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82-557: Generative artificial intelligence ( generative AI , GenAI , or GAI ) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. These models learn the underlying patterns and structures of their training data and use them to produce new data based on the input, which often comes in the form of natural language prompts . Improvements in transformer -based deep neural networks , particularly large language models (LLMs), enabled an AI boom of generative AI systems in

164-601: A Raspberry Pi 4 and one version of Stable Diffusion can run on an iPhone 11 . Larger models with tens of billions of parameters can run on laptop or desktop computers . To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by NVIDIA and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on

246-581: A loss function . Variants of gradient descent are commonly used to train neural networks. 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 ). Formal logic

328-472: 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. Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require

410-460: 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

492-489: A debate about whether artists should get royalties from audio deepfakes. Many AI music generators have been created that can be generated using a text phrase, genre options, and looped libraries of bars and riffs . Generative AI trained on annotated video can generate temporally-coherent, detailed and photorealistic video clips. Examples include Sora by OpenAI , Gen-1 and Gen-2 by Runway , and Make-A-Video by Meta Platforms. Generative AI can also be trained on

574-839: A desktop PC. The advantages of running generative AI locally include protection of privacy and intellectual property , and avoidance of rate limiting and censorship . The subreddit r/LocalLLaMA in particular focuses on using consumer -grade gaming graphics cards through such techniques as compression . That forum is one of only two sources Andrej Karpathy trusts for language model benchmarks . Yann LeCun has advocated open-source models for their value to vertical applications and for improving AI safety . Language models with hundreds of billions of parameters, such as GPT-4 or PaLM , typically run on datacenter computers equipped with arrays of GPUs (such as NVIDIA's H100 ) or AI accelerator chips (such as Google's TPU ). These very large models are typically accessed as cloud services over

656-463: A downloadable screensaver of AARON for Microsoft Windows PCs . This version of AARON can also produce printable images. AARON's source code is not publicly available, but Cohen has described AARON's operations in various essays and it is discussed in abstract in Pamela McCorduck 's book. AARON cannot learn new styles or imagery on its own; each new capability must be hand-coded by Cohen. It

738-1121: A generative AI system depend on the modality or type of the data set used. Generative AI can be either unimodal or multimodal ; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input. For example, one version of OpenAI 's GPT-4 accepts both text and image inputs. Text generated by Bing Chat , prompted with a question about Carl Jung 's concept of shadow self Generative AI systems trained on words or word tokens include GPT-3 , GPT-4 , GPT-4o , LaMDA , LLaMA , BLOOM , Gemini and others (see List of large language models ). They are capable of natural language processing , machine translation , and natural language generation and can be used as foundation models for other tasks. Data sets include BookCorpus , Misplaced Pages , and others (see List of text corpora ). In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs . Examples include OpenAI Codex . Producing high-quality visual art

820-409: A guideline that generative AI must "adhere to socialist core values". Generative AI systems such as ChatGPT and Midjourney are trained on large, publicly available datasets that include copyrighted works. AI developers have argued that such training is protected under fair use , while copyright holders have argued that it infringes their rights. Proponents of fair use training have argued that it

902-438: A more equitable society, proactive steps encompass mitigating biases, advocating transparency, respecting privacy and consent, and embracing diverse teams and ethical considerations. Strategies involve redirecting policy emphasis on regulation, inclusive design, and education's potential for personalized teaching to maximize benefits while minimizing harms. Generative AI models can reflect and amplify any cultural bias present in

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984-429: 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

1066-474: A person in an existing image or video and replace them with someone else's likeness using artificial neural networks . Deepfakes have garnered widespread attention and concerns for their uses in deepfake celebrity pornographic videos , revenge porn , fake news , hoaxes , health disinformation , financial fraud , and covert foreign election interference . This has elicited responses from both industry and government to detect and limit their use. In July 2023,

1148-423: A probabilistic text generator. The academic discipline of artificial intelligence was established at a research workshop held at Dartmouth College in 1956 and has experienced several waves of advancement and optimism in the decades since. Artificial Intelligence research began in the 1950s with works like Computing Machinery and Intelligence (1950) and the 1956 Dartmouth Summer Research Project on AI . Since

1230-510: A specified goal. Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use, process plans for manufacturing and decision plans such as in prototype autonomous spacecraft. Since its inception, the field of machine learning used both discriminative models and generative models , to model and predict data. Beginning in

1312-406: A succession of custom-built "turtle" and flatbed plotter devices. Cohen would sometimes color these images by hand in fabric dye ( Procion ), or scale them up to make larger paintings and murals . In the 1990s Cohen built a series of digital painting machines to output AARON's images in ink and fabric dye. His later work used a large-scale inkjet printer on canvas. Development of AARON began in

