Editing Artificial intelligence (section)

== Applications ==
{{Main|Applications of artificial intelligence}}AI and machine learning technology is used in most of the essential applications of the 2020s, including: [[search engines]] (such as [[Google Search]]), [[Targeted advertising|targeting online advertisements]], [[recommendation systems]] (offered by [[Netflix]], [[YouTube]] or [[Amazon (company)|Amazon]]), driving [[internet traffic]], [[Marketing and artificial intelligence|targeted advertising]] ([[AdSense]], [[Facebook]]), [[virtual assistant]]s (such as [[Siri]] or [[Amazon Alexa|Alexa]]), [[autonomous vehicles]] (including [[Unmanned aerial vehicle|drones]], [[Advanced driver-assistance system|ADAS]] and [[self-driving cars]]), [[automatic language translation]] ([[Microsoft Translator]], [[Google Translate]]), [[Facial recognition system|facial recognition]] ([[Apple Computer|Apple]]'s [[Face ID|FaceID]] or [[Microsoft]]'s [[DeepFace]] and [[Google]]'s [[FaceNet]]) and [[image labeling]] (used by [[Facebook]], Apple's [[Photos (Apple)|Photos]] and [[TikTok]]). The deployment of AI may be overseen by a [[Chief automation officer]] (CAO).

===Health and medicine===
{{Main|Artificial intelligence in healthcare}}
The application of AI in [[medicine]] and [[medical research]] has the potential to increase patient care and quality of life.<ref>{{Cite journal |last1=Davenport |first1=T |last2=Kalakota |first2=R |date=June 2019 |title=The potential for artificial intelligence in healthcare |journal=Future Healthc J. |volume=6 |issue=2 |pages=94–98 |doi=10.7861/futurehosp.6-2-94 |pmc=6616181 |pmid=31363513}}</ref> Through the lens of the [[Hippocratic Oath]], medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.<ref>{{Cite journal |last1=Lyakhova |first1=U.A. |last2=Lyakhov |first2=P.A. |date=2024 |title=Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects |url=https://linkinghub.elsevier.com/retrieve/pii/S0010482524008278 |journal=Computers in Biology and Medicine |language=en |volume=178 |pages=108742 |doi=10.1016/j.compbiomed.2024.108742 |pmid=38875908 |archive-date=3 December 2024 |access-date=10 October 2024 |archive-url=https://web.archive.org/web/20241203172502/https://linkinghub.elsevier.com/retrieve/pii/S0010482524008278 |url-status=live }}</ref><ref>{{Cite journal |last1=Alqudaihi |first1=Kawther S. |last2=Aslam |first2=Nida |last3=Khan |first3=Irfan Ullah |last4=Almuhaideb |first4=Abdullah M. |last5=Alsunaidi |first5=Shikah J. |last6=Ibrahim |first6=Nehad M. Abdel Rahman |last7=Alhaidari |first7=Fahd A. |last8=Shaikh |first8=Fatema S. |last9=Alsenbel |first9=Yasmine M. |last10=Alalharith |first10=Dima M. |last11=Alharthi |first11=Hajar M. |last12=Alghamdi |first12=Wejdan M. |last13=Alshahrani |first13=Mohammed S. |date=2021 |title=Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities |journal=IEEE Access |volume=9 |pages=102327–102344 |doi=10.1109/ACCESS.2021.3097559 |issn=2169-3536 |pmc=8545201 |pmid=34786317|bibcode=2021IEEEA...9j2327A }}</ref>

