Intelligent personal assistants use AI to understand many natural language requests in other ways than rudimentary commands. Common examples are Apple's Siri, Amazon's Alexa, and a more recent AI, ChatGPT by OpenAI.[13]
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.[15] Some models built via machine learning algorithms have over 90% accuracy in distinguishing between spam and legitimate emails.[16] 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.[17]
Speech translation technology attempts to convert one language's spoken words into another language. This potentially reduces language barriers in global commerce and cross-cultural exchange, enabling speakers of various languages to communicate with one another.[18]
AI has been used to automatically translate spoken language and textual content in products such as Microsoft Translator, Google Translate, and DeepL Translator.[19] Additionally, research and development are in progress to decode and conduct animal communication.[20][21]
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 and neural machine translations (NMTs). The old method of performing translation was to use a statistical machine translation (SMT) 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.[22]
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.[24] Facebook's DeepFace identifies human faces in digital images.
Kuki AI is a set of chatbots and other apps which were designed for entertainment and as a marketing tool.[44][45]Character.ai is another example of a chatbot being used for recreation.
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.[46]
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.[47] AI has been used to attempt to classify livestock pig call emotions,[20] automate greenhouses,[48] detect diseases and pests,[49] and optimize irrigation.[50]
Network protection: Machine learning improves intrusion detection systems by broadening the search beyond previously identified threats.
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.[52]
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.[53] In order to improve cloud computing security, Sahil (2015) created a user profile system for the cloud environment with AI techniques.[54]
Suspect user behavior: Machine learning can identify fraud or compromised applications as they occur.[55]
AI elevates teaching, focusing on significant issues like the knowledge nexus and educational equality. The evolution of AI in education and technology should be used to improve human capabilities in relationships where they do not replace humans. UNESCO recognizes the future of AI in education as an instrument to reach Sustainable Development Goal 4, called "Inclusive and Equitable Quality Education.” [56]
The World Economic Forum also stresses AI's contribution to students' overall improvement and transforming teaching into a more enjoyable process.[56]
Personalized Learning
AI driven tutoring systems, such as Khan Academy, Duolingo and Carnegie Learning are the forefoot of delivering personalized education.[57]
These platforms leverage AI algorithms to analyze individual learning patterns, strengths, and weaknesses, enabling the customization of content and Algorithm to suit each student's pace and style of learning.[57]
Administrative Efficiency
In educational institutions, AI is increasingly used to automate routine tasks like attendance tracking, grading and marking, which allows educators to devote more time to interactive teaching and direct student engagement.[58]
Furthermore, AI tools are employed to monitor student progress, analyze learning behaviors, and predict academic challenges, facilitating timely and proactive interventions for students who may be at risk of falling behind.[58]
Ethical and Privacy Concerns
Despite the benefits, the integration of AI in education raises significant ethical and privacy concerns, particularly regarding the handling of sensitive student data.[57]
It is imperative that AI systems in education are designed and operated with a strong emphasis on transparency, security, and respect for privacy to maintain trust and uphold the integrity of educational practices.[57]
Much of the regulation will be influenced by the AI Act, the world’s first comprehensive AI law. [59]
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.[60] Kasisto and Moneystream use AI.
