Artificial intelligence is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making.Artificial intelligence (AI) has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield ofMachine learning has been used for various scientific and commercial purposes[1] includinglanguage translation,image recognition,decision-making,[2][3]credit scoring, ande-commerce. In recent years, there have been massive advancements in the field ofGenerative Artificial Intelligence, which uses generative models to produce text, images, videos or other forms of data[4]. This article describes applications of AI in different sectors.
In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency.[5] AI has been used to attempt toclassify livestock pig call emotions,[6] automategreenhouses,[7] detect diseases and pests,[8] and optimize irrigation.[9]
A 2023 study found that generative AI increased productivity by 15% in contact centers.[13] Another 2023 study found it increased productivity by up to 40% in writing tasks.[14] An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI.[15] A September 2025 article by theHarvard Business Review describes how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task" the article coins the termworkslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues.[16]
AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors andIDEs asplugins.AI-assisted software development systems differ in functionality, quality, speed, and approach to privacy. Creating software primarily via AI is known as "vibe coding". Code created or suggested by AI can be incorrect or inefficient, and should be carefully reviewed by software developers before being accepted.[citation needed] The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing.[17][18] The rush to prematurely adopt AI technology can also incur additionaltechnical debt.[17] AI also requires additional consideration and careful review forcybersecurity, since AI coding software is trained on a wide range of code of inconsistent quality and often replicates poor practices.[19][20]
An overview of AI agent and its core capabilities (memory, tools usage, actions, and ability to plan)
AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies createdNASNet, a system optimized forImageNet and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet.[21]
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:[26]
Another application of AI is in human resources. AI can screen resumes and rank candidates based on their qualifications, predict candidate success in given roles, and automate repetitive communication tasks via chatbots.[citation needed]
AI underliesavatars (automated online assistants) on web pages.[27] It can reduce operation and training costs.[27]Pypestream automated customer service for its mobile application to streamline communication with customers.[28]
A Google app analyzes language and converts speech into text.[29] The platform can identify angry customers through their language and respond appropriately.[30] 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.[31] Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making.[32]
In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs.[33] AI hotel services come in the form of a chatbot,[34] application, virtual voice assistant and service robots.
In educational institutions, AI has been used to automate routine tasks like attendance tracking, grading and marking. AI tools have been used to attempt to monitor student progress and analyze learning behaviors, with the intention of facilitating interventions for students facing academic problems.[35]
TheUniversity of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness.Stanford researchers useAI to analyze satellite images to identify high poverty areas.[62]
AI applications analyze media content such as movies, TV programs, advertisement videos oruser-generated content. The solutions often involvecomputer vision.
Typical scenarios include the analysis of images usingobject recognition or face recognition techniques, or theanalysis 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 to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement.
Deep-fakes can be used for comedic purposes but are better known forfake news and hoaxes.
Deepfakes can portray individuals in harmful or compromising situations, causing significant reputational damage and emotional distress, especially when the content is defamatory or violates personal ethics. While defamation and false light laws offer some recourse, their focus on false statements rather than fabricated images or videos often leaves victims with limited legal protection and a challenging burden of proof.[75]
In January 2016,[76] theHorizon 2020 program financed the InVID Project[77][78] to help journalists and researchers detect fake documents, made available as browser plugins.[79][80]
In June 2016, the visual computing group of theTechnical University of Munich and fromStanford University developed Face2Face,[81] a program that animates photographs of faces, mimicking the facial expressions of another person. The technology has been demonstrated animating the faces of people includingBarack Obama andVladimir Putin. Other methods have been demonstrated based ondeep neural networks, from which the namedeep fake was taken.
In September 2018, U.S. SenatorMark Warner proposed to penalizesocial media companies that allow sharing of deep-fake documents on their platforms.[82]
In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames.[83]DARPA gave 68 million dollars to work on deep-fake detection.[83]
Audio deepfakes[84][85] and AI software capable of detecting deep-fakes and cloning human voices have been developed.[86][87]
Respeecher is a program that enables one person to speak with the voice of another.
Video surveillance analysis and manipulated media detection
Artificial intelligence for video surveillance utilizescomputer software programs that analyze the audio and images fromvideo surveillance cameras in order to recognize humans, vehicles, objects and events. Security contractors program is the software to define restricted areas within the camera's view (such as a fenced off area, a parking lot but not the sidewalk or public street outside the lot) and program for times of day (such as after the close of business) for the property being protected by the camerasurveillance. Theartificial intelligence ("A.I.") sends an alert if it detects a trespasser breaking the "rule" set that no person is allowed in that area during that time of day.
AI algorithms have been used to detect deepfake videos.[88][89]
Artificial intelligence is also starting to be used in video production, with tools and software being developed that utilize generative AI in order to create new video, or alter existing video. Some of the major tools that are being used in these processes currently are DALL-E, Mid-journey, and Runway.[90] Way mark Studios utilized the tools offered by bothDALL-E andMid-journey to create a fully AI generated film calledThe Frost in the summer of 2023.[90] Way mark Studios is experimenting with using these AI tools to generate advertisements and commercials for companies in mere seconds.[90] Yves Bergquist, a director of the AI &Neuroscience in Media Project at USC's Entertainment Technology Center, says post production crews in Hollywood are already using generative AI, and predicts that in the future more companies will embrace this new technology.[91]
AI has been used to compose music of various genres.
