AI Coding On Phone

AI Coding On Phone — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Trigger list

  Trigger list in its most general meaning refers to a list whose items are used to initiate ("trigger") certain actions. == United States: Private financial information == In the United States, when a person applies for a mortgage loan, the lender makes a credit inquiry about the potential borrower from the national credit bureaus, Equifax, Experian and TransUnion. Unless the borrower is opted out, the credit bureaus put the applicants onto a "trigger list" of "leads" about persons who are interested in new loans. These lists are sold to numerous lenders all over the United States, and soon after the application the applicant starts receiving offers from all parts of the country. The trigger lists contain a significant amount of personal financial information. Among the buyers of trigger lists are "lead generators" which resell filtered information to borrowers, e.g., of people who live in a certain area and have a certain credit score. While the Federal Trade Commission considers the market of "trigger lists" to be a legal business, many people and organizations (such as the National Association of Mortgage Brokers) consider this a serious breach of privacy and lobby for putting this practice under regulatory controls. As of now, American consumers may opt-out from "trigger lists" by calling 1-888-5-OPTOUT (1-888-567-8688). == Nuclear non-proliferation == The Zangger Committee and the Nuclear Suppliers Group maintain lists of items that may contribute to nuclear proliferation; The nuclear non-proliferation treaty forbids its members to export such items to non-treaty members. these items are said to trigger the countries' responsibilities under the NPT, hence the name.

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  MIT Computer Science and Artificial Intelligence Laboratory

  Computer Science and Artificial Intelligence Laboratory (CSAIL) is a research institute at the Massachusetts Institute of Technology (MIT) formed by the 2003 merger of the Laboratory for Computer Science (LCS) and the Artificial Intelligence Laboratory (AI Lab). Housed within the Ray and Maria Stata Center, CSAIL is the largest on-campus laboratory as measured by research scope and membership. It is part of the Schwarzman College of Computing but is also overseen by the MIT Vice President of Research. == Research activities == CSAIL's research activities are organized around a number of semi-autonomous research groups, each of which is headed by one or more professors or research scientists. These groups are divided up into seven general areas of research: Artificial intelligence Computational biology Graphics and vision Language and learning Theory of computation Robotics Systems (includes computer architecture, databases, distributed systems, networks and networked systems, operating systems, programming methodology, and software engineering, among others) == History == Computing Research at MIT began with Vannevar Bush's research into a differential analyzer and Claude Shannon's electronic Boolean algebra in the 1930s, the wartime MIT Radiation Laboratory, the post-war Project Whirlwind and the Research Laboratory of Electronics (RLE), and MIT Lincoln Laboratory's SAGE in the early 1950s. At MIT, research in the field of artificial intelligence began in the late 1950s. === Project MAC === On July 1, 1963, Project MAC (the Project on Mathematics and Computation, later backronymed to Multiple Access Computer, Machine Aided Cognitions, or Man and Computer) was launched with a $2 million grant from the Defense Advanced Research Projects Agency (DARPA). Project MAC's original director was Robert Fano of MIT's Research Laboratory of Electronics (RLE). Fano decided to call MAC a "project" rather than a "laboratory" for reasons of internal MIT politics – if MAC had been called a laboratory, then it would have been more difficult to raid other MIT departments for research staff. The program manager responsible for the DARPA grant was J. C. R. Licklider, who had previously been at MIT conducting research in RLE, and would later succeed Fano as director of Project MAC. Project MAC would become famous for groundbreaking research in operating systems, artificial intelligence, and the theory of computation. Its contemporaries included Project Genie at Berkeley, the Stanford Artificial Intelligence Laboratory, and (somewhat later) University of Southern California's (USC's) Information Sciences Institute. An "AI Group" including Marvin Minsky (the director), John McCarthy (inventor of Lisp), and a talented community of computer programmers were incorporated into Project MAC. They were interested principally in the problems of vision, mechanical motion and manipulation, and language, which they view as the keys to more intelligent machines. In the 1960s and 1970s the AI Group developed a time-sharing operating system called Incompatible Timesharing System (ITS) which ran on PDP-6 and later PDP-10 computers. The early Project MAC community included Fano, Minsky, Licklider, Fernando J. Corbató, and a community of computer programmers and enthusiasts among others who drew their inspiration from former colleague John McCarthy. These founders envisioned the creation of a computer utility whose computational power would be as reliable as an electric utility. To this end, Corbató brought the first computer time-sharing system, Compatible Time-Sharing System (CTSS), with him from the MIT Computation Center, using the DARPA funding to purchase an IBM 7094 for research use. One of the early focuses of Project MAC would be the development of a successor to CTSS, Multics, which was to be the first high availability computer system, developed as a part of an industry consortium including General Electric and Bell Laboratories. In 1966, Scientific American featured Project MAC in the September thematic issue devoted to computer science, that was later published in book form. At the time, the system was described as having approximately 100 TTY terminals, mostly on campus but with a few in private homes. Only 30 users could be logged in at the same time. The project enlisted students in various classes to use the terminals simultaneously in problem solving, simulations, and multi-terminal communications as tests for the multi-access computing software being developed. === AI Lab and LCS === In the late 1960s, Minsky's artificial intelligence group was seeking more space, and was unable to get satisfaction from project director Licklider. Minsky found that although Project MAC as a single entity could not get the additional space he wanted, he could split off to form his own laboratory and then be entitled to more office space. As a result, the MIT AI Lab was formed in 1970, and many of Minsky's AI colleagues left Project MAC to join him in the new laboratory, while most of the remaining members went on to form the Laboratory for Computer Science. Talented programmers such as Richard Stallman, who used TECO to develop EMACS, flourished in the AI Lab during this time. Those researchers who did not join the smaller AI Lab formed the Laboratory for Computer Science and continued their research into operating systems, programming languages, distributed systems, and the theory of computation. Two professors, Hal Abelson and Gerald Jay Sussman, chose to remain neutral—their group was referred to variously as Switzerland and Project MAC for the next 30 years. Among much else, the AI Lab led to the invention of Lisp machines and their attempted commercialization by two companies in the 1980s: Symbolics and Lisp Machines Inc. === CSAIL === On the fortieth anniversary of Project MAC's establishment, July 1, 2003, LCS was merged with the AI Lab to form the MIT Computer Science and Artificial Intelligence Laboratory, or CSAIL. This merger created the largest laboratory (over 600 personnel) on the MIT campus. In 2018, CSAIL launched a five-year collaboration program with IFlytek, a company sanctioned the following year for allegedly using its technology for surveillance and human rights abuses in Xinjiang. In October 2019, MIT announced that it would review its partnerships with sanctioned firms such as iFlyTek and SenseTime. In April 2020, the agreement with iFlyTek was terminated. CSAIL moved from the School of Engineering to the newly formed Schwarzman College of Computing by February 2020. == Offices == From 1963 to 2004, Project MAC, LCS, the AI Lab, and CSAIL had their offices at 545 Technology Square, taking over more and more floors of the building over the years. In 2004, CSAIL moved to the new Ray and Maria Stata Center, which was built specifically to house it and other departments. == Outreach activities == The IMARA (from Swahili word for "power") group sponsors a variety of outreach programs that bridge the global digital divide. Its aim is to find and implement long-term, sustainable solutions which will increase the availability of educational technology and resources to domestic and international communities. These projects are run under the aegis of CSAIL and staffed by MIT volunteers who give training, install and donate computer setups in greater Boston, Massachusetts, Kenya, Native American Indian tribal reservations in the American Southwest such as the Navajo Nation, the Middle East, and Fiji Islands. The CommuniTech project strives to empower under-served communities through sustainable technology and education and does this through the MIT Used Computer Factory (UCF), providing refurbished computers to under-served families, and through the Families Accessing Computer Technology (FACT) classes, it trains those families to become familiar and comfortable with computer technology. == Notable researchers == (Including members and alumni of CSAIL's predecessor laboratories) MacArthur Fellows Tim Berners-Lee, Erik Demaine, Dina Katabi, Daniela L. Rus, Regina Barzilay, Peter Shor, Richard Stallman, and Joshua Tenenbaum Turing Award recipients Leonard M. Adleman, Fernando J. Corbató, Shafi Goldwasser, Butler W. Lampson, John McCarthy, Silvio Micali, Marvin Minsky, Ronald L. Rivest, Adi Shamir, Barbara Liskov, and Michael Stonebraker IJCAI Computers and Thought Award recipients Terry Winograd, Patrick Winston, David Marr, Gerald Jay Sussman, Rodney Brooks Rolf Nevanlinna Prize recipients Madhu Sudan, Peter Shor, Constantinos Daskalakis Gödel Prize recipients Shafi Goldwasser (two-time recipient), Silvio Micali, Maurice Herlihy, Charles Rackoff, Johan Håstad, Peter Shor, and Madhu Sudan Grace Murray Hopper Award recipients Robert Metcalfe, Shafi Goldwasser, Guy L. Steele, Jr., Richard Stallman, and W. Daniel Hillis Textbook authors Harold Abelson and Gerald Jay Sussman, Richard Stallman, Thomas H. Cormen, Charles E. Leiserson, Patrick Winston, Ronald L.

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  Lethal autonomous weapon

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

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  Issue tree

  An issue tree, also called logic tree, is a graphical breakdown of a question that dissects it into its different components vertically and that progresses into details as it reads to the right. Issue trees are useful in problem solving to identify the root causes of a problem as well as to identify its potential solutions. They also provide a reference point to see how each piece fits into the whole picture of a problem. == Types == According to professor of strategy Arnaud Chevallier, elaborating an approach used at McKinsey & Company, there are two types of issue trees: diagnostic ones and solution ones. Diagnostic trees break down a "why" key question, identifying all the possible root causes for the problem. Solution trees break down a "how" key question, identifying all the possible alternatives to fix the problem. == Rules == Four basic rules can help ensure that issue trees are optimal, according to Chevallier: Consistently answer a "why" or a "how" question Progress from the key question to the analysis as it moves to the right Have branches that are mutually exclusive and collectively exhaustive (MECE) Use an insightful breakdown The requirement for issue trees to be collectively exhaustive implies that divergent thinking is a critical skill. == Applications == === In management interviews === Issue trees are used to answer questions in case interviews for management consulting positions. A quantitative type of question, the market sizing question, requires the interviewee to estimate the size of a data group such as a specific segment of a population, an amount of objects, a company's revenues, or similar. The candidates are expected to use a structured and logical method of arriving at their answer, and using an issue tree provides a diagram to aid the candidate's logical reasoning. Issue trees are used for other types of case interview questions as well.

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  Mobile Passport Control

  Mobile Passport Control (MPC) is a mobile app that enables eligible travelers entering the United States to submit their passport information and customs declaration form to Customs and Border Protection via smartphone or tablet and go through the inspections process using an expedited lane. It is available to "U.S. citizens, U.S. lawful permanent residents, Canadian B1/B2 citizen visitors and returning Visa Waiver Program travelers with approved ESTA". The app is available on iOS and Android devices and is operational at 34 US airports, 14 international airports offering preclearance facilities, and 4 seaports. The use of Mobile Passport Control operations have increased threefold from 2016 to 2017. == History == Mobile Passport Control operations were launched in Atlanta at the Hartsfield-Jackson International Airport in 2016 and is now available at 34 U.S. airports, 14 international airports that offer preclearance and 4 U.S. cruise ports. The Mobile Passport app is authorized by CBP and sponsored by the Airports Council International-North America, Boeing, and the Port of Everglades. Airside Mobile, Inc. secured a Series A funding of $6 million in the fall of 2017. == How it works == During the customs process at the Federal Inspection Service (FIS) area of a U.S. airport, travelers arriving from international locations typically wait in long lines before presenting passports and paperwork and verbally answering questions made by CBP officials. Eligible travelers who have downloaded the Mobile Passport app can expedite this process by submitting information regarding their passport and trip details, and a newly-taken selfie, via their mobile device to CBP officials, then access an expedited line. Mobile Passport Control users will be required to show their physical passport(s) and briefly talk to a CBP officer. == Locations == === US airports === Atlanta (ATL) Baltimore (BWI) Boston (BOS) Charlotte (CLT) Chicago (ORD) Dallas/Ft Worth (DFW) Denver (DEN) Detroit (DTW) as of 7/2024 Ft. Lauderdale (FLL) Honolulu (HNL) Houston (HOU and IAH) Kansas City (MCI) Las Vegas (LAS) Los Angeles (LAX) Miami (MIA) Minneapolis (MSP) New York (JFK) Newark (EWR) Oakland (OAK) Orlando (MCO) Palm Beach (PBI) Philadelphia (PHL) Phoenix (PHX) Pittsburgh (PIT) Portland (PDX) Sacramento (SMF) San Diego (SAN) San Francisco (SFO) San Jose (SJC) San Juan (SJU) Seattle (SEA) Tampa (TPA) Washington Dulles (IAD) === International Preclearance locations === Abu Dhabi (AUH) Aruba (AUA) Bermuda (BDA) Calgary (YYC) Dublin (DUB) Edmonton (YEG) Halifax (YHZ) Montreal (YUL) Nassau (NAS) Ottawa (YOW) Shannon (SNN) Toronto (YYZ) Vancouver (YVR) Winnipeg (YWG) Sepinggan (BPN) === Seaports === Fort Lauderdale (PEV) Miami (MSE) San Juan (PUE) West Palm Beach (WPB)

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  John Schulman

  John Schulman (born 1987 or 1988) is an American artificial intelligence researcher and co-founder of OpenAI. In August 2024, he announced he would be joining Anthropic. In February 2025, he announced he was leaving to join Thinking Machines Lab, where he is chief scientist. == Early life and education == Schulman had an interest in science and math from a young age. He enjoyed science fiction, especially the work of Isaac Asimov. When he was in seventh grade, he became deeply interested in the television program BattleBots, which featured combat between remote-controlled robots. In what he said was his first self-directed study, he read extensively in subject areas that would help him design a superior robot, but the robot he and his friends worked on was never built. He attended Great Neck South High School. He was a member of the US Physics olympiad Team in 2005. In 2010, he graduated from Caltech with a degree in physics. He has a PhD in electrical engineering and computer sciences from the University of California, Berkeley, where he was advised by Pieter Abbeel. == Career == In December 2015, shortly before finishing his PhD, Schulman co-founded OpenAI with Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk as the co-chairs. There, he led the reinforcement learning team that created ChatGPT. He has been referred to as the "architect" of ChatGPT. In August 2024, Schulman announced he would be joining Anthropic. He stated his move was to allow him to deepen his focus on AI alignment and return to more hands-on technical work. In February 2025, he announced he was leaving to join Thinking Machines Lab, where he is chief scientist. == Awards and honors == In 2025, Schulman received the Mark Bingham Award for Excellence in Achievement by Young Alumni from his alma mater, UC Berkeley.

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  New Classification Scheme for Chinese Libraries

  The New Classification Scheme for Chinese Libraries is a system of library classification developed by Lai Yung-hsiang since 1956. It is modified from "A System of Book Classification for Chinese Libraries" of Liu Guojun, which is based on the Dewey Decimal System. The scheme is developed for Chinese books and commonly used in Taiwan, Hong Kong and Macau. == Main classes == 000 Generalities 100 Philosophy 200 Religion 300 Sciences 400 Applied sciences 500 Social sciences 600 History of China and Geography of China 700 World history and Geography 800 Linguistics and Literature 900 Arts == Outline of the classification tables == 000 Generalities 000 Special collections 010 Bibliography; Literacy (Documentation) 020 Library and information science; Archive management 030 Sinology 040 General encyclopedia 050 Serial publications; Periodicals 060 General organization; Museology 070 General collected essays 080 General series 090 Collected Chinese classics 100 Philosophy 100 Philosophy: general 110 Thought; Learning 120 Chinese philosophy 130 Oriental philosophy 140 Western philosophy 150 Logic 160 Metaphysics 170 Psychology 180 Esthetics (Aesthetics) 190 Ethics 200 Religion 200 Religion: general 210 Science of religion 220 Buddhism 230 Taoism 240 Christianity 250 Islam (Mohammedanism) 260 Judaism 270 Other religions 280 Mythology 290 Astrology; Superstition 300 Sciences 300 Sciences: general 310 Mathematics 320 Astronomy 330 Physics 340 Chemistry 350 Earth science; Geology 360 Biological science 370 Botany 380 Zoology 390 Anthropology 400 Applied sciences 400 Applied sciences: general 410 Medical sciences 420 Home economics 430 Agriculture 440 Engineering 450 Mining and metallurgy 460 Chemical engineering 470 Manufacture 480 Commerce: various business 490 Commerce: administration and management 500 Social sciences 500 Social sciences: general 510 Statistics 520 Education 530 Rite and custom 540 Sociology 550 Economy 560 Finance 570 Political science 580 Law; Jurisprudence 590 Military science 600-700 History and geography 600 History and geography: General History and geography of China 610 General history of China 620 Chinese history by period 630 History of Chinese civilization 640 Diplomatic history of China 650 Historical sources 660 Geography of China 670 Local history 680 Topical topography 690 Chinese travels World history and geography 710 World: general history and geography 720 Oceans and seas 730 Asia: history and geography 740 Europe: history and geography 750 America: history and geography 760 Africa: history and geography 770 Oceania: history and geography 780 Biography 790 Antiquities and archaeology 800 Linguistics and literature 800 Linguistics: general 810 Literature: general 820 Chinese literature 830 Chinese literature: general collections 840 Chinese literature: individual works 850 Various Chinese literature 860 Oriental literature 870 Western literature 880 Other countries literatures 890 Journalism 900 Arts 900 Arts: general 910 Music 920 Architecture 930 Sculpture 940 Drawing and painting; Calligraphy 950 Photography; Computer art 960 Decorative arts 970 Arts and Crafts movement 980 Theatre 990 Recreation and leisure

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  Andrew Ng

  Andrew Yan-Tak Ng (Chinese: 吳恩達; born April 18, 1976) is a British-American computer scientist and technology entrepreneur focusing on machine learning and artificial intelligence (AI). Ng was a cofounder and head of Google Brain and was the former Chief Scientist at Baidu. Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its Stanford AI Lab or SAIL). Ng has also worked in online education, cofounding Coursera and DeepLearning.AI. He has spearheaded many efforts to "democratize deep learning" teaching over 8 million students through his online courses. Ng is renowned globally in computer science, recognized in Time magazine's 100 Most Influential People in 2012 and Fast Company's Most Creative People in 2014. His influence extends to being named in the Time100 AI Most Influential People in 2023. In 2018, he launched and currently heads the AI Fund, initially a $175-million investment fund for backing artificial intelligence startups. He has founded Landing AI, which provides AI-powered SaaS products. On April 11, 2024, Amazon announced Ng's appointment to its board of directors. == Early life and education == Andrew Yan-Tak Ng was born in London, in 1976 to Ronald Paul Ng, a hematologist and lecturer at UCL Medical School, and Tisa Ho, an arts administrator working at the London Film Festival. His parents were both immigrants from Hong Kong. His family moved back to Hong Kong and he spent his early childhood there. In 1984 he and his family moved to Singapore. Ng attended and graduated from Raffles Institution. In 1997, he earned his undergraduate degree with a triple major in computer science, statistics, and economics from Carnegie Mellon University in Pittsburgh, Pennsylvania. Between 1996 and 1998 he also conducted research on reinforcement learning, model selection, and feature selection at the AT&T Bell Labs. In 1998, Ng earned his master's degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts. At MIT, he built the first publicly available, automatically indexed web-search engine for research papers on the web. It was a precursor to CiteSeerX/ResearchIndex, but specialized in machine learning. In 2002, he received his Doctor of Philosophy (Ph.D.) in Computer Science from the University of California, Berkeley, under the supervision of Michael I. Jordan. His thesis is titled "Shaping and policy search in reinforcement learning" and is well-cited to this day. == Career == === Academia and teaching === Ng started working as an assistant professor at Stanford University in 2002 and as an associate professor in 2009. Ng is a professor at Stanford University departments of Computer Science and electrical engineering. He served as the director of the Stanford Artificial Intelligence Laboratory (SAIL), where he taught students and undertook research related to data mining, big data, and machine learning. His machine learning course CS229 at Stanford is the most popular course offered on campus with over 1,000 students enrolling some years. As of 2020, three of the most popular courses on Coursera are Ng's: Machine Learning (#1), AI for Everyone (#5), Neural Networks and Deep Learning (#6). In 2008, his group at Stanford was one of the first in the US to start advocating the use of GPUs in deep learning. The rationale was that an efficient computation infrastructure could speed up statistical model training by orders of magnitude, ameliorating some of the scaling issues associated with big data. At the time it was a controversial and risky decision, but since then and following Ng's lead, GPUs have become a cornerstone in the field. Since 2017, Ng has been advocating the shift to high-performance computing (HPC) for scaling up deep learning and accelerating progress in the field. In 2012, along with Stanford computer scientist Daphne Koller he cofounded and was CEO of Coursera, a website that offers free online courses to everyone. It took off with over 100,000 students registered for Ng's popular CS229A course. Today, several million people have enrolled in Coursera courses, making the site one of the leading massive open online courses (MOOCs) in the world. === Industry === From 2011 to 2012, he worked at Google, where he founded and directed the Google Brain Deep Learning Project with Jeff Dean, Greg Corrado, and Rajat Monga. In 2014, he joined Baidu as chief scientist, and carried out research related to big data and AI. There he set up several research teams for things like facial recognition and Melody, an AI chatbot for healthcare. He also developed for the company the AI platform called DuerOS and other technologies that positioned Baidu ahead of Google in the discourse and development of AI. In March 2017, he announced his resignation from Baidu. He soon afterward launched DeepLearning.AI, an online series of deep learning courses (including the AI for Good Specialization). Then Ng launched LandingAI, which provides AI-powered SaaS products. In January 2018, Ng unveiled the AI Fund, raising $175 million to invest in new startups. In November 2021, LandingAI secured a $57 million round of series A funding led by McRock Capital, to help enterprises adopt AI. In October 2024, Ng's AI Fund made its first investment in India, backing AI healthcare startup Jivi, which uses AI for diagnoses, treatment recommendations, and administrative tasks. The investment highlights the growth of India's AI sector, expected to reach $22 billion by 2027. === Research === Ng researches primarily in machine learning, deep learning, machine perception, computer vision, and natural language processing; and is one of the world's most famous and influential computer scientists. He's frequently won best paper awards at academic conferences and has had a huge impact on the field of AI, computer vision, and robotics. During graduate school, together with David M. Blei and Michael I. Jordan, Ng co-authored the influential paper that introduced latent Dirichlet allocation (LDA) for his thesis on reinforcement learning for drones. His early work includes the Stanford Autonomous Helicopter project, which developed one of the most capable autonomous helicopters in the world. He was the leading scientist and principal investigator on the STAIR (Stanford Artificial Intelligence Robot) project, which resulted in Robot Operating System (ROS), a widely used open source software robotics platform. His vision to build an AI robot and put a robot in every home inspired Scott Hassan to back him and create Willow Garage. He is also one of the founding team members for the Stanford WordNet project, which uses machine learning to expand the Princeton WordNet database created by Christiane Fellbaum. In 2011, Ng founded the Google Brain project at Google, which developed large-scale artificial neural networks using Google's distributed computing infrastructure. Among its notable results was a neural network trained using deep learning algorithms on 16,000 CPU cores, which learned to recognize cats after watching only YouTube videos, and without ever having been told what a "cat" is. The project's technology is also currently used in the Android operating system's speech recognition system. === Views on AI === Ng thinks that the real threat is contemplating the future of work: "Rather than being distracted by evil killer robots, the challenge to labor caused by these machines is a conversation that academia and industry and government should have." He has emphasized the importance of expanding access to AI education, stating that empowering people around the world to use AI tools is essential to building AI applications. In a December 2023 Financial Times interview, Ng highlighted concerns regarding the impact of potential regulations on open-source AI, emphasizing how reporting, licensing, and liability risks could unfairly burden smaller firms and stifle innovation. He argued that regulating basic technologies like open-source models could hinder progress without markedly enhancing safety. Ng advocated for carefully designed regulations to prevent obstacles to the development and distribution of beneficial AI technologies. In a June 2024 interview with the Financial Times, Ng expressed concerns about proposed AI legislation in California that would have required developers to implement safety mechanisms such as a "kill switch" for advanced models. He described the bill as creating "massive liabilities for science-fiction risks" and said it "stokes fear in anyone daring to innovate." Other critics argued the bill would impose burdens on open-source developers and smaller AI companies. The bill was ultimately vetoed by Governor Gavin Newsom in September 2024. == Online education: massive open online course == In 2011, Stanford launched a total of three massive open online course (MOOCs) on machine learning (CS229a), databases, and AI, taught by Ng

