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In classical planning, the agent knows exactly what the effect of any action will be. In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked. In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences. Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be. A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration), be heuristic, or it can be learned.
Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents. Learning. Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning. There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires labeling the training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input). In reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good". Transfer learning is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.
Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization. Natural language processing. Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering. Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation unless restricted to small domains called "micro-worlds" (due to the common sense knowledge problem). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure. Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning), transformers (a deep learning architecture using an attention mechanism), and others. In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text, and by 2023, these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.
Perception. Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input. The field includes speech recognition, image classification, facial recognition, object recognition,object tracking, and robotic perception. Social intelligence. Affective computing is a field that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the effects displayed by a videotaped subject.
General intelligence. A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence. Techniques. AI research uses a wide variety of techniques to accomplish the goals above. Search and optimization. AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search. State space search. State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. "Heuristics" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.
Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and countermoves, looking for a winning position. Local search. Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally. 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. 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).
Logic. Formal logic is used for reasoning and knowledge representation. 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") 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" "X"s that are "Y"s"). 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). 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. 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.
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. 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. Probabilistic methods for uncertain reasoning. 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. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.
Bayesian networks are a tool that can be used for reasoning (using the Bayesian inference algorithm), learning (using the expectation–maximization algorithm), planning (using 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). 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. 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. 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. The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability. Neural networks are also used as classifiers. Artificial neural networks. 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.
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. 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. In feedforward neural networks the signal passes in only one direction. 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. Perceptrons 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. Deep learning. Deep 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. 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. 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.
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). 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.
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.
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. 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. 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."
Military. Various countries are deploying AI military applications. 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. 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.
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. 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. 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. Risks and harm. 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.
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. 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.
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. 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. 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." 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. 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.
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. 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. 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. 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, 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. However, after 2016, the study of current and future risks and possible solutions became a serious area of research.
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. 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.
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: 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. Regulation. 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. 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.
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 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.
History. 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, 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. 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. 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". In 1967 Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". 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. In the early 1980s, AI research was revived by the commercial success of 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. 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.
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). 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).
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. For many specific tasks, other methods were abandoned. Deep learning's success was based on both hardware improvements (faster computers, graphics processing units, cloud computing) and access to large amounts of 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. 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.
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. 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. 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."
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". 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.
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." 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. 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. 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. 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, but eventually was seen as irrelevant. Modern AI has elements of both.
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. 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. 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. 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".
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. 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. Future. 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".
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. 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. 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". 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.
In fiction. Thought-capable artificial beings have appeared as storytelling devices since antiquity, 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 and "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. References. AI textbooks. The two most widely used textbooks in 2023 (see the Open Syllabus): The four most widely used AI textbooks in 2008: Other textbooks:
Afro Celt Sound System 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. 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".
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, "", 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. "" 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 "", which climbed to number one on "Billboard"s 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. 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.
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. Russell/McNally version 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. Discography. Other albums. They also recorded the soundtrack for the PC game "Magic and Mayhem", released in 1998.
Ancient philosophy This page lists some links to ancient philosophy, namely philosophical thought extending as far as early post-classical history (). 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 Iranian philosophy. 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.
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". Zoroastrianism. Anacharsis Ancient Indian philosophy. The ancient Indian philosophy is a fusion of two ancient traditions: the Vedic tradition and the śramaṇa tradition. 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: 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").
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. Classical Indian philosophy. In classical times, these inquiries were systematized in six schools of philosophy. Some of the questions asked were: The six schools of Indian philosophy are: Ancient Chinese philosophy. Chinese philosophy is the dominant philosophical thought in China and other countries within the East Asian cultural sphere that share a common language, including Japan, Korea, and Vietnam. Schools of thought. Hundred Schools of Thought.
The Hundred Schools of Thought were philosophers and schools that flourished from the 6th century to 221 BCE, an era of significant cultural and intellectual expansion in China. Even though this period – known in its earlier part as the Spring and Autumn period and the Warring States period – in its latter part was fraught with chaos and bloody battles, it is also known as the Golden Age of Chinese philosophy because a broad range of thoughts and ideas were developed and discussed freely. The thoughts and ideas discussed and refined during this period have profoundly influenced lifestyles and social consciousness up to the present day in East Asian countries. The intellectual society of this era was characterized by itinerant scholars, who were often employed by various state rulers as advisers on the methods of government, war, and diplomacy. This period ended with the rise of the Qin dynasty and the subsequent purge of dissent. The Book of Han lists ten major schools, they are: Early Imperial China. The founder of the Qin dynasty, who implemented Legalism as the official philosophy, quashed Mohist and Confucianist schools. Legalism remained influential until the emperors of the Han dynasty adopted Daoism and later Confucianism as official doctrine. These latter two became the determining forces of Chinese thought until the introduction of Buddhism.
Confucianism was particularly strong during the Han dynasty, whose greatest thinker was Dong Zhongshu, who integrated Confucianism with the thoughts of the Zhongshu School and the theory of the Five Elements. He also was a promoter of the New Text school, which considered Confucius as a divine figure and a spiritual ruler of China, who foresaw and started the evolution of the world towards the Universal Peace. In contrast, there was an Old Text school that advocated the use of Confucian works written in ancient language (from this comes the denomination "Old Text") that were so much more reliable. In particular, they refuted the assumption of Confucius as a godlike figure and considered him as the greatest sage, but simply a human and mortal. The 3rd and 4th centuries saw the rise of the "Xuanxue" (mysterious learning), also called "Neo-Taoism". The most influential philosophers of this movement were Wang Bi, Xiang Xiu and Guo Xiang. The main question of this school was whether Being came before Not-Being (in Chinese, "ming" and "wuming"). A peculiar feature of these Taoist thinkers, like the Seven Sages of the Bamboo Grove, was the concept of "feng liu" (lit. wind and flow), a sort of romantic spirit which encouraged following the natural and instinctive impulse.
Buddhism arrived in China around the 1st century AD, but it was not until the Northern and Southern, Sui and Tang dynasties that it gained considerable influence and acknowledgement. In the beginning, it was considered a sort of Taoist sect, and there was even a theory about Laozi, founder of Taoism, who went to India and taught his philosophy to Buddha. Mahayana Buddhism was far more successful in China than its rival Hinayana, and both Indian schools and local Chinese sects arose from the 5th century. Two chiefly important monk philosophers were Sengzhao and Daosheng. But probably the most influential and original of these schools was the Chan sect, which had an even stronger impact in Japan as the Zen sect.
Anaximander Anaximander ( ; "Anaximandros"; ) was a pre-Socratic Greek philosopher who lived in Miletus, a city of Ionia (in modern-day Turkey). He belonged to the Milesian school and learned the teachings of his master Thales. He succeeded Thales and became the second master of that school where he counted Anaximenes and, arguably, Pythagoras amongst his pupils. Little of his life and work is known today. According to available historical documents, he is the first philosopher known to have written down his studies, although only one fragment of his work remains. Fragmentary testimonies found in documents after his death provide a portrait of the man. Anaximander was an early proponent of science and tried to observe and explain different aspects of the universe, with a particular interest in its origins, claiming that nature is ruled by laws, just like human societies, and anything that disturbs the balance of nature does not last long. Like many thinkers of his time, Anaximander's philosophy included contributions to many disciplines. In astronomy, he attempted to describe the mechanics of celestial bodies in relation to the Earth. In physics, his postulation that the indefinite (or apeiron) was the source of all things, led Greek philosophy to a new level of conceptual abstraction. His knowledge of geometry allowed him to introduce the gnomon in Greece. He created a map of the world that contributed greatly to the advancement of geography. Anaximander was involved in the politics of Miletus and was sent as a leader to one of its colonies.
Biography. Anaximander, son of Praxiades, was born in the third year of the 42nd Olympiad (610 BC). According to Apollodorus of Athens, Greek grammarian of the 2nd century BC, he was sixty-four years old during the second year of the 58th Olympiad (547–546 BC) and died shortly afterwards. Establishing a timeline of his work is impossible, since no document provides chronological references. Themistius, a 4th-century Byzantine rhetorician, mentions that he was the "first of the known Greeks to publish a written document on nature." Therefore, his texts would be amongst the earliest written in prose, at least in the Western world. By the time of Plato, his philosophy was almost forgotten, and Aristotle, his successor Theophrastus, and a few doxographers provide us with the little information that remains. However, we know from Aristotle that Thales, also from Miletus, precedes Anaximander. It is debatable whether Thales actually was the teacher of Anaximander, but there is no doubt that Anaximander was influenced by Thales' theory that everything is derived from water. One thing that is not debatable is that even the ancient Greeks considered Anaximander to be from the Monist school which began in Miletus, with Thales followed by Anaximander and which ended with Anaximenes. 3rd-century Roman rhetorician Aelian depicts Anaximander as leader of the Milesian colony to Apollonia on the Black Sea coast, and hence some have inferred that he was a prominent citizen. Indeed, "Various History" (III, 17) explains that philosophers sometimes also dealt with political matters. It is very likely that leaders of Miletus sent him there as a legislator to create a constitution or simply to maintain the colony's allegiance.
Anaximander lived the final few years of his life as a subject of the Persian Achaemenid Empire. Theories. Anaximander's theories were influenced by the Greek mythical tradition, and by some ideas of Thales – the father of Western philosophy – as well as by observations made by older civilizations in the Near East, especially Babylon. All these were developed rationally. In his desire to find some universal principle, he assumed, like traditional religion, the existence of a cosmic order; and his ideas on this used the old language of myths which ascribed divine control to various spheres of reality. This was a common practice for the Greek philosophers in a society which saw gods everywhere, and therefore could fit their ideas into a tolerably elastic system.
The same "rational" way of thought led him to introduce the abstract "apeiron" (indefinite, infinite, boundless, unlimited) as an origin of the universe, a concept that is probably influenced by the original Chaos (gaping void, abyss, formless state) from which everything else appeared in the mythical Greek cosmogony. It also takes notice of the mutual changes between the four elements. Origin, then, must be something else unlimited in its source, that could create without experiencing decay, so that genesis would never stop. Apeiron. The "Refutation" attributed to Hippolytus of Rome (I, 5), and the later 6th century Byzantine philosopher Simplicius of Cilicia, attribute to Anaximander the earliest use of the word "apeiron" ( "infinite" or "limitless") to designate the original principle. He was the first philosopher to employ, in a philosophical context, the term "archē" (), which until then had meant beginning or origin. "That Anaximander called this something by the name of is the natural interpretation of what Theophrastos says; the current statement that the term was introduced by him appears to be due to a misunderstanding."
And "Hippolytos, however, is not an independent authority, and the only question is what Theophrastos wrote." For him, it became no longer a mere point in time, but a source that could perpetually give birth to whatever will be. The indefiniteness is spatial in early usages as in Homer (indefinite sea) and as in Xenophanes (6th century BC) who said that the Earth went down indefinitely (to "apeiron") i.e. beyond the imagination or concept of men. Burnet (1930) in "Early Greek Philosophy" says: "Nearly all we know of Anaximander's system is derived in the last resort from Theophrastos, who certainly knew his book. He seems once at least to have quoted Anaximander's own words, and he criticised his style. Here are the remains of what he said of him in the First Book: "Anaximander of Miletos, son of Praxiades, a fellow-citizen and associate of Thales, said that the material cause and first element of things was the Infinite, he being the first to introduce this name of the material cause. He says it is neither water nor any other of the so-called elements, but a substance different from them which is infinite" [apeiron, or ] "from which arise all the heavens and the worlds within them.—Phys, Op. fr. 2 (Dox. p. 476; R. P. 16)."
