The Online Safety Amendment (Social Media Minimum Age) Act 2024 is an Australian act of parliament that prohibits minors under the age of 16 from holding an account on certain social media platforms. It is an amendment to the Online Safety Act 2021 and was passed by the Parliament of Australia on 29 November 2024. It imposes monetary penalties on social media companies that fail to take reasonable steps to prevent minors under 16 that are located in Australia from having accounts on their services. The legislation allows the government to determine which social media platforms must ban age‑restricted users and proclaim a date for the commencement of the ban, with those provisions taking effect on 10 December 2025. Facebook, Instagram, Reddit, Snapchat, TikTok, Twitter, Threads, Twitch, Kick, and YouTube were age‑restricted on 10 December 2025, with the possibility that more platforms may be added. The act is being challenged in the High Court by the Digital Freedom Project. == Background == The ban on access to social media by young people by the federal government originated in November 2023, when shadow communications minister David Coleman introduced a private member's bill requiring the government to conduct a trial for age-verification technology on pornography and social media platforms. While the bill did not succeed, the Albanese government funded the trial in the 2024 Australian federal budget. In June 2024, opposition leader Peter Dutton pledged that a Coalition government would implement a ban on social media for under-16s within 100 days of taking office. The following month, prime minister Anthony Albanese announced the government would introduce legislation banning under-16s from social media. The Online Safety Amendment (Social Media Minimum Age) Bill 2024 was introduced into parliament by minister for communications Michelle Rowland on 21 November 2024, passing both houses on 28 November 2024. The ban on access to social media by young people by the federal government also gained momentum following an entreaty by the wife of the premier of South Australia, Peter Malinauskas, to her husband. She requested that he read The Anxious Generation by Jonathan Haidt and take action to address the impact of social media on the mental health of children. The couple have four young children, and, thinking of them, the premier thought that government should play a part in helping parents to regulate use of social media by their children at home. Malinauskas contacted former High Court chief justice Robert French, who agreed to look at the issue, and in September 2024 handed the premier a 267 page proposal, which he dubbed a "Swiss Army knife" rather than a machete, to adjust to social media's "changing landscape and its complexity". The leaders of other states and territories gave their support to Malinauskas's idea, and he took the French report to National Cabinet to collaborate with chief ministers, premiers, and the prime minister. Community support swelled after stories of parents who had lost their children to suicide after being bullied on social media were published. Albanese himself was moved by a personal letter received from Kelly O'Brien, whose 12-year-old daughter Charlotte had taken her own life due to bullying at school. An event took place at the sidelines of the United Nations General Assembly session in September 2025 at which a mother spoke of her daughter's suicide as "death by bullying ... enabled by social media". The speech won support from world leaders in Greece, Fiji, Tonga and the president of the European Commission Ursula von der Leyen. In early September 2024, South Australia proposed legislation similar to the federal law now in place. The state-based version was intended to ban users under the age of 14, unlike the federal law, which bans those under 16. The state-based law also proposed to require parental consent for 14 and 15‑year‑olds. Later in September, prime minister Anthony Albanese announced that his government intended to introduce legislation to set a minimum age requirement for social media. In November 2024, the federal government indicated their intention to engage the Age Check Certification Scheme following a tender process for an age assurance technology trial. The Albanese government's proposed ban was supported by the governments of every state and territory. Albanese described social media as a "scourge", and said "I want people to spend more time on the footy field or the netball court than they're spending on their phones", that family members are "worried sick about the safety of our kids online", and that social media "is having a negative impact on young people's mental health and on anxiety". Albanese's statements followed an earlier pledge by Liberal opposition leader Peter Dutton who was pushed by the early advocacy of shadow communications minister David Coleman to implement a ban on social media for under 16s within 100 days of being elected. The opposition organised an open letter signed by 140 experts who specialise in child welfare and