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Artificial intelligence | Local search | Local search
class=skin-invert-image|thumb|Illustration of gradient descent for 3 different starting points; two parameters (represented by the plan coordinates) are adjusted in order to minimize the loss function (the height) Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.Local or "optimization" search:
Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks, through the backpropagation algorithm.
Another type of local search is evolutionary computation, which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation.Evolutionary computation:
Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails). |
Artificial intelligence | Logic | Logic
Formal logic is used for reasoning and knowledge representation.Logic: , ,
Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies")Propositional logic: , , and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as "Every X is a Y" and "There are some Xs that are Ys").First-order logic and features such as equality: , , ,
Deductive reasoning in logic is the process of proving a new statement (conclusion) from other statements that are given and assumed to be true (the premises).Logical inference: Proofs can be structured as proof trees, in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules.
Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms. In the case of Horn clauses, problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem.logical deduction as search: , , , In the more general case of the clausal form of first-order logic, resolution is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.Resolution and unification:
Inference in both Horn clause logic and first-order logic is undecidable, and therefore intractable. However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog, is Turing complete. Moreover, its efficiency is competitive with computation in other symbolic programming languages.
Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.Fuzzy logic: ,
Non-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning. Other specialized versions of logic have been developed to describe many complex domains. |
Artificial intelligence | Probabilistic methods for uncertain reasoning | Probabilistic methods for uncertain reasoning
class=skin-invert-image|thumb|upright=1.7|A simple Bayesian network, with the associated conditional probability tables
Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.Stochastic methods for uncertain reasoning: , , , Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,decision theory and decision analysis: , and information value theory.Information value theory: These tools include models such as Markov decision processes,Markov decision processes and dynamic decision networks: dynamic decision networks, game theory and mechanism design.Game theory and mechanism design:
Bayesian networksBayesian networks: , , , are a tool that can be used for reasoning (using the Bayesian inference algorithm),Bayesian inference algorithm: , , , learning (using the expectation–maximization algorithm),Bayesian learning and the expectation–maximization algorithm: , , , planning (using decision networks)Bayesian decision theory and Bayesian decision networks: and perception (using dynamic Bayesian networks).
Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception systems analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).Stochastic temporal models:
Hidden Markov model:
Kalman filters:
Dynamic Bayesian networks:
thumb|upright=1.2|Expectation–maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption. |
Artificial intelligence | Classifiers and statistical learning methods | Classifiers and statistical learning methods
The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. ClassifiersStatistical learning methods and classifiers: , are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an "observation") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.
There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm.Decision trees: , K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s.Non-parameteric learning models such as K-nearest neighbor and support vector machines: , (k-nearest neighbor)
(kernel methods)
The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability.Naive Bayes classifier: ,
Neural networks are also used as classifiers. |
Artificial intelligence | Artificial neural networks | Artificial neural networks
right|thumb|A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.
An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.Neural networks: ,
Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm.Gradient calculation in computational graphs, backpropagation, automatic differentiation: , , Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.Universal approximation theorem:
The theorem: ,
In feedforward neural networks the signal passes in only one direction.Feedforward neural networks: Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks.Recurrent neural networks: PerceptronsPerceptrons: use only a single layer of neurons; deep learning uses multiple layers. Convolutional neural networks strengthen the connection between neurons that are "close" to each other—this is especially important in image processing, where a local set of neurons must identify an "edge" before the network can identify an object.Convolutional neural networks: |
Artificial intelligence | Deep learning | Deep learning
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Deep learningDeep learning: , , , uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.
Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification, and others. The reason that deep learning performs so well in so many applications is not known as of 2021. The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet. |
Artificial intelligence | GPT | GPT
Generative pre-trained transformers (GPT) are large language models (LLMs) that generate text based on the semantic relationships between words in sentences. Text-based GPT models are pretrained on a large corpus of text that can be from the Internet. The pretraining consists of predicting the next token (a token being usually a word, subword, or punctuation). Throughout this pretraining, GPT models accumulate knowledge about the world and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful, and harmless, usually with a technique called reinforcement learning from human feedback (RLHF). Current GPT models are prone to generating falsehoods called "hallucinations", although this can be reduced with RLHF and quality data. They are used in chatbots, which allow people to ask a question or request a task in simple text.
Current models and services include Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot, and LLaMA. Multimodal GPT models can process different types of data (modalities) such as images, videos, sound, and text. |
Artificial intelligence | Hardware and software | Hardware and software
In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training. Specialized programming languages such as Prolog were used in early AI research, but general-purpose programming languages like Python have become predominant.
The transistor density in integrated circuits has been observed to roughly double every 18 months—a trend known as Moore's law, named after the Intel co-founder Gordon Moore, who first identified it. Improvements in GPUs have been even faster, a trend sometimes called Huang's law, named after Nvidia co-founder and CEO Jensen Huang. |
Artificial intelligence | Applications | Applications
AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple's Face ID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used by Facebook, Apple's iPhoto and TikTok). The deployment of AI may be overseen by a Chief automation officer (CAO). |
Artificial intelligence | Health and medicine | Health and medicine
The application of AI in medicine and medical research has the potential to increase patient care and quality of life. Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.
For medical research, AI is an important tool for processing and integrating big data. This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication. It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research. New AI tools can deepen the understanding of biomedically relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein. In 2023, it was reported that AI-guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria. In 2024, researchers used machine learning to accelerate the search for Parkinson's disease drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of alpha-synuclein (the protein that characterises Parkinson's disease). They were able to speed up the initial screening process ten-fold and reduce the cost by a thousand-fold. |
Artificial intelligence | Games | Games
Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Then, in 2017, it defeated Ke Jie, who was the best Go player in the world. Other programs handle imperfect-information games, such as the poker-playing program Pluribus. DeepMind developed increasingly generalistic reinforcement learning models, such as with MuZero, which could be trained to play chess, Go, or Atari games. In 2019, DeepMind's AlphaStar achieved grandmaster level in StarCraft II, a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map. In 2021, an AI agent competed in a PlayStation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning. In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen open-world video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions. |
Artificial intelligence | Mathematics | Mathematics
Large language models, such as GPT-4, Gemini, Claude, LLaMa or Mistral, are increasingly used in mathematics. These probabilistic models are versatile, but can also produce wrong answers in the form of hallucinations. They sometimes need a large database of mathematical problems to learn from, but also methods such as supervised fine-tuning or trained classifiers with human-annotated data to improve answers for new problems and learn from corrections. A February 2024 study showed that the performance of some language models for reasoning capabilities in solving math problems not included in their training data was low, even for problems with only minor deviations from trained data. One technique to improve their performance involves training the models to produce correct reasoning steps, rather than just the correct result. The Alibaba Group developed a version of its Qwen models called Qwen2-Math, that achieved state-of-the-art performance on several mathematical benchmarks, including 84% accuracy on the MATH dataset of competition mathematics problems. In January 2025, Microsoft proposed the technique rStar-Math that leverages Monte Carlo tree search and step-by-step reasoning, enabling a relatively small language model like Qwen-7B to solve 53% of the AIME 2024 and 90% of the MATH benchmark problems.
Alternatively, dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as AlphaTensor, AlphaGeometry and AlphaProof all from Google DeepMind, Llemma from EleutherAI or Julius.
When natural language is used to describe mathematical problems, converters can transform such prompts into a formal language such as Lean to define mathematical tasks.
Some models have been developed to solve challenging problems and reach good results in benchmark tests, others to serve as educational tools in mathematics.