Burnet's quote from the "First Book" is his translation of Theophrastos' "Physic Opinion" fragment 2 as it appears in p. 476 of "Historia Philosophiae Graecae" (1898) by Ritter and Preller and section 16 of "Doxographi Graeci" (1879) by Diels. By ascribing the "Infinite" with a "material cause", Theophrastos is following the Aristotelian tradition of "nearly always discussing the facts from the point of view of his own system". Aristotle writes ("Metaphysics", I.III 3–4) that the Pre-Socratics were searching for the element that constitutes all things. While each pre-Socratic philosopher gave a different answer as to the identity of this element (water for Thales and air for Anaximenes), Anaximander understood the beginning or first principle to be an endless, unlimited primordial mass ("apeiron"), subject to neither old age nor decay, that perpetually yielded fresh materials from which everything we perceive is derived. He proposed the theory of the "apeiron" in direct response to the earlier theory of his teacher, Thales, who had claimed that the primary substance was water. The notion of temporal infinity was familiar to the Greek mind from remote antiquity in the religious concept of immortality, and Anaximander's description was in terms appropriate to this conception. This "archē" is called "eternal and ageless". (Hippolytus (?), "Refutation", I,6,I;DK B2)
""Aristotle puts things in his own way regardless of historical considerations, and it is difficult to see that it is more of an anachronism to call the Boundless " intermediate between the elements " than to say that it is " distinct from the elements." Indeed, if once we introduce the elements at all, the former description is the more adequate of the two. At any rate, if we refuse to understand these passages as referring to Anaximander, we shall have to say that Aristotle paid a great deal of attention to some one whose very name has been lost, and who not only agreed with some of Anaximander's views, but also used some of his most characteristic expressions. We may add that in one or two places Aristotle certainly seems to identify the " intermediate " with the something " distinct from " the elements"." "It is certain that he [Anaximander] cannot have said anything about elements, which no one thought of before Empedokles, and no one could think of before Parmenides. The question has only been mentioned because it has given rise to a lengthy controversy, and because it throws light on the historical value of Aristotle's statements. From the point of view of his own system, these may be justified; but we shall have to remember in other cases that, when he seems to attribute an idea to some earlier thinker, we are not bound to take what he says in an historical sense."
For Anaximander, the principle of things, the constituent of all substances, is nothing determined and not an element such as water in Thales' view. Neither is it something halfway between air and water, or between air and fire, thicker than air and fire, or more subtle than water and earth. Anaximander argues that water cannot embrace all of the opposites found in nature – for example, water can only be wet, never dry – and therefore cannot be the one primary substance; nor could any of the other candidates. He postulated the "apeiron" as a substance that, although not directly perceptible to us, could explain the opposites he saw around him. "If Thales had been right in saying that water was the fundamental reality, it would not be easy to see how anything else could ever have existed. One side of the opposition, the cold and moist, would have had its way unchecked, and the warm and dry would have been driven from the field long ago. We must, then, have something not itself one of the warring opposites, something more primitive, out of which they arise, and into which they once more pass away."
Anaximander explains how the four elements of ancient physics (air, earth, water and fire) are formed, and how Earth and terrestrial beings are formed through their interactions. Unlike other Pre-Socratics, he never defines this principle precisely, and it has generally been understood (e.g., by Aristotle and by Saint Augustine) as a sort of primal chaos. According to him, the Universe originates in the separation of opposites in the primordial matter. It embraces the opposites of hot and cold, wet and dry, and directs the movement of things; an entire host of shapes and differences then grow that are found in "all the worlds" (for he believed there were many). "Anaximander taught, then, that there was an eternal. The indestructible something out of which everything arises, and into which everything returns; a boundless stock from which the waste of existence is continually made good, "elements.". That is only the natural development of the thought we have ascribed to Thales, and there can be no doubt that Anaximander at least formulated it distinctly.
The indestructible something out of which everything arises, and into which everything returns; a boundless stock from which the waste of existence is continually made good, "elements.". That is only the natural development of the thought we have ascribed to Thales, and there can be no doubt that Anaximander at least formulated it distinctly. Indeed, we can still follow to some extent the reasoning which led him to do so. Thales had regarded water as the most likely thing to be that of which all others are forms; Anaximander appears to have asked how the primary substance could be one of these particular things. His argument seems to be preserved by Aristotle, who has the following passage in his discussion of the Infinite: "Further, there cannot be a single, simple body which is infinite, either, as some hold, one distinct from the elements, which they then derive from it, or without this qualification. For there are some who make this. (i.e. a body distinct from the elements). the infinite, and not air or water, in order that the other things may not be destroyed by their infinity.
a body distinct from the elements). the infinite, and not air or water, in order that the other things may not be destroyed by their infinity. They are in opposition one to another. air is cold, water moist, and fire hot. and therefore, if any one of them were infinite, the rest would have ceased to be by this time. Accordingly they say that what is infinite is something other than the elements, and from it the elements arise.'⁠—Aristotle Physics. F, 5 204 b 22 (Ritter and Preller (1898) Historia Philosophiae Graecae, section 16 b)." Anaximander maintains that all dying things are returning to the element from which they came ("apeiron"). The one surviving fragment of Anaximander's writing deals with this matter. Simplicius transmitted it as a quotation, which describes the balanced and mutual changes of the elements: Whence things have their origin, Thence also their destruction happens, According to necessity; For they give to each other justice and recompense For their injustice In conformity with the ordinance of Time.
