Applications of artificial intelligence

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 has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of Machine learning has been used for various scientific and commercial purposes including language translation, image recognition, decision-making, credit scoring, and e-commerce. In recent years, there have been massive advancements in the field of generative artificial intelligence, which uses generative models to produce text, images, videos or other forms of data. This article describes applications of AI in different sectors.

== Agriculture ==

In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation.

== Architecture and design ==

== Business ==

A 2023 study found that generative AI increased productivity by 15% in contact centers. Another 2023 study found it increased productivity by up to 40% in writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. A September 2025 article by the Harvard 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 term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues.

== Computer science ==

=== Programming assistance ===

==== AI-assisted software development ==== AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors and IDEs as plugins. 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. 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. The rush to prematurely adopt AI technology can also incur additional technical debt. AI also requires additional consideration and careful review for cybersecurity, since AI coding software is trained on a wide range of code of inconsistent quality and often replicates poor practices.

==== 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.

==== Quantum computing ====

Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems.

=== 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:

Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction

== Customer service ==

=== Human resources ===

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.

=== Online and telephone customer service ===

AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. 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. Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making.

=== Hospitality === In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs. AI hotel services come in the form of a chatbot, application, virtual voice assistant and service robots.

== Education ==

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.

== Energy and environment ==

=== Energy system === The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models, predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid).

=== Environmental monitoring ===

Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite monitoring-platform for analysis/tracking of plastic waste sites to help prevention of plastic pollution – primarily ocean pollution – by helping identify who and where mismanages plastic waste, dumping it into oceans.

=== Early-warning systems === Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, earthquakes, landslides, heavy rainfall, long-term water supply vulnerability, tipping-points of ecosystem collapse, cyanobacterial bloom outbreaks, and droughts.

=== Economic and social challenges === The University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness. Stanford researchers use AI to analyze satellite images to identify high poverty areas.

== Entertainment and media ==

=== Media ===

AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the 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 to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement.

Motion interpolation Pixel-art scaling algorithms Image scaling Image restoration Photo colorization Film restoration and video upscaling Photo tagging Text-to-image models such as DALL-E, Midjourney and Stable Diffusion Image to video Text to video such as Make-A-Video from Meta, Imagen video and Phenaki from Google Text to music with AI models such as MusicLM Text to speech such as ElevenLabs and 15.ai Motion capture

=== Deep-fakes === Deep-fakes can be used for comedic purposes but are better known for fake news and hoaxes. Deepfakes can portray individuals in harmful or compromising situations, causing significant reputational damage and emotional distress, especially when the content is defamatory or violates personal ethics. While defamation and false light laws offer some recourse, their focus on false statements rather than fabricated images or videos often leaves victims with limited legal protection and a challenging burden of proof. In January 2016, the Horizon 2020 program financed the InVID Project to help journalists and researchers detect fake documents, made available as browser plugins. In June 2016, the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face, a program that animates photographs of faces, mimicking the facial expressions of another person. In September 2018, U.S. Senator Mark Warner proposed to penalize social media companies that allow sharing of deep-fake documents on their platforms. In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames. DARPA gave 68 million dollars to work on deep-fake detection. Audio deepfakes and AI software capable of detecting deep-fakes and cloning human voices have been developed.

=== Video surveillance analysis and manipulated media detection ===

AI algorithms have been used to detect deepfake videos.

=== Video production === Artificial intelligence is also starting to be used in video production, with tools and software being developed that utilize generative AI in order to create new video, or alter existing video. Some of the major tools that are being used in these processes currently are DALL-E, Mid-journey, and Runway. Way mark Studios utilized the tools offered by both DALL-E and Mid-journey to create a fully AI generated film called The Frost in the summer of 2023. Way mark Studios is experimenting with using these AI tools to generate advertisements and commercials for companies in mere seconds. 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.

