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{{Short description|
{{Multiple issues|
{{Tone |date=April 2022}}
{{Undue weight |2=very obscure AI projects of questionable importance |date=April 2022}}
}}
{{Artificial intelligence|Applications}}
[[Artificial intelligence]] is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. [[Artificial intelligence]] (AI) has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of [[Machine learning]] has been used for various scientific and commercial purposes<ref>{{cite journal |last1=Brynjolfsson |first1=Erik |last2=Mitchell |first2=Tom |title=What can machine learning do? Workforce implications |journal=Science |date=22 December 2017 |volume=358 |issue=6370 |pages=1530–1534 |doi=10.1126/science.aap8062 |pmid=29269459 |bibcode=2017Sci...358.1530B }}</ref> including [[Machine translation|language translation]], [[image recognition]], [[decision-making]],<ref>{{cite journal |last1=Shin |first1=Minkyu |last2=Kim |first2=Jin |last3=van Opheusden |first3=Bas |last4=Griffiths |first4=Thomas L. |title=Superhuman artificial intelligence can improve human decision-making by increasing novelty |journal=[[Proceedings of the National Academy of Sciences of the United States of America|Proceedings of the National Academy of Sciences]] |date=2023 |volume=120 |issue=12 |pages=e2214840120 |doi=10.1073/pnas.2214840120 |pmid=36913582 |doi-access=free |pmc=10041097|arxiv=2303.07462 |bibcode=2023PNAS..12014840S }}</ref><ref>{{cite journal |last1=Chen |first1=Yiting |last2=Liu |first2=Tracy Xiao |last3=Shan |first3=You |last4=Zhong |first4=Songfa |title=The emergence of economic rationality of GPT |journal=[[Proceedings of the National Academy of Sciences of the United States of America|Proceedings of the National Academy of Sciences]] |date=2023 |volume=120 |issue=51 |pages=e2316205120 |doi=10.1073/pnas.2316205120 |doi-access=free|pmid=38085780 |pmc=10740389 |arxiv=2305.12763 |bibcode=2023PNAS..12016205C }}</ref> [[Credit score|credit scoring]], and [[e-commerce]]. In recent years, there have been massive advancements in the field of [[Generative artificial intelligence|Generative Artificial Intelligence]], which uses generative models to produce text, images, videos or other forms of data<ref>{{Cite web |date=2024-03-22 |title=What is Generative AI? {{!}} IBM |url=https://www.ibm.com/think/topics/generative-ai |access-date=2025-07-22 |website=www.ibm.com |language=en}}</ref>. This article describes applications of AI in different sectors.
==
{{See also|Precision agriculture|Digital agriculture}}
In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency.<ref>{{cite conference |last1=Gambhire |first1=Akshaya |last2=Shaikh Mohammad |first2=Bilal N. |title=Use of Artificial Intelligence in Agriculture |date=8 April 2020 |conference=Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST) 2020 |ssrn=3571733 }}</ref> AI has been used to attempt to [[Animal welfare#Farmed animals|classify livestock pig call emotions]],<ref name="10.1038/s41598-022-07174-8">{{cite journal |last1=Briefer |first1=Elodie F. |last2=Sypherd |first2=Ciara C.-R. |last3=Linhart |first3=Pavel |last4=Leliveld |first4=Lisette M. C. |last5=Padilla de la Torre |first5=Monica |last6=Read |first6=Eva R. |last7=Guérin |first7=Carole |last8=Deiss |first8=Véronique |last9=Monestier |first9=Chloé |last10=Rasmussen |first10=Jeppe H. |last11=Špinka |first11=Marek |last12=Düpjan |first12=Sandra |last13=Boissy |first13=Alain |last14=Janczak |first14=Andrew M. |last15=Hillmann |first15=Edna |last16=Tallet |first16=Céline |title=Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production |journal=Scientific Reports |date=7 March 2022 |volume=12 |issue=1 |pages=3409 |doi=10.1038/s41598-022-07174-8 |pmid=35256620 |pmc=8901661 |bibcode=2022NatSR..12.3409B }}</ref> automate [[greenhouse]]s,<ref>{{cite journal |last1=Moreno Millán |first1=M |last2=Sevilla Guzmán |first2=E |last3=Demyda |first3=S E |title=Population, Poverty, Production, Food Security, Food Sovereignty, Biotechnology and Sustainable Development: Challenges for the XXI Century |journal=Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Veterinary Medicine |volume=1 |issue=68 |date=2011 |url=https://ftp.keyboardedn.ro/index.php/veterinary/article/view/6771 }}</ref> detect diseases and pests,<ref>{{cite book |doi=10.1109/CITSM47753.2019.8965385 |chapter=Improving Rice Productivity in Indonesia with Artificial Intelligence |title=2019 7th International Conference on Cyber and IT Service Management (CITSM) |year=2019 |last1=Liundi |first1=Nicholas |last2=Darma |first2=Aditya Wirya |last3=Gunarso |first3=Rivaldi |last4=Warnars |first4=Harco Leslie Hendric Spits |pages=1–5 |isbn=978-1-7281-2909-9 }}</ref> and optimize irrigation.<ref>{{cite journal |last1=Talaviya |first1=Tanha |last2=Shah |first2=Dhara |last3=Patel |first3=Nivedita |last4=Yagnik |first4=Hiteshri |last5=Shah |first5=Manan |title=Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides |journal=Artificial Intelligence in Agriculture |year=2020 |volume=4 |pages=58–73 |doi=10.1016/j.aiia.2020.04.002 |doi-access=free }}</ref>
== Architecture and design ==
{{excerpt|Artificial intelligence in architecture}}AI in architecture has created a way for architects to create things beyond human understanding. AI implementation of machine learning text-to-render technologies, like DALL-E and stable Diffusion, gives power to visualization complex.<ref name="Dezeen-2022">{{Cite web |date=2022-11-16 |title=How AI software will change architecture and design |url=https://www.dezeen.com/2022/11/16/ai-design-architecture-product/ |access-date=2024-04-12 |website=Dezeen |language=en}}</ref>
AI allows designers to demonstrate their creativity and even invent new ideas while designing. In future, AI will not replace architects; instead, it will improve the speed of translating ideas sketching.<ref name="Dezeen-2022" />
== Business ==
{{See also|#Services}}
=== Content extraction ===
An [[Optical character recognition|optical character reader]] is used in the extraction of data in business documents like invoices and receipts. It can also be used in business contract documents e.g. [[Employment contract|employment agreements]] to extract critical data like employment terms, delivery terms, termination clauses, etc.<ref>{{Cite web |date=2021-10-25 |title=Smart Procurement Technologies for the Construction Sector - SIPMM Publications |url=https://publication.sipmm.edu.sg/smart-procurement-technologies-construction-sector/#Intelligent_Content_Extraction |access-date=2022-11-30 |website=publication.sipmm.edu.sg |language=en-US}}</ref>
== Computer science ==
=== Programming assistance ===
{{Main|Automatic programming|AI-assisted software development|Programming environment}}
==== AI-powered code assisting tools ====
AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors and [[Integrated Development Environment|IDE]]s as [[plugin (computing)|plugin]]s. They differ in functionality, quality, speed, and approach to privacy. Code suggestions could be incorrect, and should be carefully reviewed by software developers before accepted.{{cn|date=June 2025}} [[GitHub Copilot]] is one example. It was developed by [[GitHub]] and [[OpenAI]] and is able to autocomplete code in multiple programming languages.<ref>{{Cite news|last=Gershgorn|first=Dave|date=29 June 2021|title=GitHub and OpenAI launch a new AI tool that generates its own code|work=[[The Verge]]|url=https://www.theverge.com/2021/6/29/22555777/github-openai-ai-tool-autocomplete-code|access-date=3 September 2021}}</ref>
====
AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created [[NASNet]], a system optimized for [[ImageNet]] and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet.<ref>{{cite news|date=5 December 2017|title=Google AI creates its own "child" bot|work=The Independent|url=https://www.independent.co.uk/life-style/gadgets-and-tech/news/google-child-ai-bot-nasnet-automl-machine-learning-artificial-intelligence-a8093201.html|access-date=5 February 2018}}</ref>
====
{{Further|Quantum machine learning}}
{{See also|#Chemistry and biology}}
Machine learning has been used for noise-cancelling in [[quantum technology]],<ref>{{cite web |title=Cancelling quantum noise |url=https://www.uts.edu.au/about/faculty-engineering-and-information-technology/news/cancelling-quantum-noise |website=University of Technology Sydney |access-date=29 May 2022 |language=en |date=23 May 2019}}</ref> including [[quantum sensor]]s.<ref>{{cite news |title=Machine learning paves the way for next-level quantum sensing |url=https://phys.org/news/2019-05-machine-paves-next-level-quantum.html |access-date=29 May 2022 |work=University of Bristol |language=en}}</ref> Moreover, there is substantial research and development of using quantum computers with machine learning algorithms. For example, there is a prototype, photonic, {{tooltip|2=it is 'able to produce memristive dynamics on single-photon states through a scheme of measurement and classical feedback'|quantum [[memristor|memristive device]]}} for [[neuromorphic computing|neuromorphic (quantum-)computers]] (NC)/[[artificial neural network]]s and NC-using quantum materials with some variety of potential neuromorphic computing-related applications,<ref>{{cite journal |last1=Spagnolo |first1=Michele |last2=Morris |first2=Joshua |last3=Piacentini |first3=Simone |last4=Antesberger |first4=Michael |last5=Massa |first5=Francesco |last6=Crespi |first6=Andrea |last7=Ceccarelli |first7=Francesco |last8=Osellame |first8=Roberto |last9=Walther |first9=Philip |title=Experimental photonic quantum memristor |journal=Nature Photonics |date=April 2022 |volume=16 |issue=4 |pages=318–323 |doi=10.1038/s41566-022-00973-5 |arxiv=2105.04867 |bibcode=2022NaPho..16..318S }}</ref><ref>{{cite journal |last1=Ramanathan |first1=Shriram |title=Quantum materials for brain sciences and artificial intelligence |journal=MRS Bulletin |date=July 2018 |volume=43 |issue=7 |pages=534–540 |doi=10.1557/mrs.2018.147 |doi-access=free |bibcode=2018MRSBu..43..534R }}</ref> and [[quantum machine learning]] is a field with some variety of applications under development. AI could be used for [[quantum simulator]]s which may have the application of solving physics and [[Quantum chemistry|chemistry]]<ref>{{cite journal |title=Artificial intelligence makes accurate quantum chemical simulations more affordable |journal=Nature Portfolio Chemistry Community |date=2 December 2021 |url=https://chemistrycommunity.nature.com/posts/artificial-intelligence-makes-accurate-quantum-chemical-simulations-more-affordable |access-date=30 May 2022 |language=en}}</ref><ref>{{cite journal |last1=Guan |first1=Wen |last2=Perdue |first2=Gabriel |last3=Pesah |first3=Arthur |last4=Schuld |first4=Maria |last5=Terashi |first5=Koji |last6=Vallecorsa |first6=Sofia |last7=Vlimant |first7=Jean-Roch |title=Quantum machine learning in high energy physics |journal=Machine Learning: Science and Technology |date=March 2021 |volume=2 |issue=1 |pages=011003 |doi=10.1088/2632-2153/abc17d |doi-access=free |arxiv=2005.08582 }}</ref> problems as well as for [[quantum annealer]]s for training of neural networks for AI applications.<ref>{{cite news |title=Europe's First Quantum Computer with More Than 5K Qubits Launched at Jülich |url=https://www.hpcwire.com/off-the-wire/europes-first-quantum-computer-with-more-than-5k-qubits-launched-at-julich/ |access-date=30 May 2022 |work=HPCwire}}</ref> There may also be some usefulness in chemistry, e.g. for drug discovery, and in materials science, e.g. for materials optimization/discovery (with possible relevance to quantum materials manufacturing<ref name="10.1038/s43246-021-00209-z">{{cite journal |last1=Stanev |first1=Valentin |last2=Choudhary |first2=Kamal |last3=Kusne |first3=Aaron Gilad |last4=Paglione |first4=Johnpierre |last5=Takeuchi |first5=Ichiro |title=Artificial intelligence for search and discovery of quantum materials |journal=Communications Materials |date=13 October 2021 |volume=2 |issue=1 |page=105 |doi=10.1038/s43246-021-00209-z |bibcode=2021CoMat...2..105S |doi-access=free }}</ref><ref name="10.1002/adma.202109892">{{cite journal |last1=Glavin |first1=Nicholas R. |last2=Ajayan |first2=Pulickel M. |last3=Kar |first3=Swastik |title=Quantum Materials Manufacturing |journal=Advanced Materials |date=23 February 2022 |volume=35 |issue=27 |pages=2109892 |doi=10.1002/adma.202109892 |pmid=35195312 }}</ref>).<ref>{{cite book |doi=10.1007/978-1-0716-1787-8_14 |chapter=Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors |title=Artificial Intelligence in Drug Design |series=Methods in Molecular Biology |date=2022 |last1=Cova |first1=Tânia |last2=Vitorino |first2=Carla |last3=Ferreira |first3=Márcio |last4=Nunes |first4=Sandra |last5=Rondon-Villarreal |first5=Paola |last6=Pais |first6=Alberto |volume=2390 |pages=321–347 |pmid=34731476 |isbn=978-1-0716-1786-1 }}</ref><ref>{{cite journal |last1=Batra |first1=Kushal |last2=Zorn |first2=Kimberley M. |last3=Foil |first3=Daniel H. |last4=Minerali |first4=Eni |last5=Gawriljuk |first5=Victor O. |last6=Lane |first6=Thomas R. |last7=Ekins |first7=Sean |title=Quantum Machine Learning Algorithms for Drug Discovery Applications |journal=Journal of Chemical Information and Modeling |date=28 June 2021 |volume=61 |issue=6 |pages=2641–2647 |doi=10.1021/acs.jcim.1c00166 |pmid=34032436 |pmc=8254374 }}</ref><ref>{{cite journal |last1=Barkoutsos |first1=Panagiotis Kl |last2=Gkritsis |first2=Fotios |last3=Ollitrault |first3=Pauline J. |last4=Sokolov |first4=Igor O. |last5=Woerner |first5=Stefan |last6=Tavernelli |first6=Ivano |title=Quantum algorithm for alchemical optimization in material design |journal=Chemical Science |date=April 2021 |volume=12 |issue=12 |pages=4345–4352 |doi=10.1039/D0SC05718E |pmid=34163697 |pmc=8179438 }}</ref>{{better source needed|date=May 2022|reason=Better citations needed for quantum machine learning in materials science}}
===
AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. All of the following were originally developed in AI laboratories:<ref>{{Russell Norvig 2003}}</ref>
* [[Time sharing]]
* [[Interpreted language|Interactive interpreters]]
* [[Graphical user interface]]s and the [[computer mouse]]
* [[Rapid application development]] environments
* The [[linked list]] data structure
* [[Automatic storage management]]
* [[Third-generation programming language|Symbolic programming]]
* [[Functional programming]]
* [[Dynamic programming]]
* [[Object-oriented programming]]
* [[Optical character recognition]]
* [[Constraint satisfaction]]
==
=== Human resources ===
{{Main|Artificial intelligence in hiring}}
Another application of AI is in human resources. AI can screen resumes and rank candidates based on their qualifications, predict candidate success in given roles, and automate repetitive communication tasks via chatbots.{{citation needed|date=April 2025}}
=== Job search ===
AI has simplified the recruiting/job search process for both recruiters and job seekers. According to [[Raj Mukherjee]] from [[Indeed]], 65% of job searchers search again within 91 days after hire. An AI-powered engine streamlines the complexity of job hunting by assessing information on job skills, salaries, and user tendencies, matching job seekers to the most relevant positions. Machine intelligence calculates appropriate wages and highlights resume information for recruiters using NLP, which extracts relevant words and phrases from text. Another application is an AI resume builder that compiles a CV in 5 minutes.{{citation needed|date=April 2025}} [[Chatbots]] assist website visitors and refine workflows.
=== Online and telephone customer service ===
[[File:Automated online assistant.png|thumb|170px|An [[automated online assistant]] providing [[customer service]] on a web page]]
AI underlies [[Avatar (computing)#Artificial intelligence|avatars]] ([[automated online assistant]]s) on web pages.<ref name="Kongthon">{{cite book |doi=10.1145/1643823.1643908 |chapter=Implementing an online help desk system based on conversational agent |title=Proceedings of the International Conference on Management of Emergent Digital EcoSystems |date=2009 |last1=Kongthon |first1=Alisa |last2=Sangkeettrakarn |first2=Chatchawal |last3=Kongyoung |first3=Sarawoot |last4=Haruechaiyasak |first4=Choochart |pages=450–451 |isbn=978-1-60558-829-2 }}</ref> It can reduce operation and training costs.<ref name="Kongthon" /> [[Pypestream]] automated customer service for its mobile application to streamline communication with customers.<ref>{{cite web|author=Sara Ashley O'Brien|date=12 January 2016|title=Is this app the call center of the future?|url=https://money.cnn.com/2016/01/12/technology/startup-pypestream/|access-date=26 September 2016|publisher=CNN}}</ref>
A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately.<ref>{{cite news |last1=Clark |first1=Jack |title=New Google AI Brings Automation to Customer Service |url=https://www.bloomberg.com/news/articles/2016-07-20/new-google-ai-services-bring-automation-to-customer-service-iqv2rshg |work=Bloomberg.com |date=20 July 2016 |url-access=subscription }}</ref> Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds and connecting the customer with a human representative.<ref>{{Cite web|date=25 February 2020|title=Amazon.com tests customer service chatbots|url=https://www.amazon.science/blog/amazon-com-tests-customer-service-chatbots|access-date=23 April 2021|website=Amazon Science|language=en}}</ref> Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making.<ref>{{Cite journal |last1=Malatya Turgut Ozal University, Malatya, Turkey |last2=Isguzar |first2=Seda |last3=Fendoglu |first3=Eda |last4=Malatya Turgut Ozal University, Malatya, Turkey |last5=SimSek |first5=Ahmed Ihsan |date=May 2024 |title=Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence |url=http://www.amfiteatrueconomic.ro/temp/Article_3320.pdf |journal=Amfiteatru Economic |volume=26 |issue=66 |pages=511 |doi=10.24818/EA/2024/66/511 |access-date=13 June 2024}}</ref>
=== Hospitality ===
In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs.<ref>{{cite web|year=2017|title=Advanced analytics in hospitality|url=https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/advanced-analytics-in-hospitality|access-date=14 January 2020|work=McKinsey & Company}}</ref> AI hotel services come in the form of a chatbot,<ref>{{cite book |doi=10.15308/Sinteza-2019-84-90 |chapter=Current Applications of Artificial Intelligence in Tourism and Hospitality |title=Proceedings of the International Scientific Conference - Sinteza 2019 |year=2019 |last1=Zlatanov |first1=Sonja |last2=Popesku |first2=Jovan |pages=84–90 |isbn=978-86-7912-703-7 }}</ref> application, virtual voice assistant and service robots.