David Cope created an AI calledEmily Howell that managed to become well known in the field of algorithmic computer music.[92] The algorithm behind Emily Howell is registered as a US patent.[93]
In 2012, AIIamus created the first complete classical album.[94]
Melomics creates computer-generated music for stress and pain relief.[97]
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.
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.[103]
TALESPIN made up stories similar to thefables 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] 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".[104]
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 asLittle Red Riding Hood.[105] In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.[106]
South Korean company Hanteo Global uses a journalism bot to write articles.[107]
Literary authors are also exploring uses of AI. An example isDavid Jhave Johnston's workReRites (2017–2019), where the poet created a daily rite of editing the poetic output of a neural network to create a series of performances and publications.
UOL in Brazil expanded the use of AI in its writing. Rather than just generating news stories, they programmed the AI to include commonly searched words onGoogle.[109]
El Pais, a Spanish news site that covers many things including sports, allows users to make comments on each news article. They use thePerspective API to moderate these comments and if the software deems a comment to contain toxic language, the commenter must modify it in order to publish it.[109]
A local Dutch media group used AI to create automatic coverage of amateur soccer, set to cover 60,000 games in just a single season. NDC partnered with United Robots to create this algorithm and cover what would have never been possible before without an extremely large team.[109]
Lede AI has been used in 2023 to take scores fromhigh school football games to generate stories automatically for the local newspaper. This was met with significant criticism from readers for the very robotic diction that was published. With some descriptions of games being a "close encounter of the athletic kind," readers were not pleased and let the publishing company,Gannett, know on social media. Gannett has since halted their used of Lede AI until they come up with a solution for what they call an experiment.[110]
Various articles onWikipedia have been created entirely with or with the help ofartificial intelligence. AI-generated content can be detrimental to Wikipedia when unreliable or containing fake citations.
To address the issue of low-quality AI-generated content, theWikipedia community created in 2023 aWikiProject namedAI Cleanup. In August 2025, Wikipedia adopted a policy that allowed editors to nominate suspected AI-generated articles forspeedy deletion.
Millions of its articles have been edited by bots[112] which however are usually not artificial intelligence software. Many AI platforms use Wikipedia data,[113] mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences,[114]detecting covert vandalism[115] or recommending articles and tasks to new editors.
Machine translation(seeabove) has also be used for translating Wikipedia articles and could play a larger role in creating, updating, expanding, and generally improving articles in the future. A content translation tool allows editors of some Wikipedias to more easily translate articles across several select languages.[116][117]
In video games, AI is routinely used to generate behavior innon-player characters (NPCs). In addition, AI is used forpathfinding. Games with less typical AI include the AI director ofLeft 4 Dead (2008) and the neuroevolutionary training of platoons inSupreme Commander 2 (2010).[118][119] AI is also used inAlien Isolation (2014) as a way to control the actions the Alien will perform next.[120]
Kuki AI is a set ofchatbots and other apps which were designed for entertainment and as a marketing tool.[140][141]Character.ai is another example of a chatbot being used for recreation.[citation needed]
The first AI art program, calledAARON, was developed byHarold Cohen in 1968[142] with the goal of being able to code the act of drawing. It started by creating simple black and white drawings, and later to painting using special brushes and dyes that were chosen by the program itself without mediation from Cohen.[143]
AI platforms such asDALL-E,[144]Stable Diffusion,[144]Imagen,[145] andMidjourney[146] have been used for generating visual images from inputs such as text or other images.[147] Some AI tools allow users to input images and output changed versions of that image, such as to display an object or product in different environments. AI image models can also attempt to replicate the specific styles of artists, and can add visual complexity to rough sketches.
AI has been used to generate quantitative analysis of existing digital art collections.[148]Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art.[149] While distant viewing includes the analysis of large collections, close reading involves one piece of artwork.