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  Clef (app)

  Clef was a San Francisco-based technology company, known for developing a mobile app that created a two-factor authentication for websites. It allowed users to access sites with a single login password management service which stores encrypted passwords in private accounts. It had a standard verification method that requires access to data on the mobile phone to confirm the user's identity. The application required a Wi-Fi or mobile network, and the user could log in by scanning the computer screen with their phone. == History == Clef was founded in 2013 by Mark Hudnall, B. Byrne and Jesse Pollak. It raised $1.6 million in seed funding in November 2014. Clef integrated with many websites and applications, including WordPress. On March 17, 2017, Clef announced they would no longer support the plugin after June 6, 2017; Clef was acquired by Authy, another 2FA service, which later got acquired by Twilio.

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  AutoGPT

  AutoGPT is an open-source autonomous software agent that uses OpenAI's large language models, such as GPT-4, to attempt to achieve a goal specified by a user in natural language. Unlike chatbots that require continuous user commands, AutoGPT works autonomously by breaking the main goal into smaller sub-tasks and using tools like web browsing and file management to complete them. Released in March 2023, the project quickly gained popularity on GitHub and social media, with users creating agents for tasks like software development, market research, and content creation. One notable experiment, ChaosGPT, was tasked with destroying humanity, which brought mainstream attention to the technology's potential. However, AutoGPT is known for significant limitations, including a tendency to get stuck in loops, hallucinate information, and incur high operational costs due to its reliance on paid APIs. == Background == AutoGPT was released on March 30, 2023, by Toran Bruce Richards, the founder of video game company Significant Gravitas Ltd. It was one of the first widely accessible applications to showcase the autonomous capabilities of GPT-4, which had been released weeks earlier. Richards's goal was to create a model that could respond to real-time feedback and pursue objectives with a long-term outlook without needing constant human intervention. The application operates by prompting a user to define an agent's name, role, and main objective, including up to five sub-goals to achieve it. AutoGPT then works independently to reach its objective. The project is publicly available on GitHub but requires users to install it in a development environment like Docker and have a paid OpenAI account to obtain the necessary API key. In October 2023, the project's parent company, Significant Gravitas Ltd., raised $12 million in venture funding to support further development. == Capabilities == The overarching capability of AutoGPT is the breaking down of a large task into various sub-tasks without the need for user input. These sub-tasks are then chained together and performed sequentially to yield a larger result as originally laid out by the user input. One of the distinguishing features of AutoGPT is its ability to connect to the internet. This allows for up-to-date information retrieval to help complete tasks. In addition, AutoGPT maintains short-term memory for the current task, which allows it to provide context to subsequent sub-tasks needed to achieve the larger goal. Another feature is its ability to store and organize files so users can better structure their data for future analysis and extension. AutoGPT is also multimodal, which means that it can take in both text and images as input. With these features, AutoGPT is claimed to be capable of automating workflows, analyzing data, and coming up with new suggestions. == Applications == === Software === AutoGPT can be used to develop software applications from scratch. AutoGPT can also debug code and generate test cases. Observers suggest that AutoGPT's ability to write, debug, test, and edit code may extend to AutoGPT's own source code, enabling self-improvement. === Business === AutoGPT can be used to do market research, analyze investments, research products and write product reviews, create a business plan or improve operations, and create content such as a blog or podcast. One user has used AutoGPT to conduct product research and write a summary on the best headphones. Another user has used AutoGPT to summarize recent news events and prepare an outline for a podcast. === Other === AutoGPT was used to create ChefGPT, an AI agent able to independently explore the internet to generate and save unique recipes. AutoGPT was also used to create ChaosGPT, an AI agent tasked to “destroy humanity, establish global dominance, cause chaos and destruction, control humanity through manipulation, and attain immortality”. ChaosGPT reportedly researched nuclear weapons and tweeted disparagingly about humankind. == Limitations == AutoGPT is susceptible to frequent mistakes, primarily because it relies on its own feedback, which can compound errors. In contrast, non-autonomous models can be corrected by users overseeing their outputs. Furthermore, AutoGPT has a tendency to hallucinate or to present false or misleading information as fact when responding. AutoGPT can be constrained by the cost associated with running it as its recursive nature requires it to continually call the OpenAI API on which it is built. Every step required in one of AutoGPT's tasks requires a corresponding call to GPT-4 at a cost of at least about $0.03 for every 1000 tokens used for inputs and $0.06 for every 1000 tokens for output when choosing the cheapest option. For reference, 1000 tokens roughly result in 750 words. Another limitation is AutoGPT's tendency to get stuck in infinite loops. Developers believe that this is a result of AutoGPT's inability to remember, as it is unaware of what it has already done and repeatedly attempts the same subtask without end. Andrej Karpathy, co-founder of OpenAI which creates GPT-4, further explains that it is AutoGPT's “finite context window” that can limit its performance and cause it to “go off the rails”. Like other autonomous agents, AutoGPT is prone to distraction and unable to focus on its objective due to its lack of long-term memory, leading to unpredictable and unintended behavior. == Reception == AutoGPT became the top trending repository on GitHub after its release and has since repeatedly trended on Twitter. In April 2023, Avram Piltch wrote for Tom's Hardware that AutoGPT 'might be too autonomous to be useful,' as it did not ask questions to clarify requirements or allow corrective interventions by users. Piltch nonetheless noted that such tools have "a ton of potential" and should improve with better language models and further development. Malcolm McMillan from Tom's Guide mentioned that AutoGPT may not be better than ChatGPT for tasks involving conversation, as ChatGPT is well-suited for situations in which advice, rather than task completion, is sought. Will Knight from Wired wrote that AutoGPT is not a foolproof task-completion tool. When given a test task of finding a public figure's email address, he noted that it was not able to accurately find the email address. Clara Shih, Salesforce Service Cloud CEO commented that "AutoGPT illustrates the power and unknown risks of generative AI," and that due to usage risks, enterprises should include a human in the loop when using such technologies. Performance is reportedly enhanced when using AutoGPT with GPT-4 compared to GPT-3.5. For example, one reviewer who tested it on a task of finding the best laptops on the market with pros and cons found that AutoGPT with GPT-4 created a more comprehensive report than one by GPT 3.5.