technology. The opposition was concerned about the invasion of privacy that will occur with the introduction of identification-based age checks. An advocacy group for digital companies in Australia called the plans a "20th Century response to 21st Century challenges". A director of a mental health service voiced concerns, stating that "73% of young people across Australia who accessed mental health support did so through social media". == Implementation == Social media companies will receive a transition period of one year after the legislation is enacted to introduce reasonable controls preventing minors under the age of 16 from holding accounts on their services while physically located in Australia. Enforcement will involve fines of up to A$49.5 million for companies failing to take such steps, with no consequences for parents and children who violate the restrictions. There are no parental consent exceptions to the ban, and while the use of virtual private networks (VPNs) to access these services remains legal in Australia, the services are expected to try to stop under 16s from using VPNs to pretend to be outside Australia. The expectation is to make best-efforts to implement the ban on platforms including Facebook, Instagram, Reddit, Snapchat, TikTok, Twitter, Threads, Twitch, Kick and YouTube. Some social media companies are now obligated to become good enough at profiling Australian children under 16 to satisfy the Australian government they tried to implement the ban to avoid being fined. Consequently, social media companies said they will try to identify restricted users using various methods including behavioural inferencing. On 5 November 2025, it was announced that online gaming platform Roblox will not be banned, but Reddit and live-streaming platform Kick will be added to the list of platforms to be banned. A report by Age Check Certification Scheme, a UK company recruited by the government to consult on the technology used to implement the restrictions, was issued in June 2025, ahead of the December deadline to implement the ban. In June 2025, the preliminary report was released, which stated that "there are no significant technological barriers" to implementing the ban. In late July 2025, Google warned that it would sue the Australian government if YouTube was included in the ban. On 30 July, the government announced that it would extend its social media age limit to include YouTube, following advice from Grant. On 30 July 2025, the minister for communications, Anika Wells, published the Online Safety (Age-Restricted Social Media Platforms) Rules 2025, which specify exactly which types of social media platforms will be banned for certain users. On 31 August 2025, the full report was released, which stated that it would technically be possible to implement the ban; however, coordination among different services is required to successfully implement it. It also highlighted the benefits and flaws of different methods of age verification. On 16 September 2025, it was announced that the eSafety Commissioner will be able to take legal action against social media companies that have not pursued reasonable steps to bar users under the age of 16, and that fines can range up to A$49.5 million against these companies in court. On 19 November 2025, Meta announced that from 4 December their platforms (Instagram, Facebook, and Threads) would be removing users under the age of 16 ahead of the 10 December deadline. Users will be able to scan a face or provide an identity document to prove their age. On 21 November 2025, the eSafety Commissioner announced that the live-streaming platform Twitch will be included in the ban, but that Pinterest would not be. In December 2025, eSafety Commissioner Julie Inman Grant suggested efforts to block users include use by social media companies of various "signals" to identify children that are
LemonStand
LemonStand was a Canadian e-commerce company headquartered in Vancouver, British Columbia, that developed cloud-based computer software for online retailers. LemonStand was shut down on June 5, 2019. == History == LemonStand Version 1 was launched on July 28, 2001. It is written in the PHP programming language. Version 1 was released as an on-premises proprietary licensed software, and the commercial license was not free. However, there was a free trial license available. June 2012, LemonStand raised seed funding from the BDC Venture Capital, and a group of angel investors. December 20, 2013, a cloud-based SaaS version of the LemonStand eCommerce platform was released publicly. May 9, 2014, LemonStand and Payfirma, a payments processing company, partnered to provide integrated services for online retailers. May 3, 2016, LemonStand raised funding from BDC Venture Capital and Silicon Valley–based angel investors. March 5, 2019, LemonStand announced their intention to shut down on June 5, 2019. LemonStand was quietly acquired by Mailchimp at the end of February. == Pricing == LemonStand offered three levels of service plans. LemonStand did not charge any transaction fees.
