Applications of artificial intelligence

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Artificial intelligence

Major goals

* Knowledge reasoning
* Planning
* Machine learning
* Natural language processing
* Computer vision
* Robotics
* Artificial general intelligence

Approaches

* Symbolic
* Deep learning
* Bayesian networks
* Evolutionary algorithms

Philosophy

* Ethics
* Existential risk
* Turing test
* Chinese room
* Control problem
* Friendly AI

History

* Timeline
* Progress
* AI winter

Technology

* Applications
* Projects
* Programming languages

Glossary

* Glossary

* v
* t
* e

Artificial intelligence (AI) and its subfields have been used in applications throughout industry and academia. Machine learning has been used for various scientific and commercial purposes,[1] including language translation, image recognition, decision-making,[2][3] credit scoring, and e-commerce. Since the 2020s, massive advancements have been made in the field of generative artificial intelligence (GenAI), which generates text, images, music, videos, and other forms of data.[4][5][6]

## Contents

* 1 Agriculture
* 2 AI-assisted software development
* 3 Physical AI
* 4 Architecture and design
* 5 Business
* 6 Chatbots
* 7 Computer science
* 7.1 Programming assistance
* 7.1.1 AI-assisted software development
* 7.1.2 Neural network design
* 7.1.3 Quantum computing
* 7.2 Historical contributions
* 8 Customer service
* 8.1 Human resources
* 8.2 Online and telephone customer service
* 8.3 Hospitality
* 9 Education
* 10 Energy and environment
* 10.1 Energy system
* 10.2 Environmental decision-making and monitoring
* 10.3 Early-warning systems
* 10.4 Economic and social challenges
* 11 Entertainment and media
* 11.1 Media
* 11.2 Deep-fakes
* 11.3 Video surveillance analysis and manipulated media detection
* 11.4 Video production
* 11.5 Music
* 11.6 Writing and reporting
* 11.6.1 Sports writing
* 11.7 Wikipedia
* 11.8 Video games
* 11.9 Visual images
* 11.10 Computer animation
* 12 Finance
* 12.1 Expert systems
* 12.2 Trading and investment
* 12.3 Underwriting
* 12.4 Audit
* 12.5 Anti–money laundering
* 12.6 Regulatory developments in the EU
* 13 Health
* 13.1 Healthcare
* 13.2 Workplace health and safety
* 13.3 Biochemistry
* 14 Language processing
* 14.1 Language translation
* 15 Law and government
* 15.1 Government
* 15.2 Law
* 15.2.1 Legal analysis
* 15.2.2 Law enforcement and legal proceedings
* 16 Manufacturing
* 16.1 Sensors
* 16.2 Toys and games
* 16.3 Oil and gas
* 17 Mathematics
* 17.1 Automated theorem proving
* 17.2 Computational geometry
* 17.3 AI-assisted mathematical discovery
* 17.4 AI-assisted algorithm discovery
* 18 Military
* 19 Robotics
* 20 Internet and e-commerce
* 20.1 Procurement and Sourcing
* 20.2 Web feeds and posts
* 20.3 Virtual assistants and search
* 20.4 Spam filtering
* 20.5 Facial recognition and image labeling
* 21 Scientific research
* 21.1 Evidence of general impacts
* 21.2 Archaeology, history and imaging of sites
* 21.3 Physics
* 21.4 Materials science
* 21.5 Reverse engineering
* 21.6 Astronomy, space activities and ufology
* 21.7 Chemistry and biology
* 22 Security and surveillance
* 22.1 Cyber security
* 23 Transportation and logistics
* 23.1 Aviation
* 23.2 Automotive and public transit
* 23.3 Military
* 23.4 NASA
* 23.5 Maritime
* 24 See also
* 25 Footnotes
* 26 Further reading

## Agriculture

See also: Earth:Precision agriculture and Digital agriculture

In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency.[7] AI has been used to attempt to classify livestock pig call emotions,[8] automate greenhouses,[9] detect diseases and pests,[10] and optimize irrigation.[11]

## AI-assisted software development

Main articles: AI-assisted software development and List of AI-assisted software development tools

## Physical AI

Main article: Physical artificial intelligence

## Architecture and design

AI in architecture is the use of AI in automation, design, and planning in the architectural process or in assisting human skills in the field of architecture.[12]

AI has been used by some architects for design, and has been proposed as a way to automate planning and routine tasks in the field.[13][14]

## Business

A 2023 study found that generative AI increased productivity by 15% in contact centers.[15] Another 2023 study found it increased productivity by up to 40% for writing tasks.[16] An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI.[17] In September 2025, a Harvard Business Review article described 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 coined 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.[18]

In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1–2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75 million in annual profit for GLP-1 weight-loss telehealth services.[19]

## Chatbots

## Computer science

### Programming assistance

See also: Automatic programming, Programming environment, and Vibe coding

#### 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.[20] The use of AI-assisted coding can potentially speed-up software development, but it can also slow-down the process by creating more work when debugging and testing.[21][22] The rush to prematurely adopt AI technology can also incur additional technical debt.[21] 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.[23][24]

#### 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.[25]

#### 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.[26][27] The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems.[28][29]

### Historical contributions

AI researchers have created many tools to solve difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. The following were originally developed in AI laboratories:[30]

* 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

Main article: Artificial intelligence in hiring

AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots.[31] Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics,[32][33] as well as bias against disabled candidates and racial minorities.[34]

### Online and telephone customer service

An automated online assistant providing customer service on a web page

AI underlies avatars (automated online assistants) on web pages.[35] It can reduce operation and training costs.[35] In 2016, Pypestream automated customer service for its mobile application to streamline communication with customers.[36]

A Google app analyzes language and converts speech into text.[37][non-primary source needed] The platform can identify angry customers through their language and respond appropriately.[38] 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.[39][non-primary source needed]

### Hospitality

In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs.[40] AI hotel services come in the form of a chatbot,[41] application, virtual voice assistant, and service robots.

## Education

In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges.[42]

As of 2025 and 2026, respectively, about 80% of high school students[43] and nearly 60% of college students[44] were also using AI. Some schools and universities proposed restrictions on students' usage due to issues with increased cheating, over-reliance on AI tools, and widening of digital inequities.[45] [46]

## Energy and environment

### Energy system

See also: Engineering:Smart grid and Climate change mitigation

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 (LLMs), predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks.[47] 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).[48][49][50][51][52]

### Environmental decision-making and monitoring

See also: Biology:Climate-smart agriculture

The late 1980s and early 1990s 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.[53] Transportation researchers applied the same shell to balance airport capacity with noise-mitigation plans.[54] In agriculture, a potato insect expert system (PIES) supported pest management decisions for Colorado potato beetle.[55] The U.S. Environmental Protection Agency's CORMIX system for modeling pollutant discharges combined rules with Fortran hydrodynamic models.[56]

Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics[57] or remote sensing and other applications of environmental monitoring make use of machine learning.[58][59][60][61] For example, "Global Plastic Watch" is an AI-based satellite-monitoring platform for analysis/tracking of plastic waste sites to help with the prevention of plastic pollution—primarily ocean pollution—by helping identify who mismanages plastic waste and where dumping into oceans occurs.[62][63]

### Early-warning systems

Machine learning can be used to spot early warning signs of disasters and environmental issues, possibly including natural pandemics,[64][65] earthquakes,[66][67][68] landslides,[69] heavy rainfall,[70] long-term water supply vulnerability,[71] tipping-points of ecosystem collapse,[72] cyanobacterial bloom outbreaks,[73] and droughts.[74][75][76]

### Economic and social challenges

See also: § Environmental monitoring

In 2023, 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.[77][78] Stanford University researchers use AI to analyze satellite images to identify high poverty areas.[79]

## Entertainment and media

### Media

Image restoration

AI applications analyze media content such as movies, TV programs, advertisement videos, and user-generated content. The solutions often involve computer vision.

Typical scenarios include the analysis of images using object recognition or facial recognition techniques, or the analysis of video for 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[80]
* Pixel-art scaling algorithms[81]
* Image scaling[82]
* Image restoration[83][84]
* Photo colorization[85]
* Film restoration and video upscaling[86]
* Photo tagging
* Text-to-image models (such as DALL-E, Midjourney, and Stable Diffusion)
* Image to video[87]
* 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)[88][89]
* Text to speech (such as ElevenLabs and 15.ai)
* Motion capture[90]

### 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.[91] Audio deepfakes[92][93] and AI software capable of detecting deep-fakes and cloning human voices have also been developed.[94][95]

In January 2016, the Horizon 2020 program financed the InVID Project to help journalists and researchers detect fake documents, made available as browser plugins.[96][97]

In September 2018, U.S. Senator Mark Warner proposed penalizing social media companies that allow sharing of deep-fake documents on their platforms.[98] In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames.[99] DARPA gave $68 million to work on deep-fake detection.[99]

### Video surveillance analysis and manipulated media detection

See also: Web scraping, Photograph manipulation, and Video manipulation

AI for video surveillance utilizes computer software programs that analyze audio and images from video surveillance cameras to recognize humans, vehicles, objects, and events. Security contractors program the software to define restricted areas within the camera's view (such as a fenced-off area, a parking lot but not sidewalk, or a public street outside the lot) and program for times of day (such as after the close of a business) for the property being protected by the camera surveillance. The AI 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.[100][101]

### Video production

AI is also starting to be used in video production, with tools and software being developed that utilize generative AI to create new video, or alter existing video. Examples of these tools include DALL-E, Midjourney, and Runway.[102] In 2023, Waymark Studios utilized the tools offered by both DALL-E and Midjourney to create a fully AI generated film called The Frost.[102] Waymark is experimenting with using AI tools to generate advertisements and commercials for companies in seconds.[102] 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.[103]

### Music

Main article: Music and artificial intelligence

AI has been used to compose music of various genres.

David Cope created an AI called Emily Howell that became well known in the field of algorithmic computer music.[104] The algorithm behind Emily Howell is registered as a US patent.[105]

In 2012, AI Iamus created the first complete classical album.[106]

AIVA (Artificial Intelligence Virtual Artist), composes symphonic music, mainly classical music for film scores.[107] It became the first virtual composer to be recognized by a musical professional association.[108]

Melomics creates computer-generated music for stress and pain relief.[109]

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,[110] and musicians such as Taryn Southern[111] collaborated with the project.

South Korean singer Hayeon's debut song, "Eyes on You", was composed using AI which was supervised by real composers, including NUVO.[112]

### Writing and reporting

Narrative Science sells computer-generated news and reports. It summarizes sporting events based on statistical data from a game. It also creates financial reports and real estate analyses.[113] Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football.[114]

Yseop uses AI to turn structured data into natural language comments and recommendations. It writes financial reports, executive summaries, and personalized sales or marketing documents in multiple languages, including English, Spanish, French, and German.[115]

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".[116]

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.[117] In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.[118]

South Korean company Hanteo Global uses a journalism bot to write articles.[119]

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.[120]

#### Sports writing

In 2010, AI used baseball statistics to automatically generate news articles. This was launched by The Big Ten Network using software from Narrative Science.[121]

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 AI.[122]

UOL in Brazil expanded the use of AI in its writing. In addition to generating news stories, they programmed the AI to include commonly searched words on Google.[122]

El Pais, a Spanish news site, allows users to make comments on news articles. 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.[122]

A local Dutch media group used AI to create automatic coverage of amateur soccer, set to cover 60,000 games in a single season. NDC partnered with United Robots to create the algorithm.[122]

In 2023, Lede AI was used to take scores from high school football games and generate stories for the local newspaper. It was met with criticism from readers, who mocked the robotic diction and called out the publishing company, Gannett, on social media. Gannett has since halted their used of Lede AI.[123]

### Wikipedia

Some editors of Wikimedia projects use AI and machine learning programs to edit existing articles or create new ones.[124]

Some applications of AI, like using large language models (LLMs) to create new articles from scratch, have been more controversial than others for the Wikipedia community. In August 2025, English Wikipedia adopted a policy that allowed editors to nominate suspected LLM-generated articles for speedy deletion. Then, a March 2026 decision prohibited the use of LLMs to generate or rewrite article content, with exceptions for copyediting one's own writing and machine translation from another language's Wikipedia.

Wikipedia has also been a significant source of training data for some of the earliest AI projects. This has received mixed reactions, including concern about companies not citing Wikipedia when relying on it to answer a question as well as Wikipedia's increased costs from data scraping.

Millions of its articles have been edited by bots,[125] which however are usually not AI software. Many AI platforms use Wikipedia data,[126] mainly for training machine learning applications. There is research and development of various AI applications for Wikipedia, such as for identifying outdated sentences,[127] detecting covert vandalism[128] 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.[129][130]

### Video games

Main article: Software:Artificial intelligence in video games

See also: Software:Video game bot, Artificial intelligence in video games, DLAA, and DLSS

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).[131][132] AI is also used in Alien Isolation (2014) as a way to control the actions the Alien will perform next.[133]

Since the 1950s, AI's capabilities have been tested with games.[134] In the 21st century, AIs have beaten human players in many games, including chess (Deep Blue),[135][136] Jeopardy! (Watson),[137] Go (AlphaGo),[138][139][140] poker (Pluribus[141] and Cepheus),[142] E-sports (StarCraft),[143][144] and general game playing (AlphaZero[145][146][147] and MuZero).[148][149][150]

Kuki AI is a set of chatbots and other apps which were designed for entertainment and as a marketing tool.[151][152]

### Visual images

A "cyborg elf" generated by Stable Diffusion

Main article: Artificial intelligence art

The first AI art program, AARON, was developed by Harold Cohen in 1968[153] 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 chosen by the program without mediation from Cohen.[154]

AI platforms such as DALL-E,[155] Stable Diffusion,[155] Imagen,[156] and Midjourney[157] have been used for generating visual images from inputs such as text or other images.[158] Some AI tools allow users to input images and output changed versions, 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.[159] Two computational methods, close reading and distant viewing, are typical approaches used to analyze digitized art.[160] 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 the short The Dog & the Boy was met with backlash online.[161]

## Finance

Financial institutions have long used artificial neural network systems to combat fraud and financial crimes by monitoring behavioral patterns for any abnormal changes or anomalies.[162][163][164] 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.[165]

Banks use AI to organize operations for bookkeeping, investing in stocks, and managing properties. AI can adapt to changes during non-business hours.[166]

### Expert systems

In the 1980s, AI started to become prominent in finance as expert systems were commercialized. For example, Dupont created 100 expert systems, which helped them save almost $10 million per year.[167] One of the first systems was the Pro-trader expert system that predicted the 87-point drop in the Dow Jones Industrial Average in 1986.[168]

An early expert system to help with financial plans was PlanPowerm and Client Profiling System, launched by Applied Expert Systems (APEX) in 1986. It helped create personal financial plans for people.[169]

### Trading and investment

See also: Finance:Algorithmic trading

The use of AI in applications such as online trading and decision-making has changed major economic theories.[170] 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.[171] The application of AI in the financial industry can help alleviate financing constraints of non-state-owned enterprises, especially for smaller and more innovative enterprises.[172]

Large financial institutions use AI to assist with their investment practices.[173] BlackRock's AI engine, Aladdin, is used both within the company and by clients to help with investment decisions. Its functions include the use of natural language processing to analyze text such as news, broker reports, and social media feeds. It then gauges the sentiment on the companies mentioned and assigns a score. Banks such as UBS and Deutsche Bank use SQREEM (Sequential Quantum Reduction and Extraction Model) to mine data to develop consumer profiles and match them with wealth management products.[174]

### Underwriting

Online lender Upstart uses machine learning for underwriting.[175] ZestFinance's Zest Automated Machine Learning (ZAML) platform is used for credit underwriting.[176] It uses machine learning to analyze data, including purchase transactions and how a customer fills out a form, to score borrowers. It can also assign credit scores to those with limited credit histories.[177]

### Audit

AI makes continuous auditing possible. Potential benefits include reducing audit risk, increasing the level of assurance, and reducing audit duration.[178] 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.[179]

### Anti–money laundering

In 1993, FinCEN Artificial Intelligence System (FAIS) was launched. It was able to review over 200,000 transactions per week, and over two years, it helped identify 400 potential cases of money laundering equal to $1 billion.[180] These expert systems were later replaced by machine learning systems.[181]

AI software, such as LaundroGraph which uses contemporary suboptimal datasets, could be used for anti–money laundering (AML).[182][183]

### Regulatory developments in the EU

In the European Union, the Artificial 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.[184][185] These systems must meet requirements on risk management, data governance, technical documentation and logging, transparency, and human oversight.[184] 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.[185]

## Health

### Healthcare

Main article: Medicine:Artificial intelligence in healthcare

X-ray of a hand, with automatic calculation of bone age by a computer software A patient-side surgical arm of Da Vinci Surgical System

AI in healthcare is often used for classification, such as to evaluate a CT scan or electrocardiogram or to identify high-risk patients for population health. Additional potential applications like robot-assisted surgery,[186] virtual nursing assistants, and dosage error reductions could create billions in annual savings for U.S. health care.[187]

For certain disorders and diseases, AI algorithms can aid in diagnosis, recommended treatments, outcome prediction, and patient progress tracking.[188] Early detection of diseases like cancer is made possible by AI algorithms, which diagnose diseases by analyzing complex sets of medical data. For example, Microsoft's AI project Hanover helps doctors choose cancer treatments from among the more than 800 medicines and vaccines.[189][190] Studies have shown AI capable of identifying skin cancers[191] and diagnosing eye conditions, similar to an ophthalmologist, and recommending treatment referrals via transfer learning.[192] Another project monitors multiple high-risk patients by asking each patient questions based on data acquired from doctor/patient interactions.[193]

Artificial neural networks are used as clinical decision support systems for medical diagnosis,[194] such as in concept processing technology in electronic medical record (EMR) software.

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

* Screening[195]
* Companion robots for elder care[196]
* Drug creation[197] (e.g. by identifying candidate drugs[198] and by using existing drug screening data such as in life extension research)[199]
* Clinical training[200]
* Identifying genomic pathogen signatures of novel pathogens[201] or identifying pathogens via physics-based fingerprints[202] (including pandemic pathogens)
* Helping link genes to their functions,[203] otherwise analyzing genes[204] and identification of novel biological targets[205]
* Help development of biomarkers[205]
* Help tailor therapies to individuals in personalized medicine/precision medicine[205][206]

### Workplace health and safety

AI-enabled chatbots decrease the need for humans to perform basic call center tasks, and machine learning in sentiment analysis can spot fatigue to prevent overwork.[207]

Decision support systems can potentially prevent industrial disasters and make disaster response more efficient.[208] For manual workers in material handling, predictive analytics has been proposed to reduce musculoskeletal injury.[209]

AI can attempt to process workers' compensation claims.[210][211] AI has been proposed for detection of accident near misses, which are underreported.[212]

### Biochemistry

Machine learning has been used for drug design,[52] drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials.[213]

Computer-planned syntheses via computational reaction networks, described as a platform that combines "computational synthesis with AI algorithms to predict molecular properties",[214] has been used in drug-syntheses, and developing routes for recycling 200 industrial waste chemicals into important drugs and agrochemicals (chemical synthesis design).[215] It has also been used to explore the origins of life on Earth.[216]

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.[217]

The AI program AlphaFold 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).[218][219][220][221][excessive citations]

## Language processing

### Language translation

Main article: Machine translation

Speech translation technology attempts to convert one language's spoken words into another language. This could potentially reduce language barriers in global commerce and cross-cultural exchange, enabling speakers of various languages to communicate.[222] AI has been used to automatically translate spoken language and textual content in products such as Microsoft Translator, Google Translate, and DeepL Translator.[223] Additionally, research and development are in progress to decode and conduct animal communication.[8][224]

Meaning is conveyed not only by text, but also through usage and context (see semantics and pragmatics). As a result, the two primary categorization approaches for machine translations are statistical machine translation (SMT) and neural machine 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.[225]