=== Music ===

AI has been used to compose music of various genres. David Cope created an AI called Emily Howell that managed to become well known in the field of algorithmic computer music. The algorithm behind Emily Howell is registered as a US patent. In 2012, AI Iamus created the first complete classical album. AIVA (Artificial Intelligence Virtual Artist), composes symphonic music, mainly classical music for film scores. It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association. Melomics creates computer-generated music for stress and pain relief. The Watson Beat uses reinforcement learning and deep belief networks to compose music on a simple seed input melody and a select style. The software was open sourced and musicians such as Taryn Southern 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.

=== Writing and reporting ===

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. Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football. 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. TALESPIN made up stories similar to the fables of Aesop. The program started with a set of characters who wanted to achieve certain goals. 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". 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. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize. South Korean company Hanteo Global uses a journalism bot to write articles. Literary authors are also exploring uses of AI. An example is David Jhave Johnston's work ReRites (2017–2019), where the poet created a daily rite of editing the poetic output of a neural network to create a series of performances and publications.

==== Sports writing ==== In 2010, artificial intelligence used baseball statistics to automatically generate news articles. This was launched by The Big Ten Network using software from Narrative Science. After being unable to cover every Minor League Baseball game with a large team, Associated Press collaborated with Automated Insights in 2016 to create game recaps that were automated by artificial intelligence. UOL in Brazil expanded the use of AI in its writing. Rather than just generating news stories, they programmed the AI to include commonly searched words on Google. El Pais, a Spanish news site that covers many things including sports, allows users to make comments on each news article. They use the Perspective API to moderate these comments and if the software deems a comment to contain toxic language, the commenter must modify it in order to publish it. 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. Lede AI has been used in 2023 to take scores from high school football games to generate stories automatically for the local newspaper. This was met with significant criticism from readers for the very robotic diction that was published. With some descriptions of games being a "close encounter of the athletic kind," readers were not pleased and let the publishing company, Gannett, know on social media. Gannett has since halted their used of Lede AI until they come up with a solution for what they call an experiment.

=== Wikipedia === Millions of its articles have been edited by bots which however are usually not artificial intelligence software. Many AI platforms use Wikipedia data, mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences, detecting covert vandalism or recommending articles and tasks to new editors. Machine translation (see above) has also be used for translating Wikipedia articles and could play a larger role in creating, updating, expanding, and generally improving articles in the future. A content translation tool allows editors of some Wikipedias to more easily translate articles across several select languages.

=== Video games ===

In video games, AI is routinely used to generate behavior in non-player characters (NPCs). In addition, AI is used for pathfinding. Games with less typical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010). AI is also used in Alien Isolation (2014) as a way to control the actions the Alien will perform next. Games have been a major application of AI's capabilities since the 1950s. In the 21st century, AIs have beaten human players in many games, including chess (Deep Blue), Jeopardy! (Watson), Go (AlphaGo), poker (Pluribus and Cepheus), E-sports (StarCraft), and general game playing (AlphaZero and MuZero). Kuki AI is a set of chatbots and other apps which were designed for entertainment and as a marketing tool.

=== Visual images ===

The first AI art program, called AARON, was developed by Harold Cohen in 1968 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. AI platforms such as DALL-E, Stable Diffusion, Imagen, and Midjourney have been used for generating visual images from inputs such as text or other images. 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. Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art. While distant viewing includes the analysis of large collections, close reading involves one piece of artwork.

=== Computer animation === In 2023, Netflix of Japan's usage of AI to generate background images for short The Dog & the Boy was met with backlash online.

== Finance == Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking began in 1987 when Security Pacific National Bank launched a fraud prevention task-force to counter the unauthorized use of debit cards. Banks use AI to organize operations for bookkeeping, investing in stocks, and managing properties. AI can adapt to changes during non-business hours. AI is used to combat fraud and financial crimes by monitoring behavioral patterns for any abnormal changes or anomalies. The use of AI in applications such as online trading and decision-making has changed major economic theories. For example, AI-based buying and selling platforms estimate personalized demand and supply curves, thus enabling individualized pricing. AI systems reduce information asymmetry in the market and thus make markets more efficient. 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.

=== Trading and investment === 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. Such high-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 institution...

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Category

Artificial Intelligence - Computer Science