View source for Applications of artificial intelligence

{{Short description|Applications of intelligence exhibited by machines}}
{{Artificial intelligence}}
'''Artificial intelligence''' ('''AI''') has been used in applications to alleviate certain problems throughout industry and academia. [[Artificial intelligence|AI]], like electricity or [[Computer|computer]]s, is a general purpose technology that has a multitude of applications. It has been used in fields of [[Machine translation|language translation]], image recognition, credit scoring, [[Social:E-commerce|e-commerce]] and other domains.<ref>{{cite journal|last1=Brynjolfsson|first1=Erik|last2=Mitchell|first2=Tom|date=22 December 2017|title=What can machine learning do? Workforce implications|language=en|pages=1530–1534|journal=Science|volume=358 |issue=6370 |url=https://www.science.org/doi/10.1126/science.aap8062|access-date=7 May 2018|bibcode=2017Sci...358.1530B|doi=10.1126/science.aap8062|pmid=29269459 |s2cid=4036151 }}</ref>

== Internet and e-commerce ==

=== Search engines===
{{Relevance inline|date=April 2022}} 
{{Main|Search engine}}

=== Recommendation systems ===
A recommendation system predicts the "rating" or "preference" a user would give to an item.<ref name="handbook">Francesco Ricci and Lior Rokach and Bracha Shapira, [http://www.inf.unibz.it/~ricci/papers/intro-rec-sys-handbook.pdf Introduction to Recommender Systems Handbook], Recommender Systems Handbook, Springer, 2011, pp. 1-35</ref><ref>{{cite news |first=Lev |last=Grossman |date=27 May 2010|title=How Computers Know What We Want — Before We Do |magazine={{wipe|Time (magazine)}}|url=http://www.time.com/time/magazine/article/0,9171,1992403,00.html|url-status=dead|access-date=1 June 2015|archive-url=https://web.archive.org/web/20100530064045/http://www.time.com/time/magazine/article/0,9171,1992403,00.html|archive-date=30 May 2010}}</ref> Recommender systems are used in a variety of areas, such as generating playlists for video and music services, product recommendations for online stores, or content recommendations for social media platforms and open web content recommenders.<ref name="twitterwtf2">Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh Zadeh [http://dl.acm.org/citation.cfm?id=2488433 WTF:The who-to-follow system at Twitter], Proceedings of the 22nd international conference on World Wide Web</ref><ref>{{Cite journal|last1=Baran|first1=Remigiusz|last2=Dziech|first2=Andrzej|last3=Zeja|first3=Andrzej|date=1 June 2018|title=A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment|journal=Multimedia Tools and Applications|language=en|volume=77|issue=11|pages=14077–14091|doi=10.1007/s11042-017-5014-1|issn=1573-7721|doi-access=free|s2cid=36511631}}</ref>

==== Web feeds and posts ====
Machine learning is also used in [[Web feed]]s such as for determining which posts show up in 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 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 online misinformation and related [[Social:Filter bubble|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 |language=en |issn=1869-5469}}</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>

=== Targeted advertising and increasing internet engagement ===
{{Main|Software:Marketing and artificial intelligence}}
{{See also|AdSense|Facebook}}AI is used to target web advertisements to those most likely to click or engage on them. It is also used to increase time spent on a website by selecting attractive content for the viewer. It can predict or generalize the behavior of customers from their digital footprints.<ref name="Matz et al 2017">{{cite journal |last1=Matz |first1=S. C. |last2=Kosinski |first2=M. |last3=Nave |first3=G. |last4=Stillwell |first4=D. J. |title=Psychological targeting as an effective approach to digital mass persuasion |journal=Proceedings of the National Academy of Sciences of the United States of America |date=28 November 2017 |volume=114 |issue=48 |pages=12714–12719 |doi=10.1073/pnas.1710966114 |pmc=5715760 |jstor=26485255 |pmid=29133409 |doi-access=free }}</ref>

Online gambling companies use AI to improve customer targeting.<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>

[[Personality computing]] AI models add psychological targeting to more traditional sociodemographic or behavioral targeting.<ref name="Celli et al. 2017">{{cite book |doi=10.1145/3123266.3129311 |chapter=Profilio |title=Proceedings of the 25th ACM international conference on Multimedia |year=2017 |last1=Celli |first1=Fabio |last2=Massani |first2=Pietro Zani |last3=Lepri |first3=Bruno |pages=546–550 |isbn=978-1-4503-4906-2 |s2cid=767688 }}</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>

=== Virtual assistants ===
{{Main|Engineering:Virtual assistant}}
Intelligent personal assistants use AI to understand many natural language requests in other ways than rudimentary commands.<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>

=== Spam filtering ===
{{Relevance inline|date=April 2022}}

=== Language translation ===
{{Main|Machine translation}}
AI has been used to automatically translate spoken language and textual documents based context.<ref>{{cite news
 |url         = https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence
 |title       = Why 2015 Was a Breakthrough Year in Artificial Intelligence
 |last        = Clark
 |first       = Jack
 |publisher     = Bloomberg L.P.
 |date        = 8 December 2015b
|url-access  = subscription
 |access-date = 23 November 2016
|url-status  = live
 |archive-url  = https://web.archive.org/web/20161123053855/https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence
 |archive-date = 23 November 2016
}}
</ref> There also is research and development 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>

=== Facial recognition and image labeling ===
{{Main|Facial recognition system|Automatic image annotation}}
AI has been used in [[Facial recognition system|facial recognition system]]s, with a 99% accuracy rate.<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-everything-you-need-to-know-about-artificial-intelligence/ |access-date=1 March 2021 |publisher=ZDNet |date=11 December 2020 |language=en}}</ref> AI has also been demonstrated to generate speech to describe images to blind people.{{sfn|Clark|2015b}}

== Games ==
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 produced superhuman results in many games, including [[Software:Computer chess|chess]] (Deep Blue), ''[[Software:Jeopardy!|Jeopardy!]]'' (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={{wipe|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> [[Software:Go (game)|Go]] ([[Software:AlphaGo|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}}</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={{wipe|Los Angeles Times}}|date=12 March 2016}}</ref> [[Engineering:Poker|poker]] (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),<ref>{{Cite journal|last1=Bowling|first1=Michael|last2=Burch|first2=Neil|last3=Johanson|first3=Michael|last4=Tammelin|first4=Oskari|date=9 January 2015|title=Heads-up limit hold'em poker is solved|url=https://www.science.org/doi/10.1126/science.1259433|journal=Science|language=en|volume=347|issue=6218|pages=145–149|doi=10.1126/science.1259433|issn=0036-8075|pmid=25574016|bibcode=2015Sci...347..145B|s2cid=3796371}}</ref> 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|date=December 2013|title=A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft|journal=IEEE Transactions on Computational Intelligence and AI in Games|volume=5|issue=4|pages=293–311|citeseerx=10.1.1.406.2524|doi=10.1109/TCIAIG.2013.2286295|s2cid=5014732}}</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|general game playing]] ([[AlphaZero]]<ref>{{Cite journal|first1 = David|last1 = Silver |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 |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|date=5 July 2017|title=The AI revolution in science|language=en|work=Science {{!}} AAAS|url=https://www.science.org/content/article/ai-revolution-science|access-date=7 May 2018}}</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|s2cid=205242740}}</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|date=23 December 2020|title=Mastering Atari, Go, chess and shogi by planning with a learned model|url=https://www.nature.com/articles/s41586-020-03051-4|journal=Nature|language=en|volume=588|issue=7839|pages=604–609|doi=10.1038/s41586-020-03051-4|pmid=33361790|issn=1476-4687|arxiv=1911.08265|bibcode=2020Natur.588..604S|s2cid=208158225}}</ref> AI has replaced hand-coded algorithms in most chess programs.<ref>{{Cite web|last=K|first=Bharath|date=2 April 2021|title=AI In Chess: The Evolution of Artificial Intelligence In Chess Engines|url=https://towardsdatascience.com/ai-in-chess-the-evolution-of-artificial-intelligence-in-chess-engines-a3a9e230ed50|access-date=6 January 2022|website=Medium|language=en}}</ref> Unlike [[Software:Go (game)|go]] or chess, poker is an imperfect-information game, so a program that plays poker has to reason under uncertainty. The general game players work using feedback from the game system, without knowing the rules.

== Economic and social challenges ==
AI for Good is an ITU initiative supporting institutions employing AI to tackle some of the world's greatest economic and social challenges. For example, 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. At Stanford, researchers use [[Artificial intelligence|AI]] to analyze satellite images to identify high poverty areas.<ref>{{Cite book |publisher=National Science and Technology Council|title=Preparing for the future of artificial intelligence|oclc=965620122}}</ref>

== Agriculture ==
In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield.<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> Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes,<ref>{{Cite web|url=https://www.intel.com/content/www/us/en/big-data/article/agriculture-harvests-big-data.html|title=The Future of AI in Agriculture|website=Intel|language=en|access-date=5 March 2019}}</ref> monitor soil moisture, operate [[Engineering:Agricultural robot|agricultural robot]]s, conduct [[Predictive analytics|predictive analytics]],<ref>{{Cite web|url=https://emerj.com/ai-sector-overviews/ai-agriculture-present-applications-impact/|title=AI in Agriculture – Present Applications and Impact {{!}} Emerj - Artificial Intelligence Research and Insight|last=Sennaar|first=Kumba|website=Emerj|language=en-US|access-date=5 March 2019}}</ref><ref>{{Cite web|url=https://apro-software.com/artificial-intelligence-in-agriculture/|title=Artificial Intelligence in Agriculture: Farming for the 21st Century|last=G. Jones|first=Colleen |date=26 June 2019|language=en-US|access-date=8 February 2021}}</ref> 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 |language=en |issn=2045-2322}}</ref> automate greenhouses,<ref>{{cite journal |last1=Moreno |first1=Millán M. |last2=Guzmán |first2=Sevilla 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 |date=1 November 2011 |volume=1 |issue=68 |doi=10.15835/buasvmcn-vm:1:68:6771 |doi-broken-date=31 July 2022 |url=https://journals.usamvcluj.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 |s2cid=210930401 }}</ref><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 |s2cid=219064189 }}</ref> and save water.<ref>{{Cite web |last=Olick |first=Diana |date=2022-04-18 |title=How robots and indoor farming can help save water and grow crops year round |url=https://www.cnbc.com/2022/04/18/iron-ox-is-disrupting-agriculture-with-robots-and-ai.html |access-date=2022-05-09 |website=CNBC |language=en}}</ref>

