Applications of artificial intelligence

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Artificial intelligence
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Artificial intelligence (AI) has been used in applications throughout industry and academia. In a manner analogous to electricity or computers, AI serves as a general-purpose technology. AI programes emulate perception and understanding, and are designed to adapt to new information and new situations. Machine learning has been used for various scientific and commercial purposes including language translation, image recognition, decision-making, credit scoring, and e-commerce.

Internet and e-commerce

Main article: Marketing and artificial intelligence

Web feeds and posts

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

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

Virtual assistants and search

Main article: Virtual assistant

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.

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

Spam filtering

Main article: Spam filter

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. Some models built via machine learning algorithms have over 90% accuracy in distinguishing between spam and legitimate emails. 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.

Language translation

Main article: Machine translation

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.

AI has been used to automatically translate spoken language and textual content in products such as Microsoft Translator, Google Translate, and DeepL Translator. Additionally, research and development are in progress to decode and conduct animal communication.

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.

Facial recognition and image labeling

Main articles: Facial recognition system and Automatic image annotation

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.

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. Facebook's DeepFace identifies human faces in digital images.

Games and entertainment

Games have been a major application of AI's capabilities since the 1950s. In the 21st century, AIs have beaten human players in many games, including chess (Deep Blue), Jeopardy! (Watson), Go (AlphaGo), poker (Pluribus and Cepheus), E-sports (StarCraft), and general game playing (AlphaZero and MuZero).

Kuki AI is a set of chatbots and other apps which were designed for entertainment and as a marketing tool. Character.ai is another example of a chatbot being used for recreation.

Economic and social challenges

Agriculture

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

Cyber security

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

Applications of AI in cyber security include:

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.
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. In order to improve cloud computing security, Sahil (2015) created a user profile system for the cloud environment with AI techniques.

Suspect user behavior: Machine learning can identify fraud or compromised applications as they occur.

Education

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. 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. AI is used to combat fraud and financial crimes by monitoring behavioral patterns for any abnormal changes or anomalies.

The use of AI in applications such as online trading and decision-making has changed major economic theories. For example, AI-based buying and selling platforms estimate personalized demand and supply curves, thus enabling individualized pricing. AI systems reduce information asymmetry in the market and thus make markets more efficient. The application of artificial intelligence in the financial industry can alleviate the financing constraints of non-state-owned enterprises, especially for smaller and more innovative enterprises.

Trading and investment

Algorithmic trading involves 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.

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.

Underwriting

Online lender Upstart uses machine learning for underwriting.

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.

Audit

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

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.

Anti-money laundering

AI software, such as LaundroGraph which uses contemporary suboptimal datasets, could be used for anti-money laundering (AML).

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. 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."

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.

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. These expert systems were later replaced by machine learning systems.

AI can enhance entrepreneurial activity and AI is one of the most dynamic areas for start-ups, with significant venture capital flowing into AI.

Government

Main article: Artificial intelligence in government

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

Military

Main article: Military applications of artificial intelligence

Various countries are deploying AI military applications. The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. 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.

AI has been used in military operations in Iraq, Syria, Israel and Ukraine.

Health

Healthcare

Main article: Artificial intelligence in healthcare

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 $16 billion. In 2016, a study reported that an AI-derived formula derived the proper dose of immunosuppressant drugs to give to transplant patients. 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.

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. Microsoft's AI project Hanover helps doctors choose cancer treatments from among the more than 800 medicines and vaccines. 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. Another project monitors multiple high-risk patients by asking each patient questions based on data acquired from doctor/patient interactions. In one study done with transfer learning, an AI diagnosed eye conditions similar to an ophthalmologist and recommended treatment referrals.

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.

Artificial neural networks are used as clinical decision support systems for medical diagnosis, such as in concept processing technology in EMR software.

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

Screening
Heart sound analysis
Companion robots for elder care
Medical record analysis
Treatment plan design
Medication management
Assisting blind people
Consultations
Drug creation (e.g. by identifying candidate drugs and by using existing drug screening data such as in life extension research)
Clinical training
Outcome prediction for surgical procedures
HIV prognosis
Identifying genomic pathogen signatures of novel pathogens or identifying pathogens via physics-based fingerprints (including pandemic pathogens)
Helping link genes to their functions, otherwise analyzing genes and identification of novel biological targets
Help development of biomarkers
Help tailor therapies to individuals in personalized medicine/precision medicine

Workplace health and safety

Main article: Workplace impact of artificial intelligence § Health and safety applications

AI-enabled chatbots decrease the need for humans to perform basic call center tasks.

Machine learning in sentiment analysis can spot fatigue in order to prevent overwork. Similarly, decision support systems can prevent industrial disasters and make disaster response more efficient. For manual workers in material handling, predictive analytics may be used to reduce musculoskeletal injury. Data collected from wearable sensors can improve workplace health surveillance, risk assessment, and research.

AI can auto-code workers' compensation claims. AI-enabled virtual reality systems can enhance safety training for hazard recognition. AI can more efficiently detect accident near misses, which are important in reducing accident rates, but are often underreported.

Biochemistry

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).

Chemistry and biology

Machine learning has been used for drug design. It has also been used for predicting molecular properties and exploring large chemical/reaction spaces. Computer-planned syntheses via computational reaction networks, described as a platform that combines "computational synthesis with AI algorithms to predict molecular properties", have been used to explore the origins of life on Earth, drug-syntheses and developing routes for recycling 200 industrial waste chemicals into important drugs and agrochemicals (chemical synthesis design). There is research about which types of computer-aided chemistry would benefit from machine learning. It can also be used for "drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials". It has been used for the design of proteins with prespecified functional sites.

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.

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 or identifying functional DNA motifs. It is widely used in genetic research.

There also is some use of machine learning in synthetic biology, disease biology, nanotechnology (e.g. nanostructured materials and bionanotechnology), and materials science.

Novel types of machine learning

See also: Artificial brain and Automated reasoning

There are also prototype robot scientists, including robot-embodied ones like the two Robot Scientists, which show a form of "machine learning" not commonly associated with the term.

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). Polymer-based artificial neurons operate directly in biological environments and define biohybrid neurons made of artificial and living components.

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 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. An alternative or additive approach to scanning are types of reverse engineering of the brain.

A subcategory of artificial intelligence is embodied, some of which are mobile robotic systems that each consist of one or multiple robots that are able to learn in the physical world.

Digital ghosts

Main article: Resurrection § Digital ghosts

Biological computing in AI and as AI

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). 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". Technologies that integrate biology and are often AI-based include biorobotics.

Astronomy, space activities and ufology

Artificial intelligence is used in astronomy to analyze increasing amounts of available data 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. 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, and more autonomous operation.