Simplicius mentions that Anaximander said all these "in poetic terms", meaning that he used the old mythical language. The goddess Justice (Dike) keeps the cosmic order. This concept of returning to the element of origin was often revisited afterwards, notably by Aristotle, and by the Greek tragedian Euripides: "what comes from earth must return to earth." Friedrich Nietzsche, in his "Philosophy in the Tragic Age of the Greeks", stated that Anaximander viewed "... all coming-to-be as though it were an illegitimate emancipation from eternal being, a wrong for which destruction is the only penance." Physicist Max Born, in commenting upon Werner Heisenberg's arriving at the idea that the elementary particles of quantum mechanics are to be seen as different manifestations, different quantum states, of one and the same "primordial substance,"' proposed that this primordial substance be called "apeiron". A free-floating Earth. Anaximander was the first to conceive a mechanical model of the world. In his model, the Earth floats very still in the centre of the infinite, not supported by anything. It remains "in the same place because of its indifference", a point of view that Aristotle considered ingenious, in "On the Heavens". Its curious shape is that of a cylinder with a height one-third of its diameter. The flat top forms the inhabited world.
Carlo Rovelli suggests that Anaximander took the idea of the Earth's shape as a floating disk from Thales, who had imagined the Earth floating in water, the "immense ocean from which everything is born and upon which the Earth floats." Anaximander was then able to envisage the Earth at the centre of an infinite space, in which case it required no support as there was nowhere "down" to fall. In Rovelli's view, the shape – a cylinder or a sphere – is unimportant compared to the appreciation of a "finite body that floats free in space." Anaximander's realization that the Earth floats free without falling and does not need to be resting on something has been indicated by many as the first cosmological revolution and the starting point of scientific thinking. Karl Popper calls this idea "one of the boldest, most revolutionary, and most portentous ideas in the whole history of human thinking." Such a model allowed the concept that celestial bodies could pass under the Earth, opening the way to Greek astronomy. Rovelli suggests that seeing the stars circling the Pole star, and both vanishing below the horizon on one side and reappearing above it on the other, would suggest to the astronomer that there was a void both above and below the Earth.
Cosmology. Anaximander's bold use of non-mythological explanatory hypotheses considerably distinguishes him from previous cosmology writers such as Hesiod. It indicates a pre-Socratic effort to demystify physical processes. His major contribution to history was writing the oldest prose document about the Universe and the origins of life; for this he is often called the "Father of Cosmology" and founder of astronomy. However, pseudo-Plutarch states that he still viewed celestial bodies as deities. He placed the celestial bodies in the wrong order. He thought that the stars were nearest to the Earth, then the Moon, and the Sun farthest away. His scheme is compatible with the Indo-Iranian philosophical traditions contained in the Iranian Avesta and the Indian Upanishads. At the origin, after the separation of hot and cold, a ball of flame appeared that surrounded Earth like bark on a tree. This ball broke apart to form the rest of the Universe. It resembled a system of hollow concentric wheels, filled with fire, with the rims pierced by holes like those of a flute. Consequently, the Sun was the fire that one could see through a hole the same size as the Earth on the farthest wheel, and an eclipse corresponded with the occlusion of that hole. The diameter of the solar wheel was twenty-seven times that of the Earth (or twenty-eight, depending on the sources) and the lunar wheel, whose fire was less intense, eighteen (or nineteen) times. Its hole could change shape, thus explaining lunar phases. The stars and the planets, located closer, followed the same model.