== Education ==
{{See also|AI in education}}
AI elevates teaching, focusing on significant issues like the knowledge nexus and educational equality. The evolution of AI in education and technology should be used to improve human capabilities in relationships where they do not replace humans. [[UNESCO]] recognizes the future of AI in education as an instrument to reach Sustainable Development Goal 4, called "Inclusive and Equitable Quality Education."<ref name="Harvard-2023">{{Cite web |date=2023-02-09 |title=AI in Education{{!}} Harvard Graduate School of Education |url=https://www.gse.harvard.edu/ideas/edcast/23/02/educating-world-artificial-intelligence |access-date=2024-04-20 |website=www.gse.harvard.edu |language=en}}</ref>
The [[World Economic Forum]] also stresses AI's contribution to students' overall improvement and transforming teaching into a more enjoyable process.<ref name="Harvard-2023" />
===Personalized learning===
AI driven tutoring systems (such as Khan Academy, Duolingo and Carnegie Learning) are the forefoot of delivering personalized education.<ref name="nair-2021">{{Cite web |last=nair |first=madhu |date=2021-03-10 |title=AI In Education: Where Is It Now And What Is The Future |url=https://www.uopeople.edu/blog/ai-in-education-where-is-it-now-and-what-is-the-future/ |access-date=2024-04-20 |website=University of the People |language=en-US}}</ref>
These platforms leverage AI algorithms to analyze individual learning patterns, strengths, and weaknesses, enabling the customization of content and Algorithm to suit each student's pace and style of learning.<ref name="nair-2021" />
===Administrative efficiency===
In educational institutions, AI is increasingly used to automate routine tasks like attendance tracking, grading and marking, which allows educators to devote more time to interactive teaching and direct student engagement.<ref name="Brookings">{{Cite web |title=The promises and perils of new technologies to improve education and employment opportunities |url=https://www.brookings.edu/articles/the-promises-and-perils-of-new-technologies-to-improve-education-and-employment-opportunities/ |access-date=2024-04-20 |website=Brookings |language=en-US}}</ref>
Furthermore, AI tools are employed to monitor student progress, analyze learning behaviors, and predict academic challenges, facilitating timely and proactive interventions for students who may be at risk of falling behind.<ref name="Brookings" />
===Ethical and privacy concerns===
Despite the benefits, the integration of AI in education raises significant ethical and privacy concerns, particularly regarding the handling of sensitive student data.<ref name="nair-2021" />
It is imperative that AI systems in education are designed and operated with a strong emphasis on transparency, security, and respect for privacy to maintain trust and uphold the integrity of educational practices.<ref name="nair-2021" />
Much of the regulation will be influenced by the AI Act, the world's first comprehensive AI law.<ref>{{cite web | url=https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence | title=EU AI Act: First regulation on artificial intelligence | date=6 August 2023 }}</ref>
== Energy and environment ==
===Energy system===
[[Power electronics]] converters are used in [[renewable energy]], [[energy storage]], [[electric vehicle]]s and [[high-voltage direct current]] transmission. These converters are failure-prone, which can interrupt service and require costly maintenance or catastrophic consequences in mission critical applications.{{citation needed|date=January 2019}} AI can guide the design process for reliable power electronics converters, by calculating exact design parameters that ensure the required lifetime.<ref>{{cite journal |last1=Dragicevic |first1=Tomislav |last2=Wheeler |first2=Patrick |last3=Blaabjerg |first3=Frede |title=Artificial Intelligence Aided Automated Design for Reliability of Power Electronic Systems |journal=IEEE Transactions on Power Electronics |date=August 2019 |volume=34 |issue=8 |pages=7161–7171 |doi=10.1109/TPEL.2018.2883947 |bibcode=2019ITPE...34.7161D |doi-access=free }}</ref>
The U.S. Department of Energy underscores AI's pivotal role in realizing national climate goals. With AI, the ambitious target of achieving net-zero greenhouse gas emissions across the economy becomes feasible. AI also helps make room for wind and solar on the grid by avoiding congestion and increasing grid reliability.<ref name="DOE">{{cite web |title=Role of AI in Energy |url=https://www.energy.gov/cet/articles/ai-energy |website=DOE}}</ref>
Machine learning can be used for energy consumption prediction and scheduling, e.g. to help with [[100% renewable energy#Intermittency|renewable energy intermittency management]] (see also: [[smart grid]] and [[Climate change mitigation#Smart grid and load management|climate change mitigation in the power grid]]).<ref>{{cite journal |last1=Bourhnane |first1=Safae |last2=Abid |first2=Mohamed Riduan |last3=Lghoul |first3=Rachid |last4=Zine-Dine |first4=Khalid |last5=Elkamoun |first5=Najib |last6=Benhaddou |first6=Driss |title=Machine learning for energy consumption prediction and scheduling in smart buildings |journal=SN Applied Sciences |date=30 January 2020 |volume=2 |issue=2 |pages=297 |doi=10.1007/s42452-020-2024-9 |doi-access=free }}</ref><ref>{{cite journal |last1=Kanwal |first1=Sidra |last2=Khan |first2=Bilal |last3=Muhammad Ali |first3=Sahibzada |title=Machine learning based weighted scheduling scheme for active power control of hybrid microgrid |journal=International Journal of Electrical Power & Energy Systems |date=February 2021 |volume=125 |pages=106461 |doi=10.1016/j.ijepes.2020.106461 |bibcode=2021IJEPE.12506461K }}</ref><ref>{{cite book |doi=10.1109/POWERCON48463.2020.9230627 |chapter=Home Electric Vehicle Charge Scheduling Using Machine Learning Technique |title=2020 IEEE International Conference on Power Systems Technology (POWERCON) |date=2020 |last1=Mohanty |first1=Prasanta Kumar |last2=Jena |first2=Premalata |last3=Padhy |first3=Narayana Prasad |pages=1–5 |isbn=978-1-7281-6350-5 }}</ref><ref>{{cite web |last1=Foster |first1=Isabella |title=Making Smart Grids Smarter with Machine Learning |url=https://www.eit.edu.au/making-smart-grids-smarter-with-machine-learning/ |website=EIT {{!}} Engineering Institute of Technology |access-date=3 July 2022 |language=en-AU |date=15 March 2021}}</ref><ref name="Ciaramella211" />
=== Environmental monitoring ===
{{See also|Climate-smart agriculture}}
Autonomous ships that monitor the ocean, AI-driven satellite data analysis, [[passive acoustics]]<ref>{{cite journal |last1=Williams |first1=Ben |last2=Lamont |first2=Timothy A. C. |last3=Chapuis |first3=Lucille |last4=Harding |first4=Harry R. |last5=May |first5=Eleanor B. |last6=Prasetya |first6=Mochyudho E. |last7=Seraphim |first7=Marie J. |last8=Jompa |first8=Jamaluddin |last9=Smith |first9=David J. |last10=Janetski |first10=Noel |last11=Radford |first11=Andrew N. |last12=Simpson |first12=Stephen D. |title=Enhancing automated analysis of marine soundscapes using ecoacoustic indices and machine learning |journal=Ecological Indicators |date=July 2022 |volume=140 |pages=108986 |doi=10.1016/j.ecolind.2022.108986 |doi-access=free |bibcode=2022EcInd.14008986W |hdl=10871/129693 |hdl-access=free }}</ref> or [[remote sensing]] and other applications of [[environmental monitoring]] make use of machine learning.<ref>{{cite journal |last1=Hino |first1=M. |last2=Benami |first2=E. |last3=Brooks |first3=N. |title=Machine learning for environmental monitoring |journal=Nature Sustainability |date=October 2018 |volume=1 |issue=10 |pages=583–588 |doi=10.1038/s41893-018-0142-9 |bibcode=2018NatSu...1..583H }}</ref><ref>{{cite web |title=How machine learning can help environmental regulators |url=https://news.stanford.edu/2019/04/08/machine-learning-can-help-environmental-regulators/ |website=Stanford News |publisher=Stanford University |access-date=29 May 2022 |language=en |date=8 April 2019}}</ref><ref>{{cite web |title=AI empowers environmental regulators |url=https://news.stanford.edu/2021/04/19/ai-empowers-environmental-regulators/ |website=Stanford News |publisher=Stanford University |access-date=29 May 2022 |language=en |date=19 April 2021}}</ref><ref name="esaai"/>
For example, "Global Plastic Watch" is an AI-based [[Earth observation satellite#Environmental monitoring|satellite monitoring]]-platform for analysis/tracking of [[plastic waste]] sites to help [[waste management|prevention]] of [[plastic pollution]] – primarily [[ocean pollution]] – by helping identify who and where mismanages plastic waste, dumping it into oceans.<ref>{{cite news |last1=Frost |first1=Rosie |title=Plastic waste can now be found and monitored from space |url=https://www.euronews.com/green/2022/05/09/the-world-s-plastic-waste-has-been-mapped-from-space-for-the-first-time-ever |access-date=24 June 2022 |work=euronews |date=9 May 2022 |language=en}}</ref><ref>{{cite web |title=Global Plastic Watch |url=https://globalplasticwatch.org/map |website=www.globalplasticwatch.org |access-date=24 June 2022 |language=en}}</ref>
=== Early-warning systems ===
Machine learning can be used to [[early warning system|spot early-warning signs]] of disasters and environmental issues, possibly including natural [[pandemic prevention|pandemics]],<ref>{{cite news |title=AI may predict the next virus to jump from animals to humans |url=https://phys.org/news/2021-09-ai-virus-animals-humans.html |access-date=19 October 2021 |work=Public Library of Science |language=en}}</ref><ref>{{cite journal |last1=Mollentze |first1=Nardus |last2=Babayan |first2=Simon A. |last3=Streicker |first3=Daniel G. |title=Identifying and prioritizing potential human-infecting viruses from their genome sequences |journal=PLOS Biology |date=28 September 2021 |volume=19 |issue=9 |pages=e3001390 |pmid=34582436 |doi=10.1371/journal.pbio.3001390 |pmc=8478193 |doi-access=free }}</ref> earthquakes,<ref>{{cite journal |last1=Li |first1=Zefeng |last2=Meier |first2=Men-Andrin |last3=Hauksson |first3=Egill |last4=Zhan |first4=Zhongwen |last5=Andrews |first5=Jennifer |title=Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning |journal=Geophysical Research Letters |date=28 May 2018 |volume=45 |issue=10 |pages=4773–4779 |doi=10.1029/2018GL077870 |bibcode=2018GeoRL..45.4773L |doi-access=free }}</ref><ref>{{cite news |title=Machine learning and gravity signals could rapidly detect big earthquakes |url=https://www.sciencenews.org/article/machine-learning-gravity-earthquake-ai |access-date=3 July 2022 |work=Science News |date=11 May 2022}}</ref><ref>{{cite journal |last1=Fauvel |first1=Kevin |last2=Balouek-Thomert |first2=Daniel |last3=Melgar |first3=Diego |last4=Silva |first4=Pedro |last5=Simonet |first5=Anthony |last6=Antoniu |first6=Gabriel |last7=Costan |first7=Alexandru |last8=Masson |first8=Véronique |last9=Parashar |first9=Manish |last10=Rodero |first10=Ivan |last11=Termier |first11=Alexandre |title=A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning |journal=Proceedings of the AAAI Conference on Artificial Intelligence |date=3 April 2020 |volume=34 |issue=1 |pages=403–411 |doi=10.1609/aaai.v34i01.5376 |doi-access=free }}</ref> landslides,<ref>{{cite journal |last1=Thirugnanam |first1=Hemalatha |last2=Ramesh |first2=Maneesha Vinodini |last3=Rangan |first3=Venkat P. |title=Enhancing the reliability of landslide early warning systems by machine learning |journal=Landslides |date=September 2020 |volume=17 |issue=9 |pages=2231–2246 |doi=10.1007/s10346-020-01453-z |bibcode=2020Lands..17.2231T }}</ref> heavy rainfall,<ref>{{cite journal |last1=Moon |first1=Seung-Hyun |last2=Kim |first2=Yong-Hyuk |last3=Lee |first3=Yong Hee |last4=Moon |first4=Byung-Ro |title=Application of machine learning to an early warning system for very short-term heavy rainfall |journal=Journal of Hydrology |date=2019 |volume=568 |pages=1042–1054 |doi=10.1016/j.jhydrol.2018.11.060 |bibcode=2019JHyd..568.1042M }}</ref> long-term water supply vulnerability,<ref>{{cite journal |last1=Robinson |first1=Bethany |last2=Cohen |first2=Jonathan S. |last3=Herman |first3=Jonathan D. |title=Detecting early warning signals of long-term water supply vulnerability using machine learning |journal=Environmental Modelling & Software |date=September 2020 |volume=131 |pages=104781 |doi=10.1016/j.envsoft.2020.104781 |doi-access=free |bibcode=2020EnvMS.13104781R }}</ref> tipping-points of [[ecosystem collapse]],<ref>{{cite journal |last1=Bury |first1=Thomas M. |last2=Sujith |first2=R. I. |last3=Pavithran |first3=Induja |last4=Scheffer |first4=Marten |last5=Lenton |first5=Timothy M. |last6=Anand |first6=Madhur |last7=Bauch |first7=Chris T. |title=Deep learning for early warning signals of tipping points |journal=Proceedings of the National Academy of Sciences |date=28 September 2021 |volume=118 |issue=39 |pages=e2106140118 |doi=10.1073/pnas.2106140118 |pmid=34544867 |pmc=8488604 |bibcode=2021PNAS..11806140B |doi-access=free }}</ref> [[cyanobacterial bloom]] outbreaks,<ref>{{cite journal |last1=Park |first1=Yongeun |last2=Lee |first2=Han Kyu |last3=Shin |first3=Jae-Ki |last4=Chon |first4=Kangmin |last5=Kim |first5=SungHwan |last6=Cho |first6=Kyung Hwa |last7=Kim |first7=Jin Hwi |last8=Baek |first8=Sang-Soo |title=A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir |journal=Journal of Environmental Management |date=15 June 2021 |volume=288 |pages=112415 |doi=10.1016/j.jenvman.2021.112415 |pmid=33774562 |bibcode=2021JEnvM.28812415P }}</ref> and droughts.<ref>{{cite journal |last1=Li |first1=Jun |last2=Wang |first2=Zhaoli |last3=Wu |first3=Xushu |last4=Xu |first4=Chong-Yu |last5=Guo |first5=Shenglian |last6=Chen |first6=Xiaohong |last7=Zhang |first7=Zhenxing |title=Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning |journal=Water Resources Research |date=August 2021 |volume=57 |issue=8 |doi=10.1029/2020WR029413 |bibcode=2021WRR....5729413L |hdl=10852/92935 |hdl-access=free }}</ref><ref>{{cite journal |last1=Khan |first1=Najeebullah |last2=Sachindra |first2=D. A. |last3=Shahid |first3=Shamsuddin |last4=Ahmed |first4=Kamal |last5=Shiru |first5=Mohammed Sanusi |last6=Nawaz |first6=Nadeem |title=Prediction of droughts over Pakistan using machine learning algorithms |journal=Advances in Water Resources |date=May 2020 |volume=139 |pages=103562 |doi=10.1016/j.advwatres.2020.103562 |bibcode=2020AdWR..13903562K }}</ref><ref>{{cite journal |last1=Kaur |first1=Amandeep |last2=Sood |first2=Sandeep K. |title=Deep learning based drought assessment and prediction framework |journal=Ecological Informatics |date=May 2020 |volume=57 |pages=101067 |doi=10.1016/j.ecoinf.2020.101067 |bibcode=2020EcInf..5701067K }}</ref>
=== Economic and social challenges ===
{{See also|#Environmental monitoring}}
[[ITU AI for Good|AI for Good]] is a platform launched in 2017 by the [[International Telecommunication Union]] (ITU) agency of the United Nations (UN). The goal of the platform is to use AI to help achieve the UN's [[Sustainable Development Goals]].{{citation needed|date=December 2024}}
The [[University of Southern California]] launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness. [[Stanford University|Stanford]] researchers use [[Artificial intelligence|AI]] to analyze satellite images to identify high poverty areas.<ref>{{Cite book |publisher=National Science and Technology Council|url=https://www.govinfo.gov/app/details/GOVPUB-PREX23-PURL-gpo75059|access-date=7 December 2024 |title=Preparing for the future of artificial intelligence|oclc=965620122 |page=14}}</ref>
== Entertainment and media ==
===
{{See also|#Telecommunications|Synthetic media}}
[[File:Restoration using Artificial intelligence.jpg|thumb|Image restoration]]
AI applications analyze media content such as movies, TV programs, advertisement videos or [[user-generated content]]. The solutions often involve [[computer vision]].
Typical scenarios include the analysis of images using [[object recognition]] or face recognition techniques, or the [[video content analysis|analysis of video]] for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), [[speech recognition|speech to text]] for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement.
* [[Motion interpolation]]<ref>{{Cite web |title=Research at NVIDIA: Transforming Standard Video Into Slow Motion with AI | date=18 June 2018 |url=https://www.youtube.com/watch?v=MjViy6kyiqs |url-status=live |archive-url=https://ghostarchive.org/varchive/youtube/20211221/MjViy6kyiqs |archive-date=21 December 2021 |via=YouTube}}{{cbignore}}</ref>
* [[Pixel-art scaling algorithms]]<ref>{{Cite web |date=18 April 2019 |title=Artificial intelligence is helping old video games look like new |url=https://www.theverge.com/2019/4/18/18311287/ai-upscaling-algorithms-video-games-mods-modding-esrgan-gigapixel |website=The Verge}}</ref>
* [[Image scaling]]<ref>{{Cite web |date=4 March 2019 |title=Review: Topaz Sharpen AI is Amazing |url=https://petapixel.com/2019/03/04/review-topaz-sharpen-ai-is-amazing/ |website=petapixel.com}}</ref>
* [[Image restoration by artificial intelligence|Image restoration]]<ref>{{Cite web |last=Griffin |first=Matthew |date=26 April 2018 |title=AI can now restore your corrupted photos to their original condition |url=https://www.311institute.com/ai-can-now-restore-your-corrupted-photos-to-its-pristine-original-condition/}}</ref><ref>{{Cite web |title=NVIDIA's AI can fix bad photos by looking at other bad photos |url=https://www.engadget.com/2018-07-10-nvidia-ai-fix-bad-photos-deep-learning.html |website=Engadget|date=10 July 2018 }}</ref>
* [[Hand-colouring of photographs|Photo colorization]]<ref>{{Cite web |date=24 February 2020 |title=Using AI to Colorize and Upscale a 109-Year-Old Video of New York City to 4K and 60fps |url=https://petapixel.com/2020/02/24/using-ai-to-colorize-and-upscale-a-109-year-old-video-of-new-york-city-to-4k-and-60fps/ |website=petapixel.com}}</ref>
* [[Film restoration]] and video upscaling<ref>{{Cite magazine |title=YouTubers are upscaling the past to 4K. Historians want them to stop |url=https://www.wired.co.uk/article/history-colourisation-controversy |magazine=Wired UK}}</ref>
* Photo tagging<ref>{{Cite web |date=3 July 2019 |title=Facebook's image outage reveals how the company's AI tags your photos |url=https://www.theverge.com/2019/7/3/20681231/facebook-outage-image-tags-captions-ai-machine-learning-revealed |website=The Verge}}</ref>
* [[Automated species identification]] (such as identifying plants, fungi and animals with an app)<!--not media-->
* [[Text-to-image model]]s such as [[DALL-E]], [[Midjourney]] and [[Stable Diffusion]]
* Image to video<ref>{{cite web | url=https://www.popsci.com/technology/one-image-video-deepmind/?amp | title=Google's DeepMind AI can 'transframe' a single image into a video | date=18 August 2022 }}</ref>
* Text to video such as Make-A-Video from Meta, Imagen video and Phenaki from Google
* Text to music with AI models such as MusicLM<ref>{{cite web | url=https://www.theverge.com/2023/1/28/23574573/google-musiclm-text-to-music-ai | title=Google's new AI turns text into music | date=28 January 2023 }}</ref><ref>{{cite web | url=https://www.euronews.com/next/2023/01/30/musiclm-googles-new-ai-tool-can-turn-text-whistling-and-humming-into-actual-music | title=Google's new AI music generator can create - and hold - a tune | date=30 January 2023 }}</ref>
* Text to speech such as [[ElevenLabs]] and [[15.ai]]
* [[Motion capture]]<ref>{{cite web | url=https://www.computer.org/csdl/journal/tp/2019/04/08316924/13rRUEgarkK | title=CSDL | IEEE Computer Society }}</ref>
=== Deep-fakes ===
[[Deepfakes|Deep-fakes]] can be used for comedic purposes but are better known for [[fake news]] and hoaxes.
Deepfakes can portray individuals in harmful or compromising situations, causing significant reputational damage and emotional distress, especially when the content is defamatory or violates personal ethics. While defamation and false light laws offer some recourse, their focus on false statements rather than fabricated images or videos often leaves victims with limited legal protection and a challenging burden of proof.<ref>{{Cite web |last=Jodka |first=Sara |date=February 1, 2024 |title=Manipulating reality: the intersection of deepfakes and the law |url=https://www.reuters.com/legal/legalindustry/manipulating-reality-intersection-deepfakes-law-2024-02-01/ |access-date=December 8, 2024 |website=Reuters.com}}</ref>
In January 2016,<ref name="invid-project.eu-kick-off">{{cite web |title=InVID kick-off meeting |url=https://www.invid-project.eu/invid-kick-off-meeting/ |website=InVID project |access-date=23 December 2021 |date=22 January 2016 |quote=We are kicking-off the new H2020 InVID research project.}}</ref> the [[Horizon 2020]] program financed the InVID Project<ref>(In Video [[Veritas]])</ref><ref name="invid-project.eu/consortium">{{cite web |title=Consortium of the InVID project |url=https://www.invid-project.eu/consortium/ |website=InVID project |access-date=23 December 2021 |quote=The InVID vision: The InVID innovation action develops a knowledge verification platform to detect emerging stories and assess the reliability of newsworthy video files and content spread via social media.}}</ref> to help journalists and researchers detect fake documents, made available as browser plugins.<ref>{{cite book |doi=10.1007/978-3-030-26752-0_9 |chapter=Applying Design Thinking Methodology: The InVID Verification Plugin |title=Video Verification in the Fake News Era |year=2019 |last1=Teyssou |first1=Denis |pages=263–279 |isbn=978-3-030-26751-3 }}</ref><ref name="chrome-webstore-fake-news-debunker">{{cite web |title=Fake news debunker by InVID & WeVerify |url=https://chrome.google.com/webstore/detail/fake-news-debunker-by-inv/mhccpoafgdgbhnjfhkcmgknndkeenfhe |access-date=23 December 2021 |language=en}}</ref>
In June 2016, the visual computing group of the [[Technical University of Munich]] and from [[Stanford University]] developed Face2Face,<ref name="face">{{Cite web|url=http://www.niessnerlab.org/projects/thies2016face.html|title=TUM Visual Computing & Artificial Intelligence: Prof. Matthias Nießner|website=niessnerlab.org}}</ref> a program that animates photographs of faces, mimicking the facial expressions of another person. The technology has been demonstrated animating the faces of people including [[Barack Obama]] and [[Vladimir Putin]]. Other methods have been demonstrated based on [[deep neural network]]s, from which the name ''deep fake'' was taken.
In September 2018, U.S. Senator [[Mark Warner]] proposed to penalize [[social media]] companies that allow sharing of deep-fake documents on their platforms.<ref name="wpdp">{{Cite magazine|date=November 2018|title=Will "Deepfakes" Disrupt the Midterm Election?|url=https://www.wired.com/story/will-deepfakes-disrupt-the-midterm-election/|magazine=Wired}}</ref>
In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames.<ref name="Nozick2018">{{cite book |doi=10.1109/WIFS.2018.8630761 |arxiv=1809.00888 |title=2018 IEEE International Workshop on Information Forensics and Security (WIFS) |year=2018 |last1=Afchar |first1=Darius |last2=Nozick |first2=Vincent |last3=Yamagishi |first3=Junichi |last4=Echizen |first4=Isao |chapter=MesoNet: A Compact Facial Video Forgery Detection Network |pages=1–7 |isbn=978-1-5386-6536-7 }}</ref> [[DARPA]] gave 68 million dollars to work on deep-fake detection.<ref name="Nozick2018" />
[[Audio deepfake]]s<ref>{{Cite web|last=Lyons|first=Kim|date=29 January 2020|title=FTC says the tech behind audio deepfakes is getting better|url=https://www.theverge.com/2020/1/29/21080553/ftc-deepfakes-audio-cloning-joe-rogan-phone-scams|website=The Verge}}</ref><ref>{{Cite web|title=Audio samples from "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis"|url=https://google.github.io/tacotron/publications/speaker_adaptation/|website=google.github.io}}</ref> and AI software capable of detecting deep-fakes and cloning human voices have been developed.<ref>{{cite news |last1=Strickland |first1=Eliza |title=Facebook AI Launches Its Deepfake Detection Challenge |url=https://spectrum.ieee.org/facebook-ai-launches-its-deepfake-detection-challenge |work=IEEE Spectrum |date=11 December 2019 }}</ref><ref>{{Cite web|url=https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html|title=Contributing Data to Deepfake Detection Research|website=ai.googleblog.com|date=24 September 2019 }}</ref>
[[Respeecher]] is a program that enables one person to speak with the voice of another.