Topological deep learning integrates various topological approaches. |
Artificial intelligence | Finance | Finance
Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years.Matthew Finio & Amanda Downie: IBM Think 2024 Primer, "What is Artificial Intelligence (AI) in Finance?" 8 Dec. 2023
According to Nicolas Firzli, director of the World Pensions & Investments Forum, it may be too early to see the emergence of highly innovative AI-informed financial products and services. He argues that "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I'm not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."M. Nicolas, J. Firzli: Pensions Age / European Pensions magazine, "Artificial Intelligence: Ask the Industry", May–June 2024. https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ . |
Artificial intelligence | Military | Military
Various countries are deploying AI military applications.PD-notice The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles, both human operated and autonomous.
AI has been used in military operations in Iraq, Syria, Israel and Ukraine. |
Artificial intelligence | Generative AI | Generative AI
thumb|Vincent van Gogh in watercolour created by generative AI software |
Artificial intelligence | Agents | Agents
Artificial intelligent (AI) agents are software entities designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals. These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including virtual assistants, chatbots, autonomous vehicles, game-playing systems, and industrial robotics. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations. This means they are restricted to performing tasks within their defined scope and have finite memory and processing capabilities. In real-world applications, AI agents often face time constraints for decision-making and action execution. Many AI agents incorporate learning algorithms, enabling them to improve their performance over time through experience or training. Using machine learning, AI agents can adapt to new situations and optimise their behaviour for their designated tasks. |
Artificial intelligence | Sexuality | Sexuality
Applications of AI in this domain include AI-enabled menstruation and fertility trackers that analyze user data to offer prediction, AI-integrated sex toys (e.g., teledildonics), AI-generated sexual education content, and AI agents that simulate sexual and romantic partners (e.g., Replika). AI is also used for the production of non-consensual deepfake pornography, raising significant ethical and legal concerns.
AI technologies have also been used to attempt to identify online gender-based violence and online sexual grooming of minors. |
Artificial intelligence | Other industry-specific tasks | Other industry-specific tasks
There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported having incorporated "AI" in some offerings or processes. A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management.
AI applications for evacuation and disaster management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS, videos or social media. Further, AI can provide real time information on the real time evacuation conditions..
In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.
Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights." For example, it is used for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. Additionally, it could be used for activities in space, such as space exploration, including the analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.
During the 2024 Indian elections, US$50 million was spent on authorized AI-generated content, notably by creating deepfakes of allied (including sometimes deceased) politicians to better engage with voters, and by translating speeches to various local languages. |
Artificial intelligence | Ethics | Ethics
AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of DeepMind hopes to "solve intelligence, and then use that to solve everything else". However, as the use of AI has become widespread, several unintended consequences and risks have been identified. In-production systems can sometimes not factor ethics and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable in deep learning. |
Artificial intelligence | Risks and harm | Risks and harm |
Artificial intelligence | Privacy and copyright | Privacy and copyright
Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright.
AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency.
Sensitive user data collected may include online activity records, geolocation data, video, or audio. For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.
AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy experts, such as Cynthia Dwork, have begun to view privacy in terms of fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."
Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "fair use". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work". Website owners who do not wish to have their content scraped can indicate it in a "robots.txt" file. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI. Another discussed approach is to envision a separate sui generis system of protection for creations generated by AI to ensure fair attribution and compensation for human authors. |
Artificial intelligence | Dominance by tech giants | Dominance by tech giants
The commercial AI scene is dominated by Big Tech companies such as Alphabet Inc., Amazon, Apple Inc., Meta Platforms, and Microsoft. Some of these players already own the vast majority of existing cloud infrastructure and computing power from data centers, allowing them to entrench further in the marketplace. |
Artificial intelligence | Power needs and environmental impacts | Power needs and environmental impacts
In January 2024, the International Energy Agency (IEA) released Electricity 2024, Analysis and Forecast to 2026, forecasting electric power use. This is the first IEA report to make projections for data centers and power consumption for artificial intelligence and cryptocurrency. The report states that power demand for these uses might double by 2026, with additional electric power usage equal to electricity used by the whole Japanese nation.
Prodigious power consumption by AI is responsible for the growth of fossil fuels use, and might delay closings of obsolete, carbon-emitting coal energy facilities. There is a feverish rise in the construction of data centers throughout the US, making large technology firms (e.g., Microsoft, Meta, Google, Amazon) into voracious consumers of electric power. Projected electric consumption is so immense that there is concern that it will be fulfilled no matter the source. A ChatGPT search involves the use of 10 times the electrical energy as a Google search. The large firms are in haste to find power sources – from nuclear energy to geothermal to fusion. The tech firms argue that – in the long view – AI will be eventually kinder to the environment, but they need the energy now. AI makes the power grid more efficient and "intelligent", will assist in the growth of nuclear power, and track overall carbon emissions, according to technology firms.
A 2024 Goldman Sachs Research Paper, AI Data Centers and the Coming US Power Demand Surge, found "US power demand (is) likely to experience growth not seen in a generation...." and forecasts that, by 2030, US data centers will consume 8% of US power, as opposed to 3% in 2022, presaging growth for the electrical power generation industry by a variety of means. Data centers' need for more and more electrical power is such that they might max out the electrical grid. The Big Tech companies counter that AI can be used to maximize the utilization of the grid by all.
In 2024, the Wall Street Journal reported that big AI companies have begun negotiations with the US nuclear power providers to provide electricity to the data centers. In March 2024 Amazon purchased a Pennsylvania nuclear-powered data center for $650 Million (US). Nvidia CEO Jen-Hsun Huang said nuclear power is a good option for the data centers.
In September 2024, Microsoft announced an agreement with Constellation Energy to re-open the Three Mile Island nuclear power plant to provide Microsoft with 100% of all electric power produced by the plant for 20 years. Reopening the plant, which suffered a partial nuclear meltdown of its Unit 2 reactor in 1979, will require Constellation to get through strict regulatory processes which will include extensive safety scrutiny from the US Nuclear Regulatory Commission. If approved (this will be the first ever US re-commissioning of a nuclear plant), over 835 megawatts of power – enough for 800,000 homes – of energy will be produced. The cost for re-opening and upgrading is estimated at $1.6 billion (US) and is dependent on tax breaks for nuclear power contained in the 2022 US Inflation Reduction Act. The US government and the state of Michigan are investing almost $2 billion (US) to reopen the Palisades Nuclear reactor on Lake Michigan. Closed since 2022, the plant is planned to be reopened in October 2025. The Three Mile Island facility will be renamed the Crane Clean Energy Center after Chris Crane, a nuclear proponent and former CEO of Exelon who was responsible for Exelon spinoff of Constellation.
After the last approval in September 2023, Taiwan suspended the approval of data centers north of Taoyuan with a capacity of more than 5 MW in 2024, due to power supply shortages. Taiwan aims to phase out nuclear power by 2025. On the other hand, Singapore imposed a ban on the opening of data centers in 2019 due to electric power, but in 2022, lifted this ban.
Although most nuclear plants in Japan have been shut down after the 2011 Fukushima nuclear accident, according to an October 2024 Bloomberg article in Japanese, cloud gaming services company Ubitus, in which Nvidia has a stake, is looking for land in Japan near nuclear power plant for a new data center for generative AI. Ubitus CEO Wesley Kuo said nuclear power plants are the most efficient, cheap and stable power for AI.
On 1 November 2024, the Federal Energy Regulatory Commission (FERC) rejected an application submitted by Talen Energy for approval to supply some electricity from the nuclear power station Susquehanna to Amazon's data center.
According to the Commission Chairman Willie L. Phillips, it is a burden on the electricity grid as well as a significant cost shifting concern to households and other business sectors.
In 2025 a report prepared by the International Energy Agency estimated the greenhouse gas emissions from the energy consumption of AI at 180 million tons. By 2035, these emissions could rise to 300-500 million tonnes depending on what measures will be taken. This is below 1.5% of the energy sector emissions. The emissions reduction potential of AI was estimated at 5% of the energy sector emissions, but rebound effects (for example if people will pass from public transport to autonomous cars) can reduce it. |
Artificial intelligence | Misinformation | Misinformation
YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took some steps to mitigate the problem.
In 2022, generative AI began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films, or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda. One such potential malicious use is deepfakes for computational propaganda. AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks. |
Artificial intelligence | Algorithmic bias and fairness | Algorithmic bias and fairness
Machine learning applications will be biased if they learn from biased data. The developers may not be aware that the bias exists. Bias can be introduced by the way training data is selected and by the way a model is deployed. If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination.; ; ; The field of fairness studies how to prevent harms from algorithmic biases.
On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people, a problem called "sample size disparity". Google "fixed" this problem by preventing the system from labelling anything as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.
COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend. In 2017, several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.;
A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender".; ; ; Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."Quoted in .
Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as recommendations, some of these "recommendations" will likely be racist.; Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is descriptive rather than prescriptive.
Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.
There are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is distributive fairness, which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases, but it may conflict with anti-discrimination laws.
At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022), the Association for Computing Machinery, in Seoul, South Korea, presented and published findings that recommend that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe, and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed. |
Artificial intelligence | Lack of transparency | Lack of transparency
Many AI systems are so complex that their designers cannot explain how they reach their decisions. Particularly with deep neural networks, in which there are a large amount of non-linear relationships between inputs and outputs. But some popular explainability techniques exist.
It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to show the scale. Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading.
People who have been harmed by an algorithm's decision have a right to an explanation.; Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists. Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used.
DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems.
Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output. LIME can locally approximate a model's outputs with a simpler, interpretable model. Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned. Deconvolution, DeepDream and other generative methods can allow developers to see what different layers of a deep network for computer vision have learned, and produce output that can suggest what the network is learning. For generative pre-trained transformers, Anthropic developed a technique based on dictionary learning that associates patterns of neuron activations with human-understandable concepts. |
Artificial intelligence | Bad actors and weaponized AI | Bad actors and weaponized AI
Artificial intelligence provides a number of tools that are useful to bad actors, such as authoritarian governments, terrorists, criminals or rogue states.
A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision. Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and, if produced at scale, they are potentially weapons of mass destruction. Even when used in conventional warfare, they currently cannot reliably choose targets and could potentially kill an innocent person. In 2014, 30 nations (including China) supported a ban on autonomous weapons under the United Nations' Convention on Certain Conventional Weapons, however the United States and others disagreed. By 2015, over fifty countries were reported to be researching battlefield robots.;
AI tools make it easier for authoritarian governments to efficiently control their citizens in several ways. Face and voice recognition allow widespread surveillance. Machine learning, operating this data, can classify potential enemies of the state and prevent them from hiding. Recommendation systems can precisely target propaganda and misinformation for maximum effect. Deepfakes and generative AI aid in producing misinformation. Advanced AI can make authoritarian centralized decision making more competitive than liberal and decentralized systems such as markets. It lowers the cost and difficulty of digital warfare and advanced spyware. All these technologies have been available since 2020 or earlier—AI facial recognition systems are already being used for mass surveillance in China.
There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours. |
Artificial intelligence | Technological unemployment | Technological unemployment
Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.E. McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2022), 51(3) Industrial Law Journal 511–559. .
In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.; A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed. Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk".; ; The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies. In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.
Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.;
From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement. |
Artificial intelligence | Existential risk | Existential risk
It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, "spell the end of the human race". This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character. These sci-fi scenarios are misleading in several ways.
First, AI does not require human-like sentience to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a paperclip factory manager). Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead." In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is "fundamentally on our side".; ; .
Second, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are built on language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.
The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI. Personalities such as Stephen Hawking, Bill Gates, and Elon Musk,Leaders' concerns about the existential risks of AI around 2015: , , , as well as AI pioneers such as Yoshua Bengio, Stuart Russell, Demis Hassabis, and Sam Altman, have expressed concerns about existential risk from AI.
In May 2023, Geoffrey Hinton announced his resignation from Google in order to be able to "freely speak out about the risks of AI" without "considering how this impacts Google". He notably mentioned risks of an AI takeover, and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI.
In 2023, many leading AI experts endorsed the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".
Some other researchers were more optimistic. AI pioneer Jürgen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier." While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors." Andrew Ng also argued that "it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests." Yann LeCun "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction." In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine.Arguments that AI is not an imminent risk: , , , However, after 2016, the study of current and future risks and possible solutions became a serious area of research. |
Artificial intelligence | Ethical machines and alignment | Ethical machines and alignment
Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk.
Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas.
The field of machine ethics is also called computational morality,
and was founded at an AAAI symposium in 2005.
Other approaches include Wendell Wallach's "artificial moral agents" and Stuart J. Russell's three principles for developing provably beneficial machines. |
Artificial intelligence | Open source | Open source
Active organizations in the AI open-source community include Hugging Face, Google, EleutherAI and Meta. Various AI models, such as Llama 2, Mistral or Stable Diffusion, have been made open-weight, meaning that their architecture and trained parameters (the "weights") are publicly available. Open-weight models can be freely fine-tuned, which allows companies to specialize them with their own data and for their own use-case. Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitate bioterrorism) and that once released on the Internet, they cannot be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses. |
Artificial intelligence | Frameworks | Frameworks
Artificial Intelligence projects can be guided by ethical considerations during the design, development, and implementation of an AI system. An AI framework such as the Care and Act Framework, developed by the Alan Turing Institute and based on the SUM values, outlines four main ethical dimensions, defined as follows:
Respect the dignity of individual people
Connect with other people sincerely, openly, and inclusively
Care for the wellbeing of everyone
Protect social values, justice, and the public interest
Other developments in ethical frameworks include those decided upon during the Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others; however, these principles are not without criticism, especially regards to the people chosen to contribute to these frameworks.
Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers.
The UK AI Safety Institute released in 2024 a testing toolset called 'Inspect' for AI safety evaluations available under a MIT open-source licence which is freely available on GitHub and can be improved with third-party packages. It can be used to evaluate AI models in a range of areas including core knowledge, ability to reason, and autonomous capabilities. |
Artificial intelligence | Regulation | Regulation
upright=1.2|thumb|alt=AI Safety Summit|The first global AI Safety Summit was held in the United Kingdom in November 2023 with a declaration calling for international cooperation.
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms.Regulation of AI to mitigate risks: , , , , The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone. Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years. In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics. In 2024, the Council of Europe created the first international legally binding treaty on AI, called the "Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law". It was adopted by the European Union, the United States, the United Kingdom, and other signatories.
In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks". A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".
In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks. 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence. In May 2024 at the AI Seoul Summit, 16 global AI tech companies agreed to safety commitments on the development of AI. |
Artificial intelligence | History | History
thumb |In 2024, AI patents in China and the US numbered more than three-fourths of AI patents worldwide. Though China had more AI patents, the US had 35% more patents per AI patent-applicant company than China.
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of mathematical reasoning. This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an "electronic brain". They developed several areas of research that would become part of AI,AI's immediate precursors: , , , such as McCullouch and Pitts design for "artificial neurons" in 1943, and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that "machine intelligence" was plausible.
The field of AI research was founded at a workshop at Dartmouth College in 1956.Dartmouth workshop: , , The proposal: The attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.Successful programs of the 1960s: , , , Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s.
Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field. In 1965 Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". quoted in In 1967 Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". quoted in They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects. Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The "AI winter", a period when obtaining funding for AI projects was difficult, followed.First AI Winter, Lighthill report, Mansfield Amendment: , , , ,
In the early 1980s, AI research was revived by the commercial success of expert systems,Expert systems: , , , , , a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.Funding initiatives in the early 1980s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): , , , , However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.Second AI Winter: , , , ,
Up to this point, most of AI's funding had gone to projects that used high-level symbols to represent mental objects like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition, and began to look into "sub-symbolic" approaches. Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that move and survive. Judea Pearl, Lofti Zadeh, and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic. But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others., In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.
AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics).Formal and narrow methods adopted in the 1990s: , By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence" (a tendency known as the AI effect).AI widely used in the late 1990s: , ,
However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.
Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.Deep learning revolution, AlexNet: , ,
For many specific tasks, other methods were abandoned.
Deep learning's success was based on both hardware improvements (faster computers,Moore's Law and AI: graphics processing units, cloud computing) and access to large amounts of dataBig data: (including curated datasets, such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.
thumb|The number of Google searches for the term "AI" accelerated in 2022.
In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study.
In the late 2010s and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program taught only the game's rules and developed a strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. ChatGPT, launched on November 30, 2022, became the fastest-growing consumer software application in history, gaining over 100 million users in two months. It marked what is widely regarded as AI's breakout year, bringing it into the public consciousness. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions of dollars in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI". About 800,000 "AI"-related U.S. job openings existed in 2022. According to PitchBook research, 22% of newly funded startups in 2024 claimed to be AI companies. |
Artificial intelligence | Philosophy | Philosophy
Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines. Another major focus has been whether machines can be conscious, and the associated ethical implications. Many other topics in philosophy are relevant to AI, such as epistemology and free will. Rapid advancements have intensified public discussions on the philosophy and ethics of AI. |
Artificial intelligence | Defining artificial intelligence | Defining artificial intelligence
Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?" He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation.Turing's original publication of the Turing test in "Computing machinery and intelligence":
Historical influence and philosophical implications: , , , Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks."
thumb|The Turing test can provide some evidence of intelligence, but it penalizes non-human intelligent behavior.
Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure. However, they are critical that the test requires the machine to imitate humans. "Aeronautical engineering texts", they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons. AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".
McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world". Another AI founder, Marvin Minsky, similarly describes it as "the ability to solve hard problems". The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals. These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible.
Another definition has been adopted by Google, a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.
Some authors have suggested in practice, that the definition of AI is vague and difficult to define, with contention as to whether classical algorithms should be categorised as AI, with many companies during the early 2020s AI boom using the term as a marketing buzzword, often even if they did "not actually use AI in a material way". |
Artificial intelligence | Evaluating approaches to AI | Evaluating approaches to AI
No established unifying theory or paradigm has guided AI research for most of its history. The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, soft and narrow. Critics argue that these questions may have to be revisited by future generations of AI researchers. |
Artificial intelligence | Symbolic AI and its limits | Symbolic AI and its limits
Symbolic AI (or "GOFAI") simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."Physical symbol system hypothesis:
Historical significance: ,
However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.Moravec's paradox: , , Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge.Dreyfus' critique of AI: ,
Historical significance and philosophical implications: , , , Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.
The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence, in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neuro-symbolic artificial intelligence attempts to bridge the two approaches. |
Artificial intelligence | Neat vs. scruffy | Neat vs. scruffy
"Neats" hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s,Neats vs. scruffies, the historic debate: , , ,
A classic example of the "scruffy" approach to intelligence:
A modern example of neat AI and its aspirations in the 21st century: but eventually was seen as irrelevant. Modern AI has elements of both. |
Artificial intelligence | Soft vs. hard computing | Soft vs. hard computing
Finding a provably correct or optimal solution is intractable for many important problems. Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks. |
Artificial intelligence | Narrow vs. general AI | Narrow vs. general AI
AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals. General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The sub-field of artificial general intelligence studies this area exclusively. |
Artificial intelligence | Machine consciousness, sentience, and mind | Machine consciousness, sentience, and mind
The philosophy of mind does not know whether a machine can have a mind, consciousness and mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on." However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction. |
Artificial intelligence | Consciousness | Consciousness
David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). While human information processing is easy to explain, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to know what red looks like. |
Artificial intelligence | Computationalism and functionalism | Computationalism and functionalism
Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind–body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.
Philosopher John Searle characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle challenges this claim with his Chinese room argument, which attempts to show that even a computer capable of perfectly simulating human behavior would not have a mind.Searle's Chinese room argument: . Searle's original presentation of the thought experiment., .
Discussion: , , |
Artificial intelligence | AI welfare and rights | AI welfare and rights
It is difficult or impossible to reliably evaluate whether an advanced AI is sentient (has the ability to feel), and if so, to what degree. But if there is a significant chance that a given machine can feel and suffer, then it may be entitled to certain rights or welfare protection measures, similarly to animals. Sapience (a set of capacities related to high intelligence, such as discernment or self-awareness) may provide another moral basis for AI rights. Robot rights are also sometimes proposed as a practical way to integrate autonomous agents into society.
In 2017, the European Union considered granting "electronic personhood" to some of the most capable AI systems. Similarly to the legal status of companies, it would have conferred rights but also responsibilities. Critics argued in 2018 that granting rights to AI systems would downplay the importance of human rights, and that legislation should focus on user needs rather than speculative futuristic scenarios. They also noted that robots lacked the autonomy to take part to society on their own.
Progress in AI increased interest in the topic. Proponents of AI welfare and rights often argue that AI sentience, if it emerges, would be particularly easy to deny. They warn that this may be a moral blind spot analogous to slavery or factory farming, which could lead to large-scale suffering if sentient AI is created and carelessly exploited. |
Artificial intelligence | Future | Future |
Artificial intelligence | Superintelligence and the singularity | Superintelligence and the singularity
A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to what I. J. Good called an "intelligence explosion" and Vernor Vinge called a "singularity".The Intelligence explosion and technological singularity: , ,
I. J. Good's "intelligence explosion":
Vernor Vinge's "singularity":
However, technologies cannot improve exponentially indefinitely, and typically follow an S-shaped curve, slowing when they reach the physical limits of what the technology can do. |
Artificial intelligence | Transhumanism | Transhumanism
Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines may merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in the writings of Aldous Huxley and Robert Ettinger.Transhumanism: , ,
Edward Fredkin argues that "artificial intelligence is the next step in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his 1998 book Darwin Among the Machines: The Evolution of Global Intelligence.AI as evolution: Edward Fredkin is quoted in , , |
Artificial intelligence | Decomputing | Decomputing
Arguments for decomputing have been raised by Dan McQuillan (Resisting AI: An Anti-fascist Approach to Artificial Intelligence, 2022), meaning an opposition to the sweeping application and expansion of artificial intelligence. Similar to degrowth, the approach criticizes AI as an outgrowth of the systemic issues and capitalist world we live in. It argues that a different future is possible, in which distance between people is reduced rather than increased through AI intermediaries. |
Artificial intelligence | In fiction | In fiction
thumb|upright=1.2|The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots".
Thought-capable artificial beings have appeared as storytelling devices since antiquity,AI in myth: and have been a persistent theme in science fiction.
A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.
Isaac Asimov introduced the Three Laws of Robotics in many stories, most notably with the "Multivac" super-intelligent computer. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence. |
Artificial intelligence | See also | See also
Organoid intelligence – Use of brain cells and brain organoids for intelligent computing
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Artificial intelligence | Explanatory notes | Explanatory notes |
Artificial intelligence | References | References |
Artificial intelligence | AI textbooks | AI textbooks
The two most widely used textbooks in 2023 (see the Open Syllabus):
The four most widely used AI textbooks in 2008:
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Later edition:
Other textbooks:
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Artificial intelligence | History of AI | History of AI
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Artificial intelligence | Other sources | Other sources
AI & ML in Fusion
AI & ML in Fusion, video lecture
Presidential Address to the Association for the Advancement of Artificial Intelligence.
Later published as
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Artificial intelligence | Further reading | Further reading
Autor, David H., "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015) 29(3) Journal of Economic Perspectives 3.
Boyle, James, The Line: AI and the Future of Personhood, MIT Press, 2024.
Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192–198. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.)