Banks use AI to organize operations for bookkeeping, investing in stocks, and managing properties. AI can adapt to changes during non-business hours.[61]AI is used to combat fraud and financial crimes by monitoring behavioral patterns for any abnormal changes or anomalies.[62][63][64]
The use of AI in applications such as online trading and decision-making has changed major economic theories.[65] 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.[66] 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.[67]
Trading and investment
Algorithmic trading involves the use of AI systems to make trading decisions at speeds orders 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 entire portfolios that are AI-managed. Automated trading systems are typically used by large institutional investors but include smaller firms trading with their own AI systems.[68]
Large financial institutions use AI to assist with their investment practices. 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.[69]
ZestFinance's Zest Automated Machine Learning (ZAML) platform is used for credit underwriting. This platform uses machine learning to analyze data including purchase transactions and how a customer fills out a form to score borrowers. The platform is particularly useful to assign credit scores to those with limited credit histories.[71]
Audit
AI makes continuous auditing possible. Potential benefits include reducing audit risk, increasing the level of assurance, and reducing audit duration.[72][quantify]
Continuous auditing with AI allows real-time monitoring and reporting of financial activities and provides businesses with timely insights that can lead to quick decision making.[73]
Anti-money laundering
AI software, such as LaundroGraph which uses contemporary suboptimal datasets, could be used for anti-money laundering (AML).[74][75]
History
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 to save almost $10 million per year.[76] 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. "The major junctions of the system were to monitor premiums in the market, determine the optimum investment strategy, execute transactions when appropriate and modify the knowledge base through a learning mechanism."[77]
One of the first expert systems to help with financial plans was PlanPowerm and Client Profiling System, created by Applied Expert Systems (APEX). It was launched in 1986. It helped create personal financial plans for people.[78]
In the 1990s AI was applied to fraud detection. In 1993 FinCEN Artificial Intelligence System (FAIS) 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.[79] These expert systems were later replaced by machine learning systems.[80]
AI can enhance entrepreneurial activity and AI is one of the most dynamic areas for start-ups, with significant venture capital flowing into AI.[81]
Various countries are deploying AI military applications.[85] The main applications enhance command and control, communications, sensors, integration and interoperability.[86] Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles.[85] AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams.[86] AI was incorporated into military operations in Iraq and Syria.[85]
In 2023, the United States Department of Defense tested generative AI based on large language models to digitize and integrate data across the military.[87]
In the 2023 Israel–Hamas war, Israel used two AI systems to generate targets to strike: Habsora (translated: "the gospel") was used to compile a list of buildings to target, while "Lavender" produced a list of people. "Lavender" produced a list of 37,000 people to target.[88][89] The list of buildings to target included Gazan private homes of people that were suspected of affiliation to Hamas operatives. The combination of AI targeting technology with policy shift away from avoiding civilian targets resulted in unprecedented numbers of civilian deaths. IDF officials say the program addresses the previous issue of the air force running out of targets. Using Habsora, officials say that suspected and junior Hamas members homes significantly expand the "AI target bank." An internal source describes the process as a “mass assassination factory”.[90][89]
In 2024, the U.S. military trained artificial intelligence to identify airstrike targets during its operations in Iraq and Syria.[91]
In 2024 a Chinese laboratory at the Joint Operations College of the National Defense University in Shijiazhuang has created an AI military commander, for use in large-scale war simulations in the role of the commander-in-chief.[92]
Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[93][94] Military drones capable of autonomous action are in wide use.[95] The Ukrainian Army has developed 2024 autonomous Kamikazedrones in oder to make Russian interference during flight ineffective.[96] Many researchers avoid military applications.[86]
AI in healthcare is often used for classification, to evaluate a CT scan or electrocardiogram or to identify high-risk patients for population health. AI is helping with the high-cost problem of dosing. One study suggested that AI could save $16billion. In 2016, a study reported that an AI-derived formula derived the proper dose of immunosuppressant drugs to give to transplant patients.[97] Current research has indicated that non-cardiac vascular illnesses are also being treated with artificial intelligence (AI). For certain disorders, AI algorithms can aid in diagnosis, recommended treatments, outcome prediction, and patient progress tracking. As AI technology advances, it is anticipated that it will become more significant in the healthcare industry.[98]