== Cyber security ==
Cyber security companies are adopting [[Neural network|neural network]]s, [[Machine learning|machine learning]], and [[Natural language processing|natural language processing]] to improve their systems.<ref>{{Cite book|title=Implications of artificial intelligence for cybersecurity: proceedings of a workshop|date=2019 |author=Anne Johnson |author2=Emily Grumbling |publisher=National Academies Press |isbn=978-0-309-49451-9|location=Washington, DC|oclc=1134854973}}</ref>

Applications of AI in cyber security include:

* Network protection: Machine learning improves [[Intrusion detection system|intrusion detection system]]s by broadening the search beyond previously identified threats.
* Endpoint protection: Attacks such as [[Ransomware|ransomware]] can be thwarted by learning typical malware behaviors.
* Application security: can help counterattacks such as [[Server-side request forgery|server-side request forgery]], [[SQL injection]], [[Cross-site scripting|cross-site scripting]], and distributed denial-of-service.
* Suspect user behavior: Machine learning can identify fraud or compromised applications as they occur.<ref>{{Cite book|last=Parisi|first=Alessandro|title=Hands-on artificial intelligence for cybersecurity: implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies|date=2019|isbn=978-1-78980-517-8|location=Birmingham, UK|oclc=1111967955}}</ref>
Google fraud czar Shuman Ghosemajumder has said that AI will be used to completely automate most cyber security operations over time.<ref>{{Cite web |date=2020-09-06 |title=How AI will automate cybersecurity in the post-COVID world |url=https://venturebeat.com/2020/09/06/how-ai-will-automate-cybersecurity-in-the-post-covid-world/ |access-date=2022-05-09 |website=VentureBeat |language=en-US}}</ref>

== Education ==
AI tutors allow students to get one-on-one help. They can reduce anxiety and stress for students stemming from tutor labs or human tutors.<ref>{{cite web|url=https://elearningindustry.com/artificial-intelligence-in-the-classroom-role|title=The Role Of Artificial Intelligence In The Classroom|date=14 April 2018|website=eLearning Industry|access-date=14 January 2019}}</ref>

AI can also create a dysfunctional environment with revenge effects<ref>{{Cite book|last=Anabel|first=Quan-Haase|title=TECHNOLOGY AND SOCIETY: social networks, power, and inequality.|date=2020|publisher=Oxford University Press|isbn=978-0-19-903225-9|oclc=1142724334}}</ref> such as technology that hinders students' ability to stay on task.<ref>{{Cite news|url=https://www.nytimes.com/2010/11/21/technology/21brain.html|title=Growing Up Digital, Wired for Distraction|first=Matt|last=Richtel|newspaper=The New York Times|date=21 November 2010}}</ref> In other scenario, AI can help educator for student early prediction in virtual learning environment (VLE) such as Moodle.<ref>{{Cite journal|last1=Chen|first1=Hsing-Chung|last2=Prasetyo|first2=Eko|last3=Tseng|first3=Shian-Shyong|last4=Putra|first4=Karisma Trinanda|last5=Prayitno|last6=Kusumawardani|first6=Sri Suning|last7=Weng|first7=Chien-Erh|date=January 2022|title=Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence|journal=Applied Sciences|language=en|volume=12|issue=4|pages=1885|doi=10.3390/app12041885|doi-access=free}}</ref> Especially, during the COVID-19 pandemic, learning activity has been required to be conducted online to reduce the virus spread through face-to-face meeting.

== Finance ==
{{Split section|Artificial intelligence in finance|date=April 2022}}
[[Finance:Financial institution|Financial institution]]s have long used [[Artificial neural network|artificial neural network]] systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in [[Finance:Banking|banking]] can be began in 1987 when Security Pacific National Bank launched a fraud prevention taskforce 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> Kasisto and Moneystream use AI.

Banks use AI to organize operations, for bookkeeping, invest in stocks, and manage properties. AI can react to changes when business is not taking place.<ref name="Eleanor">{{cite web|last=O'Neill|first=Eleanor|date=31 July 2016|title=Accounting, automation and AI|url=https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|url-status=live|archive-url=https://web.archive.org/web/20161118165901/https://www.icas.com/ca-today-news/how-accountancy-and-finance-are-using-artificial-intelligence|archive-date=18 November 2016|access-date=18 November 2016|website=icas.com|language=en}}</ref> [[Artificial intelligence in fraud detection|AI is used to combat fraud]] and financial crimes by monitoring behavioral patterns for any [[Anomaly detection|abnormal changes or anomalies]].<ref name="fsroundtable.org">{{Cite news|date=2 April 2015|title=CTO Corner: Artificial Intelligence Use in Financial Services – Financial Services Roundtable|language=en-US|work=Financial Services Roundtable|url=http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|url-status=dead|access-date=18 November 2016|archive-url=https://web.archive.org/web/20161118165842/http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/|archive-date=18 November 2016}}</ref><ref>{{Cite web|title=Artificial Intelligence Solutions, AI Solutions|url=https://www.sas.com/en_ae/solutions/ai.html|website=sas.com}}</ref><ref>{{Cite web|last=Chapman|first=Lizette|date=7 January 2019|title=Palantir once mocked the idea of salespeople. Now it's hiring them|url=https://www.latimes.com/business/la-fi-palantir-sales-ipo-20190107-story.html|access-date=28 February 2019|website=Los Angeles Times}}</ref>

The use of AI in applications such as online trading and decision making has changed major economic theories.<ref>{{cite book|last1=Marwala|first1=Tshilidzi|title=Artificial Intelligence and Economic Theory: Skynet in the Market|last2=Hurwitz|first2=Evan|publisher=[[Company:Springer Science+Business Media|Springer]]|year=2017|isbn=978-3-319-66104-9|location=London}}</ref> For example, AI-based buying and selling platforms estimate individualized demand and supply curves and thus enable individualized pricing. AI machines reduce [[Physics:Information asymmetry|information asymmetry]] in the market and thus make markets more efficient.<ref>{{Citation|last1=Marwala|first1=Tshilidzi|title=Efficient Market Hypothesis|year=2017|url=http://link.springer.com/10.1007/978-3-319-66104-9_9|work=Artificial Intelligence and Economic Theory: Skynet in the Market|pages=101–110|place=Cham|publisher=Springer International Publishing|doi=10.1007/978-3-319-66104-9_9|isbn=978-3-319-66103-2|access-date=11 November 2020|last2=Hurwitz|first2=Evan}}</ref>

=== Trading and investment ===
[[Finance:Algorithmic trading|Algorithmic trading]] involves the use of AI systems to make trading decisions at speeds orders of magnitude greater than any human is capable of, making millions of trades in a day without human intervention. Such [[Finance:High-frequency trading|high-frequency trading]] represents is a fast-growing sector. Many banks, funds, and proprietary trading firms now have entire portfolios that are AI-managed. [[Finance:Automated trading system|Automated trading system]]s are typically used by large institutional investors, but include smaller firms trading with their own AI systems.<ref>{{Cite web|url=http://www.investopedia.com/terms/a/algorithmictrading.asp|title=Algorithmic Trading|date=18 May 2005|website=Investopedia}}</ref>

Large financial institutions use AI to assist with their investment practices. [[Company:BlackRock|BlackRock]]'s AI engine, [[Software:Aladdin (BlackRock)|Aladdin]], is used both within the company and by clients to help with investment decisions. Its functions includes the use of [[Natural language processing|natural language processing]] to analyze text such as news, broker reports, and social media feeds. It then gauges the sentiment on the companies mentioned and assigns a score. Banks such as [[Finance:UBS tax evasion controversies|UBS]] and [[Company:Deutsche Bank|Deutsche Bank]] use SQREEM (Sequential Quantum Reduction and Extraction Model) to mine data to develop consumer profiles and match them with [[Finance:Wealth management|wealth management]] products.<ref>{{Cite web|url=https://www.americanbanker.com/news/beyond-robo-advisers-how-ai-could-rewire-wealth-management|title=Beyond Robo-Advisers: How AI Could Rewire Wealth Management|date=5 January 2017}}</ref>

=== Underwriting ===
Online lender Upstart uses machine learning for [[Underwriting|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 particularly useful to assign credit scores to those with limited credit histories.<ref>{{Cite web|url=http://www.businesswire.com/news/home/20170214005357/en/ZestFinance-Introduces-Machine-Learning-Platform-Underwrite-Millennials|title=ZestFinance Introduces Machine Learning Platform to Underwrite Millennials and Other Consumers with Limited Credit History|date=14 February 2017}}</ref>

=== 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.|s2cid=157787279|date=10 April 2017|title=Technical Inefficiency, Allocative Inefficiency, and Audit Pricing|journal=Journal of Accounting, Auditing & Finance|volume=33|issue=4|pages=580–600|doi=10.1177/0148558X17696760}}</ref>{{Quantify|date=December 2021}}

=== 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 Protrader 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=1 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) launched. It was able to review over 200,000 transactions per week and over two years it helped identify 400 potential cases of [[Finance:Money laundering|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}}</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>

== Government ==

In 2019, Bengaluru, India deployed AI-managed traffic signal. 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>

=== Military ===
{{Further|Artificial intelligence arms race|Lethal autonomous weapon|Engineering:Unmanned combat aerial vehicle}}
Various countries are deploying AI military applications.<ref name=":22">{{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}}PD-notice</ref> The main applications enhance command and control, communications, sensors, integration and interoperability.<ref name="AI">{{cite journal |last1=Slyusar |first1=Vadym |title=Artificial intelligence as the basis of future control networks |url=https://www.researchgate.net/publication/334573170 |website=ResearchGate |year=2019 |doi=10.13140/RG.2.2.30247.50087 }}</ref> Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and [[Engineering:Vehicular automation|autonomous vehicles]].<ref name=":22" /> AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams.<ref name="AI" /> AI was incorporated into military operations in Iraq and Syria.<ref name=":22" />

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.<ref>{{cite news|date=25 January 2018|title=Getting to grips with military robotics|language=en|newspaper=The Economist|url=https://www.economist.com/news/special-report/21735478-autonomous-robots-and-swarms-will-change-nature-warfare-getting-grips|access-date=7 February 2018}}</ref><ref>{{cite web|title=Autonomous Systems: Infographic|url=https://www.siemens.com/innovation/en/home/pictures-of-the-future/digitalization-and-software/autonomous-systems-infographic.html|access-date=7 February 2018|website=siemens.com|language=en}}</ref> Military drones capable of autonomous action are in wide use.<ref>{{Cite web|last=Allen|first=Gregory|date=6 February 2019|title=Understanding China's AI Strategy|url=https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy|archive-url=https://web.archive.org/web/20190317004017/https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy|archive-date=17 March 2019|access-date=17 March 2019|publisher=Center for a New American Security}}</ref> Many researchers avoid military applications.<ref name="AI"/>

== Health{{anchor|Hospitals_and_medicine}} ==

=== Healthcare ===
{{Main|Medicine:Artificial intelligence in healthcare}}
[[File:X-ray of hand, where bone age is automatically found by BoneXpert software.jpg|thumb|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 [[Engineering:Da Vinci Surgical System|Da Vinci Surgical System]]]]
AI in healthcare is often used for classification, to evaluate a [[CT scan]] or [[Biology: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&nbsp;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>

Microsoft's AI project Hanover helps doctors choose [[Medicine:Treatment of cancer|cancer treatments]] from among the more than 800 medicines and vaccines.<ref>{{Cite web|date=29 October 2019|title=Microsoft Using AI to Accelerate Cancer Precision Medicine|url=https://healthitanalytics.com/news/microsoft-using-ai-to-accelerate-cancer-precision-medicine|access-date=29 November 2020|website=HealthITAnalytics|language=en-US}}</ref><ref>{{cite news|author=Dina Bass|date=20 September 2016|title=Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments|publisher=Bloomberg L.P.|url=https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments|url-status=live|archive-url=https://web.archive.org/web/20170511103625/https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments|archive-date=11 May 2017}}</ref> Its goal is to memorize all the relevant papers to predict which (combinations of) drugs will be most effective for each patient. [[Medicine:Acute myeloid leukemia|Myeloid leukemia]] is one target. Another study reported on an AI that was as good as doctors in identifying skin cancers.<ref>{{Cite news|last=Gallagher|first=James|date=26 January 2017|title=Artificial intelligence 'as good as cancer doctors'|language=en-GB|work=BBC News|url=https://www.bbc.co.uk/news/health-38717928|url-status=live|access-date=26 January 2017|archive-url=https://web.archive.org/web/20170126133849/http://www.bbc.co.uk/news/health-38717928|archive-date=26 January 2017}}</ref> Another project monitors multiple high-risk patients by asking each patient questions based on data acquired from doctor/patient interactions.<ref>{{Citation|title=Remote monitoring of high-risk patients using artificial intelligence|date=18 October 1994|url=https://www.google.com/patents/US5357427|issue=US5357427 A|editor-last=Langen|editor-first=Pauline A.|archive-url=https://web.archive.org/web/20170228090520/https://www.google.com/patents/US5357427|access-date=27 February 2017|archive-date=28 February 2017|editor2-last=Katz|editor2-first=Jeffrey S.|editor3-last=Dempsey|editor3-first=Gayle|url-status=live}}</ref> In one study done with [[Transfer learning|transfer learning]], an AI diagnosed eye conditions similar to an ophthalmologist and recommended treatment referrals.<ref>{{cite journal |last1=Kermany |first1=Daniel S. |last2=Goldbaum |first2=Michael |last3=Cai |first3=Wenjia |last4=Valentim |first4=Carolina C.S. |last5=Liang |first5=Huiying |last6=Baxter |first6=Sally L. |last7=McKeown |first7=Alex |last8=Yang |first8=Ge |last9=Wu |first9=Xiaokang |last10=Yan |first10=Fangbing |last11=Dong |first11=Justin |last12=Prasadha |first12=Made K. |last13=Pei |first13=Jacqueline |last14=Ting |first14=Magdalene Y.L. |last15=Zhu |first15=Jie |last16=Li |first16=Christina |last17=Hewett |first17=Sierra |last18=Dong |first18=Jason |last19=Ziyar |first19=Ian |last20=Shi |first20=Alexander |last21=Zhang |first21=Runze |last22=Zheng |first22=Lianghong |last23=Hou |first23=Rui |last24=Shi |first24=William |last25=Fu |first25=Xin |last26=Duan |first26=Yaou |last27=Huu |first27=Viet A.N. |last28=Wen |first28=Cindy |last29=Zhang |first29=Edward D. |last30=Zhang |first30=Charlotte L. |last31=Li |first31=Oulan |last32=Wang |first32=Xiaobo |last33=Singer |first33=Michael A. |last34=Sun |first34=Xiaodong |last35=Xu |first35=Jie |last36=Tafreshi |first36=Ali |last37=Lewis |first37=M. Anthony |last38=Xia |first38=Huimin |last39=Zhang |first39=Kang |title=Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning |journal=Cell |date=February 2018 |volume=172 |issue=5 |pages=1122–1131.e9 |doi=10.1016/j.cell.2018.02.010 |pmid=29474911 |s2cid=3516426 |doi-access=free }}</ref>

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 [[Medicine:Clinical decision support system|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 | s2cid = 238990562 }}</ref> such as in [[Medicine:Concept processing|concept processing]] technology in EMR software.

Other healthcare tasks thought suitable for an AI that are in development include:

*[[Medicine:Screening|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 |language=en |issn=2730-664X}}
* 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>
*Heart sound analysis<ref>{{cite journal |doi=10.1016/j.simpat.2003.11.005 |title=Heart sound analysis for symptom detection and computer-aided diagnosis |journal=Simulation Modelling Practice and Theory |volume=12 |issue=2 |pages=129–146 |year=2004 |last1=Reed |first1=Todd R. |last2=Reed |first2=Nancy E. |last3=Fritzson |first3=Peter }}</ref>
* Companion robots for 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 |s2cid=13797196 |citeseerx=10.1.1.607.342 }}</ref>
* Medical record analysis
* Treatment plan design{{citation needed|date=July 2022}}
* Medication management
* Assisting blind people<ref>{{cite web |last1=Ray |first1=Dr Amit |title=Artificial intelligence for Assisting Navigation of Blind People |url=https://amitray.com/artificial-intelligence-for-assisting-blind-people/ |publisher=Inner Light Publishers |date=14 May 2018}}</ref>
* 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 [[Philosophy:Life extension|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 |url=https://www.biorxiv.org/content/10.1101/2022.04.26.489505v1 |pages=2022.04.26.489505 |language=en |doi=10.1101/2022.04.26.489505 |date=27 April 2022|s2cid=248452963 }}</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>
* Outcome prediction for surgical procedures
* HIV prognosis
* 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 |language=en |issn=1932-6203|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|pmid=35653572 |pmc=9191668 |arxiv=2206.02788 |bibcode=2022PNAS..11918836Y |s2cid=235372800 |pmc-embargo-date=December 2, 2022 }}</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=1 January 2019 |volume=49 |pages=49–66 |doi=10.1016/j.arr.2018.11.003 |pmid=30472217 |s2cid=53755842 |language=en |issn=1568-1637}}</ref>
* Help development of [[Medicine:Biomarker|biomarkers]]<ref name="10.1016/j.arr.2018.11.003"/>
* Help tailor therapies to individuals in [[Medicine:Personalized medicine|personalized medicine]]/[[Medicine:Precision 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 |language=en |issn=0935-9648}}</ref>

=== Workplace health and safety ===
AI-enabled [[Chatbot|chatbot]]s decrease the need for humans to perform basic call center tasks.<ref name=":1">{{Cite web|last=Moore|first=Phoebe V.|date=7 May 2019|title=OSH and the Future of Work: benefits and risks of artificial intelligence tools in workplaces|url=https://osha.europa.eu/en/publications/osh-and-future-work-benefits-and-risks-artificial-intelligence-tools-workplaces/view|access-date=30 July 2020|website=EU-OSHA|pages=3–7}}</ref>

Machine learning in [[Sentiment analysis|sentiment analysis]] can spot fatigue in order to prevent overwork.<ref name=":1" /> Similarly, [[Decision support system|decision support system]]s can prevent industrial disasters and make disaster response more efficient.<ref name=Howard2019>{{cite journal |last1=Howard |first1=John |title=Artificial intelligence: Implications for the future of work |journal=American Journal of Industrial Medicine |date=November 2019 |volume=62 |issue=11 |pages=917–926 |doi=10.1002/ajim.23037 |pmid=31436850 |s2cid=201275028 }}</ref> For manual workers in [[Finance:Material handling|material handling]], [[Predictive analytics|predictive analytics]] may be used to reduce [[Medicine:Musculoskeletal injury|musculoskeletal injury]].<ref name=":02">{{Cite web|last=Gianatti|first=Toni-Louise|date=14 May 2020|title=How AI-Driven Algorithms Improve an Individual's Ergonomic Safety|url=https://ohsonline.com/articles/2020/05/14/how-aidriven-algorithms-improve-an-individuals-ergonomic-safety.aspx|access-date=30 July 2020|website=Occupational Health & Safety|language=en}}</ref> Data collected from [[Engineering:Wearable technology|wearable sensors]] can improve [[Medicine:Workplace health surveillance|workplace health surveillance]], [[Occupational risk assessment|risk assessment]], and research.<ref name=Howard2019/>{{How|date=December 2021}}

