Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data. The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data. This principle was first set out in a 1974 book by Vladimir Vapnik and Alexey Chervonenkis and uses the VC dimension. In practical terms, Structural Risk Minimization is implemented by minimizing E t r a i n + β H ( W ) {\displaystyle E_{train}+\beta H(W)} , where E t r a i n {\displaystyle E_{train}} is the train error, the function H ( W ) {\displaystyle H(W)} is called a regularization function, and β {\displaystyle \beta } is a constant. H ( W ) {\displaystyle H(W)} is chosen such that it takes large values on parameters W {\displaystyle W} that belong to high-capacity subsets of the parameter space. Minimizing H ( W ) {\displaystyle H(W)} in effect limits the capacity of the accessible subsets of the parameter space, thereby controlling the trade-off between minimizing the training error and minimizing the expected gap between the training error and test error. The SRM problem can be formulated in terms of data. Given n data points consisting of data x and labels y, the objective J ( θ ) {\displaystyle J(\theta )} is often expressed in the following manner: J ( θ ) = 1 2 n ∑ i = 1 n ( h θ ( x i ) − y i ) 2 + λ 2 ∑ j = 1 d θ j 2 {\displaystyle J(\theta )={\frac {1}{2n}}\sum _{i=1}^{n}(h_{\theta }(x^{i})-y^{i})^{2}+{\frac {\lambda }{2}}\sum _{j=1}^{d}\theta _{j}^{2}} The first term is the mean squared error (MSE) term between the value of the learned model, h θ {\displaystyle h_{\theta }} , and the given labels y {\displaystyle y} . This term is the training error, E t r a i n {\displaystyle E_{train}} , that was discussed earlier. The second term, places a prior over the weights, to favor sparsity and penalize larger weights. The trade-off coefficient, λ {\displaystyle \lambda } , is a hyperparameter that places more or less importance on the regularization term. Larger λ {\displaystyle \lambda } encourages sparser weights at the expense of a more optimal MSE, and smaller λ {\displaystyle \lambda } relaxes regularization allowing the model to fit to data. Note that as λ → ∞ {\displaystyle \lambda \to \infty } the weights become zero, and as λ → 0 {\displaystyle \lambda \to 0} , the model typically suffers from overfitting.
Operation Serenata de Amor
Operation Serenata de Amor is an artificial intelligence project designed to analyze public spending in Brazil. The project has been funded by a recurrent financing campaign since September 7, 2016, and came in the wake of major scandals of misappropriation of public funds in Brazil, such as the Mensalão scandal and what was revealed in the Operation Car Wash investigations. The analysis began with data from the National Congress then expanded to other types of budget and instances of government, such as the Federal Senate. The project is built through collaboration on GitHub and using a public group with more than 600 participants on Telegram. The name "Serenata de Amor," which means "serenade of love," was taken from a popular cashew cream bonbon produced by Chocolates Garoto in Brazil. == Modules == Throughout development of the project, new modules have been newly introduced in addition to the main repository: The main repository, serenata-de-amor, serves as the starting point for investigative work. Rosie is the robot programmed to identify public funds expenses with discrepancies, starting with CEAP (Quota for Exercise of Parliamentary Activity); it analyzes each of the reimbursements requested by the deputies and senators, indicating the reasons that lead it to believe they are suspicious. From Rosie was born whistleblower, which tweets under the name of @RosieDaSerenata, distributing the results found on social media. Jarbas (Github repository) is a data visualization tool which shows a complete list of reimbursements made available by the Chamber of Deputies and mined by Rosie. Toolbox is a Python installable package that supports the development of Serenata de Amor and Rosie. == History == Operation Serenata de Amor is an Artificial intelligence project for analysis of public expenditures. It was conceived in March 2016 by data scientist Irio Musskopf, sociologist Eduardo Cuducos and entrepreneur Felipe Cabral. The project was financed collectively in the Catarse platform, where it reached 131% of the collection goal paying 3 months of project development. Ana Schwendler, also a data scientist, Pedro Vilanova "Tonny", data journalist, Bruno Pazzim, software engineer, Filipe Linhares, a frontend engineer, Leandro Devegili, an entrepreneur and André Pinho took the first steps towards constructing the platform, such as collecting and structuring the first datasets. Jessica Temporal, data scientist and Yasodara Córdova "Yaso", researcher, Tatiana Balachova "Russa", UX designer, joined the project after the financing took place. The members created a recurring financing campaign, expanding the analysis of public spending to the Federal Senate. Donors make monthly payments ranging from 5 BRL to 200 BRL to maintain group activities. The monthly amount collected is around 10,000 BRL. == Results == In January 2017, concluding the period financed by the initial campaign, the group carried out an investigation into the suspicious activities found by the data analysis system. 