=== Video surveillance analysis and manipulated media detection ===
{{See also|Web scraping|Photograph manipulation|Video manipulation}}
{{Excerpt|Video content analysis|Artificial Intelligence}}
AI algorithms have been used to detect deepfake videos.<ref>{{cite news |last1=Ober |first1=Holly |title=New method detects deepfake videos with up to 99% accuracy |url=https://techxplore.com/news/2022-05-method-deepfake-videos-accuracy.html |access-date=3 July 2022 |work=University of California-Riverside |language=en}}</ref><ref>{{cite news |title=AI algorithm detects deepfake videos with high accuracy |url=https://techxplore.com/news/2020-07-ai-algorithm-deepfake-videos-high.html |access-date=3 July 2022 |work=techxplore.com |language=en}}</ref>
=== Video production ===
[[Artificial intelligence]] is also starting to be used in video production, with tools and software being developed that utilize generative AI in order to create new video, or alter existing video. Some of the major tools that are being used in these processes currently are DALL-E, Mid-journey, and Runway.<ref name="MIT-2023">{{Cite web |title=Welcome to the new surreal. How AI-generated video is changing film. |url=https://www.technologyreview.com/2023/06/01/1073858/surreal-ai-generative-video-changing-film/ |access-date=2023-12-05 |website=MIT Technology Review |language=en}}</ref> Way mark Studios utilized the tools offered by both [[DALL-E]] and [[Midjourney|Mid-journey]] to create a fully AI generated film called ''The Frost'' in the summer of 2023.<ref name="MIT-2023" /> Way mark Studios is experimenting with using these AI tools to generate advertisements and commercials for companies in mere seconds.<ref name="MIT-2023" /> Yves Bergquist, a director of the AI & [[Neuroscience]] in Media Project at USC's Entertainment Technology Center, says post production crews in Hollywood are already using generative AI, and predicts that in the future more companies will embrace this new technology.<ref>{{Cite web |last=Bean |first=Thomas H. Davenport and Randy |date=2023-06-19 |title=The Impact of Generative AI on Hollywood and Entertainment |url=https://sloanreview.mit.edu/article/the-impact-of-generative-ai-on-hollywood-and-entertainment/ |access-date=2023-12-05 |website=MIT Sloan Management Review |language=en-US}}</ref>
=== Music ===
{{main|Music and artificial intelligence}}
AI has been used to compose music of various genres.
[[David Cope]] created an AI called [[Emily Howell]] that managed to become well known in the field of algorithmic computer music.<ref name="ARS">{{cite web|url=https://arstechnica.com/science/2009/09/virtual-composer-makes-beautiful-musicand-stirs-controversy/|title=Virtual composer makes beautiful music—and stirs controversy|last=Cheng|first=Jacqui|website=Ars Technica|date=30 September 2009}}</ref> The algorithm behind Emily Howell is registered as a US patent.<ref>{{cite patent |country=US|status=patent|number=7696426|url= https://www.google.com/patents/US7696426}}</ref>
In 2012, AI [[Iamus (computer)|Iamus]] created the first complete classical album.<ref>{{Cite web|date=4 July 2012|title=Computer composer honours Turing's centenary|url=https://www.newscientist.com/article/mg21528724-300-computer-composer-honours-turings-centenary/|url-status=live|access-date=27 December 2021|website=New Scientist|language=en-US|archive-url=https://web.archive.org/web/20160413104322/https://www.newscientist.com/article/mg21528724-300-computer-composer-honours-turings-centenary/ |archive-date=2016-04-13 }}</ref>
[[AIVA]] (Artificial Intelligence Virtual Artist), composes symphonic music, mainly [[classical music]] for [[film scores]].<ref name="LWO">{{cite news|url=http://www.wort.lu/de/kultur/aiva-une-jeune-start-up-qui-ne-manque-pas-d-ambitions-la-musique-classique-recomposee-57fbba6b5061e01abe83a1c2|title=La musique classique recomposée|last=Hick|first=Thierry|newspaper=Luxemburger Wort|date=11 October 2016}}</ref> It achieved a world first by becoming the first virtual composer to be recognized by a musical [[Société des auteurs, compositeurs et éditeurs de musique|professional association]].<ref>{{Cite web|url=https://repertoire.sacem.fr/resultats?filters=parties&query=aiva&nbWorks=20|title=Résultats de recherche - La Sacem|website=repertoire.sacem.fr}}</ref>
[[Melomics]] creates computer-generated music for stress and pain relief.<ref name="alergy">{{cite journal |doi=10.1111/pai.12263 |pmid=25115240 |title=Melomics music medicine (M3) to lessen pain perception during pediatric prick test procedure |journal=Pediatric Allergy and Immunology |volume=25 |issue=7 |pages=721–724 |year=2014 |last1=Requena |first1=Gloria |last2=Sánchez |first2=Carlos |last3=Corzo-Higueras |first3=José Luis |last4=Reyes-Alvarado |first4=Sirenia |last5=Rivas-Ruiz |first5=Francisco |last6=Vico |first6=Francisco |last7=Raglio |first7=Alfredo }}</ref>
At Sony CSL Research Laboratory, the Flow Machines software creates pop songs by learning music styles from a huge database of songs. It can compose in multiple styles.
The Watson Beat uses [[reinforcement learning]] and [[deep belief network]]s to compose music on a simple seed input melody and a select style. The software was open sourced<ref name="twb">{{cite web |url=https://github.com/cognitive-catalyst/watson-beat |title= Watson Beat on GitHub|website= [[GitHub]]|date= 10 October 2018}}</ref> and musicians such as [[Taryn Southern]]<ref name="taryn">{{cite magazine|url=https://www.wired.com/story/music-written-by-artificial-intelligence/|title=Songs in the Key of AI|magazine=Wired|date=17 May 2018}}</ref> collaborated with the project to create music.
South Korean singer, Hayeon's, debut song, "Eyes on You" was composed using AI which was supervised by real composers, including NUVO.<ref>{{Cite web|title=Hayeon, sister of Girls' Generation's Taeyeon, debuts with song made by AI|url=https://koreajoongangdaily.joins.com/2020/10/07/entertainment/kpop/hayeon-tayeon-girls-generation/20201007180200377.html|access-date=23 October 2020|website=koreajoongangdaily.joins.com|date=7 October 2020 |language=en}}</ref>
=== Writing and reporting ===
{{See also|#Web feeds and posts}}
[[Narrative Science]] sells [[computer generated journalism|computer-generated news]] and reports. It summarizes sporting events based on statistical data from the game. It also creates financial reports and real estate analyses.<ref>[http://www.narrativescience.com/solutions.html business intelligence solutions] {{webarchive|url=https://web.archive.org/web/20111103192105/http://www.narrativescience.com/solutions.html|date=3 November 2011}}. Narrative Science. Retrieved 21 July 2013.</ref> [[Automated Insights]] generates personalized recaps and previews for [[Yahoo Sports]] [[Fantasy football (American)|Fantasy Football]].<ref>{{cite web|url=http://online.barrons.com/news/articles/SB50001424052748704719204579028752466630862?mod=rss_barrons_technology_trader|title=Big Data and Yahoo's Quest for Mass Personalization|website=Barron's|last1=Eule|first1=Alexander}}</ref>
[[Yseop]], uses AI to turn structured data into natural language comments and recommendations. [[Yseop]] writes financial reports, executive summaries, personalized sales or marketing documents and more in multiple languages, including English, Spanish, French, and German.<ref>{{Cite web |url=http://yseop.com/EN/solutions.html |title=Artificial Intelligence Software that Writes like a Human Being |access-date=11 March 2013 |archive-url=https://archive.today/20130412055015/http://yseop.com/EN/solutions.html |archive-date=12 April 2013 |url-status=dead }}</ref>
TALESPIN made up stories similar to the [[Aesop's Fables|fables of Aesop]]. The program started with a set of characters who wanted to achieve certain goals. The story narrated their attempts to satisfy these goals.{{citation needed|date=July 2021}} Mark Riedl and Vadim Bulitko asserted that the essence of storytelling was experience management, or "how to balance the need for a coherent story progression with user agency, which is often at odds".<ref>{{cite journal |last1=Riedl |first1=Mark Owen |last2=Bulitko |first2=Vadim |title=Interactive Narrative: An Intelligent Systems Approach |journal=[[AI Magazine]] |date=6 December 2012 |volume=34 |issue=1 |pages=67 |doi=10.1609/aimag.v34i1.2449 |doi-access=free }}</ref>
While AI storytelling focuses on story generation (character and plot), story communication also received attention. In 2002, researchers developed an architectural framework for narrative prose generation. They faithfully reproduced text variety and complexity on stories such as [[Little Red Riding Hood]].<ref>{{cite journal |last1=Callaway |first1=Charles B. |last2=Lester |first2=James C. |title=Narrative prose generation |journal=Artificial Intelligence |date=August 2002 |volume=139 |issue=2 |pages=213–252 |doi=10.1016/S0004-3702(02)00230-8 |doi-access=free }}</ref> In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.<ref>{{Cite news|url=http://www.digitaltrends.com/cool-tech/japanese-ai-writes-novel-passes-first-round-nationanl-literary-prize/|title=A Japanese AI program just wrote a short novel, and it almost won a literary prize|date=23 March 2016|access-date=18 November 2016|language=en-US|newspaper=Digital Trends}}</ref>
South Korean company Hanteo Global uses a journalism bot to write articles.<ref>{{Cite web|url=https://hanteonews.com/en/article/bot%20news|title=Bot News|date=20 October 2020|access-date=20 October 2020|language=en-US|website=Hanteo News}}</ref>
Literary authors are also exploring uses of AI. An example is [[David Jhave Johnston]]'s work ''[[ReRites]]'' (2017–2019), where the poet created a daily rite of editing the poetic output of a neural network to create a series of performances and publications.
==== Sports writing ====
In 2010, artificial intelligence used [[baseball]] statistics to automatically generate news articles. This was launched by [[Big Ten Network|The Big Ten Network]] using software from [[Narrative Science]].<ref>{{Cite journal |last=Canavilhas |first=João |date=September 2022 |title=Artificial Intelligence and Journalism: Current Situation and Expectations in the Portuguese Sports Media |journal=Journalism and Media |language=en |volume=3 |issue=3 |pages=510–520 |doi=10.3390/journalmedia3030035 |doi-access=free |hdl=10400.6/12308 |hdl-access=free }}</ref>
After being unable to cover every [[Minor League Baseball]] game with a large team, [[Associated Press]] collaborated with [[Automated Insights]] in 2016 to create game recaps that were automated by artificial intelligence.<ref name="Galily 47–51">{{cite journal |last1=Galily |first1=Yair |title=Artificial intelligence and sports journalism: Is it a sweeping change? |journal=Technology in Society |date=August 2018 |volume=54 |pages=47–51 |doi=10.1016/j.techsoc.2018.03.001 }}</ref>
UOL in Brazil expanded the use of AI in its writing. Rather than just generating news stories, they programmed the AI to include commonly searched words on [[Google Insights for Search|Google]].<ref name="Galily 47–51"/>
[[El País|El Pais]], a Spanish news site that covers many things including sports, allows users to make comments on each news article. They use the [[Jigsaw (company)|Perspective API]] to moderate these comments and if the software deems a comment to contain toxic language, the commenter must modify it in order to publish it.<ref name="Galily 47–51"/>
A local Dutch media group used AI to create automatic coverage of amateur soccer, set to cover 60,000 games in just a single season. NDC partnered with United Robots to create this algorithm and cover what would have never been possible before without an extremely large team.<ref name="Galily 47–51"/>
Lede AI has been used in 2023 to take scores from [[high school football]] games to generate stories automatically for the local newspaper. This was met with significant criticism from readers for the very robotic diction that was published. With some descriptions of games being a "close encounter of the athletic kind," readers were not pleased and let the publishing company, [[Gannett]], know on social media. Gannett has since halted their used of Lede AI until they come up with a solution for what they call an experiment.<ref>{{Cite news |last=Wu |first=Daniel |date=2023-08-31 |title=Gannett halts AI-written sports recaps after readers mocked the stories |language=en-US |newspaper=Washington Post |url=https://www.washingtonpost.com/nation/2023/08/31/gannett-ai-written-stories-high-school-sports/ |access-date=2023-10-31 }}</ref>
===Wikipedia===
{{Excerpt|Artificial intelligence in Wikimedia projects}} Millions of its articles have been edited by bots<ref>{{cite news |title=Study reveals bot-on-bot editing wars raging on Wikipedia's pages |url=https://www.theguardian.com/technology/2017/feb/23/wikipedia-bot-editing-war-study |access-date=10 January 2023 |work=The Guardian |date=23 February 2017 |language=en}}</ref> which however are usually not artificial intelligence software. Many AI platforms use Wikipedia data,<ref>{{cite magazine |last1=Cole |first1=K. C. |title=The Shaky Ground Truths of Wikipedia |url=https://www.wired.com/story/shaky-ground-truths-wikipedia/ |access-date=10 January 2023 |magazine=Wired}}</ref> mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences,<ref>{{cite news |title=AI can automatically rewrite outdated text in Wikipedia articles |url=https://www.engadget.com/2020-02-12-mit-ai-automatically-rewrites-outdated-wikipedia-text.html |access-date=10 January 2023 |work=Engadget}}</ref> [[Vandalism on Wikipedia|detecting covert vandalism]]<ref>{{cite magazine |last1=Metz |first1=Cade |title=Wikipedia Deploys AI to Expand Its Ranks of Human Editors |url=https://www.wired.com/2015/12/wikipedia-is-using-ai-to-expand-the-ranks-of-human-editors/ |access-date=10 January 2023 |magazine=Wired}}</ref> or recommending articles and tasks to new editors.
Machine translation {{see above|[[#Language translation|above]]}} has also be used for translating Wikipedia articles and could play a larger role in creating, updating, expanding, and generally improving articles in the future. A content translation tool allows editors of some Wikipedias to more easily translate articles across several select languages.<ref>{{cite news |title=Wikipedia taps Google to help editors translate articles |url=https://venturebeat.com/ai/wikipedia-taps-google-to-help-editors-translate-articles/ |access-date=9 January 2023 |work=VentureBeat |date=9 January 2019}}</ref><ref>{{cite news |last1=Wilson |first1=Kyle |title=Wikipedia has a Google Translate problem |url=https://www.theverge.com/2019/5/8/18526739/wikipedia-translation-tool-machine-learning-ai-english |access-date=9 January 2023 |work=The Verge |date=8 May 2019}}</ref>
=== Video games ===
{{Main|Artificial intelligence in video games}}
{{See also|Video game bot|Artificial intelligence in video games}}
In video games, AI is routinely used to generate behavior in [[non-player character]]s (NPCs). In addition, AI is used for [[pathfinding]]. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks.{{Who|date=May 2022}} Games with less typical AI include the AI director of ''[[Left 4 Dead]]'' (2008) and the neuroevolutionary training of platoons in ''[[Supreme Commander 2]]'' (2010).<ref>{{cite news|title=Why AI researchers like video games|newspaper=The Economist|url=https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|url-status=live|archive-url=https://web.archive.org/web/20171005051028/https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games|archive-date=5 October 2017}}</ref><ref>{{cite book |doi=10.1145/2212908.2212954 |chapter=Game AI revisited |title=Proceedings of the 9th conference on Computing Frontiers - CF '12 |year=2012 |last1=Yannakakis |first1=Geogios N. |page=285 |isbn=978-1-4503-1215-8 }}</ref> AI is also used in [[Alien: Isolation|''Alien Isolation'']] (2014) as a way to control the actions the Alien will perform next.<ref>{{Cite web|last=Maass|first=Laura E. Shummon|date=1 July 2019|title=Artificial Intelligence in Video Games|url=https://towardsdatascience.com/artificial-intelligence-in-video-games-3e2566d59c22|access-date=23 April 2021|website=Medium|language=en}}</ref>
Games have been a major application{{relevance inline|date=May 2022|reason=Demonstration of capabilities is not an application by itself or is it?}} of AI's capabilities since the 1950s. In the 21st century, AIs have beaten human players in many games, including [[Computer chess|chess]] ([[IBM Deep Blue|Deep Blue]]), ''[[Jeopardy!]]'' ([[Watson (artificial intelligence software)|Watson]]),<ref>{{cite news|last=Markoff|first=John|date=16 February 2011<!-- corrected 24 February 2011-->|title=Computer Wins on 'Jeopardy!': Trivial, It's Not|work=[[The New York Times]]|url=https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html|url-status=live|access-date=25 October 2014|archive-url=https://web.archive.org/web/20141022023202/http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html|archive-date=22 October 2014}}</ref> [[Go (game)|Go]] ([[AlphaGo]]),<ref name="bbc-alphago">{{cite web|url=https://deepmind.com/alpha-go.html|title=AlphaGo – Google DeepMind |url-status=live|archive-url=https://web.archive.org/web/20160310191926/https://www.deepmind.com/alpha-go.html |archive-date=10 March 2016}}</ref><ref>{{cite news|title=Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol|url=https://www.bbc.com/news/technology-35785875|access-date=1 October 2016 |work=BBC News |date=12 March 2016|url-status=live|archive-url=https://web.archive.org/web/20160826103910/http://www.bbc.com/news/technology-35785875|archive-date=26 August 2016}}</ref><ref>{{cite magazine|url=https://www.wired.com/2017/05/win-china-alphagos-designers-explore-new-ai/|title=After Win in China, AlphaGo's Designers Explore New AI |magazine=Wired|date=27 May 2017|url-status=live|archive-url=https://web.archive.org/web/20170602234726/https://www.wired.com/2017/05/win-china-alphagos-designers-explore-new-ai/|archive-date=2 June 2017 |last1=Metz |first1=Cade}}</ref><ref>{{cite web|url=http://www.goratings.org/|title=World's Go Player Ratings|date=May 2017|url-status=live|archive-url=https://web.archive.org/web/20170401123616/https://www.goratings.org/|archive-date=1 April 2017}}</ref><ref>{{cite web|title=柯洁迎19岁生日 雄踞人类世界排名第一已两年|url=http://sports.sina.com.cn/go/2016-08-02/doc-ifxunyya3020238.shtml |language=zh |date=May 2017|url-status=live|archive-url=https://web.archive.org/web/20170811222849/http://sports.sina.com.cn/go/2016-08-02/doc-ifxunyya3020238.shtml|archive-date=11 August 2017}}</ref><ref>{{Cite web|title=MuZero: Mastering Go, chess, shogi and Atari without rules|url=https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules|access-date=1 March 2021|website=Deepmind|date=23 December 2020 }}</ref><ref>{{cite news|author1=Steven Borowiec|author2=Tracey Lien|title=AlphaGo beats human Go champ in milestone for artificial intelligence|url=https://www.latimes.com/world/asia/la-fg-korea-alphago-20160312-story.html|access-date=13 March 2016|work=[[Los Angeles Times]]|date=12 March 2016}}</ref> [[poker]] ([[Pluribus (poker bot)|Pluribus]]<ref>{{Cite web|url=https://www.smithsonianmag.com/smart-news/poker-playing-ai-knows-when-hold-em-when-fold-em-180972643/|title=This Poker-Playing A.I. Knows When to Hold 'Em and When to Fold 'Em|first=Meilan|last=Solly|website=Smithsonian|quote=Pluribus has bested poker pros in a series of six-player no-limit Texas Hold'em games, reaching a milestone in artificial intelligence research. It is the first bot to beat humans in a complex multiplayer competition.