Applications of artificial intelligence
Artificial intelligence is the capability of computational systems to perform tasks that are typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. Artificial intelligence has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of machine learning has been used for various scientific and commercial purposes, including language translation, image recognition, decision-making, credit scoring, and e-commerce. In recent years, massive advancements have been made in the field of generative artificial intelligence, which uses generative models to generate text, images, videos, and other forms of data. This article describes applications of AI in different sectors. == Agriculture == In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation. == AI-assisted software develoment == == Architecture and design == == Business == A 2023 study found that generative AI increased productivity by 15% in contact centers. Another 2023 study found it increased productivity by up to 40% in writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. A September 2025 article by the Harvard Business Review describes how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task" the article coins the term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues. In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1-2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75M in annual profit for GLP-1 weight-loss telehealth services. == Chatbots == == Computer science == === Programming assistance === ==== AI-assisted software development ==== AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors and IDEs as plugins. AI-assisted software development systems differ in functionality, quality, speed, and approach to privacy. Creating software primarily via AI is known as "vibe coding". Code created or suggested by AI can be incorrect or inefficient. The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing. The rush to prematurely adopt AI technology can also incur additional technical debt. AI also requires additional consideration and careful review for cybersecurity, since AI coding software is trained on a wide range of code of inconsistent quality and often replicates poor practices. ==== Neural network design ==== AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet. ==== Quantum computing ==== Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems. === Historical contributions === AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. All of the following were originally developed in AI laboratories: Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction == Customer service == === Human resources === AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots. Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics, as well as bias against disabled candidates and racial minorities. === Online and telephone customer service === AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds and connecting the customer with a human representative. Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making. === Hospitality === In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs. AI hotel services come in the form of a chatbot, application, virtual voice assistant and service robots. == Education == In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges. == Energy and environment == === Energy system === The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models, predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid). === Environmental monitoring === Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite monitoring-platform for analysis/tracking of plastic waste sites to help prevention of plastic pollution – primarily ocean pollution – by helping identify who and where mismanages plastic waste, dumping it into oceans. === Early-warning systems === Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, earthquakes, landslides, heavy rainfall, long-term water supply vulnerability, tipping-points of ecosystem collapse, cyanobacterial bloom outbreaks, and droughts. === Economic and social challenges === The University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness. Stanford researchers use AI to analyze satellite images to identify high poverty areas. == Entertainment and media == === Media === AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement. Motion interpolation Pixel-art scaling algorithms Image scaling Imag
Traité de Documentation
Traité de documentation: le livre sur le livre, théorie et pratique is a landmark book by Belgian author Paul Otlet, first published in 1934. == Legacy == The book is considered a landmark in the history of information science, with concepts predicting the rise of the World Wide Web and search engines. In [Otlet's] most famous publication of 1934, Traité de Documentation, he wrote of a desk in the form of a wheel from which different projects (workspaces) could be switched as they rotated — foreshadowing the multiple desktops and tabs of contemporary computer interfaces. Inspired by the arrival of radio, phonograph, cinema, and television, Otlet also posited that there were as yet many “inventions to be discovered,” including the reading and annotation of remote documents and computer speech.
Five safes
The Five Safes is a framework for helping make decisions about making effective use of data which is confidential or sensitive. It is mainly used to describe or design research access to statistical data held by government and health agencies, and by data archives such as the UK Data Service. It is not an internationally accepted standard. Two of the Five Safes refer to statistical disclosure control, and so the Five Safes is usually used to contrast statistical and non-statistical controls when comparing data management options. == Concept == The Five Safes proposes that data management decisions be considered as solving problems in five 'dimensions': projects, people, settings, data and outputs. The combination of the controls leads to 'safe use'. These are most commonly expressed as questions, for example: These dimensions are scales, not limits. That is, solutions can have a mix of more or fewer controls in each dimension, but the overall aim of 'safe use' independent of the particular mix. For example, a public use file available for open download cannot control who uses it, where or for what purpose, and so all the control (protection) must be in the data itself. In contrast, a