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Artificial intelligence - Wikipedia

# Artificial intelligence Artificial intelligence Intelligence in machines "AI" redirects here. For other uses, see [AI (disambiguation)](https://en.wikipedia.org/wiki/AI_(disambiguation)) and [Artificial intelligence (disambiguation)](https://en.wikipedia.org/wiki/Artificial_intelligence_(disambiguation)). **Artificial intelligence** ( **AI**) is the capability of [computational systems](https://en.wikipedia.org/wiki/Computer) to perform tasks typically associated with [human intelligence](https://en.wikipedia.org/wiki/Human_intelligence), such as [learning](https://en.wikipedia.org/wiki/Learning), [reasoning](https://en.wikipedia.org/wiki/Reason), [problem-solving](https://en.wikipedia.org/wiki/Problem-solving), [perception](https://en.wikipedia.org/wiki/Perception), and [decision-making](https://en.wikipedia.org/wiki/Decision-making). It is a field of research in [engineering](https://en.wikipedia.org/wiki/Engineering), [mathematics](https://en.wikipedia.org/wiki/Mathematics), and [computer science](https://en.wikipedia.org/wiki/Computer_science) that develops and studies methods and [software](https://en.wikipedia.org/wiki/Software) that enable machines to perceive their environment and use [learning](https://en.wikipedia.org/wiki/Machine_learning) and [intelligence](https://en.wikipedia.org/wiki/Intelligence) to take actions that maximise their chances of achieving defined goals. [[1]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-FOOTNOTERussellNorvig20211%E2%80%934-1) High-profile [applications of AI](https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence) include advanced [web search engines](https://en.wikipedia.org/wiki/Web_search_engine), [chatbots](https://en.wikipedia.org/wiki/Chatbot), [virtual assistants](https://en.wikipedia.org/wiki/Virtual_assistant), [autonomous vehicles](https://en.wikipedia.org/wiki/Autonomous_vehicles), play and analysis in [strategy games](https://en.wikipedia.org/wiki/Strategy_game) (e.g., [chess](https://en.wikipedia.org/wiki/Chess) and [Go](https://en.wikipedia.org/wiki/Go_(game))), and [content generation](https://en.wikipedia.org/wiki/Generative_AI) (e.g., text, images, audio, and videos). The traditional goals of AI research include learning, [reasoning](https://en.wikipedia.org/wiki/Automated_reasoning), [knowledge representation](https://en.wikipedia.org/wiki/Knowledge_representation), [planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling), [natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing), and [perception](https://en.wikipedia.org/wiki/Machine_perception), as well as support for [robotics](https://en.wikipedia.org/wiki/Robotics). [[a]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Problems_of_AI-2) To reach these goals, AI researchers use techniques including [state space search](https://en.wikipedia.org/wiki/State_space_search) and [mathematical optimisation](https://en.wikipedia.org/wiki/Mathematical_optimization), [formal logic](https://en.wikipedia.org/wiki/Formal_logic), [artificial neural networks](https://en.wikipedia.org/wiki/Artificial_neural_network), and methods based on [statistics](https://en.wikipedia.org/wiki/Statistics), [operations research](https://en.wikipedia.org/wiki/Operations_research), and [economics](https://en.wikipedia.org/wiki/Economics). [[b]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Tools_of_AI-3) AI also draws upon [psychology](https://en.wikipedia.org/wiki/Psychology), [linguistics](https://en.wikipedia.org/wiki/Linguistics), [philosophy](https://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence), [neuroscience](https://en.wikipedia.org/wiki/Neuroscience), and other fields. [[2]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-4) Some companies, such as [OpenAI](https://en.wikipedia.org/wiki/OpenAI), [Google DeepMind](https://en.wikipedia.org/wiki/Google_DeepMind), and [Meta](https://en.wikipedia.org/wiki/Meta_Platforms), aim to create [artificial general intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence) (AGI)—AI that can complete nearly any [cognitive](https://en.wikipedia.org/wiki/Cognition) task at least as well as a human. [[3]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-5) Artificial intelligence was founded as an [academic discipline](https://en.wikipedia.org/wiki/Academic_discipline) in 1956. [[4]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Dartmouth_workshop-6) The field went through multiple cycles of optimism throughout [its history](https://en.wikipedia.org/wiki/History_of_artificial_intelligence),[[5]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Succ1-7)[[6]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Fund01-8) followed by periods of disappointment and loss of funding, known as [AI winters](https://en.wikipedia.org/wiki/AI_winter). [[7]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-First_AI_Winter-9)[[8]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Second_AI_Winter-10) Funding and interest increased substantially after 2012, when [graphics processing units](https://en.wikipedia.org/wiki/Graphics_processing_unit) (GPUs) started being used to accelerate neural networks, and [deep learning](https://en.wikipedia.org/wiki/Deep_learning) outperformed previous AI techniques. [[9]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Deep_learning_revolution-11) This growth accelerated further after 2017 with the [transformer architecture](https://en.wikipedia.org/wiki/Transformer_architecture). [[10]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-FOOTNOTEToews2023-12) In the 2020s, an [AI boom](https://en.wikipedia.org/wiki/AI_boom) coincided with advances in [generative AI](https://en.wikipedia.org/wiki/Generative_AI), which became widespread and allowed for the creation and modification of media. In addition to [AI safety](https://en.wikipedia.org/wiki/AI_safety) and [unintended consequences and harms](https://en.wikipedia.org/wiki/Generative_AI#Concerns) from the use of AI, [ethical concerns](https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence), [AI's long-term effects](https://en.wikipedia.org/wiki/AI_aftermath_scenarios), [environmental effects](https://en.wikipedia.org/wiki/Environmental_impacts_of_artificial_intelligence),[[11]](https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-UN-13) and [potential existential risks](https://en.wikipedia.org/wiki/Existential_risk_from_artificial_intelligence) have prompted discussions of [AI regulation](https://en.wikipedia.org/wiki/Regulation_of_artificial_intelligence). ## External links | [Artificial intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) (AI) | |-| | - [History](https://en.wikipedia.org/wiki/History_of_artificial_intelligence) <br> - [timeline](https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence)<br>- [Glossary](https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence)<br>- Lists<br> - [Algorithms](https://en.wikipedia.org/wiki/List_of_artificial_intelligence_algorithms)<br> - [Companies](https://en.wikipedia.org/wiki/List_of_artificial_intelligence_companies)<br> - [Institutions](https://en.wikipedia.org/wiki/List_of_artificial_intelligence_institutions)<br> - [Projects](https://en.wikipedia.org/wiki/List_of_artificial_intelligence_projects)<br> - Software<br> - [Open-source](https://en.wikipedia.org/wiki/Lists_of_open-source_artificial_intelligence_software)<br> - [Proprietary](https://en.wikipedia.org/wiki/List_of_proprietary_artificial_intelligence_software) | | Concepts | - [Automated reasoning](https://en.wikipedia.org/wiki/Automated_reasoning)<br>- [Automated planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling)<br>- [Constraint satisfaction](https://en.wikipedia.org/wiki/Constraint_satisfaction_problem)<br>- [Knowledge representation](https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning)<br>- [Parameter](https://en.wikipedia.org/wiki/Parameter) <br> - [Hyperparameter](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning))<br>- [Loss