In the search for extraterrestrial intelligence (SETI), machine learning has been used in attempts to identify artificially generated electromagnetic waves in available data – such as real-time observations – and other technosignatures, e.g. via anomaly detection. In ufology, the SkyCAM-5 project headed by Prof. Hakan Kayal and the Galileo Project headed by Avi Loeb use machine learning to attempt to detect and classify types of UFOs. 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.

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.

Other fields of research

Evidence of general impacts

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

As benefits, the evidence review 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

Physics

Main article: 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. 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. In the future, it may be possible that such can be used to automate the discovery of physical laws of complex systems.

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.

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.

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, and for quickly understanding the behavior of malware. It can be used to reverse engineer artificial intelligence models. 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 or protein design for prespecified functional sites. Biological network reverse engineering could model interactions in a human understandable way, e.g. bas on time series data of gene expression levels.

Law

Main article: Legal informatics § Artificial intelligence

Legal analysis

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

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

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. 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.

COMPAS is a commercial system used by U.S. courts to assess the likelihood of recidivism.

One concern relates to algorithmic bias, AI programs may become biased after processing data that exhibits bias. ProPublica claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than that of white defendants.

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. Parties appear before the court via videoconference and AI evaluates the evidence presented and applies relevant legal standards.

Services

Human resources

Main article: Artificial intelligence in hiring

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.

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. Chatbots assist website visitors and refine workflows.

Online and telephone customer service

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

A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds and connecting the customer with a human representative. Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making.

Hospitality

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

Media

See also: § Telecommunications, and Synthetic media

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

Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement.

Motion interpolation
Pixel-art scaling algorithms
Image scaling
Image restoration
Photo colorization
Film restoration and video upscaling
Photo tagging
Automated species identification (such as identifying plants, fungi and animals with an app)
Text-to-image models such as DALL-E, Midjourney and Stable Diffusion
Image to video
Text to video such as Make-A-Video from Meta, Imagen video and Phenaki from Google
Text to music with AI models such as MusicLM
Text to speech such as ElevenLabs and 15.ai
Motion capture
Make image transparent

Deep-fakes

Deep-fakes can be used for comedic purposes but are better known for fake news and hoaxes.

Deepfakes can portray individuals in harmful or compromising situations, causing significant reputational damage and emotional distress, especially when the content is defamatory or violates personal ethics. While defamation and false light laws offer some recourse, their focus on false statements rather than fabricated images or videos often leaves victims with limited legal protection and a challenging burden of proof.

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

In June 2016, the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face, a program that animates photographs of faces, mimicking the facial expressions of another person. 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.

In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames. DARPA gave 68 million dollars to work on deep-fake detection.

Audio deepfakes and AI software capable of detecting deep-fakes and cloning human voices have been developed.

Respeecher is a program that enables one person to speak with the voice of another.

Video surveillance analysis and manipulated media detection

See also: Web scraping, Photograph manipulation, and Video manipulation This section is an excerpt from Video content analysis § Artificial Intelligence. 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.

Video production

Artificial intelligence is also starting to be used in video production, with tools and software being developed that utilize generative AI in order to create new video, or alter existing video. Some of the major tools that are being used in these processes currently are DALL-E, Mid-journey, and Runway. Way mark Studios utilized the tools offered by both DALL-E and Mid-journey to create a fully AI generated film called The Frost in the summer of 2023. Way mark Studios is experimenting with using these AI tools to generate advertisements and commercials for companies in mere seconds. Yves Bergquist, a director of the AI & Neuroscience in Media Project at USC's Entertainment Technology Center, says post production crews in Hollywood are already using generative AI, and predicts that in the future more companies will embrace this new technology.

Music

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 managed to become well known in the field of algorithmic computer music. The algorithm behind Emily Howell is registered as a US patent.

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

AIVA (Artificial Intelligence Virtual Artist), composes symphonic music, mainly classical music for film scores. It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.

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

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.

The Watson Beat uses reinforcement learning and deep belief networks to compose music on a simple seed input melody and a select style. The software was open sourced and musicians such as Taryn Southern collaborated with the project to create music.

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

Writing and reporting

Sports writing

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

After being unable to cover every Minor League Baseball game with a large team, Associated Press collaborated with Automated Insights in 2016 to create game recaps that were automated by artificial intelligence.

UOL in Brazil expanded the use of AI in its writing. Rather than just generating news stories, they programmed the AI to include commonly searched words on Google.

El Pais, a Spanish news site that covers many things including sports, allows users to make comments on each news article. They use the Perspective API to moderate these comments and if the software deems a comment to contain toxic language, the commenter must modify it in order to publish it.

A local Dutch media group used AI to create automatic coverage of amateur soccer, set to cover 60,000 games in just a single season. NDC partnered with United Robots to create this algorithm and cover what would have never been possible before without an extremely large team.

Lede AI has been used in 2023 to take scores from high school football games to generate stories automatically for the local newspaper. This was met with significant criticism from readers for the very robotic diction that was published. With some descriptions of games being a "close encounter of the athletic kind," readers were not pleased and let the publishing company, Gannett, know on social media. Gannett has since halted their used of Lede AI until they come up with a solution for what they call an experiment.

Misplaced Pages

This section is an excerpt from Artificial intelligence in Wikimedia projects. Artificial intelligence is used in Misplaced Pages and other Wikimedia projects for the purpose of developing those projects. Human and bot interaction in Wikimedia projects is routine and iterative.

Millions of its articles have been edited by bots which however are usually not artificial intelligence software. Many AI platforms use Misplaced Pages data, mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Misplaced Pages such as for identifying outdated sentences, detecting covert vandalism or recommending articles and tasks to new editors.

Machine translation (see above) has also be used for translating Misplaced Pages articles and could play a larger role in creating, updating, expanding, and generally improving articles in the future. A content translation tool allows editors of some Wikipedias to more easily translate articles across several select languages.

Video games

Main article: Artificial intelligence in video games

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. Games with less typical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010). AI is also used in Alien Isolation (2014) as a way to control the actions the Alien will perform next.

Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from AI research.

Art

Main article: Artificial intelligence art

AI has been used to produce visual art. The first AI art program, called AARON, was developed by Harold Cohen in 1968 with the goal of being able to code the act of drawing. It started by creating simple black and white drawings, and later to painting using special brushes and dyes that were chosen by the program itself without mediation from Cohen.

AI platforms such as "DALL-E", Stable Diffusion, Imagen, and Midjourney have been used for generating visual images from inputs such as text or other images. Some AI tools allow users to input images and output changed versions of that image, such as to display an object or product in different environments. AI image models can also attempt to replicate the specific styles of artists, and can add visual complexity to rough sketches.