The early detection of diseases like cancer is made possible by AI algorithms, which diagnose diseases by analyzing complex sets of medical data. For example, the IBM Watson system might be used to comb through massive data such as medical records and clinical trials to help diagnose a problem.[99] Microsoft's AI project Hanover helps doctors choose cancer treatments from among the more than 800 medicines and vaccines.[100][101] Its goal is to memorize all the relevant papers to predict which (combinations of) drugs will be most effective for each patient. Myeloid leukemia is one target. Another study reported on an AI that was as good as doctors in identifying skin cancers.[102] Another project monitors multiple high-risk patients by asking each patient questions based on data acquired from doctor/patient interactions.[103] In one study done with transfer learning, an AI diagnosed eye conditions similar to an ophthalmologist and recommended treatment referrals.[104]
Another study demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel judged better than a surgeon.[105]
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).[128][129][130][131]
Machine learning has been used for drug design.[132] It has also been used for predicting molecular properties and exploring large chemical/reaction spaces.[133] Computer-planned syntheses via computational reaction networks, described as a platform that combines "computational synthesis with AI algorithms to predict molecular properties",[134] have been used to explore the origins of life on Earth,[135] drug-syntheses and developing routes for recycling 200 industrial waste chemicals into important drugs and agrochemicals (chemical synthesis design).[136] There is research about which types of computer-aided chemistry would benefit from machine learning.[137] It can also be used for "drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials".[138] It has been used for the design of proteins with prespecified functional sites.[139][140]
It 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.[141]
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[142] or identifying functional DNA motifs.[143] It is widely used in genetic research.[144]
Similarly, there is research and development of biological "wetware computers" that can learn (e.g. for use as biosensors) and/or implantation into an organism's body (e.g. for use to control prosthetics).[154][155][156] Polymer-based artificial neurons operate directly in biological environments and define biohybrid neurons made of artificial and living components.[157][158]
Moreover, if whole brain emulation is possible via both scanning and replicating the, at least, bio-chemical brain – as premised in the form of digital replication in The Age of Em, possibly using physical neural networks – that may have applications as or more extensive than e.g. valued human activities and may imply that society would face substantial moral choices, societal risks and ethical problems[159][160] such as whether (and how) such are built, sent through space and used compared to potentially competing e.g. potentially more synthetic and/or less human and/or non/less-sentient types of artificial/semi-artificial intelligence.[additional citation(s) needed] An alternative or additive approach to scanning are types of reverse engineering of the brain.[161][162]
A subcategory of artificial intelligence is embodied,[163][164] some of which are mobile robotic systems that each consist of one or multiple robots that are able to learn in the physical world.
However, biological computers, even if both highly artificial and intelligent, are typically distinguished from synthetic, often silicon-based, computers – they could however be combined or used for the design of either. Moreover, many tasks may be carried out inadequately by artificial intelligence even if its algorithms were transparent, understood, bias-free, apparently effective, and goal-aligned and its trained data sufficiently large and cleansed – such as in cases were the underlying or available metrics, values or data are inappropriate. Computer-aided is a phrase used to describe human activities that make use of computing as tool in more comprehensive activities and systems such as AI for narrow tasks or making use of such without substantially relying on its results (see also: human-in-the-loop).[citation needed] A study described the biological as a limitation of AI with "as long as the biological system cannot be understood, formalized, and imitated, we will not be able to develop technologies that can mimic it" and that if it was understood this does not mean there being "a technological solution to imitate natural intelligence".[165] Technologies that integrate biology and are often AI-based include biorobotics.
Artificial intelligence is used in astronomy to analyze increasing amounts of available data[166][167] 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.[168] 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,[169] and more autonomous operation.[170][171][172][167]
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.[185]
Other fields of research
Evidence of general impacts
In April 2024, the Scientific Advice Mechanism to the European Commission published advice[186] including a comprehensive evidence review of the opportunities and challenges posed by artificial intelligence in scientific research.