functions](https://en.wikipedia.org/wiki/Loss_functions_for_classification)<br>- [Regression](https://en.wikipedia.org/wiki/Regression_analysis) <br> - [Bias–variance tradeoff](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff)<br> - [Double descent](https://en.wikipedia.org/wiki/Double_descent)<br> - [Overfitting](https://en.wikipedia.org/wiki/Overfitting)<br>- [Clustering](https://en.wikipedia.org/wiki/Cluster_analysis)<br>- [Gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) <br> - [SGD](https://en.wikipedia.org/wiki/Stochastic_gradient_descent)<br> - [Quasi-Newton method](https://en.wikipedia.org/wiki/Quasi-Newton_method)<br> - [Conjugate gradient method](https://en.wikipedia.org/wiki/Conjugate_gradient_method)<br>- [Backpropagation](https://en.wikipedia.org/wiki/Backpropagation)<br>- [Attention](https://en.wikipedia.org/wiki/Attention_(machine_learning))<br>- [Convolution](https://en.wikipedia.org/wiki/Convolution)<br>- [Normalization](https://en.wikipedia.org/wiki/Normalization_(machine_learning)) <br> - [Batchnorm](https://en.wikipedia.org/wiki/Batch_normalization)<br>- [Activation](https://en.wikipedia.org/wiki/Activation_function) <br> - [Softmax](https://en.wikipedia.org/wiki/Softmax_function)<br> - [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function)<br> - [Rectifier](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))<br>- [Gating](https://en.wikipedia.org/wiki/Gating_mechanism)<br>- [Weight initialization](https://en.wikipedia.org/wiki/Weight_initialization)<br>- [Regularization](https://en.wikipedia.org/wiki/Regularization_(mathematics))<br>- [Datasets](https://en.wikipedia.org/wiki/Training,_validation,_and_test_data_sets) <br> - [Augmentation](https://en.wikipedia.org/wiki/Data_augmentation)<br>- [Prompt engineering](https://en.wikipedia.org/wiki/Prompt_engineering)<br>- [Reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning) <br> - [Q-learning](https://en.wikipedia.org/wiki/Q-learning)<br> - [SARSA](https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action)<br> - [Imitation](https://en.wikipedia.org/wiki/Imitation_learning)<br> - [Policy gradient](https://en.wikipedia.org/wiki/Policy_gradient_method)<br>- [Diffusion](https://en.wikipedia.org/wiki/Diffusion_process)<br>- [Latent diffusion model](https://en.wikipedia.org/wiki/Latent_diffusion_model)<br>- [Autoregression](https://en.wikipedia.org/wiki/Autoregressive_model)<br>- [Adversary](https://en.wikipedia.org/wiki/Adversarial_machine_learning)<br>- [RAG](https://en.wikipedia.org/wiki/Retrieval-augmented_generation)<br>- [Uncanny valley](https://en.wikipedia.org/wiki/Uncanny_valley)<br>- [LLM post-training](https://en.wikipedia.org/wiki/Post-training_of_large_language_models)<br>- [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback)<br>- [Self-supervised learning](https://en.wikipedia.org/wiki/Self-supervised_learning)<br>- [Reflection](https://en.wikipedia.org/wiki/Reflection_(artificial_intelligence))<br>- [Recursive self-improvement](https://en.wikipedia.org/wiki/Recursive_self-improvement)<br>- [Hallucination](https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence))<br>- [Word embedding](https://en.wikipedia.org/wiki/Word_embedding)<br>- [Vibe coding](https://en.wikipedia.org/wiki/Vibe_coding)<br>- [Blended AI](https://en.wikipedia.org/wiki/Blended_artificial_intelligence)<br>- [Open-source AI](https://en.wikipedia.org/wiki/Open-source_artificial_intelligence)<br>- [Sovereign AI](https://en.wikipedia.org/wiki/Sovereign_AI)<br>- [Symbolic AI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)<br>- [Neuro-symbolic AI](https://en.wikipedia.org/wiki/Neuro-symbolic_AI)<br>- [Situated approach](https://en.wikipedia.org/wiki/Situated_approach_(artificial_intelligence))<br>- [Actor-critic algorithm](https://en.wikipedia.org/wiki/Actor-critic_algorithm) | | [Applications](https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence) | - [Automated theorem proving](https://en.wikipedia.org/wiki/Automated_theorem_proving)<br>- [General game playing](https://en.wikipedia.org/wiki/General_game_playing)<br>- [Machine learning](https://en.wikipedia.org/wiki/Machine_learning) <br> - [In-context learning](https://en.wikipedia.org/wiki/Prompt_engineering#In-context_learning)<br>- [Artificial neural network](https://en.wikipedia.org/wiki/Neural_network_(machine_learning)) <br> - [Deep learning](https://en.wikipedia.org/wiki/Deep_learning)<br>- [Language model](https://en.wikipedia.org/wiki/Language_model) <br> - [Large](https://en.wikipedia.org/wiki/Large_language_model)<br> - [NMT](https://en.wikipedia.org/wiki/Neural_machine_translation)<br> - [Reasoning](https://en.wikipedia.org/wiki/Reasoning_model)<br>- [Model Context Protocol](https://en.wikipedia.org/wiki/Model_Context_Protocol)<br>- [Intelligent agent](https://en.wikipedia.org/wiki/Intelligent_agent) <br> - [AI agent](https://en.wikipedia.org/wiki/AI_agent)<br>- [Artificial human companion](https://en.wikipedia.org/wiki/Artificial_human_companion)<br>- [Humanity's Last Exam](https://en.wikipedia.org/wiki/Humanity's_Last_Exam)<br>- [Lethal autonomous weapons (LAWs)](https://en.wikipedia.org/wiki/Lethal_autonomous_weapon)<br>- [Generative AI](https://en.wikipedia.org/wiki/Generative_AI)<br>- [Weak AI](https://en.wikipedia.org/wiki/Weak_artificial_intelligence)<br>- Hypothetical<br> - [Artificial general intelligence (AGI)](https://en.wikipedia.org/wiki/Artificial_general_intelligence)<br> - [Artificial superintelligence (ASI)](https://en.wikipedia.org/wiki/Superintelligence)<br>- [Agent2Agent protocol](https://en.wikipedia.org/wiki/Agent2Agent)<br>- [Physical AI](https://en.wikipedia.org/wiki/Physical_AI) | | Implementations | | Audio–visual | - [AlexNet](https://en.wikipedia.org/wiki/AlexNet)<br>- [WaveNet](https://en.wikipedia.org/wiki/WaveNet)<br>- [Human image synthesis](https://en.wikipedia.org/wiki/Human_image_synthesis)<br>- [HWR](https://en.wikipedia.org/wiki/Handwriting_recognition)<br>- [OCR](https://en.wikipedia.org/wiki/Optical_character_recognition)<br>- [Computer vision](https://en.wikipedia.org/wiki/Computer_vision)<br>- [Speech synthesis](https://en.wikipedia.org/wiki/Deep_learning_speech_synthesis) <br> - [15.ai](https://en.wikipedia.org/wiki/15.ai)<br> - [ElevenLabs](https://en.wikipedia.org/wiki/ElevenLabs)<br>- [Speech recognition](https://en.wikipedia.org/wiki/Speech_recognition) <br> - [Whisper](https://en.wikipedia.org/wiki/Whisper_(speech_recognition_system))<br>- [Facial recognition](https://en.wikipedia.org/wiki/Facial_recognition_system)<br>- [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold)<br>- [Text-to-image models](https://en.wikipedia.org/wiki/Text-to-image_model) <br> - [Aurora](https://en.wikipedia.org/wiki/Aurora_(text-to-image_model))<br> - [DALL-E](https://en.wikipedia.org/wiki/DALL-E)<br> - [Firefly](https://en.wikipedia.org/wiki/Adobe_Firefly)<br> - [Flux](https://en.wikipedia.org/wiki/Flux_(text-to-image_model))<br> - [GPT Image](https://en.wikipedia.org/wiki/GPT_Image)<br> - [Ideogram](https://en.wikipedia.org/wiki/Ideogram_(text-to-image_model))<br> - [Imagen](https://en.wikipedia.org/wiki/Imagen_(text-to-image_model))<br> - [Midjourney](https://en.wikipedia.org/wiki/Midjourney)<br> - [Recraft](https://en.wikipedia.org/wiki/Recraft)<br> - [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion)<br>- [Text-to-video models](https://en.wikipedia.org/wiki/Text-to-video_model) <br> - [Dream Machine](https://en.wikipedia.org/wiki/Dream_Machine_(text-to-video_model))<br> - [Runway Gen](https://en.wikipedia.org/wiki/Runway_(company)#Services_and_technologies)<br> - [Hailuo AI](https://en.wikipedia.org/wiki/MiniMax_(company)#Hailuo_AI)<br> - [Kling](https://en.wikipedia.org/wiki/Kling_AI)<br> - [Sora](https://en.wikipedia.org/wiki/Sora_(text-to-video_model))<br> - [Seedance](https://en.wikipedia.org/wiki/Seedance)<br> - [Veo](https://en.wikipedia.org/wiki/Veo_(text-to-video_model))<br>- [Music generation](https://en.wikipedia.org/wiki/Artificial_intelligence_in_music) <br> - [Riffusion](https://en.wikipedia.org/wiki/Riffusion)<br> - [Suno](https://en.wikipedia.org/wiki/Suno_(platform))<br> - [Udio](https://en.wikipedia.org/wiki/Udio)<br>- [World