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. 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. Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art. While distant viewing includes the analysis of large collections, close reading involves one piece of artwork.

Computer animation

AI has been in use since the early 2000s, most notably by a system designed by Pixar called "Genesis". 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. 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. 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.

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. AI can guide the design process for reliable power electronics converters, by calculating exact design parameters that ensure the required lifetime.

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.

Machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid).

Telecommunications

Many telecommunications companies make use of heuristic search to manage their workforces. For example, BT Group deployed heuristic search 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.

Manufacturing

Main articles: Artificial intelligence in industry and Artificial intelligence in heavy industry

Sensors

Artificial intelligence has been combined with digital spectrometry by IdeaCuria Inc., 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.

Oil and gas

Oil and gas companies have used artificial intelligence tools to automate functions, foresee equipment issues, and increase oil and gas output.

Transport

Automotive

Main articles: Vehicular automation and Self-driving car

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.

AI-based fuzzy logic controllers operate gearboxes. For example, the 2006 Audi TT, VW Touareg and VW Caravell feature the DSP transmission. A number of Škoda variants (Škoda Fabia) include a fuzzy logic-based controller. Cars have AI-based driver-assist features such as self-parking and adaptive cruise control.

There are also prototypes of autonomous automotive public transport vehicles such as electric mini-buses as well as autonomous rail transport in operation.

There also are prototypes of autonomous delivery vehicles, sometimes including delivery robots.

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.

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.

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

Autonomous vehicles require accurate maps to be able to navigate between destinations. Some autonomous vehicles do not allow human drivers (they have no steering wheels or pedals).

Traffic management

AI has been used to optimize traffic management, which reduces wait times, energy use, and emissions by as much as 25 percent.

Cameras with radar and ultrasonic acoustic location sensors, while using predictive algorithms to have artificially intelligent traffic lights to make traffic flow better

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.

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.

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.

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, 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. The software compensated for damaged components by relying on the remaining undamaged components.

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.

Maritime

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

Environmental monitoring

Early-warning systems

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

Computer science

Programming assistance

Main articles: Automatic programming and Programming environment

AI-powered code assisting tools

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. 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. 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. Tabnine tool is available as plugin to most popular IDEs. It offers multiple pricing options, including limited "starter" free version.

CodiumAI by CodiumAI, small startup in Tel Aviv, offers automated test creation. Currently supports Python, JS, and TS.

Ghostwriter by Replit offers code completion and chat. 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. Individual plan is free, professional plan is $19/user/month.

Other tools: SourceGraph Cody, CodeCompleteFauxPilot, Tabby

Neural network design

AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet.

Quantum computing

Further information: Quantum machine learning See also: § Chemistry and biology

Machine learning has been used for noise-cancelling in quantum technology, including quantum sensors. 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, 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 problems as well as for quantum annealers for training of neural networks for AI applications. 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).

Historical contributions

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

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

Business

Content extraction

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.

Architecture

This section is an excerpt from Artificial intelligence in architecture.

Artificial intelligence in architecture describes the use of artificial intelligence in automation, design and planning in the architectural process or in assisting human skills in the field of architecture. Artificial Intelligence is thought to potentially lead to and ensue major changes in architecture.

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.

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.

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.

List of applications

This section is in list format but may read better as prose. You can help by converting this section, if appropriate. Editing help is available. (December 2021)

Optical character recognition
Handwriting recognition
Speech recognition
Face recognition
Generative artificial intelligence
Synthetic media
Artificial creativity
Computer vision
Virtual reality
Image processing
Photo and video manipulation
Diagnosis (artificial intelligence)
Game theory and strategic planning
Game artificial intelligence and computer game bot
Natural language processing, translation and chatterbots
Nonlinear control and robotics
Chatbots and assistant apps like Alexa, Google Assistant, Siri
Social bot
To transcribe music
Law related services
Healthcare
Education and Learning Disabilities related issues
User activity monitoring, personalized targeted promotion and marketing via ads
Humanoids
Games like DeepBlue
Agent-based models
- Agent-based computational economics
- Artificial life
Automated reasoning
- Automated theorem proving
- Proof assistants
Automation
Bio-inspired computing
Concept mining
Data mining
Knowledge representation
Semantic Web
Email spam filtering
Filtering hate speech, nudity, and other unwanted content.
Robotics
- Behavior-based robotics
- Cognitive robotics
- Cybernetics
- Developmental robotics (epigenetic)
- Evolutionary robotics
- Human-robot interaction
Hybrid intelligent system
Intelligent agent
Intelligent control
Litigation