629 complaints were made to the Ombudsman's Office of the Chamber of Deputies, questioning expenses of 216 federal deputies. In addition, the Facebook project page has more than 25,000 followers, and users frequently cite the operation as a benchmark in transparency in the Brazilian government. One of the examples of results obtained by the operation is the case of the Deputy who had to return about 700 BRL to the House after his expenses were analyzed by the platform. The platform was able to analyze more than 3 million notes, raising about 8,000 suspected cases in public spending. The community that supports the work of the team benefits from open source repositories, with licenses open for the collaboration. So much so that the two main data scientists of the project presented it at the CivicTechFest in Taipei, obtaining several mentions even in the international press. The technical leader presented the project in Poland during DevConf2017 in Kraków. It was also presented in the Google News Lab in 2017. It was presented by Yaso, when she was the Director of the initiative, at the MIT Media Lab/Berkman Klein Center Initiative for Artificial Intelligence ethics, and at the Artificial Intelligence and Inclusion Symposium, an initiative of the Global Network of Internet & Society Centers (NoC). It was also presented both by Irio and Yaso at the Digital Harvard Kennedy School, over a lunch seminar, where the transparency of the platform and the main solutions found were discussed, so that the code and data are always available to verify its suitability. This infographic provides information about the first results of Operation Serenata de Amor, a project that analyzes open data on public spending to find discrepancies. The project was presented by Yaso to the House Audit and Control Committee of the Chamber of Deputies in August 2017, and raised the interest of House officials who work with open data. The operation has been a source of inspiration for other civic projects that aim to work with similar goals, demonstrating the broader impact of artificial intelligence also in industry in Brazil. Participation of several team members in events throughout Brazil and abroad can be found on the Internet, such as presentation at OpenDataDay, held at Calango Hackerspace in the Federal District, Campus Party Bahia, Campus Party Brasilia, Friends of Tomorrow, XIII National Meeting of Internal Control, in the event USP Talks Hackfest against corruption in João Pessoa, the latter being also highlighted in the National Press.
Versata
Versata is a privately held software company, one of several business units under the ESW Capital umbrella. Versata acquires underperforming or financially struggling enterprise software companies, integrates them into their portfolio, and makes operational changes to improve the viability and performance of the companies. == History == === Early years (1991–2000) === This company was founded in 1991 with the name Image Innovations; Naren Bakshi was co-founder and president, Kevin Fletcher Tweedy was vice president of technology, and they sold a development tool set named Image Application WorkBench that worked with Plexus Software's imaging platform. In 1997, the company name changed to Vision Software. They sold a small suite of software: Vision Builder for accelerated coding; and Vision StoryBoard Pro for creating software documentation. In 1998, their flagship product was a Java development tool named Vision JADE. In January 2000, the company changed names again, this time to Versata, and their e-business automation system, Versata Logic Suite, had three components: Versata Logic Server to host business rules written in Java, Versata Studio for developing the business rules, and Versata Connectors for connecting the logic server to IBM database servers. === Public company (2000–2006) === They went public in March 2000 during the dot-com bubble, raising about $94 million and reaching a market capitalization of over $2.5 billion despite reporting just $13 million in revenue and a $21 million loss in the prior year. In November 2000, Versata expanded into the business workflow area with the acquisition of Verve, Inc. and its workflow management system by the same name. From early 2001 through mid-2003, Versata's revenues were in quarter-over-quarter decline until Alan Baratz took over as CEO. Five consecutive quarters of growth followed until early 2005, when revenues once again took a downward plunge. In mid-2005, the company was notified by NASDAQ that it no longer met NASDAQ's requirements for continued listing, related to maintenance of a minimum amount of shareholder's equity, market value, or net income. In July 2005, Versata was delisted from NASDAQ and publicly traded on the OTC (also known as the Pink Sheets). == Versata, a business unit of ESW Capital == In January 2006, Austin-based Trilogy, Inc. acquired the company and took it private. Trilogy then proceeded to merge portions of Trilogy, specifically, Trilogy Technology Group, into Versata and began acquiring further companies, reorganizing dramatically and offshoring most technical positions to its office in Bangalore, India. From 2006 to 2008, Versata continued to make acquisitions mostly in US. Most of the employees in the acquired companies were laid -off with the majority work being offshored to its India office in Bangalore. In early 2009, Versata made another major overhaul of its business model when it asked all its employees in India to work as contractors through oDesk for a gDev which is an entity incorporated by Trilogy to manage its outsourcing activities. The only employees left in Versata were the ones in US. == Acquisitions == a Corizon was acquired by Metatomix, while Metatomix was part of Versata. b Infopia