models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)) <br> - [Genie](https://en.wikipedia.org/wiki/Genie_(world_model))<br> - [_Oasis_](https://en.wikipedia.org/wiki/Oasis_(Minecraft_clone)) | | Text | - [List of large language models](https://en.wikipedia.org/wiki/List_of_large_language_models)<br>- [Project Debater](https://en.wikipedia.org/wiki/Project_Debater)<br>- [IBM Watson](https://en.wikipedia.org/wiki/IBM_Watson) <br> - [IBM Watsonx](https://en.wikipedia.org/wiki/IBM_Watsonx) | | Decisional | - [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo)<br>- [AlphaZero](https://en.wikipedia.org/wiki/AlphaZero)<br>- [OpenAI Five](https://en.wikipedia.org/wiki/OpenAI_Five)<br>- [Self-driving car](https://en.wikipedia.org/wiki/Self-driving_car)<br>- [MuZero](https://en.wikipedia.org/wiki/MuZero)<br>- [Action selection](https://en.wikipedia.org/wiki/Action_selection) <br> - [AutoGPT](https://en.wikipedia.org/wiki/AutoGPT)<br>- [Robot control](https://en.wikipedia.org/wiki/Robot_control) | | [Reasoning systems](https://en.wikipedia.org/wiki/Reasoning_system) | - [Deductive classifiers](https://en.wikipedia.org/wiki/Deductive_classifier)<br>- [Expert systems](https://en.wikipedia.org/wiki/Expert_system)<br>- [Inference engines](https://en.wikipedia.org/wiki/Inference_engine)<br>- [Knowledge-based systems](https://en.wikipedia.org/wiki/Knowledge-based_system)<br>- [Logic programs](https://en.wikipedia.org/wiki/Logic_program)<br>- [Procedural reasoning systems](https://en.wikipedia.org/wiki/Procedural_reasoning_system)<br>- [Semantic reasoners](https://en.wikipedia.org/wiki/Semantic_reasoner)<br>- [Rule-based systems](https://en.wikipedia.org/wiki/Rule-based_system) | | [Cognitive architectures](https://en.wikipedia.org/wiki/Cognitive_architecture) | - [ACT-R](https://en.wikipedia.org/wiki/ACT-R)<br>- [Soar](https://en.wikipedia.org/wiki/Soar_(cognitive_architecture))<br>- [CLARION](https://en.wikipedia.org/wiki/CLARION_(cognitive_architecture))<br>- [LIDA](https://en.wikipedia.org/wiki/LIDA_(cognitive_architecture))<br>- [OpenCog](https://en.wikipedia.org/wiki/OpenCog) | | [Knowledge bases](https://en.wikipedia.org/wiki/Knowledge_base) | - [ConceptNet](https://en.wikipedia.org/wiki/ConceptNet)<br>- [Wikidata](https://en.wikipedia.org/wiki/Wikidata)<br>- [DBpedia](https://en.wikipedia.org/wiki/DBpedia)<br>- [YAGO](https://en.wikipedia.org/wiki/YAGO_(database)) | | | People | - [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing)<br>- [Warren Sturgis McCulloch](https://en.wikipedia.org/wiki/Warren_Sturgis_McCulloch)<br>- [Walter Pitts](https://en.wikipedia.org/wiki/Walter_Pitts)<br>- [John von Neumann](https://en.wikipedia.org/wiki/John_von_Neumann)<br>- [Christopher D. Manning](https://en.wikipedia.org/wiki/Christopher_D._Manning)<br>- [Claude Shannon](https://en.wikipedia.org/wiki/Claude_Shannon)<br>- [Shun'ichi Amari](https://en.wikipedia.org/wiki/Shun'ichi_Amari)<br>- [Kunihiko Fukushima](https://en.wikipedia.org/wiki/Kunihiko_Fukushima)<br>- [Takeo Kanade](https://en.wikipedia.org/wiki/Takeo_Kanade)<br>- [Marvin Minsky](https://en.wikipedia.org/wiki/Marvin_Minsky)<br>- [John McCarthy](https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist))<br>- [Nathaniel Rochester](https://en.wikipedia.org/wiki/Nathaniel_Rochester_(computer_scientist))<br>- [Allen Newell](https://en.wikipedia.org/wiki/Allen_Newell)<br>- [Cliff Shaw](https://en.wikipedia.org/wiki/Cliff_Shaw)<br>- [Herbert A. Simon](https://en.wikipedia.org/wiki/Herbert_A._Simon)<br>- [Oliver Selfridge](https://en.wikipedia.org/wiki/Oliver_Selfridge)<br>- [Frank Rosenblatt](https://en.wikipedia.org/wiki/Frank_Rosenblatt)<br>- [Bernard Widrow](https://en.wikipedia.org/wiki/Bernard_Widrow)<br>- [Joseph Weizenbaum](https://en.wikipedia.org/wiki/Joseph_Weizenbaum)<br>- [Seymour Papert](https://en.wikipedia.org/wiki/Seymour_Papert)<br>- [Seppo Linnainmaa](https://en.wikipedia.org/wiki/Seppo_Linnainmaa)<br>- [Paul Werbos](https://en.wikipedia.org/wiki/Paul_Werbos)<br>- [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton)<br>- [John Hopfield](https://en.wikipedia.org/wiki/John_Hopfield)<br>- [Jürgen Schmidhuber](https://en.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber)<br>- [Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun)<br>- [Yoshua Bengio](https://en.wikipedia.org/wiki/Yoshua_Bengio)<br>- [Lotfi A. Zadeh](https://en.wikipedia.org/wiki/Lotfi_A._Zadeh)<br>- [Stephen Grossberg](https://en.wikipedia.org/wiki/Stephen_Grossberg)<br>- [Alex Graves](https://en.wikipedia.org/wiki/Alex_Graves_(computer_scientist))<br>- [James Goodnight](https://en.wikipedia.org/wiki/James_Goodnight)<br>- [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)<br>- [Fei-Fei Li](https://en.wikipedia.org/wiki/Fei-Fei_Li)<br>- [Alex Krizhevsky](https://en.wikipedia.org/wiki/Alex_Krizhevsky)<br>- [Ilya Sutskever](https://en.wikipedia.org/wiki/Ilya_Sutskever)<br>- [Oriol Vinyals](https://en.wikipedia.org/wiki/Oriol_Vinyals)<br>- [Quoc V. Le](https://en.wikipedia.org/wiki/Quoc_V._Le)<br>- [Ian Goodfellow](https://en.wikipedia.org/wiki/Ian_Goodfellow)<br>- [Demis Hassabis](https://en.wikipedia.org/wiki/Demis_Hassabis)<br>- [David Silver](https://en.wikipedia.org/wiki/David_Silver_(computer_scientist))<br>- [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy)<br>- [Ashish Vaswani](https://en.wikipedia.org/wiki/Ashish_Vaswani)<br>- [Noam Shazeer](https://en.wikipedia.org/wiki/Noam_Shazeer)<br>- [Aidan Gomez](https://en.wikipedia.org/wiki/Aidan_Gomez)<br>- [John Schulman](https://en.wikipedia.org/wiki/John_Schulman)<br>- [Mustafa Suleyman](https://en.wikipedia.org/wiki/Mustafa_Suleyman)<br>- [Jan Leike](https://en.wikipedia.org/wiki/Jan_Leike)<br>- [Daniel Kokotajlo](https://en.wikipedia.org/wiki/Daniel_Kokotajlo_(researcher))<br>- [François Chollet](https://en.wikipedia.org/wiki/Fran%C3%A7ois_Chollet) | | Neural network<br>architectures | - [Neural Turing machine](https://en.wikipedia.org/wiki/Neural_Turing_machine)<br>- [Differentiable neural computer](https://en.wikipedia.org/wiki/Differentiable_neural_computer)<br>- [Transformer](https://en.wikipedia.org/wiki/Transformer_(deep_learning)) <br> - [Vision transformer (ViT)](https://en.wikipedia.org/wiki/Vision_transformer)<br>- [Recurrent neural network (RNN)](https://en.wikipedia.org/wiki/Recurrent_neural_network)<br>- [Long short-term memory (LSTM)](https://en.wikipedia.org/wiki/Long_short-term_memory)<br>- [Gated recurrent unit (GRU)](https://en.wikipedia.org/wiki/Gated_recurrent_unit)<br>- [Echo state network](https://en.wikipedia.org/wiki/Echo_state_network)<br>- [Multilayer perceptron (MLP)](https://en.wikipedia.org/wiki/Multilayer_perceptron)<br>- [Convolutional neural network (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network)<br>- [Residual neural network (RNN)](https://en.wikipedia.org/wiki/Residual_neural_network)<br>- [Highway network](https://en.wikipedia.org/wiki/Highway_network)<br>- [Mamba](https://en.wikipedia.org/wiki/Mamba_(deep_learning_architecture))<br>- [Autoencoder](https://en.wikipedia.org/wiki/Autoencoder)<br>- [Variational autoencoder (VAE)](https://en.wikipedia.org/wiki/Variational_autoencoder)<br>- [Generative adversarial network (GAN)](https://en.wikipedia.org/wiki/Generative_adversarial_network)<br>- [Graph neural network (GNN)](https://en.wikipedia.org/wiki/Graph_neural_network) | | Political | - [AI Cold War](https://en.wikipedia.org/wiki/Artificial_Intelligence_Cold_War)<br>- [AI in government](https://en.wikipedia.org/wiki/Artificial_intelligence_in_government)<br>- [AI safety](https://en.wikipedia.org/wiki/AI_safety) ( [Alignment](https://en.wikipedia.org/wiki/AI_alignment))<br>- [AI takeover](https://en.wikipedia.org/wiki/AI_takeover)<br>- [Elections](https://en.wikipedia.org/wiki/Artificial_intelligence_and_elections)<br>- [Ethics of AI](https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence)<br>- EU [AI Act](https://en.wikipedia.org/wiki/Artificial_Intelligence_Act)<br>- [Nationalism](https://en.wikipedia.org/wiki/AI_nationalism)<br>- [Precautionary