file which is only accessed through a secure environment with certified users can contain very sensitive information: the non-statistical controls allow the data to be 'unsafe'. One academic likened the process to a graphic equalizer, where bass and treble can be combined independently to produce a sound the listener likes, which has proven to be a very useful metaphor. This 2023 Data Foundation webinar is an expert discussion of how the elements interact, including an excellent introductory representation. There is no 'order' to the Five Safes, in that one is necessarily more important than the others. However, Ritchie argued that the 'managerial' controls (projects, people, setting) should be addressed before the 'statistical' controls (data, output). The Five Safes concept is associated with other topics which developed from the same programme at ONS, although these are not necessarily implemented. Safe people is associated with 'active researcher management', while safe outputs is linked with principles-based output statistical disclosure control. The Five Safes is a positive framework, describing what is and is not. The EDRU ('evidence-based, default-open, risk-managed, user-centred') attitudinal model is sometimes used to give a normative context == The 'data access spectrum' == From 2003 the Five Safes was also represented in a simpler form as a 'Data Access Spectrum'. The non-data controls (project, people, setting, outputs) tend to work together, in that organisations often see these as a complementary set of restrictions on access. These can then be contrasted with choices about data anonymisation to present a linear representation of data access options. This presentation is consistent with the idea of 'data as a residual', as well as data protection laws of the time which often characterised data simply as anonymous or not anonymous. A similar idea had already been developed independently in 2001 by Chuck Humphrey of the Canadian RDC network, the 'continuum of access'. More recently, The Open Data Institute has developed a 'Data Spectrum toolkit' which includes industry-specific examples. == History and terminology == The Five Safes was devised in the winter of 2002/2003 by Felix Ritchie at the UK Office for National Statistics (ONS) to describe its secure remote-access Virtual Microdata Laboratory (VML). It was described at this time as the 'VML Security Model'. This was adopted by the NORC data enclave, and more widely in the US, as the 'portfolio model' (although this is now also used to refer to a slightly different legal/statistical/educational breakdown). In 2012 the framework as was still being referred to as the 'VML security model', but its increasing use among non-UK organisations led to the adoption of the more general and informative phrase 'Five Safes'. The original framework only had four safes (projects, people, settings and outputs): the framework was used to describe highly detailed data access through a secure environment, and so the 'data' dimension was irrelevant. From 2007 onwards, 'safe data' was included as the framework was used to a describe a wider range of ONS activities. As the US version was based upon the 2005 specification, some US iterations uses have the original four dimensions (eg). Some discussions, such as the OECD, use the term 'secure' instead 'safe'. However, the use of both these terms can cause presentational problems: less control in a particular dimension could be seen to imply 'unsafe users' or 'insecure settings', for example, which distracts from the main message. Hence, the Australian government uses the term "five data sharing principles". The 'Anonymisation Decision-Making Framework' uses a framework based on the Five Safes but relabelling "projects", "people", and "settings" as "governance", "agency" and "infrastructure", respectively; "Output" is omitted, and "safe use" becomes "functional anonymisation". There is no reference to the Five Safes or any associated literature. The Australian version was required to include references to the Five Safes, and presented it as an alternative without comment. == Application == The framework has had three uses: pedagogical, descriptive, and design. Since 2016, it has also been used, directly and indirectly in legislation. See for more detailed examples. === Pedagogy === The first significant use of the framework, other than internal administrative use, was to structure researcher training courses at the UK Office for National Statistics from 2003. UK Data Archive, Administrative Data Research Network, Eurostat, Statistics New Zealand, the Mexican National Institute of Statistics and Geography, NORC, Statistics Canada and the Australian Bureau of Statistics, amongst others, have also used this framework. Most of these courses are for researchers using restricted-access facilities; the Eurostat courses are unusual in that they are designed for all users of sensitive data. === Description === The framework is often used to describe existing data access solutions (e.g. UK HMRC Data Lab, UK Data Service, Statistics New Zealand) or planned/conceptualised ones (e.g. Eurostat in 2011). An early use was to help identify areas where ONS' still had 'irreducible risks' in its provision of secure remote access. The framework is mostly used for confidential social science data. To date it appears to have made little impact on medical research planning, although it is now included in the revised guidelines on implementing HIPAA regulations in the US, and by Cancer Research UK and the Health Foundation in the UK. It has also been used to describe a security model for the Scottish Health Informatics Programme. === Design === In general the Five Safes has been used to describe solutions post-factum, and to explain/justify choices made, but an increasing number of organisations have used the framework to design data access solutions. For example, the Hellenic Statistical Agency developed a data strategy built around the Five Safes in 2016; the UK Health Foundation used the Five Safes to design its data management and training programmes. Use in the private sector is less common but some organisations have incorporated the Five Safes into consulting services. In 2015 the UK Data Service organized a workshop to encourage data users from the academic and private sectors to think about how to manage confidential research data, using the Five Safes to demonstrate alternative options and best