For medical research, AI is an important tool for processing and integrating [[big data]]. This is particularly important for [[organoid]] and [[tissue engineering]] development which use [[microscopy]] imaging as a key technique in fabrication.<ref name="Bax-2023">{{Cite journal |last1=Bax |first1=Monique |last2=Thorpe |first2=Jordan |last3=Romanov |first3=Valentin |date=December 2023 |title=The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence |journal=Frontiers in Sensors |volume=4 |doi=10.3389/fsens.2023.1294721 |issn=2673-5067 |doi-access=free}}</ref> It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research.<ref name="Bax-2023"/><ref>{{Cite journal |last=Dankwa-Mullan |first=Irene |date=2024 |title=Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine |url=https://www.cdc.gov/pcd/issues/2024/24_0245.htm |journal=Preventing Chronic Disease |language=en-us |volume=21 |pages=E64 |doi=10.5888/pcd21.240245 |pmid=39173183 |issn=1545-1151|pmc=11364282 }}</ref> New AI tools can deepen the understanding of biomedically relevant pathways. For example, [[AlphaFold 2]] (2021) demonstrated the ability to approximate, in hours rather than months, the 3D [[Protein structure|structure of a protein]].<ref>{{Cite journal |last1=Jumper |first1=J |last2=Evans |first2=R |last3=Pritzel |first3=A |date=2021 |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |volume=596 |issue=7873 |pages=583–589 |bibcode=2021Natur.596..583J |doi=10.1038/s41586-021-03819-2 |pmc=8371605 |pmid=34265844}}</ref> In 2023, it was reported that AI-guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria.<ref>{{Cite web |date=2023-12-20 |title=AI discovers new class of antibiotics to kill drug-resistant bacteria |url=https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/ |access-date=5 October 2024 |archive-date=16 September 2024 |archive-url=https://web.archive.org/web/20240916014421/https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/ |url-status=live }}</ref> In 2024, researchers used machine learning to accelerate the search for [[Parkinson's disease]] drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of [[alpha-synuclein]] (the protein that characterises Parkinson's disease). They were able to speed up the initial screening process ten-fold and reduce the cost by a thousand-fold.<ref>{{Cite web |date=2024-04-17 |title=AI speeds up drug design for Parkinson's ten-fold |url=https://www.cam.ac.uk/research/news/ai-speeds-up-drug-design-for-parkinsons-ten-fold |publisher=Cambridge University |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165755/https://www.cam.ac.uk/research/news/ai-speeds-up-drug-design-for-parkinsons-ten-fold |url-status=live }}</ref><ref>{{Cite journal |last1=Horne |first1=Robert I. |last2=Andrzejewska |first2=Ewa A. |last3=Alam |first3=Parvez |last4=Brotzakis |first4=Z. Faidon |last5=Srivastava |first5=Ankit |last6=Aubert |first6=Alice |last7=Nowinska |first7=Magdalena |last8=Gregory |first8=Rebecca C. |last9=Staats |first9=Roxine |last10=Possenti |first10=Andrea |last11=Chia |first11=Sean |last12=Sormanni |first12=Pietro |last13=Ghetti |first13=Bernardino |last14=Caughey |first14=Byron |last15=Knowles |first15=Tuomas P. J. |last16=Vendruscolo |first16=Michele |date=2024-04-17 |title=Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning |journal=Nature Chemical Biology |publisher=Nature |volume=20 |issue=5 |pages=634–645 |doi=10.1038/s41589-024-01580-x |pmc=11062903 |pmid=38632492}}</ref>

