires both low level programming and reverse engineering. === Simulation software === Open source robotics simulators include Gazebo, MuJoCo and Webots. Open source 3D game engines such as Godot are also sometimes used as simulators, when equipped with suitable middleware interfaces. === Automation software === At the level of AI, many standard algorithms have open source software implementations, mostly in ROS2. Major components include: Machine vision systems such as the YOLO object detector. 3D photogrammetry Navigation including SLAM and planning such as nav2 Arm inverse kinematics such as moveIt2 == Community == The first signs of the increasing popularity of building and sharing robot designs were found with the maker culture community. What began with small competitions for remote operated vehicles (e.g. Robot combat), soon developed to the building of autonomous telepresence robots such as Sparky and then true robots (being able to take decisions themselves) as the Open Automaton Project. Several commercial companies now also produce kits for making simple robots. The community has adopted open source hardware licenses, certifications, and peer-reviewed publications, which check that source has been made correctly and permanently available under community definitions, and which validate that this has been done. These processes have become critically important due to many historical projects claiming to be open source but them reverting on the promise due to commercialisation or other pressures. As with other forms of open source hardware, the community continues to debate precise criteria for 'ease of build'. A common standard is that designs should be buildable by a technical university student, in a few days, using typical fablab tools, but definitions of all of these subterms can also be debated. Compared to other forms of open source hardware, open source robotics typically includes a large software element, so involves software as well as hardware engineers. Open source concepts are more established in open source software than hardware, so robotics is a field in which those concepts can be shared and transferred from software to hardware. While the community in open source robotics is multi-faceted with a wide range of backgrounds, a sizable sub-community uses the ROS middleware and meets at the ROSCon conferences to discuss development of ROS itself and automation components built on it.

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  Alex Krizhevsky

  Alex Krizhevsky is a Canadian computer scientist most noted for his work on artificial neural networks and deep learning. In 2012, Krizhevsky, Ilya Sutskever and their PhD advisor Geoffrey Hinton, at the University of Toronto, developed a powerful visual-recognition network AlexNet using only two GeForce-branded GPU cards. This revolutionized research in neural networks. Previously neural networks were trained on CPUs. The transition to GPUs opened the way to the development of advanced AI models. == AlexNet == Motivated by Sutskever and inspired by Hinton, Krizhevsky developed AlexNet to expand the limits in image recognition and classification. Building on Convolutional Neural Networks and Sutskever’s Deep Neural Network approach of deepening the neural layers far beyond the convention of the time—as well as adding Dropout for training resilience—AlexNet won the ImageNet challenge in 2012. The team presented their paper for AlexNet at NeurIPS (NIPS) 2012. Shortly after AlexNet’s debut, Krizhevsky and Sutskever sold their startup, DNN Research Inc., to Google. Krizhevsky left Google in September 2017 after losing interest in the work, to work at the company Dessa in support of new deep-learning techniques. Many of his numerous papers on machine learning and computer vision are frequently cited by other researchers. He is also the main author of the CIFAR-10 and CIFAR-100 datasets. == Legacy == AlexNet is widely credited with igniting the deep learning revolution. Its success demonstrated the effectiveness of deep neural networks trained on GPUs, leading to rapid progress across multiple domains of artificial intelligence beyond computer vision. The techniques and momentum generated by AlexNet helped shape the development of modern natural language processing models, including large-scale transformer-based models such as BERT and GPT, which power tools like ChatGPT.

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  Artificial intelligence safety institute