practice. Early adopters for strategic design use were in Australia: both the Australian Bureau of Statistics and the Australian Department of Social Service used the Five Safes as an ex ante design tool. In 2017 the Australian Productivity Commission recommended adopting a version of the framework to support cross-government data sharing and re-use. This underwent extensive consultation and culminated in the DAT Act 2022. Since 2020 the Five Safes has been the overriding framework for the design of new secure facilities and data sharing arrangements in the UK for public health and social sciences. This has been promoted by the Office for Statistics Regulation, the UK Statistics Authority, NHS DIgital, and the research funding bodies Administrative Data Research UK and DARE UK. === Regulation and legislation === Three laws have incorporated the Fives Safes. They are explicit in the South Australian Public Sector (Data Sharing) Act 2016, and implicit in the research provisions of the UK Digital Economy Act 2017. The Australian Data Availability and Transparency Act 2022 renames the Five Safes as the Five Data Sharing Principles.A 2025 statutory review of the DAT Act 2022 found "that the DAT Act has not been effective in achieving its objectives.". The review includes specific referen
Scene text
Scene text is text that appears in an image captured by a camera in an outdoor environment. The detection and recognition of scene text from camera captured images are computer vision tasks which became important after smart phones with good cameras became ubiquitous. The text in scene images varies in shape, font, colour and position. The recognition of scene text is further complicated sometimes by non-uniform illumination and focus. To improve scene text recognition, the International Conference on Document Analysis and Recognition (ICDAR) conducts a robust reading competition once in two years. The competition was held in 2003, 2005 and during every ICDAR conference. International association for pattern recognition (IAPR) has created a list of datasets as Reading systems. == Text detection == Text detection is the process of detecting the text present in the image, followed by surrounding it with a rectangular bounding box. Text detection can be carried out using image based techniques or frequency based techniques. In image based techniques, an image is segmented into multiple segments. Each segment is a connected component of pixels with similar characteristics. The statistical features of connected components are utilised to group them and form the text. Machine learning approaches such as support vector machine and convolutional neural networks are used to classify the components into text and non-text. In frequency based techniques, discrete Fourier transform (DFT) or discrete wavelet transform (DWT) are used to extract the high frequency coefficients. It is assumed that the text present in an image has high frequency components and selecting only the high frequency coefficients filters the text from the non-text regions in an image. == Word recognition == In word recognition, the text is assumed to be already detected and located and the rectangular bounding box containing the text is available. The word present in the bounding box needs to be recognized. The methods available to perform word recognition can be broadly classified into top-down and bottom-up approaches. In the top-down approaches, a set of words from a dictionary is used to identify which word suits the given image. Images are not segmented in most of these methods. Hence, the top-down approach is sometimes referred as segmentation free recognition. In the bottom-up approaches, the image is segmented into multiple components and the segmented image is passed through a recognition engine. Either an off the shelf Optical character recognition (OCR) engine or a custom-trained one is used to recognise the text.
Artificial intelligence industry in China
The roots of the development of artificial intelligence in the People's Republic of China started in the late 1970s following Deng Xiaoping's reform and opening up emphasizing science and technology as the country's primary productive force. The initial stages of China's AI development were slow and encountered significant challenges due to lack of resources and talent. At the beginning China was behind most Western countries in terms of AI development. A majority of the research was led by scientists who had received higher education abroad. Since 2006, the Chinese government has steadily developed a national agenda for artificial intelligence development and emerged as one of the leading nations in artificial intelligence research and development. In 2016, the Chinese Communist Party (CCP) released its 13th Five-Year Plan in which it aimed to become a global AI leader by 2030. As of 2025, China is considered to be a world leader in AI technology along with the United States. The State Council has a list of "national AI teams" including fifteen China-based companies, including Baidu, Tencent, Alibaba, SenseTime, and iFlytek. Each company should lead the development of a designated specialized AI sector in China, such as facial recognition, software/hardware, and speech recognition. China's rapid AI development has significantly impacted Chinese society in many areas, including the socio-economic, military, intelligence, and political spheres. Agriculture, transportation, accommodation and food services, and manufacturing are the top industries that would be the most impacted by further AI deployment. The private sector, university laboratories, and the military are working collaboratively in many aspects as there are few current existing boundaries. In 2021, China published the Data Security Law of the People's Republic of China, its first national law addressing AI-related ethical concerns. In October 2022, the United States federal government announced a series of export controls and trade restrictions intended to restrict China's access to advanced computer chips for AI applications. In 2023, the Cyberspace Administration of China issued guidelines requiring that AI content upholds the ideology of the CCP including Core Socialist Values, avoids discrimination, respects intellectual property rights, and safeguards user data. In 2025, the Chinese government issued a document regarding training data, requiring companies to use as little as data deemed "unsafe" as