=== Games ===
{{Main|Game artificial intelligence}}

[[Game AI|Game playing]] programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.<ref>{{Cite magazine |last1=Grant |first1=Eugene F. |last2=Lardner |first2=Rex |date=1952-07-25 |title=The Talk of the Town – It |url=https://www.newyorker.com/magazine/1952/08/02/it |access-date=2024-01-28 |magazine=The New Yorker |issn=0028-792X |archive-date=16 February 2020 |archive-url=https://web.archive.org/web/20200216034025/https://www.newyorker.com/magazine/1952/08/02/it |url-status=live }}</ref> [[IBM Deep Blue|Deep Blue]] became the first computer chess-playing system to beat a reigning world chess champion, [[Garry Kasparov]], on 11 May 1997.<ref>{{Cite web |last=Anderson |first=Mark Robert |date=2017-05-11 |title=Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution |url=http://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |access-date=2024-01-28 |website=The Conversation |archive-date=17 September 2024 |archive-url=https://web.archive.org/web/20240917000827/https://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |url-status=live }}</ref> In 2011, in a ''[[Jeopardy!]]'' [[quiz show]] exhibition match, [[IBM]]'s [[question answering system]], [[Watson (artificial intelligence software)|Watson]], defeated the two greatest ''Jeopardy!'' champions, [[Brad Rutter]] and [[Ken Jennings]], by a significant margin.<ref>{{Cite news |last=Markoff |first=John |date=2011-02-16 |title=Computer Wins on 'Jeopardy!': Trivial, It's Not |url=https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-access=subscription |access-date=2024-01-28 |work=The New York Times |issn=0362-4331 |archive-date=22 October 2014 |archive-url=https://web.archive.org/web/20141022023202/http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-status=live }}</ref> In March 2016, [[AlphaGo]] won 4 out of 5 games of [[Go (game)|Go]] in a match with Go champion [[Lee Sedol]], becoming the first [[computer Go]]-playing system to beat a professional Go player without [[Go handicaps|handicaps]]. Then, in 2017, it [[AlphaGo versus Ke Jie|defeated Ke Jie]], who was the best Go player in the world.<ref>{{Cite web |last=Byford |first=Sam |date=2017-05-27 |title=AlphaGo retires from competitive Go after defeating world number one 3–0 |url=https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |access-date=2024-01-28 |website=The Verge |archive-date=7 June 2017 |archive-url=https://web.archive.org/web/20170607184301/https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |url-status=live }}</ref> Other programs handle [[Imperfect information|imperfect-information]] games, such as the [[poker]]-playing program [[Pluribus (poker bot)|Pluribus]].<ref>{{Cite journal |last1=Brown |first1=Noam |last2=Sandholm |first2=Tuomas |date=2019-08-30 |title=Superhuman AI for multiplayer poker |url=https://www.science.org/doi/10.1126/science.aay2400 |journal=Science |volume=365 |issue=6456 |pages=885–890 |bibcode=2019Sci...365..885B |doi=10.1126/science.aay2400 |issn=0036-8075 |pmid=31296650}}</ref> [[DeepMind]] developed increasingly generalistic [[reinforcement learning]] models, such as with [[MuZero]], which could be trained to play chess, Go, or [[Atari]] games.<ref>{{Cite web |date=2020-12-23 |title=MuZero: Mastering Go, chess, shogi and Atari without rules |url=https://deepmind.google/discover/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules |access-date=2024-01-28 |website=Google DeepMind}}</ref> In 2019, DeepMind's AlphaStar achieved grandmaster level in [[StarCraft II]], a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map.<ref>{{Cite news |last=Sample |first=Ian |date=2019-10-30 |title=AI becomes grandmaster in 'fiendishly complex' StarCraft II |url=https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |access-date=2024-01-28 |work=The Guardian |issn=0261-3077 |archive-date=29 December 2020 |archive-url=https://web.archive.org/web/20201229185547/https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |url-status=live }}</ref> In 2021, an AI agent competed in a PlayStation [[Gran Turismo (series)|Gran Turismo]] competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.<ref>{{Cite journal |last1=Wurman |first1=P. R. |last2=Barrett |first2=S. |last3=Kawamoto |first3=K. |date=2022 |title=Outracing champion Gran Turismo drivers with deep reinforcement learning |journal=Nature |volume=602 |issue=7896 |pages=223–228 |bibcode=2022Natur.602..223W |doi=10.1038/s41586-021-04357-7 |pmid=35140384|url=https://www.researchsquare.com/article/rs-795954/latest.pdf }}</ref> In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen [[open-world]] video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions.<ref>{{Cite web |last=Wilkins |first=Alex |date=13 March 2024 |title=Google AI learns to play open-world video games by watching them |url=https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them |access-date=2024-07-21 |website=New Scientist |archive-date=26 July 2024 |archive-url=https://web.archive.org/web/20240726182946/https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them/ |url-status=live }}</ref>