1394-721: 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 ). 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

1476-701: A toy dinosaur when given the prompt pick up the extinct animal at a table filled with toy animals and other objects. Artificially intelligent computer-aided design (CAD) can use text-to-3D, image-to-3D, and video-to-3D to automate 3D modeling . AI-based CAD libraries could also be developed using linked open data of schematics and diagrams . AI CAD assistants are used as tools to help streamline workflow. Generative AI models are used to power chatbot products such as ChatGPT , programming tools such as GitHub Copilot , text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2. Generative AI features have been integrated into

1558-599: A variety of existing commercially available products such as Microsoft Office ( Microsoft Copilot ), Google Photos , and the Adobe Suite ( Adobe Firefly ). Many generative AI models are also available as open-source software , including Stable Diffusion and the LLaMA language model. Smaller generative AI models with up to a few billion parameters can run on smartphones , embedded devices, and personal computers . For example, LLaMA-7B (a version with 7 billion parameters) can run on

1640-558: A wide range of industries, including software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, fashion, and product design. However, concerns have been raised about the potential misuse of generative AI such as cybercrime , the use of fake news or deepfakes to deceive or manipulate people, and the mass replacement of human jobs . Intellectual property law concerns also exist around generative models that are trained on and emulate copyrighted works of art. Since its inception, researchers in

1722-669: A wide range of techniques, including search and mathematical optimization , formal logic , artificial neural networks , and methods based on statistics , operations research , and economics . AI also draws upon psychology , linguistics , philosophy , neuroscience , and other fields. Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism, followed by periods of disappointment and loss of funding, known as AI winter . Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with

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1804-487: A wide variety of techniques to accomplish the goals above. 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 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

1886-1060: Is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs. Some high-profile applications of AI include advanced web search engines (e.g., Google Search ); recommendation systems (used by YouTube , Amazon , and Netflix ); interacting via human speech (e.g., Google Assistant , Siri , and Alexa ); autonomous vehicles (e.g., Waymo ); generative and creative tools (e.g., ChatGPT , and AI art ); and superhuman play and analysis in strategy games (e.g., chess and Go ). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore ." The various subfields of AI research are centered around particular goals and

1968-453: Is a transformative use and does not involve making copies of copyrighted works available to the public. Critics have argued that image generators such as Midjourney can create nearly-identical copies of some copyrighted images, and that generative AI programs compete with the content they are trained on. As of 2024, several lawsuits related to the use of copyrighted material in training are ongoing. Getty Images has sued Stability AI over

2050-641: Is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects; situations, events, states, and time; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge. Among

2132-785: Is a prominent application of generative AI. Generative AI systems trained on sets of images with text captions include Imagen , DALL-E , Midjourney , Adobe Firefly , FLUX.1 , Stable Diffusion and others (see Artificial intelligence art , Generative art , and Synthetic media ). They are commonly used for text-to-image generation and neural style transfer . Datasets include LAION-5B and others (see List of datasets in computer vision and image processing ). Generative AI can also be trained extensively on audio clips to produce natural-sounding speech synthesis and text-to-speech capabilities, exemplified by ElevenLabs ' context-aware synthesis tools or Meta Platform 's Voicebox. Generative AI systems such as MusicLM and MusicGen can also be trained on

2214-459: 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

2296-462: Is an interdisciplinary umbrella 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

2378-444: Is an unsolved problem. Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases ), and other areas. A knowledge base

2460-422: Is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning , the agent has a specific goal. In automated decision-making , the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called

2542-458: Is capable of producing a practically infinite supply of distinct images in its own style. Examples of these images have been exhibited in galleries worldwide. AARON's artwork has been used as an artistic equivalent of the Turing test . It does seem however that AARON's output follows a noticeable formula (figures standing next to a potted plant, framed within a colored square is a common theme). Cohen

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2624-413: 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

2706-413: 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

2788-405: Is not open source, its development effectively ended with Cohen's death in 2016. The name "AARON" does not seem to be an acronym; rather, it was a name chosen to start with the letter "A" so that the names of successive programs could follow it alphabetically. However, Cohen did not create any other major programs. Initial versions of AARON created abstract drawings that grew more complex through

2870-400: Is reportedly the "most widely used learner" at Google, due in part to its scalability. Neural networks are also used as classifiers. 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

2952-410: 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. AARON Proceeding from Cohen's initial question "What are the minimum conditions under which a set of marks functions as an image?", AARON was in development between 1972 and the 2010s. As the software

3034-404: 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

3116-440: Is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position. 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

3198-455: 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

3280-436: Is used in AI programs that make decisions that involve other agents. 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

3362-905: 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 (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