Pixar began experimenting with a machine learning project called "Genesis" in the early 2000s. It was designed to learn algorithms and create 3D models for its characters and props.[citation needed]
In 2023, Netflix of Japan's usage of AI to generate background images for shortThe Dog & the Boy was met with backlash online.[150]
The use of AI in applications such as online trading and decision-making has changed major economic theories.[156] For example, AI-based buying and selling platforms estimate personalized demand and supply curves, thus enabling individualizedpricing. AI systems reduceinformation asymmetry in the market and thusmake markets more efficient.[157] The application of artificial intelligence in the financial industry can alleviate the financing constraints of non-state-owned enterprises, especially for smaller and more innovative enterprises.[158]
Algorithmic trading involves using AI systems to make trading decisions at speeds of magnitude greater than any human is capable of, making millions of trades in a day without human intervention. Suchhigh-frequency trading represents a fast-growing sector. Many banks, funds, and proprietary trading firms now have AI-managed portfolios.Automated trading systems are typically used by large institutional investors but include smaller firms trading with their own AI systems.[159]
Large financial institutions use AI to assist with their investment practices.[160]BlackRock's AI engine,Aladdin, is used both within the company and by clients to help with investment decisions. Its functions include the use ofnatural 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 asUBS andDeutsche Bank use SQREEM (Sequential Quantum Reduction and Extraction Model) to mine data to develop consumer profiles and match them withwealth management products.[161]
ZestFinance's Zest Automated Machine Learning (ZAML) platform is used for credit underwriting.[163] 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 handy for assigning credit scores to those with limited credit histories.[164]
AI makes continuous auditing possible. Potential benefits include reducing audit risk, increasing the level of assurance, and reducing audit duration.[165][quantify]
Continuous auditing with AI allows real-time monitoring and reporting of financial activities and provides businesses with timely insights that can lead to quick decision-making.[166]
AI software, such as LaundroGraph which uses contemporary suboptimal datasets, could be used foranti–money laundering (AML).[167][168]Anti–money laundering
In recent years, thedebt collection industry has begun to adopt AI-driven "agents" to automate routine outreach and negotiation tasks. Platforms use natural-language processing and machine learning to interact with consumers.
Proponents claim these systems can handle high volumes of standard enquiries, freeing human collectors to focus on more complex cases, while delivering more consistent, 24/7 service. However, critics warn of potential compliance pitfalls, such as the risk of unintended bias in algorithmic decision-making.[169]
In the 1980s, AI started to become prominent in finance asexpert systems were commercialized. For example, Dupont created 100 expert systems, which helped them to save almost $10 million per year.[170] One of the first systems was the Pro-trader expert system that predicted the 87-point drop in theDow 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."[171]
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.[172]
In the 1990s, AI was applied tofraud detection. In 1993, FinCEN Artificial Intelligence System (FAIS) was launched. It was able to review over 200,000 transactions per week, and over two years, it helped identify 400 potential cases ofmoney laundering equal to $1 billion.[173] These expert systems were later replaced by machine learning systems.[174]
Outside finance, the late 1980s and early 1990s also saw expert systems used in technical and environmental domains. For example, researchers built a fishway design advisor to recommend fish passage structures under varying hydraulic and biological conditions using the VP-Expert shell.[175] Transportation researchers applied the same shell to balance airport capacity with noise-mitigation plans.[176] In agriculture, a potato insect expert system (PIES) supported pest management decisions for Colorado potato beetle.[177] The U.S. Environmental Protection Agency’s CORMIX system for modeling pollutant discharges combined rules with Fortran hydrodynamic models.[178]
AI can enhance entrepreneurial activity, and AI is one of the most dynamic areas for start-ups, with significant venture capital flowing into AI.[179]
In theEuropean Union, theArtificial Intelligence Act (Regulation (EU) 2024/1689) classifies several finance‑sector uses of AI as "high‑risk", including systems used to evaluate the creditworthiness of natural persons or to establish a credit score and AI used for risk assessment and pricing in life or health insurance.[180][181][182] These systems must meet requirements on risk management, data governance, technical documentation and logging, transparency, and human oversight.[181][183]The Act's obligations are phased in: prohibitions and AI‑literacy rules apply from 2 February 2025, governance and most GPAI duties from 2 August 2025, the bulk of obligations from 2 August 2026, and certain safety‑component high‑risk obligations from 2 August 2027.[182]
AI in healthcare is often used for classification, to evaluate aCT scan orelectrocardiogram or to identify high-risk patients for population health. AI is helping with the high-cost problem of dosing. One study suggested that AI could save $16 billion. In 2016, a study reported that an AI-derived formula derived the proper dose of immunosuppressant drugs to give to transplant patients.[184] Current research has indicated that non-cardiac vascular illnesses are also being treated with artificial intelligence (AI). For certain disorders, AI algorithms can aid in diagnosis, recommended treatments, outcome prediction, and patient progress tracking. As AI technology advances, it is anticipated that it will become more significant in the healthcare industry.[185]