=== Mathematics ===
Large language models, such as [[GPT-4]], [[Gemini (chatbot)|Gemini]], [[Claude (language model)|Claude]], [[Llama (language model)|LLaMa]] or [[Mistral AI|Mistral]], are increasingly used in mathematics. These probabilistic models are versatile, but can also produce wrong answers in the form of [[Hallucination (artificial intelligence)|hallucinations]]. They sometimes need a large database of mathematical problems to learn from, but also methods such as [[Supervised learning|supervised]] [[Fine-tuning (deep learning)|fine-tuning]]<ref>{{Cite journal |date=2024 |title=ReFT: Representation Finetuning for Language Models |journal=NeurIPS |arxiv=2404.03592 |last1=Wu |first1=Zhengxuan |last2=Arora |first2=Aryaman |last3=Wang |first3=Zheng |last4=Geiger |first4=Atticus |last5=Jurafsky |first5=Dan |last6=Manning |first6=Christopher D. |last7=Potts |first7=Christopher }}</ref> or trained [[Statistical classification|classifiers]] with human-annotated data to improve answers for new problems and learn from corrections.<ref>{{Cite web |date=2023-05-31 |title=Improving mathematical reasoning with process supervision |url=https://openai.com/index/improving-mathematical-reasoning-with-process-supervision/ |access-date=2025-01-26 |website=OpenAI |language=en-US}}</ref> A February 2024 study showed that the performance of some language models for reasoning capabilities in solving math problems not included in their training data was low, even for problems with only minor deviations from trained data.<ref>{{Cite arXiv |eprint=2402.19450 |class=cs.AI |first=Saurabh |last=Srivastava |title=Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap |date=2024-02-29}}</ref> One technique to improve their performance involves training the models to produce correct [[Automated reasoning|reasoning]] steps, rather than just the correct result.<ref>{{cite arXiv |eprint=2305.20050v1 |class=cs.LG |first1=Hunter |last1=Lightman |first2=Vineet |last2=Kosaraju |title=Let's Verify Step by Step |date=2023 |last3=Burda |first3=Yura |last4=Edwards |first4=Harri |last5=Baker |first5=Bowen |last6=Lee |first6=Teddy |last7=Leike |first7=Jan |last8=Schulman |first8=John |last9=Sutskever |first9=Ilya |last10=Cobbe |first10=Karl}}</ref> The [[Alibaba Group]] developed a version of its ''[[Qwen]]'' models called ''Qwen2-Math'', that achieved state-of-the-art performance on several mathematical benchmarks, including 84% accuracy on the MATH dataset of competition mathematics problems.<ref name="VentureBeat 8 August 2024">{{cite web |last1=Franzen |first1=Carl |title=Alibaba claims no. 1 spot in AI math models with Qwen2-Math |url=https://venturebeat.com/ai/alibaba-claims-no-1-spot-in-ai-math-models-with-qwen2-math/ |website=VentureBeat |date=2024-08-08|access-date=2025-02-16}}</ref> In January 2025, Microsoft proposed the technique ''rStar-Math'' that leverages [[Monte Carlo tree search]] and step-by-step reasoning, enabling a relatively small language model like ''Qwen-7B'' to solve 53% of the [[American Invitational Mathematics Examination|AIME]] 2024 and 90% of the MATH benchmark problems.<ref>{{Cite web |last=Franzen |first=Carl |date=2025-01-09 |title=Microsoft's new rStar-Math technique upgrades small models to outperform OpenAI's o1-preview at math problems |url=https://venturebeat.com/ai/microsofts-new-rstar-math-technique-upgrades-small-models-to-outperform-openais-o1-preview-at-math-problems/ |access-date=2025-01-26 |website=VentureBeat |language=en-US}}</ref>