Gertner, Jon. (2023) "Wikipedia's Moment of Truth: Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process?" New York Times Magazine (July 18, 2023) online
Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27–28, 30. "Agency is what distinguishes us from machines. For biological creatures, reason and purpose come from acting in the world and experiencing the consequences. Artificial intelligences – disembodied, strangers to blood, sweat, and tears – have no occasion for that." (p. 30.)
Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44–46. "'We can't realistically expect that those who hope to get rich from AI are going to have the interests of the rest of us close at heart,' ... writes [Gary Marcus]. 'We can't count on governments driven by campaign finance contributions [from tech companies] to push back.'... Marcus details the demands that citizens should make of their governments and the tech companies. They include transparency on how AI systems work; compensation for individuals if their data [are] used to train LLMs (large language model)s and the right to consent to this use; and the ability to hold tech companies liable for the harms they cause by eliminating Section 230, imposing cash penalties, and passing stricter product liability laws... Marcus also suggests... that a new, AI-specific federal agency, akin to the FDA, the FCC, or the FTC, might provide the most robust oversight.... [T]he Fordham law professor Chinmayi Sharma... suggests... establish[ing] a professional licensing regime for engineers that would function in a similar way to medical licenses, malpractice suits, and the Hippocratic oath in medicine. 'What if, like doctors,' she asks..., 'AI engineers also vowed to do no harm?'" (p. 46.)
Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped humans for decades, reveals the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81–82. "This murder mystery competition has revealed that although NLP (natural-language processing) models are capable of incredible feats, their abilities are very much limited by the amount of context they receive. This [...] could cause [difficulties] for researchers who hope to use them to do things such as analyze ancient languages. In some cases, there are few historical records on long-gone civilizations to serve as training data for such a purpose." (p. 82.)
Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate fake videos indistinguishable from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54–59. "If by 'deepfakes' we mean realistic videos produced using artificial intelligence that actually deceive people, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, operating in our media as counterfeited evidence. Their role better resembles that of cartoons, especially smutty ones." (p. 59.)
Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press.
Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning models used in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80–81.
Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a conversation?", The New Yorker, 7 October 2024, pp. 12–16.
Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42–45.
Introduced DQN, which produced human-level performance on some Atari games.
Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?", The New Yorker, 20 November 2023, pp. 20–26.
Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test but showed that intelligence cannot be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at tasks that require real humanlike reasoning or an understanding of the physical and social world.... ChatGPT seemed unable to reason logically and tried to rely on its vast database of... facts derived from online texts."
Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–144. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.)
Tarnoff, Ben, "The Labor Theory of AI" (review of Matteo Pasquinelli, The Eye of the Master: A Social History of Artificial Intelligence, Verso, 2024, 264 pp.), The New York Review of Books, vol. LXXII, no. 5 (27 March 2025), pp. 30–32. The reviewer, Ben Tarnoff, writes: "The strangeness at the heart of the generative AI boom is that nobody really knows how the technology works. We know how the large language models within ChatGPT and its counterparts are trained, even if we don't always know which data they're being trained on: they are asked to predict the next string of characters in a sequence. But exactly how they arrive at any given prediction is a mystery. The computations that occur inside the model are simply too intricate for any human to comprehend." (p. 32.)
Vaswani, Ashish, Noam Shazeer, Niki Parmar et al. "Attention is all you need." Advances in neural information processing systems 30 (2017). Seminal paper on transformers.
Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29–32. "[AI chatbot] programs are made possible by new technologies but rely on the timelelss human tendency to anthropomorphise." (p. 29.)
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Artificial intelligence | External links | External links
*
Category:Computational fields of study
Category:Computational neuroscience
Category:Cybernetics
Category:Data science
Category:Formal sciences
Category:Intelligence by type |
Artificial intelligence | Table of Content | Short description, Goals, Reasoning and problem-solving, Knowledge representation, Planning and decision-making, Learning, Natural language processing, Perception, Social intelligence, General intelligence, Techniques, Search and optimization, State space search, Local search, Logic, Probabilistic methods for uncertain reasoning, Classifiers and statistical learning methods, Artificial neural networks, Deep learning, GPT, Hardware and software, Applications, Health and medicine, Games, Mathematics, Finance, Military, Generative AI, Agents, Sexuality, Other industry-specific tasks, Ethics, Risks and harm, Privacy and copyright, Dominance by tech giants, Power needs and environmental impacts, Misinformation, Algorithmic bias and fairness, Lack of transparency, Bad actors and weaponized AI, Technological unemployment, Existential risk, Ethical machines and alignment, Open source, Frameworks, Regulation, History, Philosophy, Defining artificial intelligence, Evaluating approaches to AI, Symbolic AI and its limits, Neat vs. scruffy, Soft vs. hard computing, Narrow vs. general AI, Machine consciousness, sentience, and mind, Consciousness, Computationalism and functionalism, AI welfare and rights, Future, Superintelligence and the singularity, Transhumanism, Decomputing, In fiction, See also, Explanatory notes, References, AI textbooks, History of AI, Other sources, Further reading, External links |
Afro Celt Sound System | short description | Afro Celt Sound System are a European and African group who fuse electronic music with traditional Gaelic and West African music. Afro Celt Sound System was formed in 1995 by producer-guitarist Simon Emmerson, and feature a wide range of guest artists. In 2003, they temporarily changed their name to Afrocelts before reverting to their original name.
Their albums have been released through Peter Gabriel's Real World Records, and they have frequently performed at WOMAD festivals worldwide. Their sales on the label are exceeded only by Gabriel himself. Their recording contract with Real World was for five albums, of which Volume 5: Anatomic was the last.
After a number of festival dates in 2007, the band went on hiatus. In 2010, they regrouped to play a number of shows (including a return to WOMAD), and released a remastered retrospective titled Capture.
On 20 May 2014, Afro Celt Sound System announced the release of the album Born. In January 2016, a posting on their website revealed that due to a dispute with Emmerson, who announced his departure from the band in 2015, there were two active versions of the band, one led by Emmerson and another with a separate line-up headed by James McNally and Martin Russell. Emmerson's version of the band released the album The Source in 2016. The dispute ended on 21 December 2016, with an announcement on social media.
The band released their seventh studio album, Flight, on 23 November 2018. |
Afro Celt Sound System | Formation | Formation
The inspiration behind the project dates back to 1991, when Simon Emmerson, a Grammy Award-nominated British producer and guitarist, collaborated with Afro-pop star Baaba Maal. While making an album with Maal in Senegal, Emmerson was struck by the similarity between one African melody and a traditional Irish air. Back in London, Irish musician Davy Spillane told Emmerson about a belief that nomadic Celts lived in Africa or India before they migrated to Western Europe. Whether or not the theory was true, Emmerson was intrigued by the two regions' musical affinities.
In an experiment that would prove successful, Emmerson brought two members of Baaba Maal's band together with traditional Irish musicians to see what kind of music the two groups would create. Adding a dash of modern sound, Emmerson also brought in English dance mixers for an electronic beat. "People thought I was mad when I touted the idea," Emmerson told Jim Carroll of The Irish Times. "At the time, I was out of favour with the London club scene. I was broke and on income support but the success was extraordinary". |
Afro Celt Sound System | Career | Career
Jamming in the studios at Real World, musician Peter Gabriel's recording facilities in Wiltshire, England, the group of musicians recorded the basis of their first album in one week. This album, Volume 1: Sound Magic, was released by Real World Records in 1996, and marked the debut of the Afro Celt Sound System.
"Prior to that first album being made, none of us knew if it would work," musician James McNally told Larry Katz of the Boston Herald. "We were strangers who didn't even speak the same language. But we were bowled over by this communication that took place beyond language." McNally, who grew up second-generation Irish in London, played whistles, keyboards, piano, bodhran, and bamboo flute.