}}</ref> and [[Cepheus (poker bot)|Cepheus]]),<ref>{{cite journal |last1=Bowling |first1=Michael |last2=Burch |first2=Neil |last3=Johanson |first3=Michael |last4=Tammelin |first4=Oskari |title=Heads-up limit hold'em poker is solved |journal=Science |date=9 January 2015 |volume=347 |issue=6218 |pages=145–149 |doi=10.1126/science.1259433 |pmid=25574016 |bibcode=2015Sci...347..145B }}</ref> [[Esports|E-sports]] ([[StarCraft]]),<ref>{{cite journal |last1=Ontanon |first1=Santiago |last2=Synnaeve |first2=Gabriel |last3=Uriarte |first3=Alberto |last4=Richoux |first4=Florian |last5=Churchill |first5=David |last6=Preuss |first6=Mike |title=A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft |journal=IEEE Transactions on Computational Intelligence and AI in Games |date=December 2013 |volume=5 |issue=4 |pages=293–311 |doi=10.1109/TCIAIG.2013.2286295 |url=https://hal.archives-ouvertes.fr/hal-00871001 }}</ref><ref>{{cite news|year=2017|title=Facebook Quietly Enters StarCraft War for AI Bots, and Loses|magazine=WIRED|url=https://www.wired.com/story/facebook-quietly-enters-starcraft-war-for-ai-bots-and-loses/|access-date=7 May 2018}}</ref> and [[general game playing]] ([[AlphaZero]]<ref>{{Cite journal|first1 = David|last1 = Silver |author-link1=David Silver (programmer)|first2 = Thomas |last2 = Hubert|first3 = Julian |last3 = Schrittwieser|first4 = Ioannis |last4 = Antonoglou|first5 = Matthew |last5 = Lai|first6 = Arthur |last6 = Guez |first7 = Marc |last7 = Lanctot |first8 = Laurent |last8 = Sifre |first9 = Dharshan |last9 = Kumaran |first10 = Thore |last10 = Graepel |first11 = Timothy |last11 = Lillicrap |first12 = Karen |last12 = Simonyan |first13 = Demis |last13 = Hassabis |author-link13=Demis Hassabis |title = A general reinforcement learning algorithm that masters chess, shogi, and go through self-play |journal = [[Science (journal)|Science]] |pages = 1140–1144|volume = 362|issue = 6419|doi = 10.1126/science.aar6404|pmid = 30523106|date = 7 December 2018|bibcode = 2018Sci...362.1140S|doi-access = free}}</ref><ref>{{cite news|last1=Sample|first1=Ian|date=18 October 2017|title='It's able to create knowledge itself': Google unveils AI that learns on its own|language=en|work=The Guardian|url=https://www.theguardian.com/science/2017/oct/18/its-able-to-create-knowledge-itself-google-unveils-ai-learns-all-on-its-own|access-date=7 May 2018}}</ref><ref>{{cite news |last1=Appenzeller |first1=Tim |title=The AI revolution in science |journal=Science |date=7 July 2017 |doi=10.1126/science.aan7064 }}</ref> and [[MuZero]]).<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|access-date=26 April 2018|work=The Guardian|date=16 February 2016|archive-date=23 April 2018|archive-url=https://web.archive.org/web/20180423220101/https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|url-status=live}}</ref><ref>{{cite journal |last1=Mnih |first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A. |last5=Veness|first5=Joel |last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller |first8=Martin|last9=Fidjeland|first9=Andreas K. |last10=Ostrovski|first10=Georg|last11=Petersen |first11=Stig|last12=Beattie|first12=Charles|last13=Sadik |first13=Amir|last14=Antonoglou|first14=Ioannis |last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg |first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning |journal=Nature |date=26 February 2015|volume=518|issue=7540|pages=529–533 |doi=10.1038/nature14236 |pmid=25719670|bibcode=2015Natur.518..529M }}</ref><ref>{{cite news |last1=Sample|first1=Ian |title=Google's DeepMind makes AI program that can learn like a human |url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|access-date=26 April 2018 |work=The Guardian |date=14 March 2017|archive-date=26 April 2018|archive-url=https://web.archive.org/web/20180426212908/https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human |url-status=live}}</ref><ref>{{cite journal |last1=Schrittwieser |first1=Julian |last2=Antonoglou |first2=Ioannis |last3=Hubert |first3=Thomas |last4=Simonyan |first4=Karen |last5=Sifre |first5=Laurent |last6=Schmitt |first6=Simon |last7=Guez |first7=Arthur |last8=Lockhart |first8=Edward |last9=Hassabis |first9=Demis |last10=Graepel |first10=Thore |last11=Lillicrap |first11=Timothy |last12=Silver |first12=David |title=Mastering Atari, Go, chess and shogi by planning with a learned model |journal=Nature |date=24 December 2020 |volume=588 |issue=7839 |pages=604–609 |doi=10.1038/s41586-020-03051-4 |pmid=33361790 |arxiv=1911.08265 |bibcode=2020Natur.588..604S }}</ref>
Kuki AI is a set of [[chatbot]]s and other apps which were designed for entertainment and as a marketing tool.<ref>{{cite web |last1=Ortiz |first1=Sabrina |title=You can now chat with a famous AI character on Viber. Here's how |url=https://www.zdnet.com/article/you-can-now-chat-with-a-famous-ai-character-on-viber-heres-how/#google_vignette |website=zdnet.com |publisher=[[ZDNET]] |access-date=5 December 2024 |quote=ICONIQ created Kuki, an AI character whose sole purpose is to entertain humans and has even been used as a brand ambassador for H&M, modeled for Vogue, and starred in its own Roblox game.}}</ref><ref>{{cite news |last1=Lewis |first1=Nell |title=Robot friends: Why people talk to chatbots in times of trouble |url=https://edition.cnn.com/2020/08/19/world/chatbot-social-anxiety-spc-intl/index.html |date=19 August 2020 |website=cnn.com |publisher=[[CNN]] |access-date=5 December 2024 |quote=Since 2016, when the bot landed on major messaging platforms, an estimated 5 million unique users hailing from all corners of the world have chatted with her.}}</ref> [[Character.ai]] is another example of a chatbot being used for recreation.{{citation needed|date=May 2025}}
[[Kinect]], which provides a 3D body–motion interface for the [[Xbox 360]] and the [[Xbox One]], uses algorithms that emerged from AI research.<ref>{{cite web|url=http://www.i-programmer.info/news/105-artificial-intelligence/2176-kinects-ai-breakthrough-explained.html|title=Kinect's AI breakthrough explained|last=Fairhead |first=Harry |date=26 March 2011 |orig-date=Update 30 March 2011 |work=I Programmer |url-status=live|archive-url=https://web.archive.org/web/20160201031242/http://www.i-programmer.info/news/105-artificial-intelligence/2176-kinects-ai-breakthrough-explained.html|archive-date=1 February 2016}}</ref>{{Which|date=December 2021}}
=== Art ===
[[File:Cyborg elf, science fantasy, Stable Diffusion AI art.png|thumb|A "cyborg elf" generated by [[Stable Diffusion]]]]
{{Main|Artificial intelligence art}}
AI has been used to produce visual art. The first AI art program, called [[AARON]], was developed by [[Harold Cohen (artist)|Harold Cohen]] in 1968<ref name="Poltronieri-2019">{{cite book |doi=10.1145/3359852.3359865 |chapter=Technical Images and Visual Art in the Era of Artificial Intelligence: From GOFAI to GANs |title=Proceedings of the 9th International Conference on Digital and Interactive Arts |date=2019 |last1=Poltronieri |first1=Fabrizio Augusto |last2=Hänska |first2=Max |pages=1–8 |isbn=978-1-4503-7250-3 }}</ref> with the goal of being able to code the act of drawing. It started by creating simple black and white drawings, and later to painting using special brushes and dyes that were chosen by the program itself without mediation from Cohen.<ref>{{Cite web |title=Fine art print - crypto art |url=https://www.katevassgalerie.com/blog/harold-cohen-aaron-computer-art |access-date=2022-05-07 |website=Kate Vass Galerie |language=en-US}}</ref>
AI platforms such as [[DALL-E]],<ref name="2022/08/24/751f9a5"/> [[Stable Diffusion]],<ref name="2022/08/24/751f9a5">{{cite news |title=Analysis {{!}} Is That Trump Photo Real? Free AI Tools Come With Risks |url=https://www.washingtonpost.com/business/is-that-trump-photo-real-free-ai-tools-comewith-risks/2022/08/24/751f9a54-236a-11ed-a72f-1e7149072fbc_story.html |access-date=30 August 2022 |newspaper=Washington Post}}</ref> [[Imagen (text-to-image model)|Imagen]],<ref>{{cite news |title=Google's image generator rivals DALL-E in shiba inu drawing |url=https://techcrunch.com/2022/05/23/openai-look-at-our-awesome-image-generator-google-hold-my-shiba-inu/ |access-date=30 August 2022 |work=TechCrunch}}</ref> and [[Midjourney]]<ref>{{cite news |title=Midjourney's enthralling AI art generator goes live for everyone |url=https://www.pcworld.com/article/820518/midjourneys-ai-art-goes-live-for-everyone.html |work=PCWorld |language=en}}</ref> have been used for generating visual images from inputs such as text or other images.<ref>{{cite web |title=After Photos, Here's How AI Made A Trippy Music Video Out Of Thin Air |url=https://fossbytes.com/ai-made-a-trippy-music-video/ |website=Fossbytes |access-date=30 May 2022 |date=19 May 2022}}</ref> Some AI tools allow users to input images and output changed versions of that image, such as to display an object or product in different environments. AI image models can also attempt to replicate the specific styles of artists, and can add visual complexity to rough sketches.
Since their design in 2014, [[generative adversarial network]]s (GANs) have been used by AI artists. GAN computer programming, generates technical images through machine learning frameworks that surpass the need for human operators.<ref name="Poltronieri-2019" /> Examples of GAN programs that generate art include [[Artbreeder]] and [[DeepDream]].
==== Art analysis ====
In addition to the creation of original art, research methods that utilize AI have been generated to quantitatively analyze digital art collections. Although the main goal of the large-scale digitization of artwork in the past few decades was to allow for accessibility and exploration of these collections, the use of AI in analyzing them has brought about new research perspectives.<ref>{{Cite journal |last1=Cetinic |first1=Eva |last2=She |first2=James |date=2022-02-16 |title=Understanding and Creating Art with AI: Review and Outlook |journal=ACM Transactions on Multimedia Computing, Communications, and Applications |volume=18 |issue=2 |pages=66:1–66:22 |doi=10.1145/3475799 |arxiv=2102.09109 }}</ref>
Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art.<ref>{{Cite conference |last1=Lang |first1=Sabine |last2=Ommer |first2=Bjorn |date=2018 |title=Reflecting on How Artworks Are Processed and Analyzed by Computer Vision: Supplementary Material |book-title=Proceedings of the European Conference on Computer Vision (ECCV) Workshops |url=https://openaccess.thecvf.com/content_eccv_2018_workshops/w13/html/Lang_Reflecting_on_How_Artworks_Are_Processed_and_Analyzed_by_Computer_ECCVW_2018_paper.html |via=Computer Vision Foundation}}</ref> While distant viewing includes the analysis of large collections, close reading involves one piece of artwork.
=== Computer animation ===
AI has been in use since the early 2000s, most notably by a system designed by Pixar called "Genesis".<ref>{{Cite web |last=admin |date=2023-09-12 |title=Top 2 Technologies that will Influence the Future of Animation |url=https://vgenmedia.com/top-2-technologies-that-will-influence-the-future-of-animation/ |access-date=2023-12-04 |website=VGenMedia |language=en-US}}</ref> It was designed to learn algorithms and create 3D models for its characters and props. Notable movies that used this technology included Up and The Good Dinosaur.<ref>{{Cite web |title=Artificial Intelligence Animation: What Is It and How Does It Function? |url=https://studiopigeon.com/blog/artificial-intelligence-animation-what-is-it-and-how-does-it-function/ |access-date=2023-12-04 |website=Pigeon Studio |language=en-US}}</ref> AI has been used less ceremoniously in recent years. In 2023, it was revealed Netflix of Japan was using AI to generate background images for their upcoming show to be met with backlash online.<ref>{{Cite web |last=Cole |first=Samantha |date=2023-02-01 |title=Netflix Made an Anime Using AI Due to a 'Labor Shortage,' and Fans Are Pissed |url=https://www.vice.com/en/article/netflix-anime-dog-and-the-boy-ai-generated-art/ |access-date=2023-12-04 |website=Vice |language=en}}</ref> In recent years, motion capture became an easily accessible form of AI animation. For example, Move AI is a program built to capture any human movement and reanimate it in its animation program using learning AI.<ref>{{Cite web |date=2023-09-12 |title=What is Move AI? A Revolution in Motion Capture |url=https://www.openaigeek.com/what-is-move-ai/ |access-date=2023-12-04 |language=en-US}}</ref>
== Finance ==
[[Financial institution]]s have long used [[artificial neural network]] systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in [[banking]] began in 1987 when [[Security Pacific National Bank]] launched a fraud prevention task-force to counter the unauthorized use of debit cards.<ref>{{Cite web|last=Christy|first=Charles A.|date=17 January 1990|title=Impact of Artificial Intelligence on Banking|url=https://www.latimes.com/archives/la-xpm-1990-01-17-fi-233-story.html|access-date=10 September 2019|website=Los Angeles Times}}</ref>
Banks use AI to organize operations
The use of AI in applications such as online trading and decision
=== Trading and investment ===
[[Algorithmic trading]] involves
Large financial institutions use AI to assist with their investment practices. [[BlackRock]]'s AI engine, [[Aladdin (BlackRock)|Aladdin]], is used both within the company and by clients to help with investment decisions. Its functions
=== Underwriting ===
Online lender [[Upstart (company)|Upstart]] uses machine learning for [[underwriting]].<ref>{{Cite web |last=Asatryan |first=Diana |date=3 April 2017 |title=Machine Learning Is the Future of Underwriting, But Startups Won't be Driving It |url=http://bankinnovation.net/2017/04/machine-learning-is-the-future-of-underwriting-but-startups-wont-be-driving-it/ |access-date=15 April 2022 |website=bankinnovation.net}}</ref>
ZestFinance's Zest Automated Machine Learning (ZAML) platform is used for credit underwriting. This platform uses machine learning to analyze data, including purchase transactions and how a customer fills out a form, to score borrowers. The platform is
=== Audit ===
AI makes continuous auditing possible. Potential benefits include reducing audit risk, increasing the level of assurance, and reducing audit duration.<ref>{{cite journal|last1=Chang|first1=Hsihui|last2=Kao|first2=Yi-Ching|last3=Mashruwala|first3=Raj|last4=Sorensen|first4=Susan M.
Continuous auditing with AI allows real-time monitoring and reporting of financial activities and provides businesses with timely insights that can lead to quick decision-making.<ref>{{cite journal |last1=Munoko |first1=Ivy |last2=Brown-Liburd |first2=Helen L. |last3=Vasarhelyi |first3=Miklos |title=The Ethical Implications of Using Artificial Intelligence in Auditing |journal=Journal of Business Ethics |date=November 2020 |volume=167 |issue=2 |pages=209–234 |doi=10.1007/s10551-019-04407-1 }}</ref>
=== Anti-money laundering ===
AI software, such as LaundroGraph which uses contemporary suboptimal datasets, could be used for [[anti-money laundering]] (AML).<ref>{{cite news |last1=Fadelli |first1=Ingrid |title=LaundroGraph: Using deep learning to support anti-money laundering efforts |url=https://techxplore.com/news/2022-11-laundrograph-deep-anti-money-laundering-efforts.html |access-date=18 December 2022 |work=techxplore.com |language=en}}</ref><ref>{{cite book |doi=10.1145/3533271.3561727 |chapter=LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering |title=Proceedings of the Third ACM International Conference on AI in Finance |date=2022 |last1=Cardoso |first1=Mário |last2=Saleiro |first2=Pedro |last3=Bizarro |first3=Pedro |pages=130–138 |arxiv=2210.14360 |isbn=978-1-4503-9376-8 }}</ref>Anti-money laundering
=== Collections and Account Receivables ===
In recent years, the [[debt collection]] industry has begun to adopt AI-driven “agents” to automate routine outreach and negotiation tasks. Platforms use natural-language processing and machine learning to interact with consumers.
Proponents claim these systems can handle high volumes of standard enquiries, freeing human collectors to focus on more complex cases, while delivering more consistent, 24/7 service. However, critics warn of potential compliance pitfalls, such as the risk of unintended bias in algorithmic decision-making.<ref>{{Cite journal |last=Sivamayilvelan |first=Keerthana |last2=Rajasekar |first2=Elakkiya |last3=Vairavasundaram |first3=Subramaniyaswamy |last4=Balachandran |first4=Santhi |last5=Suresh |first5=Vishnu |date=2025-11-01 |title=Building explainable artificial intelligence for reinforcement learning based debt collection recommender system using large language models |url=https://www.sciencedirect.com/science/article/pii/S0952197625016240 |journal=Engineering Applications of Artificial Intelligence |volume=159 |pages=111622 |doi=10.1016/j.engappai.2025.111622 |issn=0952-1976}}</ref>
=== History ===
In the 1980s, AI started to become prominent in finance as [[expert systems]] were commercialized. For example, Dupont created 100 expert systems, which helped them to save almost $10 million per year.<ref>{{cite book |doi=10.1016/B978-012443880-4/50045-4 |chapter=History and applications |title=Expert Systems |year=2002 |last1=Durkin |first1=J. |volume=1 |pages=1–22 |isbn=978-0-12-443880-4 }}</ref> One of the first systems was the Pro-trader expert system that predicted the 87-point drop in the [[Dow Jones Industrial Average]] in 1986. "The major junctions of the system were to monitor premiums in the market, determine the optimum investment strategy, execute transactions when appropriate and modify the knowledge base through a learning mechanism."<ref>{{cite journal |last1=Chen |first1=K.C. |last2=Liang |first2=Ting-peng |title=Protrader: An Expert System for Program Trading |journal=Managerial Finance |date=May 1989 |volume=15 |issue=5 |pages=1–6 |doi=10.1108/eb013623 }}</ref>
One of the first expert systems to help with financial plans was PlanPowerm and Client Profiling System, created by Applied Expert Systems (APEX). It was launched in 1986. It helped create personal financial plans for people.<ref>{{cite journal |last1=Nielson |first1=Norma |last2=Brown |first2=Carol E. |last3=Phillips |first3=Mary Ellen |title=Expert Systems for Personal Financial Planning |journal=Journal of Financial Planning |date=July 1990 |pages=137–143 |doi=10.11575/PRISM/33995 |hdl=1880/48295 }}</ref>
In the 1990s, AI was applied to [[Artificial intelligence in fraud detection|fraud detection]]. In 1993, FinCEN Artificial Intelligence System (FAIS) was launched. It was able to review over 200,000 transactions per week, and over two years, it helped identify 400 potential cases of [[money laundering]] equal to $1 billion.<ref>{{Cite journal|last1=Senator|first1=Ted E.|last2=Goldberg|first2=Henry G.|last3=Wooton|first3=Jerry|last4=Cottini|first4=Matthew A.|last5=Khan|first5=A.F. Umar|last6=Kilinger|first6=Christina D.|last7=Llamas|first7=Winston M.|last8=Marrone|first8=MichaeI P.|last9=Wong|first9=Raphael W.H.|year=1995|title=The FinCEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports of Large Cash Transactions|url=https://www.aaai.org/Papers/IAAI/1995/IAAI95-015.pdf|journal=IAAI-95 Proceedings|access-date=2019-01-14|archive-date=2015-10-20|archive-url=https://web.archive.org/web/20151020030150/http://www.aaai.org/Papers/IAAI/1995/IAAI95-015.pdf|url-status=dead}}</ref> These expert systems were later replaced by machine learning systems.<ref>{{cite journal |last1=Sutton |first1=Steve G. |last2=Holt |first2=Matthew |last3=Arnold |first3=Vicky |title='The reports of my death are greatly exaggerated'—Artificial intelligence research in accounting |journal=International Journal of Accounting Information Systems |date=September 2016 |volume=22 |pages=60–73 |doi=10.1016/j.accinf.2016.07.005 }}</ref>
AI can enhance entrepreneurial activity, and AI is one of the most dynamic areas for start-ups, with significant venture capital flowing into AI.<ref>{{cite journal |last1=Chalmers |first1=Dominic |last2=MacKenzie |first2=Niall G. |last3=Carter |first3=Sara |title=Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution |journal=Entrepreneurship Theory and Practice |date=September 2021 |volume=45 |issue=5 |pages=1028–1053 |doi=10.1177/1042258720934581 |doi-access=free }}</ref>
== Health{{anchor|Hospitals_and_medicine}} ==
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[[File:X-ray of hand, where bone age is automatically found by BoneXpert software.jpg|thumb|[[Projectional radiography|X-ray]] of a hand, with automatic calculation of [[bone age]] by a computer software]]
[[File:Laproscopic_Surgery_Robot.jpg|thumb|A patient-side surgical arm of [[Da Vinci Surgical System]]]]
AI in healthcare is often used for classification, to evaluate a [[CT scan]] or [[Electrocardiography|electrocardiogram]] or to identify high-risk patients for population health. AI is helping with the high-cost problem of dosing. One study suggested that AI could save $16 billion. In 2016, a study reported that an AI-derived formula derived the proper dose of immunosuppressant drugs to give to transplant patients.<ref>{{Cite news|date=10 May 2018|title=10 Promising AI Applications in Health Care|work=Harvard Business Review|url=https://hbr.org/2018/05/10-promising-ai-applications-in-health-care|url-status=dead|access-date=28 August 2018|archive-url=https://web.archive.org/web/20181215015645/https://hbr.org/2018/05/10-promising-ai-applications-in-health-care|archive-date=15 December 2018}}</ref> Current research has indicated that non-cardiac vascular illnesses are also being treated with artificial intelligence (AI). For certain disorders, AI algorithms can aid in diagnosis, recommended treatments, outcome prediction, and patient progress tracking. As AI technology advances, it is anticipated that it will become more significant in the healthcare industry.<ref>{{cite journal |last1=Lareyre |first1=Fabien |last2=Lê |first2=Cong Duy |last3=Ballaith |first3=Ali |last4=Adam |first4=Cédric |last5=Carrier |first5=Marion |last6=Amrani |first6=Samantha |last7=Caradu |first7=Caroline |last8=Raffort |first8=Juliette |title=Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis |journal=Angiology |date=August 2022 |volume=73 |issue=7 |pages=606–614 |doi=10.1177/00033197211062280 |pmid=34996315 }}</ref>