As benefits, the evidence review[187] highlighted:
its role in accelerating research and innovation
its capacity to automate workflows
enhancing dissemination of scientific work
As challenges:
limitations and risks around transparency, reproducibility and interpretability
poor performance (inaccuracy)
risk of harm through misuse or unintended use
societal concerns including the spread of misinformation and increasing inequalities
Machine learning can help to restore and attribute ancient texts.[188] It can help to index texts for example to enable better and easier searching[189] and classification of fragments.[190]
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.[193][194] 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.[195][196] In the future, it may be possible that such can be used to automate the discovery of physical laws of complex systems.[195]
Materials science
AI could be used for materials optimization and discovery such as the discovery of stable materials and the prediction of their crystal structure.[197][198][199]
In November 2023, researchers at Google DeepMind and Lawrence Berkeley National Laboratory announced that they had developed an AI system known as GNoME. This system has contributed to materials science by discovering over 2 million new materials within a relatively short timeframe. GNoME employs deep learning techniques to efficiently explore potential material structures, achieving a significant increase in the identification of stable inorganic crystal structures. The system's predictions were validated through autonomous robotic experiments, demonstrating a noteworthy success rate of 71%. The data of newly discovered materials is publicly available through the Materials Project database, offering researchers the opportunity to identify materials with desired properties for various applications. This development has implications for the future of scientific discovery and the integration of AI in material science research, potentially expediting material innovation and reducing costs in product development. The use of AI and deep learning suggests the possibility of minimizing or eliminating manual lab experiments and allowing scientists to focus more on the design and analysis of unique compounds.[200][201][202]
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,[203] and for quickly understanding the behavior of malware.[204][205][206] It can be used to reverse engineer artificial intelligence models.[207] 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[208] or protein design for prespecified functional sites.[139][140] Biological network reverse engineering could model interactions in a human understandable way, e.g. bas on time series data of gene expression levels.[209]
AI is a mainstay of law-related professions. Algorithms and machine learning do some tasks previously done by entry-level lawyers.[210] While its use is common, it is not expected to replace most work done by lawyers in the near future.[211]
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. Furthermore, FRS has shown to have much a better ability to identify individuals when video clarity and visibility are low in comparison to human participants. [213]
One concern relates to algorithmic bias, AI programs may become biased after processing data that exhibits bias.[215]ProPublica claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than that of white defendants.[214]
In 2019, the city of Hangzhou, China established a pilot program artificial intelligence-based Internet Court to adjudicate disputes related to ecommerce and internet-related intellectual property claims.[216]:124 Parties appear before the court via videoconference and AI evaluates the evidence presented and applies relevant legal standards.[216]:124
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.[217]
Job search
AI has simplified the recruiting /job search process for both recruiters and job seekers. According to Raj Mukherjee from Indeed, 65% of job searchers search again within 91 days after hire. An AI-powered engine streamlines the complexity of job hunting by assessing information on job skills, salaries, and user tendencies, matching job seekers to the most relevant positions. Machine intelligence calculates appropriate wages and highlights resume information for recruiters using NLP, which extracts relevant words and phrases from text. Another application is an AI resume builder that compiles a CV in 5 minutes.[218]Chatbots assist website visitors and refine workflows.
A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately.[221] 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.[222] Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making.[223]
Hospitality
In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs.[224] AI hotel services come in the form of a chatbot,[225] application, virtual voice assistant and service robots.
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.
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.[239]
In January 2016,[240] the Horizon 2020 program financed the InVID Project[241][242] to help journalists and researchers detect fake documents, made available as browser plugins.[243][244]
In June 2016, the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face,[245] a program that animates photographs of faces, mimicking the facial expressions of another person. The technology has been demonstrated animating the faces of people including Barack Obama and Vladimir Putin. Other methods have been demonstrated based on deep neural networks, from which the name deep fake was taken.
In September 2018, U.S. Senator Mark Warner proposed to penalize social media companies that allow sharing of deep-fake documents on their platforms.[246]
In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames.[247]DARPA gave 68 million dollars to work on deep-fake detection.[247]
Artificial intelligence for video surveillance utilizes computer software programs that analyze the audio and images from video surveillance cameras in order to recognize humans, vehicles, objects and events. Security contractors program is the software to define restricted areas within the camera's view (such as a fenced off area, a parking lot but not the sidewalk or public street outside the lot) and program for times of day (such as after the close of business) for the property being protected by the camera surveillance. The artificial intelligence ("A.I.") sends an alert if it detects a trespasser breaking the "rule" set that no person is allowed in that area during that time of day.