AI can auto-[[Coding (social sciences)|code]] [[Workers' compensation|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> AI‐enabled [[Virtual reality|virtual reality]] systems can enhance safety training for hazard recognition.<ref name=Howard2019/> AI can more efficiently detect accident [[Near miss (safety)|near misses]], which are important in reducing accident rates, but are often underreported.<ref>{{Cite web|last=Ferguson|first=Murray|date=19 April 2016|title=Artificial Intelligence: What's To Come for EHS… And When?|url=https://www.ehstoday.com/safety-leadership/article/21917682/artificial-intelligence-whats-to-come-for-ehs-and-when|access-date=30 July 2020|website=EHS Today}}</ref>

===Biochemistry===

AlphaFold 2 can determine the 3D structure of a ([[Biology: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|issn=0013-0613}}</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'', 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>

== Chemistry and biology ==
Machine learning has been used for [[Drug design#Computer-aided drug design|drug design]]. It has also been used for 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 |language=en |issn=2041-1723}}</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> have been used to explore the [[Biology: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 |s2cid=221882090 }}</ref> drug-syntheses and developing routes for [[Finance:Circular economy|recycling]] 200 industrial [[Chemistry: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 |s2cid=248415772 |language=en |issn=1476-4687}}</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> It can also be used for "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> It has been used for the [[Biology: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"/>

It has been used with databases for the development of a 46-day process to design, synthesize and test a drug which inhibits enzymes of a particular gene, [[Biology:DDR1|DDR1]]. DDR1 is involved in cancers and fibrosis which is one reason for the high-quality datasets that enabled these results.<ref>{{cite journal |title=Deep learning enables rapid identification of potent DDR1 kinase inhibitors |volume=37 |issue=9 |pages=1038–1040 |date=2 September 2019 |journal=Nature|doi=10.1038/s41587-019-0224-x |pmid=31477924 |last1=Zhavoronkov |first1=Alex |last2=Ivanenkov |first2=Yan A. |last3=Aliper |first3=Alex |last4=Veselov |first4=Mark S. |last5=Aladinskiy |first5=Vladimir A. |last6=Aladinskaya |first6=Anastasiya V. |last7=Terentiev |first7=Victor A. |last8=Polykovskiy |first8=Daniil A. |last9=Kuznetsov |first9=Maksim D. |last10=Asadulaev |first10=Arip |last11=Volkov |first11=Yury |last12=Zholus |first12=Artem |last13=Shayakhmetov |first13=Rim R. |last14=Zhebrak |first14=Alexander |last15=Minaeva |first15=Lidiya I. |last16=Zagribelnyy |first16=Bogdan A. |last17=Lee |first17=Lennart H. |last18=Soll |first18=Richard |last19=Madge |first19=David |last20=Xing |first20=Li |last21=Guo |first21=Tao |last22=Aspuru-Guzik |first22=Alán |s2cid=201716327 |url=https://www.researchgate.net/publication/335565604|url-access=subscription}}</ref>

There are various types of applications for machine learning in decoding human biology, such as helping to map [[Biology:Gene expression|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 |s2cid=232367225 |language=en |issn=2397-3374}}</ref> or identifying functional DNA motifs.<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 |language=en |issn=1476-4687}}</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 |doi=10.3389/fgene.2022.951939 |language=English |issn=1664-8021|doi-access=free }}</ref>

There also is some use of machine learning in [[Biology:Synthetic biology|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 |language=en |issn=2041-1723}}</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 |s2cid=197664634 }}</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=1 February 2022 |volume=17 |issue=1 |pages=1–9 |doi=10.1134/S2635167622010049 |s2cid=248701168 |language=en |issn=2635-1684}}</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 |language=en |issn=2079-4991|doi-access=free }}</ref> and [[Physics:Materials science|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 |s2cid=53632880 }}</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 |language=en |issn=2041-1723}}</ref>

=== Novel types of machine learning ===
[[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 [[Organization:Laboratory robotics#Applications|prototype robot scientists]], including robot-embodied ones like the two Robot Scientists, 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=July 2020 |volume=583 |issue=7815 |pages=237–241 |doi=10.1038/s41586-020-2442-2 |pmid=32641813 |bibcode=2020Natur.583..237B |s2cid=220420261 |url=https://www.nature.com/articles/s41586-020-2442-2 |access-date=16 August 2020 |language=en |issn=1476-4687}}</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|wetware computer]]s" that can learn (e.g. for use as [[Biology:Biosensor|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 |language=en |issn=2375-2548}}
* 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>

Moreover, if whole brain emulation is possible via both scanning and replicating the, at least, bio-chemical brain – as premised in the form of digital replication in ''The Age of Em'', possibly using [[Physical neural network|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 web |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 |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|s2cid=14545074 }}</ref> such as whether (and how) such are built, [[Philosophy: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=September 2021 |volume=4 |issue=9 |pages=635–644 |doi=10.1038/s41928-021-00646-1 |s2cid=240580331 |url=https://www.nature.com/articles/s41928-021-00646-1 |language=en |issn=2520-1131|url-access=subscription}}</ref>

A subcategory of artificial intelligence is embodied,<ref>{{cite journal |last1=Pfeifer |first1=Rolf |last2=Iida |first2=Fumiya |title=Embodied Artificial Intelligence: Trends and Challenges |journal=Embodied Artificial Intelligence: International Seminar, Dagstuhl Castle, Germany, July 7–11, 2003. Revised Papers |series=Lecture Notes in Computer Science |date=2004 |volume=3139 |pages=1–26 |doi=10.1007/978-3-540-27833-7_1 |publisher=Springer |isbn=978-3-540-22484-6 |language=en}}</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 |s2cid=233687524 |language=en |issn=2522-5839|url=http://urn.nb.no/URN:NBN:no-88559 }}</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. {{Excerpt|Robotic sensing|Collective sensing and sensemaking}}

==== Biological computing in AI and as AI ====
However, [[Biology:Biological computing|biological computers]], even if both highly artificial and intelligent, are typically distinguished from synthetic, often silicon-based, computers – they could however be combined or used for the design of either. Moreover, many tasks may be carried out inadequately by artificial intelligence even if its algorithms were transparent, understood, bias-free, apparently effective, and goal-aligned and its trained data sufficiently large and [[Data cleansing|cleansed]] – such as in cases were the underlying or available metrics, [[Philosophy:Value (ethics)|values]] or data are inappropriate. 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|human-in-the-loop]]).{{citation needed|date=May 2022}} A study described the biological as a limitation of AI with "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 if it was understood this doesn't mean there being "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 |s2cid=146091906 |url=http://www.univagora.ro/jour/index.php/ijccc/article/view/3536 |language=en |issn=1841-9844}}</ref> Technologies that integrate biology and are often AI-based include [[Biorobotics|biorobotics]].

== Astronomy, space activities and ufology ==
Artificial intelligence is used in [[Astronomy:Astronomy|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=1 July 2010 |volume=19 |issue=7 |pages=1049–1106 |doi=10.1142/S0218271810017160 |arxiv=0906.2173 |bibcode=2010IJMPD..19.1049B |s2cid=119277652 |issn=0218-2718}}</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 [[Astronomy:Exoplanet|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 |s2cid=208857777 |language=en |issn=1942-4787}}</ref> It could also be used for activities in space such as [[Astronomy:Space exploration|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 journal |last1=Mohan |first1=Jaya Preethi |last2=Tejaswi |first2=N. |title=A Study on Embedding the Artificial Intelligence and Machine Learning into Space Exploration and Astronomy |journal=Emerging Trends in Computing and Expert Technology |series=Lecture Notes on Data Engineering and Communications Technologies |date=2020 |volume=35 |pages=1295–1302 |doi=10.1007/978-3-030-32150-5_131 |publisher=Springer International Publishing |isbn=978-3-030-32149-9 |s2cid=209081154 |language=en}}</ref><ref>{{cite web |last1=Rees |first1=Martin |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 [[Astronomy:Search for extraterrestrial intelligence|search for extraterrestrial intelligence]] (SETI), machine learning has been used in attempts to identify artificially generated [[Physics:Electromagnetic waves|electromagnetic waves]] in available data<ref>{{cite journal |last1=McCarren |first1=Andrew |title=Identifying extra-terrestrial intelligence using machine learning |url=https://core.ac.uk/reader/335049114 |website=CORE}}</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 |s2cid=52232565 |language=en}}</ref> – such as real-time observations<ref>{{cite journal |last1=Nanda |first1=Lakshay |last2=V |first2=Santhi |title=SETI (Search for Extra Terrestrial Intelligence) Signal Classification using Machine Learning |journal=2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) |date=November 2019 |pages=499–504 |doi=10.1109/ICSSIT46314.2019.8987793|isbn=978-1-7281-2119-2 |s2cid=211120812 }}</ref> – and other [[Astronomy:Technosignature|technosignature]]s, e.g. via [[Anomaly detection|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 [[Unsolved:Ufology|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=[[Organization:University of Würzburg|University of Würzburg]] |access-date=29 May 2022}}</ref> and the [[Biography:Avi Loeb#The Galileo Project|Galileo Project]] headed by Prof. [[Biography:Avi Loeb|Avi Loeb]] use machine learning to detect and classify peculiar 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 [[Astronomy:Interstellar object|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>

=== Future or non-human applications ===
Loeb has speculated that one type of technological equipment the project may detect could be "AI astronauts"<ref>{{cite web |last1=Loeb |first1=Avi |title=A.I. Astronauts from Advanced Civilizations |url=https://medium.com/on-the-trail-of-the-saucers/a-i-astronauts-from-advanced-civilizations-4d2ffe852a4c |website=Trail of the Saucers |access-date=29 May 2022 |language=en |date=12 October 2021}}</ref> and in 2021 – in an opinion piece – that AI "will" "supersede natural intelligence",<ref>{{cite web |last1=Loeb |first1=Avi |title=Microbes, Natural Intelligence and Artificial Intelligence |url=https://www.scientificamerican.com/article/microbes-natural-intelligence-and-artificial-intelligence/ |website=Scientific American |access-date=29 May 2022 |language=en}}</ref> while [[Biography:Martin Rees|Martin Rees]] stated that there "may" be more civilisations than thought with the "majority of them" being artificial.<ref>{{cite web |last1=Rees |first1=Martin |title=Why extraterrestrial intelligence is more likely to be artificial than biological |url=https://phys.org/news/2021-10-extraterrestrial-intelligence-artificial-biological.html |website=phys.org |access-date=30 May 2022 |language=en}}</ref> In particular, [[Unsolved:Hypothetical technology|mid/far future or non-human applications]] of artificial intelligence could include advanced forms of [[Artificial general intelligence|artificial general intelligence]] that engages in [[Engineering:Space colonization|space colonization]] or more narrow spaceflight-specific types of AI. In contrast, there have been concerns in relation to potential AGI or AI capable of [[Unsolved:Embryo space colonization|embryo space colonization]], or more generally natural intelligence-based space colonization, such as "safety of encounters with an alien AI",<ref>{{cite journal |last1=Crowl |first1=A. |last2=Hunt |first2=J. |last3=Hein |first3=A. M. |title=Embryo Space Colonisation to Overcome the Interstellar Time Distance Bottleneck |journal=Journal of the British Interplanetary Society |date=1 January 2012 |volume=65 |pages=283–285 |bibcode=2012JBIS...65..283C |url=https://www.researchgate.net/publication/235975216 |issn=0007-084X}}</ref><ref>{{cite arXiv |last1=Hein |first1=Andreas M. |last2=Baxter |first2=Stephen |title=Artificial Intelligence for Interstellar Travel |eprint=1811.06526 |date=19 November 2018|class=physics.pop-ph }}</ref> suffering risks (or inverse goals),<ref>{{cite web |last1=Davies |first1=Jim |title=We Shouldn't Try to Make Conscious Software&mdash;Until We Should |url=https://www.scientificamerican.com/article/we-shouldn-rsquo-t-try-to-make-conscious-software-mdash-until-we-should/ |website=Scientific American |access-date=30 May 2022 |language=en}}</ref><ref>{{cite journal |last1=Torres |first1=Phil |title=Space colonization and suffering risks: Reassessing the "maxipok rule" |journal=Futures |date=June 2018 |volume=100 |pages=74–85 |doi=10.1016/j.futures.2018.04.008|s2cid=149794325 }}</ref> moral license/responsibility in respect to colonization-effects,<ref>{{cite journal |last1=Edwards |first1=Matthew R. |title=Android Noahs and embryo Arks: ectogenesis in global catastrophe survival and space colonization |journal=International Journal of Astrobiology |date=April 2021 |volume=20 |issue=2 |pages=150–158 |doi=10.1017/S147355042100001X |bibcode=2021IJAsB..20..150E |s2cid=232148456 |language=en |issn=1473-5504}}
* Explanation by author: {{cite news |title=Humans could recolonize Earth after mass extinctions with ectogenesis |url=https://sciencex.com/news/2021-08-humans-recolonize-earth-mass-extinctions.html |access-date=30 May 2022 |work=ScienceX}}</ref> or AI gone rogue (e.g. as portrayed with fictional David⁸ and HAL 9000). See also: [[Astronomy:Space law|space law]] and [[Physics:Space ethics|space ethics]]. Loeb has described the possibility of "AI astronauts" that engage in "supervised evolution" (see also: [[Philosophy:Directed evolution (transhumanism)|directed evolution]], Uplift, [[Unsolved:Directed panspermia|directed panspermia]], [[Engineering:Space colonization|space colonization]]).<ref>{{cite web |last1=Loeb |first1=Avi |title=Intelligent Adaptation or Barbarian Duplication |url=https://avi-loeb.medium.com/intelligent-adaptation-or-barbarian-duplication-93eadff574ba |website=Medium |access-date=30 May 2022 |language=en |date=27 January 2022}}</ref>

=== Astrochemistry ===
It 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 [[Astronomy: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 |language=English |issn=2296-987X|doi-access=free }}</ref>

==Other fields of research==
===Archaeology, history and imaging of sites===
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 |bibcode=2022Natur.603..280A |language=en |issn=1476-4687|doi-access=free}}</ref> It can help to index texts for example to enable better and easier searching<ref>{{cite journal |title=Searching in Archaeological Texts. Problems andSolutions Using an Artiﬁcial Intelligence Approach |journal=Palarch's Journal of Archaeology of Egypt/Egyptology |date=2010 |url=https://www.researchgate.net/publication/242752455 |issn=1567-214X}}</ref> and classify 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=14 August 2020 |volume=1 |issue=5 |pages=267 |doi=10.1007/s42979-020-00286-w |arxiv=2005.02863 |s2cid=218516977 |url=https://link.springer.com/article/10.1007/s42979-020-00286-w |language=en |issn=2661-8907|url-access=subscription}}</ref>

Artificial intelligence can also be used to investigate genomes to uncover genetic history, such as [[Biology:Interbreeding between archaic and modern humans|interbreeding between archaic and modern humans]] by which for example the past existence of a [[Biology:Ghost population|ghost population]], not [[Biology:Neanderthal|Neanderthal]] or [[History:Denisovan|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 |language=en |issn=1932-6203|doi-access=free }}</ref> {{Further|Remote sensing in archaeology}}

=== Physics ===
{{Excerpt|Machine learning in physics}}
A [[Deep learning|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 |pages=1–11 |doi=10.1038/s41562-022-01394-8 |pmid=35817932 |language=en |issn=2397-3374|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 |s2cid=251087119 |language=en |issn=2662-8457}}</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"/>

=== Materials science ===
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 |language=en |issn=2057-3960|doi-access=free}}</ref><ref name="10.1038/s43246-021-00209-z"/><ref name="10.1002/adma.202109892"/>

=== Reverse engineering ===
Machine learning is used in diverse types of [[Reverse engineering|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 |s2cid=225749339 |url=https://www.sciencedirect.com/science/article/abs/pii/S0266353820313452 |language=en |issn=0266-3538|url-access=subscription}}</ref> and for quickly understanding the behavior of [[Malware|malware]].<ref>{{cite journal |last1=Anderson |first1=Blake |last2=Storlie |first2=Curtis |last3=Yates |first3=Micah |last4=McPhall |first4=Aaron |title=Automating Reverse Engineering with Machine Learning Techniques |journal=Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop |date=7 November 2014 |pages=103–112 |doi=10.1145/2666652.2666665 |url=https://dl.acm.org/doi/10.1145/2666652.2666665 |publisher=Association for Computing Machinery|isbn=9781450331531 |s2cid=14367892 |url-access=subscription}}</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 |s2cid=50787617 |url=https://www.science.org/doi/10.1126/science.aat2663 |language=en |issn=0036-8075|url-access=subscription}}</ref> or [[Biology:Protein design|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 |s2cid=250953434 |url=https://www.ipd.uw.edu/wp-content/uploads/2022/07/science.abn2100.pdf |language=en |issn=0036-8075}}</ref>

== Law ==

=== Legal analysis ===
AI is a mainstay of law-related professions. Algorithms and machine learning do some tasks previously done by entry-level lawyers.<ref>{{Cite book|last=Ashley|first=Kevin D.|url=http://ebooks.cambridge.org/ref/id/CBO9781316761380|title=Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age|date=2017|publisher=Cambridge University Press|isbn=978-1-316-76138-0|location=Cambridge|doi=10.1017/9781316761380|s2cid=209050358 }}{{page needed|date=July 2021}}</ref> While its use is common, it is not expected to replace most work done by lawyers in the near future.<ref>{{Cite news |last=Lohr |first=Steve |date=2017-03-19 |title=A.I. Is Doing Legal Work. But It Won't Replace Lawyers, Yet. |language=en-US |work=The New York Times |url=https://www.nytimes.com/2017/03/19/technology/lawyers-artificial-intelligence.html |access-date=2022-05-09 |issn=0362-4331}}</ref>

The [[Electronic discovery|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=[[Social:Financial Times|Financial Times]]|language=en-GB}}</ref>

=== Law enforcement and legal proceedings ===
COMPAS is a commercial system used by U.S. courts to assess the likelihood of recidivism.<ref name=":3">{{Cite web|author1=Jeff Larson|author2=Julia Angwin|date=23 May 2016|title=How We Analyzed the COMPAS Recidivism Algorithm|url=https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm|url-status=live|archive-url=https://web.archive.org/web/20190429190950/https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm|archive-date=29 April 2019|access-date=19 June 2020|website=ProPublica|language=en}}</ref>

One concern relates to [[Algorithmic bias|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=":3" />

== Services ==

=== 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.<ref>{{cite journal |last1=Nawaz |first1=Nishad |last2=Gomes |first2=Anjali Mary |title=Artificial Intelligence Chatbots are New Recruiters |journal=International Journal of Advanced Computer Science and Applications |year=2020 |volume=10 |issue=9 |doi=10.2139/ssrn.3521915 |ssrn=3521915 |s2cid=233762238 }}</ref>

=== Job search ===
AI has simplified the recruiting /job search process for both recruiters and job seekers. According to Raj Mukherjee from [[Organization:Indeed|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.<ref>{{Cite journal|last=Kafre|first=Sumit|date=15 April 2018|title=Automatic Curriculum Vitae using Machine learning and Artificial Intelligence|url=http://asianssr.org/index.php/ajct/article/view/479|journal=Asian Journal for Convergence in Technology (AJCT)|volume=4}}</ref> 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 assistants) on web pages.<ref name="Kongthon">{{Cite book|last1=Kongthon|first1=Alisa|title=Proceedings of the International Conference on Management of Emergent Digital Eco ''Systems'' - MEDES '09|last2=Sangkeettrakarn|first2=Chatchawal|last3=Kongyoung|first3=Sarawoot|last4=Haruechaiyasak|first4=Choochart|s2cid=1046438|year=2009|isbn=9781605588292|pages=450|chapter=Implementing an online help desk system based on conversational agent|doi=10.1145/1643823.1643908}}</ref> It can reduce operation and training costs.<ref name="Kongthon" /> [[Company:Pypestream|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|last=jackclarkSF|first=Jack Clark|date=20 July 2016|title=New Google AI Brings Automation to Customer Service|publisher=Bloomberg L.P.|url=https://www.bloomberg.com/news/articles/2016-07-20/new-google-ai-services-bring-automation-to-customer-service-iqv2rshg|access-date=18 November 2016}}</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>

=== 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 |s2cid=182061194 }}</ref> application, virtual voice assistant and service robots.