was acquired by Everest Software, while Everest Software was part of Versata. c Symphony Commerce was acquired by Quantum Retail, while Quantum Retail was part of Versata. == Legal disputes == === Patent infringement and "poison pill" lawsuits with Selectica === The legal disputes with Selectica began in 2004 (before Trilogy acquired Versata in January 2006) and lasted until 2010. While there were many suits and counter-suits, they largely centered around three issues: 2004–2006: Patent infringement in configure, price, and quote (CPQ) software 2005–2007: Patent infringement in contract lifecycle management (CLM) software 2008–2010: The "poison pill" lawsuit In 2004, Selectica and Trilogy had competing CPQ software: Selectica sold Solutions Advisor and Deal Optimization, while Trilogy sold Selling Chain. In April of that year, Trilogy Software sued Selectica for patent infringement. In 2005, before the court ruling, Trilogy made several offers to buy Selectica, but the board rejected them. In January 2006, the court ordered Selectica to pay Trilogy $7.5 million in damages. Four days after the January 2006 judgment in the first lawsuit, Trilogy announced its acquisition of Versata for an undisclosed amount. In 2005, Selectica had acquired the Determine CLM software platform, which included features that overlapped with some offered by Versata. In October 2006, Versata filed a second patent infringement lawsuit. The case was settled in 2007, with Selectica agreeing to pay Trilogy and Versata $10 million, plus up to $7.5 million in additional contingent payments. In 2008, Versata began acquiring Selectica stock. By December, Selectica's board amended its shareholder rights plan to adopt a "poison pill" with an unusually low trigger threshold: if any shareholder acquired more than 4.99% of company stock, their ownership would be diluted. The board explained that the move was meant to protect Selectica's net operating losses (NOLs), which were tax-deductible if the company returned to profitability. Under IRS Section 382, a significant change in stock ownership could cause those NOLs to be disqualified. Versata intentionally triggered the poison pill and also offered to sell back the stocks at a profit (greenmailing them), which prompted a legal dispute over whether Selectica's board had the authority to set such a low threshold and whether defending NOLs justified triggering shareholder dilution. The case ultimately reached the Delaware Supreme Court, which upheld the poison pill in October 2010, ruling in favor of Selectica. === Intellectual property lawsuit over joint development with Sun Microsystems === In 1998, Sun Microsystems hired Trilogy to help Sun's developers in California create a software configurator (later named the WC5 Configurator) that Sun's customers could use to modify products they wanted to buy, customizing products to have the features they wanted. Trilogy worked on the WC5 Configurator for several years, then Sun transferred the work to Oracle to finish. Trilogy believed that they owned the copyright to the work they'd done for Sun, and in 2006 after the merger with Versata they sued Sun for more than $100 million in damages. In April 2009, a jury ruled in favor of Sun and rejected Versata's claims. === Patent lawsuit and ruling on patents of abstract ideas with SAP === SAP developed Pricing Engine, a component in their enterprise resource planning (ERP) system. It competed with an older Trilogy product called Pricer, which was part of Trilogy's Selling Chain platform in the mid-1990s before they merged with Versata. In April 2007—the year after Trilogy acquired Versata—Versata filed a lawsuit against SAP for patent infringement. In August 2009, the jury agreed with Versata and awarded them $139 million. The court granted a new trial on damages and in September 2011, in the retrial, the jury awarded Versata $345 million. This then went to the US Court of Appeals, which in May 2013 affirmed the $345 million damages award, plus interest that had accumulated. In October 2014, Versata and SAP settled their litigation for an undisclosed amount of money. With the dispute between Versata and SAP settled, in June 2013 the Patent Trial and Appeal Board (PTAB) reviewed the validity of the patent itself, and issued a decision in a Covered Business Method (CBM) review, stating that the disputed items were abstract ideas and thus under the US patent law not patentable. In July 2015, the Federal Circuit agreed with PTAB's decision that the challenged items were not patentable. === Trade secrets and damages dispute with Internet Brands === Internet Brands was formerly known as CarsDirect and AutoData Solutions. Like Trilogy, they made software for automakers that helped customers compare vehicles online. In the late 1990s, Trilogy and Internet Brands tried to combine their products but failed to do so, and after a December 1999 lawsuit they made a settlement agreement in May 2001. In 2008, Versata sued Internet Brands claiming they had violated the settlement agreement by making presentations to potential clients stating they had a license from Versata to use and sell Versata technical solutions; and doing so had cost Versata business with Chrysler. Internet Brands' countersuit argued that Versata had misappropriated trade secrets and asked the jury to use Versata's business relationship with Toyota—including revenue from Toyota contracts—as a benchmark to calculate damages. The jury agreed and used that data to determine a $2 million damages award in favor of Internet Brands’ subsidiary, AutoData Solutions. Versata appealed the decision, and in January 2014 the court upheld the $2 million award to Internet Brands. === Patent challenges a
VP-Expert
VP-Expert is an artificial intelligence development tool that gained popularity in the late 1980s and early 1990s. Published by Paperback Software, VP-Expert was designed to facilitate the creation of rule-based expert systems, primarily for applications in business and industry. It was the best-selling expert-system software for microcomputers in the late 1980s. == History == VP-Expert was created by Brian Sawyer and published by Paperback Software in 1987. VP-Expert was widely adopted during the late 1980s. By April 1989, InfoWorld described it as "the best-selling expert-system software for personal computers." In June 1991, ownership of VP-Expert transferred from Paperback Software to WordTech Systems, Inc. following Paperback Software’s liquidation after a legal dispute with Lotus Development Corporation regarding its VP-Planner spreadsheet. VP-Expert continued to receive positive reviews with InfoWorld stating in 1992 "for automatically creating simple expert systems and being able to edit them into more sophisticated applications, hardly a better product exists than VP-Expert". == Features == VP-Expert used an inference engine based on backward chaining to reach conclusions through English-like if/then rules. It operated through a text interface and included an explanation facility that showed the reasoning steps used to justify its conclusions. == Applications == VP-Expert found applications across various domains. In environmental analysis, researchers used VP-Expert to develop a knowledge-based system for analyzing the impact of particulate matter air pollution on human health. In engineering design, VP-Expert was utilized in the creation of a prototype expert system to assist in fishway design. In aviation management, the tool was employed to develop an expert system aimed at maximizing airport capacity while adhering to noise-mitigation plans. == Limitations == While VP-Expert offered certain advantages, it also had limitations. Its rule-based approach could become challenging to manage for large and complex knowledge bases, and the process of eliciting and encoding knowledge from experts could be time-consuming and difficult.
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.
Outline of automation
The following outline is provided as an overview of and topical guide to automation: Automation – use of control systems and information technologies to reduce the need for human work in the production of goods and services. In the scope of industrialization, automation is a step beyond mechanization. == Essence of automation == Control system – a device, or set of devices to manage, command, direct or regulate the behavior of other devices or systems. Industrial control system (ICS) – encompasses several types of control systems used in industrial production, including supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), and other smaller control system configurations such as skid-mounted programmable logic controllers (PLC) often found in industrial sectors and critical infrastructures. Industrialization – period of social and economic change that transforms a human group from an agrarian society into an industrial one. Numerical control (NC) – refers to the automation of machine tools that are operated by abstractly programmed commands encoded on a storage medium, as opposed to controlled manually via handwheels or levers, or mechanically automated via cams alone. Robotics – the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots and computer systems for their control, sensory feedback, and information processing. == Branches of automation == === General purpose === Autonomous automation – autonomous software agents to adapt the controllers of computer controlled industrial machinery and processes Banking automation Broadcast automation Building automation – advanced functionality provided by the control system of a building. A building automation system (BAS) is an example of a distributed control system. Home automation – control system of a home. Office automation – the varied computer machinery and software used to digitally create, collect, store, manipulate, and relay office information needed for accomplishing basic tasks such as business process automation and robotic process automation. Console automation Database automation Integrated library system Laboratory automation === Specific purpose === Automated attendant Automated guided vehicle Autonomous mobile robot Automated highway system Automated pool cleaner Automated teller machine Automatic painting (robotic) Pop music automation Remotely operated vehicle Robotic lawn mower Telephone switchboard Vending machine == Fields contributing to automation == Cybernetics – the interdisciplinary study of the structure of regulatory systems. Cognitive science – interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. Robotics – the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots and computer systems for their control, sensory feedback, and information processing. == History of automation == History of mass production – Prerequisites of mass production were interchangeable parts, machine tools and power, especially in the form of electricity. Mass production was popularized in the 1910s and 1920s by Henry Ford's Ford Motor Company, which introduced electric motors to the then-well-known technique of chain