Footnotes

Brynjolfsson, Erik; Mitchell, Tom (22 December 2017). "What can machine learning do? Workforce implications". Science. 358 (6370): 1530–1534. Bibcode:2017Sci...358.1530B. doi:10.1126/science.aap8062. PMID 29269459.
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): e2214840120. arXiv:2303.07462. Bibcode:2023PNAS..12014840S. doi:10.1073/pnas.2214840120. PMC 10041097. PMID 36913582.
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): e2316205120. arXiv:2305.12763. Bibcode:2023PNAS..12016205C. doi:10.1073/pnas.2316205120. PMC 10740389. PMID 38085780.
"What are the security risks of open sourcing the Twitter algorithm?". VentureBeat. 27 May 2022. Retrieved 29 May 2022.
"Examining algorithmic amplification of political content on Twitter". Retrieved 29 May 2022.
Park, SoHyun; Oh, Heung-Kwon; Park, Gibeom; Suh, Bongwon; Bae, Woo Kyung; Kim, Jin Won; Yoon, Hyuk; Kim, Duck-Woo; Kang, Sung-Bum (February 2016). "The Source and Credibility of Colorectal Cancer Information on Twitter". Medicine. 95 (7): e2775. doi:10.1097/MD.0000000000002775. PMC 4998625. PMID 26886625.
Efthimion, Phillip; Payne, Scott; Proferes, Nicholas (20 July 2018). "Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots". SMU Data Science Review. 1 (2).
"The online information environment" (PDF). Retrieved 21 February 2022.
Islam, Md Rafiqul; Liu, Shaowu; Wang, Xianzhi; Xu, Guandong (29 September 2020). "Deep learning for misinformation detection on online social networks: a survey and new perspectives". Social Network Analysis and Mining. 10 (1): 82. doi:10.1007/s13278-020-00696-x. PMC 7524036. PMID 33014173.
Mohseni, Sina; Ragan, Eric (4 December 2018). "Combating Fake News with Interpretable News Feed Algorithms". arXiv:1811.12349 .
"How artificial intelligence may be making you buy things". BBC News. 9 November 2020. Retrieved 9 November 2020.
Busby, Mattha (30 April 2018). "Revealed: how bookies use AI to keep gamblers hooked". The Guardian.
Rowinski, Dan (15 January 2013). "Virtual Personal Assistants & The Future Of Your Smartphone [Infographic]". ReadWrite. Archived from the original on 22 December 2015.
Roose, Kevin (2023-02-16). "Bing's A.I. Chat: 'I Want to Be Alive. 😈'". The New York Times. ISSN 0362-4331. Retrieved 2024-04-23.
Galego Hernandes, Paulo R.; Floret, Camila P.; Cardozo De Almeida, Katia F.; Da Silva, Vinicius Camargo; Papa, Joso Paulo; Pontara Da Costa, Kelton A. (2021). "Phishing Detection Using URL-based XAI Techniques". 2021 IEEE Symposium Series on Computational Intelligence (SSCI). pp. 01–06. doi:10.1109/SSCI50451.2021.9659981. ISBN 978-1-7281-9048-8.
Jáñez-Martino, Francisco; Alaiz-Rodríguez, Rocío; González-Castro, Víctor; Fidalgo, Eduardo; Alegre, Enrique (2023-02-01). "A review of spam email detection: analysis of spammer strategies and the dataset shift problem". Artificial Intelligence Review. 56 (2): 1145–1173. doi:10.1007/s10462-022-10195-4. hdl:10612/14967. S2CID 248738572.
Kapan, Sibel; Sora Gunal, Efnan (January 2023). "Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features". Applied Sciences. 13 (24): 13269. doi:10.3390/app132413269. ISSN 2076-3417.
Nakamura, Satoshi (2009). "Overcoming the language barrier with speech translation technology" (PDF). Science & Technology Trends-Quarterly Review.
Clark, Jack (8 December 2015b). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg L.P. Archived from the original on 23 November 2016. Retrieved 23 November 2016.
^ 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; Monestier, Chloé; Rasmussen, Jeppe H.; Špinka, Marek; Düpjan, Sandra; Boissy, Alain; Janczak, Andrew M.; Hillmann, Edna; Tallet, Céline (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. Bibcode:2022NatSR..12.3409B. doi:10.1038/s41598-022-07174-8. PMC 8901661. PMID 35256620.
"Can artificial intelligence really help us talk to the animals?". The Guardian. 31 July 2022. Retrieved 30 August 2022.
K. Mandal, G. S. Pradeep Ghantasala, Firoz Khan, R. Sathiyaraj, B. Balamurugan (2020). Natural Language Processing in Artificial Intelligence (1st ed.). Apple Academic Press. pp. 53–54. ISBN 9780367808495.{{cite book}}: CS1 maint: multiple names: authors list (link)
Heath, Nick (11 December 2020). "What is AI? Everything you need to know about Artificial Intelligence". ZDNet. Retrieved 1 March 2021.
Clark 2015b.
Markoff, John (16 February 2011). "Computer Wins on 'Jeopardy!': Trivial, It's Not". The New York Times. Archived from the original on 22 October 2014. Retrieved 25 October 2014.
"AlphaGo – Google DeepMind". Archived from the original on 10 March 2016.
"Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol". BBC News. 12 March 2016. Archived from the original on 26 August 2016. Retrieved 1 October 2016.
Metz, Cade (27 May 2017). "After Win in China, AlphaGo's Designers Explore New AI". Wired. Archived from the original on 2 June 2017.
"World's Go Player Ratings". May 2017. Archived from the original on 1 April 2017.
"柯洁迎19岁生日雄踞人类世界排名第一已两年" (in Chinese). May 2017. Archived from the original on 11 August 2017.
"MuZero: Mastering Go, chess, shogi and Atari without rules". Deepmind. 23 December 2020. Retrieved 1 March 2021.
Steven Borowiec; Tracey Lien (12 March 2016). "AlphaGo beats human Go champ in milestone for artificial intelligence". Los Angeles Times. Retrieved 13 March 2016.
Solly, Meilan. "This Poker-Playing A.I. Knows When to Hold 'Em and When to Fold 'Em". Smithsonian. Pluribus has bested poker pros in a series of six-player no-limit Texas Hold'em games, reaching a milestone in artificial intelligence research. It is the first bot to beat humans in a complex multiplayer competition.
Bowling, Michael; Burch, Neil; Johanson, Michael; Tammelin, Oskari (9 January 2015). "Heads-up limit hold'em poker is solved". Science. 347 (6218): 145–149. Bibcode:2015Sci...347..145B. doi:10.1126/science.1259433. PMID 25574016.
Ontanon, Santiago; Synnaeve, Gabriel; Uriarte, Alberto; Richoux, Florian; Churchill, David; Preuss, Mike (December 2013). "A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft". IEEE Transactions on Computational Intelligence and AI in Games. 5 (4): 293–311. CiteSeerX 10.1.1.406.2524. doi:10.1109/TCIAIG.2013.2286295. S2CID 5014732.