Anaximander was the first astronomer to consider the Sun as a huge mass, and consequently, to realize how far from Earth it might be, and the first to present a system where the celestial bodies turned at different distances. Furthermore, according to Diogenes Laertius (II, 2), he built a celestial sphere. This invention undoubtedly made him the first to realize the obliquity of the Zodiac as the Roman philosopher Pliny the Elder reports in "Natural History" (II, 8). It is a little early to use the term ecliptic, but his knowledge and work on astronomy confirm that he must have observed the inclination of the celestial sphere in relation to the plane of the Earth to explain the seasons. The doxographer and theologian Aetius attributes to Pythagoras the exact measurement of the obliquity. Multiple worlds. According to Simplicius, Anaximander already speculated on the plurality of worlds, similar to atomists Leucippus and Democritus, and later philosopher Epicurus. These thinkers supposed that worlds appeared and disappeared for a while, and that some were born when others perished. They claimed that this movement was eternal, "for without movement, there can be no generation, no destruction".
In addition to Simplicius, Hippolytus reports Anaximander's claim that from the infinite comes the principle of beings, which themselves come from the heavens and the worlds (several doxographers use the plural when this philosopher is referring to the worlds within, which are often infinite in quantity). Cicero writes that he attributes different gods to the countless worlds. This theory places Anaximander close to the Atomists and the Epicureans who, more than a century later, also claimed that an infinity of worlds appeared and disappeared. In the timeline of the Greek history of thought, some thinkers conceptualized a single world (Plato, Aristotle, Anaxagoras and Archelaus), while others instead speculated on the existence of a series of worlds, continuous or non-continuous (Anaximenes, Heraclitus, Empedocles and Diogenes). Meteorological phenomena. Anaximander attributed some phenomena, such as thunder and lightning, to the intervention of elements, rather than to divine causes. In his system, thunder results from the shock of clouds hitting each other; the loudness of the sound is proportionate with that of the shock. Thunder without lightning is the result of the wind being too weak to emit any flame, but strong enough to produce a sound. A flash of lightning without thunder is a jolt of the air that disperses and falls, allowing a less active fire to break free. Thunderbolts are the result of a thicker and more violent air flow.
He saw the sea as a remnant of the mass of humidity that once surrounded Earth. A part of that mass evaporated under the Sun's action, thus causing the winds and even the rotation of the celestial bodies, which he believed were attracted to places where water is more abundant. He explained rain as a product of the humidity pumped up from Earth by the sun. For him, the Earth was slowly drying up and water only remained in the deepest regions, which someday would go dry as well. According to Aristotle's "Meteorology" (II, 3), Democritus also shared this opinion. Origin of mankind. Anaximander speculated about the beginnings and origin of animal life, and that humans came from other animals in waters. According to his evolutionary theory, animals sprang out of the sea long ago, born trapped in a spiny bark, but as they got older, the bark would dry up and animals would be able to break it. The 3rd century Roman writer Censorinus reports: Anaximander put forward the idea that humans had to spend part of this transition inside the mouths of big fish to protect themselves from the Earth's climate until they could come out in open air and lose their scales. He thought that, considering humans' extended infancy, we could not have survived in the primeval world in the same manner we do presently.
Other accomplishments. Cartography. Both Strabo and Agathemerus (later Greek geographers) claim that, according to the geographer Eratosthenes, Anaximander was the first to publish a map of the world. The map probably inspired the Greek historian Hecataeus of Miletus to draw a more accurate version. Strabo viewed both as the first geographers after Homer. Maps were produced in ancient times, also notably in Egypt, Lydia, the Middle East, and Babylon. Only some small examples survived until today. The unique example of a world map comes from the late Babylonian Map of the World later than 9th century BC but is based probably on a much older map. These maps indicated directions, roads, towns, borders, and geological features. Anaximander's innovation was to represent the entire inhabited land known to the ancient Greeks. Such an accomplishment is more significant than it at first appears. Anaximander most likely drew this map for three reasons. First, it could be used to improve navigation and trade between Miletus's colonies and other colonies around the Mediterranean Sea and Black Sea. Second, Thales would probably have found it easier to convince the Ionian city-states to join in a federation in order to push the Median threat away if he possessed such a tool. Finally, the philosophical idea of a global representation of the world simply for the sake of knowledge was reason enough to design one.