Alternatively, dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as ''AlphaTensor'', ''[[AlphaGeometry]]'' and ''AlphaProof'' all from [[Google DeepMind]],<ref>{{Cite web |last=Roberts |first=Siobhan |date=July 25, 2024 |title=AI achieves silver-medal standard solving International Mathematical Olympiad problems |url=https://www.nytimes.com/2024/07/25/science/ai-math-alphaproof-deepmind.html |access-date=2024-08-07 |website=[[The New York Times]] |archive-date=26 September 2024 |archive-url=https://web.archive.org/web/20240926131402/https://www.nytimes.com/2024/07/25/science/ai-math-alphaproof-deepmind.html |url-status=live }}</ref> ''Llemma'' from [[EleutherAI]]<ref>{{Cite web |last1=Azerbayev |first1=Zhangir |last2=Schoelkopf |first2=Hailey |last3=Paster |first3=Keiran |last4=Santos |first4=Marco Dos |last5=McAleer' |first5=Stephen |last6=Jiang |first6=Albert Q. |last7=Deng |first7=Jia |last8=Biderman |first8=Stella |last9=Welleck |first9=Sean |date=2023-10-16 |title=Llemma: An Open Language Model For Mathematics |url=https://blog.eleuther.ai/llemma/ |access-date=2025-01-26 |website=EleutherAI Blog |language=en}}</ref> or ''Julius''.<ref>{{Cite web |title=Julius AI |url=https://julius.ai/home/ai-math |access-date= |website=julius.ai |language=en}}</ref>

When natural language is used to describe mathematical problems, converters can transform such prompts into a formal language such as [[Lean (proof assistant)|Lean]] to define mathematical tasks.

Some models have been developed to solve challenging problems and reach good results in benchmark tests, others to serve as educational tools in mathematics.<ref>{{Cite web |last=McFarland |first=Alex |date=2024-07-12 |title=8 Best AI for Math Tools (January 2025) |url=https://www.unite.ai/best-ai-for-math-tools/ |access-date=2025-01-26 |website=Unite.AI |language=en-US}}</ref>

[[Topological deep learning]] integrates various [[topology|topological]] approaches.

=== Finance ===
Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years.<ref>Matthew Finio & Amanda Downie: IBM Think 2024 Primer, "What is Artificial Intelligence (AI) in Finance?" 8 Dec. 2023</ref>

According to Nicolas Firzli, director of the [[World Pensions & Investments Forum]], it may be too early to see the emergence of highly innovative AI-informed financial products and services. He argues that "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I'm not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."<ref>M. Nicolas, J. Firzli: Pensions Age / European Pensions magazine, "Artificial Intelligence: Ask the Industry", May–June 2024. https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ {{Webarchive|url=https://web.archive.org/web/20240911125502/https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ |date=11 September 2024}}.</ref>

=== Military ===
{{main|Military applications of artificial intelligence}}

Various countries are deploying AI military applications.<ref name="CRS-2019">{{Cite book|last=Congressional Research Service|url=https://fas.org/sgp/crs/natsec/R45178.pdf|title=Artificial Intelligence and National Security|publisher=Congressional Research Service|year=2019|location=Washington, DC|archive-date=8 May 2020|access-date=25 February 2024|archive-url=https://web.archive.org/web/20200508062631/https://fas.org/sgp/crs/natsec/R45178.pdf|url-status=live}}[[Template:PD-notice|PD-notice]]</ref> The main applications enhance [[command and control]], communications, sensors, integration and interoperability.<ref name="Slyusar-2019">{{cite report |type=Preprint |last1=Slyusar |first1=Vadym |title=Artificial intelligence as the basis of future control networks |date=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 [[Vehicular automation|autonomous vehicles]].<ref name="CRS-2019" /> AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, [[target acquisition]], coordination and deconfliction of distributed [[Forward observers in the U.S. military|Joint Fires]] between networked combat vehicles, both human operated and [[Vehicular automation|autonomous]].<ref name="Slyusar-2019" />