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  Composite portrait

  Composite portraiture (also known as composite photographs) is a technique invented by Sir Francis Galton in the 1880s after a suggestion by Herbert Spencer for registering photographs of human faces on the two eyes to create an "average" photograph of all those in the photographed group. Spencer had suggested using onion paper and line drawings, but Galton devised a technique for multiple exposures on the same photographic plate. He noticed that these composite portraits were more attractive than any individual member, and this has generated a large body of research on human attractiveness and averageness one hundred years later. He also suggested in a Royal Society presentation in 1883 that the composites provided an interesting concrete representation of human ideal types and concepts. He discussed using the technique to investigate characteristics of common types of humanity, such as criminals. In his mind, it was an extension of the statistical techniques of averages and correlation. In this sense, it represents one of the first implementations of convolution factor analysis and neural networks in the understanding of knowledge representation in the human mind. Galton also suggested that the technique could be used for creating natural types of common objects. During the late 19th century, English psychometrician Sir Francis Galton attempted to define physiognomic characteristics of health, disease, beauty, and criminality, via a method of composite photography. Galton's process involved the photographic superimposition of two or more faces by multiple exposures. After averaging together photographs of violent criminals, he found that the composite appeared "more respectable" than any of the faces comprising it; this was likely due to the irregularities of the skin across the constituent images being averaged out in the final blend. Since the advancement of computer graphics technology in the early 1990s, Galton's composite technique has been adopted and greatly improved using computer graphics software.

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  Effective accelerationism

  Effective accelerationism (e/acc) is a 21st-century ideological movement that advocates for an explicitly pro-technology stance. Its proponents believe that unrestricted technological progress, especially driven by artificial intelligence, is a solution to universal human problems, such as poverty, war, and climate change. They perceive themselves as a counterweight to more cautious views on technological innovation and often label their opponents derogatorily as "doomers" or "decels" (short for decelerationists). The movement carries utopian undertones and advocates for faster AI progress to ensure human survival and propagate consciousness throughout the universe. Although effective accelerationism has been described as a fringe movement and as cult-like, it has gained mainstream visibility in 2023. A number of high-profile Silicon Valley figures, including investors Marc Andreessen and Garry Tan, explicitly endorsed it by adding "e/acc" to their public social media profiles. == Etymology and central beliefs == Effective accelerationism, a portmanteau of "effective altruism" and "accelerationism", is a fundamentally techno-optimist movement. According to Guillaume Verdon, one of the movement's founders, its aim is for human civilization to "clim[b] the Kardashev gradient", meaning its purpose is for human civilization to rise to next levels on the Kardashev scale by maximizing energy usage. To achieve this goal, effective accelerationism wants to accelerate technological progress. It is strongly focused on artificial general intelligence (AGI), because it sees AGI as fundamental for climbing the Kardashev scale. The movement therefore advocates for unrestricted development and deployment of artificial intelligence. Regulation of artificial intelligence and government intervention in markets more generally is met with opposition. Many of its proponents have libertarian views and think that AGI will be most aligned if many AGIs compete against each other on the marketplace. The founders of the movement see it as rooted in Jeremy England's theory on the origin of life, which is focused on entropy and thermodynamics. According to them, the universe aims to increase entropy, and life is a way of increasing it. By spreading life throughout the universe and making life use up ever increasing amounts of energy, the universe's purpose would thus be fulfilled. == History == === Intellectual origins === While Nick Land is seen as the intellectual originator of contemporary accelerationism in general, the precise origins of effective accelerationism remain unclear. The earliest known reference to the movement can be traced back to a May 2022 newsletter published by four pseudonymous authors known by their X (formerly Twitter) usernames @BasedBeffJezos, @bayeslord, @zestular and @creatine_cycle. Effective accelerationism is an extension of the TESCREAL movement, being etymologically derived from Effective Altruism and heavily rooted in the older Silicon Valley subcultures of transhumanism and extropianism (which similarly emphasized the value of progress and resisted efforts to restrain the development of technology), alongside elements of singularitarianism, cosmism, and longtermism. It is also often considered to have emerged at least in part from the work of the Cybernetic Culture Research Unit (of which Nick Land was a leading member, alongside writers such as Mark Fisher and Sadie Plant). It is sometimes compared and contrasted with the work of philosopher Benjamin Bratton on planetary computation. === Disclosure of the identity of BasedBeffJezos === Forbes disclosed in December 2023 that the @BasedBeffJezos persona is maintained by Guillaume Verdon, a Canadian former Google quantum computing engineer and theoretical physicist. The revelation was supported by a voice analysis conducted by the National Center for Media Forensics of the University of Colorado Denver, which further confirmed the match between Jezos and Verdon. The magazine justified its decision to disclose Verdon's identity on the grounds of it being "in the public interest". On 29 December 2023 Guillaume Verdon was interviewed by Lex Fridman on the Lex Fridman Podcast and introduced as the "creator of the effective accelerationism movement". === Second Trump presidency === Following Donald Trump's victory in the 2024 U.S. presidential election, several prominent tech industry figures expressed support for positions aligned with effective accelerationism, particularly regarding deregulation and technological advancement. The potential appointment of Elon Musk to government roles focused on auditing federal programs drew support from venture capitalists who anticipated reduced regulatory oversight of the technology sector. Notable tech figures publicly connected these developments to the movement's principles. Aaron Levie, CEO of Box, expressed support for "removing unnecessary red tape and over-regulation", while Mark Pincus, early Facebook investor and Zynga founder, explicitly referenced "effective accelerationism" in his post-election commentary. Venture capitalists viewed the incoming administration as an opportunity to ease regulations that had affected technology mergers and acquisitions during the previous years. == Relation to other movements == === Traditional accelerationism === Traditional accelerationism, as developed by the British philosopher Nick Land, sees the acceleration of technological change as a way to bring about a fundamental transformation of current culture, society, and the political economy. This is done through capitalism, which Land views as "an autonomous force that’s reconfiguring society" that can overcome its limits if intensified. Land's work has also been characterized as concerning "the supposedly inevitable 'disintegration of the human species' when artificial intelligence improves sufficiently." While both concern ideas like a technocapital singularity and AGI progress, effective accelerationism focuses on using AGI for the greatest ethical good for conscious life and civilization (whether human or machine), as well as expanding civilization and maximizing energy usage in order to align with the "will of the universe". Land focuses on capitalist self-optimization as the driver of modernity, progress, and the eroding of existing social orders. Land has expressed support for effective accelerationism, while Thomas Murphy referred to the movement as "Nick Land diluted for LinkedIn". === Effective altruism === Effective accelerationism diverges from the principles of effective altruism, which prioritizes using evidence and reasoning to identify the most effective ways to altruistically improve the world. This divergence comes primarily from one of the causes effective altruists focus on – AI existential risk. Effective altruists (particularly longtermists) argue that AI companies should be cautious and strive to develop safe AI systems, as they fear that any misaligned AGI could eventually lead to human extinction. Proponents of effective accelerationism generally consider existential risks from AGI to be negligible, and claim that even if they were not, decentralized free markets would much better mitigate this risk than centralized governmental regulation. === Degrowth === Effective accelerationism stands in stark contrast with the degrowth movement, sometimes described by it as "decelerationism" or "decels". The degrowth movement advocates for reducing economic activity and consumption to address ecological and social issues. Effective accelerationism on the contrary embraces technological progress, energy consumption and the dynamics of capitalism, rather than advocating for a reduction in economic activity. == Reception == The "Techno-Optimist Manifesto", a 2023 essay by Marc Andreessen, has been described by the Financial Times and the German Süddeutsche Zeitung as espousing the views of effective accelerationism. Mother Jones also characterized it as expressing effective accelerationism and reported that Andressen cited Land's work. David Swan of The Sydney Morning Herald has criticized effective accelerationism due to its opposition to government and industry self-regulation. He argues that "innovations like AI needs thoughtful regulations and guardrails ... to avoid the myriad mistakes Silicon Valley has already made." During the 2023 Reagan National Defense Forum, U.S. Secretary of Commerce Gina Raimondo cautioned against embracing the "move fast and break things" mentality associated with "effective acceleration [sic]". She emphasized the need to exercise caution in dealing with AI, stating "that's too dangerous. You can't break things when you are talking about AI." In a similar vein, Ellen Huet argued on Bloomberg News that some of the ideas of the movement were "deeply unsettling", focusing especially on Guillaume Verdon's "post-humanism" and the view that "natural selection could lead AI to replace us as the dominant spe