"Facebook Quietly Enters StarCraft War for AI Bots, and Loses". WIRED. 2017. Retrieved 7 May 2018.
Silver, David; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc; Sifre, Laurent; Kumaran, Dharshan; Graepel, Thore; Lillicrap, Timothy; Simonyan, Karen; Hassabis, Demis (7 December 2018). "A general reinforcement learning algorithm that masters chess, shogi, and go through self-play". Science. 362 (6419): 1140–1144. Bibcode:2018Sci...362.1140S. doi:10.1126/science.aar6404. PMID 30523106.
Sample, Ian (18 October 2017). "'It's able to create knowledge itself': Google unveils AI that learns on its own". The Guardian. Retrieved 7 May 2018.
"The AI revolution in science". Science | AAAS. 5 July 2017. Retrieved 7 May 2018.
"The superhero of artificial intelligence: can this genius keep it in check?". The Guardian. 16 February 2016. Archived from the original on 23 April 2018. Retrieved 26 April 2018.
Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis (26 February 2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740.
Sample, Ian (14 March 2017). "Google's DeepMind makes AI program that can learn like a human". The Guardian. Archived from the original on 26 April 2018. Retrieved 26 April 2018.
Schrittwieser, Julian; Antonoglou, Ioannis; Hubert, Thomas; Simonyan, Karen; Sifre, Laurent; Schmitt, Simon; Guez, Arthur; Lockhart, Edward; Hassabis, Demis; Graepel, Thore; Lillicrap, Timothy; Silver, David (24 December 2020). "Mastering Atari, Go, chess and shogi by planning with a learned model". Nature. 588 (7839): 604–609. arXiv:1911.08265. Bibcode:2020Natur.588..604S. doi:10.1038/s41586-020-03051-4. PMID 33361790.
Ortiz, Sabrina. "You can now chat with a famous AI character on Viber. Here's how". zdnet.com. ZDNET. Retrieved 5 December 2024. ICONIQ created Kuki, an AI character whose sole purpose is to entertain humans and has even been used as a brand ambassador for H&M, modeled for Vogue, and starred in its own Roblox game.
Lewis, Nell (19 August 2020). "Robot friends: Why people talk to chatbots in times of trouble". cnn.com. CNN. Retrieved 5 December 2024. Since 2016, when the bot landed on major messaging platforms, an estimated 5 million unique users hailing from all corners of the world have chatted with her.
Preparing for the future of artificial intelligence. National Science and Technology Council. p. 14. OCLC 965620122. Retrieved 7 December 2024.
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. SSRN 3571733.
Moreno, Millán M.; Guzmán, Sevilla E.; Demyda, S. E. (November 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). doi:10.15835/buasvmcn-vm:1:68:6771 (inactive 1 November 2024).{{cite journal}}: CS1 maint: DOI inactive as of November 2024 (link)
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. S2CID 210930401.
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. S2CID 219064189.
Anne Johnson; Emily Grumbling (2019). Implications of artificial intelligence for cybersecurity: proceedings of a workshop. Washington, DC: National Academies Press. ISBN 978-0-309-49451-9. OCLC 1134854973.
Kant, Daniel; Johannsen, Andreas (2022-01-16). "Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs)". Electronic Imaging. 34 (3): 387–3. doi:10.2352/EI.2022.34.3.MOBMU-387. ISSN 2470-1173.
Randrianasolo, Arisoa (2012). "Artificial intelligence in computer security: Detection, temporary repair and defense". Texas Tech University Libraries. hdl:2346/45196.
Sahil; Sood, Sandeep; Mehmi, Sandeep; Dogra, Shikha (2015). "Artificial intelligence for designing user profiling system for cloud computing security: Experiment". 2015 International Conference on Advances in Computer Engineering and Applications. IEEE. pp. 51–58. doi:10.1109/ICACEA.2015.7164645. ISBN 978-1-4673-6911-4.
Parisi, Alessandro (2019). Hands-on artificial intelligence for cybersecurity: implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies. Birmingham, UK. ISBN 978-1-78980-517-8. OCLC 1111967955.{{cite book}}: CS1 maint: location missing publisher (link)
^ "AI in Education| Harvard Graduate School of Education". www.gse.harvard.edu. 2023-02-09. Retrieved 2024-04-20.
^ nair, madhu (2021-03-10). "AI In Education: Where Is It Now And What Is The Future". University of the People. Retrieved 2024-04-20.
^ "The promises and perils of new technologies to improve education and employment opportunities". Brookings. Retrieved 2024-04-20.
"EU AI Act: First regulation on artificial intelligence". 6 August 2023.
Christy, Charles A. (17 January 1990). "Impact of Artificial Intelligence on Banking". Los Angeles Times. Retrieved 10 September 2019.
O'Neill, Eleanor (31 July 2016). "Accounting, automation and AI". icas.com. Archived from the original on 18 November 2016. Retrieved 18 November 2016.
"CTO Corner: Artificial Intelligence Use in Financial Services – Financial Services Roundtable". Financial Services Roundtable. 2 April 2015. Archived from the original on 18 November 2016. Retrieved 18 November 2016.
"Artificial Intelligence Solutions, AI Solutions". sas.com.
Chapman, Lizette (7 January 2019). "Palantir once mocked the idea of salespeople. Now it's hiring them". Los Angeles Times. Retrieved 28 February 2019.
Artificial Intelligence and Economic Theory: Skynet in the Market. Advanced Information and Knowledge Processing. 2017. doi:10.1007/978-3-319-66104-9. ISBN 978-3-319-66103-2.
Marwala, Tshilidzi; Hurwitz, Evan (2017). "Efficient Market Hypothesis". Artificial Intelligence and Economic Theory: Skynet in the Market. Advanced Information and Knowledge Processing. pp. 101–110. doi:10.1007/978-3-319-66104-9_9. ISBN 978-3-319-66103-2.
Shao, Jun; Lou, Zhukun; Wang, Chong; Mao, Jinye; Ye, Ailin (16 May 2022). "The impact of artificial intelligence (AI) finance on financing constraints of non-SOE firms in emerging markets". International Journal of Emerging Markets. 17 (4): 930–944. doi:10.1108/IJOEM-02-2021-0299.
"Algorithmic Trading". Investopedia. 18 May 2005.
"Beyond Robo-Advisers: How AI Could Rewire Wealth Management". 5 January 2017.