## Law and government

### Government

Main article: Artificial intelligence in government

AI facial recognition systems are used for mass surveillance, notably in China.[226][227] In 2019, Bengaluru, India deployed AI-managed traffic signals that uses cameras to monitor traffic density and adjust signal timing based on the interval needed to clear traffic.[228]

### Law

#### Legal analysis

AI is a mainstay of law-related professions. Algorithms and machine learning do some tasks previously done by entry-level lawyers.[229] While its use is common, it is not expected to replace most work done by lawyers in the near future.[230]

The electronic discovery industry uses machine learning to reduce manual searching.[231]

#### Law enforcement and legal proceedings

Law enforcement has begun using facial recognition systems (FRS) to identify suspects from visual data. FRS results have proven to be more accurate when compared to eyewitness results, as well as better at identifying individuals when video clarity and visibility are low compared to human participants.[232]

COMPAS is a commercial system used by U.S. courts to assess the likelihood of recidivism.[233] One concern relates to algorithmic bias, AI programs may become biased after processing data that exhibits bias.[234] ProPublica claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than that of white defendants.[233]

In 2019, the city of Hangzhou, China established a pilot program AI-based Internet Court to adjudicate disputes related to ecommerce and internet-related intellectual property claims.[235]: 124 Parties appear before the court via videoconference and AI evaluates the evidence presented and applies relevant legal standards.[235]: 124

## Manufacturing

Main article: Artificial intelligence in industry

### Sensors

AI has been combined with digital spectrometry by IdeaCuria Inc.,[236][237] to enable applications such as at-home water quality monitoring.

### Toys and games

In the 1990s, early AI tools controlled toys like Aibo, a domestic robot in the form of a robotic dog with intelligent features and autonomy.[238][239] In the mid-2010s, Mattel's Hello Barbie could "understand" conversations, give intelligent responses, and remember.[240]

AI in toys could offer personalized learning.[241][242] However, in May 2026, after early tests to see how toddlers interact with AI, researchers called for tighter regulation on AI-powered toys.[243] Similarly, nonprofit organization Common Sense Media warned against children under five using AI.[244]

### Oil and gas

Oil and gas companies have used AI tools to automate functions, foresee equipment issues, and increase oil and gas output.[245][246]

## Mathematics

AI tools have been used to translate mathematical proofs into formal proofs in order to automatically verify them.[247]

### Automated theorem proving

This section is an excerpt from Automated theorem proving[ edit ]

Automated theorem proving (also known as ATP or automated deduction) is a subfield of automated reasoning and mathematical logic dealing with proving mathematical theorems by computer programs. Automated reasoning over mathematical proof was a major motivating factor for the development of computer science.

### Computational geometry

This section is an excerpt from AlphaGeometry[ edit ]

AlphaGeometry is an artificial intelligence (AI) program that can solve hard problems in Euclidean geometry. The system comprises a data-driven large language model (LLM) and a rule-based symbolic engine (Deductive Database Arithmetic Reasoning). It was developed by DeepMind, a subsidiary of Google. The program solved 25 geometry problems out of 30 from the International Mathematical Olympiad (IMO) under competition time limits—a performance almost as good as the average human gold medallist. For comparison, the previous AI program, called Wu's method, managed to solve only 10 problems.[248][249]

DeepMind published a paper about AlphaGeometry in the peer-reviewed journal Nature on 17 January 2024.[250] AlphaGeometry was featured in MIT Technology Review on the same day.[251]

Traditional geometry programs are symbolic engines that rely exclusively on human-coded rules to generate rigorous proofs, which makes them lack flexibility in unusual situations. AlphaGeometry combines such a symbolic engine with a specialized large language model trained on synthetic data of geometrical proofs. When the symbolic engine doesn't manage to find a formal and rigorous proof on its own, it solicits the large language model, which suggests a geometrical construct to move forward. However, it is unclear how applicable this method is to other domains of mathematics or reasoning, because symbolic engines rely on domain-specific rules and because of the need for synthetic data.[252]

### AI-assisted mathematical discovery

FunSearch, developed by Google DeepMind, combines a large language model with automated evaluation to search for programs that can help solve mathematical problems. It has been applied to extremal combinatorics, including the cap set problem, and to online bin packing. [253]

### AI-assisted algorithm discovery

AI systems have also been used to search for improved algorithms and computational routines. AlphaTensor, AlphaDev, and AlphaEvolve have been applied to matrix multiplication algorithms, sorting routines, and broader scientific and algorithmic discovery. [254][255][256]

## Military

Main article: Military applications of artificial intelligence

Various countries are deploying AI military applications.[257] Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles.[257]

AI has been used in military operations in Iraq, Syria, Israel and Ukraine.[257][258][259][260][excessive citations]

## Robotics

See also: Software:List of robotics software

Artificial intelligence is used in robotics to help robots perceive their surroundings, plan actions, navigate, manipulate objects, and adapt to changing environments. AI systems are used to connect sensor data with physical action, especially in systems operating outside controlled factory settings.[261]

## Internet and e-commerce

Main article: Software:Marketing and artificial intelligence

### Procurement and Sourcing

AI has been used in B2B sourcing and procurement.[262] Academic literature and industry reports point out uses of AI for supplier evaluation and selection, predictive demand forecasting, automated contract and invoice processing, and supply-chain risk assessment. Machine learning, natural language processing, and robotic process automation are commonly used in procurement.[263]

### Web feeds and posts

Machine learning has been used for recommendation systems in determining which posts should show up in social media feeds.[264][265] Various types of social media analysis also make use of machine learning[266][267] and there is research into its use for (semi-)automated tagging/enhancement/correction of online misinformation and related filter bubbles.[268][269][270]

AI has been used to customize shopping options and personalize offers.[271] Online gambling companies have used AI for targeting gamblers.[272]

### Virtual assistants and search

Main article: Engineering:Virtual assistant

Intelligent personal assistants use AI to attempt to respond to natural language requests. Siri, released in 2010 for Apple smartphones, popularized the concept.[273]

Bing Chat has used artificial intelligence as part of its search engine.[274]

### Spam filtering

Machine learning can be used to combat spam, scams, and phishing. It can scrutinize the contents of spam and phishing attacks to attempt to identify malicious elements.[275] Some models built via machine learning algorithms have over 90% accuracy in distinguishing between spam and legitimate emails.[276] 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.[277]

### Facial recognition and image labeling

Main articles: Automatic image annotation and Artificial intelligence for video surveillance

AI has been used in facial recognition systems. Some examples are Apple's Face ID and Android's Face Unlock, which are used to secure mobile devices.[278]

China has used facial recognition and artificial intelligence technology in Xinjiang. 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.[279][280] Human rights groups have criticized the Chinese government for using artificial intelligence facial recognition technology for use in political suppression.[281][282]

The Netherlands has deployed facial recognition and artificial intelligence technology since 2016.[283] The database of the Dutch police currentlyTemplate:Reltime 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.[284]

Image labeling has been used by Google Image Labeler to detect products in photos and to allow people to search based on a photo. Image labeling has also been demonstrated to generate speech to describe images to blind people.[223]

## Scientific research

### Evidence of general impacts

In April 2024, the Scientific Advice Mechanism to the European Commission published advice[285] including a comprehensive evidence review of the opportunities and challenges posed by artificial intelligence in scientific research.