Surely aware of the sea's convexity, he may have designed his map on a slightly rounded metal surface. The centre or "navel" of the world ( "omphalós gẽs") could have been Delphi, but is more likely in Anaximander's time to have been located near Miletus. The Aegean Sea was near the map's centre and enclosed by three continents, themselves located in the middle of the ocean and isolated like islands by sea and rivers. Europe was bordered on the south by the Mediterranean Sea and was separated from Asia by the Black Sea, the Lake Maeotis, and, further east, either by the Phasis River (now called the Rioni in Georgia) or the Tanais. The Nile flowed south into the ocean, separating Libya (which was the name for the part of the then-known African continent) from Asia. Gnomon. The "Suda" relates that Anaximander explained some basic notions of geometry. It also mentions his interest in the measurement of time and associates him with the introduction in Greece of the gnomon. In Lacedaemon, he participated in the construction, or at least in the adjustment, of sundials to indicate solstices and equinoxes. Indeed, a gnomon required adjustments from a place to another because of the difference in latitude.
In his time, the gnomon was simply a vertical pillar or rod mounted on a horizontal plane. The position of its shadow on the plane indicated the time of day. As it moves through its apparent course, the Sun draws a curve with the tip of the projected shadow, which is shortest at noon, when pointing due south. The variation in the tip's position at noon indicates the solar time and the seasons; the shadow is longest on the winter solstice and shortest on the summer solstice. The invention of the gnomon itself cannot be attributed to Anaximander because its use, as well as the division of days into twelve parts, came from the Babylonians. It is they, according to Herodotus' Histories (II, 109), who gave the Greeks the art of time measurement. It is likely that he was not the first to determine the solstices, because no calculation is necessary. On the other hand, equinoxes do not correspond to the middle point between the positions during solstices, as the Babylonians thought. As the "Suda" seems to suggest, it is very likely that with his knowledge of geometry, he became the first Greek to determine accurately the equinoxes.
Prediction of an earthquake. In his philosophical work "De Divinatione" (I, 50, 112), Cicero states that Anaximander convinced the inhabitants of Lacedaemon to abandon their city and spend the night in the country with their weapons because an earthquake was near. The city collapsed when the top of the Taygetus split like the stern of a ship. Pliny the Elder also mentions this anecdote (II, 81), suggesting that it came from an "admirable inspiration", as opposed to Cicero, who did not associate the prediction with divination. Scientific method. Rovelli credits Anaximander with pioneering the "first great scientific revolution in history" by introducing the naturalistic approach to understanding the universe, according to which the universe operates by inviolable laws, without recourse to supernatural explanations. According to Rovelli, Anaximander not only paved the way for modern science, but revolutionized the process for how we form our worldview, by constantly questioning and rejecting certainty. Rovelli further states that Anaximander has not been given his due credit, largely because his naturalistic approach was strongly opposed in antiquity (among others by Aristotle) and had yet to yield the tangible benefits it has today.
Legacy. Bertrand Russell in the "History of Western Philosophy" interprets Anaximander's theories as an assertion of the necessity of an appropriate balance between earth, fire, and water, all of which may be independently seeking to aggrandize their proportions relative to the others. Anaximander seems to express his belief that a natural order ensures balance among these elements, that where there was fire, ashes (earth) now exist. His Greek peers echoed this sentiment with their belief in natural boundaries beyond which not even the gods could operate. Friedrich Nietzsche, in "Philosophy in the Tragic Age of the Greeks", claimed that Anaximander was a pessimist who asserted that the primal being of the world was a state of indefiniteness. In accordance with this, anything definite has to eventually pass back into indefiniteness. In other words, Anaximander viewed "...all coming-to-be as though it were an illegitimate emancipation from eternal being, a wrong for which destruction is the only penance". ("Ibid.", § 4) The world of individual objects, in this way of thinking, has no worth and should perish.