Asatryan, Diana (3 April 2017). "Machine Learning Is the Future of Underwriting, But Startups Won't be Driving It". bankinnovation.net. Retrieved 15 April 2022.
"ZestFinance Introduces Machine Learning Platform to Underwrite Millennials and Other Consumers with Limited Credit History" (Press release). 14 February 2017.
Chang, Hsihui; Kao, Yi-Ching; Mashruwala, Raj; Sorensen, Susan M. (10 April 2017). "Technical Inefficiency, Allocative Inefficiency, and Audit Pricing". Journal of Accounting, Auditing & Finance. 33 (4): 580–600. doi:10.1177/0148558X17696760. S2CID 157787279.
Munoko, Ivy; Brown-Liburd, Helen L.; Vasarhelyi, Miklos (November 2020). "The Ethical Implications of Using Artificial Intelligence in Auditing". Journal of Business Ethics. 167 (2): 209–234. doi:10.1007/s10551-019-04407-1.
Fadelli, Ingrid. "LaundroGraph: Using deep learning to support anti-money laundering efforts". techxplore.com. Retrieved 18 December 2022.
Cardoso, Mário; Saleiro, Pedro; Bizarro, Pedro (2022). "LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering". Proceedings of the Third ACM International Conference on AI in Finance. pp. 130–138. arXiv:2210.14360. doi:10.1145/3533271.3561727. ISBN 978-1-4503-9376-8.
Durkin, J. (2002). "History and applications". Expert Systems. Vol. 1. pp. 1–22. doi:10.1016/B978-012443880-4/50045-4. ISBN 978-0-12-443880-4.
Chen, K.C.; Liang, Ting-peng (May 1989). "Protrader: An Expert System for Program Trading". Managerial Finance. 15 (5): 1–6. doi:10.1108/eb013623.
Nielson, Norma; Brown, Carol E.; Phillips, Mary Ellen (July 1990). "Expert Systems for Personal Financial Planning". Journal of Financial Planning: 137–143. doi:10.11575/PRISM/33995. hdl:1880/48295.
Senator, Ted E.; Goldberg, Henry G.; Wooton, Jerry; Cottini, Matthew A.; Khan, A.F. Umar; Kilinger, Christina D.; Llamas, Winston M.; Marrone, MichaeI P.; Wong, Raphael W.H. (1995). "The FinCEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports of Large Cash Transactions" (PDF). IAAI-95 Proceedings. Archived from the original (PDF) on 2015-10-20. Retrieved 2019-01-14.
Sutton, Steve G.; Holt, Matthew; Arnold, Vicky (September 2016). "'The reports of my death are greatly exaggerated'—Artificial intelligence research in accounting". International Journal of Accounting Information Systems. 22: 60–73. doi:10.1016/j.accinf.2016.07.005.
Chalmers, Dominic; MacKenzie, Niall G.; Carter, Sara (September 2021). "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution". Entrepreneurship Theory and Practice. 45 (5): 1028–1053. doi:10.1177/1042258720934581. S2CID 225625933.
Buckley, Chris; Mozur, Paul (22 May 2019). "How China Uses High-Tech Surveillance to Subdue Minorities". The New York Times.
"Security lapse exposed a Chinese smart city surveillance system". 3 May 2019. Archived from the original on 7 March 2021. Retrieved 14 September 2020.
"AI traffic signals to be installed in Bengaluru soon". NextBigWhat. 24 September 2019. Retrieved 1 October 2019.
^ Congressional Research Service (2019). Artificial Intelligence and National Security (PDF). Washington, DC: Congressional Research Service.PD-notice
^ Slyusar, Vadym (2019). Artificial intelligence as the basis of future control networks (Preprint). doi:10.13140/RG.2.2.30247.50087.
Iraqi, Amjad (2024-04-03). "'Lavender': The AI machine directing Israel's bombing spree in Gaza". +972 Magazine. Retrieved 2024-04-06.
Davies, Harry; McKernan, Bethan; Sabbagh, Dan (2023-12-01). "'The Gospel': how Israel uses AI to select bombing targets in Gaza". The Guardian. Retrieved 2023-12-04.
Marti, J Werner (10 August 2024). "Drohnen haben den Krieg in der Ukraine revolutioniert, doch sie sind empfindlich auf Störsender – deshalb sollen sie jetzt autonom operieren". Neue Zürcher Zeitung (in German). Retrieved 10 August 2024.
"10 Promising AI Applications in Health Care". Harvard Business Review. 10 May 2018. Archived from the original on 15 December 2018. Retrieved 28 August 2018.
Lareyre, Fabien; Lê, Cong Duy; Ballaith, Ali; Adam, Cédric; Carrier, Marion; Amrani, Samantha; Caradu, Caroline; Raffort, Juliette (August 2022). "Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis". Angiology. 73 (7): 606–614. doi:10.1177/00033197211062280. PMID 34996315. S2CID 245812907.
"What is artificial intelligence in medicine?". IBM. 28 March 2024. Retrieved 19 April 2024.
"Microsoft Using AI to Accelerate Cancer Precision Medicine". HealthITAnalytics. 29 October 2019. Retrieved 29 November 2020.
Dina Bass (20 September 2016). "Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments". Bloomberg L.P. Archived from the original on 11 May 2017.
Gallagher, James (26 January 2017). "Artificial intelligence 'as good as cancer doctors'". BBC News. Archived from the original on 26 January 2017. Retrieved 26 January 2017.
Langen, Pauline A.; Katz, Jeffrey S.; Dempsey, Gayle, eds. (18 October 1994), Remote monitoring of high-risk patients using artificial intelligence, archived from the original on 28 February 2017, retrieved 27 February 2017
Kermany, Daniel S.; Goldbaum, Michael; Cai, Wenjia; Valentim, Carolina C.S.; Liang, Huiying; Baxter, Sally L.; McKeown, Alex; Yang, Ge; Wu, Xiaokang; Yan, Fangbing; Dong, Justin; Prasadha, Made K.; Pei, Jacqueline; Ting, Magdalene Y.L.; Zhu, Jie; Li, Christina; Hewett, Sierra; Dong, Jason; Ziyar, Ian; Shi, Alexander; Zhang, Runze; Zheng, Lianghong; Hou, Rui; Shi, William; Fu, Xin; Duan, Yaou; Huu, Viet A.N.; Wen, Cindy; Zhang, Edward D.; Zhang, Charlotte L.; Li, Oulan; Wang, Xiaobo; Singer, Michael A.; Sun, Xiaodong; Xu, Jie; Tafreshi, Ali; Lewis, M. Anthony; Xia, Huimin; Zhang, Kang (February 2018). "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning". Cell. 172 (5): 1122–1131.e9. doi:10.1016/j.cell.2018.02.010. PMID 29474911. S2CID 3516426.
Senthilingam, Meera (12 May 2016). "Are Autonomous Robots Your next Surgeons?". CNN. Archived from the original on 3 December 2016. Retrieved 4 December 2016.