Sound Magic has now sold over 300,000 copies. The band performed at festivals, raves, and dance clubs and regularly included two African musicians, Moussa Sissokho on talking drum and djembe and N'Faly Kouyate on vocals, kora and balafon.
Just as the second album was getting off the ground, one of the group's core musicians, 27-year-old keyboardist Jo Bruce, (son of Cream bass player Jack Bruce), died suddenly of an asthma attack. The band was devastated, and the album was put on hold. Sinéad O'Connor then collaborated with the band and helped them cope with their loss. "[O'Connor] blew into the studio on a windy November night and blew away again leaving us something incredibly emotional and powerful," McNally told Katz. "We had this track we didn't know what to do with. Sinéad scribbled a few lyrics and bang! She left us completely choked up." The band used the name of O'Connor's song, "Release", for the title of their album. Volume 2: Release was released in 1999, and by the spring of 2000 it had sold more than half a million copies worldwide. Release is also used as one of the GCSE music set works in the UK that students are required to study for their exam.
In 2000, the group was nominated for a Grammy Award in the Best World Music category. The band, composed at the time of eight members from six countries (the UK, Senegal, Guinea, Ireland, France and Kenya), took pride in its ability to bring people together through music. "We can communicate anywhere at any corner of the planet and feel that we're at home," McNally told Patrick MacDonald of The Seattle Times. "We're breaking down categories of world music and rock music and black music. We leave a door open to communicate with each other's traditions. And it's changed our lives".
In 2001, the group released Volume 3: Further in Time, which climbed to number one on Billboards Top World Music Albums chart. Featuring guest spots by Peter Gabriel and Robert Plant, the album also incorporated a heightened African sound. "On the first two records, the pendulum swung more toward the Celtic, London club side of the equation," Emmerson told The Irish Times Carroll. "For this one, we wanted to have more African vocals and input than we'd done before." Again the Afro Celt Sound System met with success. Chuck Taylor of Billboard praised the album as "a cultural phenomenon that bursts past the traditional boundaries of contemporary music." The single "When You're Falling", with vocals by Gabriel, became a radio hit in the United States.
In 2003, for the Seed album, they changed their name to Afrocelts. They reverted to the longer band name for their subsequent albums, Pod, a compilation of new mixes of songs from the first four albums, Volume 5: Anatomic (their fifth studio album), and Capture (1995–2010).
They played a number of shows to promote Volume 5: Anatomic in 2006 and summer 2007, ending with a gig in Korea, before taking an extended break to work on side projects, amongst them The Imagined Village featuring Simon Emmerson and Johnny Kalsi. Starting in the summer of 2010, the band performed a series of live shows to promote Capture (1995–2010), released on 6 September 2010 on Real World Records. Further performances continue to the present day, and a new album-in-progress titled Born was announced on their website in 2014. Following the split (see below), Emmerson's version of the band released the album The Source in 2016. |
Afro Celt Sound System | Split | Split
During 2015, the band had split into two formations, one of them including Simon Emmerson, N'Faly Kouyate and Johnny Kalsi, the other one James McNally and Martin Russell. The split was announced on the band's website in January 2016. The dispute officially ended with an announcement on social media on 21 December 2016. |
Afro Celt Sound System | Members | Members
When Afro Celt Sound System formed in the mid-1990s during the Real World Recording Week, the difference between a guest artist and a band member was virtually non-existent. However, over time, a combination of people became most often associated with the name Afro Celt Sound System (while Volume 5: Anatomic only lists Emmerson, McNally, Ó Lionáird and Russell as regulars). The divided grouping of the band into two versions, both operating under the name Afro Celt Sound System, began in January 2016 and was resolved in December 2016 after McNally and Russell agreed to work under a different name from Emmerson.
Simon Emmerson who died on 13 March 2023 after falling ill.
N'Faly Kouyate
Johnny Kalsi
Moussa Sissokho
Griogair Labhruidh
Ronan Browne
Emer Mayock
Davy Spillane
Russell/McNally version
Martin Russell
James McNally
Ian Markin
Tim Bradshaw
Babara Bangoura
Dorothee Munyaneza
Kadially Kouyaté
Dav Daheley
Other musicians who have performed or recorded with Afro Celt Sound System include: Jimmy Mahon, Demba Barry, Babara Bangoura, Iarla Ó Lionáird, Peter Gabriel, Robert Plant, Pete Lockett, Sinéad O'Connor, Pina Kollar, Dorothee Munyaneza, Sevara Nazarkhan, Simon Massey, Jesse Cook, Martin Hayes, Eileen Ivers, Mundy, Mairéad Ní Mhaonaigh and Ciarán Tourish of Altan, Ronan Browne, Michael McGoldrick, Steáfán Hannigan, Myrdhin, Shooglenifty, Mairead Nesbitt, Nigel Eaton, Davy Spillane, Jonas Bruce, Heather Nova, Julie Murphy, Ayub Ogada, Caroline Lavelle, and Ross Ainslie. |
Afro Celt Sound System | Discography | Discography |
Afro Celt Sound System | Studio albums | Studio albums
+ List of studio albums, with selected chart positions Title Year Peak chart positions UKPeaks in the UK:
All except noted:
"When You're Falling": AUS FRA IRE NZ US USWorld Volume 1: Sound Magic 1996 59 53 — — 32 — 15 Volume 2: Release 1999 38 93 63 — 42 — 6 Volume 3: Further in Time 2001 77 — — 33 — 176 1 Seed 2003 — — — — — — 5 Volume 5: Anatomic 2005 — — — — — — — The Source 2016 86 — — — — — — Flight 2018 — — — — — — — OVA 2024 — — — — — — — |
Afro Celt Sound System | Other albums | Other albums
Pod (remix album) (2004)
Capture (1995–2010) (2010) (compilation) No. 14 NZ
They also recorded the soundtrack for the PC game Magic and Mayhem, released in 1998. |
Afro Celt Sound System | Charted singles | Charted singles
+ List of charted singles, with selected chart positions Title Year Peak chart positions Album UK NLD USDance "Whirl-Y-Reel" 1997 91 — — Volume 1: Sound Magic "Release" 2000 71 — 3 Volume 2: Release "When You're Falling" 2001 139 86 — Volume 3: Further in Time |
Afro Celt Sound System | References | References |
Afro Celt Sound System | External links | External links
Category:Celtic fusion groups
Category:Worldbeat groups
Category:Real World Records artists
Category:British world music groups
Category:Musical groups established in 1995 |
Afro Celt Sound System | Table of Content | short description, Formation, Career, Split, Members, Discography, Studio albums, Other albums, Charted singles, References, External links |
Ancient philosophy | short description | This page lists some links to ancient philosophy, namely philosophical thought extending as far as early post-classical history (). |
Ancient philosophy | Overview | Overview
Genuine philosophical thought, depending upon original individual insights, arose in many cultures roughly contemporaneously. Karl Jaspers termed the intense period of philosophical development beginning around the 7th century BCE and concluding around the 3rd century BCE an Axial Age in human thought.
In Western philosophy, the spread of Christianity in the Roman Empire marked the ending of Hellenistic philosophy and ushered in the beginnings of medieval philosophy, whereas in the Middle East, the spread of Islam through the Arab Empire marked the end of Old Iranian philosophy and ushered in the beginnings of early Islamic philosophy. |
Ancient philosophy | Ancient Greek and Roman philosophy | Ancient Greek and Roman philosophy
thumb|250px|right|Graphical relationship among the various pre-Socratic philosophers and thinkers; red arrows indicate a relationship of opposition.
thumb|250px|right|Raphael's School of Athens, depicting an array of ancient Greek philosophers engaged in discussion. |
Ancient philosophy | Philosophers | Philosophers |
Ancient philosophy | Pre-Socratic philosophers | Pre-Socratic philosophers
Milesian School
Thales (624 – c 546 BCE)
Anaximander (610 – 546 BCE)
Anaximenes of Miletus (c. 585 – c. 525 BCE)
Pythagoreans
Pythagoras (582 – 496 BCE)
Philolaus (470 – 380 BCE)
Alcmaeon of Croton
Archytas (428 – 347 BCE)
Heraclitus (535 – 475 BCE)
Eleatic School
Xenophanes (570 – 470 BCE)
Parmenides (510 – 440 BCE)
Zeno of Elea (490 – 430 BCE)
Melissus of Samos (c. 470 BCE – ?)