== Media ==
[[File:Restoration using Artificial intelligence.jpg|thumb|Image restoration]]
AI applications analyze media content such as movies, TV programs, advertisement videos or [[Social:User-generated content|user-generated content]]. The solutions often involve [[Computer vision|computer vision]].

Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the [[Engineering: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.

{{Prose|date=April 2022|list}}

* [[Motion interpolation]]<ref>{{Cite web |title=Research at NVIDIA: Transforming Standard Video Into Slow Motion with AI |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]]<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/amp/2018-07-10-nvidia-ai-fix-bad-photos-deep-learning.html |website=Engadget}}</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<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>
* [[Biology:Automated species identification|Automated species identification]]

=== Deep-fakes ===

Deep-fakes can be used for comedic purposes, but are better known for fake news and hoaxes.

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 [[Philosophy:Veritas|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 |s2cid=202717914 }}</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 [[Organization:Stanford University|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 [[Biography:Barack Obama|Barack Obama]] and Vladimir Putin. Other methods have been demonstrated based on deep neural networks, from which the name ''deep fake'' was taken.

In September 2018, U.S. Senator Mark Warner proposed to penalize [[Social:Social media|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, Vincent Nozick found a way to detect faked content by analyzing eyelid movements.<ref name="Nozick2018">{{cite book |doi=10.1109/WIFS.2018.8630761 |arxiv=1809.00888 |chapter=Meso ''Net'': A Compact Facial Video Forgery Detection Network |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 |pages=1–7 |isbn=978-1-5386-6536-7 |s2cid=52157475 }}</ref> DARPA gave 68 million dollars to work on deep-fake detection.<ref name="Nozick2018" />

Audio deep-fakes<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/tech-talk/artificial-intelligence/machine-learning/facebook-ai-launches-its-deepfake-detection-challenge |work=IEEE Spectrum |date=11 December 2019 }}</ref><ref>{{Cite web|url=http://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html|title=Contributing Data to Deepfake Detection Research}}</ref>

=== Video content analysis, surveillance and manipulated media detection ===
{{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>

=== Music ===
{{main|Music and artificial intelligence}}
AI has been used to compose music of various genres.

[[Biography:David Cope|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 [[Engineering: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 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 |s2cid=43273958 }}</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|reinforcement learning]] and [[Deep belief network|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|language=en}}</ref>

=== Writing and reporting ===
[[Company:Narrative Science|Narrative Science]] sells 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> [[Company:Automated Insights|Automated Insights]] generates personalized recaps and previews for [[Software:Yahoo Sports|Yahoo Sports]] 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 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 }}</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 }}</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>

=== Video games ===
{{Main|Software:Artificial intelligence in video games}}
In video games, AI is routinely used to generate behavior in [[Software:Non-player character|non-player character]]s (NPCs). In addition, AI is used for [[Pathfinding|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 ''[[Software:Left 4 Dead|Left 4 Dead]]'' (2008) and the neuroevolutionary training of platoons in ''[[Software:Supreme Commander 2|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 |s2cid=4335529 }}</ref> AI is also used in [[Software: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>

[[Engineering:Kinect|Kinect]], which provides a 3D body–motion interface for the [[Engineering:Xbox 360|Xbox 360]] and the [[Engineering:Xbox One|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 ===
{{Main|Artificial intelligence art}}

==== Art from language input ====
[[File:DALL-E 2 "A photo of a robot hand drawing, digital art".jpg|thumb|An artificial intelligence generated image given the prompt "A photo of a robot hand drawing, digital art".]]
[[File:DALL-E sample.png|alt=Images generated by artificial intelligence DALL-E of giraffe-dragon combinations|thumb|Images generated by [[Software:DALL-E|DALL-E]] based on the prompt "a professional high quality illustration of a giraffe dragon chimera. a giraffe imitating a dragon. a giraffe made of dragon."]]
AI like "Disco Diffusion", "[[Software:DALL-E|DALL·E]]" (1 and 2),<ref>{{cite web |title=DALL·E: Creating Images from Text |url=https://openai.com/blog/dall-e/ |website=OpenAI |access-date=30 May 2022 |language=en |date=5 January 2021}}</ref><ref name="2022/08/24/751f9a5"/> [[Software:Stable Diffusion|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><ref>{{cite web |title=Stable Diffusion launch announcement |url=https://stability.ai/blog/stable-diffusion-announcement |website=Stability.Ai |access-date=30 August 2022}}</ref> 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> "Dream by Wombo",<ref>{{cite web |last1=Nanou |first1=Electra |title=How to Create AI Artwork With the Wombo Dream Mobile App |url=https://www.makeuseof.com/how-to-create-ai-artwork-wombo-dream-mobile-app/ |website=MUO |access-date=30 May 2022 |date=14 January 2022}}</ref><ref>{{cite web |title=This AI-powered art app lets you paint pictures with words |url=https://techcrunch.com/2021/12/23/wombo-dream-app/ |website=TechCrunch |access-date=30 May 2022}}</ref><ref>{{cite web |last1=Vincent |first1=James |title=This AI art app is a glimpse at the future of synthetic media |url=https://www.theverge.com/2021/12/6/22820106/ai-art-app-dream-synthetic-media-wombo |website=The Verge |access-date=30 May 2022 |language=en |date=6 December 2021}}</ref> [[Software:Midjourney|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> has also been used for visualizing conceptual inputs such as song lyrics, certain texts or specific imagined concepts (or imaginations) in artistic ways or artistic images in general.<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 of the tools also allow users to input images and various parameters e.g. to display an object or product in various environments, some can replicate artistic styles of popular artists, and some can create elaborate artistic images from rough sketches.

==== History ====

===== GOFAI =====
AI has been used to produce visual art. The first AI art program, called [[Software:AARON|AARON]], was developed by Harold Cohen in 1968 at the University of California at San Diego. AARON is the most notable example of AI art in the era of [[Symbolic artificial intelligence|GOFAI]] programming because of its use of a symbolic rule-based approach to generate technical images.<ref name=":03">{{Cite journal |last1=Poltronieri |first1=Fabrizio Augusto |last2=Hänska |first2=Max |date=2019-10-23 |title=Technical Images and Visual Art in the Era of Artificial Intelligence: From GOFAI to GANs |url=https://dl.acm.org/doi/10.1145/3359852.3359865 |journal=Proceedings of the 9th International Conference on Digital and Interactive Arts |language=en |location=Braga Portugal |publisher=ACM |pages=1–8 |doi=10.1145/3359852.3359865 |isbn=978-1-4503-7250-3|s2cid=208109113 }}</ref> Cohen developed AARON with the goal of being able to code the act of drawing. In its primitive form, AARON created simple black and white drawings. Cohen would later finish the drawings by painting them. Throughout the years, he also began to develop a way for AARON to also paint. Cohen designed AARON to paint using special brushes and dyes that were chosen by the program itself without mediation from Cohen.<ref name=":23">{{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>

===== GAN/Modern =====
In recent years, AI art has shifted into a new paradigm with the emergence of [[Generative adversarial network|GAN]] computer programming, which generates technical images through machine learning frameworks that surpass the need for human operators.<ref name=":03" /> One example is [https://magenta.tensorflow.org/ Magenta], which began as a research project in 2016 from the Google Brain team that aimed to build programs and algorithms that can generate art and music, without need of human intervention. Other examples of GAN programs that generate art include [[Artbreeder]] and [[DeepDream]].

AI art generated from GANs programming challenged the parameters of art and only recently entered the art auction market.<ref name=":32">{{Cite journal |last1=Wu |first1=Chujun |last2=Seokin |first2=Ko |last3=Zhang |first3=Lina |date=2021-01-29 |title=On GANs Art in Context of Artificial Intelligence Art |journal=2021 the 5th International Conference on Machine Learning and Soft Computing |series=ICMLSC'21 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=168–171 |doi=10.1145/3453800.3453831 |isbn=978-1-4503-8761-3|s2cid=235474022 }}</ref> On October 25, 2018, Portrait of Edmond [[Edmond de Belamy|Belamy]] by the Parisian collective, Obvious, was the first art piece created by artificial intelligence to be offered at Christie's Auction House and was sold for $432,500.<ref name=":4">{{Cite web |title=Is artificial intelligence set to become art's next medium? {{!}} Christie's |url=https://www.christies.com/features/A-collaboration-between-two-artists-one-human-one-a-machine-9332-1.aspx |access-date=2022-05-07 |website=www.christies.com |language=en}}</ref>

The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMA provided an overview of AI applications for art, architecture, and design. Exhibitions showcasing the usage of AI to produce art include the 2016 Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the [[DeepDream]] algorithm and the 2017 exhibition "Unhuman: Art in the Age of AI", which took place in Los Angeles and Frankfurt. In spring 2018, the [[Organization:Association for Computing Machinery|Association for Computing Machinery]] dedicated a magazine issue to the subject of computers and art. In June 2018, "Duet for Human and Machine", an art piece permitting viewers to interact with an artificial intelligence, premiered at the Beall Center for Art + Technology. The Austrian [[Organization:Ars Electronica|Ars Electronica]] and Museum of Applied Arts, Vienna opened exhibitions on AI in 2019. Ars Electronica's 2019 festival "Out of the box" explored art's role in a sustainable societal transformation.