Pumplun L, Fecho M, Wahl N, Peters F, Buxmann P (2021). "Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study". Journal of Medical Internet Research. 23 (10): e29301. doi:10.2196/29301. PMC 8556641. PMID 34652275. S2CID 238990562.
Inglese, Marianna; Patel, Neva; Linton-Reid, Kristofer; Loreto, Flavia; Win, Zarni; Perry, Richard J.; Carswell, Christopher; Grech-Sollars, Matthew; Crum, William R.; Lu, Haonan; Malhotra, Paresh A.; Aboagye, Eric O. (20 June 2022). "A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease". Communications Medicine. 2 (1): 70. doi:10.1038/s43856-022-00133-4. PMC 9209493. PMID 35759330.
- News report: "Single MRI scan of the brain could detect Alzheimer's disease". Physics World. 13 July 2022. Retrieved 19 July 2022.
Reed, Todd R.; Reed, Nancy E.; Fritzson, Peter (2004). "Heart sound analysis for symptom detection and computer-aided diagnosis". Simulation Modelling Practice and Theory. 12 (2): 129–146. doi:10.1016/j.simpat.2003.11.005.
Yorita, Akihiro; Kubota, Naoyuki (2011). "Cognitive Development in Partner Robots for Information Support to Elderly People". IEEE Transactions on Autonomous Mental Development. 3: 64–73. CiteSeerX 10.1.1.607.342. doi:10.1109/TAMD.2011.2105868. S2CID 13797196.
Ray, Dr Amit (14 May 2018). "Artificial intelligence for Assisting Navigation of Blind People". Inner Light Publishers.
"Artificial Intelligence Will Redesign Healthcare – The Medical Futurist". The Medical Futurist. 4 August 2016. Retrieved 18 November 2016.
Dönertaş, Handan Melike; Fuentealba, Matías; Partridge, Linda; Thornton, Janet M. (February 2019). "Identifying Potential Ageing-Modulating Drugs In Silico". Trends in Endocrinology & Metabolism. 30 (2): 118–131. doi:10.1016/j.tem.2018.11.005. PMC 6362144. PMID 30581056.
Smer-Barreto, Vanessa; Quintanilla, Andrea; Elliot, Richard J. R.; Dawson, John C.; Sun, Jiugeng; Carragher, Neil O.; Acosta, Juan Carlos; Oyarzún, Diego A. (27 April 2022). "Discovery of new senolytics using machine learning". bioRxiv. doi:10.1101/2022.04.26.489505. hdl:10261/269843.
Luxton, David D. (2014). "Artificial intelligence in psychological practice: Current and future applications and implications". Professional Psychology: Research and Practice. 45 (5): 332–339. doi:10.1037/a0034559.
Randhawa, Gurjit S.; Soltysiak, Maximillian P. M.; Roz, Hadi El; Souza, Camila P. E. de; Hill, Kathleen A.; Kari, Lila (24 April 2020). "Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study". PLOS ONE. 15 (4): e0232391. Bibcode:2020PLoSO..1532391R. doi:10.1371/journal.pone.0232391. PMC 7182198. PMID 32330208.
Ye, Jiarong; Yeh, Yin-Ting; Xue, Yuan; Wang, Ziyang; Zhang, Na; Liu, He; Zhang, Kunyan; Ricker, RyeAnne; Yu, Zhuohang; Roder, Allison; Perea Lopez, Nestor; Organtini, Lindsey; Greene, Wallace; Hafenstein, Susan; Lu, Huaguang; Ghedin, Elodie; Terrones, Mauricio; Huang, Shengxi; Huang, Sharon Xiaolei (7 June 2022). "Accurate virus identification with interpretable Raman signatures by machine learning". Proceedings of the National Academy of Sciences. 119 (23): e2118836119. arXiv:2206.02788. Bibcode:2022PNAS..11918836Y. doi:10.1073/pnas.2118836119. PMC 9191668. PMID 35653572. S2CID 235372800.
"Artificial intelligence finds disease-related genes". Linköping University. Retrieved 3 July 2022.
"Researchers use AI to detect new family of genes in gut bacteria". UT Southwestern Medical Center. Retrieved 3 July 2022.
^ Zhavoronkov, Alex; Mamoshina, Polina; Vanhaelen, Quentin; Scheibye-Knudsen, Morten; Moskalev, Alexey; Aliper, Alex (2019). "Artificial intelligence for aging and longevity research: Recent advances and perspectives". Ageing Research Reviews. 49: 49–66. doi:10.1016/j.arr.2018.11.003. PMID 30472217. S2CID 53755842.
Adir, Omer; Poley, Maria; Chen, Gal; Froim, Sahar; Krinsky, Nitzan; Shklover, Jeny; Shainsky-Roitman, Janna; Lammers, Twan; Schroeder, Avi (April 2020). "Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine". Advanced Materials. 32 (13): 1901989. Bibcode:2020AdM....3201989A. doi:10.1002/adma.201901989. PMC 7124889. PMID 31286573.
Bax, Monique; Thorpe, Jordan; Romanov, Valentin (December 2023). "The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence". Frontiers in Sensors. 4. doi:10.3389/fsens.2023.1294721.
^ Moore, Phoebe V. (7 May 2019). "OSH and the Future of Work: benefits and risks of artificial intelligence tools in workplaces". EU-OSHA. pp. 3–7. Retrieved 30 July 2020.
^ Howard, John (November 2019). "Artificial intelligence: Implications for the future of work". American Journal of Industrial Medicine. 62 (11): 917–926. doi:10.1002/ajim.23037. PMID 31436850. S2CID 201275028.
Gianatti, Toni-Louise (14 May 2020). "How AI-Driven Algorithms Improve an Individual's Ergonomic Safety". Occupational Health & Safety. Retrieved 30 July 2020.
Meyers, Alysha R. (1 May 2019). "AI and Workers' Comp". NIOSH Science Blog. Retrieved 3 August 2020.
Webb, Sydney; Siordia, Carlos; Bertke, Stephen; Bartlett, Diana; Reitz, Dan (26 February 2020). "Artificial Intelligence Crowdsourcing Competition for Injury Surveillance". NIOSH Science Blog. Retrieved 3 August 2020.
Ferguson, Murray (19 April 2016). "Artificial Intelligence: What's To Come for EHS... And When?". EHS Today. Retrieved 30 July 2020.
"DeepMind is answering one of biology's biggest challenges". The Economist. 30 November 2020. Retrieved 30 November 2020.
Jeremy Kahn, Lessons from DeepMind's breakthrough in protein-folding A.I., Fortune, 1 December 2020
"DeepMind uncovers structure of 200m proteins in scientific leap forward". The Guardian. 2022-07-28. Retrieved 2022-07-28.
"AlphaFold reveals the structure of the protein universe". DeepMind. 2022-07-28. Retrieved 2022-07-28.
^ Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from data analysis to generative AI. Intellisemantic Editions. p. 211. ISBN 978-8894787603.
Stocker, Sina; Csányi, Gábor; Reuter, Karsten; Margraf, Johannes T. (30 October 2020). "Machine learning in chemical reaction space". Nature Communications. 11 (1): 5505. Bibcode:2020NatCo..11.5505S. doi:10.1038/s41467-020-19267-x. PMC 7603480. PMID 33127879.
"Allchemy – Resource-aware AI for drug discovery". Retrieved 29 May 2022.