AI has been used in military operations in Iraq, Syria, Israel and Ukraine.<ref name="CRS-2019" /><ref>{{Cite web |last=Iraqi |first=Amjad |date=2024-04-03 |title='Lavender': The AI machine directing Israel's bombing spree in Gaza |url=https://www.972mag.com/lavender-ai-israeli-army-gaza/ |access-date=2024-04-06 |website=+972 Magazine |language=en-US |archive-date=10 October 2024 |archive-url=https://web.archive.org/web/20241010022042/https://www.972mag.com/lavender-ai-israeli-army-gaza/ |url-status=live }}</ref><ref name="Davies-2023">{{Cite news |last1=Davies |first1=Harry |last2=McKernan |first2=Bethan |last3=Sabbagh |first3=Dan |date=2023-12-01 |title='The Gospel': how Israel uses AI to select bombing targets in Gaza |language=en-GB |work=The Guardian |url=https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets |access-date=2023-12-04 |archive-date=6 December 2023 |archive-url=https://web.archive.org/web/20231206213901/https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets |url-status=live }}</ref><ref>{{Cite news|last=Marti|first=J Werner|title=Drohnen haben den Krieg in der Ukraine revolutioniert, doch sie sind empfindlich auf Störsender – deshalb sollen sie jetzt autonom operieren|url=https://www.nzz.ch/international/die-ukraine-setzt-auf-drohnen-die-autonom-navigieren-und-toeten-koennen-ld.1838731|date=10 August 2024|access-date=10 August 2024|newspaper=Neue Zürcher Zeitung|language=German|archive-date=10 August 2024|archive-url=https://web.archive.org/web/20240810054043/https://www.nzz.ch/international/die-ukraine-setzt-auf-drohnen-die-autonom-navigieren-und-toeten-koennen-ld.1838731|url-status=live}}</ref>

=== Generative AI ===
[[File:Vincent van Gogh in watercolour.png|thumb|[[Vincent van Gogh]] in watercolour created by generative AI software]]{{Excerpt|Generative artificial intelligence|only=paragraphs|paragraphs=1-3}}

===Agents===
{{Main|Agentic AI}}
Artificial intelligent (AI) agents are software entities designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals. These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including [[virtual assistant]]s, [[chatbots]], [[autonomous vehicles]], [[Video game console|game-playing systems]], and [[industrial robotics]]. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations. This means they are restricted to performing tasks within their defined scope and have finite memory and processing capabilities. In real-world applications, AI agents often face time constraints for decision-making and action execution. Many AI agents incorporate learning algorithms, enabling them to improve their performance over time through experience or training. Using machine learning, AI agents can adapt to new situations and optimise their behaviour for their designated tasks.<ref>{{Cite book |last1=Poole |first1=David |url=https://doi.org/10.1017/9781009258227 |title=Artificial Intelligence, Foundations of Computational Agents |last2=Mackworth |first2=Alan |date=2023 |publisher=Cambridge University Press |isbn=978-1-0092-5819-7 |edition=3rd |doi=10.1017/9781009258227 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://www.cambridge.org/highereducation/books/artificial-intelligence/C113F6CE284AB00F5489EBA5A59B93B7#overview |url-status=live }}</ref><ref>{{Cite book |last1=Russell |first1=Stuart |title=[[Artificial Intelligence: A Modern Approach]] |last2=Norvig |first2=Peter |publisher=Pearson |date=2020 |isbn=978-0-1346-1099-3 |edition=4th}}</ref><ref>{{Cite web |date=2024-07-24 |title=Why agents are the next frontier of generative AI |url=https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai |access-date=2024-08-10 |website=McKinsey Digital |archive-date=3 October 2024 |archive-url=https://web.archive.org/web/20241003212335/https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai |url-status=live }}</ref>