principle](https://en.wikipedia.org/wiki/Precautionary_principle)<br>- [Regulation of AI](https://en.wikipedia.org/wiki/Regulation_of_artificial_intelligence) <br> - [US](https://en.wikipedia.org/wiki/Regulation_of_artificial_intelligence_in_the_United_States)<br>- [Virtual politician](https://en.wikipedia.org/wiki/Virtual_politician)<br>- [Propaganda](https://en.wikipedia.org/wiki/Computational_propaganda)<br>- [Opposition to AI data centers](https://en.wikipedia.org/wiki/Opposition_to_AI_data_centers) | | Social<br>and economic | - [AI boom](https://en.wikipedia.org/wiki/AI_boom)<br>- [AI bubble](https://en.wikipedia.org/wiki/AI_bubble)<br>- [AI data center](https://en.wikipedia.org/wiki/AI_data_center)<br>- [AI effect](https://en.wikipedia.org/wiki/AI_effect)<br>- [AI infrastructure](https://en.wikipedia.org/wiki/AI_infrastructure)<br>- [AI literacy](https://en.wikipedia.org/wiki/AI_literacy)<br>- [AI slop](https://en.wikipedia.org/wiki/AI_slop)<br>- [AI winter](https://en.wikipedia.org/wiki/AI_winter)<br>- [Anthropomorphism](https://en.wikipedia.org/wiki/AI_anthropomorphism)<br>- [Arms race](https://en.wikipedia.org/wiki/Artificial_intelligence_arms_race)<br>- [Competition](https://en.wikipedia.org/wiki/Competition_in_artificial_intelligence)<br>- [Environmental impact](https://en.wikipedia.org/wiki/Environmental_impact_of_artificial_intelligence)<br>- [Explainable AI](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence)<br>- [Generative engine optimization](https://en.wikipedia.org/wiki/Generative_engine_optimization)<br>- [In architecture](https://en.wikipedia.org/wiki/Artificial_intelligence_in_architecture)<br>- [In education](https://en.wikipedia.org/wiki/Artificial_intelligence_in_education)<br>- [In fiction](https://en.wikipedia.org/wiki/Artificial_intelligence_in_fiction)<br>- [In healthcare](https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare) <br> - [Chatbot psychosis](https://en.wikipedia.org/wiki/Chatbot_psychosis)<br>- [In marketing](https://en.wikipedia.org/wiki/Artificial_intelligence_in_marketing)<br>- [In video games](https://en.wikipedia.org/wiki/Artificial_intelligence_in_video_games)<br>- [In visual art](https://en.wikipedia.org/wiki/Artificial_intelligence_visual_art)<br>- [Military applications](https://en.wikipedia.org/wiki/Military_applications_of_artificial_intelligence) <br> - [AI warfare](https://en.wikipedia.org/wiki/AI_warfare)<br>- [Workplace impact](https://en.wikipedia.org/wiki/Workplace_impact_of_artificial_intelligence) | | - ![](https://thumb.wikimedia.org/wikipedia/en/thumb/9/96/Symbol_category_class.svg/40px-Symbol_category_class.svg.png?utm_source=en.wikipedia.org&utm_campaign=parser&utm_content=thumbnail)[Category](https://en.wikipedia.org/wiki/Category:Artificial_intelligence) | | Articles related to artificial intelligence | |-| | | [Artificial intelligence algorithms](https://en.wikipedia.org/wiki/List_of_artificial_intelligence_algorithms) | | --- | | [Search](https://en.wikipedia.org/wiki/Search_algorithm) | - [A* search algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm)<br>- [Beam search](https://en.wikipedia.org/wiki/Beam_search)<br>- [Beam stack search](https://en.wikipedia.org/wiki/Beam_stack_search)<br>- [Best-first search](https://en.wikipedia.org/wiki/Best-first_search)<br>- [Breadth-first search](https://en.wikipedia.org/wiki/Breadth-first_search)<br>- [Depth-first search](https://en.wikipedia.org/wiki/Depth-first_search)<br>- [General Problem Solver](https://en.wikipedia.org/wiki/General_Problem_Solver)<br>- [Iterative deepening A*](https://en.wikipedia.org/wiki/Iterative_deepening_A*)<br>- [Iterative deepening depth-first search](https://en.wikipedia.org/wiki/Iterative_deepening_depth-first_search)<br>- [Uniform-cost search](https://en.wikipedia.org/wiki/Uniform-cost_search) | | [Game-tree search](https://en.wikipedia.org/wiki/Game_tree) | - [Alpha–beta pruning](https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning)<br>- [Expectiminimax](https://en.wikipedia.org/wiki/Expectiminimax)<br>- [Minimax](https://en.wikipedia.org/wiki/Minimax)<br>- [Monte Carlo tree search](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search)<br>- [SSS*](https://en.wikipedia.org/wiki/SSS*) | | [Optimization](https://en.wikipedia.org/wiki/Mathematical_optimization) | - [ALOPEX](https://en.wikipedia.org/wiki/ALOPEX)<br>- [Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization)<br>- [Conjugate gradient method](https://en.wikipedia.org/wiki/Conjugate_gradient_method)<br>- [Gradient descent](https://en.wikipedia.org/wiki/Gradient_descent)<br>- [Hill climbing](https://en.wikipedia.org/wiki/Hill_climbing)<br>- [Levenberg–Marquardt algorithm](https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm)<br>- [Quadratic unconstrained binary optimization](https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization)<br>- [Quasi-Newton method](https://en.wikipedia.org/wiki/Quasi-Newton_method)<br>- [Simulated annealing](https://en.wikipedia.org/wiki/Simulated_annealing)<br>- [Stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) | | [Evolutionary computation](https://en.wikipedia.org/wiki/Evolutionary_computation) | - [Differential evolution](https://en.wikipedia.org/wiki/Differential_evolution)<br>- [Evolutionary multimodal optimization](https://en.wikipedia.org/wiki/Evolutionary_multimodal_optimization)<br>- [Genetic algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm)<br>- [Genetic programming](https://en.wikipedia.org/wiki/Genetic_programming) | | [Bio-inspired methods](https://en.wikipedia.org/wiki/Bio-inspired_computing) | - [Ant colony optimization algorithms](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms)<br>- [Particle swarm optimization](https://en.wikipedia.org/wiki/Particle_swarm_optimization) | | [Automated reasoning](https://en.wikipedia.org/wiki/Automated_reasoning) | - [Backward chaining](https://en.wikipedia.org/wiki/Backward_chaining)<br>- [Forward chaining](https://en.wikipedia.org/wiki/Forward_chaining)<br>- [Rete algorithm](https://en.wikipedia.org/wiki/Rete_algorithm) | | [Logic](https://en.wikipedia.org/wiki/Mathematical_logic) | - [DPLL algorithm](https://en.wikipedia.org/wiki/DPLL_algorithm)<br>- [Resolution (logic)](https://en.wikipedia.org/wiki/Resolution_(logic))<br>- [WalkSAT](https://en.wikipedia.org/wiki/WalkSAT) | | [Statistical inference](https://en.wikipedia.org/wiki/Statistical_inference) | - [Baum–Welch algorithm](https://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm)<br>- [Belief propagation](https://en.wikipedia.org/wiki/Belief_propagation)<br>- [Expectation–maximization algorithm](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)<br>- [Forward–backward algorithm](https://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm)<br>- [Kalman filter](https://en.wikipedia.org/wiki/Kalman_filter)<br>- [Markov chain Monte Carlo (MCMC)](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo)<br>- [Viterbi algorithm](https://en.wikipedia.org/wiki/Viterbi_algorithm) | | [Motion planning](https://en.wikipedia.org/wiki/Motion_planning) | - [Bug algorithm](https://en.wikipedia.org/wiki/Bug_algorithm)<br>- [Dynamic window approach](https://en.wikipedia.org/wiki/Dynamic_window_approach)<br>- [Graphplan](https://en.wikipedia.org/wiki/Graphplan)<br>- [Probabilistic roadmap](https://en.wikipedia.org/wiki/Probabilistic_roadmap)<br>- [Rapidly-exploring random tree](https://en.wikipedia.org/wiki/Rapidly-exploring_random_tree)<br>- [Vector Field Histogram](https://en.wikipedia.org/wiki/Vector_Field_Histogram) | | [Ensemble learning](https://en.wikipedia.org/wiki/Ensemble_learning) | - [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost)<br>- [Bootstrap aggregating](https://en.wikipedia.org/wiki/Bootstrap_aggregating)<br>- [BrownBoost](https://en.wikipedia.org/wiki/BrownBoost)<br>- [Gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting)<br>- [LogitBoost](https://en.wikipedia.org/wiki/LogitBoost)<br>- [LPBoost](https://en.wikipedia.org/wiki/LPBoost)<br>- [Random forest](https://en.wikipedia.org/wiki/Random_forest)<br>- [Randomized weighted majority