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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 (AI) 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.
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.
Artificial intelligence in architecture is the use of artificial intelligence in automation, design, and planning in the architectural process or in assisting human skills in the field of architecture.
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.
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, and should be carefully reviewed by software developers before being accepted.[citation needed] The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing. 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.
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.
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.[better source needed]
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:
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Applications of artificial intelligence AI simulator
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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 (AI) 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.
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.
Artificial intelligence in architecture is the use of artificial intelligence in automation, design, and planning in the architectural process or in assisting human skills in the field of architecture.
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.
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, and should be carefully reviewed by software developers before being accepted.[citation needed] The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing. 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.
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.
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.[better source needed]
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: