Michael Irwin Jordan (born February 25, 1956) is an American scientist, professor at the University of California, Berkeley, research scientist at the Inria Paris, and researcher in machine learning, statistics, and artificial intelligence. Jordan was elected a member of the National Academy of Engineering in 2010 for contributions to the foundations and applications of machine learning. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. In 2022, Jordan won the inaugural World Laureates Association Prize in Computer Science or Mathematics, "for fundamental contributions to the foundations of machine learning and its application." == Education == Jordan received a Bachelor of Science magna cum laude in psychology from the Louisiana State University in 1978, a Master of Science in mathematics from Arizona State University in 1980, and a Doctor of Philosophy in cognitive science from the University of California, San Diego in 1985. At UC San Diego, Jordan was a student of David Rumelhart and a member of the Parallel Distributed Processing (PDP) Group in the 1980s. == Career and research == Jordan is the Pehong Chen Distinguished Professor at the University of California, Berkeley, where his appointment is split across EECS and Statistics. He was a professor at the Department of Brain and Cognitive Sciences at MIT from 1988 to 1998. In the 1980s Jordan started developing recurrent neural networks as a cognitive model. In recent years, his work is less driven from a cognitive perspective and more from the background of traditional statistics. Jordan popularised Bayesian networks in the machine learning community and is known for pointing out links between machine learning and statistics. He was also prominent in the formalisation of variational methods for approximate inference and the popularisation of the expectation–maximization algorithm in machine learning. === Resignation from Machine Learning === In 2001, Jordan and others resigned from the editorial board of the journal Machine Learning. In a public letter, they argued for less restrictive access and pledged support for a new open access journal, the Journal of Machine Learning Research, which was created by Leslie Kaelbling to support the evolution of the field of machine learning. === Honors and awards === Jordan has received numerous awards, including a best student paper award (with X. Nguyen and M. Wainwright) at the International Conference on Machine Learning (ICML 2004), a best paper award (with R. Jacobs) at the American Control Conference (ACC 1991), the ACM-AAAI Allen Newell Award, the IEEE Neural Networks Pioneer Award, and an NSF Presidential Young Investigator Award. In 2002 he was named an AAAI Fellow "for significant contributions to reasoning under uncertainty, machine learning, and human motor control." In 2004 he was named an IMS Fellow "for contributions to graphical models and machine learning." In 2005 he was named an IEEE Fellow "for contributions to probabilistic graphical models and neural information processing systems." In 2007 he was named an ASA Fellow. In 2010 he was named a Cognitive Science Society Fellow and named an ACM Fellow "for contributions to the theory and application of machine learning." In 2012 he was named a SIAM Fellow "for contributions to machine learning, in particular variational approaches to statistical inference." In 2014 he was named an International Society for Bayesian Analysis Fellow "for his outstanding research contributions at the interface of statistics, computer sciences and probability, for his leading role in promoting Bayesian methods in machine learning, engineering and other fields, and for his extensive service to ISBA in many roles." Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He also won the 2020 IEEE John von Neumann Medal. In 2016, Jordan was identified as the "most influential computer scientist", based on an analysis of the published literature by the Semantic Scholar project. In 2019, Jordan argued that the artificial intelligence revolution hasn't happened yet and that the AI revolution required a blending of computer science with statistics. In 2022, Jordan was awarded the inaugural World Laureates Association Prize by non-governmental and non-profit international organization World Laureates Association, for fundamental contributions to the foundations of machine learning and its application. For 2024 he received the BBVA Foundation Frontiers of Knowledge Award in the category of "Information and Communication Technologies".