algorithm](https://en.wikipedia.org/wiki/Randomized_weighted_majority_algorithm)<br>- [Weighted majority algorithm](https://en.wikipedia.org/wiki/Weighted_majority_algorithm_(machine_learning)) | | [Classification](https://en.wikipedia.org/wiki/Statistical_classification) | - [Alternating decision tree](https://en.wikipedia.org/wiki/Alternating_decision_tree)<br>- [C4.5 algorithm](https://en.wikipedia.org/wiki/C4.5_algorithm)<br>- [CN2 algorithm](https://en.wikipedia.org/wiki/CN2_algorithm)<br>- [Decision tree learning](https://en.wikipedia.org/wiki/Decision_tree_learning)<br>- [Dominance-based rough set approach](https://en.wikipedia.org/wiki/Dominance-based_rough_set_approach)<br>- [Genetic Algorithm for Rule Set Production](https://en.wikipedia.org/wiki/Genetic_Algorithm_for_Rule_Set_Production)<br>- [ID3 algorithm](https://en.wikipedia.org/wiki/ID3_algorithm)<br>- [_k_-nearest neighbors algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)<br>- [Logic learning machine](https://en.wikipedia.org/wiki/Logic_learning_machine)<br>- [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression)<br>- [Naive Bayes classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)<br>- [Relevance vector machine](https://en.wikipedia.org/wiki/Relevance_vector_machine)<br>- [RIPPER](https://en.wikipedia.org/wiki/Repeated_incremental_pruning_to_produce_error_reduction_(RIPPER))<br>- [Structured kNN](https://en.wikipedia.org/wiki/Structured_kNN)<br>- [Support vector machine](https://en.wikipedia.org/wiki/Support_vector_machine)<br>- [Winnow algorithm](https://en.wikipedia.org/wiki/Winnow_algorithm) | | [Dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) | - [Diffusion map](https://en.wikipedia.org/wiki/Diffusion_map)<br>- [FastICA](https://en.wikipedia.org/wiki/FastICA)<br>- [Kernel principal component analysis](https://en.wikipedia.org/wiki/Kernel_principal_component_analysis)<br>- [Manifold alignment](https://en.wikipedia.org/wiki/Manifold_alignment)<br>- [Minimum redundancy feature selection](https://en.wikipedia.org/wiki/Minimum_redundancy_feature_selection)<br>- [Non-negative matrix factorization](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)<br>- [Principal component analysis](https://en.wikipedia.org/wiki/Principal_component_analysis)<br>- [Sparse PCA](https://en.wikipedia.org/wiki/Sparse_PCA)<br>- [T-distributed stochastic neighbor embedding](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) | | [Neural learning](https://en.wikipedia.org/wiki/Artificial_neural_network) | - [Almeida–Pineda recurrent backpropagation](https://en.wikipedia.org/wiki/Almeida%E2%80%93Pineda_recurrent_backpropagation)<br>- [Backpropagation](https://en.wikipedia.org/wiki/Backpropagation)<br>- [GeneRec](https://en.wikipedia.org/wiki/GeneRec)<br>- [Generalized Hebbian algorithm](https://en.wikipedia.org/wiki/Generalized_Hebbian_algorithm)<br>- [Growing self-organizing map](https://en.wikipedia.org/wiki/Growing_self-organizing_map)<br>- [Learning vector quantization](https://en.wikipedia.org/wiki/Learning_vector_quantization)<br>- [Leabra](https://en.wikipedia.org/wiki/Leabra)<br>- [Perceptron](https://en.wikipedia.org/wiki/Perceptron)<br>- [Quickprop](https://en.wikipedia.org/wiki/Quickprop)<br>- [Rprop](https://en.wikipedia.org/wiki/Rprop)<br>- [Self-organizing map](https://en.wikipedia.org/wiki/Self-organizing_map)<br>- [Wake-sleep algorithm](https://en.wikipedia.org/wiki/Wake-sleep_algorithm) | | [Reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning) | - [Actor-critic algorithm](https://en.wikipedia.org/wiki/Actor-critic_algorithm)<br>- [Constructing skill trees](https://en.wikipedia.org/wiki/Constructing_skill_trees)<br>- [Error-driven learning](https://en.wikipedia.org/wiki/Error-driven_learning)<br>- [Policy gradient method](https://en.wikipedia.org/wiki/Policy_gradient_method)<br>- [Prefrontal cortex basal ganglia working memory](https://en.wikipedia.org/wiki/Prefrontal_cortex_basal_ganglia_working_memory)<br>- [Proximal policy optimization](https://en.wikipedia.org/wiki/Proximal_policy_optimization)<br>- [PVLV](https://en.wikipedia.org/wiki/PVLV)<br>- [Q-learning](https://en.wikipedia.org/wiki/Q-learning)<br>- [Skill chaining](https://en.wikipedia.org/wiki/Skill_chaining)<br>- [State–action–reward–state–action](https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action)<br>- [Temporal difference learning](https://en.wikipedia.org/wiki/Temporal_difference_learning) | | [Deep learning](https://en.wikipedia.org/wiki/Deep_learning) | - [PagedAttention](https://en.wikipedia.org/wiki/PagedAttention)<br>- [vAttention](https://en.wikipedia.org/wiki/PagedAttention#vAttention) | | [Natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing) | - [Byte-pair encoding](https://en.wikipedia.org/wiki/Byte-pair_encoding)<br>- [Cocke–Younger–Kasami algorithm](https://en.wikipedia.org/wiki/Cocke%E2%80%93Younger%E2%80%93Kasami_algorithm)<br>- [Earley parser](https://en.wikipedia.org/wiki/Earley_parser)<br>- [Inside-outside algorithm](https://en.wikipedia.org/wiki/Inside-outside_algorithm) | | [Computer vision](https://en.wikipedia.org/wiki/Computer_vision) | - [Canny edge detector](https://en.wikipedia.org/wiki/Canny_edge_detector)<br>- [GrabCut](https://en.wikipedia.org/wiki/GrabCut)<br>- [RANSAC](https://en.wikipedia.org/wiki/RANSAC)<br>- [Scale-invariant feature transform](https://en.wikipedia.org/wiki/Scale-invariant_feature_transform) | | [Game play](https://en.wikipedia.org/wiki/Game_artificial_intelligence) | - [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo)<br>- [AlphaGo Zero](https://en.wikipedia.org/wiki/AlphaGo_Zero)<br>- [AlphaZero](https://en.wikipedia.org/wiki/AlphaZero)<br>- [MuZero](https://en.wikipedia.org/wiki/MuZero)<br>- [TD-Gammon](https://en.wikipedia.org/wiki/TD-Gammon) | | Related | - [Glossary of artificial intelligence](https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence)<br>- [List of algorithms](https://en.wikipedia.org/wiki/List_of_algorithms)<br>- [List of algorithms for automated planning](https://en.wikipedia.org/wiki/List_of_algorithms_for_automated_planning)<br>- [List of artificial intelligence journals](https://en.wikipedia.org/wiki/List_of_artificial_intelligence_journals)<br>- [List of machine learning algorithms](https://en.wikipedia.org/wiki/List_of_machine_learning_algorithms)<br>- [List of quantum algorithms](https://en.wikipedia.org/wiki/List_of_quantum_algorithms)<br>- [Lists of open-source artificial intelligence software](https://en.wikipedia.org/wiki/Lists_of_open-source_artificial_intelligence_software)<br>- [Motion planning](https://en.wikipedia.org/wiki/Motion_planning)<br>- [Outline of algorithms](https://en.wikipedia.org/wiki/Outline_of_algorithms)<br>- [Outline of artificial intelligence](https://en.wikipedia.org/wiki/Outline_of_artificial_intelligence)<br>- [Satplan](https://en.wikipedia.org/wiki/Satplan)<br>- [TurboQuant](https://en.wikipedia.org/wiki/TurboQuant)<br>- [AlphaDev](https://en.wikipedia.org/wiki/AlphaDev)<br>- [AlphaEvolve](https://en.wikipedia.org/wiki/AlphaEvolve)<br>- [AlphaTensor](https://en.wikipedia.org/wiki/AlphaTensor)<br>- [Algorithm engineering](https://en.wikipedia.org/wiki/Algorithm_engineering) | | [Computer science](https://en.wikipedia.org/wiki/Computer_science) | |-| | [Artificial<br><br>intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) | | - [Computational intelligence](https://en.wikipedia.org/wiki/Computational_intelligence)<br>- [Natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing)<br>- [Knowledge representation and reasoning](https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning)<br>- [Computer vision](https://en.wikipedia.org/wiki/Computer_vision)<br>- [Automated planning and scheduling](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling)<br>- [Search methodology](https://en.wikipedia.org/wiki/Mathematical_optimization)<br>- [Control method](https://en.wikipedia.org/wiki/Control_theory)<br>- [Philosophy of](https://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence)<br>- [Distributed](https://en.wikipedia.org/wiki/Distributed_artificial_intelligence) |