AI algorithms have been used to detect deepfake videos.[252][253]
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.[254] 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.[254] Way mark Studios is experimenting with using these AI tools to generate advertisements and commercials for companies in mere seconds.[254] 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.[255]
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.[256] The algorithm behind Emily Howell is registered as a US patent.[257]
In 2012, AI Iamus created the first complete classical album.[258]
Melomics creates computer-generated music for stress and pain relief.[261]
At Sony CSL Research Laboratory, the Flow Machines software creates pop songs by learning music styles from a huge database of songs. It can compose in multiple styles.
Yseop, uses AI to turn structured data into natural language comments and recommendations. Yseop writes financial reports, executive summaries, personalized sales or marketing documents and more in multiple languages, including English, Spanish, French, and German.[267]
TALESPIN made up stories similar to the fables of Aesop. The program started with a set of characters who wanted to achieve certain goals. The story narrated their attempts to satisfy these goals.[citation needed] Mark Riedl and Vadim Bulitko asserted that the essence of storytelling was experience management, or "how to balance the need for a coherent story progression with user agency, which is often at odds".[268]
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.[269] In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.[270]
South Korean company Hanteo Global uses a journalism bot to write articles.[271]
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.
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.[273]
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.[273]
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.[273]
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.[274]
Millions of its articles have been edited by bots[278] which however are usually not artificial intelligence software. Many AI platforms use Wikipedia data,[279] mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences,[280]detecting covert vandalism[281] 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.[282][283]
In video games, AI is routinely used to generate behavior in non-player characters (NPCs). In addition, AI is used for pathfinding. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks.[who?] 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).[284][285] AI is also used in Alien Isolation (2014) as a way to control the actions the Alien will perform next.[286]
Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from AI research.[287][which?]
AI has been used to produce visual art. The first AI art program, called AARON, was developed by Harold Cohen in 1968[288] 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.[289]
AI platforms such as "DALL-E",[290]Stable Diffusion,[290]Imagen,[291] and Midjourney[292] have been used for generating visual images from inputs such as text or other images.[293] 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.
Since their design in 2014, generative adversarial networks (GANs) have been used by AI artists. GAN computer programming, generates technical images through machine learning frameworks that surpass the need for human operators.[288] Examples of GAN programs that generate art include Artbreeder and DeepDream.
Art analysis
In addition to the creation of original art, research methods that utilize AI have been generated to quantitatively analyze digital art collections. Although the main goal of the large-scale digitization of artwork in the past few decades was to allow for accessibility and exploration of these collections, the use of AI in analyzing them has brought about new research perspectives.[294] Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art.[295] While distant viewing includes the analysis of large collections, close reading involves one piece of artwork.
Computer animation
AI has been in use since the early 2000s, most notably by a system designed by Pixar called "Genesis".[296] It was designed to learn algorithms and create 3D models for its characters and props. Notable movies that used this technology included Up and The Good Dinosaur.[297] AI has been used less ceremoniously in recent years. In 2023, it was revealed Netflix of Japan was using AI to generate background images for their upcoming show to be met with backlash online.[298] In recent years, motion capture became an easily accessible form of AI animation. For example, Move AI is a program built to capture any human movement and reanimate it in its animation program using learning AI.[299]
Utilities
Energy system
Power electronics converters are used in renewable energy, energy storage, electric vehicles and high-voltage direct current transmission. These converters are failure-prone, which can interrupt service and require costly maintenance or catastrophic consequences in mission critical applications.[citation needed] AI can guide the design process for reliable power electronics converters, by calculating exact design parameters that ensure the required lifetime.[300]
The U.S. Department of Energy underscores AI's pivotal role in realizing national climate goals. With AI, the ambitious target of achieving net-zero greenhouse gas emissions across the economy becomes feasible. AI also helps make room for wind and solar on the grid by avoiding congestion and increasing grid reliability. [301]
Many telecommunications companies make use of heuristic search to manage their workforces. For example, BT Group deployed heuristic search[306] in an application that schedules 20,000 engineers. Machine learning is also used for speech recognition (SR), including of voice-controlled devices, and SR-related transcription, including of videos.[307][308]
Artificial intelligence has been combined with digital spectrometry by IdeaCuria Inc.,[309][310] enable applications such as at-home water quality monitoring.