==== Understanding art with AI ====
In addition to the creation of original art, research methods that utilize AI have been generated to quantitatively analyze digital art collections. This has been made possible due to large-scale digitization of artwork in the past few decades. Although the main goal of digitization was to allow for accessibility and exploration of these collections, the use of AI in analyzing them has brought about new research perspectives.<ref name=":12">{{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 |s2cid=231951381 |issn=1551-6857}}</ref>

Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art.<ref name=":6">{{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> Close reading focuses on specific visual aspects of one piece. Some tasks performed by machines in close reading methods include computational artist authentication and analysis of brushstrokes or texture properties. In contrast, through distant viewing methods, the similarity across an entire collection for a specific feature can be statistically visualized. Common tasks relating to this method include automatic classification, object detection, multimodal tasks, knowledge discovery in art history, and computational aesthetics.<ref name=":12" /> Whereas distant viewing includes the analysis of large collections, close reading involves one piece of artwork.

Researchers have also introduced models that predict emotional responses to art such as [https://www.artemisdataset.org/ ArtEmis], a large-scale dataset with machine learning models that contain emotional reactions to visual art as well as predictions of emotion from images or text.<ref name=":7">{{Cite arXiv |last1=Achlioptas |first1=Panos |last2=Ovsjanikov |first2=Maks |last3=Haydarov |first3=Kilichbek |last4=Elhoseiny |first4=Mohamed |last5=Guibas |first5=Leonidas |date=2021-01-18 |title=ArtEmis: Affective Language for Visual Art |class=cs.CV |eprint=2101.07396}}</ref>

== Utilities ==
===Energy system===
[[Engineering:Power electronics|Power electronics]] converters are used in [[Engineering:Renewable energy|renewable energy]], [[Engineering:Energy storage|energy storage]], [[Engineering:Electric vehicle|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 |s2cid=116390072 |bibcode=2019ITPE...34.7161D |doi-access=free }}</ref>

Machine learning can be used for energy consumption prediction and scheduling, e.g. to help with [[100% renewable energy#Intermittency management|renewable energy intermittency management]] (see also: [[Engineering:Smart grid|smart grid]] and [[Earth: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 |s2cid=213274176 |language=en |issn=2523-3971}}</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=1 February 2021 |volume=125 |pages=106461 |doi=10.1016/j.ijepes.2020.106461 |s2cid=224876246 |language=en |issn=0142-0615}}</ref><ref>{{cite journal |last1=Mohanty |first1=Prasanta Kumar |last2=Jena |first2=Premalata |last3=Padhy |first3=Narayana Prasad |title=Home Electric Vehicle Charge Scheduling Using Machine Learning Technique |journal=2020 IEEE International Conference on Power Systems Technology (POWERCON) |date=September 2020 |pages=1–5 |doi=10.1109/POWERCON48463.2020.9230627|isbn=978-1-7281-6350-5 |s2cid=226268097 }}</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>{{better source needed|date=July 2022}}

===Telecommunications===
Many telecommunications companies make use of [[Search algorithm|heuristic search]] to manage their workforces. For example, [[Company:BT Group|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|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 |s2cid=62127575 |issn=0256-4602}}</ref><ref>{{cite web |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}}</ref>

== Manufacturing ==
{{Main|Artificial intelligence in industry|Artificial intelligence in heavy industry}}

=== Sensors ===
Artificial intelligence has been combined with digital [[Physics: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 AIs controlled Tamagotchis and Giga Pets, the [[Internet]], and the first widely released robot, [[Engineering:Furby|Furby]]. Aibo was a [[Engineering:Domestic robot|domestic robot]] in the form of a robotic dog with intelligent features and [[Autonomy|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 ===
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>

== Transport ==

=== Automotive ===
{{Main|Engineering:Vehicular automation|Engineering:Self-driving car}}
[[File:Waymo self-driving car side view.gk.jpg|thumb|Side view of a [[Company:Waymo|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|url=http://www.trb.org/Publications/Blurbs/158428.aspx|title=Artificial Intelligence in Transportation: Information for Application|date=29 January 2007|isbn=978-0-309-42929-0|doi=10.17226/23208}}{{page needed|date=July 2021}}</ref>

AI-based [[Fuzzy logic|fuzzy logic]] controllers operate [[Engineering:Transmission (mechanics)|gearboxes]]. For example, the 2006 [[Engineering:Audi TT|Audi TT]], [[Engineering:Volkswagen Touareg|VW Touareg]] {{Citation needed|date=February 2011}} and [[Engineering:Volkswagen Transporter (T5)|VW Caravell]] feature the DSP transmission. A number of Škoda variants ([[Engineering:Škoda Fabia|Škoda Fabia]]) include a fuzzy logic-based controller. Cars have AI-based [[Engineering:Advanced driver-assistance systems|driver-assist]] features such as [[Engineering:Automatic parking|self-parking]] and [[Engineering:Adaptive cruise control|adaptive cruise control]].

There are also prototypes of autonomous automotive public transport vehicles such as electric mini-buses<ref>{{cite news |last1=Benson |first1=Thor |title=Self-driving buses to appear on public roads for the first time |url=https://www.inverse.com/innovation/americas-first-self-driving-buses-are-coming-to-a-town-in-florida |access-date=26 August 2021 |work=Inverse |language=en}}</ref><ref>{{cite news |title=Europe's first full-sized self-driving urban electric bus has arrived |url=https://www.weforum.org/agenda/2021/03/europe-first-autonomous-electric-buses-spain/ |access-date=26 August 2021 |work=World Economic Forum |language=en}}</ref><ref>{{cite news |title=Self-driving bus propels Swiss town into the future |url=https://edition.cnn.com/2018/06/27/sport/trapeze-self-driving-autonomous-electric-bus-switzerland-spt-intl/index.html |access-date=26 August 2021 |work=CNN}}</ref><ref>{{cite journal |last1=Huber |first1=Dominik |last2=Viere |first2=Tobias |last3=Horschutz Nemoto |first3=Eliane |last4=Jaroudi |first4=Ines |last5=Korbee |first5=Dorien |last6=Fournier |first6=Guy |title=Climate and environmental impacts of automated minibuses in future public transportation |journal=Transportation Research Part D: Transport and Environment |date=1 January 2022 |volume=102 |pages=103160 |doi=10.1016/j.trd.2021.103160 |s2cid=245777788 |language=en |issn=1361-9209}}</ref> as well as [[Engineering:Automatic train operation|autonomous rail transport]] in operation.<ref>{{cite web |title=Transportation Germany Unveils the World's First Fully Automated Train in Hamburg |date=12 October 2021 |url=https://interestingengineering.com/germany-unveils-the-worlds-first-fully-automated-train-in-hamburg |access-date=3 July 2022}}</ref><ref>{{cite web |title=Railway digitalisation using drones |url=https://www.euspa.europa.eu/railway-digitalisation-using-drones |website=www.euspa.europa.eu |access-date=3 July 2022 |language=en |date=25 February 2021}}</ref><ref>{{cite news |title=World's fastest driverless bullet train launches in China |url=https://www.theguardian.com/travel/2020/jan/09/worlds-fastest-driverless-automated-bullet-train-launches-beijing-china-olympics |access-date=3 July 2022 |work=The Guardian |date=9 January 2020 |language=en}}</ref>

There also are prototypes of autonomous delivery vehicles, sometimes including [[Engineering:Delivery robot|delivery robot]]s.<ref>{{cite news |title=JD.com, Meituan and Neolix to test autonomous deliveries on Beijing public roads |url=https://techcrunch.com/2021/05/25/meituan-jd-com-and-neolix-begin-autonomous-deliveries-in-beijing/ |access-date=28 April 2022 |work=TechCrunch}}</ref><ref>{{cite news |last1=Hawkins |first1=Andrew J. |title=Waymo is designing a self-driving Ram delivery van with FCA |url=https://www.theverge.com/2020/7/22/21334012/waymo-fca-ram-delivery-self-driving-van |access-date=28 April 2022 |work=The Verge |date=22 July 2020 |language=en}}</ref><ref>{{cite news |title=Arrival's delivery van demos its autonomous chops at a UK parcel depot |url=https://newatlas.com/automotive/arrivals-delivery-van-autonomous-demo-parcel-depot/ |access-date=28 April 2022 |work=New Atlas |date=3 August 2021}}</ref><ref>{{cite news |last1=Buss |first1=Dale |title=Walmart Presses Its Distribution Legacy To Lead In Automated Delivery |url=https://www.forbes.com/sites/dalebuss/2021/08/31/walmart-presses-its-distribution-legacy-to-lead-in-automated-delivery/ |access-date=28 April 2022 |work=Forbes |language=en}}</ref><ref>{{cite news |last1=Cooley |first1=Patrick |last2=Dispatch |first2=The Columbus |title=Grubhub testing delivery robots |url=https://techxplore.com/news/2021-09-grubhub-delivery-robots.html |access-date=28 April 2022 |work=techxplore.com |language=en}}</ref><ref>{{cite news |title=Self-driving delivery van ditches 'human controls' |url=https://www.bbc.com/news/technology-51409031 |access-date=28 April 2022 |work=BBC News |date=6 February 2020}}</ref><ref>{{cite news |last1=Krok |first1=Andrew |title=Nuro's self-driving delivery van wants to run errands for you |url=https://www.cnet.com/roadshow/news/nuros-self-driving-delivery-van-wants-to-run-errands-for-you/ |access-date=28 April 2022 |work=CNET |language=en}}</ref>

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>

Transportation's complexity means that in most cases training an AI in a real-world driving environment is impractical. Simulator-based testing can reduce the risks of on-road training.<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, WayMo, and [[Company:General Motors|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>

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 is testing its [[Engineering:Freightliner Inspiration|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 February 2015 }}</ref> 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>

=== Military ===
The {{wipe|Royal Australian Air Force}} Force (RAAF) 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>

Aircraft simulators use AI for training aviators. Flight conditions can be simulated that allow pilots to make mistakes without risking themselves or expensive aircraft. Air combat can also be simulated.