  An artificial intelligence safety institute is a type of state-backed organization aiming to evaluate and ensure the safety of advanced artificial intelligence (AI) models, also called frontier AI models. AI safety gained prominence in 2023, notably with public declarations about potential existential risks from AI. During the AI Safety Summit in November 2023, the United Kingdom and the United States both created their own AISI. During the AI Seoul Summit in May 2024, international leaders agreed to form a network of AI Safety Institutes, comprising institutes from the UK, the US, Japan, France, Germany, Italy, Singapore, South Korea, Australia, Canada and the European Union. In 2025, the UK's AI Safety Institute was renamed the "AI Security Institute", and its US counterpart became the Center for AI Standards and Innovation (CAISI). == Timeline == In 2023, Rishi Sunak, the Prime Minister of the United Kingdom, expressed his intention to "make the UK not just the intellectual home but the geographical home of global AI safety regulation" and unveiled plans for an AI Safety Summit. He emphasized the need for independent safety evaluations, stating that AI companies cannot "mark their own homework". During the summit in November 2023, the UK AISI was officially established as an evolution of the Frontier AI Taskforce, and the US AISI as part of the National Institute of Standards and Technology. Japan followed by launching an AI safety institute in February 2024. Politico reported in April 2024 that many AI companies had not shared pre-deployment access to their most advanced AI models for evaluation. Meta's president of global affairs Nick Clegg said that many AI companies were waiting for the UK and the US AI Safety Institutes to work out common evaluation rules and procedures. An agreement was indeed concluded between the UK and the US in April 2024 to collaborate on at least one joint safety test. Initially established in London, the UK AI Safety Institute announced in May 2024 that it would open an office in San Francisco, where many AI companies are located. This is part of a plan to "set new, international standards on AI safety", according to UK's technology minister Michele Donelan. == International network == At the AI Seoul Summit in May 2024, the European Union and other countries agreed to create their own AI safety institutes, forming an international network. In July 2025, the international network held an exercise to explore issues with evaluating AI agents, especially when it came to leaking sensitive information or cybersecurity. Network members also met at NeurIPS 2025 in the city of San Diego. == Specific institutes == === Australia === The Albanese government announced the creation of the Australian AI Safety Institute on 25 November 2025. === Canada === Canada announced in April 2024 that it would create an AI safety institute, and such an institute was officially founded in November 2024. The institute is housed under Innovation, Science and Economic Development Canada, though it also partners with the Canadian Institute for Advanced Research (CIFAR). It is supported by a budget of CA$50,000,000 for a five-year timespan. === European Union === The EU AI office, founded in May 2024, is a member of the international network of AI safety institutes. === France === On 31 January 2025, the government of France created the Institut national pour l'évaluation et la sécurité de l'intelligence artificielle (INESIA), or the National Institute for AI Evaluation and Security. === India === The Ministry of Electronics and Information Technology held consultations with Meta Platforms, Google, Microsoft, IBM, OpenAI, NASSCOM, Broadband India Forum, Software Alliance, Indian Institutes of Technology (IITs), The Quantum Hub, Digital Empowerment Foundation, and Access Now on October 7, 2024, in relation to the establishment of the AI Safety Institute. The decision was made to shift focus from regulation to standards-setting, risk identification, and damage detection—all of which require interoperable technologies. The AISI may spend the ₹20 crore allotted to the Safe and Trusted Pillar of the IndiaAI Mission for the initial budget. Future funding may come from other components of the IndiaAI Mission. UNESCO and MeitY began consulting on AI Readiness Assessment Methodology under Safety and Ethics in Artificial Intelligence from 2024. It is to encourage the ethical and responsible use of AI in industries. The study will find areas where government can become involved, especially in attempts to strengthen institutional and regulatory capabilities. Minister for Electronics & Information Technology Ashwini Vaishnaw announced the creation of an IndiaAI Safety Institute on January 30, 2025, to ensure the ethical and safe application of AI models. The institute will promote domestic R&D that is grounded in India's social, economic, cultural, and linguistic diversity and is based on Indian datasets. With the help of academic and research institutions, as well as private sector partners, the institute will follow the hub-and-spoke approach to carry out projects within Safe and Trusted Pillar of the IndiaAI Mission. It operates under a "hub-and-spoke" model with collaboration from academic institutions (e.g., IITs), tech firms, and international organizations like UNESCO. === Japan === The Japan AISI (or J-AISI) was founded in February 2024. Part of the Information Technology Promotion Agency, it employs about 23 people. The institute consists of the Council of AISI, the AISI Steering Committee, and a secretariat with six teams. Akiko Murakami (previously of IBM Japan and Sompo Japan) serves as the institute's executive director, and Kenji Hiramoto and Suguru Nishimura serve as the institute's two deputy executive directors. === Kenya === Kenya agreed to join the international network of AI safety institutes, but the country has not announced any details yet. It is the only African state in the network. === Singapore === The Digital Trust Centre was initially founded in June 2022. In May 2024, it was renamed to the Singapore AISI. Part of Nanyang Technological University, the institute partners with Infocomm Media Development Authority and is supported by an investment of S$10,000,000 per year. === South Korea === South Korea announced in May 2024 that it would create an AI safety institute under the umbrella of the Electronics and Telecommunications Research Institute. It will be supported by a tentative investment of somewhere between 10 and 20 million South Korean won per year, and employ at least 30 people. The institute was founded in November 2024 and is based in Bundang District within the city of Seongnam. === United Kingdom === The United Kingdom founded in April 2023 a safety organisation called Frontier AI Taskforce, with an initial budget of £100 million. In November 2023, it evolved into the AI Safety Institute, and continued to be led by Ian Hogarth. The AISI is part of the United Kingdom's Department for Science, Innovation and Technology. The United Kingdom's AI strategy aims to balance safety and innovation. Unlike the European Union which adopted the AI Act, the UK is reluctant to legislate early, considering that it may lower the sector's growth, and that laws might be rendered obsolete by technological progress. In May 2024, the institute open-sourced an AI safety tool called "Inspect", which evaluates AI model capabilities such as reasoning and their degree of autonomy. In February 2025, the UK body was renamed the AI Security Institute. Observers saw the name change as a signal that the institute will not focus on ethical issues such as algorithmic bias or freedom of speech in AI applications. === United States === The US AISI was founded in November 2023 as part of the National Institute of Standards and Technology (NIST). This happened the day after the signature of the Executive Order 14110. In February 2024, Joe Biden's former economic policy adviser Elizabeth Kelly was appointed to lead it. In February 2024, the US government created the US AI Safety Institute Consortium (AISIC), regrouping more than 200 organizations such as Google, Anthropic or Microsoft. In March 2024, a budget of $10 million was allocated. Observers noted that this investment is relatively small, especially considering the presence of many big AI companies in the US. The NIST itself, which hosts the AISI, is also known for its chronic lack of funding. Biden administration's request for additional funding was met with further budget cuts from congressional appropriators. Under President Trump, plans for members of the agency to attend the February 2025 AI Action Summit in Paris were scrapped. The US and the UK refused to sign the summit's final communique. US Vice President JD Vance said "pro-growth AI policies" should be prioritised over safety. The name of the agency was changed in June 2025 to the Center for AI Standards and Innovation

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