possible, as well as requiring companies to test models regularly. Concerns have been raised about the effects of the Chinese government's censorship regime on the development of generative artificial intelligence and long-term talent acquisition with state of the country's demographics. Others have noted that official notions of AI safety require following the priorities of the CCP and are antithetical to standards in democratic societies and raised concerns about the extension of China's system of mass surveillance and censorship abroad. == History == The Chinese term for artificial intelligence (réngōngzhìnéng 人工智能) connotes "humanmade" intelligence. The term developed as mid-20th century localisation of the Japanese term jinko chino. The research and development of artificial intelligence in China started in the 1980s, with the announcement by Deng Xiaoping of the importance of science and technology for China's economic growth. === Late 1970s to early 2010s === Chinese artificial intelligence research and development began in late 1970s after Deng Xiaoping's reform and opening up. China's first national conference on AI occurred in 1979. Academic journals in the late 1970s began publishing literature reviews of Western research on AI topics. In the 1980s, a group of Chinese scientists launched AI research led by Qian Xuesen and Wu Wenjun. However, during the time, China's society still had a generally conservative view towards AI. In the early 1980s, Science Press published translated versions of Western textbooks such as Patrick Winston's Artificial Intelligence and Nils John Nilsson's Principles of Artificial Intelligence. In 1980, a journal of the Chinese Academy of Sciences convened its first annual National Symposium on Artificial Intelligence, which included national and international scholars like Herbert A. Simon. The Chinese Association for Artificial Intelligence (CAAI) was founded in September 1981 and was authorized by the Ministry of Civil Affairs. CAAI has continued to be the largest AI association in China as of 2025. In 1982, CAAI began publishing the Artificial Intelligence Journal, which published early AI research by Chinese academics. In the 1980s, Chinese research on AI was influenced by the field of cybernetics, particularly the work of Norbert Weiner and his text Cybernetics: Or Control and Communication in the Animal and the Machine. Chinese researchers at the time sought to situate AI as part of a broader "Intelligence Science" field which would include disciplines like mathematics, computer science, cognitive science, social sciences, and philosophy. In 1987, Tsinghua University began a research publication on AI. Beginning in 1993, smart automation and intelligence have been part of China's national technology plan. Since the 2000s, the Chinese government has further expanded its research and development funds for AI and the number of government-sponsored research projects has dramatically increased. In 2006, China announced a policy priority for the development of artificial intelligence, which was included in the National Medium and Long Term Plan for the Development of Science and Technology (2006–2020), released by the State Council. In the same year, artificial intelligence was also mentioned in the 11th Five-Year Plan. In 2011, the Association for the Advancement of Artificial Intelligence (AAAI) established a branch in Beijing, China. At same year, the Wu Wenjun Artificial Intelligence Science and Technology Award was founded in honor of Chinese mathematician Wu Wenjun, and it became the highest award for Chinese achievements in the field of artificial intelligence. The first award ceremony was held on May 14, 2012. In 2013, the International Joint Conferences on Artificial Intelligence (IJCAI) was held in Beijing, marking the first time the conference was held in China. This event coincided with the Chinese government's announcement of the "Chinese Intelligence Year," a significant milestone in China's development of artificial intelligence. === Late 2010s to early 2020s === AI became a major issue of commercial, public, and political focus in China in the latter half of the 2010s. Various interpretations of the primary cause for this increased focus exist, with some analyses focusing on the 2016 Go match between Google's AlphaGo and Lee Sedol, others emphasising the U.S. increasing trade restrictions on China's technology industries and the desire to achieve national technological self-sufficiency. The State Council of China issued "A Next Generation Artificial Intelligence Development Plan" (State Council Document [2017] No. 35) on 20 July 2017. In the document, the CCP Central Committee and the State Council urged governing bodies in China to promote the development of artificial intelligence. Specifically, the plan described AI as a strategic technology that has become a "focus of international competition".:2 The document urged significant investment in a number of strategic areas related to AI and called for close cooperation between the state and private sectors. It set the goal of China becoming the preeminent country for AI research and application by 2030. During the general secretaryship of Xi Jinping, artificial intelligence has been a focus of the CCP's military-civil fusion efforts. On the occasion of Xi's speech at the first plenary meeting of the Central Military-Civil Fusion Development Committee (CMCFDC), scholars from the National Defense University wrote in the PLA Daily that the "transferability of social resources" between economic and military ends is an essential component to being a great power. During the Two Sessions 2017,"artificial intelligence plus" was proposed to be elevated to a strategic level. The same year witnessed the emergence of multiple application-level usages in the medical field according to reports. In 2018, Xinhua News Agency, in partnership with Tencent's subsidiary Sogou, launched its first artificial intelligence-generated news anchor. In 2018, the State Council budgeted $2.1 billion for an AI industrial park in Mentougou district. In order to achieve this the State Council stated the need for massive talent acquisition, theoretical and practical developments, as well as public and private investments. Some of the stated motivations that the State Council gave for pursuing its AI strategy include the potential of artificial intelligence for industrial transformation, better social