The early detection of diseases like cancer is made possible by AI algorithms, which diagnose diseases by analyzing complex sets of medical data. For example, the IBM Watson system might be used to comb through massive data such as medical records and clinical trials to help diagnose a problem.<ref>{{cite web |title=What is artificial intelligence in medicine? |date=28 March 2024 |url=https://www.ibm.com/topics/artificial-intelligence-medicine |publisher=IBM |access-date=19 April 2024}}</ref> Microsoft's AI project Hanover helps doctors choose [[Treatment of cancer|cancer treatments]] from among the more than 800 medicines and vaccines.<ref>{{Cite
Another study demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel judged better than a surgeon.<ref>{{cite news|author=Senthilingam, Meera|date=12 May 2016|title=Are Autonomous Robots Your next Surgeons?|publisher=CNN|url=http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation/|url-status=live|access-date=4 December 2016|archive-url=https://web.archive.org/web/20161203154119/http://www.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation|archive-date=3 December 2016}}</ref>
[[Artificial neural networks]] are used as [[clinical decision support system]]s for medical diagnosis,<ref name="Pumplun_2021">{{cite journal | vauthors = Pumplun L, Fecho M, Wahl N, Peters F, Buxmann P | title = Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study | journal = Journal of Medical Internet Research | volume = 23 | issue = 10 | pages = e29301 |year = 2021 | pmid = 34652275 | doi = 10.2196/29301| pmc = 8556641 |
Other healthcare tasks thought suitable for an AI that are in development include:
* [[Screening (medicine)|Screening]]<ref>{{cite journal |last1=Inglese |first1=Marianna |last2=Patel |first2=Neva |last3=Linton-Reid |first3=Kristofer |last4=Loreto |first4=Flavia |last5=Win |first5=Zarni |last6=Perry |first6=Richard J. |last7=Carswell |first7=Christopher |last8=Grech-Sollars |first8=Matthew |last9=Crum |first9=William R. |last10=Lu |first10=Haonan |last11=Malhotra |first11=Paresh A. |last12=Aboagye |first12=Eric O. |title=A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease |journal=Communications Medicine |date=20 June 2022 |volume=2 |issue=1 |page=70 |doi=10.1038/s43856-022-00133-4 |pmid=35759330 |pmc=9209493 }}
* News report: {{cite news |title=Single MRI scan of the brain could detect Alzheimer's disease |url=https://physicsworld.com/a/single-mri-scan-of-the-brain-could-detect-alzheimers-disease/ |access-date=19 July 2022 |work=Physics World |date=13 July 2022}}</ref>
* Companion robots for [[elderly care|elder care]]<ref>{{cite journal |doi=10.1109/TAMD.2011.2105868 |title=Cognitive Development in Partner Robots for Information Support to Elderly People |journal=IEEE Transactions on Autonomous Mental Development |volume=3 |pages=64–73 |year=2011 |last1=Yorita |first1=Akihiro |last2=Kubota |first2=Naoyuki
* Drug creation<ref>{{Cite news|url=http://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare/|title=Artificial Intelligence Will Redesign Healthcare – The Medical Futurist|date=4 August 2016|access-date=18 November 2016|language=en-US|newspaper=The Medical Futurist}}</ref> (e.g. by identifying candidate drugs<ref>{{cite journal |last1=Dönertaş |first1=Handan Melike |last2=Fuentealba |first2=Matías |last3=Partridge |first3=Linda |last4=Thornton |first4=Janet M. |title=Identifying Potential Ageing-Modulating Drugs In Silico |journal=Trends in Endocrinology & Metabolism |date=February 2019 |volume=30 |issue=2 |pages=118–131 |doi=10.1016/j.tem.2018.11.005|pmid=30581056 |pmc=6362144 }}</ref> and by using existing drug screening data such as in [[life extension]] research)<ref>{{cite journal |last1=Smer-Barreto |first1=Vanessa |last2=Quintanilla |first2=Andrea |last3=Elliot |first3=Richard J. R. |last4=Dawson |first4=John C. |last5=Sun |first5=Jiugeng |last6=Carragher |first6=Neil O. |last7=Acosta |first7=Juan Carlos |last8=Oyarzún |first8=Diego A. |title=Discovery of new senolytics using machine learning |date=27 April 2022 |journal=bioRxiv |doi=10.1101/2022.04.26.489505 |hdl=10261/269843 |hdl-access=free }}</ref>
* Clinical training<ref>{{cite journal |doi=10.1037/a0034559 |title=Artificial intelligence in psychological practice: Current and future applications and implications |journal=Professional Psychology: Research and Practice |volume=45 |issue=5 |pages=332–339 |year=2014 |last1=Luxton |first1=David D. }}</ref>
* Identifying genomic pathogen signatures of novel pathogens<ref>{{cite journal |last1=Randhawa |first1=Gurjit S. |last2=Soltysiak |first2=Maximillian P. M. |last3=Roz |first3=Hadi El |last4=Souza |first4=Camila P. E. de |last5=Hill |first5=Kathleen A. |last6=Kari |first6=Lila |title=Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study |journal=PLOS ONE |date=24 April 2020 |volume=15 |issue=4 |pages=e0232391 |doi=10.1371/journal.pone.0232391 |pmid=32330208 |pmc=7182198 |bibcode=2020PLoSO..1532391R |doi-access=free }}</ref> or identifying pathogens via physics-based fingerprints<ref name="10.1073/pnas.2118836119">{{cite journal |last1=Ye |first1=Jiarong |last2=Yeh |first2=Yin-Ting |last3=Xue |first3=Yuan |last4=Wang |first4=Ziyang |last5=Zhang |first5=Na |last6=Liu |first6=He |last7=Zhang |first7=Kunyan |last8=Ricker |first8=RyeAnne |last9=Yu |first9=Zhuohang |last10=Roder |first10=Allison |last11=Perea Lopez |first11=Nestor |last12=Organtini |first12=Lindsey |last13=Greene |first13=Wallace |last14=Hafenstein |first14=Susan |last15=Lu |first15=Huaguang |last16=Ghedin |first16=Elodie |last17=Terrones |first17=Mauricio |last18=Huang |first18=Shengxi |last19=Huang |first19=Sharon Xiaolei |title=Accurate virus identification with interpretable Raman signatures by machine learning |journal=Proceedings of the National Academy of Sciences |date=7 June 2022 |volume=119 |issue=23 |pages=e2118836119 |doi=10.1073/pnas.2118836119 |doi-access=free |pmid=35653572 |pmc=9191668 |arxiv=2206.02788 |bibcode=2022PNAS..11918836Y }}</ref> (including pandemic pathogens)
* Helping link genes to their functions,<ref>{{cite news |title=Artificial intelligence finds disease-related genes |url=https://techxplore.com/news/2020-02-artificial-intelligence-disease-related-genes.html |access-date=3 July 2022 |work=Linköping University |language=en}}</ref> otherwise analyzing genes<ref>{{cite news |title=Researchers use AI to detect new family of genes in gut bacteria |url=https://phys.org/news/2022-07-ai-family-genes-gut-bacteria.html |access-date=3 July 2022 |work=UT Southwestern Medical Center |language=en}}</ref> and identification of novel biological targets<ref name="10.1016/j.arr.2018.11.003">{{cite journal |last1=Zhavoronkov |first1=Alex |last2=Mamoshina |first2=Polina |last3=Vanhaelen |first3=Quentin |last4=Scheibye-Knudsen |first4=Morten |last5=Moskalev |first5=Alexey |last6=Aliper |first6=Alex |title=Artificial intelligence for aging and longevity research: Recent advances and perspectives |journal=Ageing Research Reviews |date=2019 |volume=49 |pages=49–66 |doi=10.1016/j.arr.2018.11.003 |pmid=30472217 |doi-access=free }}</ref>
* Help development of [[biomarker (medicine)|biomarkers]]<ref name="10.1016/j.arr.2018.11.003"/>
* Help tailor therapies to individuals in [[personalized medicine]]/[[precision medicine]]<ref name="10.1016/j.arr.2018.11.003"/><ref>{{cite journal |last1=Adir |first1=Omer |last2=Poley |first2=Maria |last3=Chen |first3=Gal |last4=Froim |first4=Sahar |last5=Krinsky |first5=Nitzan |last6=Shklover |first6=Jeny |last7=Shainsky-Roitman |first7=Janna |last8=Lammers |first8=Twan |last9=Schroeder |first9=Avi |title=Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine |journal=Advanced Materials |date=April 2020 |volume=32 |issue=13 |pages=1901989 |doi=10.1002/adma.201901989 |pmid=31286573 |pmc=7124889 |bibcode=2020AdM....3201989A }}</ref>
=== Workplace health and safety ===
{{Main|Workplace impact of artificial intelligence#Health and safety applications}}
AI-enabled [[chatbot]]s decrease the need for humans to perform basic call center tasks.<ref name="
Machine learning in [[sentiment analysis]] can spot fatigue in order to prevent [[overwork]].<ref name="
AI can auto-[[Coding (social sciences)|code]] [[workers' compensation]] claims.<ref>{{Cite web|last=Meyers|first=Alysha R.|date=1 May 2019|title=AI and Workers' Comp|url=https://blogs.cdc.gov/niosh-science-blog/2019/05/01/ai-workers-comp/|access-date=3 August 2020|website=NIOSH Science Blog|language=en-us}}</ref><ref>{{Cite web|last1=Webb|first1=Sydney|last2=Siordia|first2=Carlos|last3=Bertke|first3=Stephen|last4=Bartlett|first4=Diana|last5=Reitz|first5=Dan|date=26 February 2020|title=Artificial Intelligence Crowdsourcing Competition for Injury Surveillance|url=https://blogs.cdc.gov/niosh-science-blog/2020/02/26/ai-crowdsourcing/|access-date=3 August 2020|website=NIOSH Science Blog|language=en-us}}</ref>
===Biochemistry===
Machine learning has been used for [[Drug design#Computer-aided drug design|drug design]],<ref name="Ciaramella211">{{cite book|first1=Alberto|last1=Ciaramella|author-link=Alberto Ciaramella|first2=Marco|last2=Ciaramella|title=Introduction to Artificial Intelligence: from data analysis to generative AI|date=2024|isbn=978-8894787603|page=211|publisher=Intellisemantic Editions }}</ref> [[drug discovery]] and development, [[drug repurposing]], improving pharmaceutical productivity, and clinical trials.<ref>{{cite journal |last1=Paul |first1=Debleena |last2=Sanap |first2=Gaurav |last3=Shenoy |first3=Snehal |last4=Kalyane |first4=Dnyaneshwar |last5=Kalia |first5=Kiran |last6=Tekade |first6=Rakesh K. |title=Artificial intelligence in drug discovery and development |journal=Drug Discovery Today |date=January 2021 |volume=26 |issue=1 |pages=80–93 |doi=10.1016/j.drudis.2020.10.010|pmid=33099022 |pmc=7577280 }}</ref>
Computer-planned syntheses via computational reaction networks, described as a platform that combines "computational synthesis with AI algorithms to predict molecular properties",<ref>{{cite web |title=Allchemy – Resource-aware AI for drug discovery |url=https://allchemy.net/ |access-date=29 May 2022}}</ref> has been used in drug-syntheses, and developing routes for [[circular economy|recycling]] 200 industrial [[Chemical waste#Methods of disposal of laboratory chemical wastes|waste chemicals]] into important drugs and agrochemicals (chemical synthesis design).<ref>{{cite journal |last1=Wołos |first1=Agnieszka |last2=Koszelewski |first2=Dominik |last3=Roszak |first3=Rafał |last4=Szymkuć |first4=Sara |last5=Moskal |first5=Martyna |last6=Ostaszewski |first6=Ryszard |last7=Herrera |first7=Brenden T. |last8=Maier |first8=Josef M. |last9=Brezicki |first9=Gordon |last10=Samuel |first10=Jonathon |last11=Lummiss |first11=Justin A. M. |last12=McQuade |first12=D. Tyler |last13=Rogers |first13=Luke |last14=Grzybowski |first14=Bartosz A. |title=Computer-designed repurposing of chemical wastes into drugs |journal=Nature |date=April 2022 |volume=604 |issue=7907 |pages=668–676 |doi=10.1038/s41586-022-04503-9 |pmid=35478240 |bibcode=2022Natur.604..668W |doi-access=free }}</ref> It has also been used to explore the [[abiogenesis|origins of life on Earth]].<ref>{{cite journal |last1=Wołos |first1=Agnieszka |last2=Roszak |first2=Rafał |last3=Żądło-Dobrowolska |first3=Anna |last4=Beker |first4=Wiktor |last5=Mikulak-Klucznik |first5=Barbara |last6=Spólnik |first6=Grzegorz |last7=Dygas |first7=Mirosław |last8=Szymkuć |first8=Sara |last9=Grzybowski |first9=Bartosz A. |title=Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry |journal=Science |date=25 September 2020 |volume=369 |issue=6511 |pages=eaaw1955 |doi=10.1126/science.aaw1955|pmid=32973002 }}</ref>
The AI program [[AlphaFold 2]] can determine the 3D structure of a ([[Protein folding|folded]]) protein in hours rather than the months required by earlier automated approaches and was used to provide the likely structures of all proteins in the human body and essentially all proteins known to science (more than 200 million).<ref name="economist20201130">{{Cite news|date=30 November 2020|title=DeepMind is answering one of biology's biggest challenges|newspaper=The Economist|url=https://www.economist.com/science-and-technology/2020/11/30/deepmind-is-answering-one-of-biologys-biggest-challenges|access-date=30 November 2020 }}</ref><ref name="KahnLessons">Jeremy Kahn, [https://fortune.com/2020/12/01/lessons-from-deepminds-a-i-breakthrough-eye-on-ai/ Lessons from DeepMind's breakthrough in protein-folding A.I.], ''[[Fortune (magazine)|Fortune]]'', 1 December 2020</ref><ref>{{Cite web |title=DeepMind uncovers structure of 200m proteins in scientific leap forward |url=https://www.theguardian.com/technology/2022/jul/28/deepmind-uncovers-structure-of-200m-proteins-in-scientific-leap-forward |access-date=2022-07-28|date=2022-07-28 |website=The Guardian}}</ref><ref>{{Cite web |title=AlphaFold reveals the structure of the protein universe |url=https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe |access-date=2022-07-28|date=2022-07-28 |website=DeepMind}}</ref>
== Language processing ==
=== Language translation ===
{{Main|Machine translation}}
Speech translation technology attempts to convert one language's spoken words into another language. This potentially reduces language barriers in global commerce and cross-cultural exchange, enabling speakers of various languages to communicate with one another.<ref>{{Cite journal |last=Nakamura |first=Satoshi |date=2009 |title=Overcoming the language barrier with speech translation technology |journal=Science & Technology Trends Quarterly Review |volume= |issue=31 |pages=35–48 |id={{CORE output|236667511}} }}</ref>
AI has been used to automatically translate spoken language and textual content in products such as [[Microsoft Translator]], [[Google Translate]], and [[DeepL Translator]].<ref name="Clark Breakthrough Year">{{cite news |last1=Clark |first1=Jack |title=Why 2015 Was a Breakthrough Year in Artificial Intelligence |url=https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence |work=Bloomberg.com |date=8 December 2015 |url-access =subscription }}</ref> Additionally, research and development are in progress to decode and conduct animal communication.<ref name="10.1038/s41598-022-07174-8" /><ref>{{cite news |title=Can artificial intelligence really help us talk to the animals? |url=https://www.theguardian.com/science/2022/jul/31/can-artificial-intelligence-really-help-us-talk-to-the-animals |access-date=30 August 2022 |work=The Guardian |date=31 July 2022 |language=en}}</ref>
Meaning is conveyed not only by text, but also through usage and context (see [[semantics]] and [[pragmatics]]). As a result, the two primary categorization approaches for machine translations are [[statistical machine translation]] (SMT) and [[neural machine translation]]s (NMTs). The old method of performing translation was to use statistical methodology to forecast the best probable output with specific algorithms. However, with NMT, the approach employs dynamic algorithms to achieve better translations based on context.<ref>{{Cite book |last=K. Mandal, G. S. Pradeep Ghantasala, Firoz Khan, R. Sathiyaraj, B. Balamurugan |title=Natural Language Processing in Artificial Intelligence |date=2020 |publisher=Apple Academic Press |isbn=9780367808495 |edition=1st |pages=53–54}}</ref>
== Law and government ==
===
{{Main|Artificial intelligence in government}}
AI [[facial recognition system]]s are used for [[mass surveillance]], notably in China.<ref>{{Cite news|last1=Buckley|first1=Chris|last2=Mozur|first2=Paul|date=22 May 2019|title=How China Uses High-Tech Surveillance to Subdue Minorities|work=The New York Times|url=https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html}}</ref><ref>{{Cite web|url=https://techcrunch.com/2019/05/03/china-smart-city-exposed/|title=Security lapse exposed a Chinese smart city surveillance system|date=3 May 2019|access-date=14 September 2020|archive-date=7 March 2021|archive-url=https://web.archive.org/web/20210307203740/https://consent.yahoo.com/v2/collectConsent?sessionId=3_cc-session_c8562b93-9863-4915-8523-6c7b930a3efc|url-status=live}}</ref> In 2019, [[Bangalore|Bengaluru, India]] deployed AI-managed traffic signals. This system uses cameras to monitor traffic density and adjust signal timing based on the interval needed to clear traffic.<ref>{{Cite web|date=24 September 2019|title=AI traffic signals to be installed in Bengaluru soon|url=https://nextbigwhat.com/ai-traffic-signals-to-be-installed-in-bengaluru-soon/|access-date=1 October 2019|website=NextBigWhat|language=en-US}}</ref>
=== Law ===
{{Main|Legal informatics#Artificial intelligence
==== Legal analysis ====
AI is a mainstay of law-related professions. Algorithms and machine learning do some tasks previously done by entry-level lawyers.<ref>{{
The [[electronic discovery]] industry uses machine learning to reduce manual searching.<ref>{{Cite news|last=Croft|first=Jane|date=2 May 2019|title=AI learns to read Korean, so you don't have to|url=https://www.ft.com/content/fef40df0-4a6a-11e9-bde6-79eaea5acb64|access-date=19 December 2019|website=[[Financial Times]]|language=en-GB}}</ref>
==== Law enforcement and legal proceedings ====
Law enforcement has begun using [[facial recognition system]]s (FRS) to identify suspects from visual data. FRS results have proven to be more accurate when compared to eyewitness results. Furthermore, FRS has shown to have much a better ability to identify individuals when video clarity and visibility are low in comparison to human participants.<ref>{{cite journal |last1=Kleider-Offutt |first1=Heather |last2=Stevens |first2=Beth |last3=Mickes |first3=Laura |last4=Boogert |first4=Stewart |title=Application of artificial intelligence to eyewitness identification |journal=Cognitive Research: Principles and Implications |date=3 April 2024 |volume=9 |issue=1 |page=19 |doi=10.1186/s41235-024-00542-0 |doi-access=free |pmid=38568356 |pmc=10991253 }}</ref>
One concern relates to [[algorithmic bias]], AI programs may become biased after processing data that exhibits bias.<ref>{{Cite web|date=12 January 2019|title=Commentary: Bad news. Artificial intelligence is biased|url=https://www.channelnewsasia.com/news/commentary/artificial-intelligence-big-data-bias-hiring-loans-key-challenge-11097374|url-status=live|archive-url=https://web.archive.org/web/20190112104421/https://www.channelnewsasia.com/news/commentary/artificial-intelligence-big-data-bias-hiring-loans-key-challenge-11097374|archive-date=12 January 2019|access-date=19 June 2020|website=CNA|language=en}}</ref> [[ProPublica]] claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than that of white defendants.<ref name="Julia Angwin-2016" />
In 2019, the city of [[Hangzhou]], China established a pilot program artificial intelligence-based Internet Court to adjudicate disputes related to ecommerce and internet-related [[Intellectual property in China|intellectual property]] claims.<ref name="Šimalčík-2023">{{Cite book |last=Šimalčík |first=Matej |title=Contemporary China: a New Superpower? |publisher=[[Routledge]] |year=2023 |isbn=978-1-03-239508-1 |editor-last=Kironska |editor-first=Kristina |chapter=Rule by Law |editor-last2=Turscanyi |editor-first2=Richard Q.}}</ref>{{Rp|page=124}} Parties appear before the court via videoconference and AI evaluates the evidence presented and applies relevant legal standards.<ref name="Šimalčík-2023" />{{Rp|page=124}}
{{Main|Artificial intelligence in industry|Artificial intelligence in heavy industry}}
=== Sensors ===
Artificial intelligence has been combined with digital [[Spectrometer|spectrometry]] by IdeaCuria Inc.,<ref>{{Cite web|date=8 October 2018|title=Digital Spectrometry|url=https://www.ideacuria.com/ic/technology.html}}</ref><ref>{{Cite patent|title=Digital Spectrometry Patent |country=US|number=9967696B2|url=https://patents.google.com/patent/US9967696B2/en?assignee=ideacuria&oq=ideacuria|pubdate=2018-10-08}}</ref> enable applications such as at-home water quality monitoring.
=== Toys and games ===
In the 1990s, early artificial intelligence tools controlled [[Tamagotchi]]s and [[Giga Pet]]s, the [[Internet]], and the first widely released robot, [[Furby]]. [[Aibo]] was a [[domestic robot]] in the form of a robotic dog with intelligent features and [[autonomy]].