As benefits, the evidence review[286] highlighted:

* its role in accelerating research and innovation
* its capacity to automate workflows
* enhancing dissemination of scientific work

As challenges:

* limitations and risks around transparency, reproducibility and interpretability
* poor performance (inaccuracy)
* risk of harm through misuse or unintended use
* societal concerns including the spread of misinformation and increasing inequalities

### Archaeology, history and imaging of sites

See also: Digital archaeology

Machine learning can help to restore and attribute ancient texts.[287] It can help to index texts for example to enable better and easier searching and classification of fragments.[288]

Artificial intelligence can also be used to investigate genomes to uncover genetic history, such as interbreeding between archaic and modern humans by which for example the past existence of a ghost population, not Neanderthal or Denisovan, was inferred.[289] It can also be used for "non-invasive and non-destructive access to internal structures of archaeological remains".[290]

### Physics

Main article: Physics:Machine learning in physics

A deep learning system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on an unpublished approach inspired by studies of visual cognition in infants.[291][292] 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.[293][294] In the future, it may be possible that such can be used to automate the discovery of physical laws of complex systems.[293]

### Materials science

In November 2023, researchers at Google DeepMind and Lawrence 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 inorganic crystal 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 the Materials Project database.[295][296][297]

### Reverse engineering

Machine learning is used in diverse types of reverse engineering. For example, machine learning has been used to reverse engineer a composite material part, enabling unauthorized production of high quality parts,[298] and for quickly understanding the behavior of malware.[299][300][301] It can be used to reverse engineer artificial intelligence models.[302] 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[303] or protein design for pre-specified functional sites.[304][305] Biological network reverse engineering could model interactions in a human understandable way, e.g. bas on time series data of gene expression levels.[306]

### Astronomy, space activities and ufology

Artificial intelligence is used in astronomy to analyze increasing amounts of available data[307][308] and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy.[309] It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance,[310] and more autonomous operation.[311][312][61][308]

In the search for extraterrestrial intelligence (SETI), machine learning has been used in attempts to identify artificially generated electromagnetic waves in available data[313][314] – such as real-time observations[315] – and other technosignatures, e.g. via anomaly detection.[316] In ufology, the SkyCAM-5 project headed by Prof. Hakan Kayal[317] and the Galileo Project headed by Avi Loeb use machine learning to attempt to detect and classify types of UFOs.[318][319][320][321][322][excessive citations] The Galileo Project also seeks to detect two further types of potential extraterrestrial technological signatures with the use of AI: 'Oumuamua-like interstellar objects, and non-manmade artificial satellites.[323][324]

Machine learning can also be used to produce datasets of spectral signatures of molecules that may be involved in the atmospheric production or consumption of particular chemicals – such as phosphine possibly detected on Venus – which could prevent miss assignments and, if accuracy is improved, be used in future detections and identifications of molecules on other planets.[325]

### Chemistry and biology

There is research about which types of computer-aided chemistry would benefit from machine learning.[326] A deep learning AI-based process has been developed that uses genome databases to design novel proteins based on evolutionary algorithms.[327][328] Machine learning has also been used for protein design with pre-specified functional sites,[304][305] predicting molecular properties, and exploring large chemical/reaction spaces.[329]

Using drug discovery AI algorithms, researchers generated 40,000 potential chemical weapon candidates, helping in the regulation of such chemicals to prevent synthesizing them for real harm.[330][331][332]

There are various types of applications for machine learning in decoding human biology, such as helping to map gene expression patterns to functional activation patterns[333] or identifying functional DNA motifs.[334] It is widely used in genetic research.[335] There also is some use of machine learning in synthetic biology,[336][337] disease biology,[337] nanotechnology (e.g. nanostructured materials and bionanotechnology),[338][339] and materials science.[340][341][342]

## Security and surveillance

### Cyber security

Cyber security companies are adopting neural networks, machine learning, and natural language processing to improve their systems.[343]

Applications of AI in cyber security include:

* Network protection: Machine learning improves intrusion detection systems by broadening the search beyond previously identified threats.[344]
* Endpoint protection: Attacks such as ransomware can be thwarted by learning typical malware behaviors.
* AI-related cyber security application cases vary in both benefit and complexity. Security features such as Security Orchestration, Automation, and Response (SOAR) and Extended Endpoint Detection and Response (XDR) offer significant benefits for businesses, but require significant integration and adaptation efforts.[345]
* Application security: can help counterattacks such as server-side request forgery, SQL injection, cross-site scripting, and distributed denial-of-service.
* AI technology can also be utilized to improve system security and safeguard our privacy. Randrianasolo (2012) suggested a security system based on artificial intelligence that can recognize intrusions and adapt to perform better.[346] In order to improve cloud computing security, Sahil (2015) created a user profile system for the cloud environment with AI techniques.[347]
* Suspect user behavior: Machine learning can identify fraud or compromised applications as they occur.[348]

## Transportation and logistics

### Aviation

The use of artificial intelligence is increasingly observed in the aviation industry for predictive maintenance. This has been helpful in reducing delays. Companies such as Boeing and Airbus are using AI to optimize repair procedures while maintaining operational performance.[349]

### Automotive and public transit

Main articles: Engineering:Vehicular automation, Self-driving car, and Smart traffic light

Side view of a Waymo-branded self-driving car

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.[350] AI-based systems control functions such as braking, lane changing, collision prevention, navigation and mapping.[351]

Some autonomous vehicles do not allow human drivers (they have no steering wheels or pedals).[352][353]

There are prototypes of autonomous automotive public transport vehicles such as autonomous rail transport in operation,[354][355][356] electric mini-buses,[357][358][359] and autonomous delivery vehicles,[360][361][353] including delivery robots.[362][363]

Autonomous trucks are in the testing phase. The UK government passed legislation to begin testing of autonomous truck platoons in 2018.[364] A group of autonomous trucks follow closely behind each other. German corporation Daimler is testing its Freightliner Inspiration.[365]

AI has been used to optimize traffic management, which can reduce wait times, energy use, and emissions.[366]

### Military

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

AI can also be used to operate planes analogously to their control of ground vehicles. Autonomous drones can fly independently or in swarms.[367]

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

Speech recognition allows traffic controllers to give verbal directions to drones.

Artificial intelligence supported design of aircraft,[368] or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule-based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.