https://en.wikipedia.org/wiki/Artificial_intelligence
ENwww.ibm.comfirecrawl

What Is Artificial Intelligence (AI)? - IBM

# What is artificial intelligence (AI)? ## Generative AI ![Outside view of a commercial building](https://www.ibm.com/adobe/dynamicmedia/deliver/dm-aid--3d4105bc-8250-45e4-9ac4-1bfcddb0e066/agents-hiu-dach-975x861.png?preferwebp=true&width=320) ## Benefits of AI ### Enhanced decision-making Whether used for decision support or for fully automated decision-making, AI enables faster, more accurate predictions and reliable, [data-driven decisions](https://www.ibm.com/think/topics/data-driven-decision-making). Combined with automation, AI enables businesses to act on opportunities and respond to crises as they emerge, in real time and without human intervention. ## History of AI From there, he offers a test, now famously known as the "Turing Test," where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since it was published, it remains an important part of the history of AI, and an ongoing concept within philosophy as it uses ideas around linguistics. John McCarthy coins the term "artificial intelligence" at the first-ever AI conference at Dartmouth College. (McCarthy went on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw and Herbert Simon create the Logic Theorist, the first-ever running AI computer program. Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that "learned" through trial and error. Neural networks, which use a backpropagation algorithm to train itself, became widely used in AI applications. Stuart Russell and Peter Norvig publish [Artificial Intelligence: A Modern Approach](https://aima.cs.berkeley.edu/), which becomes one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems based on rationality and thinking versus acting. John McCarthy writes a paper, [What Is Artificial Intelligence?](https://www-formal.stanford.edu/jmc/whatisai.pdf), and proposes an often-cited definition of AI. By this time, the era of big data and cloud computing is underway, enabling organizations to manage ever-larger data estates, which will one day be used to train AI models. **2011** IBM Watson® beats champions Ken Jennings and Brad Rutter at Jeopardy! **2015** Baidu's Minwa supercomputer uses a special deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human. **2016** DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. Later, Google purchased DeepMind for a reported USD 400 million. **2022** A rise in [large language models](https://www.ibm.com/think/topics/large-language-models) or LLMs, such as OpenAI’s ChatGPT, creates an enormous change in performance of AI and its potential to drive enterprise value. With these new generative AI practices, deep-learning models can be pretrained on large amounts of data. **2024** The latest [AI trends](https://www.ibm.com/think/insights/artificial-intelligence-trends) point to a continuing AI renaissance. Multimodal models that can take multiple types of data as input are providing richer, more robust experiences. These models bring together [computer vision](https://www.ibm.com/think/topics/computer-vision) image recognition and NLP speech recognition capabilities. Smaller models are also making strides in an age of diminishing returns with massive models with large parameter counts. Authors Staff Editor, AI Models IBM Think ![ Eda](https://assets.ibm.com/is/image/ibm/eda-kavlakoglu?wid=128) Business Development + Partnerships IBM Research Link copied Next step in your AI journey See watsonx Orchestrate in action Start your free trial ## Resources Carousel Slide 1 of 3. Showing 4 items. Related solutions Related solutions ## Related solutions 2. [Explore AI solutions](https://www.ibm.com/solutions/artificial-intelligence) Loading START END START END [![close icon](https://consent.trustarc.com/get?name=ibm_close_icon.svg)](https://www.ibm.com/think/topics/artificial-intelligence#) To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed [here](https://www.ibm.com/think/topics/artificial-intelligence#truste_domain_list). Retry The chat is loading. Return to conversation Return to conversation Return to conversation Options Contact IBM Chat History AI Chat history begin. Select this button to focus the first message then use the arrow keys to move between messages. Press escape to exit the message list. Chat history end. Press escape to exit the message list. Powered by IBM watsonx IBM watsonx is powered by the latest AI models to intelligently process conversations and provide help whenever and wherever you may need it. Chat message input Type your message to start a conversation Contact IBM Open chat history

https://www.ibm.com/think/topics/artificial-intelligence
ENcloud.google.comfirecrawl

What is Artificial Intelligence (AI)? | Google Cloud

# Artificial intelligence (AI): a simple-to-understand guide The answer is artificial intelligence (AI). Far from being science fiction, or limited to just the chatbots we know and enjoy, AI is part of our daily lives in countless ways. It's one of the most transformative technologies of our time, acting as the engine behind modern innovation. But what does "Artificial Intelligence" actually mean? # Key takeaways ## Types of artificial intelligence ### Automation AI can help automate workflows and processes or work independently from a team of workers. For example, AI can help [automate aspects of cybersecurity](https://cloud.google.com/security/products/security-operations) by continuously monitoring and analyzing network traffic. Similarly, a smart factory may have many different kinds of AI in use, such as robots using computer vision to navigate the factory floor or to inspect products for defects, create digital twins, or use real-time analytics to measure efficiency and output. ### Reduce human error AI can minimize manual errors in data processing, analytics, assembly in manufacturing, and other tasks through automation and algorithms that follow the same processes every single time. ### Eliminate repetitive tasks AI can be used to perform repetitive tasks, freeing up people to work on more complex problems. AI excels at automating these repetitive or tedious job functions. ### Fast and accurate AI can process more information more quickly than a person, finding patterns and discovering relationships in data that someone might miss. ### Accelerated research and development For instance, AI can help with predictive modeling for potential new pharmaceutical treatments or with quantifying the human genome.

https://cloud.google.com/learn/what-is-artificial-intelligence
ENwww.britannica.comfirecrawl

Artificial intelligence (AI) | Definition, Examples, Types, Applications ...