Mattel created an assortment of AI-enabled toys that "understand" conversations, give intelligent responses, and learn.<ref>{{Cite news|url=https://www.washingtonpost.com/news/innovations/wp/2015/10/15/how-artificial-intelligence-is-moving-from-the-lab-to-your-kids-playroom/|title=How artificial intelligence is moving from the lab to your kid's playroom|newspaper=The Washington Post|access-date=18 November 2016}}</ref>
===
[[Oil and gas]] companies have used artificial intelligence tools to automate functions, foresee equipment issues, and increase oil and gas output.<ref>{{Cite web |date=15 May 2019 |title=Application of artificial intelligence in oil and gas industry: Exploring its impact |url=https://www.offshore-technology.com/features/application-of-artificial-intelligence-in-oil-and-gas-industry/}}</ref><ref>{{Cite news |last=Salvaterra |first=Neanda |date=14 October 2019 |title=Oil and Gas Companies Turn to AI to Cut Costs |newspaper=The Wall Street Journal |url=https://www.wsj.com/articles/oil-and-gas-companies-turn-to-ai-to-cut-costs-11571018460}}</ref>
== Military ==
{{main|Military applications of artificial intelligence}}
Various countries are deploying AI military applications.<ref name="CRS-2019">{{Cite book|last=Congressional Research Service|url=https://fas.org/sgp/crs/natsec/R45178.pdf|title=Artificial Intelligence and National Security|publisher=Congressional Research Service|year=2019|___location=Washington, DC}}[[Template:PD-notice|PD-notice]]</ref> The main applications enhance [[command and control]], communications, sensors, integration and interoperability.{{citation needed|date=April 2025}} Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and [[Vehicular automation|autonomous vehicles]].<ref name="CRS-2019" /> AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, [[target acquisition]], coordination and deconfliction of distributed [[Forward observers in the U.S. military|Joint Fires]] between networked combat vehicles involving manned and unmanned teams.{{citation needed|date=April 2025}}
AI has been used in military operations in Iraq, Syria, Israel and Ukraine.<ref name="CRS-2019" /><ref>{{Cite web |last=Iraqi |first=Amjad |date=2024-04-03 |title='Lavender': The AI machine directing Israel's bombing spree in Gaza |url=https://www.972mag.com/lavender-ai-israeli-army-gaza/ |access-date=2024-04-06 |website=+972 Magazine |language=en-US}}</ref><ref name="Davies-2023">{{Cite news |last1=Davies |first1=Harry |last2=McKernan |first2=Bethan |last3=Sabbagh |first3=Dan |date=2023-12-01 |title='The Gospel': how Israel uses AI to select bombing targets in Gaza |language=en-GB |work=The Guardian |url=https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets |access-date=2023-12-04 }}</ref><ref>{{Cite news|last=Marti|first=J Werner|title=Drohnen haben den Krieg in der Ukraine revolutioniert, doch sie sind empfindlich auf Störsender – deshalb sollen sie jetzt autonom operieren|url=https://www.nzz.ch/international/die-ukraine-setzt-auf-drohnen-die-autonom-navigieren-und-toeten-koennen-ld.1838731|date=10 August 2024|access-date=10 August 2024|newspaper=Neue Zürcher Zeitung|language=German}}</ref>
=== Internet and e-commerce ===
{{Main|Marketing and artificial intelligence}}
==== Web feeds and posts ====
Machine learning has been used for [[recommendation system]]s in determining which posts should show up in [[web feed|social media feeds]].<ref>{{cite web |title=What are the security risks of open sourcing the Twitter algorithm? |url=https://venturebeat.com/2022/05/27/open-source-twitter-security-risks/ |website=VentureBeat |access-date=29 May 2022 |date=27 May 2022}}</ref><ref>{{cite web |title=Examining algorithmic amplification of political content on Twitter |url=https://blog.twitter.com/en_us/topics/company/2021/rml-politicalcontent |access-date=29 May 2022 |language=en-us}}</ref> Various types of [[social media analysis]] also make use of machine learning<ref>{{cite journal |last1=Park |first1=SoHyun |last2=Oh |first2=Heung-Kwon |last3=Park |first3=Gibeom |last4=Suh |first4=Bongwon |last5=Bae |first5=Woo Kyung |last6=Kim |first6=Jin Won |last7=Yoon |first7=Hyuk |last8=Kim |first8=Duck-Woo |last9=Kang |first9=Sung-Bum |title=The Source and Credibility of Colorectal Cancer Information on Twitter |journal=Medicine |date=February 2016 |volume=95 |issue=7 |pages=e2775 |doi=10.1097/MD.0000000000002775|pmid=26886625 |pmc=4998625 }}</ref><ref>{{cite journal |last1=Efthimion |first1=Phillip |last2=Payne |first2=Scott |last3=Proferes |first3=Nicholas |title=Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots |journal=SMU Data Science Review |date=20 July 2018 |volume=1 |issue=2|url=https://scholar.smu.edu/datasciencereview/vol1/iss2/5/}}</ref> and there is research into its use for (semi-)automated tagging/enhancement/correction of [[Misinformation|online misinformation]] and related [[filter bubble]]s.<ref name="onlenv">{{cite web |title=The online information environment |url=https://royalsociety.org/-/media/policy/projects/online-information-environment/the-online-information-environment.pdf |access-date=21 February 2022}}</ref><ref>{{cite journal |last1=Islam |first1=Md Rafiqul |last2=Liu |first2=Shaowu |last3=Wang |first3=Xianzhi |last4=Xu |first4=Guandong |title=Deep learning for misinformation detection on online social networks: a survey and new perspectives |journal=Social Network Analysis and Mining |date=29 September 2020 |volume=10 |issue=1 |pages=82 |doi=10.1007/s13278-020-00696-x |pmid=33014173 |pmc=7524036 }}</ref><ref>{{Cite arXiv|last1=Mohseni |first1=Sina |last2=Ragan |first2=Eric |title=Combating Fake News with Interpretable News Feed Algorithms |date=4 December 2018|class=cs.SI |eprint=1811.12349 }}</ref>
AI has been used to customize shopping options and personalize offers.<ref>{{cite news|url=https://www.bbc.com/news/technology-54522442/|title=How artificial intelligence may be making you buy things|work=BBC News|date=9 November 2020|access-date=9 November 2020}}</ref> [[Online gambling]] companies have used AI for targeting gamblers.<ref>{{cite web|last1=Busby|first1=Mattha|date=30 April 2018|title=Revealed: how bookies use AI to keep gamblers hooked|url=https://www.theguardian.com/technology/2018/apr/30/bookies-using-ai-to-keep-gamblers-hooked-insiders-say|website=The Guardian|language=en}}</ref>
==== Virtual assistants and search ====
{{Main|Virtual assistant}}
[[Intelligent personal assistant]]s use AI to understand many natural language requests in other ways than rudimentary commands. Common examples are Apple's Siri, Amazon's Alexa, and a more recent AI, ChatGPT by OpenAI.<ref>{{cite web |url=http://readwrite.com/2013/01/15/virtual-personal-assistants-the-future-of-your-smartphone-infographic|title=Virtual Personal Assistants & The Future Of Your Smartphone [Infographic]|date=15 January 2013|author=Rowinski, Dan|work=ReadWrite|url-status=live|archive-url=https://web.archive.org/web/20151222083034/http://readwrite.com/2013/01/15/virtual-personal-assistants-the-future-of-your-smartphone-infographic|archive-date=22 December 2015}}</ref>
[[Microsoft Copilot|Bing Chat]] has used artificial intelligence as part of its [[search engine]].<ref>{{cite news |last1=Roose |first1=Kevin |title=Bing's A.I. Chat: 'I Want to Be Alive. 😈' |url=https://www.nytimes.com/2023/02/16/technology/bing-chatbot-transcript.html |work=The New York Times |date=16 February 2023 }}</ref>
==== Spam filtering ====
{{Main|Spam filter}}
Machine learning can be used to combat spam, scams, and [[phishing]]. It can scrutinize the contents of spam and phishing attacks to attempt to identify malicious elements.<ref>{{cite book |doi=10.1109/SSCI50451.2021.9659981 |chapter=Phishing Detection Using URL-based XAI Techniques |title=2021 IEEE Symposium Series on Computational Intelligence (SSCI) |date=2021 |last1=Galego Hernandes |first1=Paulo R. |last2=Floret |first2=Camila P. |last3=Cardozo De Almeida |first3=Katia F. |last4=Da Silva |first4=Vinicius Camargo |last5=Papa |first5=Joso Paulo |last6=Pontara Da Costa |first6=Kelton A. |pages=01–06 |isbn=978-1-7281-9048-8 }}</ref> Some models built via machine learning algorithms have over 90% accuracy in distinguishing between spam and legitimate emails.<ref>{{Cite journal |last1=Jáñez-Martino |first1=Francisco |last2=Alaiz-Rodríguez |first2=Rocío |last3=González-Castro |first3=Víctor |last4=Fidalgo |first4=Eduardo |last5=Alegre |first5=Enrique |date=2023-02-01 |title=A review of spam email detection: analysis of spammer strategies and the dataset shift problem |journal=Artificial Intelligence Review |language=en |volume=56 |issue=2 |pages=1145–1173 |doi=10.1007/s10462-022-10195-4 |doi-access=free |hdl=10612/14967 |hdl-access=free }}</ref> These models can be refined using new data and evolving spam tactics. Machine learning also analyzes traits such as sender behavior, email header information, and attachment types, potentially enhancing spam detection.<ref>{{Cite journal |last1=Kapan |first1=Sibel |last2=Sora Gunal |first2=Efnan |date=January 2023 |title=Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features |journal=Applied Sciences |language=en |volume=13 |issue=24 |pages=13269 |doi=10.3390/app132413269 |doi-access=free }}</ref>
==== Facial recognition and image labeling ====
{{Main|Automatic image annotation}}
AI has been used in [[facial recognition system]]s. Some examples are Apple's [[Face ID]] and Android's [[Face Unlock]], which are used to secure mobile devices.<ref>{{cite news |last1=Heath |first1=Nick |title=What is AI? Everything you need to know about Artificial Intelligence |url=https://www.zdnet.com/article/what-is-ai-heres-everything-you-need-to-know-about-artificial-intelligence/ |access-date=1 March 2021 |publisher=ZDNet |date=11 December 2020 |language=en}}</ref>
Image labeling has been used by [[Google Image Labeler]] to detect products in photos and to allow people to search based on a photo. Image labeling has also been demonstrated to generate speech to describe images to blind people.<ref name="Clark Breakthrough Year"/> Facebook's [[DeepFace]] identifies human faces in digital images.{{citation needed|date=May 2025}}
== Scientific research ==
===Evidence of general impacts===
In April 2024, the [[Scientific Advice Mechanism]] to the [[European Commission]] published advice<ref>{{Cite web |title=Successful and timely uptake of artificial intelligence in science in the EU – Scientific Advice Mechanism |url=https://scientificadvice.eu/advice/artificial-intelligence-in-science/ |access-date=2024-04-16 |language=en-GB}}</ref> including a comprehensive evidence review of the opportunities and challenges posed by artificial intelligence in scientific research.
As benefits, the evidence review<ref>{{Cite web |title=AI in science evidence review report – Scientific Advice Mechanism |url=https://scientificadvice.eu/scientific-outputs/ai-in-science-evidence-review-report/ |access-date=2024-04-16 |language=en-GB}}</ref> highlighted:
* its role in accelerating research and innovation
* its capacity to automate workflows
* enhancing dissemination of scientific work
As challenges:
* limitations and risks around transparency, reproducibility and interpretability
* poor performance (inaccuracy)
* risk of harm through misuse or unintended use
* societal concerns including the spread of misinformation and increasing inequalities
===Archaeology,
{{See also|Digital archaeology}}
Machine learning can help to restore and attribute ancient texts.<ref>{{cite journal |last1=Assael |first1=Yannis |last2=Sommerschield |first2=Thea |last3=Shillingford |first3=Brendan |last4=Bordbar |first4=Mahyar |last5=Pavlopoulos |first5=John |last6=Chatzipanagiotou |first6=Marita |last7=Androutsopoulos |first7=Ion |last8=Prag |first8=Jonathan |last9=de Freitas |first9=Nando |title=Restoring and attributing ancient texts using deep neural networks |journal=Nature |date=March 2022 |volume=603 |issue=7900 |pages=280–283 |doi=10.1038/s41586-022-04448-z |pmid=35264762 |pmc=8907065 |bibcode=2022Natur.603..280A |doi-access=free}}</ref> It can help to index texts for example to enable better and easier searching and classification of fragments.<ref>{{cite journal |last1=Mantovan |first1=Lorenzo |last2=Nanni |first2=Loris |title=The Computerization of Archaeology: Survey on Artificial Intelligence Techniques |journal=SN Computer Science |date=September 2020 |volume=1 |issue=5 |doi=10.1007/s42979-020-00286-w |arxiv=2005.02863 }}</ref>
Artificial intelligence can also be used to investigate genomes to uncover [[genetic history]], such as [[interbreeding between archaic and modern humans]] by which for example the past existence of a [[ghost population]], not [[Neanderthal]] or [[Denisovan]], was inferred.<ref>{{cite journal |last1=Mondal |first1=Mayukh |last2=Bertranpetit |first2=Jaume |last3=Lao |first3=Oscar |title=Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania |journal=Nature Communications |date=December 2019 |volume=10 |issue=1 |pages=246 |doi=10.1038/s41467-018-08089-7|pmid=30651539 |pmc=6335398 |bibcode=2019NatCo..10..246M |doi-access=free}}</ref> {{Further|Ancient DNA#Human aDNA|Genetic history of Europe}}
It can also be used for "non-invasive and non-destructive access to internal structures of archaeological remains".<ref>{{cite journal |last1=Tanti |first1=Marc |last2=Berruyer |first2=Camille |last3=Tafforeau |first3=Paul |last4=Muscat |first4=Adrian |last5=Farrugia |first5=Reuben |last6=Scerri |first6=Kenneth |last7=Valentino |first7=Gianluca |last8=Solé |first8=V. Armando |last9=Briffa |first9=Johann A. |title=Automated segmentation of microtomography imaging of Egyptian mummies |journal=PLOS ONE |date=15 December 2021 |volume=16 |issue=12 |pages=e0260707 |doi=10.1371/journal.pone.0260707 |pmid=34910736 |pmc=8673632 |arxiv=2105.06738 |bibcode=2021PLoSO..1660707T |doi-access=free }}</ref> {{Further|Remote sensing in archaeology}}
=== Physics ===
{{main|Machine learning in physics}}
A [[deep learning]] system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on an [[reproducibility|unpublished]] approach inspired by studies of visual cognition in infants.<ref>{{cite news |title=DeepMind AI learns physics by watching videos that don't make sense |url=https://www.newscientist.com/article/2327766-deepmind-ai-learns-physics-by-watching-videos-that-dont-make-sense |access-date=21 August 2022 |work=New Scientist}}</ref><ref>{{cite journal |last1=Piloto |first1=Luis S. |last2=Weinstein |first2=Ari |last3=Battaglia |first3=Peter |last4=Botvinick |first4=Matthew |title=Intuitive physics learning in a deep-learning model inspired by developmental psychology |journal=Nature Human Behaviour |date=11 July 2022 |volume=6 |issue=9 |pages=1257–1267 |doi=10.1038/s41562-022-01394-8 |pmid=35817932 |pmc=9489531 |doi-access=free}}</ref> Other researchers have developed a machine learning algorithm that could discover sets of basic variables of various physical systems and predict the systems' future dynamics from video recordings of their behavior.<ref name="advancedsciencenews.com/an-artific">{{cite news |last1=Feldman |first1=Andrey |title=Artificial physicist to unravel the laws of nature |url=https://www.advancedsciencenews.com/an-artificial-physicist-to-unravel-the-laws-of-nature/ |access-date=21 August 2022 |work=Advanced Science News |date=11 August 2022}}</ref><ref>{{cite journal |last1=Chen |first1=Boyuan |last2=Huang |first2=Kuang |last3=Raghupathi |first3=Sunand |last4=Chandratreya |first4=Ishaan |last5=Du |first5=Qiang |last6=Lipson |first6=Hod |title=Automated discovery of fundamental variables hidden in experimental data |journal=Nature Computational Science |date=July 2022 |volume=2 |issue=7 |pages=433–442 |doi=10.1038/s43588-022-00281-6 |pmid=38177869 }}</ref> In the future, it may be possible that such can be used to automate the discovery of physical laws of complex systems.<ref name="advancedsciencenews.com/an-artific"/>
===
AI could be used for materials optimization and discovery such as the discovery of stable materials and the prediction of their crystal structure.<ref>{{cite journal |last1=Schmidt |first1=Jonathan |last2=Marques |first2=Mário R. G. |last3=Botti |first3=Silvana |last4=Marques |first4=Miguel A. L. |title=Recent advances and applications of machine learning in solid-state materials science |journal=npj Computational Materials |date=8 August 2019 |volume=5 |issue=1 |page=83 |doi=10.1038/s41524-019-0221-0 |bibcode=2019npjCM...5...83S |doi-access=free}}</ref><ref name="10.1038/s43246-021-00209-z"/><ref name="10.1002/adma.202109892"/>
In November 2023, researchers at [[Google DeepMind]] and [[Lawrence Berkeley National Laboratory]] announced that they had developed an AI system known as GNoME. This system has contributed to [[materials science]] by discovering over 2 million new materials within a relatively short timeframe. GNoME employs deep learning techniques to efficiently explore potential material structures, achieving a significant increase in the identification of stable inorganic [[crystal structure]]s. The system's predictions were validated through autonomous robotic experiments, demonstrating a noteworthy success rate of 71%. The data of newly discovered materials is publicly available through the [[Materials Project]] database, offering researchers the opportunity to identify materials with desired properties for various applications. This development has implications for the future of scientific discovery and the integration of AI in material science research, potentially expediting material innovation and reducing costs in product development. The use of AI and deep learning suggests the possibility of minimizing or eliminating manual lab experiments and allowing scientists to focus more on the design and analysis of unique compounds.<ref>{{Cite web |last=Nuñez |first=Michael |date=2023-11-29 |title=Google DeepMind's materials AI has already discovered 2.2 million new crystals |url=https://venturebeat.com/ai/google-deepminds-materials-ai-has-already-discovered-2-2-million-new-crystals/ |access-date=2023-12-19 |website=VentureBeat |language=en-US}}</ref><ref>{{Cite journal |last1=Merchant |first1=Amil |last2=Batzner |first2=Simon |last3=Schoenholz |first3=Samuel S. |last4=Aykol |first4=Muratahan |last5=Cheon |first5=Gowoon |last6=Cubuk |first6=Ekin Dogus |date=December 2023 |title=Scaling deep learning for materials discovery |journal=Nature |language=en |volume=624 |issue=7990 |pages=80–85 |doi=10.1038/s41586-023-06735-9 |doi-access=free |pmid=38030720 |pmc=10700131 |bibcode=2023Natur.624...80M }}</ref><ref>{{cite journal |last1=Peplow |first1=Mark |title=Google AI and robots join forces to build new materials |journal=Nature |date=29 November 2023 |doi=10.1038/d41586-023-03745-5 |pmid=38030771 }}</ref>
===
Machine learning is used in diverse types of [[reverse engineering]]. For example, machine learning has been used to reverse engineer a composite material part, enabling unauthorized production of high quality parts,<ref>{{cite journal |last1=Yanamandra |first1=Kaushik |last2=Chen |first2=Guan Lin |last3=Xu |first3=Xianbo |last4=Mac |first4=Gary |last5=Gupta |first5=Nikhil |title=Reverse engineering of additive manufactured composite part by toolpath reconstruction using imaging and machine learning |journal=Composites Science and Technology |date=29 September 2020 |volume=198 |pages=108318 |doi=10.1016/j.compscitech.2020.108318 |doi-access=free }}</ref> and for quickly understanding the behavior of [[malware]].<ref>{{cite book |doi=10.1145/2666652.2666665 |chapter=Automating Reverse Engineering with Machine Learning Techniques |title=Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop |date=2014 |last1=Anderson |first1=Blake |last2=Storlie |first2=Curtis |last3=Yates |first3=Micah |last4=McPhall |first4=Aaron |pages=103–112 |isbn=978-1-4503-3153-1 }}</ref><ref>{{cite journal |last1=Liu |first1=Wenye |last2=Chang |first2=Chip-Hong |last3=Wang |first3=Xueyang |last4=Liu |first4=Chen |last5=Fung |first5=Jason M. |last6=Ebrahimabadi |first6=Mohammad |last7=Karimi |first7=Naghmeh |last8=Meng |first8=Xingyu |last9=Basu |first9=Kanad |title=Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security |journal=IEEE Journal on Emerging and Selected Topics in Circuits and Systems |date=June 2021 |volume=11 |issue=2 |pages=228–251 |doi=10.1109/JETCAS.2021.3084400 |bibcode=2021IJEST..11..228L |doi-access=free |hdl=10356/155876 |hdl-access=free }}</ref><ref>{{cite web |title=DARPA Taps GrammaTech for Artificial Intelligence Exploration (AIE) Program |url=https://www.businesswire.com/news/home/20210107005103/en/DARPA-Taps-GrammaTech-for-Artificial-Intelligence-Exploration-AIE-Program |website=www.businesswire.com |access-date=10 January 2023 |language=en |date=7 January 2021}}</ref> It can be used to reverse engineer artificial intelligence models.<ref>{{cite magazine |last1=Greenberg |first1=Andy |title=How to Steal an AI |url=https://www.wired.com/2016/09/how-to-steal-an-ai/ |magazine=Wired |access-date=10 January 2023}}</ref> It can also design components by engaging in a type of reverse engineering of not-yet existent virtual components such as inverse molecular design for particular desired functionality<ref>{{cite journal |last1=Sanchez-Lengeling |first1=Benjamin |last2=Aspuru-Guzik |first2=Alán |title=Inverse molecular design using machine learning: Generative models for matter engineering |journal=Science |date=27 July 2018 |volume=361 |issue=6400 |pages=360–365 |doi=10.1126/science.aat2663 |pmid=30049875 |bibcode=2018Sci...361..360S |doi-access=free }}</ref> or [[protein design]] for prespecified functional sites.<ref name="2022-07-biologist"/><ref name="10.1126/science.abn2100">{{cite journal |last1=Wang |first1=Jue |last2=Lisanza |first2=Sidney |last3=Juergens |first3=David |last4=Tischer |first4=Doug |last5=Watson |first5=Joseph L. |last6=Castro |first6=Karla M. |last7=Ragotte |first7=Robert |last8=Saragovi |first8=Amijai |last9=Milles |first9=Lukas F. |last10=Baek |first10=Minkyung |last11=Anishchenko |first11=Ivan |last12=Yang |first12=Wei |last13=Hicks |first13=Derrick R. |last14=Expòsit |first14=Marc |last15=Schlichthaerle |first15=Thomas |last16=Chun |first16=Jung-Ho |last17=Dauparas |first17=Justas |last18=Bennett |first18=Nathaniel |last19=Wicky |first19=Basile I. M. |last20=Muenks |first20=Andrew |last21=DiMaio |first21=Frank |last22=Correia |first22=Bruno |last23=Ovchinnikov |first23=Sergey |last24=Baker |first24=David |title=Scaffolding protein functional sites using deep learning |journal=Science |date=22 July 2022 |volume=377 |issue=6604 |pages=387–394 |doi=10.1126/science.abn2100 |pmid=35862514 |pmc=9621694 |bibcode=2022Sci...377..387W }}</ref> Biological network reverse engineering could model interactions in a human understandable way, e.g. bas on time series data of gene expression levels.<ref>{{cite thesis |last1=Teemu |first1=Rintala |title=Using Boolean network extraction of trained neural networks to reverse-engineer gene-regulatory networks from time-series data |degree=Master’s in Life Science Technologies |publisher=Aalto University |date=17 June 2019 |url=https://aaltodoc.aalto.fi/handle/123456789/38941?show=full }}{{page needed|date=May 2025}}</ref>
=== Astronomy, space activities and ufology ===
{{See also|#Novel types of machine learning}}