Hello World: How to be Human in the Age of the Machine
Hello World: How to Be Human in the Age of the Machine (also titled Hello World: Being Human in the Age of Algorithms) is a book on the growing influence of algorithms and artificial intelligence (AI) on human life, authored by mathematician and science communicator Hannah Fry. The book examines how algorithms are increasingly shaping decisions in critical areas such as healthcare, transportation, justice, finance, and the arts. == Overview == Fry uses real-world examples, such as driverless cars and predictive policing, to illustrate her points. She emphasizes that algorithms are not inherently objective; they reflect biases embedded in their design and data inputs. While acknowledging their potential to improve efficiency and accuracy, Fry cautions against over-reliance on machines without human judgment. Fry explores moral questions surrounding algorithmic decision-making, such as whether machines can replace human empathy in critical situations. She advocates for greater scrutiny of algorithms to ensure fairness and avoid harmful biases. The book proposes a "cyborg future", where humans work alongside algorithms to enhance decision-making while retaining ultimate control. == Reception == Hello World has been praised for its clarity, engaging storytelling, and balanced perspective. Critics have highlighted Fry's ability to make complex topics accessible to general audiences while raising important questions about technology's impact on society. The book was shortlisted for awards such as the 2018 Baillie Gifford Prize and the Royal Society Science Book Prize.
Strategic Computing Initiative
The United States government's Strategic Computing Initiative funded research into advanced computer hardware and artificial intelligence from 1983 to 1993. The initiative was designed to support various projects that were required to develop machine intelligence in a prescribed ten-year time frame, from chip design and manufacture, computer architecture to artificial intelligence software. The Department of Defense spent a total of $1 billion on the project. The inspiration for the program was Japan's fifth generation computer project, an enormous initiative that set aside billions for research into computing and artificial intelligence. As with Sputnik in 1957, the American government saw the Japanese project as a challenge to its technological dominance. The British government also funded a program of their own around the same time, known as Alvey, and a consortium of U.S. companies funded another similar project, the Microelectronics and Computer Technology Corporation. The goal of SCI, and other contemporary projects, was nothing less than full machine intelligence. "The machine envisioned by SC", according to Alex Roland and Philip Shiman, "would run ten billion instructions per second to see, hear, speak, and think like a human. The degree of integration required would rival that achieved by the human brain, the most complex instrument known to man." The initiative was conceived as an integrated program, similar to the Apollo moon program, where different subsystems would be created by various companies and academic projects and eventually brought together into a single integrated system. Roland and Shiman wrote that "While most research programs entail tactics or strategy, SC boasted grand strategy, a master plan for an entire campaign." The project was funded by the Defense Advanced Research Projects Agency and directed by the Information Processing Technology Office (IPTO). By 1985 it had spent $100 million, and 92 projects were underway at 60 institutions: half in industry, half in universities and government labs. Robert Kahn, who directed IPTO in those years, provided the project with its early leadership and inspiration. Clint Kelly managed the SC Initiative for three years and developed many of the specific application programs for DARPA, such as the Autonomous Land Vehicle. By the late 1980s, it was clear that the project would fall short of realizing the hoped-for levels of machine intelligence. Program insiders pointed to issues with integration, organization, and communication. When Jack Schwarz ascended to the leadership of IPTO in 1987, he cut funding to artificial intelligence research (the software component) "deeply and brutally", "eviscerating" the program (wrote Pamela McCorduck). Schwarz felt that DARPA should focus its funding only on those technologies which showed the most promise. In his words, DARPA should "surf", rather than "dog paddle", and he felt strongly AI was not "the next wave". The project was superseded in the 1990s by the Accelerated Strategic Computing Initiative and then by the Advanced Simulation and Computing Program. These later programs did not include artificial general intelligence as a goal, but instead focused on supercomputing for large scale simulation, such as atomic bomb simulations. The Strategic Computing Initiative of the 1980s is distinct from the 2015 National Strategic Computing Initiative—the two are unrelated. == Results == Although the program failed to meet its goal of high-level machine intelligence, it did meet some of its specific technical objectives, for example those of autonomous land navigation. The Autonomous Land Vehicle program and its sister Navlab project at Carnegie Mellon University, in particular, laid the scientific and technical foundation for many of the driverless vehicle programs that came after it, such as the Demo II and III programs (ALV being Demo I), Perceptor, and the DARPA Grand Challenge. The use of video cameras plus laser scanners and inertial navigation units pioneered by the SCI ALV program form the basis of almost all commercial driverless car developments today. It also helped to advance the state of the art of computer hardware to a considerable degree. On the software side, the initiative funded development of the Dynamic Analysis and Replanning Tool (DART), a program that handled logistics using artificial intelligence techniques. This was a huge success, saving the Department of Defense billions during Desert Storm. Introduced in 1991, DART had by 1995 offset the monetary equivalent of all funds DARPA had channeled into AI research for the previous 30 years combined.
Common Crawl
The Common Crawl Foundation (Common Crawl) is a nonprofit 501(c)(3) organization that crawls the web and freely provides its archives and datasets to the public. Common Crawl was founded by Gil Elbaz. The data had mostly been primarily used by researchers and some startups until the 2020s, when AI companies started training large language models using the data. In November 2025, an investigation by The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and it was not honoring requests from publishers to have their content removed from its databases. == History == Common Crawl was founded in 2007 in San Francisco. It began publishing its crawls in 2011. By 2013, sites like TinEye were building their products off of Common Crawl. The crawl reduces the reliance of companies and researchers on Google, which has the biggest dataset. Common Crawl was designed to have more and fresher data that was more efficient to analyze and utilize than the Wayback Machine created by the Internet Archive. By 2015, 1.8 billion webpages were on the Common Crawl, which started by crawling a list of URLs donated by the search engine Blekko. They use Amazon Web Services, which provides some of its services for free, allowing computing costs to average $2-4000/month. The Common Crawl website listed 30 studies based on Common Crawl data. Before 2023, Common Crawl was not very well known outside of academic researchers who utilize the data. Common Crawl received its first requests to redact information in 2023 and increasingly started seeing its crawler, CCBot, blocked. In 2023, it began receiving significant financial support from AI companies, including Anthropic and OpenAI, each of which donated $250,000. It was also used to train Google DeepMind's large language model Gemini. By April 2023, Common Crawl was capturing 3.1 billion webpages, with an estimated 5% of pages before 2021 containing hate speech or slurs. As of 2024, Common Crawl had been cited in more than 10,000 academic studies. By 2024, The Pile and Common Crawl had been the two main training datasets being used to train AI models. In November 2025, an investigation by technology journalist Alex Reisner for The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and when it said that it was honoring requests from publishers to have their content removed from its databases. It included misleading results in the public search function on its website that showed no entries for websites that had requested their archives be removed, when in fact those sites were still included in its scrapes used by AI companies. As of 2025, Reisner found that CCBot was the most widely-blocked bot by the top 1000 websites. A 2026 article in LWN.net discussed an advantage to services like Common Crawl being that it can limit the scraping costs to websites by allowing companies and researchers to download the data from Common Crawl instead of scraping it themselves. In April 2026, Common Crawl experimentally began to distribute its data through Hugging Face Storage Bucket, in addition to its standard storage on Amazon S3. == Organization == Peter Norvig and Joi Ito have served on the advisory board. Rich Skrenta is the executive director. It has received funding almost exclusively from the Elbaz Family Foundation Trust until 2023 when it started receiving donations from the AI industry. == Refined versions == A number of organizations take raw Common Crawl data and refine it into datasets that exclude edgy content or are otherwise higher-quality for their purposes, such as FineWeb, DCLM and C4. === Colossal Clean Crawled Corpus === Google version of the Common Crawl is called the Colossal Clean Crawled Corpus, or C4 for short. It was constructed for the training of the T5 language model series in 2019. As of 2023, there were some concerns over copyrighted content in the C4 as well as racist content. A 2024 study found that 45% of content was explicitly restricted by websites' terms of service to be used for purposes like AI training by for-profit companies.
Copyright and artificial intelligence in the United Kingdom
The interaction of artificial intelligence and copyright law has become one of the most contentious tech policy debates in the United Kingdom, centering on whether AI developers should be permitted to train their models on copyrighted material without explicit consent or remuneration. This debate has exposed a deep fracture between the creative industries, which seek to protect their intellectual property from unauthorised commercial exploitation, and tech companies. The academic and library sectors are also impacted, and argue that overly restrictive copyright laws hinder scientific research and the UK's sovereign AI capabilities. In 2024, the UK government proposed a broad text and data mining (TDM) exception to copyright that would have allowed AI companies to use publicly available copyrighted material for training, offering creators only an "opt-out" mechanism, similar to the exception introduced in Europe. This proposal faced intense opposition from across the creative sector. Trade unions representing writers, musicians, performers, and journalists argued that such an exception would effectively expropriate their members' work for the commercial benefit of tech giants. A report from the House of Lords Communications and Digital Committee, warned that generative AI posed a "clear and present danger" to the £124 billion creative economy. The government abandoned the opt-out model in March 2026, opting instead to build a stronger evidence base before pursuing any copyright reform. Conversely, the academic and library sectors have raised significant concerns that the UK's current TDM exception, which is strictly limited to non-commercial research, is too narrow. Universities and research libraries occupy a dual role as both creators of vast datasets and beneficiaries of TDM exceptions. They argue that the current legal framework restricts their ability to computationally analyse the very research they produce, thereby hobbling the UK's "AI for Science" strategy. Advocacy groups have highlighted a "triple payment" problem, wherein publicly funded research is handed over to publishers, who then charge universities substantial subscription fees and demand additional payments for specific TDM licences. This tension is further complicated by the commercial practices of major academic publishers. While publishers often restrict universities from using subscribed databases for AI training, they have simultaneously entered into lucrative, multi-million-dollar licensing agreements to sell access to this academic content to commercial AI developers. Furthermore, academics have accused publishers of actively steering authors away from permissive open-access licences towards more restrictive variants. By doing so, publishers retain the exclusive commercial rights necessary to strike these AI training deals, often without consulting the original authors or offering them any additional remuneration. This dynamic has not only reopened debates within the Open Access movement but has also created complex legal scenarios where publishers, rather than authors, control the terms of copyright litigation against major tech companies. == Training on copyrighted material == The question of whether AI developers should be permitted to train their models on copyrighted material without payment or consent has been one of the most contentious policy debates in the UK AI landscape. In 2024, the then-Conservative government proposed a broad text and data mining (TDM) exception that would have allowed AI companies to use any publicly available copyrighted material for training purposes, with creators able only to "opt out" of having their work used. This proposal provoked intense opposition from writers, musicians, visual artists, publishers, and broadcasters, who argued it would effectively expropriate their intellectual property for the commercial benefit of AI companies. The debate over text and data mining exceptions extends significantly beyond generative AI and the creative industries, implicating a wide range of scientific, industrial, and academic research applications. TDM is a foundational process for analysing large datasets to identify patterns, trends, and correlations, which is heavily utilised in fields such as medical research, climate modelling, and financial services. In the scientific and academic sectors, researchers rely on TDM to process vast amounts of published literature. For example, in biomedical research, TDM is used to accelerate drug discovery, identify new uses for existing medicines, and extract insights from clinical notes and genomic datasets. However, the application of traditional copyright frameworks to scientific literature has been criticised by academics. Researchers argue that scientific writing is intended to convey factual, verifiable information rather than creative originality, and that copyright restrictions on TDM hinder reproducibility, validation, and the advancement of science. The current UK copyright exception for TDM (Section 29A of the Copyright, Designs and Patents Act 