The early detection of diseases like cancer is made possible by AI algorithms, which diagnose diseases by analyzing complex sets of medical data. For example, the IBM Watson system might be used to comb through massive data such as medical records and clinical trials to help diagnose a problem.[186] Microsoft's AI project Hanover helps doctors choosecancer treatments from among the more than 800 medicines and vaccines.[187][188] Its goal is to memorize all the relevant papers to predict which (combinations of) drugs will be most effective for each patient.Myeloid leukemia is one target. Another study reported on an AI that was as good as doctors in identifying skin cancers.[189] Another project monitors multiple high-risk patients by asking each patient questions based on data acquired from doctor/patient interactions.[190] In one study done withtransfer learning, an AI diagnosed eye conditions similar to anophthalmologist and recommended treatment referrals.[191]
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.[192]
AI-enabledchatbots decrease the need for humans to perform basic call center tasks, and machine learning insentiment analysis can spot fatigue in order to preventoverwork.[206]
Computer-planned syntheses via computational reaction networks, described as a platform that combines "computational synthesis with AI algorithms to predict molecular properties",[213] has been used in drug-syntheses, and developing routes forrecycling 200 industrialwaste chemicals into important drugs and agrochemicals (chemical synthesis design).[214] It has also been used to explore theorigins of life on Earth.[215]
Deep learning 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,DDR1. DDR1 is involved in cancers and fibrosis which is one reason for the high-quality datasets that enabled these results.[216]
The AI programAlphaFold 2 can determine the 3D structure of a (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).[217][218][219][220]
Speech translation technology attempts to convert one language's spoken words into another language. This potentially reduces language barriers in global commerce and cross-cultural exchange, enabling speakers of various languages to communicate with one another.[221]
AI has been used to automatically translate spoken language and textual content in products such asMicrosoft Translator,Google Translate, andDeepL Translator.[222] Additionally, research and development are in progress to decode and conduct animal communication.[6][223]
Meaning is conveyed not only by text, but also through usage and context (seesemantics andpragmatics). As a result, the two primary categorization approaches for machine translations arestatistical machine translation (SMT) andneural machine translations (NMTs). The old method of performing translation was to use statistical methodology to forecast the best probable output with specific algorithms. However, with NMT, the approach employs dynamic algorithms to achieve better translations based on context.[224]
AI is a mainstay of law-related professions. Algorithms and machine learning do some tasks previously done by entry-level lawyers.[228] While its use is common, it is not expected to replace most work done by lawyers in the near future.[229]
Law enforcement has begun usingfacial recognition systems (FRS) to identify suspects from visual data. FRS results have proven to be more accurate when compared to eyewitness results. Furthermore, FRS has shown to have much a better ability to identify individuals when video clarity and visibility are low in comparison to human participants.[231]
One concern relates toalgorithmic bias, AI programs may become biased after processing data that exhibits bias.[233]ProPublica claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than that of white defendants.[232]
In 2019, the city ofHangzhou, China established a pilot program artificial intelligence-based Internet Court to adjudicate disputes related to ecommerce and internet-relatedintellectual property claims.[234]: 124 Parties appear before the court via videoconference and AI evaluates the evidence presented and applies relevant legal standards.[234]: 124
Artificial intelligence has been combined with digitalspectrometry by IdeaCuria Inc.,[235][236] enable applications such as at-home water quality monitoring.
Oil and gas companies have used artificial intelligence tools to automate functions, foresee equipment issues, and increase oil and gas output.[238][239]
Various countries are deploying AI military applications.[240] The main applications enhancecommand and control, communications, sensors, integration and interoperability.[citation needed] Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous andautonomous vehicles.[240] AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions,target acquisition, coordination and deconfliction of distributedJoint Fires between networked combat vehicles, both piloted and autonomous.[citation needed]
AI has been used in military operations in Iraq, Syria, Israel and Ukraine.[240][241][242][243]
Intelligent personal assistants use AI to attempt to respond to natural language requests.Siri, released in 2010 for Apple smartphones, popularized the concept.[253]
Machine learning can be used to combat spam, scams, andphishing. It can scrutinize the contents of spam and phishing attacks to attempt to identify malicious elements.[255] Some models built via machine learning algorithms have over 90% accuracy in distinguishing between spam and legitimate emails.[256] These models can be refined using new data and evolving spam tactics. Machine learning also analyzes traits such as sender behavior, email header information, and attachment types, potentially enhancing spam detection.[257]
China has used facial recognition andartificial intelligence technology inXinjiang. In 2017, reporters visiting the region found surveillance cameras installed every hundred meters or so in several cities, as well as facial recognition checkpoints at areas like gas stations, shopping centers, and mosque entrances.[259][260] Human rights groups have criticized the Chinese government for using artificial intelligence facial recognition technology for use in political suppression.[261][262]
TheNetherlands has deployed facial recognition and artificial intelligence technology since 2016.[263] The database of the Dutch police currently contains over 2.2 million pictures of 1.3 million Dutch citizens. This accounts for about 8% of the population. In The Netherlands, face recognition is not used by the police on municipal CCTV.[264]
Image labeling has been used byGoogle Image Labeler to detect products in photos and to allow people to search based on a photo. Image labeling has also been demonstrated to generate speech to describe images to blind people.[222] Facebook'sDeepFace identifies human faces in digital images.[citation needed]
In April 2024, theScientific Advice Mechanism to theEuropean Commission published advice[265] including a comprehensive evidence review of the opportunities and challenges posed by artificial intelligence in scientific research.