- [Methods and goals in AI](https://www.britannica.com/technology/artificial-intelligence/Methods-and-goals-in-AI) - [Artificial general intelligence (AGI), applied AI, and cognitive simulation](https://www.britannica.com/technology/artificial-intelligence/Methods-and-goals-in-AI#ref219086) - [AI technology](https://www.britannica.com/technology/artificial-intelligence/Methods-and-goals-in-AI#ref380007) - [Machine learning](https://www.britannica.com/technology/artificial-intelligence/Methods-and-goals-in-AI#ref380008) - [Large language models and natural language processing (NLP)](https://www.britannica.com/technology/artificial-intelligence/Methods-and-goals-in-AI#ref380009) - [Autonomous vehicles](https://www.britannica.com/technology/artificial-intelligence/Methods-and-goals-in-AI#ref380010) - [Virtual assistants](https://www.britannica.com/technology/artificial-intelligence/Methods-and-goals-in-AI#ref380011) - [Is artificial general intelligence (AGI) possible?](https://www.britannica.com/technology/artificial-intelligence/Is-artificial-general-intelligence-AGI-possible) [![Artificial intelligence](https://cdn.britannica.com/47/246247-050-F1021DE9/AI-text-to-image-photo-robot-with-computer.jpg?w=400&h=300&c=crop)](https://cdn.britannica.com/47/246247-050-F1021DE9/AI-text-to-image-photo-robot-with-computer.jpg) [Artificial intelligence](https://cdn.britannica.com/47/246247-050-F1021DE9/AI-text-to-image-photo-robot-with-computer.jpg) Image generated by the Stable Diffusion model from the prompt “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings,” which is the definition of artificial intelligence in the _Encyclopædia Britannica_ article on the subject. # artificial intelligence ### What is artificial intelligence? Artificial intelligence is the ability of a [computer](https://www.britannica.com/technology/computer) or computer-controlled [robot](https://www.britannica.com/technology/robot-technology) to perform tasks that are commonly associated with the [intellectual processes characteristic of humans](https://www.britannica.com/science/human-intelligence-psychology), such as the ability to reason. Although there are as of yet no AIs that match full human flexibility over wider domains or in tasks requiring much everyday knowledge, some AIs perform specific tasks as well as humans. [Learn more](https://www.britannica.com/technology/Artificial-Intelligence-AI-At-a-Glance-2235722). ## News • 1, 2026, 3:33 PM ET (AP) ...( Show more) 1, 2026, 11:01 AM ET (AP) 1, 2026, 7:38 AM ET (AP) 1, 2026, 6:34 AM ET (AP) 30, 2026, 7:46 PM ET (AP) Show less **artificial intelligence (AI)**, the ability of a digital [computer](https://www.britannica.com/technology/computer) or computer-controlled [robot](https://www.britannica.com/technology/robot-technology) to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the [intellectual](https://www.merriam-webster.com/dictionary/intellectual) processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since their development in the 1940s, [digital computers](https://www.britannica.com/technology/digital-computer) have been programmed to carry out very complex tasks—such as discovering proofs for mathematical theorems or playing [chess](https://www.britannica.com/topic/chess)—with great proficiency. On the other hand, some programs have attained the performance levels of human experts and professionals in executing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as [diverse](https://www.merriam-webster.com/dictionary/diverse) as medical [diagnosis](https://www.merriam-webster.com/dictionary/diagnosis), computer [search engines](https://www.britannica.com/technology/search-engine), voice or handwriting recognition, and [chatbots](https://www.britannica.com/topic/chatbot). ## What is intelligence? **What Do You Think?** - **[Is Artificial Intelligence Good for Society?](https://www.britannica.com/procon/artificial-intelligence-AI-debate)** ### Thank you for signing up! Thank you for signing up!" ## [Learning](https://www.britannica.com/technology/machine-learning) Artificial intelligence (AI) # Reasoning ## [Language](https://www.britannica.com/topic/language) - [Lifewire - What is artificial intelligence?](https://www.lifewire.com/what-is-artificial-intelligence-5119206) - [Internet Encyclopedia of Philosophy - Artificial Intelligence](https://iep.utm.edu/artificial-intelligence/)

https://www.britannica.com/technology/artificial-intelligence
ENwww.nasa.govfirecrawl

What is Artificial Intelligence? - NASA

# What is Artificial Intelligence? ## Defining Artificial Intelligence Artificial intelligence refers to computer systems that can perform complex tasks normally done by human-reasoning, decision making, creating, etc. There is no single, simple definition of artificial intelligence because AI tools are capable of a wide range of tasks and outputs, but NASA follows the definition of AI found within [EO 13960](https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government), which references Section 238(g) of the National Defense Authorization Act of 2019. - Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets. - An artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action. - An artificial system designed to think or act like a human, including cognitive architectures and neural networks. - A set of techniques, including machine learning that is designed to approximate a cognitive task. - An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision-making, and acting. ![Large circle that says "Artificial Intelligence," inside that is a smaller circle that says "Machine Learning," and inside the Machine Learning circle is a smaller circle that says "Deep Learning."](https://www.nasa.gov/wp-content/uploads/2024/05/ai-ml-dl-03.png) _How Artificial Intelligence, Machine Learning, and Deep Learning Fit Together_ # Artificial Intelligence and Machine Learning (ML): AI tools used at NASA sometimes use machine learning, which uses data and algorithms to train computers to make classifications, generatepredictions, or uncover similarities or trends across large datasets. ### **Decision Support** This method of artificial intelligence considers multiple outcomes and probabilities to inform decisions.

https://www.nasa.gov/what-is-artificial-intelligence/
ENwww.mtu.edufirecrawl

What is Artificial intelligence (AI)? - Michigan Technological University

# What is Artificial intelligence (AI)? ## Four Basic Functions of AI - **Learning.** A key aspect of AI is learning, which allows AI systems to digest data and enhance their functions without direct human coding. - **Reasoning and decision-making.** Reasoning and decision-making systems employ logical rules, probability models, and algorithms to reach conclusions and make reliable decisions based on inference. - **Problem-solving.** Problem-solving in AI involves processing data, manipulating it, and applying it to devise solutions for specific issues. - **Perception.** The perception component of AI includes tasks like image recognition, object detection, image segmentation, and video analysis. ## Some Artificial Intelligence Terms ### A Brief History of AI made in the middle of the 20th century, when pioneers like Alan Turing began exploring foundational concepts like artificial neural networks, machine learning, and symbolic reasoning. The term artificial intelligence was coined and came into popular use in the mid-1950s following Turing's publication of "Computer Machinery and Intelligence," a paper that proposed a test of machine intelligence called the Imitation Game. Turing's publication eventually became the **Turing Test,** which experts used to measure computer intelligence. became faster, cheaper, more accessible, and could store more information. During this time, the first AI programming languages were created, machine learning algorithms were improved, and books and films began to explore the idea of robots. But computers were still millions of times too weak to exhibit intelligence. Research funding declined in research and additional government funding. Deep learning techniques and the use their mistakes and make independent decisions. The early 1990s showed some strides forward in AI research, including the first AI system that could defeat a reigning world champion chess player. innovations such as the first robot vacuum and the first commercially available speech recognition software. The late 1990s and the early 2000s also saw significant advances in artificial intelligence. immense collections of data, and the application of advanced mathematical methods. **Deep learning** started to take off in the early 2010s. An abundance of data, advancements in learning algorithms, and increases in computational power led to achievements in speech recognition, natural language processing, visual recognition, and reinforcement learning. AI continues to evolve at a rapid pace as an integral part of daily life. of AI into everyday applications impacts industries such as finance, healthcare, transportation, and entertainment. The convergence of AI with other technologies, such as the Internet of Things (IoT), blockchain, and quantum computing, continues. and accountability have grown. There is a growing focus on ethical and responsible AI practices. frameworks to ensure AI is developed and deployed responsibly. ## How Can AI Benefit Humanity? Artificial intelligence has enormous potential to serve society. ## Is AI Dangerous? AI is a powerful and promising technology that can bring many benefits and opportunities ## Who Uses AI? Tech companies are at the forefront of AI, but industries of all kinds use AI. - Astronomy - Agriculture - Finance - Government - Healthcare - Marketing - Navigation - Travel and transport ## What Skills Do You Need In Artificial Intelligence? - Natural language processing (NLP) ## Artificial Intelligence at Michigan Tech Michigan Tech's College of Computing is the first college in Michigan fully dedicated to computing, and one of only a few nationwide. Michigan Tech computing students gain knowledge and experience through a wide range of classroom and hands-on learning opportunities. Multiple programs and research projects provide avenues for students to pursue an AI-intensive education.

https://www.mtu.edu/data-science/undergraduate/ai/what-is/
ENwww.sas.comfirecrawl

Artificial Intelligence (AI): What it is and why it matters - SAS

# Artificial Intelligence ## What it is and why it matters - [Who Uses It](https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html#industries) ## Artificial Intelligence History The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. Early research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names. This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities. While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of this technology isn’t that scary – or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Keep reading for examples in health care, retail and more. Today's generative AI technologies have made the benefits of AI clear to a growing number of professionals. LLM-powered assistants are showing up inside many existing software products, from forecasting tools to marketing stacks. The fast adoption of GenAI has also raised questions and concerns about [AI anxiety](https://www.sas.com/en_us/insights/articles/analytics/ai-anxiety-calm-in-the-face-of-change.html), [AI hallucinations](https://www.sas.com/en_us/insights/articles/analytics/what-are-ai-hallucinations.html) and [AI ethics](https://www.sas.com/en_us/insights/analytics/ai-ethics.html). As a result, trustworthy AI and [responsible AI](https://www.sas.com/en_us/company-information/innovation/responsible-innovation.html) discussions are becoming crucial in every industry. Neural Network graphic 1950s–1970s [Neural Networks](https://www.sas.com/en_us/insights/analytics/neural-networks.html) Early work with neural networks stirs excitement for “thinking machines.” machine-learningMachine Learning icon 1980s–2010s Machine learning becomes popular. Deep Learning icon [Deep Learning](https://www.sas.com/en_us/insights/analytics/deep-learning.html) Deep learning breakthroughs drive AI boom. Deep Learning icon Present Day Generative AI, a disruptive tech, soars in popularity. ## What is generative AI? ### Why is artificial intelligence important? ![artificial intelligence icon](https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence/_jcr_content/par/styledcontainer_2f7b/par/styledcontainer_e229/par/textimage_8fd5/image.img.jpg/1568056760404.jpg) **Automates repetitive learning and discovery through data.** Instead of automating manual tasks, artificial intelligence performs frequent, high-volume, computerized tasks. And it does so reliably and without fatigue. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies. **Adapts through progressive learning algorithms** to let the data do the programming. Artificial intelligence finds structure and regularities in data so that algorithms can acquire skills. And the models adapt when given new data. **Analyzes more and deeper data** using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers used to be impossible. You need lots of data to train deep learning models because they learn directly from the data. **Achieves incredible accuracy** through deep neural networks. In the medical field, AI techniques from deep learning and object recognition can now be used to pinpoint cancer on medical images with improved accuracy. **Gets the most out of data.** This entails having solid [AI governance](https://www.sas.com/en_us/insights/analytics/ai-governance.html) to support ethical, equitable and sustainable AI systems. ## Artificial Intelligence in Today's World ### Pondering AI podcast What will AI do next? Join Kimberly Nevala to ponder AI’s progress with a diverse group of guests, including innovators, activists and data experts. ## How Artificial Intelligence Is Being Used ### Manufacturing AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data. ## How Artificial Intelligence Works AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. machine-learning ## Next Steps See how Artificial Intelligence solutions augment human creativity and endeavors. Featured capability for ARTIFICIAL INTELLIGENCE