Martin Heidegger lectured extensively on Anaximander, and delivered a lecture entitled "Anaximander's Saying" which was subsequently included in "Off the Beaten Track". The lecture examines the ontological difference and the oblivion of Being or "Dasein" in the context of the Anaximander fragment. Heidegger's lecture is, in turn, an important influence on the French philosopher Jacques Derrida. In the 2017 essay collection "Anaximander in Context: New Studies on the Origins of Greek Philosophy", Dirk Couprie, Robert Hahn and Gerald Naddaf describe Anaximander as "one of the greatest minds in history", but one that has not been given his due. Couprie goes to state that he considers him on par with Newton. Similar sentiments are expressed in Carlo Rovelli's 2011 book "The First Scientist: Anaximander and His Legacy." The Anaximander (31st) High School of Thessaloniki, Greece is named after Anaximander. Works. According to the "Suda":
APL APL is an abbreviation, acronym, or initialism that may refer to:
Architect An architect is a person who plans, designs, and oversees the construction of buildings. To practice architecture means to provide services in connection with the design of buildings and the space within the site surrounding the buildings that have human occupancy or use as their principal purpose. Etymologically, the term architect derives from the Latin , which derives from the Greek ("-", chief + , builder), i.e., chief builder. The professional requirements for architects vary from location to location. An architect's decisions affect public safety, and thus the architect must undergo specialised training consisting of advanced education and a "practicum" (or internship) for practical experience to earn a license to practice architecture. Practical, technical, and academic requirements for becoming an architect vary by jurisdiction though the formal study of architecture in academic institutions has played a pivotal role in the development of the profession. Origins. Throughout ancient and medieval history, most architectural design and construction was carried out by artisans—such as stone masons and carpenters—who rose to the role of master builders. Until modern times, there was no clear distinction between architect and engineer. In Europe, the titles "architect" and "engineer" were primarily geographical variations that referred to the same person, often used interchangeably.
"Architect" derives from Greek (, "master builder," "chief ). It is suggested that various developments in technology and mathematics allowed the development of the professional 'gentleman' architect, separate from the hands-on craftsman. Paper was not used in Europe for drawing until the 15th century but became increasingly available after 1500. Pencils were used for drawing by 1600. The availability of both paper and pencils allowed pre-construction drawings to be made by professionals. Concurrently, the introduction of linear perspective and innovations such as the use of different projections to describe a three-dimensional building in two dimensions, together with an increased understanding of dimensional accuracy, helped building designers communicate their ideas. However, development was gradual and slow-going. Until the 18th century, buildings continued to be designed and set out by craftsmen, with the exception of high-status projects. Architecture. In most developed countries only those qualified with an appropriate license, certification, or registration with a relevant body (often a government) may legally practice architecture. Such licensure usually requires a university degree, successful completion of exams, and a training period. Representation of oneself as an architect through the use of terms and titles were restricted to licensed individuals by law, although in general, derivatives such as architectural designer were not legally protected.
To practice architecture implies the ability to practice independently of supervision. The term "building design professional" (or "design professional)", by contrast, is a much broader term that includes professionals who practice independently under an alternate profession, such as engineering professionals, or those who assist in the practice of architecture under the supervision of a licensed architect, such as intern architects. In many places, independent, non-licensed individuals may perform design services outside of professional restrictions, such as the design of houses or other smaller structures. Practice. In the architectural profession, technical and environmental knowledge, design, and construction management require an understanding of business as well as design. However, design is the driving force throughout the project and beyond. An architect accepts a commission from a client. The commission might involve preparing feasibility reports, building audits, and designing a building or several buildings, structures, and the spaces among them. The architect participates in developing the requirements the client wants in the building. Throughout the project (planning to occupancy), the architect coordinates a design team. Structural, mechanical, and electrical engineers are hired by the client or architect, who must ensure that the work is coordinated to construct the design.
Design role. The architect, once hired by a client, is responsible for creating a design concept that meets the requirements of that client and provides a facility suitable to the required use. The architect must meet with and ask questions to the client, to ascertain all the requirements (and nuances) of the planned project. Often, the full brief is not clear in the beginning. It involves a degree of risk in the design undertaking. The architect may make early proposals to the client which may rework the terms of the brief. The "program" (or brief) is essential to producing a project that meets all the needs of the owner. This becomes a guide for the architect in creating the design concept. Design proposal(s) are generally expected to be both imaginative and pragmatic. Much depends upon the time, place, finance, culture, and available crafts and technology in which the design takes place. The extent and nature of these expectations will vary. Foresight is a prerequisite when designing buildings as it is a very complex and demanding undertaking.

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