As benefits, the evidence review[266] highlighted:
its role in accelerating research and innovation
its capacity to automate workflows
enhancing dissemination of scientific work
As challenges:
limitations and risks around transparency, reproducibility and interpretability
poor performance (inaccuracy)
risk of harm through misuse or unintended use
societal concerns including the spread of misinformation and increasing inequalities
Machine learning can help to restore and attribute ancient texts.[267] It can help to index texts for example to enable better and easier searching and classification of fragments.[268]
Adeep learning system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on anunpublished approach inspired by studies of visual cognition in infants.[271][272] 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.[273][274] In the future, it may be possible that such can be used to automate the discovery of physical laws of complex systems.[273]
In November 2023, researchers atGoogle DeepMind andLawrence Berkeley National Laboratory announced that the AI system GNoME had documented over 2 million new materials. GNoME uses deep learning techniques to examine potential material structures, and identify stable inorganiccrystal structures. The system's predictions were validated through autonomous robotic experiments, with a success rate of 71%. The data of newly discovered materials is publicly available through theMaterials Project database.[275][276][277]
Machine learning is used in diverse types ofreverse engineering. For example, machine learning has been used to reverse engineer a composite material part, enabling unauthorized production of high quality parts,[278] and for quickly understanding the behavior ofmalware.[279][280][281] It can be used to reverse engineer artificial intelligence models.[282] 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[283] orprotein design for pre-specifiedfunctional sites.[284][285] Biological network reverse engineering could model interactions in a human understandable way, e.g. bas on time series data of gene expression levels.[286]
Artificial intelligence is used inastronomy to analyze increasing amounts of available data[287][288] and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discoveringexoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects ingravitational wave astronomy.[289] It could also be used for activities in space such asspace exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance,[290] and more autonomous operation.[291][292][46][288]
Machine learning can also be used to produce datasets of spectral signatures of molecules that may be involved in the atmospheric production or consumption of particular chemicals – such asphosphine 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.[305]
There is research about which types of computer-aided chemistry would benefit from machine learning.[306] A deep learning AI-based process has been developed that usesgenome databases todesign novel proteins based onevolutionary algorithms.[307][308]Machine learning has also been used for protein design with pre-specifiedfunctional sites,[284][285] predicting molecular properties, and exploring large chemical/reaction spaces.[309]
Network protection: Machine learning improvesintrusion detection systems by broadening the search beyond previously identified threats.[324]
Endpoint protection: Attacks such asransomware can be thwarted by learning typical malware behaviors.
AI-related cyber security application cases vary in both benefit and complexity. Security features such as Security Orchestration, Automation, and Response (SOAR) and Extended Endpoint Detection and Response (XDR) offer significant benefits for businesses, but require significant integration and adaptation efforts.[325]
AI technology can also be utilized to improve system security and safeguard our privacy. Randrianasolo (2012) suggested a security system based on artificial intelligence that can recognize intrusions and adapt to perform better.[326] In order to improve cloud computing security, Sahil (2015) created a user profile system for the cloud environment with AI techniques.[327]
Suspect user behavior: Machine learning can identify fraud or compromised applications as they occur.[328]
Transportation's complexity means that in most cases, training an AI in a real-world driving environment is impractical, and is achieved through simulator-based testing.[329] AI-based systems control functions such as braking, lane changing, collision prevention, navigation and mapping.[330] AI-basedfuzzy logic controllers operategearboxes. AI-baseddriver-assist systems include features such asself-parking andadaptive cruise control.[citation needed]
Some autonomous vehicles do not allow human drivers (they have no steering wheels or pedals).[331][332]
Autonomous trucks are in the testing phase. The UK government passed legislation to begin testing of autonomous truck platoons in 2018.[343] A group of autonomous trucks follow closely behind each other. German corporationDaimler is testing itsFreightliner Inspiration.[344]
AI has been used to optimize traffic management, which can reduce wait times, energy use, and emissions.[345]
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 inswarms.[346]
AOD uses the Interactive Fault Diagnosis and Isolation System, or IFDIS, which is a rule-based expert system using information fromTF-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 theF-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,[347] 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.
In 2003 aDryden Flight Research Center project created software that could enable a damaged aircraft to continue flight until a safe landing can be achieved.[348] The software compensated for damaged components by relying on the remaining undamaged components.[349]
The 2016 Intelligent Autopilot System combinedapprenticeship 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.[350]
^Gambhire, Akshaya; Shaikh Mohammad, Bilal N. (8 April 2020).Use of Artificial Intelligence in Agriculture. Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST) 2020.SSRN3571733.
^Liundi, Nicholas; Darma, Aditya Wirya; Gunarso, Rivaldi; Warnars, Harco Leslie Hendric Spits (2019). "Improving Rice Productivity in Indonesia with Artificial Intelligence".2019 7th International Conference on Cyber and IT Service Management (CITSM). pp. 1–5.doi:10.1109/CITSM47753.2019.8965385.ISBN978-1-7281-2909-9.
^abKongthon, Alisa; Sangkeettrakarn, Chatchawal; Kongyoung, Sarawoot; Haruechaiyasak, Choochart (2009). "Implementing an online help desk system based on conversational agent".Proceedings of the International Conference on Management of Emergent Digital EcoSystems. pp. 450–451.doi:10.1145/1643823.1643908.ISBN978-1-60558-829-2.
^Zlatanov, Sonja; Popesku, Jovan (2019). "Current Applications of Artificial Intelligence in Tourism and Hospitality".Proceedings of the International Scientific Conference - Sinteza 2019. pp. 84–90.doi:10.15308/Sinteza-2019-84-90.ISBN978-86-7912-703-7.