### NASA

In 2003 a Dryden Flight Research Center project created software that could enable a damaged aircraft to continue flight until a safe landing can be achieved.[369] The software compensated for damaged components by relying on the remaining undamaged components.[370]

The 2016 Intelligent Autopilot System combined apprenticeship learning and behavioral cloning whereby the autopilot observed low-level actions required to maneuver the airplane and high-level strategy used to apply those actions.[371]

### Maritime

Neural networks are used by situational awareness systems in ships and boats.[372] There also are autonomous boats.

## See also

* Applications of artificial intelligence to legal informatics
* Applications of deep learning
* Applications of machine learning
* Artificial intelligence and elections
* Collective intelligence § Applications
* List of artificial intelligence projects
* List of datasets for machine-learning research
* Open data
* Progress in artificial intelligence
* Timeline of computing Template:CURRENTDECADE–present

## Footnotes

1. ↑ Brynjolfsson, Erik; Mitchell, Tom (22 December 2017). "What can machine learning do? Workforce implications". Science 358 (6370): 1530–1534. doi:10.1126/science.aap8062. PMID 29269459. Bibcode: 2017Sci...358.1530B.
2. ↑ Shin, Minkyu; Kim, Jin; van Opheusden, Bas; Griffiths, Thomas L. (2023). "Superhuman artificial intelligence can improve human decision-making by increasing novelty". Proceedings of the National Academy of Sciences 120 (12). doi:10.1073/pnas.2214840120. PMID 36913582. Bibcode: 2023PNAS..12014840S.
3. ↑ Chen, Yiting; Liu, Tracy Xiao; Shan, You; Zhong, Songfa (2023). "The emergence of economic rationality of GPT". Proceedings of the National Academy of Sciences 120 (51). doi:10.1073/pnas.2316205120. PMID 38085780. Bibcode: 2023PNAS..12016205C.
4. ↑ "What is Generative AI? | IBM" (in en). 2024-03-22. https://www.ibm.com/think/topics/generative-ai.
5. ↑ Wilkinson, Lindsey (2023-11-22). "The rise of generative AI: A timeline of triumphs, hiccups and hype" (in en). https://www.ciodive.com/news/generative-ai-one-year-chatgpt-openai-timeline/698110/.
6. ↑ Robinson, Kristin (2026-03-17). "A Short History of AI-Generated Music: From ‘Fake Drake’ to Blockbuster Legal Settlements" (in en-US). https://www.billboard.com/lists/ai-music-timeline-fake-drake-suno-udio-label-settlements/.
7. ↑ 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.
8. ↑ 8.0 8.1 Briefer, Elodie F.; Sypherd, Ciara C.-R.; Linhart, Pavel; Leliveld, Lisette M. C.; Padilla de la Torre, Monica; Read, Eva R.; Guérin, Carole; Deiss, Véronique et al. (7 March 2022). "Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production". Scientific Reports 12 (1): 3409. doi:10.1038/s41598-022-07174-8. PMID 35256620. Bibcode: 2022NatSR..12.3409B.
9. ↑ Moreno Millán, M; Sevilla Guzmán, E; Demyda, S E (2011). "Population, Poverty, Production, Food Security, Food Sovereignty, Biotechnology and Sustainable Development: Challenges for the XXI Century". Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Veterinary Medicine 1 (68). https://ftp.keyboardedn.ro/index.php/veterinary/article/view/6771.
10. ↑ 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. ISBN 978-1-7281-2909-9.
11. ↑ Talaviya, Tanha; Shah, Dhara; Patel, Nivedita; Yagnik, Hiteshri; Shah, Manan (2020). "Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides". Artificial Intelligence in Agriculture 4: 58–73. doi:10.1016/j.aiia.2020.04.002.
12. ↑ Bernstein, Phillip (2022). Machine Learning: Architecture in the Age of Artificial Intelligence. London: RIBA Publishing. ISBN 978-1-914124-01-3.
13. ↑ Heathcote, Edwin (2024-01-20). "AI is coming for architecture" (in en-GB). https://www.ft.com/content/e0ba1b34-ddc9-4b42-941e-6f8114ba41c9.
14. ↑ Matoso, Marília (2023-10-18). "Will Artificial Intelligence Replace Architects?" (in en-US). https://www.archdaily.com/1007802/will-artificial-intelligence-replace-architects.
15. ↑ Brynjolfsson, Erik; Li, Danielle; Raymond, Lindsey (2025-02-04). "Generative AI at Work" (in en). The Quarterly Journal of Economics 140 (2): 889–942. doi:10.1093/qje/qjae044. ISSN 0033-5533. https://academic.oup.com/qje/article/140/2/889/7990658.
16. ↑ Noy, Shakked; Zhang, Whitney (2023-07-14). "Experimental evidence on the productivity effects of generative artificial intelligence". Science 381 (6654): 187–192. doi:10.1126/science.adh2586. PMID 37440646. Bibcode: 2023Sci...381..187N. https://www.science.org/doi/10.1126/science.adh2586.
17. ↑ Estrada, Sheryl (18 August 2025). "MIT report: 95% of generative AI pilots at companies are failing" (in en). Fortune. https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/.
18. ↑ Niederhoffer, Kate; Kellerman, Gabriella Rosen; Lee, Angela; Liebscher, Alex; Rapuano, Kristina; Hancock, Jeffrey T. (22 September 2025). "AI-Generated "Workslop" Is Destroying Productivity" (in en). Harvard Business Review. https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity.
19. ↑ Griffith, Erin (2 April 2026). "How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company". The New York Times. https://www.nytimes.com/2026/04/02/technology/ai-billion-dollar-company-medvi.html.
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22. ↑ Davis, Dominic-Madori (14 September 2025). "Vibe coding has turned senior devs into 'AI babysitters,' but they say it's worth it". TechCrunch. https://techcrunch.com/2025/09/14/vibe-coding-has-turned-senior-devs-into-ai-babysitters-but-they-say-its-worth-it/.
23. ↑ Newman, Lily Hay. "Vibe Coding Is the New Open Source—in the Worst Way Possible". Wired. https://www.wired.com/story/vibe-coding-is-the-new-open-source/. Retrieved 25 October 2025.
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26. ↑ Spagnolo, Michele; Morris, Joshua; Piacentini, Simone; Antesberger, Michael; Massa, Francesco; Crespi, Andrea; Ceccarelli, Francesco; Osellame, Roberto et al. (April 2022). "Experimental photonic quantum memristor". Nature Photonics 16 (4): 318–323. doi:10.1038/s41566-022-00973-5. Bibcode: 2022NaPho..16..318S.
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32. ↑ Sivakaminathan, Siva Sankari; Musi, Elena (2026-04-01). "ChatGPT is a gender bias echo-chamber in HR recruitment: an NLP analysis and framework to uncover the language roots of bias" (in en). AI & Society 41 (4): 2841–2861. doi:10.1007/s00146-025-02564-8. ISSN 1435-5655. https://doi.org/10.1007/s00146-025-02564-8.
33. ↑ Rozado, David (2026-02-17). "Gender and positional biases in LLM-based hiring decisions: evidence from comparative CV/résumé evaluations" (in en). PeerJ Computer Science 12. doi:10.7717/peerj-cs.3628. ISSN 2376-5992. https://peerj.com/articles/cs-3628.
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37. ↑ "Using Google AI to convert speech to text". https://cloud.google.com/speech-to-text.
38. ↑ Clark, Jack (20 July 2016). "New Google AI Brings Automation to Customer Service". Bloomberg.com. https://www.bloomberg.com/news/articles/2016-07-20/new-google-ai-services-bring-automation-to-customer-service-iqv2rshg.
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43. ↑ "New Research: Majority of High School Students Use Generative AI for Schoolwork – Newsroom" (in en). https://newsroom.collegeboard.org/new-research-majority-high-school-students-use-generative-ai-schoolwork.
44. ↑ MARKEN, STEPHANIE (2026-04-01). "AI Is Routine for College Students, Despite Campus Limits" (in en). https://news.gallup.com/poll/704090/routine-college-students-despite-campus-limits.aspx.
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## Further reading