=== Sexuality ===

Applications of AI in this domain include AI-enabled menstruation and fertility trackers that analyze user data to offer prediction,<ref>{{Cite journal |last1=Figueiredo |first1=Mayara Costa |last2=Ankrah |first2=Elizabeth |last3=Powell |first3=Jacquelyn E. |last4=Epstein |first4=Daniel A. |last5=Chen |first5=Yunan |date=2024-01-12 |title=Powered by AI: Examining How AI Descriptions Influence Perceptions of Fertility Tracking Applications |url=https://dl.acm.org/doi/10.1145/3631414 |journal=Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. |volume=7 |issue=4 |pages=154:1–154:24 |doi=10.1145/3631414}}</ref> AI-integrated sex toys (e.g., [[teledildonics]]),<ref>{{Cite journal |last1=Power |first1=Jennifer |last2=Pym |first2=Tinonee |last3=James |first3=Alexandra |last4=Waling |first4=Andrea |date=2024-07-05 |title=Smart Sex Toys: A Narrative Review of Recent Research on Cultural, Health and Safety Considerations |journal=Current Sexual Health Reports |language=en |volume=16 |issue=3 |pages=199–215 |doi=10.1007/s11930-024-00392-3 |issn=1548-3592 |doi-access=free}}</ref> AI-generated sexual education content,<ref>{{Cite journal |last1=Marcantonio |first1=Tiffany L. |last2=Avery |first2=Gracie |last3=Thrash |first3=Anna |last4=Leone |first4=Ruschelle M. |date=2024-09-10 |title=Large Language Models in an App: Conducting a Qualitative Synthetic Data Analysis of How Snapchat's "My AI" Responds to Questions About Sexual Consent, Sexual Refusals, Sexual Assault, and Sexting |url=https://www.tandfonline.com/doi/full/10.1080/00224499.2024.2396457 |url-status=live |journal=The Journal of Sex Research |language=en |pages=1–15 |doi=10.1080/00224499.2024.2396457 |pmid=39254628 |pmc=11891083 |pmc-embargo-date=March 10, 2026 |issn=0022-4499 |archive-url=https://web.archive.org/web/20241209185843/https://www.tandfonline.com/doi/full/10.1080/00224499.2024.2396457 |archive-date=9 December 2024 |access-date=9 December 2024}}</ref> and AI agents that simulate sexual and romantic partners (e.g., [[Replika]]).<ref>{{Cite journal |last1=Hanson |first1=Kenneth R. |last2=Bolthouse |first2=Hannah |date=2024 |title="Replika Removing Erotic Role-Play Is Like Grand Theft Auto Removing Guns or Cars": Reddit Discourse on Artificial Intelligence Chatbots and Sexual Technologies |journal=Socius: Sociological Research for a Dynamic World |language=en |volume=10 |doi=10.1177/23780231241259627 |issn=2378-0231 |doi-access=free}}</ref>  AI is also used for the production of non-consensual [[deepfake pornography]], raising significant ethical and legal concerns.<ref>{{Cite journal |last=Mania |first=Karolina |date=2024-01-01 |title=Legal Protection of Revenge and Deepfake Porn Victims in the European Union: Findings From a Comparative Legal Study |url=https://journals.sagepub.com/doi/abs/10.1177/15248380221143772?journalCode=tvaa |journal=Trauma, Violence, & Abuse |language=en |volume=25 |issue=1 |pages=117–129 |doi=10.1177/15248380221143772 |pmid=36565267 |issn=1524-8380}}</ref>

AI technologies have also been used to attempt to identify [[online gender-based violence]] and online [[sexual grooming]] of minors.<ref>{{Cite journal |last1=Singh |first1=Suyesha |last2=Nambiar |first2=Vaishnavi |date=2024 |title=Role of Artificial Intelligence in the Prevention of Online Child Sexual Abuse: A Systematic Review of Literature |url=https://www.tandfonline.com/doi/full/10.1080/19361610.2024.2331885 |url-status=live |journal=Journal of Applied Security Research |language=en |volume=19 |issue=4 |pages=586–627 |doi=10.1080/19361610.2024.2331885 |issn=1936-1610 |archive-url=https://web.archive.org/web/20241209171923/https://www.tandfonline.com/doi/full/10.1080/19361610.2024.2331885 |archive-date=9 December 2024 |access-date=9 December 2024}}</ref><ref>{{Cite journal |last1=Razi |first1=Afsaneh |last2=Kim |first2=Seunghyun |last3=Alsoubai |first3=Ashwaq |last4=Stringhini |first4=Gianluca |last5=Solorio |first5=Thamar |last6=De Choudhury |first6=Munmun|author6-link=Munmun De Choudhury |last7=Wisniewski |first7=Pamela J. |date=2021-10-13 |title=A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection |url=https://dl.acm.org/doi/10.1145/3479609 |url-status=live |journal=Proceedings of the ACM on Human-Computer Interaction |language=en |volume=5 |issue=CSCW2 |pages=1–38 |doi=10.1145/3479609 |issn=2573-0142 |archive-url=https://web.archive.org/web/20241209171735/https://dl.acm.org/doi/10.1145/3479609 |archive-date=9 December 2024 |access-date=9 December 2024}}</ref>