or sequential production. History of home automation == Automated machines == Machine to Machine OLE for process control (OPC) Process control – a statistics and engineering discipline that deals with architectures, mechanisms and algorithms for maintaining the output of a specific process within a desired range. Run Book Automation (RBA) Robot – a mechanical or virtual intelligent agent that can perform tasks automatically or with guidance, typically by remote control. == Automated machine components == Artificial intelligence – the intelligence of machines and the branch of computer science that aims to create it. Friendly artificial intelligence – an artificial intelligence that has a positive rather than negative effect on humanity, and the field of knowledge required to build such an artificial intelligence. === Automation tools === Artificial neural network (ANN) – mathematical model or computational model that is inspired by the structure or functional aspects of biological neural networks. Human machine interface (HMI) – operator level local control panel that monitors field devices Laboratory information management system (LIMS) – software package that offers a set of key features that support a modern laboratory's operations. Industrial control system – encompasses several types of control systems used in industrial production, including supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), and other smaller control system configurations such as skid-mounted programmable logic controllers (PLC) often found in the industrial sectors and critical infrastructures. Distributed control system (DCS) – control system usually of a manufacturing system, process or any kind of dynamic system, in which the controller elements are not central in location (like the brain) but are distributed throughout the system with each component sub-system controlled by one or more controllers. Manufacturing execution system (MES) – system that manages manufacturing operations in a factory, including management of resources, scheduling production processes, dispatching production orders, execution of production orders, etc. Programmable automation controller (PAC) – digital computer used for automation of electromechanical processes, such as control of machinery on factory assembly lines, amusement rides, or light fixtures. Programmable logic controller (PLC)A Programmable Logic Controller, PLC or Programmable Controller is a digital computer used for automation of electromechanical processes, such as control of machinery on factory assembly lines, amusement rides, or light fixtures. The abbreviation "PLC" and the term "Programmable Logic Controller" are registered trademarks of the Allen-Bradley Company (Rockwell Automation). PLCs are used in many industries and machines. Unlike general-purpose computers, the PLC is designed for multiple inputs and output arrangements, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. Programs to control machine operation are typically stored in battery-backed-up or non-volatile memory. A PLC is an example of a hard real time system since output results must be produced in response to input conditions within a limited time, otherwise unintended operation will result. Supervisory control and data acquisition (SCADA) – generally refers to industrial control systems (ICS): computer systems that monitor and control industrial, infrastructure, or facility-based processes, as described below: Industrial processes include those of manufacturing, production, power generation, fabrication, and refining, and may run in continuous, batch, repetitive, or discrete modes. Simulation § Engineering Technology simulation or Process simulation == Social movements == Automation-related social movement – a movement that advocates semi- or fully automatic systems to provide for human needs globally. For example, automation of farming and food distribution throughout the world so that no one will go hungry. One goal is to automate all mundane labor, to free humans to engage in more creative activities (or less work). The Technocracy movement – social movement active from the Great Depression (1930s) to date that proposes replacing politicians and business people with scientists and engineers who have the technical expertise to manage the economy. The Zeitgeist Movement – movement advocating the replacement of the market economy with an economy in which all resources are equitably, commonly and sustainably shared. == Automation in the future == Android – a robot or synthetic organism designed to look and act like a human, and with a body having a flesh-like resemblance Technological singularity – the hypothetical future emergence of greater-than-human intelligence through technological means Semi-automation – using a centralized computer controller to orchestrate the activities of man and machine. == Automation-related publications == IEEE Spectrum – the flagship publication of the Institute of Electrical and Electronics Engineers (IEEE), explores the development, applications and implications of new technologies, and provides a forum for understanding, discussion and leadership in these areas. IEEE Transactions on Information Theory – peer-reviewed scientific journal published by the Institute of Electrical and Electronics Engineers (IEEE), focused on the study of information theory, the mathematics of communications, including computer communications, robotics communications, etc. IEEE Transactions on Control S
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