* 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.
* Kurzweil, Ray (2005). The Singularity is Near: When Humans Transcend Biology. New York: Viking. ISBN 978-0-670-03384-3. https://archive.org/details/singularityisnea00kurz.
* National Research Council (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press. ISBN 978-0-309-06278-7. OCLC 246584055. https://archive.org/details/fundingrevolutio00nati.
* 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.
* Felten, Ed (3 May 2016). "Preparing for the Future of Artificial Intelligence". https://obamawhitehouse.archives.gov/blog/2016/05/03/preparing-future-artificial-intelligence.

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* Parameter
* Hyperparameter
* Loss functions
* Regression
* Bias–variance tradeoff
* Double descent
* Overfitting
* Clustering
* Gradient descent
* SGD
* Quasi-Newton method
* Conjugate gradient method
* Backpropagation
* Attention
* Convolution
* Normalization
* Batchnorm
* Activation
* Softmax
* Sigmoid
* Rectifier
* Gating
* Weight initialization
* Regularization
* Datasets
* Augmentation
* Prompt engineering
* Reinforcement learning
* Q-learning
* SARSA
* Imitation
* Policy gradient
* Diffusion
* Latent diffusion model
* Autoregression
* Adversary
* RAG
* Uncanny valley
* RLHF
* Self-supervised learning
* Reflection
* Recursive self-improvement
* Hallucination
* Word embedding
* Vibe coding

Applications|

* Machine learning
* In-context learning
* Artificial neural network
* Deep learning
* Language model
* Large
* NMT
* Reasoning
* Model Context Protocol
* Intelligent agent
* Artificial human companion
* Humanity's Last Exam
* Lethal autonomous weapons (LAWs)
* Generative artificial intelligence (GenAI)
* (Hypothetical: Artificial general intelligence (AGI))
* (Hypothetical: Artificial superintelligence (ASI))
* Agent2Agent protocol

Implementations|

| Audio–visual|

* AlexNet
* WaveNet
* Human image synthesis
* HWR
* OCR
* Computer vision
* Speech synthesis
* 15.ai
* ElevenLabs
* Speech recognition
* Whisper
* Facial recognition
* AlphaFold
* Text-to-image models
* Aurora
* DALL-E
* Firefly
* Flux
* GPT Image
* Ideogram
* Imagen
* Midjourney
* Recraft
* Stable Diffusion
* Text-to-video models
* Dream Machine
* Runway Gen
* Hailuo AI
* Kling
* Sora
* Seedance
* Veo
* Music generation
* Riffusion
* Suno AI
* Udio

|

Text|

* Word2vec
* Seq2seq
* GloVe
* BERT
* T5
* Llama
* Chinchilla AI
* PaLM
* GPT
* 1
* 2
* 3
* J
* ChatGPT
* 4
* 4o
* o1
* o3
* 4.5
* 4.1
* o4-mini
* 5
* 5.1
* 5.2
* Claude
* Gemini
* Gemini (language model)
* Gemma
* Grok
* LaMDA
* BLOOM
* DBRX
* Project Debater
* IBM Watson
* IBM Watsonx
* Granite
* PanGu-Σ
* DeepSeek
* Qwen

Decisional|

* AlphaGo
* AlphaZero
* OpenAI Five
* Self-driving car
* MuZero
* Action selection
* AutoGPT
* Robot control

People|

* Alan Turing
* Warren Sturgis McCulloch
* Walter Pitts
* John von Neumann
* Christopher D. Manning
* Claude Shannon
* Shun'ichi Amari
* Kunihiko Fukushima
* Takeo Kanade
* Marvin Minsky
* John McCarthy
* Nathaniel Rochester
* Allen Newell
* Cliff Shaw
* Herbert A. Simon
* Oliver Selfridge
* Frank Rosenblatt
* Bernard Widrow
* Joseph Weizenbaum
* Seymour Papert
* Seppo Linnainmaa
* Paul Werbos
* Geoffrey Hinton
* John Hopfield
* Jürgen Schmidhuber
* Yann LeCun
* Yoshua Bengio
* Lotfi A. Zadeh
* Stephen Grossberg
* Alex Graves
* James Goodnight
* Andrew Ng
* Fei-Fei Li
* Alex Krizhevsky
* Ilya Sutskever
* Oriol Vinyals
* Quoc V. Le
* Ian Goodfellow
* Demis Hassabis
* David Silver
* Andrej Karpathy
* Ashish Vaswani
* Noam Shazeer
* Aidan Gomez
* John Schulman
* Mustafa Suleyman
* Jan Leike
* Daniel Kokotajlo
* François Chollet

Architectures|

* Neural Turing machine
* Differentiable neural computer
* Transformer
* Vision transformer (ViT)
* Recurrent neural network (RNN)
* Long short-term memory (LSTM)
* Gated recurrent unit (GRU)
* Echo state network
* Multilayer perceptron (MLP)
* Convolutional neural network (CNN)
* Residual neural network (RNN)
* Highway network
* Mamba
* Autoencoder
* Variational autoencoder (VAE)
* Generative adversarial network (GAN)
* Graph neural network (GNN)

Political|

* AI safety (Alignment)
* Ethics of AI
* EU AI Act
* Precautionary principle
* Regulation of AI
* Virtual politician

Social and economic|

* AI boom
* AI bubble
* AI literacy
* AI slop
* AI veganism
* AI winter
* Anthropomorphism
* In architecture
* In education
* In healthcare
* Chatbot psychosis
* Mental health
* In visual art