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  Grammatik

  Grammatik was the first grammar-checking program for home computers. Aspen Software of Albuquerque, NM, released the earliest version of this diction and style checker for personal computers. It was first released no later than 1981, and was inspired by the Writer's Workbench. Grammatik was first available for the TRS-80, and soon had versions for CP/M and the IBM PC. Reference Software International of San Francisco, California, acquired Grammatik in 1985. Development of Grammatik continued, and it became an actual grammar checker that could detect writing errors beyond simple style checking. Subsequent versions were released for MS-DOS, Windows, Macintosh, and Unix. Grammatik was ultimately acquired by WordPerfect Corporation and is integrated into the WordPerfect word processor.

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  Karen Hao

  Karen Hao (born in the United States c. 1993) is an American journalist and author. Currently a freelancer for publications like The Atlantic and previously a foreign correspondent based in Hong Kong for The Wall Street Journal and senior artificial intelligence editor at the MIT Technology Review, she is best known for her coverage on AI research, technology ethics and the social impact of AI. Hao also co-produced the podcast In Machines We Trust and wrote the newsletter The Algorithm. Previously, she worked at Quartz as a tech reporter and data scientist and was an application engineer at the first startup to spin out of X Development. Hao's writing has also appeared in Mother Jones, Sierra Magazine, The New Republic, and other publications. == Early life and education == Hao is the daughter of Chinese immigrant parents, and grew up in New Jersey. She is a native speaker of both English and Mandarin Chinese. She graduated from The Lawrenceville School in 2011. She then studied at the Massachusetts Institute of Technology (MIT), graduating with a B.S. in mechanical engineering and a minor in energy studies in 2015. == Career == Hao is known in the technology world for her coverage of new AI research findings and their societal and ethical impacts. Her writing has spanned research and issues regarding big tech data privacy, misinformation, deepfakes, facial recognition, and AI healthcare tools. In March 2021, Hao published a piece that uncovered previously unknown information about how attempts to combat misinformation by different teams at Facebook using machine learning were impeded and constantly at odds with Facebook's drive to grow user engagement. Upon its release, leaders at Facebook including Mike Schroepfer and Yann LeCun immediately criticized the piece through Twitter responses. AI researchers and AI ethics experts Timnit Gebru and Margaret Mitchell responded in support of Hao's writing and advocated for more change and improvement for all. Hao also co-produced the podcast In Machines We Trust, which discusses the rise of AI with people developing, researching, and using AI technologies. The podcast won the 2020 Front Page Award in investigative reporting. Hao has occasionally created data visualizations that have been featured in her work at the MIT Technology Review and elsewhere. In 2018, her "What is AI?" flowchart visualization was exhibited as an installation at the Museum of Applied Arts in Vienna. She has been an invited speaker at TEDxGateway, the United Nations Foundation, EmTech, WNPR, and many other conferences and podcasts. Her TEDx talk discussed the importance of democratizing how AI is built. In March 2022, she was hired by The Wall Street Journal to cover China technology and society, while being based in Hong Kong. She left the WSJ in 2023. In May 2025, Hao released the book Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI. The book became a New York Times Bestseller and was named a Book of the Year by the Financial Times. In December 2025, after criticism from readers, Hao issued a correction to her book where she had previously overestimated the water consumption of a data center in Chile compared to the community's water consumption by factor of 1,000, due to an error in a government document. In April 2026 the book won the New York Public Library's Helen Bernstein Book Award for Excellence in Journalism. === Selected awards and honors === 2019 Webby Award nominee for best newsletter, as a writer of The Algorithm 2021 Front Page Award in investigative reporting, as a co-producer for In Machines We Trust 2021 Ambies Award nominee for best knowledge and science podcast, as a co-producer for In Machines We Trust 2021 Webby Award nominee for best technology podcast, as a co-producer for In Machines We Trust 2024 American Humanist Media Award 2025 TIME100 AI, named by TIME magazine as one of the 100 most influential people in artificial intelligence 2026 New York Public Library's Helen Bernstein Book Award for Excellence in Journalism 2026 Whiting Award in Non-fiction