Artificial intelligence is used in [[astronomy]] to analyze increasing amounts of available data<ref>{{cite journal |last1=Ball |first1=Nicholas M. |last2=Brunner |first2=Robert J. |title=Data mining and machine learning in astronomy |journal=International Journal of Modern Physics D |date=July 2010 |volume=19 |issue=7 |pages=1049–1106 |doi=10.1142/S0218271810017160 |arxiv=0906.2173 |bibcode=2010IJMPD..19.1049B }}</ref><ref name="nasaai"/> and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering [[exoplanet]]s, forecasting solar activity, and distinguishing between signals and instrumental effects in [[gravitational wave astronomy]].<ref>{{cite journal |last1=Fluke |first1=Christopher J. |last2=Jacobs |first2=Colin |title=Surveying the reach and maturity of machine learning and artificial intelligence in astronomy |journal=WIREs Data Mining and Knowledge Discovery |date=March 2020 |volume=10 |issue=2 |doi=10.1002/widm.1349 |arxiv=1912.02934 |bibcode=2020WDMKD..10.1349F }}</ref> It could also be used for activities in space such as [[space exploration]], including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance,<ref>{{cite news |last1=Pultarova |first1=Tereza |title=Artificial intelligence is learning how to dodge space junk in orbit |url=https://www.space.com/AI-autonomous-space-debris-avoidance-esa |access-date=3 July 2022 |work=Space.com |date=29 April 2021 |language=en}}</ref> and more autonomous operation.<ref>{{cite book |doi=10.1007/978-3-030-32150-5_131 |chapter=A Study on Embedding the Artificial Intelligence and Machine Learning into Space Exploration and Astronomy |title=Emerging Trends in Computing and Expert Technology |series=Lecture Notes on Data Engineering and Communications Technologies |date=2020 |last1=Mohan |first1=Jaya Preethi |last2=Tejaswi |first2=N. |volume=35 |pages=1295–1302 |isbn=978-3-030-32149-9 }}</ref><ref>{{cite web |last1=Rees |first1=Martin |author1-link=Martin Rees |title=Could space-going billionaires be the vanguard of a cosmic revolution? {{!}} Martin Rees |url=https://www.theguardian.com/commentisfree/2022/apr/30/space-billionaires-cosmic-earth-elon-musk-jeff-bezos |website=The Guardian |access-date=29 May 2022 |language=en |date=30 April 2022}}</ref><ref name="esaai">{{cite web |title=Artificial intelligence in space |url=https://www.esa.int/Enabling_Support/Preparing_for_the_Future/Discovery_and_Preparation/Artificial_intelligence_in_space |website=www.esa.int |access-date=30 May 2022 |language=en}}</ref><ref name="nasaai">{{cite web |last1=Shekhtman |first1=Svetlana |title=NASA Applying AI Technologies to Problems in Space Science |url=https://www.nasa.gov/feature/goddard/2019/nasa-takes-a-cue-from-silicon-valley-to-hatch-artificial-intelligence-technologies |website=NASA |access-date=30 May 2022 |date=15 November 2019}}</ref>
In the [[search for extraterrestrial intelligence]] (SETI), machine learning has been used in attempts to identify artificially generated [[electromagnetic waves]] in available data<ref>{{cite conference |last1=Gutowska |first1=Małgorzata |last2=Scriney |first2=Michael |last3=McCarren |first3=Andrew |title=Identifying extra-terrestrial intelligence using machine learning |conference=27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science |date=December 2019 |url=https://doras.dcu.ie/24020/ }}</ref><ref>{{cite journal |last1=Zhang |first1=Yunfan Gerry |last2=Gajjar |first2=Vishal |last3=Foster |first3=Griffin |last4=Siemion |first4=Andrew |last5=Cordes |first5=James |last6=Law |first6=Casey |last7=Wang |first7=Yu |title=Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach |journal=The Astrophysical Journal |year=2018 |volume=866 |issue=2 |page=149 |doi=10.3847/1538-4357/aadf31 |arxiv=1809.03043 |bibcode=2018ApJ...866..149Z |doi-access=free }}</ref> – such as real-time observations<ref>{{cite book |doi=10.1109/ICSSIT46314.2019.8987793 |chapter=SETI (Search for Extra Terrestrial Intelligence) Signal Classification using Machine Learning |title=2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) |date=2019 |last1=Nanda |first1=Lakshay |last2=V |first2=Santhi |pages=499–504 |isbn=978-1-7281-2119-2 }}</ref> – and other [[technosignature]]s, e.g. via [[anomaly detection]].<ref>{{cite journal |last1=Gajjar |first1=Vishal |last2=Siemion |first2=Andrew |last3=Croft |first3=Steve |last4=Brzycki |first4=Bryan |last5=Burgay |first5=Marta |last6=Carozzi |first6=Tobia |last7=Concu |first7=Raimondo |last8=Czech |first8=Daniel |last9=DeBoer |first9=David |last10=DeMarines |first10=Julia |last11=Drew |first11=Jamie |last12=Enriquez |first12=J. Emilio |last13=Fawcett |first13=James |last14=Gallagher |first14=Peter |last15=Garrett |first15=Michael |last16=Gizani |first16=Nectaria |last17=Hellbourg |first17=Greg |last18=Holder |first18=Jamie |last19=Isaacson |first19=Howard |last20=Kudale |first20=Sanjay |last21=Lacki |first21=Brian |last22=Lebofsky |first22=Matthew |last23=Li |first23=Di |last24=MacMahon |first24=David H. E. |last25=McCauley |first25=Joe |last26=Melis |first26=Andrea |last27=Molinari |first27=Emilio |last28=Murphy |first28=Pearse |last29=Perrodin |first29=Delphine |last30=Pilia |first30=Maura |last31=Price |first31=Danny C. |last32=Webb |first32=Claire |last33=Werthimer |first33=Dan |last34=Williams |first34=David |last35=Worden |first35=Pete |last36=Zarka |first36=Philippe |last37=Zhang |first37=Yunfan Gerry |title=The Breakthrough Listen Search for Extraterrestrial Intelligence |journal=Bulletin of the American Astronomical Society |arxiv=1907.05519 |date=2 August 2019|volume=51 |issue=7 |page=223 |bibcode=2019BAAS...51g.223G }}</ref> In [[ufology]], the SkyCAM-5 project headed by Prof. Hakan Kayal<ref>{{cite web |title=SkyCAM-5 - Chair of Computer Science VIII - Aerospace Information Technology |url=https://www.informatik.uni-wuerzburg.de/en/aerospaceinfo/wissenschaft-forschung/skycam-5/ |publisher=[[University of Würzburg]] |access-date=29 May 2022}}</ref> and the [[The Galileo Project|Galileo Project]] headed by [[Avi Loeb]] use machine learning to attempt to detect and classify types of UFOs.<ref>{{cite news |title=Project Galileo: The search for alien tech hiding in our Solar System |url=https://www.sciencefocus.com/space/alien-technology-project-galileo/ |access-date=29 May 2022 |work=BBC Science Focus Magazine |language=en}}</ref><ref>{{cite news |title='Something's coming': is America finally ready to take UFOs seriously? |url=https://www.theguardian.com/world/2022/feb/05/ufos-america-aliens-government-report |access-date=29 May 2022 |work=The Guardian |date=5 February 2022 |language=en}}</ref><ref>{{cite news |last1=David |first1=Leonard |title=2022 could be a turning point in the study of UFOs |url=https://www.livescience.com/ufo-study-turning-point-2022 |access-date=29 May 2022 |work=livescience.com |date=27 January 2022 |language=en}}</ref><ref>{{cite news |last1=Gritz |first1=Jennie Rothenberg |title=The Wonder of Avi Loeb |url=https://www.smithsonianmag.com/science-nature/wonder-avi-loeb-180978579/ |access-date=29 May 2022}}</ref><ref>{{cite news |last1=Mann |first1=Adam |title=Avi Loeb's Galileo Project Will Search for Evidence of Alien Visitation |url=https://www.scientificamerican.com/article/avi-loebs-galileo-project-will-search-for-evidence-of-alien-visitation/ |access-date=29 May 2022 |work=Scientific American |language=en}}</ref> The Galileo Project also seeks to detect two further types of potential extraterrestrial technological signatures with the use of AI: [['Oumuamua]]-like [[interstellar object]]s, and non-manmade artificial satellites.<ref>{{cite web |title=Galileo Project – Activities |url=https://projects.iq.harvard.edu/galileo/activities |website=projects.iq.harvard.edu |access-date=29 May 2022 |language=en}}</ref><ref>{{cite news |title=The Galileo Project: Harvard researchers to search for signs of alien technology |url=https://news.sky.com/story/the-galileo-project-harvard-researchers-to-search-for-signs-of-alien-technology-12365304 |work=Sky News |language=en}}</ref>
Machine learning can also be used to produce datasets of spectral signatures of molecules that may be involved in the atmospheric production or consumption of particular chemicals – such as [[Life on Venus#Phosphine|phosphine possibly detected on Venus]] – which could prevent miss assignments and, if accuracy is improved, be used in future detections and identifications of molecules on other planets.<ref>{{cite journal |last1=Zapata Trujillo |first1=Juan C. |last2=Syme |first2=Anna-Maree |last3=Rowell |first3=Keiran N. |last4=Burns |first4=Brendan P. |last5=Clark |first5=Ebubekir S. |last6=Gorman |first6=Maire N. |last7=Jacob |first7=Lorrie S. D. |last8=Kapodistrias |first8=Panayioti |last9=Kedziora |first9=David J. |last10=Lempriere |first10=Felix A. R. |last11=Medcraft |first11=Chris |last12=O'Sullivan |first12=Jensen |last13=Robertson |first13=Evan G. |last14=Soares |first14=Georgia G. |last15=Steller |first15=Luke |last16=Teece |first16=Bronwyn L. |last17=Tremblay |first17=Chenoa D. |last18=Sousa-Silva |first18=Clara |last19=McKemmish |first19=Laura K. |title=Computational Infrared Spectroscopy of 958 Phosphorus-Bearing Molecules |journal=Frontiers in Astronomy and Space Sciences |date=2021 |volume=8 |page=43 |doi=10.3389/fspas.2021.639068 |arxiv=2105.08897 |bibcode=2021FrASS...8...43Z |doi-access=free }}</ref>
{{See also|#Health|#Quantum computing|Regulation of chemicals|Computational chemistry#Fields of application|Laboratory robotics}}
Machine learning has been used for the [[protein design|design of proteins]] with prespecified functional sites,<ref name="2022-07-biologist">{{cite news |title=Biologists train AI to generate medicines and vaccines |url=https://medicalxpress.com/news/2022-07-biologists-ai-medicines-vaccines.html |work=University of Washington-Harborview Medical Center |language=en}}</ref><ref name="10.1126/science.abn2100"/> predicting molecular properties, and exploring large chemical/reaction spaces.<ref>{{cite journal |last1=Stocker |first1=Sina |last2=Csányi |first2=Gábor |last3=Reuter |first3=Karsten |last4=Margraf |first4=Johannes T. |title=Machine learning in chemical reaction space |journal=Nature Communications |date=30 October 2020 |volume=11 |issue=1 |pages=5505 |doi=10.1038/s41467-020-19267-x |pmid=33127879 |pmc=7603480 |bibcode=2020NatCo..11.5505S }}</ref>
There is research about which types of computer-aided chemistry would benefit from machine learning.<ref>{{cite web |title=Chemists debate machine learning's future in synthesis planning and ask for open data |url=https://cen.acs.org/physical-chemistry/computational-chemistry/Chemists-debate-machine-learnings-future/100/i18 |website=cen.acs.org |access-date=29 May 2022}}</ref>
There are various types of applications for machine learning in decoding human biology, such as helping to map [[gene expression]] patterns to functional activation patterns<ref>{{cite journal |last1=Hansen |first1=Justine Y. |last2=Markello |first2=Ross D. |last3=Vogel |first3=Jacob W. |last4=Seidlitz |first4=Jakob |last5=Bzdok |first5=Danilo |last6=Misic |first6=Bratislav |title=Mapping gene transcription and neurocognition across human neocortex |journal=Nature Human Behaviour |date=September 2021 |volume=5 |issue=9 |pages=1240–1250 |doi=10.1038/s41562-021-01082-z |pmid=33767429 }}</ref> or identifying functional [[DNA motif]]s.<ref>{{cite journal |last1=Vo ngoc |first1=Long |last2=Huang |first2=Cassidy Yunjing |last3=Cassidy |first3=California Jack |last4=Medrano |first4=Claudia |last5=Kadonaga |first5=James T. |title=Identification of the human DPR core promoter element using machine learning |journal=Nature |date=September 2020 |volume=585 |issue=7825 |pages=459–463 |doi=10.1038/s41586-020-2689-7 |pmid=32908305 |pmc=7501168 |bibcode=2020Natur.585..459V }}</ref> It is widely used in genetic research.<ref>{{cite journal |last1=Bijun |first1=Zhang |last2=Ting |first2=Fan |title=Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021] |journal=Frontiers in Genetics |date=2022 |volume=13 |page=951939 |doi=10.3389/fgene.2022.951939 |pmid=36081985 |pmc=9445221 |doi-access=free }}</ref>
There also is some use of machine learning in [[synthetic biology]],<ref>{{cite journal |last1=Radivojević |first1=Tijana |last2=Costello |first2=Zak |last3=Workman |first3=Kenneth |last4=Garcia Martin |first4=Hector |title=A machine learning Automated Recommendation Tool for synthetic biology |journal=Nature Communications |date=25 September 2020 |volume=11 |issue=1 |pages=4879 |doi=10.1038/s41467-020-18008-4 |pmid=32978379 |pmc=7519645 |arxiv=1911.11091 |bibcode=2020NatCo..11.4879R }}</ref><ref name="10.1021/acssynbio.8b00540">{{cite journal |author=Pablo Carbonell |author2=Tijana Radivojevic |author3=Héctor García Martín*|title=Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation |journal=ACS Synthetic Biology |date=2019 |volume=8 |issue=7 |pages=1474–1477 |doi=10.1021/acssynbio.8b00540 |pmid=31319671 |hdl=20.500.11824/998 |doi-access=free |hdl-access=free }}</ref> disease biology,<ref name="10.1021/acssynbio.8b00540"/> nanotechnology (e.g. nanostructured materials and [[bionanotechnology]]),<ref>{{cite journal |last1=Gadzhimagomedova |first1=Z. M. |last2=Pashkov |first2=D. M. |last3=Kirsanova |first3=D. Yu. |last4=Soldatov |first4=S. A. |last5=Butakova |first5=M. A. |last6=Chernov |first6=A. V. |last7=Soldatov |first7=A. V. |title=Artificial Intelligence for Nanostructured Materials |journal=Nanobiotechnology Reports |date=February 2022 |volume=17 |issue=1 |pages=1–9 |doi=10.1134/S2635167622010049 }}</ref><ref>{{cite journal |last1=Mirzaei |first1=Mahsa |last2=Furxhi |first2=Irini |last3=Murphy |first3=Finbarr |last4=Mullins |first4=Martin |title=A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles |journal=Nanomaterials |date=July 2021 |volume=11 |issue=7 |pages=1774 |doi=10.3390/nano11071774 |pmid=34361160 |pmc=8308172 |doi-access=free }}</ref> and [[materials science]].<ref>{{cite news |last1=Chen |first1=Angela |title=How AI is helping us discover materials faster than ever |url=https://www.theverge.com/2018/4/25/17275270/artificial-intelligence-materials-science-computation |access-date=30 May 2022 |work=The Verge |date=25 April 2018 |language=en}}</ref><ref>{{cite journal |last1=Talapatra |first1=Anjana |last2=Boluki |first2=S. |last3=Duong |first3=T. |last4=Qian |first4=X. |last5=Dougherty |first5=E. |last6=Arróyave |first6=R. |title=Autonomous efficient experiment design for materials discovery with Bayesian model averaging |journal=Physical Review Materials |date=26 November 2018 |volume=2 |issue=11 |pages=113803 |doi=10.1103/PhysRevMaterials.2.113803|arxiv=1803.05460 |bibcode=2018PhRvM...2k3803T }}</ref><ref>{{cite journal |last1=Zhao |first1=Yicheng |last2=Zhang |first2=Jiyun |last3=Xu |first3=Zhengwei |last4=Sun |first4=Shijing |last5=Langner |first5=Stefan |last6=Hartono |first6=Noor Titan Putri |last7=Heumueller |first7=Thomas |last8=Hou |first8=Yi |last9=Elia |first9=Jack |last10=Li |first10=Ning |last11=Matt |first11=Gebhard J. |last12=Du |first12=Xiaoyan |last13=Meng |first13=Wei |last14=Osvet |first14=Andres |last15=Zhang |first15=Kaicheng |last16=Stubhan |first16=Tobias |last17=Feng |first17=Yexin |last18=Hauch |first18=Jens |last19=Sargent |first19=Edward H. |last20=Buonassisi |first20=Tonio |last21=Brabec |first21=Christoph J. |title=Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning |journal=Nature Communications |date=13 April 2021 |volume=12 |issue=1 |pages=2191 |doi=10.1038/s41467-021-22472-x |pmid=33850155 |pmc=8044090 |bibcode=2021NatCo..12.2191Z }}</ref>
==== Novel types of machine learning ====
{{See also|Artificial brain|Automated reasoning}}
[[File:Semi-automated testing of reproducibility and robustness of the cancer biology literature by robot.jpg|thumb|Schema of the process of a semi-automated robot scientist process that includes Web statement extraction and biological laboratory testing]]
There are also [[Laboratory robotics#Applications|prototype robot scientists]], including robot-embodied ones like the two [[Robot Scientist]]s, which show a form of "machine learning" not commonly associated with the term.<ref>{{cite journal |last1=Burger |first1=Benjamin |last2=Maffettone |first2=Phillip M. |last3=Gusev |first3=Vladimir V. |last4=Aitchison |first4=Catherine M. |last5=Bai |first5=Yang |last6=Wang |first6=Xiaoyan |last7=Li |first7=Xiaobo |last8=Alston |first8=Ben M. |last9=Li |first9=Buyi |last10=Clowes |first10=Rob |last11=Rankin |first11=Nicola |last12=Harris |first12=Brandon |last13=Sprick |first13=Reiner Sebastian |last14=Cooper |first14=Andrew I. |title=A mobile robotic chemist |journal=Nature |date=9 July 2020 |volume=583 |issue=7815 |pages=237–241 |doi=10.1038/s41586-020-2442-2 |pmid=32641813 |bibcode=2020Natur.583..237B |url=https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf }}</ref><ref>{{cite journal |last1=Roper |first1=Katherine |last2=Abdel-Rehim |first2=A. |last3=Hubbard |first3=Sonya |last4=Carpenter |first4=Martin |last5=Rzhetsky |first5=Andrey |last6=Soldatova |first6=Larisa |last7=King |first7=Ross D. |title=Testing the reproducibility and robustness of the cancer biology literature by robot |journal=Journal of the Royal Society Interface |year=2022 |volume=19 |issue=189 |pages=20210821 |doi=10.1098/rsif.2021.0821|pmid=35382578 |pmc=8984295 }}</ref>
Similarly, there is research and development of biological "[[wetware computer]]s" that can learn (e.g. for use as [[biosensor]]s) and/or implantation into an organism's body (e.g. for use to control prosthetics).<ref>{{cite journal |last1=Krauhausen |first1=Imke |last2=Koutsouras |first2=Dimitrios A. |last3=Melianas |first3=Armantas |last4=Keene |first4=Scott T. |last5=Lieberth |first5=Katharina |last6=Ledanseur |first6=Hadrien |last7=Sheelamanthula |first7=Rajendar |last8=Giovannitti |first8=Alexander |last9=Torricelli |first9=Fabrizio |last10=Mcculloch |first10=Iain |last11=Blom |first11=Paul W. M. |last12=Salleo |first12=Alberto |last13=van de Burgt |first13=Yoeri |last14=Gkoupidenis |first14=Paschalis |title=Organic neuromorphic electronics for sensorimotor integration and learning in robotics |journal=Science Advances |date=10 December 2021 |volume=7 |issue=50 |pages=eabl5068 |doi=10.1126/sciadv.abl5068 |pmid=34890232 |pmc=8664264 |bibcode=2021SciA....7.5068K }}
* News article: {{cite news |last1=Bolakhe |first1=Saugat |title=Lego Robot with an Organic "Brain" Learns to Navigate a Maze |url=https://www.scientificamerican.com/article/lego-robot-with-an-organic-brain-learns-to-navigate-a-maze/ |access-date=29 May 2022 |work=Scientific American |language=en}}</ref><ref>{{cite bioRxiv |last1=Kagan |first1=Brett J. |last2=Kitchen |first2=Andy C. |last3=Tran |first3=Nhi T. |last4=Parker |first4=Bradyn J. |last5=Bhat |first5=Anjali |last6=Rollo |first6=Ben |last7=Razi |first7=Adeel |last8=Friston |first8=Karl J. |title=In vitro neurons learn and exhibit sentience when embodied in a simulated game-world |language=en |biorxiv=10.1101/2021.12.02.471005 |date=3 December 2021}}
* News article: {{cite news |title=Human brain cells in a dish learn to play Pong faster than an AI |url=https://www.newscientist.com/article/2301500-human-brain-cells-in-a-dish-learn-to-play-pong-faster-than-an-ai |access-date=26 January 2022 |work=New Scientist}}</ref><ref>{{cite journal |last1=Fu |first1=Tianda |last2=Liu |first2=Xiaomeng |last3=Gao |first3=Hongyan |last4=Ward |first4=Joy E. |last5=Liu |first5=Xiaorong |last6=Yin |first6=Bing |last7=Wang |first7=Zhongrui |last8=Zhuo |first8=Ye |last9=Walker |first9=David J. F. |last10=Joshua Yang |first10=J. |last11=Chen |first11=Jianhan |last12=Lovley |first12=Derek R. |last13=Yao |first13=Jun |title=Bioinspired bio-voltage memristors |journal=Nature Communications |date=20 April 2020 |volume=11 |issue=1 |pages=1861 |doi=10.1038/s41467-020-15759-y|pmid=32313096 |pmc=7171104 |bibcode=2020NatCo..11.1861F }}
* News article: {{cite news |title=Researchers unveil electronics that mimic the human brain in efficient learning |url=https://phys.org/news/2020-04-unveil-electronics-mimic-human-brain.html |access-date=29 May 2022 |work=University of Massachusetts Amherst |language=en}}</ref> Polymer-based artificial neurons operate directly in biological environments and define biohybrid neurons made of artificial and living components.<ref>{{cite journal |last1=Sarkar |first1=Tanmoy |last2=Lieberth |first2=Katharina |last3=Pavlou |first3=Aristea |last4=Frank |first4=Thomas |last5=Mailaender |first5=Volker |last6=McCulloch |first6=Iain |last7=Blom |first7=Paul W. M. |last8=Torriccelli |first8=Fabrizio |last9=Gkoupidenis |first9=Paschalis |title=An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing |journal=Nature Electronics |date=7 November 2022 |volume=5 |issue=11 |pages=774–783 |doi=10.1038/s41928-022-00859-y |doi-access=free |hdl=10754/686016 |hdl-access=free }}</ref><ref>{{cite journal |title=Artificial neurons emulate biological counterparts to enable synergetic operation |journal=Nature Electronics |date=10 November 2022 |volume=5 |issue=11 |pages=721–722 |doi=10.1038/s41928-022-00862-3 }}</ref>
Moreover, if [[whole brain emulation]] is possible via both scanning and replicating, at a minimum, the bio-chemical brain – as premised in the form of digital replication in ''[[The Age of Em]]'', possibly using [[physical neural network]]s – that may have applications as or more extensive than e.g. valued human activities and may imply that society would face substantial moral choices, societal risks and ethical problems<ref>{{cite news |last1=Sloat |first1=Sarah |title=Brain Emulations Pose Three Massive Moral Questions and a Scarily Practical One |url=https://www.inverse.com/article/14515-brain-emulations-pose-three-massive-moral-questions-and-a-scarily-practical-one |website=Inverse |date=21 April 2016 |access-date=3 July 2022 |language=en}}</ref><ref>{{cite journal |last1=Sandberg |first1=Anders |title=Ethics of brain emulations |journal=Journal of Experimental & Theoretical Artificial Intelligence |date=3 July 2014 |volume=26 |issue=3 |pages=439–457 |doi=10.1080/0952813X.2014.895113 }}</ref> such as whether (and how) such are built, [[Mind uploading#Space exploration|sent through space]] and used compared to potentially competing e.g. potentially more synthetic and/or less human and/or non/less-sentient types of artificial/semi-artificial intelligence.{{additional citation needed|date=August 2022}} An alternative or additive approach to scanning are types of reverse engineering of the brain.<ref>{{cite web |title=To advance artificial intelligence, reverse-engineer the brain |url=https://science.mit.edu/reverse-engineer-the-brain/ |website=MIT School of Science |access-date=30 August 2022}}</ref><ref>{{cite journal |last1=Ham |first1=Donhee |last2=Park |first2=Hongkun |last3=Hwang |first3=Sungwoo |last4 =Kim |first4=Kinam |title=Neuromorphic electronics based on copying and pasting the brain |journal=Nature Electronics |date=23 September 2021 |volume=4 |issue=9 |pages=635–644 |doi=10.1038/s41928-021-00646-1 }}</ref>
A subcategory of artificial intelligence is embodied,<ref>{{cite book |doi=10.1007/978-3-540-27833-7_1 |chapter=Embodied Artificial Intelligence: Trends and Challenges |title=Embodied Artificial Intelligence |series=Lecture Notes in Computer Science |date=2004 |last1=Pfeifer |first1=Rolf |last2=Iida |first2=Fumiya |volume=3139 |pages=1–26 |isbn=978-3-540-22484-6 }}</ref><ref>{{cite journal |last1=Nygaard |first1=Tønnes F. |last2=Martin |first2=Charles P. |last3=Torresen |first3=Jim |last4=Glette |first4=Kyrre |last5=Howard |first5=David |title=Real-world embodied AI through a morphologically adaptive quadruped robot |journal=Nature Machine Intelligence |date=May 2021 |volume=3 |issue=5 |pages=410–419 |doi=10.1038/s42256-021-00320-3 |hdl=10852/85867 |url=http://urn.nb.no/URN:NBN:no-88559 |hdl-access=free }}</ref> some of which are mobile robotic systems that each consist of one or multiple robots that are able to learn in the physical world.