Toys and games
In the 1990s, early artificial intelligence tools controlled Tamagotchis and Giga Pets, the Internet, and the first widely released robot, Furby. Aibo was a domestic robot in the form of a robotic dog with intelligent features and autonomy.
Mattel created an assortment of AI-enabled toys that "understand" conversations, give intelligent responses, and learn.[311]
Oil and gas
Oil and gas companies have used artificial intelligence tools to automate functions, foresee equipment issues, and increase oil and gas output.[312][313]
AI in transport is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major development challenge is the complexity of transportation systems that involves independent components and parties, with potentially conflicting objectives.[314]
Transportation's complexity means that in most cases training an AI in a real-world driving environment is impractical. Simulator-based testing can reduce the risks of on-road training.[329]
AI underpins self-driving vehicles. Companies involved with AI include Tesla, Waymo, and General Motors. AI-based systems control functions such as braking, lane changing, collision prevention, navigation and mapping.[330]
Autonomous trucks are in the testing phase. The UK government passed legislation to begin testing of autonomous truck platoons in 2018.[331] A group of autonomous trucks follow closely behind each other. German corporation Daimler is testing its Freightliner Inspiration.[332]
Autonomous vehicles require accurate maps to be able to navigate between destinations.[333] Some autonomous vehicles do not allow human drivers (they have no steering wheels or pedals).[334]
Traffic management
AI has been used to optimize traffic management, which reduces wait times, energy use, and emissions by as much as 25 percent.[335]
Smart traffic lights have been developed at Carnegie Mellon since 2009. Professor Stephen Smith has started a company since then Surtrac that has installed smart traffic control systems in 22 cities. It costs about $20,000 per intersection to install. Drive time has been reduced by 25% and traffic jam waiting time has been reduced by 40% at the intersections it has been installed.[336]
Military
The Royal Australian Air Force (RAAF) Air Operations Division (AOD) uses AI for expert systems. AIs operate as surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[337]
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.[338]
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,[339] 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.[340] The software compensated for damaged components by relying on the remaining undamaged components.[341]
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.[342]
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. They differ in functionality, quality, speed, and approach to privacy.[363] Code suggestions could be incorrect, and should be carefully reviewed by software developers before accepted.
GitHub Copilot is an artificial intelligence model developed by GitHub and OpenAI that is able to autocomplete code in multiple programming languages.[364] Price for individuals: $10/mo or $100/yr, with one free month trial.
Tabnine was created by Jacob Jackson and was originally owned by Tabnine company. In late 2019, Tabnine was acquired by Codota.[365] Tabnine tool is available as plugin to most popular IDEs. It offers multiple pricing options, including limited "starter" free version.[366]
CodiumAI by CodiumAI, small startup in Tel Aviv, offers automated test creation. Currently supports Python, JS, and TS.[367]
Ghostwriter by Replit offers code completion and chat.[368] They have multiple pricing plans, including a free one and a "Hacker" plan for $7/month.
CodeWhisperer by Amazon collects individual users' content, including files open in the IDE. They claim to focus on security both during transmission and when storing.[369] Individual plan is free, professional plan is $19/user/month.