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3444-449: The 2023 SAG-AFTRA strike . Voice generation AI has been seen as a potential challenge to the voice acting sector. The intersection of AI and employment concerns among underrepresented groups globally remains a critical facet. While AI promises efficiency enhancements and skill acquisition, concerns about job displacement and biased recruiting processes persist among these groups, as outlined in surveys by Fast Company . To leverage AI for

3526-574: The C programming language then switched to Lisp in the early 1990s. Cohen credits Lisp with helping him solve the challenges he faced in adding color capabilities to AARON. An article about Cohen appeared in Computer Answers that describes AARON and shows two line drawings that were exhibited at the Tate gallery . The article goes on to describe the workings of AARON, then running on a DEC VAX 750 minicomputer. Raymond Kurzweil 's company has produced

3608-520: The bar exam , SAT test, GRE test, and many other real-world applications. 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 . Affective computing

3690-426: The supervised learning typical of discriminative models. Unsupervised learning removed the need for humans to manually label data , allowing for larger networks to be trained. In 2021, the release of DALL-E , a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts. In 2022,

3772-416: The transformer architecture , and by the early 2020s hundreds of billions of dollars were being invested in AI (known as the " AI boom "). The widespread use of AI in the 21st century exposed several unintended consequences and harms in the present and raised concerns about its risks and long-term effects in the future, prompting discussions about regulatory policies to ensure the safety and benefits of

3854-436: The " utility ") that measures how much the agent prefers it. For each possible action, it can calculate the " expected utility ": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility. In classical planning , the agent knows exactly what the effect of any action will be. In most real-world problems, however,

3936-474: The 1950s, artists and researchers have used artificial intelligence to create artistic works. By the early 1970s, Harold Cohen was creating and exhibiting generative AI works created by AARON , the computer program Cohen created to generate paintings. The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning , used to generate sequences of actions to reach

4018-405: The 1970s. More representational imagery was added in the 1980s; first rocks, then plants, then people. In the 1990s more representational figures set in interior scenes were added, along with color. AARON returned to more abstract imagery, this time in color, in the early 2000s. Cohen used machines that allowed AARON to produce physical artwork. The first machines drew in black and white using

4100-1133: The Internet. In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI. Chips such as the NVIDIA A800 and the Biren Technology BR104 were developed to meet the requirements of the sanctions. There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero ), as well as images, audio or video coming from it. Potential mitigation strategies for detecting generative AI content include digital watermarking , content authentication , information retrieval , and machine learning classifier models . Despite claims of accuracy, both free and paid AI text detectors have frequently produced false positives, mistakenly accusing students of submitting AI-generated work. In

4182-680: The United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden administration in July 2023 to watermark AI-generated content. In October 2023, Executive Order 14110 applied the Defense Production Act to require all US companies to report information to the federal government when training certain high-impact AI models. In the European Union,

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4264-421: 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

4346-510: 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

4428-529: 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

4510-451: The audio waveforms of recorded music along with text annotations, in order to generate new musical samples based on text descriptions such as a calming violin melody backed by a distorted guitar riff . Audio deepfakes of lyrics have been generated, like the song Savages, which used AI to mimic rapper Jay-Z 's vocals. Music artist's instrumentals and lyrics are copyrighted but their voices aren't protected from regenerative AI yet, raising

4592-420: The benchmark of ‘general human intelligence’" as of 2023. In 2023, Meta released an AI model called ImageBind which combines data from text, images, video, thermal data, 3D data, audio, and motion which is expected to allow for more immersive generative AI content. According to a survey by SAS and Coleman Parkes Research, China is leading the world in adopting generative AI, with 83% of Chinese respondents using

4674-648: 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

4756-623: The difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements. In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost. In July 2023, developments in generative AI contributed to the 2023 Hollywood labor disputes . Fran Drescher , president of the Screen Actors Guild , declared that "artificial intelligence poses an existential threat to creative professions" during

4838-402: The early 1800s. Markov chains have long been used to model natural languages since their development by Russian mathematician Andrey Markov in the early 20th century. Markov published his first paper on the topic in 1906, and analyzed the pattern of vowels and consonants in the novel Eugeny Onegin using Markov chains. Once a Markov chain is learned on a text corpus , it can then be used as

4920-446: The early 2020s. These include chatbots such as ChatGPT , Copilot , Gemini , and LLaMA ; text-to-image artificial intelligence image generation systems such as Stable Diffusion , Midjourney , and DALL-E ; and text-to-video AI generators such as Sora . Companies such as OpenAI , Anthropic , Microsoft , Google , and Baidu as well as numerous smaller firms have developed generative AI models. Generative AI has uses across