1988) is limited strictly to non-commercial research, which creates barriers for public-private research partnerships and commercial scientific development. Beyond academia, non-generative AI and TDM are critical to various industrial and commercial operations. In the financial services sector, TDM is employed to monitor transactions, detect fraud, and analyse market feeds. Other non-generative applications include search engine indexing, plagiarism detection software, and media monitoring. A 2026 report by Public First estimated that 19% of UK businesses use specialised TDM tools, and that a restrictive copyright regime requiring licenses for all copyrighted content could cost the UK economy £220 billion in lost AI-driven GDP growth by 2035 compared to a broad commercial TDM exemption. Industry advocates argue that the lack of a commercial TDM exception in the UK creates legal uncertainty that stifles innovation across these broader, non-generative applications of data analysis. === Tech and AI industry positions === The technology and artificial intelligence industries lobbied for a broad text and data mining (TDM) exception to UK copyright law, arguing that such an exception is essential for the UK to remain globally competitive in AI development. Industry bodies such as techUK have argued that without a TDM exception, the UK risks becoming an "AI taker rather than an AI maker," as developers will relocate training operations to jurisdictions with more permissive copyright regimes, such as the United States, Japan, Singapore, and the European Union. During the UK government's 2024–2025 consultation on copyright and AI, major AI developers and trade associations strongly supported "Option 2" (a broad TDM exception) or "Option 3" (a TDM exception with an opt-out mechanism). OpenAI stated in its consultation response that a broad TDM exception is "necessary to drive AI innovation and investment in the UK," arguing that developers should be permitted to train models on lawfully accessed copies without further distribution. The Computer and Communications Industry Association (CCIA) similarly argued that restricting TDM to non-commercial development would undermine the government's ambitions for the UK tech sector and frustrate partnerships between commercial entities and research institutions. Tech industry advocates have also highlighted the economic implications of copyright policy. According to analysis by the think tank UK Day One, adopting an overly restrictive licensing-only approach could result in the UK economy losing up to £182 billion over 20 years, whereas a broad TDM exception could generate a positive impact of £131.61 billion over the same period. Following the government's March 2026 decision to drop plans for a TDM exception in favour of a market-led licensing approach, techUK's Deputy CEO Antony Walker criticised the move, stating that "copyright material cannot be used for AI development and training without permission" under the current framework, which he argued would push AI model training to the US. === Creative sector and political opposition to text and data mining === In March 2026, the House of Lords Communications and Digital Committee published a report, AI, Copyright and the Creative Industries, which concluded that the creative industries face "a clear and present danger from generative AI" and that it would be "a very poor bet" for the government to weaken copyright protections to attract AI investment. The Committee noted that the creative industries contributed £124 billion to the UK economy in 2023 and employed 2.4 million people, compared to the AI sector's £12 billion GVA and 86,000 employees in 2024. The Committee called on the government to develop a "licensing-first" regime underpinned by mandatory transparency requirements, and to rule out any new commercial TDM exception with an opt-out model. Tra
Coolgorilla
Coolgorilla was one of the earliest software developers that created 3rd party native applications for Apple iPod devices. Coolgorilla was an early adopter of using a sponsorship business model to enable mobile applications to be given away freely. Coolgorilla developed a series of Talking Phrasebooks for iPods in 2006. They partnered with online travel company lastminute.com who sponsored the applications enabling them to be made available to download completely free of charge. As mobile devices became more sophisticated, Coolgorilla developed the Talking Phrasebooks for Sony Ericsson and Nokia Mobile Devices which at the time were considerably noteworthy since the applications used real voice audio translations. With Apple's introduction of the iPhone in 2007, Coolgorilla developed a Web App before having four of the iPhone Talking Phrasebooks available to download from Apple's App Store on the day it opened in 2008. == Almanac in Chronological Order == On 23 December 2005, CoolGorilla, a new start-up, launched a trivia game for the iPod. It was titled "Rock and Pop Quiz". It was a quiz game that tested users' knowledge on bands such as U2, Metallica, Beyonce, and the Beatles. The quiz contained twenty megabytes