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  Retrieval-based Voice Conversion

  Retrieval-based Voice Conversion (RVC) is an open source voice conversion AI algorithm that enables realistic speech-to-speech transformations, accurately preserving the intonation and audio characteristics of the original speaker. == Overview == In contrast to text-to-speech systems such as ElevenLabs, RVC differs by providing speech-to-speech outputs instead. It maintains the modulation, timbre and vocal attributes of the original speaker, making it suitable for applications where emotional tone is crucial. The algorithm enables both pre-processed and real-time voice conversion with low latency. This real-time capability marks a significant advancement over previous AI voice conversion technologies, such as So-vits SVC. Its speed and accuracy have led many to note that its generated voices sound near-indistinguishable from "real life", provided that sufficient computational specifications and resources (e.g., a powerful GPU and ample RAM) are available when running it locally and that a high-quality voice model is used. == Technical foundation == Retrieval-based Voice Conversion (RVC) utilizes a hybrid approach that integrates feature extraction with retrieval-based synthesis. Instead of directly mapping source speaker features to the target speaker using statistical models, RVC retrieves relevant segments from a target speech database, aiming to enhance the naturalness and speaker fidelity of the converted speech. At a high level, the RVC system typically comprises three main components: (1) a content feature extractor, such as a phonetic posteriorgram (PPG) encoder or self-supervised models like HuBERT; (2) a vector retrieval module that searches a target voice database for the most similar speech units; and (3) a vocoder or neural decoder that synthesizes waveform output from the retrieved representations. The retrieval-based paradigm aims to mitigate the oversmoothing effect commonly observed in fully neural sequence-to-sequence models, potentially leading to more expressive and natural-sounding speech. Furthermore, with the incorporation of high-dimensional embeddings and k-nearest-neighbor search algorithms, the model can perform efficient matching across large-scale databases without significant computational overhead. Recent RVC frameworks have incorporated adversarial learning strategies and GAN-based vocoders, such as HiFi-GAN, to enhance synthesis quality. These integrations have been shown to produce clearer harmonics and reduce reconstruction errors. == Research developments == Research on RVC has recently explored the use of self-supervised learning (SSL) encoders such as wav2vec 2.0 and HuBERT to replace hand-engineered features like MFCCs. These encoders improve content preservation, especially when source and target speakers have dissimilar speaking styles or accents. Moreover, modern RVC models leverage vector quantization methods to discretize the acoustic space, improving both synthesis accuracy and generalization across unseen speakers. For example, retrieval-augmented VQ models can condition the synthesis stage on quantized speech tokens, which enhances controllability and style transfer. Despite its strengths, RVC still faces limitations related to database coverage, especially in real-time or few-shot settings. Inadequate diversity in the target voice corpus may lead to suboptimal retrieval or unnatural prosody. These advances demonstrate the viability of RVC as a strong alternative to conventional deep learning VC systems, balancing both flexibility and efficiency in diverse voice synthesis applications. == Training process == The training pipeline for retrieval-based voice conversion typically includes a preprocessing step where the target speaker's dataset is segmented and normalized. A pitch extractor such as librosa or DDSP-DDC may be used to obtain fundamental frequency (F0) features. During training, the model learns to map content features from the source speaker to the acoustic representation of the target speaker while maintaining pitch and prosody. The training objective often combines reconstruction loss with feature consistency loss across intermediate layers, and may incorporate cycle consistency loss to preserve speaker identity. Fine-tuning on small datasets is feasible due to the use of pre-trained models, particularly for the SSL encoder and content extractor components. This approach allows transfer learning to be applied effectively, enabling the model to converge faster and generalize better to unseen inputs. Most open implementations support batch training, gradient accumulation, and mixed-precision acceleration (e.g., FP16), especially when utilizing NVIDIA CUDA-enabled GPUs. == Real-time deployment == RVC systems can be deployed in real-time scenarios through WebUI interfaces and streaming audio frameworks. Optimizations include converting the inference graph to ONNX or TensorRT formats, reducing latency. Audio buffers are typically processed in chunks of 0.2–0.5 seconds to ensure minimal delay and seamless conversion. Cross-platform compatibility with tools such as OBS Studio and Voicemeeter enables integration into live streaming, video production, or virtual avatar environments. == Applications and concerns == The technology enables voice changing and mimicry, allowing users to create accurate models of others using only a negligible amount of minutes of clear audio samples. These voice models can be saved as .pth (PyTorch) files. While this capability facilitates numerous creative applications, it has also raised concerns about potential misuse as deepfake software for identity theft and malicious impersonation through voice calls. == Ethical and legal considerations == As with other deep generative models, the rise of RVC technology has led to increasing debate about copyright, consent, and authorship. While some jurisdictions may allow parody or fair use in creative contexts, impersonating living individuals without permission may infringe upon privacy and likeness rights. As a result, some platforms have begun issuing takedown notices against AI-generated voice content that closely mimics celebrities or musicians. === In pop culture === RVC inference has been used to create realistic depictions of song covers, such as replacing original vocals with characters like Twilight Sparkle and Mordecai to have them sing duets of popular music like "Airplanes" and "Somebody That I Used to Know." These AI-generated covers, which can sound strikingly similar to the voice imitated, have gained popularity on platforms like YouTube as humorous memes.

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