===
{{main|Resurrection#Digital ghosts}}
===== Biological computing in AI and as AI =====
Additionally, [[biological computing|biological computers]], even if both artificial and highly intelligent, are typically distinguishable from synthetic, predominantly silicon-based, computers. The two technologies could, however, be combined and used for the design of either. Moreover, many tasks may be poorly carried out by AI even if it uses algorithms that are transparent, understood, bias-free, apparently effective and goal-aligned in addition to having trained data sets that are sufficiently large and [[data cleansing|cleansed]]. This may occur, for instance, when the underlying data, available metrics, [[value (ethics)|values]] or training methods are incorrect, flawed or used inappropriately. [[Computer-aided]] is a phrase used to describe human activities that make use of computing as tool in more comprehensive activities and systems such as AI for narrow tasks or making use of such without substantially relying on its results (see also: [[human-in-the-loop]]).{{citation needed|date=May 2022}} One study described the biological component as a limitation of AI stating that "as long as the biological system cannot be understood, formalized, and imitated, we will not be able to develop technologies that can mimic it" and that, even if it were understood, this does not necessarily mean there will be "a technological solution to imitate natural intelligence".<ref>{{cite journal |last1=Tugui |first1=Alexandru |last2=Danciulescu |first2=Daniela |last3=Subtirelu |first3=Mihaela-Simona |title=The Biological as a Double Limit for Artificial Intelligence: Review and Futuristic Debate |journal=International Journal of Computers Communications & Control |date=14 April 2019 |volume=14 |issue=2 |pages=253–271 |doi=10.15837/ijccc.2019.2.3536 |doi-access=free }}</ref> Technologies that integrate biology and AI include [[biorobotics]].
== Security and surveillance ==
===
[[Computer security|Cyber security]] companies are adopting [[neural network]]s, [[machine learning]], and [[natural language processing]] to improve their systems.<ref>{{Cite book |author=Anne Johnson |url=https://nap.nationalacademies.org/catalog/25488/implications-of-artificial-intelligence-for-cybersecurity-proceedings-of-a-workshop |title=Implications of artificial intelligence for cybersecurity: proceedings of a workshop |author2=Emily Grumbling |date=2019 |publisher=National Academies Press |isbn=978-0-309-49451-9 |___location=Washington, DC |pages=4–5 |oclc=1134854973 |access-date=2025-05-12 |url-access=registration}}</ref>
Applications of AI in cyber security include:
* Network protection: Machine learning improves [[intrusion detection system]]s by broadening the search beyond previously identified threats.<ref>{{cite journal |last1=Kocher |first1=Geeta |last2=Kumar |first2=Gulshan |title=Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges |journal=Soft Computing |date=August 2021 |volume=25 |issue=15 |pages=9731–9763 |doi=10.1007/s00500-021-05893-0 }}</ref>
* Endpoint protection: Attacks such as [[ransomware]] can be thwarted by learning typical malware behaviors.
** AI-related cyber security application cases vary in both benefit and complexity. Security features such as Security Orchestration, Automation, and Response (SOAR) and Extended Endpoint Detection and Response (XDR) offer significant benefits for businesses, but require significant integration and adaptation efforts.<ref>{{cite journal |last1=Kant |first1=Daniel |last2=Johannsen |first2=Andreas |title=Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs) |journal=Electronic Imaging |date=16 January 2022 |volume=34 |issue=3 |pages=387–1–387–8 |doi=10.2352/EI.2022.34.3.MOBMU-387 }}</ref>
* Application security: can help counterattacks such as [[server-side request forgery]], [[SQL injection]], [[cross-site scripting]], and [[Distributed-denial-of-service|distributed denial-of-service]].
** AI technology can also be utilized to improve system security and safeguard our privacy. Randrianasolo (2012) suggested a security system based on artificial intelligence that can recognize intrusions and adapt to perform better.<ref>{{cite thesis |last=Randrianasolo |first=Arisoa |title=Artificial intelligence in computer security: Detection, temporary repair and defense |date=2012 |page=vii |hdl=2346/45196 |hdl-access=free}}</ref> In order to improve cloud computing security, Sahil (2015) created a user profile system for the cloud environment with AI techniques.<ref>{{cite book |doi=10.1109/ICACEA.2015.7164645 |chapter=Artificial intelligence for designing user profiling system for cloud computing security: Experiment |title=2015 International Conference on Advances in Computer Engineering and Applications |date=2015 |last1=Sahil |last2=Sood |first2=Sandeep |last3=Mehmi |first3=Sandeep |last4=Dogra |first4=Shikha |pages=51–58 |isbn=978-1-4673-6911-4 }}</ref>
* Suspect user behavior: Machine learning can identify fraud or compromised applications as they occur.<ref>{{cite book |last1=Parisi |first1=Alessandro |title=Hands-On Artificial Intelligence for Cybersecurity: Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies |date=2019 |publisher=Packt Publishing Ltd |isbn=978-1-78980-517-8 |oclc=1111967955 }}{{page needed|date=April 2024}}</ref>
== Transportation and logistics ==
=== Automotive ===
{{Main|Vehicular automation|Self-driving car}}
[[File:Waymo self-driving car side view.gk.jpg|thumb|Side view of a [[Waymo]]-branded self-driving car]]
AI in transport is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major development challenge is the complexity of transportation systems that involves independent components and parties, with potentially conflicting objectives.<ref>{{cite book |
Transportation's complexity means that in most cases, training an AI in a real-world driving environment is impractical, and is achieved through simulator-based testing.<ref>{{cite journal|last1=Hallerbach|first1=Sven|last2=Xia|first2=Yiqun|last3=Eberle|first3=Ulrich|last4=Koester|first4=Frank|date=3 April 2018|title=Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles|journal=SAE International Journal of Connected and Automated Vehicles|volume=1|issue=2|pages=93–106|doi=10.4271/2018-01-1066}}</ref>
AI underpins self-driving vehicles. Companies involved with AI include [[Tesla Motors|Tesla]], [[Waymo]], and [[General Motors]]. AI-based systems control functions such as braking, lane changing, collision prevention, navigation and mapping.<ref>{{cite news |last1=West |first1=Darrell M. |title=Moving forward: Self-driving vehicles in China, Europe, Japan, Korea, and the United States |url=https://www.brookings.edu/research/moving-forward-self-driving-vehicles-in-china-europe-japan-korea-and-the-united-states/ |work=Brookings |date=20 September 2016 }}</ref>
AI-based [[fuzzy logic]] controllers operate [[Transmission (mechanics)|gearboxes]]. AI-based [[Advanced driver-assistance systems|driver-assist systems]] include features such as [[Automatic parking|self-parking]] and [[adaptive cruise control]].{{cn|date=August 2025}}
Some autonomous vehicles do not allow human drivers (they have no steering wheels or pedals).<ref>{{cite news |title=Programming safety into self-driving cars |url=https://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=134033 |work=National Science Foundation |date=2 February 2015 }}</ref><ref name="bbc"/>
There are
Autonomous trucks are in the testing phase. The UK government passed legislation to begin testing of autonomous truck platoons in 2018.<ref>{{cite magazine|last1=Burgess|first1=Matt|date=24 August 2017|title=The UK is about to Start Testing Self-Driving Truck Platoons|url=https://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|url-status=live|magazine=Wired UK|archive-url=https://web.archive.org/web/20170922055917/http://www.wired.co.uk/article/uk-trial-self-driving-trucks-platoons-roads|archive-date=22 September 2017|access-date=20 September 2017}}</ref> A group of autonomous trucks follow closely behind each other. German corporation [[Daimler AG|Daimler]] is testing its [[Freightliner Inspiration]].<ref>{{cite magazine|last1=Davies|first1=Alex|date=5 May 2015|title=World's First Self-Driving Semi-Truck Hits the Road|url=https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|url-status=live|magazine=Wired|archive-url=https://web.archive.org/web/20171028222802/https://www.wired.com/2015/05/worlds-first-self-driving-semi-truck-hits-road/|archive-date=28 October 2017|access-date=20 September 2017}}</ref>
Autonomous vehicles require accurate maps to be able to navigate between destinations.<ref>{{cite news |last1=McFarland |first1=Matt |title=Google's artificial intelligence breakthrough may have a huge impact on self-driving cars and much more |url=https://www.washingtonpost.com/news/innovations/wp/2015/02/25/googles-artificial-intelligence-breakthrough-may-have-a-huge-impact-on-self-driving-cars-and-much-more/ |newspaper=The Washington Post |date=25
AI has been used to optimize traffic management, which reduces wait times, energy use, and emissions by as much as 25 percent.<ref>{{Cite book|publisher=National Science and Technology Council |title=Preparing for the future of artificial intelligence|oclc=965620122}}</ref>
A technology for [[smart traffic light]]s was developed in 2009 by [[Carnegie Mellon University]] research professor Stephen Smith, and deployed in [[Pittsburgh, Pennsylvania]]. His company then installed the [[Scalable Urban Traffic Control|Surtrac]] technology in 22 cities. It cost about $20,000 per intersection to install, drive time reduced by 25%, and traffic jam waiting time reduced by 40% at the intersections it was installed in.<ref>{{cite web | url=https://science.howstuffworks.com/engineering/civil/smart-traffic-lights-news.htm | title=Going Nowhere Fast? Smart Traffic Lights Can Help Ease Gridlock | date=18 May 2022 |author=Patrick J. Kiger}}</ref>
{{multiple image
| direction = vertical
| width = 200
| footer = Cameras with [[radar]] and [[Acoustic ___location|ultrasonic acoustic]] ___location sensors, while using [[Predictive analytics|predictive algorithms]] to have [[smart traffic light|artificially intelligent traffic lights]] to make traffic flow better
| image1 = Smart traffic light.webp
| alt1 =
| caption1 =
| image2 = Smart traffic light intersection.webp
| alt2 =
| caption2 =
}}
=== Military ===
The [[Royal Australian Air Force|Royal Australian Air]] Force (RAAF) [[Air and Space Operations Center|Air Operations Division]] (AOD) uses AI for [[expert systems]]. AIs operate as surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.<ref>{{Cite news|date=29 June 2016|title=AI bests Air Force combat tactics experts in simulated dogfights|work=[[Ars Technica]]|url=https://arstechnica.com/information-technology/2016/06/ai-bests-air-force-combat-tactics-experts-in-simulated-dogfights/}}</ref>
Line 398 ⟶ 539:
[[Speech recognition]] allows traffic controllers to give verbal directions to drones.
Artificial intelligence supported design of aircraft,<ref>[http://www.kbs.twi.tudelft.nl/Research/Projects/AIDA/ AIDA Homepage]. Kbs.twi.tudelft.nl (17 April 1997). Retrieved 21 July 2013.</ref> or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule
=== NASA ===
In 2003 a [[Dryden Flight Research Center]] project created software that could enable a damaged aircraft to continue flight until a safe landing can be achieved.<ref>[http://crgis.ndc.nasa.gov/crgis/images/c/c9/88798main_srfcs.pdf The Story of Self-Repairing Flight Control Systems.] NASA Dryden. (April 2003). Retrieved 25 August 2016.</ref> The software compensated for damaged components by relying on the remaining undamaged components.<ref
The 2016 Intelligent Autopilot System combined [[apprenticeship learning]] and behavioral cloning whereby the autopilot observed low-level actions required to maneuver the airplane and high-level strategy used to apply those actions.<ref
=== Maritime ===
[[Neural network]]s are used by [[Situation awareness|situational awareness]] systems in ships and boats.<ref>{{Cite web|title=UB invests in student-founded startup|url=http://www.buffalo.edu/ubnow/stories/2020/10/buffalo-automation-seed-funding.html|access-date=24 December 2020|website=buffalo.edu|language=en}}</ref> There also are [[Unmanned surface vehicle|autonomous boats]].
== Utilities ==
===Telecommunications===
Many telecommunications companies make use of [[Search algorithm|heuristic search]] to manage their workforces. For example, [[BT Group]] deployed heuristic search<ref name="TheorSoc">[http://www.theorsociety.com/Science_of_Better/htdocs/prospect/can_do/success_stories/dwsbt.htm Success Stories] {{webarchive|url=https://web.archive.org/web/20111004194517/http://www.theorsociety.com/Science_of_Better/htdocs/prospect/can_do/success_stories/dwsbt.htm|date=4 October 2011}}.</ref> in an application that schedules 20,000 engineers. Machine learning is also used for [[speech recognition]] (SR), including of voice-controlled devices, and SR-related transcription, including of videos.<ref>{{cite journal |last1=Padmanabhan |first1=Jayashree |last2=Johnson Premkumar |first2=Melvin Jose |title=Machine Learning in Automatic Speech Recognition: A Survey |journal=IETE Technical Review |date=4 July 2015 |volume=32 |issue=4 |pages=240–251 |doi=10.1080/02564602.2015.1010611 }}</ref><ref>{{cite book |last1=Ahmed |first1=Shimaa |last2=Chowdhury |first2=Amrita Roy |last3=Fawaz |first3=Kassem |last4=Ramanathan |first4=Parmesh |title=Preech: A System for {Privacy-Preserving} Speech Transcription |url=https://www.usenix.org/conference/usenixsecurity20/presentation/ahmed-shimaa |pages=2703–2720 |language=en |date=2020 |isbn=978-1-939133-17-5 }}</ref>
== List of applications ==
{{Prose|date=December 2021|section}}
The following are applications of artificial intelligence (AI) organized by category:
=== Agriculture ===
* [[Agricultural robot]]
* Crop monitoring
* [[Digital agriculture]]
* [[Precision agriculture]]
* Yield prediction
=== Architecture and design ===
* [[Computer-aided design]]
* [[Smart city]]
* [[Structural analysis]]
===
* [[Agent-based computational economics]]
* [[Business process automation]]
* [[Market analysis]]
* [[Network optimization]]
* [[User activity monitoring]]
=== Computer science ===
* Algorithm development
* [[Automatic programming]]
* [[Automated reasoning]]
* [[Automated theorem proving]]
* [[Concept mining]]
* [[Data mining]]
* Data structure optimization
* [[Knowledge representation]]
* [[Proof assistant]]s
* [[Semantic Web]]
* [[Signal processing]]
* [[Software development]]
===
* [[Computer vision]]
* [[Facial recognition system|Face recognition]]
* [[Handwriting recognition]]
* [[Image processing]]
* [[Optical character recognition]]
* [[Photo manipulation|Photo]]
* [[video manipulation]]
* [[Chatbot]]
* [[Sentiment analysis]]
* [[Social bot]]
* [[Virtual assistant]]
* [[Educational technology]]
* [[Intelligent tutoring system]]
* [[Personalized learning]]
===
* [[Carbon footprint]]
* Earth sciences applications
* [[Environmental monitoring]]
* [[Smart grid]]
=== Entertainment and media ===
* [[
* [[Generative artificial intelligence]]
* [[Recommender system]]
* [[Synthetic media]]
* [[Virtual reality]]
=== Finance ===
* [[
* [[
* [[
===
* [[Game artificial intelligence]]
* [[computer game bot]]
* [[Game theory]]
* [[
=== Healthcare ===
* [[Artificial intelligence in healthcare]]
* [[Diagnosis (artificial intelligence)]]
* [[Health informatics]]
* [[Mental health]]
* [[Biochemistry]]
* [[Bioinformatics]]
* [[Drug discovery]]
=== Human resources ===
* [[Employee engagement]]
* [[Recruitment]]
* Training programs
=== Language processing ===
* [[Natural language processing]]
* [[translation]]
* [[chatterbot]]
* [[Speech recognition]]
=== Law and government ===
* [[
* [[
* [[
* [[Public service]]
=== Manufacturing ===
* [[Automation]]
* [[
* Predictive maintenance
* Quality control
=== Military ===
* Autonomous weapons
* Intelligence analysis
* Simulation training
=== Retail and e-commerce ===
* [[Dynamic pricing]]
* [[Inventory management (business)|Inventory management]]
=== Robotics ===
* [[Behavior-based robotics]]
* [[Cognitive robotics]]
* [[Cybernetics]]
* [[Developmental robotics]]
* [[Evolutionary robotics]]
* [[Human-robot interaction]]
* [[Humanoid robot]]
* [[Hybrid intelligent system]]
* [[Intelligent agent]]
* [[Intelligent control]]
* [[
=== Scientific research ===
* [[Agent-based model]]s
* [[Artificial life]]
* [[Bio-inspired computing]]
* Data analysis
* Earth sciences
* [[Materials Science]]
* [[Physics]]
* Simulations
=== Security and surveillance ===
* [[Cybersecurity]]
* [[Deepfake]]
* [[Email spam]] filtering
* [[Facial recognition system|Face recognition]]
=== Social impact ===
* [[Economic forecasting]]
* [[Poverty reduction]]
* [[Social equity]]
=== Transportation and logistics ===
* [[Self-driving car]]
* [[Traffic management]]
* [[Vehicle routing problem]]
== See also ==
Line 503 ⟶ 717:
* [[Applications of deep learning]]
* [[Applications of machine learning]]
* [[
* {{section link|Collective intelligence|Applications}}
* [[List of artificial intelligence projects]]
* [[List of datasets for machine-learning research]]
* [[Open data]]
* [[Progress in artificial intelligence]]
* [[Timeline of computing {{CURRENTDECADE}}–present]]
==Footnotes==
Line 513 ⟶ 730:
== Further reading ==
<!-- This is not a list of your pet website or article, or favorite AI software and books. Please add those to the appropriate links in the see also section. Keep this list short and use only famous and clear examples -->
* {{cite journal |doi=10.1016/j.bushor.2018.08.004 |title=Siri, Siri in my Hand, who's the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence |journal=Business Horizons |volume=62 |issue=1 |pages=15–25 |year=2018 |last1=Kaplan |first1=A.M. |last2=Haenlein |first2=M.
* {{Cite book
| first=Ray
| last=Kurzweil
| author-link=Ray Kurzweil
| title=The Singularity is Near: When Humans Transcend Biology
| ___location=New York
| publisher=Viking
| year=2005
Line 536 ⟶ 754:
| chapter-url=https://archive.org/details/fundingrevolutio00nati
}}
* {{cite journal |doi=10.1007/s10845-013-0788-0 |title=Sequence planning for stamping operations in progressive dies |journal=Journal of Intelligent Manufacturing |volume=26 |issue=2 |pages=347–357 |year=2015 |last1=Moghaddam |first1=M. J. |last2=Soleymani |first2=M. R. |last3=Farsi |first3=M. A.
* {{cite web |first1=Ed |last1=Felten |date=3 May 2016 |title=Preparing for the Future of Artificial Intelligence |url=https://obamawhitehouse.archives.gov/blog/2016/05/03/preparing-future-artificial-intelligence }}
{{emerging technologies|topics=yes|infocom=yes}}
[[Category:Applications of artificial intelligence| ]]
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