5002-596: The fact-checking company Logically found that the popular generative AI models Midjourney , DALL-E 2 and Stable Diffusion would produce plausible disinformation images when prompted to do so, such as images of electoral fraud in the United States and Muslim women supporting India's Hindu nationalist Bharatiya Janata Party . In April 2024, a paper proposed to use blockchain ( distributed ledger technology) to promote "transparency, verifiability, and decentralization in AI development and usage". Instances of users abusing software to generate controversial statements in

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5084-661: The field have raised philosophical and ethical arguments about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth , fiction and philosophy since antiquity. The concept of automated art dates back at least to the automata of ancient Greek civilization , where inventors such as Daedalus and Hero of Alexandria were described as having designed machines capable of writing text, generating sounds, and playing music. The tradition of creative automations has flourished throughout history, exemplified by Maillardet's automaton created in

5166-403: The first generative pre-trained transformer (GPT), known as GPT-1 , in 2018. This was followed in 2019 by GPT-2 which demonstrated the ability to generalize unsupervised to many different tasks as a Foundation model . The new generative models introduced during this period allowed for large neural networks to be trained using unsupervised learning or semi-supervised learning , rather than

5248-452: The first practical deep neural networks capable of learning generative models, as opposed to discriminative ones, for complex data such as images. These deep generative models were the first to output not only class labels for images but also entire images. In 2017, the Transformer network enabled advancements in generative models compared to older Long-Short Term Memory models, leading to

5330-410: The global economy by 2030, but that its malicious use "could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale". From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given

5412-440: 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 affects displayed by a videotaped subject. A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence . AI research uses

5494-537: The late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics . Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Accurate and efficient reasoning

5576-407: The late 2000s, the emergence of deep learning drove progress and research in image classification , speech recognition , natural language processing and other tasks. Neural networks in this era were typically trained as discriminative models, due to the difficulty of generative modeling. In 2014, advancements such as the variational autoencoder and generative adversarial network produced

5658-457: The most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition , the problem of obtaining knowledge for AI applications. An "agent"

5740-419: The motions of a robotic system to generate new trajectories for motion planning or navigation . For example, UniPi from Google Research uses prompts like "pick up blue bowl" or "wipe plate with yellow sponge" to control movements of a robot arm. Multimodal "vision-language-action" models such as Google's RT-2 can perform rudimentary reasoning in response to user prompts and visual input, such as picking up

5822-665: The office has also begun taking public input to determine if these rules need to be refined for generative AI. The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments , and actions by multiple governments. In a July 2023 briefing of the United Nations Security Council , Secretary-General António Guterres stated "Generative AI has enormous potential for good and evil at scale", that AI may "turbocharge global development" and contribute between $ 10 and $ 15 trillion to

5904-405: 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

5986-411: 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

6068-696: The proposed Artificial Intelligence Act includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such. In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and

6150-430: The public release of ChatGPT popularized the use of generative AI for general-purpose text-based tasks. In March 2023, GPT-4 was released. A team from Microsoft Research argued that "it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system". Other scholars have disputed that GPT-4 reaches this threshold, calling generative AI "still far from reaching

6232-471: The technology . The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions . By

6314-430: The technology, surpassing the global average of 54% and the U.S. at 65%. A UN report revealed China filed over 38,000 GenAI patents from 2014 to 2023, far exceeding the U.S. A generative AI system is constructed by applying unsupervised machine learning (invoking for instance neural network architectures such as GANs , VAE , Transformer , ...) or self-supervised machine learning to a data set. The capabilities of

6396-451: 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

6478-576: The underlying data. For example, a language model might assume that doctors and judges are male, and that secretaries or nurses are female, if those biases are common in the training data. Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts and reweighting training data. Deepfakes (a portmanteau of "deep learning" and "fake") are AI-generated media that take

6560-610: The use of its images to train Stable diffusion . Both the Authors Guild and The New York Times have sued Microsoft and OpenAI over the use of their works to train ChatGPT . A separate question is whether AI-generated works can qualify for copyright protection. The United States Copyright Office has ruled that works created by artificial intelligence without any human input cannot be copyrighted, because they lack human authorship. However,

6642-420: The use of particular tools. The traditional goals of AI research include reasoning , knowledge representation , planning , learning , natural language processing , perception, and support for robotics . General intelligence —the ability to complete any task performable by a human on an at least equal level—is among the field's long-term goals. To reach these goals, AI researchers have adapted and integrated

6724-458: The vocal style of celebrities, public officials, and other famous individuals have raised ethical concerns over voice generation AI. In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse through safeguards and identity verification . Artificial intelligence Artificial intelligence ( AI ), in its broadest sense, is intelligence exhibited by machines , particularly computer systems . It

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