TAH_CARTRIDGE FORMAT: text/plain VERSION: 1 GENERATED_AT: 2026-07-08T22:46:29.930Z SLUG: wiki-artificial-intelligence TITLE: Wiki Artificial Intelligence CANONICAL_TITLE: Wiki Artificial Intelligence TYPE: hat DOMAIN: AI & Learning FORMAT: memoria-pair SHARDS: 134 BYTES: 23628 SOURCE: wiki_artificial_intelligence.hat QUERY_SEED: Wiki Artificial Intelligence SUMMARY: ARTICLE: Artificial intelligence Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-m HTML: https://www.sunsetpulse.app/tah/wiki-artificial-intelligence HEADLESS: https://www.sunsetpulse.app/tah/wiki-artificial-intelligence/headless JSON_INDEX: https://www.sunsetpulse.app/tah/index.json QUERY_API: https://www.sunsetpulse.app/api/tah?q=Wiki%20Artificial%20Intelligence&limit=10 META_API: https://www.sunsetpulse.app/api/tah/wiki-artificial-intelligence/meta PREVIEW_SHARDS: [0] SCORE: 63.1664 SOURCE: wiki_artificial_intelligence.hat TEXT: ARTICLE: Artificial intelligence Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in engineering, mathematics and 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. High-profile applications of AI include advanced web search engines, chatbots, virtual assistants, autonomous vehicles, and play and analysis in strategy games (e.g., chess and Go). Since the 2020s, generative AI has become widely available to generate images, audio, and videos from text prompts. The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception, as well as support for robotics. To reach these goals, AI researchers have used techniques including state space 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. Some companies, such as OpenAI, Google DeepMind and Meta, aim to create artificial general intelligence (AGI) – AI that can complete virtually any cognitive task at least as well as a human. Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest increased substantially after 2012, when graphics processing units began being used to accelerate neural networks, and deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture. In the 2020s, an AI boom has coincided with advances in generative AI, which allowed for the creation and modification of media. In addition to AI safety and unintended consequences and harms from the use of AI, ethical concerns, AI's long-term effects, and potential existential risks have prompted discussions of AI regulation. [1] SCORE: 63.1664 SOURCE: wiki_artificial_intelligence.hat TEXT: General intelligence A machine with artificial general intelligence would 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 There are two 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. [2] SCORE: 63.1664 SOURCE: wiki_artificial_intelligence.hat TEXT: 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.