Pluralists
Empedocles (490 – 430 BCE)
Anaxagoras (500 – 428 BCE)
Atomists
Leucippus (first half of 5th century BCE)
Democritus (460 – 370 BCE)
Metrodorus of Chios (4th century BCE)
Pherecydes of Syros (6th century BCE)
Sophists
Protagoras (490 – 420 BCE)
Gorgias (487 – 376 BCE)
Antiphon (480 – 411 BCE)
Prodicus (465/450 – after 399 BCE)
Hippias (middle of the 5th century BCE)
Thrasymachus (459 – 400 BCE)
Callicles
Critias
Lycophron
Diogenes of Apollonia ( – ?) |
Ancient philosophy | Classical Greek philosophers | Classical Greek philosophers
Socrates (469 – 399 BCE)
Euclid of Megara (450 – 380 BCE)
Antisthenes (445 – 360 BCE)
Aristippus (435 – 356 BCE)
Plato (428 – 347 BCE)
Speusippus (407 – 339 BCE)
Diogenes of Sinope (400 – 325 BCE)
Xenocrates (396 – 314 BCE)
Aristotle (384 – 322 BCE)
Stilpo (380 – 300 BCE)
Theophrastus (370 – 288 BCE) |
Ancient philosophy | Hellenistic philosophy | Hellenistic philosophy
Pyrrho (365 – 275 BCE)
Epicurus (341 – 270 BCE)
Metrodorus of Lampsacus (the younger) (331 – 278 BCE)
Zeno of Citium (333 – 263 BCE)
Cleanthes ( – )
Timon (320 – 230 BCE)
Arcesilaus (316 – 232 BCE)
Menippus (3rd century BCE)
Archimedes ( – 212 BCE)
Chrysippus (280 – 207 BCE)
Carneades (214 – 129 BCE)
Clitomachus (187 – 109 BCE)
Metrodorus of Stratonicea (late 2nd century BCE)
Philo of Larissa (160 – 80 BCE)
Posidonius (135 – 51 BCE)
Antiochus of Ascalon (130 – 68 BCE)
Aenesidemus (1st century BCE)
Agrippa (1st century CE) |
Ancient philosophy | Hellenistic schools of thought | Hellenistic schools of thought
Academic skepticism
Cynicism
Cyrenaicism
Eclecticism
Epicureanism
Middle Platonism
Neo-Platonism
Neopythagoreanism
Peripatetic School
Pyrrhonism
Stoicism
Sophism |
Ancient philosophy | Early Roman and Christian philosophy | Early Roman and Christian philosophy
Neoplatonism in Christianity
School of the Sextii |
Ancient philosophy | Philosophers during Roman times | Philosophers during Roman times
thumb|150px|right|Plotinus
Cicero (106 – 43 BCE)
Lucretius (94 – 55 BCE)
Seneca (4 BCE – 65 CE)
Musonius Rufus (30 – 100 CE)
Plutarch (45 – 120 CE)
Epictetus (55 – 135 CE)
Favorinus ( – )
Marcus Aurelius (121 – 180 CE)
Clement of Alexandria (150 – 215 CE)
Alcinous (philosopher) (2nd century CE)
Sextus Empiricus (3rd century CE)
Alexander of Aphrodisias (3rd century CE)
Ammonius Saccas (3rd century CE)
Plotinus (205 – 270 CE)
Porphyry (232 – 304 CE)
Iamblichus (242 – 327 CE)
Themistius (317 – 388 CE)
Ambrose (340 – 397 CE)
Hypatia of Alexandria (350 – 415 CE)
Augustine of Hippo (354 – 430 CE)
Proclus (411 – 485 CE)
Damascius (462 – 540 CE)
Boethius (472 – 524 CE)
Simplicius of Cilicia (490 – 560 CE)
John Philoponus (490 – 570 CE) |
Ancient philosophy | Ancient Iranian philosophy | Ancient Iranian philosophy
right|250px|thumb|Zarathustra as depicted in Raphael's The School of Athens beside Raphael who appears as the ancient painter Apelles of Kos.
See also: Dualism, Dualism (philosophy of mind)
While there are ancient relations between the Indian Vedas and the Iranian Avesta, the two main families of the Indo-Iranian philosophical traditions were characterized by fundamental differences in their implications for the human being's position in society and their view of man's role in the universe. The first charter of human rights by Cyrus the Great as understood in the Cyrus cylinder is often seen as a reflection of the questions and thoughts expressed by Zarathustra and developed in Zoroastrian schools of thought of the Achaemenid Era of Iranian history.Philip G. Kreyenbroek: "Morals and Society in Zoroastrian Philosophy" in "Persian Philosophy". Companion Encyclopedia of Asian Philosophy: Brian Carr and Indira Mahalingam. Routledge, 2009.Mary Boyce: "The Origins of Zoroastrian Philosophy" in "Persian Philosophy". Companion Encyclopedia of Asian Philosophy: Brian Carr and Indira Mahalingam. Routledge, 2009. |
Ancient philosophy | Schools of thought | Schools of thought
Ideas and tenets of Zoroastrian schools of Early Persian philosophy are part of many works written in Middle Persian and of the extant scriptures of the Zoroastrian religion in Avestan language. Among these are treatises such as the Shikand-gumanic Vichar by Mardan-Farrux Ohrmazddadan, selections of Denkard, Wizidagīhā-ī Zātspram ("Selections of Zātspram") as well as older passages of the book Avesta, the Gathas which are attributed to Zarathustra himself and regarded as his "direct teachings".An Anthology of Philosophy in Persia. From Zoroaster to 'Umar Khayyam. S. H. Nasr & M. Aminrazavi. I. B. Tauris Publishers, London & New York, 2008. . |
Ancient philosophy | Zoroastrianism | Zoroastrianism
Zarathustra
Jamasp
Ostanes
Mardan-Farrux OhrmazddadanZurvan. A Zoroastrian Dilemma. Robert Charles Zaehner. Biblo and Tannen, 1972. .
Adurfarnbag FarroxzadanSasanian Iran - intellectual life. A. Tafazzoli and A. L. Khromov in: History of Civilizations of Central Asia: The Crossroads of Civilization. B. A. Litvinsky, Zhang Guand-Da, R. Shabani Samghabadi. Unesco, 1996. .