of audible trivia questions. The free game was compatible with 3rd, 4th and 5th generation iPods, iPod mini and nano. In March 2006, Coolgorilla released "Movie Quiz for iPods" with a price of $5. It was an audio game narrated by New York's DJ Thomas, a radio and television host, voice over artist and event Master of Ceremonies. There were questions on Star Wars, Spiderman, The Godfather, Pulp Fiction, The Matrix, James Bond, and others. The user could keep track of their score. The game included a secret code for players who answered all questions correctly which enabled users to enter their name on the Coolgorilla Hall of Fame. In May 2006, Coolgorilla launched a World Cup Encyclopedia which was released prior to the 2006 FIFA World Cup. It had information on the World Cup schedule, details of every player from every team, every score from every world cup game ever played, stadium details, and manager profiles. It was a free download. In June 2006, Coolgorilla released a series of iPod Phrasebooks in German, Greek, French and Spanish. They were sponsored by lastminute.com and were free. The phrasebooks included common words and phrases for tourists with 750 sound files. They were accessed through the iPod's Notes feature. In April 2007, Coolgorilla released a downloadable version of the Talking Phrasebooks for Nokia and Sony Ericsson mobile devices. French, Spanish, German, Greek, Italian, and Portuguese were produced. The application provided real voice translations. They initially sold for £3 but 3 months later were offered for free. The branding was lastminute.com branding. Apple's iPhone was released at the end of June 2007. Soon after, Coolgorilla released an online all-in-one version of their Talking Phrasebooks for iPhone (Web App). The Phrasebooks were made available online in the form of a web app as iPhone did not yet allow for the download of additional apps. The app provided both text and audio translations in French, Spanish, Portuguese, Italian, German, and Greek. The iPhone translated the phrases using the recordings of real, native voice-over artists. A text translation on screen was also displayed. Apple's App Store opened in July 2008 with approximately 500 native apps available. Four of these Apps were Coolgorilla's Talking Phrasebooks for iPhone (Native Apps). There was French, German, Italian, and Spanish. These Apps carried lastminute.com branding and were available for free download. In the first three weeks following their release, the phrasebooks had over 350,000 downloads. Subsequently, Dutch, Arabic, Mandarin and Cantonese were also released. In October 2008, Coolgorilla released an iPhone London Travel Guide. Coolgorilla featured on NBC News in August 2009. In 2010, FIAT used the Italian Phrasebook to help promote the release of their FIAT 500 in the US. There has been no further activity since.
Interim Measures for the Management of Anthropomorphic AI Interactive Services
The Interim Measures for the Management of Anthropomorphic AI Interactive Services (Chinese: 人工智能拟人化互动服务管理暂行办法) is a document proposed by the Cyberspace Administration of China to regulate anthropomorphic artificial intelligence systems. The draft was released on December 27, 2026 for public comment period until January 25, 2026. The proposed document would prohibit AI companies and users of AI services from generating certain types of content deemed harmful to national interests or the social order, and impose various regulatory and safety requirements on providers of AI systems. The proposed regulation is motivated by concerns about the psychological and social effects of AI systems that are perceived as personalities by their users, including addiction, encouragement of self-harm, or generation of illegal content. == Description == === Scope === The regulation would apply to AI systems that are offered to the general public within China. They would not apply to company-internal or research use, or to products that are only available outside of China. For the purpose of the regulation, anthropomorphic Ai systems are defined as those that "simulate human personality traits, modes of thinking, and communication styles, and that engage in emotional interaction with humans through text, images, audio, video, or other means". === Requirements === The regulation would require AI providers to monitor users for signs of harmful use and to take various interventions when indications of harmful use are detected. It would also prohibit AI systems from certain types of behaviors and generation of certain types of content. In some circumstances where a user appears to be at risk of self harm, the system would be required to hand over control to a human operator who would manually intervene. The regulation would also require more rigorous practices for managing the provenance of training data used to develop these systems, and would require explicit opt-in consent from users before their interactions with an AI system were used as training data. Data used to train the regulated systems would be required to reflect core socialist values and traditional Chinese culture.