AI can also be used to operate planes analogously to their control of ground vehicles. Autonomous drones can fly independently or in [[Engineering:Swarm robotics|swarms]].<ref>{{cite journal |last1=Jones |first1=Randolph M. |last2=Laird |first2=John E. |last3=Nielsen |first3=Paul E. |last4=Coulter |first4=Karen J. |last5=Kenny |first5=Patrick |last6=Koss |first6=Frank V. |title=Automated Intelligent Pilots for Combat Flight Simulation |journal=AI Magazine |date=15 March 1999 |volume=20 |issue=1 |pages=27 |doi=10.1609/aimag.v20i1.1438 }}</ref>

AOD uses the Interactive Fault Diagnosis and Isolation System, or IFDIS, which is a rule-based expert system using information from TF-30 documents and expert advice from mechanics that work on the TF-30. This system was designed to be used for the development of the TF-30 for the F-111C. The system replaced specialized workers. The system allowed regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers.

[[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 based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.

=== 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 name=":0">{{Cite magazine|last=Adams|first=Eric|date=28 March 2017|title=AI Wields the Power to Make Flying Safer—and Maybe Even Pleasant|url=https://www.wired.com/2017/03/ai-wields-power-make-flying-safer-maybe-even-pleasant/|magazine=Wired|access-date=7 October 2017}}</ref>

The 2016 Intelligent Autopilot System combined [[Apprenticeship learning|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 name=":2">{{cite book|last1=Baomar|first1=Haitham|url=https://discovery.ucl.ac.uk/id/eprint/1520870/|title=2016 IEEE Symposium Series on Computational Intelligence (SSCI)|last2=Bentley|first2=Peter J.|year=2016|isbn=978-1-5090-4240-1|pages=1–9|chapter=An Intelligent Autopilot System that learns flight emergency procedures by imitating human pilots|doi=10.1109/SSCI.2016.7849881|s2cid=2021875}}</ref>

=== Maritime ===
[[Neural network]]s are used by [[Philosophy: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 [[Earth:Unmanned surface vehicle|autonomous boats]].

== Environmental monitoring ==
Autonomous ships that monitor the ocean, AI-driven satellite data analysis, [[Biology:Passive acoustics|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=1 July 2022 |volume=140 |pages=108986 |doi=10.1016/j.ecolind.2022.108986 |s2cid=248955278 |language=en |issn=1470-160X}}</ref> or [[Earth:Remote sensing|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 |s2cid=169513589 |language=en |issn=2398-9629}}</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 [[Astronomy:Earth observation satellite#Environmental monitoring|satellite monitoring]]-platform for analysis/tracking of plastic waste sites to help [[Social: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 spot early-warning signs of disasters and environmental issues, possibly including natural [[Social: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 |language=en |issn=1545-7885}}</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 |s2cid=54926314 |language=en}}</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 |s2cid=208877225 |language=en |issn=2374-3468}}</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=1 September 2020 |volume=17 |issue=9 |pages=2231–2246 |doi=10.1007/s10346-020-01453-z |s2cid=220294377 |language=en |issn=1612-5118}}</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=1 January 2019 |volume=568 |pages=1042–1054 |doi=10.1016/j.jhydrol.2018.11.060 |bibcode=2019JHyd..568.1042M |s2cid=134910487 |language=en |issn=0022-1694}}</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=1 September 2020 |volume=131 |pages=104781 |doi=10.1016/j.envsoft.2020.104781 |s2cid=221823295 |language=en |issn=1364-8152}}</ref> tipping-points of [[Biology:Ecosystem collapse|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 |language=en |issn=0027-8424}}</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 |s2cid=232407435 |language=en |issn=0301-4797}}</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 |s2cid=237716175 |language=en |issn=0043-1397|url=http://urn.nb.no/URN:NBN:no-95519 }}</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=1 May 2020 |volume=139 |pages=103562 |doi=10.1016/j.advwatres.2020.103562 |bibcode=2020AdWR..13903562K |s2cid=216447098 |language=en |issn=0309-1708}}</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=1 May 2020 |volume=57 |pages=101067 |doi=10.1016/j.ecoinf.2020.101067 |s2cid=215964704 |language=en |issn=1574-9541}}</ref>

== Computer science ==
{{Split section|Artificial intelligence in computer science|date=April 2022}}

=== Programming assistance ===
[[Software:GitHub Copilot|GitHub Copilot]] is an artificial intelligence model developed by [[GitHub]] and [[Company:OpenAI|OpenAI]] that 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>

==== Neural network design ====
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>

==== Quantum computing ====
{{Further|Quantum machine learning}}
Machine learning has been used for noise-cancelling in [[Engineering:Quantum technology|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|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, {{Hover title|it is 'able to produce memristive dynamics on single-photon states through a scheme of measurement and classical feedback'|quantum [[Engineering:Memristor|memristive device]]}} for neuromorphic (quantum-)computers (NC)/[[Artificial neural network|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 |s2cid=234358015 |language=en |issn=1749-4893}}</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 |s2cid=140048632 |language=en |issn=0883-7694}}</ref> and [[Quantum machine learning|quantum machine learning]] is a field with some variety of applications under development. AI could be used for [[Quantum simulator|quantum simulator]]s which may have the application of solving physics and [[Chemistry: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=1 March 2021 |volume=2 |issue=1 |pages=011003 |doi=10.1088/2632-2153/abc17d |s2cid=218674486 |language=en}}</ref> problems as well as for quantum annealers 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 |s2cid=238640632 |language=en |issn=2662-4443}}</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 |pages=2109892 |doi=10.1002/adma.202109892 |pmid=35195312 |s2cid=247056685 |url=https://www.researchgate.net/publication/358815219 |language=en |issn=0935-9648|url-access=subscription}}</ref>).<ref>{{cite journal |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 |title=Artificial Intelligence and Quantum Computing Quantum computing (QC) as the Next Pharma Disruptors |journal=Artificial Intelligence in Drug Design |date=2022 |volume=2390 |pages=321–347 |doi=10.1007/978-1-0716-1787-8_14 |publisher=Springer US |pmid=34731476 |s2cid=242947877 |language=en}}</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 |language=en |issn=1549-9596}}</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=1 April 2021 |volume=12 |issue=12 |pages=4345–4352 |doi=10.1039/D0SC05718E |pmid=34163697 |pmc=8179438 |language=en |issn=2041-6539}}</ref>{{better source needed|date=May 2022|reason=Better citations needed for quantum machine learning in materials science}}

=== Historical contributions ===

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|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]]

== List of applications ==
{{Prose|date=December 2021|section}}{{Duplication|date=April 2022|section}}
{{columns-list|colwidth=10em|style=width: 800px;|
* [[Optical character recognition]]
* [[Software:Handwriting recognition|Handwriting recognition]]
* [[Speech recognition]]
* [[Facial recognition system|Face recognition]]
* Artificial creativity
* [[Computer vision]] 
*[[Virtual reality]]
*[[Image processing]]
* [[Photo manipulation|Photo]] and [[Video manipulation|video manipulation]]
* [[Diagnosis (artificial intelligence)]]
* [[Game theory]] and [[Social:Strategic planning|strategic planning]]
* Game artificial intelligence and computer game bot
* [[Natural language processing]], [[Philosophy:Translation|translation]] and chatterbots
* [[Nonlinear control]] and [[Engineering:Robot|robot]]ics
* Chatbots and assistant apps like Alexa, Google Assistant, Siri
* To transcribe music
* Law related services
* Healthcare
* Education and Learning Disabilities related issues
* User activity analyzation, personalized targeted promotion and marketing via ads
* Humanoids
* Games like DeepBlue
* [[Agent-based model]]s
** [[Finance:Agent-based computational economics|Agent-based computational economics]]
** [[Artificial life]]
* [[Automated reasoning]]
** [[Automated theorem proving]]
** [[Proof assistant]]s
* [[Engineering:Automation|Automation]]
* [[Bio-inspired computing]]
* [[Concept mining]]
* [[Data mining]]
* Knowledge representation
* [[Semantic Web]]
* [[Email spam]] filtering
* [[Engineering:Robot|Robot]]ics
** [[Engineering:Behavior-based robotics|Behavior-based robotics]]
** [[Cognitive robotics]]
** [[Cybernetics]]
** [[Developmental robotics]] (epigenetic)
** [[Evolutionary robotics]]
** Human-robot interaction
* [[Hybrid intelligent system]]
* [[Intelligent agent]]
* [[Intelligent control]]
* Litigation
}}

== See also ==
* [[Applications of artificial intelligence to legal informatics]]
* Applications of deep learning
* Applications of machine learning
* [[Social:Collective intelligence#Applications|Collective intelligence]]
* [[List of artificial intelligence projects]]
* [[Progress in artificial intelligence]]
* [[Open data]]
* Timeline of computing {{CURRENTDECADE}}–present

==Footnotes==
{{Reflist}}

== 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.|s2cid=158433736 }}
* {{Cite book
| first=Ray
| last=Kurzweil
| title=The Singularity is Near: When Humans Transcend Biology
| location=New York 
| publisher=Viking
| year=2005
| isbn=978-0-670-03384-3
| url=https://archive.org/details/singularityisnea00kurz
}}
* {{Cite book
| ref={{harvid|NRC|1999}}
| author=National Research Council
| chapter=Developments in Artificial Intelligence
| title=Funding a Revolution: Government Support for Computing Research
| publisher=National Academy Press
| year=1999
| isbn=978-0-309-06278-7
| oclc=246584055
| 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. |s2cid=7843287 }}
*{{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 }}

== External links ==
* [https://web.archive.org/web/20171213010136/https://www.buzzle.com/articles/applications-of-artificial-intelligence.html How AI can be applied in many fields]

{{emerging technologies|topics=yes|infocom=yes}}

[[Category:Artificial intelligence]]
[[Category:Applications of artificial intelligence| ]]

{{Sourceattribution|Applications of artificial intelligence|1}}