Adurbad Emedan
Avesta
Gathas
Anacharsis |
Ancient philosophy | Pre-Manichaean thought | Pre-Manichaean thought
BardesanesMansour Shaki. Falsafa. Philosophy in the pre-Islamic period. Encyclopædia Iranica. Volume IX. 1999. .Prods Oktor Skjaervo. Bardesanes. Encyclopædia Iranica. Volume III. Fasc. 7–8. . |
Ancient philosophy | Manichaeism | Manichaeism
Mani ( – 276 CE)
AmmoDavid A. Scott. Manichaean Views of Buddhism in: History of Religions. Vol. 25, No. 2, Nov. 1985. University of Chicago Press. |
Ancient philosophy | Mazdakism | Mazdakism
Mazdak the ElderYarshater, Ehsan. 1983. The Cambridge history of Iran, volume 2. pp. 995–997
Mazdak (died c. 524 or 528 CE) |
Ancient philosophy | Zurvanism | Zurvanism
Aesthetic Zurvanism
Materialist Zurvanism
Fatalistic Zurvanism |
Ancient philosophy | Philosophy and the Empire | Philosophy and the Empire
Political philosophy
Tansar
University of Gundishapur
Borzouye
Bakhtshooa Gondishapuri
Emperor Khosrau's philosophical discourses
Paul the Persian |
Ancient philosophy | Literature | Literature
Pahlavi literature |
Ancient philosophy | Ancient Jewish philosophy | Ancient Jewish philosophy
Qohelet ()
Pseudo-Aristeas ()
Ben Sira (fl. 180–175 BCE)
Aristobulus of Alexandria (181–124 BCE)
Philo of Alexandria (30 BCE – 45 CE)
Wisdom of Solomon ()
4 Maccabees ()
Rabbi Akiva ( – ) |
Ancient philosophy | Ancient Indian philosophy<!--'Ancient Indian philosophy' redirects here--> | Ancient Indian philosophy
The ancient Indian philosophy is a fusion of two ancient traditions: the Vedic tradition and the śramaṇa tradition. |
Ancient philosophy | Vedic philosophy | Vedic philosophy
Indian philosophy begins with the Vedas wherein questions pertaining to laws of nature, the origin of the universe, and the place of man in it are asked. In the famous Rigvedic Hymn of Creation (Nasadiya Sukta) the poet asks:
"Whence all creation had its origin,
he, whether he fashioned it or whether he did not,
he, who surveys it all from highest heaven,
he knows—or maybe even he does not know."
In the Vedic view, creation is ascribed to the self-consciousness of the primeval being (Purusha). This leads to the inquiry into the one being that underlies the diversity of empirical phenomena and the origin of all things. Cosmic order is termed rta and causal law by karma. Nature (prakriti) is taken to have three qualities (sattva, rajas, and tamas).
Vedas
Upanishads
Hindu philosophy |
Ancient philosophy | Sramana philosophy | Sramana philosophy
Jainism and Buddhism are a continuation of the Sramana school of thought. The Sramanas cultivated a pessimistic worldview of the samsara as full of suffering and advocated renunciation and austerities. They laid stress on philosophical concepts like Ahimsa, Karma, Jnana, Samsara and Moksa. Cārvāka (Sanskrit: चार्वाक) (atheist) philosophy, also known as Lokāyata, it is a system of Hindu philosophy that assumes various forms of philosophical skepticism and religious indifference. It is named after its founder, Cārvāka, author of the Bārhaspatya-sūtras. |
Ancient philosophy | Classical Indian philosophy | Classical Indian philosophy
In classical times, these inquiries were systematized in six schools of philosophy. Some of the questions asked were:
What is the ontological nature of consciousness?
How is cognition itself experienced?
Is mind (chit) intentional or not?
Does cognition have its own structure?
The six schools of Indian philosophy are:
Nyaya
Vaisheshika
Samkhya
Yoga
Mimamsa (Purva Mimamsa)
Vedanta (Uttara Mimamsa) |
Ancient philosophy | Ancient Indian philosophers | Ancient Indian philosophers |
Ancient philosophy | 1st millennium BCE | 1st millennium BCE
Parashara – writer of Viṣṇu Purāṇa. |
Ancient philosophy | Philosophers of Vedic Age (c. 1500 – c. 600 BCE) | Philosophers of Vedic Age (c. 1500 – c. 600 BCE)
Rishi Narayana – seer of the Purusha Sukta of the Rig Veda.The significance of Purusha Sukta in Daily Invocations by Swami Krishnananda
Seven Rishis – Atri, Bharadwaja, Gautama, Jamadagni, Kasyapa, Vasishtha, Viswamitra.P. 285 Indian sociology through Ghurye, a dictionary By S. Devadas Pillai
Other Vedic Rishis – Gritsamada, Sandilya, Kanva etc.
Rishaba – Rishi mentioned in Rig Veda and later in several Puranas, and believed by Jains to be the first official religious guru of Jainism, as accredited by later followers.
Yajnavalkya – one of the Vedic sages, greatly influenced Buddhistic thought.
Lopamudra
Gargi Vachaknavi
Maitreyi
Parshvanatha
Ghosha
Angiras – one of the seers of the Atharva Veda and author of Mundaka Upanishad.
Uddalaka Aruni – an Upanishadic sage who authored major portions of Chāndogya Upaniṣad.
Ashvapati – a King in the Later Vedic age who authored Vaishvanara Vidya of Chāndogya Upaniṣad.
Ashtavakra – an Upanishadic Sage mentioned in the Mahabharata, who authored Ashtavakra Gita. |
Ancient philosophy | Philosophers of Axial Age (600–185 BCE) | Philosophers of Axial Age (600–185 BCE)
Gotama (), logician, author of Nyaya Sutra
Kanada (), founded the philosophical school of Vaisheshika, gave theory of atomism
Mahavira (599–527 BCE) – heavily influenced Jainism, the 24th Tirthankara of Jainism.
Purana Kassapa
Ajita Kesakambali
Payasi
Makkhali Gośāla
Sañjaya Belaṭṭhiputta
Mahavira
Dandamis
Nagasena
Lakulisha
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Pakudha Kaccayana
Pāṇini (520–460 BCE), grammarian, author of Ashtadhyayi
Kapila (), proponent of the Samkhya system of philosophy.
Badarayana (lived between 500 BCE and 400 BCE) – Author of Brahma Sutras.
Jaimini (), author of Purva Mimamsa Sutras.
Pingala (), author of the Chandas shastra
Gautama Buddha ( – ), founder of Buddhist school of thought
Śāriputra
Chanakya ( – ), author of Arthashastra, professor (acharya) of political science at the Takshashila University
Patañjali (), developed the philosophy of Raja Yoga in his Yoga Sutras.
Shvetashvatara – Author of earliest textual exposition of a systematic philosophy of Shaivism. |
Ancient philosophy | Philosophers of Golden Age (184 BCE – 600 CE) | Philosophers of Golden Age (184 BCE – 600 CE)
Aśvaghoṣa, believed to have been the first Sanskrit dramatist, and is considered the greatest Indian poet before Kālidāsa
Vatsyana, known for "Kama Sutra"
Samantabhadra, a proponent of the Jaina doctrine of Anekantavada
Isvarakrsna
Aryadeva, a student of Nagarjuna and contributed significantly to the Madhyamaka
Dharmakirti
Haribhadra
Pujyapada
Buddhaghosa
Kamandaka
Maticandra
Prashastapada
Bhāviveka
Dharmapala
Udyotakara
Gaudapada
Valluvar (), wrote the Kural text, a Tamil-language treatise on morality and secular ethics
Dignāga (), one of the founders of Buddhist school of Indian logic
Asanga (), exponent of the Yogacara
Bhartrihari (–510 CE), early figure in Indic linguistic theory
Bodhidharma (–528 CE), founder of the Zen school of Buddhism
Siddhasenadivākarasuri (5th century CE), Jain logician and author of important works in Sanskrit and Prakrit, such as Nyāyāvatāra (on logic) and Sanmatisūtra (dealing with the seven Jaina standpoints, knowledge and the objects of knowledge)
Vasubandhu (), one of the main founders of the Indian Yogacara school
Kundakunda (2nd century CE), exponent of Jain mysticism and Jain nayas dealing with the nature of the soul and its contamination by matter, author of Pañcāstikāyasāra (Essence of the Five Existents), the Pravacanasāra (Essence of the Scripture) and the Samayasāra (Essence of the Doctrine)
Nagarjuna ( – 250 CE), the founder of the Madhyamaka (Middle Path) school of Mahāyāna Buddhism
Umāsvāti or Umasvami (2nd century CE), author of first Jain work in Sanskrit, Tattvārthasūtra, expounding the Jain philosophy in a most systematized form acceptable to all sects of Jainism
Adi Shankara – philosopher and theologian, most renowned exponent of the Advaita Vedanta school of philosophy |