===Other industry-specific tasks===
There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported having incorporated "AI" in some offerings or processes.<ref>{{Cite journal |last1=Ransbotham |first1=Sam |last2=Kiron |first2=David |last3=Gerbert |first3=Philipp |last4=Reeves |first4=Martin |date=2017-09-06 |title=Reshaping Business With Artificial Intelligence |url=https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence |url-status=live |journal=MIT Sloan Management Review |archive-url=https://web.archive.org/web/20240213070751/https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence |archive-date=Feb 13, 2024}}</ref> A few examples are [[energy storage]], medical diagnosis, military logistics, applications that predict the result of judicial decisions, [[foreign policy]], or supply chain management.

AI applications for evacuation and [[disaster]] management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS, videos or social media. Further, AI can provide real time information on the real time evacuation conditions.<ref>{{Citation |last1=Sun |first1=Yuran |title=8 – AI for large-scale evacuation modeling: promises and challenges |date=2024-01-01 |work=Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure |pages=185–204 |editor-last=Naser |editor-first=M. Z. |url=https://www.sciencedirect.com/science/article/pii/B9780128240731000149 |access-date=2024-06-28 |series=Woodhead Publishing Series in Civil and Structural Engineering |publisher=Woodhead Publishing |isbn=978-0-1282-4073-1 |last2=Zhao |first2=Xilei |last3=Lovreglio |first3=Ruggiero |last4=Kuligowski |first4=Erica |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121547/https://www.sciencedirect.com/science/article/abs/pii/B9780128240731000149 |url-status=live }}.</ref><ref>{{Cite journal |last1=Gomaa |first1=Islam |last2=Adelzadeh |first2=Masoud |last3=Gwynne |first3=Steven |last4=Spencer |first4=Bruce |last5=Ko |first5=Yoon |last6=Bénichou |first6=Noureddine |last7=Ma |first7=Chunyun |last8=Elsagan |first8=Nour |last9=Duong |first9=Dana |last10=Zalok |first10=Ehab |last11=Kinateder |first11=Max |date=2021-11-01 |title=A Framework for Intelligent Fire Detection and Evacuation System |url=https://doi.org/10.1007/s10694-021-01157-3 |journal=Fire Technology |volume=57 |issue=6 |pages=3179–3185 |doi=10.1007/s10694-021-01157-3 |issn=1572-8099 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://link.springer.com/article/10.1007/s10694-021-01157-3 |url-status=live }}</ref><ref>{{Cite journal |last1=Zhao |first1=Xilei |last2=Lovreglio |first2=Ruggiero |last3=Nilsson |first3=Daniel |date=2020-05-01 |title=Modelling and interpreting pre-evacuation decision-making using machine learning |url=https://www.sciencedirect.com/science/article/pii/S0926580519313184 |journal=Automation in Construction |volume=113 |pages=103140 |doi=10.1016/j.autcon.2020.103140 |issn=0926-5805 |access-date=5 October 2024 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121548/https://www.sciencedirect.com/science/article/abs/pii/S0926580519313184 |url-status=live |hdl=10179/17315 |hdl-access=free }}</ref>

In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct [[predictive analytics]], classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.

Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights." For example, it is used for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. Additionally, it could be used for activities in space, such as space exploration, including the analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.

During the [[2024 Indian general election|2024 Indian elections]], US$50 million was spent on authorized AI-generated content, notably by creating [[deepfake]]s of allied (including sometimes deceased) politicians to better engage with voters, and by translating speeches to various local languages.<ref>{{Cite web |date=2024-06-12 |title=India's latest election embraced AI technology. Here are some ways it was used constructively |url=https://www.pbs.org/newshour/world/indias-latest-election-embraced-ai-technology-here-are-some-ways-it-was-used-constructively |access-date=2024-10-28 |website=PBS News |language=en-us |archive-date=17 September 2024 |archive-url=https://web.archive.org/web/20240917194950/https://www.pbs.org/newshour/world/indias-latest-election-embraced-ai-technology-here-are-some-ways-it-was-used-constructively |url-status=live }}</ref>