* Category

* v
* t
* e

Emerging technologies

Fields|

| Agriculture|

* Agricultural robot
* Cellular agriculture
* Closed ecological systems
* Cultured meat
* Genetically modified food
* Precision agriculture
* Vertical farming

|

Architecture|

* Arcology
* Building printing
* Contour crafting
* Domed city

Biomedical|

* Artificial uterus
* Ampakine
* Brain transplant
* Cryonics
* Cryoprotectant
* Cryopreservation
* Vitrification
* Suspended animation
* De-extinction
* Genetic engineering
* Gene therapy
* Head transplant
* Isolated brain
* Life extension
* Strategies for Engineered Negligible Senescence
* Nanomedicine
* Nanosensors
* Organ printing
* Personalized medicine
* Regenerative medicine
* Stem-cell therapy
* Tissue engineering
* Robot-assisted surgery
* Synthetic biology
* Synthetic genomics
* Virotherapy
* Oncolytic virus
* Tricorder
* Whole genome sequencing

Displays|

| Next generation|

* FED
* FLCD
* iMoD
* Laser
* LPD
* OLED
* OLET
* QD-LED
* SED
* TPD
* TDEL
* TMOS
* MicroLED

|

Screenless|

* Bionic contact lens
* Head-mounted display
* Head-up display
* Optical head-mounted display
* Virtual retinal display

Other|

* Autostereoscopy
* Flexible display
* Holographic display
* Computer-generated holography
* Multi-primary color display
* Ultra HD
* Volumetric display

Electronics|

* Electronic nose
* E-textiles
* Flexible electronics
* Molecular electronics
* Nanoelectromechanical systems
* Memristor
* Spintronics
* Thermal copper pillar bump
* Twistronics

Energy|

| Production|

* Airborne wind turbine
* Artificial photosynthesis
* Biofuels
* Carbon-neutral fuel
* Concentrated solar power
* Fusion power
* Home fuel cell
* Hydrogen economy
* Methanol economy
* Molten salt reactor
* Nantenna
* Photovoltaic pavement
* Space-based solar power
* Vortex engine

|

Storage|

* Beltway battery
* Compressed air energy storage
* Flywheel energy storage
* Grid energy storage
* Lithium–air battery
* Molten-salt battery
* Nanowire battery
* Research in lithium-ion batteries
* Silicon–air battery
* Thermal energy storage
* Ultracapacitor

Other|

* Smart grid
* Wireless power

Information and
communications

* Ambient intelligence
* Internet of things
* Artificial intelligence
* Applications of artificial intelligence
* Progress in artificial intelligence
* Machine translation
* Mobile translation
* Machine vision
* Semantic Web
* Speech recognition
* Atomtronics
* Carbon nanotube field-effect transistor
* Cybermethodology
* Fourth-generation optical discs
* 3D optical data storage
* Holographic data storage
* GPGPU
* Memory
* CBRAM
* FRAM
* Millipede
* MRAM
* NRAM
* PRAM
* Racetrack memory
* RRAM
* SONOS
* Optical computing
* RFID
* Chipless RFID
* Software-defined radio
* Three-dimensional integrated circuit

Manufacturing|

* 3D printing
* Claytronics
* Molecular assembler
* Utility fog

Materials science|

* Aerogel
* Amorphous metal
* Artificial muscle
* Conductive polymer
* Femtotechnology
* Fullerene
* Graphene
* High-temperature superconductivity
* High-temperature superfluidity
* Linear acetylenic carbon
* Metamaterials
* Metamaterial cloaking
* Metal foam
* Multi-function structures
* Nanotechnology
* Carbon nanotubes
* Molecular nanotechnology
* Nanomaterials
* Picotechnology
* Programmable matter
* Quantum dots
* Silicene
* Superalloy
* Synthetic diamond

Military|

* Antimatter weapon
* Caseless ammunition
* Directed-energy weapon
* Laser
* Maser
* Particle-beam weapon
* Sonic weapon
* Coilgun
* Railgun
* Plasma weapon
* Pure fusion weapon
* Stealth technology
* Vortex ring gun

Neuroscience|

* Artificial brain
* Brain–computer interface
* Electroencephalography
* Mind uploading
* Brain-reading
* Neuroinformatics
* Neuroprosthetics
* Bionic eye
* Brain implant
* Exocortex
* Retinal implant

Quantum|

* Quantum algorithms
* Quantum amplifier
* Quantum bus
* Quantum channel
* Quantum circuit
* Quantum complexity theory
* Quantum computing
* Quantum cryptography
* Quantum dynamics
* Quantum electronics
* Quantum error correction
* Quantum imaging
* Quantum information
* Quantum key distribution
* Quantum logic
* Quantum logic gates
* Quantum machine
* Quantum machine learning
* Quantum metamaterial
* Quantum metrology
* Quantum network
* Quantum neural network
* Quantum optics
* Quantum programming
* Quantum sensing
* Quantum simulator
* Quantum teleportation

Robotics|

* Domotics
* Nanorobotics
* Powered exoskeleton
* Self-reconfiguring modular robot
* Swarm robotics
* Uncrewed vehicle

Space science|

| Launch|

* Fusion rocket
* Non-rocket spacelaunch
* Mass driver
* Orbital ring
* Skyhook
* Space elevator
* Space fountain
* Space tether
* Reusable launch system

|

Propulsion|

* Beam-powered propulsion
* Ion thruster
* Laser propulsion
* Plasma propulsion engine
* Helicon thruster
* VASIMR
* Nuclear pulse propulsion
* Solar sail

Other|

* Interstellar travel
* Propellant depot
* Laser communication in space

Transport|

| Aerial|

* Adaptive compliant wing
* Backpack helicopter
* Delivery drone
* Flying car
* High-altitude platform
* Jet pack
* Pulse detonation engine
* Scramjet
* Spaceplane
* Supersonic transport

|

Land|

* Airless tire
* Alternative fuel vehicle
* Hydrogen vehicle
* Driverless car
* Ground effect train
* Hyperloop
* Maglev train
* Personal rapid transit
* Transit Elevated Bus
* Vactrain
* Vehicular communication systems

Pipeline|

* Pneumatic transport
* Automated vacuum collection

Other|

* Anti-gravity
* Cloak of invisibility
* Digital scent technology
* Force field
* Plasma window
* Immersive virtual reality
* Magnetic refrigeration
* Phased-array optics

Topics|

* Collingridge dilemma
* Differential technological development
* Disruptive Innovation
* Ephemeralization
* Exploratory engineering
* Fictional technology
* Proactionary principle
* Technological change
* Technological unemployment
* Technological convergence
* Technological evolution
* Technological paradigm
* Technology forecasting
* Accelerating change
* Moore's law
* Technological singularity
* Technology scouting
* Technology readiness level
* Technology roadmap
* Transhumanism

* Category
* List

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