Other tools: SourceGraph Cody, CodeCompleteFauxPilot, Tabby[363]
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.[370]
Machine learning has been used for noise-cancelling in quantum technology,[371] including quantum sensors.[372] Moreover, there is substantial research and development of using quantum computers with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic (quantum-)computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications,[373][374] and quantum machine learning is a field with some variety of applications under development. AI could be used for quantum simulators which may have the application of solving physics and chemistry[375][376] problems as well as for quantum annealers for training of neural networks for AI applications.[377] There may also be some usefulness in chemistry, e.g. for drug discovery, and in materials science, e.g. for materials optimization/discovery (with possible relevance to quantum materials manufacturing[198][199]).[378][379][380][bettersourceneeded]
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:[381]
An optical character reader is used in the extraction of data in business documents like invoices and receipts. It can also be used in business contract documents e.g. employment agreements to extract critical data like employment terms, delivery terms, termination clauses, etc.[382]
AI's potential in optimization of design, planning and productivity have been noted as accelerators in the field of architectural work. The ability of AI to potentially amplify an architect's design process has also been noted. Fears of the replacement of aspects or core processes of the architectural profession by Artificial Intelligence have also been raised, as well as the philosophical implications on the profession and creativity.[383][384][385]
AI in architecture has created a way for architects to create things beyond human understanding. AI implementation of machine learning text-to-render technologies, like DALL-E and stable Diffusion, gives power to visualization complex.[386]
AI allows designers to demonstrate their creativity and even invent new ideas while designing. In future, AI will not replace architects; instead, it will improve the speed of translating ideas sketching.[386]
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Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.
Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form of decisions. "Understanding" in this context signifies the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Advances in the field of deep learning have allowed neural networks to surpass many previous approaches in performance.
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
An AI takeover is an imagined scenario in which artificial intelligence (AI) emerges as the dominant form of intelligence on Earth and computer programs or robots effectively take control of the planet away from the human species, which relies on human intelligence. Possible scenarios include replacement of the entire human workforce due to automation, takeover by a superintelligent AI (ASI), and the notion of a robot uprising. Stories of AI takeovers have been popular throughout science fiction, but recent advancements have made the threat more real. Some public figures, such as Stephen Hawking and Elon Musk, have advocated research into precautionary measures to ensure future superintelligent machines remain under human control.
Weak artificial intelligence is artificial intelligence that implements a limited part of the mind, or, as Artificial Narrow Intelligece, is focused on one narrow task.
The following outline is provided as an overview of and topical guide to artificial intelligence:
The ethics of artificial intelligence covers a broad range of topics within the field that are considered to have particular ethical stakes. This includes algorithmic biases, fairness, automated decision-making, accountability, privacy, and regulation. It also covers various emerging or potential future challenges such as machine ethics, lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status, artificial superintelligence and existential risks.
Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a multidisciplinary branch of computer science that aims to create machines and systems capable of performing tasks that typically require human intelligence. AI applications have been used in a wide range of fields including medical diagnosis, finance, robotics, law, video games, agriculture, and scientific discovery. However, many AI applications are not perceived as AI: "A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore." "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 1990s and early 2000s, AI technology became widely used as elements of larger systems, but the field was rarely credited for these successes at the time.
Eric Joel Horvitz is an American computer scientist, and Technical Fellow at Microsoft, where he serves as the company's first Chief Scientific Officer. He was previously the director of Microsoft Research Labs, including research centers in Redmond, WA, Cambridge, MA, New York, NY, Montreal, Canada, Cambridge, UK, and Bangalore, India.
Machine ethics is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. It should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with technology's grander social effects.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence (AI), its subdisciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
Artificial intelligence in healthcare is the application of artificial intelligence (AI) to analyze and understand complex medical and healthcare data. In some cases, it can exceed or augment human capabilities by providing better or faster ways to diagnose, treat, or prevent disease.
Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. The main focus is usually on the reasoning behind the decisions or predictions made by the AI which are made more understandable and transparent. This has been brought up again as a topic of active research as users now need to know the safety and explain what automated decision making is in different applications. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.
This article presents a detailed timeline of events in the history of computing from 2020 to the present. For narratives explaining the overall developments, see the history of computing.
Government by algorithm is an alternative form of government or social ordering where the usage of computer algorithms is applied to regulations, law enforcement, and generally any aspect of everyday life such as transportation or land registration. The term "government by algorithm" has appeared in academic literature as an alternative for "algorithmic governance" in 2013. A related term, algorithmic regulation, is defined as setting the standard, monitoring and modifying behaviour by means of computational algorithms – automation of judiciary is in its scope. In the context of blockchain, it is also known as blockchain governance.
The impact of artificial intelligence on workers includes both applications to improve worker safety and health, and potential hazards that must be controlled.
Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.
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