^Kanwal, Sidra; Khan, Bilal; Muhammad Ali, Sahibzada (February 2021). "Machine learning based weighted scheduling scheme for active power control of hybrid microgrid".International Journal of Electrical Power & Energy Systems.125 106461.Bibcode:2021IJEPE.12506461K.doi:10.1016/j.ijepes.2020.106461.
^Mohanty, Prasanta Kumar; Jena, Premalata; Padhy, Narayana Prasad (2020). "Home Electric Vehicle Charge Scheduling Using Machine Learning Technique".2020 IEEE International Conference on Power Systems Technology (POWERCON). pp. 1–5.doi:10.1109/POWERCON48463.2020.9230627.ISBN978-1-7281-6350-5.
^abCiaramella, Alberto; Ciaramella, Marco (2024).Introduction to Artificial Intelligence: from data analysis to generative AI. Intellisemantic Editions. p. 211.ISBN978-88-947876-0-3.
^Thirugnanam, Hemalatha; Ramesh, Maneesha Vinodini; Rangan, Venkat P. (September 2020). "Enhancing the reliability of landslide early warning systems by machine learning".Landslides.17 (9):2231–2246.Bibcode:2020Lands..17.2231T.doi:10.1007/s10346-020-01453-z.
^Moon, Seung-Hyun; Kim, Yong-Hyuk; Lee, Yong Hee; Moon, Byung-Ro (2019). "Application of machine learning to an early warning system for very short-term heavy rainfall".Journal of Hydrology.568:1042–1054.Bibcode:2019JHyd..568.1042M.doi:10.1016/j.jhydrol.2018.11.060.
^Park, Yongeun; Lee, Han Kyu; Shin, Jae-Ki; Chon, Kangmin; Kim, SungHwan; Cho, Kyung Hwa; Kim, Jin Hwi; Baek, Sang-Soo (15 June 2021). "A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir".Journal of Environmental Management.288 112415.Bibcode:2021JEnvM.28812415P.doi:10.1016/j.jenvman.2021.112415.PMID33774562.
^Khan, Najeebullah; Sachindra, D. A.; Shahid, Shamsuddin; Ahmed, Kamal; Shiru, Mohammed Sanusi; Nawaz, Nadeem (May 2020). "Prediction of droughts over Pakistan using machine learning algorithms".Advances in Water Resources.139 103562.Bibcode:2020AdWR..13903562K.doi:10.1016/j.advwatres.2020.103562.
^"Consortium of the InVID project".InVID project. Retrieved23 December 2021.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.
^abAfchar, Darius; Nozick, Vincent; Yamagishi, Junichi; Echizen, Isao (2018). "MesoNet: A Compact Facial Video Forgery Detection Network".2018 IEEE International Workshop on Information Forensics and Security (WIFS). pp. 1–7.arXiv:1809.00888.doi:10.1109/WIFS.2018.8630761.ISBN978-1-5386-6536-7.
^Requena, Gloria; Sánchez, Carlos; Corzo-Higueras, José Luis; Reyes-Alvarado, Sirenia; Rivas-Ruiz, Francisco; Vico, Francisco; Raglio, Alfredo (2014). "Melomics music medicine (M3) to lessen pain perception during pediatric prick test procedure".Pediatric Allergy and Immunology.25 (7):721–724.doi:10.1111/pai.12263.PMID25115240.
^abcdGalily, Yair (August 2018). "Artificial intelligence and sports journalism: Is it a sweeping change?".Technology in Society.54:47–51.doi:10.1016/j.techsoc.2018.03.001.
^Solly, Meilan."This Poker-Playing A.I. Knows When to Hold 'Em and When to Fold 'Em".Smithsonian.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.
^Schrittwieser, Julian; Antonoglou, Ioannis; Hubert, Thomas; Simonyan, Karen; Sifre, Laurent; Schmitt, Simon; Guez, Arthur; Lockhart, Edward; Hassabis, Demis; Graepel, Thore; Lillicrap, Timothy; Silver, David (24 December 2020). "Mastering Atari, Go, chess and shogi by planning with a learned model".Nature.588 (7839):604–609.arXiv:1911.08265.Bibcode:2020Natur.588..604S.doi:10.1038/s41586-020-03051-4.PMID33361790.
^Ortiz, Sabrina."You can now chat with a famous AI character on Viber. Here's how".zdnet.com.ZDNET. Retrieved5 December 2024.ICONIQ created Kuki, an AI character whose sole purpose is to entertain humans and has even been used as a brand ambassador for H&M, modeled for Vogue, and starred in its own Roblox game.
^Lewis, Nell (19 August 2020)."Robot friends: Why people talk to chatbots in times of trouble".cnn.com.CNN. Retrieved5 December 2024.Since 2016, when the bot landed on major messaging platforms, an estimated 5 million unique users hailing from all corners of the world have chatted with her.
^Poltronieri, Fabrizio Augusto; Hänska, Max (2019). "Technical Images and Visual Art in the Era of Artificial Intelligence: From GOFAI to GANs".Proceedings of the 9th International Conference on Digital and Interactive Arts. pp. 1–8.doi:10.1145/3359852.3359865.ISBN978-1-4503-7250-3.
^Cetinic, Eva; She, James (2022-02-16). "Understanding and Creating Art with AI: Review and Outlook".ACM Transactions on Multimedia Computing, Communications, and Applications.18 (2): 66:1–66:22.arXiv:2102.09109.doi:10.1145/3475799.
^Marwala, Tshilidzi; Hurwitz, Evan (2017). "Efficient Market Hypothesis".Artificial Intelligence and Economic Theory: Skynet in the Market. Advanced Information and Knowledge Processing. pp. 101–110.doi:10.1007/978-3-319-66104-9_9.ISBN978-3-319-66103-2.
^Shao, Jun; Lou, Zhukun; Wang, Chong; Mao, Jinye; Ye, Ailin (16 May 2022). "The impact of artificial intelligence (AI) finance on financing constraints of non-SOE firms in emerging markets".International Journal of Emerging Markets.17 (4):930–944.doi:10.1108/IJOEM-02-2021-0299.
^Chang, Hsihui; Kao, Yi-Ching; Mashruwala, Raj; Sorensen, Susan M. (10 April 2017). "Technical Inefficiency, Allocative Inefficiency, and Audit Pricing".Journal of Accounting, Auditing & Finance.33 (4):580–600.doi:10.1177/0148558X17696760.
^Munoko, Ivy; Brown-Liburd, Helen L.; Vasarhelyi, Miklos (November 2020). "The Ethical Implications of Using Artificial Intelligence in Auditing".Journal of Business Ethics.167 (2):209–234.doi:10.1007/s10551-019-04407-1.
^Cardoso, Mário; Saleiro, Pedro; Bizarro, Pedro (2022). "LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering".Proceedings of the Third ACM International Conference on AI in Finance. pp. 130–138.arXiv:2210.14360.doi:10.1145/3533271.3561727.ISBN978-1-4503-9376-8.
^Chen, K.C.; Liang, Ting-peng (May 1989). "Protrader: An Expert System for Program Trading".Managerial Finance.15 (5):1–6.doi:10.1108/eb013623.
^Nielson, Norma; Brown, Carol E.; Phillips, Mary Ellen (July 1990). "Expert Systems for Personal Financial Planning".Journal of Financial Planning:137–143.doi:10.11575/PRISM/33995.hdl:1880/48295.
^Sutton, Steve G.; Holt, Matthew; Arnold, Vicky (September 2016). "'The reports of my death are greatly exaggerated'—Artificial intelligence research in accounting".International Journal of Accounting Information Systems.22:60–73.doi:10.1016/j.accinf.2016.07.005.
^Yorita, Akihiro; Kubota, Naoyuki (2011). "Cognitive Development in Partner Robots for Information Support to Elderly People".IEEE Transactions on Autonomous Mental Development.3 (1):64–73.Bibcode:2011ITAMD...3...64Y.doi:10.1109/TAMD.2011.2105868.
^Smer-Barreto, Vanessa; Quintanilla, Andrea; Elliot, Richard J. R.; Dawson, John C.; Sun, Jiugeng; Carragher, Neil O.; Acosta, Juan Carlos; Oyarzún, Diego A. (27 April 2022). "Discovery of new senolytics using machine learning".bioRxiv.doi:10.1101/2022.04.26.489505.hdl:10261/269843.
^Luxton, David D. (2014). "Artificial intelligence in psychological practice: Current and future applications and implications".Professional Psychology: Research and Practice.45 (5):332–339.doi:10.1037/a0034559.
^Howard, John (November 2019). "Artificial intelligence: Implications for the future of work".American Journal of Industrial Medicine.62 (11):917–926.doi:10.1002/ajim.23037.PMID31436850.
^K. Mandal, G. S. Pradeep Ghantasala, Firoz Khan, R. Sathiyaraj, B. Balamurugan (2020).Natural Language Processing in Artificial Intelligence (1st ed.). Apple Academic Press. pp. 53–54.ISBN978-0-367-80849-5.{{cite book}}: CS1 maint: multiple names: authors list (link)
^abŠimalčík, Matej (2023). "Rule by Law". In Kironska, Kristina; Turscanyi, Richard Q. (eds.).Contemporary China: a New Superpower?.Routledge.ISBN978-1-03-239508-1.
^Galego Hernandes, Paulo R.; Floret, Camila P.; Cardozo De Almeida, Katia F.; Da Silva, Vinicius Camargo; Papa, Joso Paulo; Pontara Da Costa, Kelton A. (2021). "Phishing Detection Using URL-based XAI Techniques".2021 IEEE Symposium Series on Computational Intelligence (SSCI). pp. 01–06.doi:10.1109/SSCI50451.2021.9659981.ISBN978-1-7281-9048-8.
^Anderson, Blake; Storlie, Curtis; Yates, Micah; McPhall, Aaron (2014). "Automating Reverse Engineering with Machine Learning Techniques".Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop. pp. 103–112.doi:10.1145/2666652.2666665.ISBN978-1-4503-3153-1.
^Fluke, Christopher J.; Jacobs, Colin (March 2020). "Surveying the reach and maturity of machine learning and artificial intelligence in astronomy".WIREs Data Mining and Knowledge Discovery.10 (2) e1349.arXiv:1912.02934.Bibcode:2020WDMKD..10.1349F.doi:10.1002/widm.1349.
^Mohan, Jaya Preethi; Tejaswi, N. (2020). "A Study on Embedding the Artificial Intelligence and Machine Learning into Space Exploration and Astronomy".Emerging Trends in Computing and Expert Technology. Lecture Notes on Data Engineering and Communications Technologies. Vol. 35. pp. 1295–1302.doi:10.1007/978-3-030-32150-5_131.ISBN978-3-030-32149-9.
^Nanda, Lakshay; V, Santhi (2019). "SETI (Search for Extra Terrestrial Intelligence) Signal Classification using Machine Learning".2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). pp. 499–504.doi:10.1109/ICSSIT46314.2019.8987793.ISBN978-1-7281-2119-2.
^Gajjar, Vishal; Siemion, Andrew; Croft, Steve; Brzycki, Bryan; Burgay, Marta; Carozzi, Tobia; Concu, Raimondo; Czech, Daniel; DeBoer, David; DeMarines, Julia; Drew, Jamie; Enriquez, J. Emilio; Fawcett, James; Gallagher, Peter; Garrett, Michael; Gizani, Nectaria; Hellbourg, Greg; Holder, Jamie; Isaacson, Howard; Kudale, Sanjay; Lacki, Brian; Lebofsky, Matthew; Li, Di; MacMahon, David H. E.; McCauley, Joe; Melis, Andrea; Molinari, Emilio; Murphy, Pearse; Perrodin, Delphine; Pilia, Maura; Price, Danny C.; Webb, Claire; Werthimer, Dan; Williams, David; Worden, Pete; Zarka, Philippe; Zhang, Yunfan Gerry (2 August 2019). "The Breakthrough Listen Search for Extraterrestrial Intelligence".Bulletin of the American Astronomical Society.51 (7): 223.arXiv:1907.05519.Bibcode:2019BAAS...51g.223G.
^Hansen, Justine Y.; Markello, Ross D.; Vogel, Jacob W.; Seidlitz, Jakob; Bzdok, Danilo; Misic, Bratislav (September 2021). "Mapping gene transcription and neurocognition across human neocortex".Nature Human Behaviour.5 (9):1240–1250.doi:10.1038/s41562-021-01082-z.PMID33767429.
^Gadzhimagomedova, Z. M.; Pashkov, D. M.; Kirsanova, D. Yu.; Soldatov, S. A.; Butakova, M. A.; Chernov, A. V.; Soldatov, A. V. (February 2022). "Artificial Intelligence for Nanostructured Materials".Nanobiotechnology Reports.17 (1):1–9.doi:10.1134/S2635167622010049.
^Kocher, Geeta; Kumar, Gulshan (August 2021). "Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges".Soft Computing.25 (15):9731–9763.doi:10.1007/s00500-021-05893-0.
^Kant, Daniel; Johannsen, Andreas (16 January 2022). "Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs)".Electronic Imaging.34 (3): 387–1–387–8.doi:10.2352/EI.2022.34.3.MOBMU-387.
^Randrianasolo, Arisoa (2012).Artificial intelligence in computer security: Detection, temporary repair and defense (Thesis). p. vii.hdl:2346/45196.
^Sahil; Sood, Sandeep; Mehmi, Sandeep; Dogra, Shikha (2015). "Artificial intelligence for designing user profiling system for cloud computing security: Experiment".2015 International Conference on Advances in Computer Engineering and Applications. pp. 51–58.doi:10.1109/ICACEA.2015.7164645.ISBN978-1-4673-6911-4.
^Parisi, Alessandro (2019).Hands-On Artificial Intelligence for Cybersecurity: Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies. Packt Publishing Ltd.ISBN978-1-78980-517-8.OCLC1111967955.[page needed]
^Hallerbach, Sven; Xia, Yiqun; Eberle, Ulrich; Koester, Frank (3 April 2018). "Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles".SAE International Journal of Connected and Automated Vehicles.1 (2):93–106.doi:10.4271/2018-01-1066.
^Preparing for the future of artificial intelligence. National Science and Technology Council.OCLC965620122.
^Jones, Randolph M.; Laird, John E.; Nielsen, Paul E.; Coulter, Karen J.; Kenny, Patrick; Koss, Frank V. (15 March 1999). "Automated Intelligent Pilots for Combat Flight Simulation".AI Magazine.20 (1): 27.doi:10.1609/aimag.v20i1.1438.
^AIDA Homepage. Kbs.twi.tudelft.nl (17 April 1997). Retrieved 21 July 2013.
Kaplan, A.M.; Haenlein, M. (2018). "Siri, Siri in my Hand, who's the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence".Business Horizons.62 (1):15–25.doi:10.1016/j.bushor.2018.08.004.
Moghaddam, M. J.; Soleymani, M. R.; Farsi, M. A. (2015). "Sequence planning for stamping operations in progressive dies".Journal of Intelligent Manufacturing.26 (2):347–357.doi:10.1007/s10845-013-0788-0.
_artificial_intelligence_icon.png){kind=icon}