https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html
ENwww.iso.orgfirecrawl

Artificial intelligence: What it is, how it works and why it matters - ISO

# Artificial intelligence: What it is, how it works and why it matters ![](https://www.iso.org/files/live/sites/isoorg/files/news/insights/ai/svg/ai_pillar.svg)![Artificial intelligence: What it is, how it works and why it matters ](https://www.iso.org/files/live/sites/isoorg/files/news/insights/ai/svg/ai_pillar.svg/thumbnails/300x300) While not always obvious, artificial intelligence has been a fixture of day-to-day life for millions of people for years. Virtual assistants like Siri and Alexa are prime examples of how AI can support humans in a variety of ways – if only by making things more convenient. Yet when generative AI like ChatGPT burst onto the scene, its uncanny ability to mimic human response and ready availability to everyone with a computer suddenly pushed discussions about machine learning and ethics into the public sphere. Concepts like deep learning, NLP and neural networks have seeped into everyday professional and even personal conversation. Here, we break down what artificial intelligence is, how it works, the difference between machine learning, deep learning, natural language processing and more. Let’s dive in. ## What is artificial intelligence? At its core, AI refers to a machine or computer system’s ability to perform tasks that would typically require human intelligence. It involves programming systems to analyse data, learn from experiences, and make smart decisions – guided by human input. The most familiar form of AI is virtual assistants like Siri or Alexa, but there are many iterations of the technology. AI has the potential to revolutionize various industries by enabling machines to solve complex problems and think intuitively, going beyond mere automation. This encompasses various subfields and technologies, such as machine learning and natural language processing. Uncover more with our [in-depth guide on what is artificial intelligence](https://www.iso.org/artificial-intelligence/what-is-ai). ### Sign up for email updates Subscribe **Almost done!** You are only one step away from joining the ISO subscriber list. How your data will be used ## Standards and artificial intelligence This standard aims to ensure that AI algorithms and models are understandable and can be audited for bias and fairness, thereby building trust and confidence in AI systems. This is especially important as AI becomes more integrated into various industries and applications. As the development and adoption of AI continues to accelerate, developing rigorous standards will be key to ensuring artificial intelligence becomes a technology for good.

https://www.iso.org/artificial-intelligence
ENmeng.uic.edufirecrawl

What is (AI) Artificial Intelligence? | UIC Online MEng

# What is (AI) Artificial Intelligence? Posted on **December 21, 2023** ## (AI) Artificial Intelligence: What is the definition of AI and how does AI work? ![3D interpretation of artificial intelligence](https://meng.uic.edu/wp-content/uploads/sites/1019/2023/12/UIC-MENG-AI-Graphic-1090x1090.jpg) A recurring theme in science fiction, artificial intelligence (AI) has captured our collective imagination and enthralled audiences for over a century. [Artificial Intelligence (AI)](https://meng.uic.edu/online-program/meng/) enables machines to learn from experience, adapt to new inputs, and execute tasks resembling human capabilities. By leveraging AI technologies, computers can undergo training to perform particular tasks through the analysis of extensive data sets and the identification of patterns within the data. ## What is the definition of Artificial Intelligence? Artificial intelligence represents a branch of computer science that aims to create machines capable of performing tasks that typically require human intelligence. These tasks include learning from experience (machine learning), understanding natural language, recognizing patterns, solving problems, and making decisions. From self-driving cars to virtual personal assistants, AI is reshaping various aspects of our daily lives, and its significance continues to grow. “Computer science is about building recipes to achieve different goals and objectives,” said Dr. “In many areas of computer science, we can build things that are guaranteed to do what we want. However, there are a lot of extremely difficult problems in the world. So, to me, the field of AI is a set of techniques and tools that have been developed to solve these hard problems even when we can’t get a fully satisfying ‘just follow this recipe’ solution.” One pivotal moment in the exploration of AI came in 1950 with the visionary work of British polymath, [Alan Turing](https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/). In his paper, “Computing Machinery and Intelligence,” Turing introduced the Turing test and explored the mathematical possibilities of AI and questioned why machines couldn’t leverage available information, just as humans do, to solve problems and make decisions. This marked a crucial step in the journey from speculative fiction to tangible innovation. Unlike traditional computer programs that follow predetermined instructions, AI systems can learn and adapt from data, allowing them to improve their performance over time. This ability to learn and evolve is a key characteristic that sets AI apart from conventional computing. ## How does artificial intelligence work? Artificial Intelligence (AI) works by simulating human intelligence through the use of algorithms, data, and computational power. The goal is to enable machines or software to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding. ### AI Subsets Artificial Intelligence comprises various subsets or subfields, each focusing on specific aspects of replicating human intelligence or solving particular types of problems. ## **Weak AI vs. Strong AI** There are two different types of artificial intelligence capabilities, particularly in terms of mimicking human intelligence. These concepts help distinguish the extent to which AI systems can replicate cognitive functions and exhibit intelligence. ### Weak AI (or Narrow AI) AI systems that are designed and trained for a specific task or a narrow set of tasks and are most of the AI that we see today. It enables some very robust applications, such as Amazon’s Alexa and Tesla’s self-driving vehicles. ### Strong AI (or General AI) AI systems with the capacity to comprehend, learn, and apply knowledge across a diverse spectrum of tasks at a level equivalent to human intelligence. It’s a theoretical form of AI where a machine would have an intelligence equal to humans. ## Education in AI UIC’s [online Master of Engineering with a concentration in AI and Machine Learning program](https://meng.uic.edu/online-program/meng/) offers a unique opportunity to dive headfirst into the cutting-edge world of artificial intelligence. They will apply this knowledge more deeply in the courses of Image Analysis and Computer Vision, Deep Neural Networks, and Natural Language Processing. Kash believes AI is a powerful set of tools that can help individuals in all careers solve complex problems.

https://meng.uic.edu/news-stories/ai-artificial-intelligence-what-is-the-definition-of-ai-and-how-does-ai-work/
ENwww.atlassian.comfirecrawl

Artificial Intelligence 101: The basics of AI everyone should know

# Artificial Intelligence 101: The basics of AI everyone should know ## What is artificial intelligence? Artificial intelligence (AI) involves creating machines that can think like humans and imitate their actions. This field uses various technologies to enable computers to do things that normally need human intelligence, like recognizing images, understanding speech, making decisions, and translating languages. Essentially, artificial intelligence is like having a smart computer that can learn from experience, solve problems, and make decisions on its own — just like a human. ## How AI works Instead of being explicitly programmed for every task, AI uses algorithms to learn from experiences. This ability to learn and improve without constant human instruction makes AI so powerful and versatile in solving complex problems. ## Using AI in our everyday lives Artificial intelligence is already seamlessly integrated into our daily routines. Artificial intelligence analyzes your viewing or listening habits and suggests content that matches your preferences, making entertainment choices more enjoyable and tailored to your tastes. It allows computers to respond to human commands and queries by letting you ask questions like a human and get answers tailored to your needs. These tools streamline workflows by [automating repetitive tasks](https://www.atlassian.com/platform/automation), providing intelligent insights from [analytics](https://www.atlassian.com/platform/analytics), and enhancing collaboration among teams. [Learn more](https://www.atlassian.com/platform/artificial-intelligence)

https://www.atlassian.com/blog/ai-at-work/artificial-intelligence-101-the-basics-of-ai