Wołos, Agnieszka; Roszak, Rafał; Żądło-Dobrowolska, Anna; Beker, Wiktor; Mikulak-Klucznik, Barbara; Spólnik, Grzegorz; Dygas, Mirosław; Szymkuć, Sara; Grzybowski, Bartosz A. (25 September 2020). "Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry". Science. 369 (6511): eaaw1955. doi:10.1126/science.aaw1955. PMID 32973002. S2CID 221882090.
Wołos, Agnieszka; Koszelewski, Dominik; Roszak, Rafał; Szymkuć, Sara; Moskal, Martyna; Ostaszewski, Ryszard; Herrera, Brenden T.; Maier, Josef M.; Brezicki, Gordon; Samuel, Jonathon; Lummiss, Justin A. M.; McQuade, D. Tyler; Rogers, Luke; Grzybowski, Bartosz A. (April 2022). "Computer-designed repurposing of chemical wastes into drugs". Nature. 604 (7907): 668–676. Bibcode:2022Natur.604..668W. doi:10.1038/s41586-022-04503-9. PMID 35478240. S2CID 248415772.
"Chemists debate machine learning's future in synthesis planning and ask for open data". cen.acs.org. Retrieved 29 May 2022.
Paul, Debleena; Sanap, Gaurav; Shenoy, Snehal; Kalyane, Dnyaneshwar; Kalia, Kiran; Tekade, Rakesh K. (January 2021). "Artificial intelligence in drug discovery and development". Drug Discovery Today. 26 (1): 80–93. doi:10.1016/j.drudis.2020.10.010. PMC 7577280. PMID 33099022.
^ "Biologists train AI to generate medicines and vaccines". University of Washington-Harborview Medical Center.
^ Wang, Jue; Lisanza, Sidney; Juergens, David; Tischer, Doug; Watson, Joseph L.; Castro, Karla M.; Ragotte, Robert; Saragovi, Amijai; Milles, Lukas F.; Baek, Minkyung; Anishchenko, Ivan; Yang, Wei; Hicks, Derrick R.; Expòsit, Marc; Schlichthaerle, Thomas; Chun, Jung-Ho; Dauparas, Justas; Bennett, Nathaniel; Wicky, Basile I. M.; Muenks, Andrew; DiMaio, Frank; Correia, Bruno; Ovchinnikov, Sergey; Baker, David (22 July 2022). "Scaffolding protein functional sites using deep learning". Science. 377 (6604): 387–394. Bibcode:2022Sci...377..387W. doi:10.1126/science.abn2100. PMC 9621694. PMID 35862514. S2CID 250953434.
Zhavoronkov, Alex; Ivanenkov, Yan A.; Aliper, Alex; Veselov, Mark S.; Aladinskiy, Vladimir A.; Aladinskaya, Anastasiya V.; Terentiev, Victor A.; Polykovskiy, Daniil A.; Kuznetsov, Maksim D.; Asadulaev, Arip; Volkov, Yury; Zholus, Artem; Shayakhmetov, Rim R.; Zhebrak, Alexander; Minaeva, Lidiya I.; Zagribelnyy, Bogdan A.; Lee, Lennart H.; Soll, Richard; Madge, David; Xing, Li; Guo, Tao; Aspuru-Guzik, Alán (September 2019). "Deep learning enables rapid identification of potent DDR1 kinase inhibitors". Nature Biotechnology. 37 (9): 1038–1040. doi:10.1038/s41587-019-0224-x. PMID 31477924.
Hansen, Justine Y.; Markello, Ross D.; Vogel, Jacob W.; Seidlitz, Jakob; Bzdok, Danilo; Misic, Bratislav (September 2021). "Mapping gene transcription and neurocognition across human neocortex". Nature Human Behaviour. 5 (9): 1240–1250. doi:10.1038/s41562-021-01082-z. PMID 33767429. S2CID 232367225.
Vo ngoc, Long; Huang, Cassidy Yunjing; Cassidy, California Jack; Medrano, Claudia; Kadonaga, James T. (September 2020). "Identification of the human DPR core promoter element using machine learning". Nature. 585 (7825): 459–463. Bibcode:2020Natur.585..459V. doi:10.1038/s41586-020-2689-7. PMC 7501168. PMID 32908305.
Bijun, Zhang; Ting, Fan (2022). "Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]". Frontiers in Genetics. 13: 951939. doi:10.3389/fgene.2022.951939. PMC 9445221. PMID 36081985.
Radivojević, Tijana; Costello, Zak; Workman, Kenneth; Garcia Martin, Hector (25 September 2020). "A machine learning Automated Recommendation Tool for synthetic biology". Nature Communications. 11 (1): 4879. arXiv:1911.11091. Bibcode:2020NatCo..11.4879R. doi:10.1038/s41467-020-18008-4. PMC 7519645. PMID 32978379.
^ Pablo Carbonell; Tijana Radivojevic; Héctor García Martín* (2019). "Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation". ACS Synthetic Biology. 8 (7): 1474–1477. doi:10.1021/acssynbio.8b00540. hdl:20.500.11824/998. PMID 31319671. S2CID 197664634.
Gadzhimagomedova, Z. M.; Pashkov, D. M.; Kirsanova, D. Yu.; Soldatov, S. A.; Butakova, M. A.; Chernov, A. V.; Soldatov, A. V. (February 2022). "Artificial Intelligence for Nanostructured Materials". Nanobiotechnology Reports. 17 (1): 1–9. doi:10.1134/S2635167622010049. S2CID 248701168.
Mirzaei, Mahsa; Furxhi, Irini; Murphy, Finbarr; Mullins, Martin (July 2021). "A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles". Nanomaterials. 11 (7): 1774. doi:10.3390/nano11071774. PMC 8308172. PMID 34361160.
Chen, Angela (25 April 2018). "How AI is helping us discover materials faster than ever". The Verge. Retrieved 30 May 2022.
Talapatra, Anjana; Boluki, S.; Duong, T.; Qian, X.; Dougherty, E.; Arróyave, R. (26 November 2018). "Autonomous efficient experiment design for materials discovery with Bayesian model averaging". Physical Review Materials. 2 (11): 113803. arXiv:1803.05460. Bibcode:2018PhRvM...2k3803T. doi:10.1103/PhysRevMaterials.2.113803. S2CID 53632880.
Zhao, Yicheng; Zhang, Jiyun; Xu, Zhengwei; Sun, Shijing; Langner, Stefan; Hartono, Noor Titan Putri; Heumueller, Thomas; Hou, Yi; Elia, Jack; Li, Ning; Matt, Gebhard J.; Du, Xiaoyan; Meng, Wei; Osvet, Andres; Zhang, Kaicheng; Stubhan, Tobias; Feng, Yexin; Hauch, Jens; Sargent, Edward H.; Buonassisi, Tonio; Brabec, Christoph J. (13 April 2021). "Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning". Nature Communications. 12 (1): 2191. Bibcode:2021NatCo..12.2191Z. doi:10.1038/s41467-021-22472-x. PMC 8044090. PMID 33850155.
Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V.; Aitchison, Catherine M.; Bai, Yang; Wang, Xiaoyan; Li, Xiaobo; Alston, Ben M.; Li, Buyi; Clowes, Rob; Rankin, Nicola; Harris, Brandon; Sprick, Reiner Sebastian; Cooper, Andrew I. (9 July 2020). "A mobile robotic chemist" (PDF). Nature. 583 (7815): 237–241. Bibcode:2020Natur.583..237B. doi:10.1038/s41586-020-2442-2. PMID 32641813.
Roper, Katherine; Abdel-Rehim, A.; Hubbard, Sonya; Carpenter, Martin; Rzhetsky, Andrey; Soldatova, Larisa; King, Ross D. (2022). "Testing the reproducibility and robustness of the cancer biology literature by robot". Journal of the Royal Society Interface. 19 (189): 20210821. doi:10.1098/rsif.2021.0821. PMC 8984295. PMID 35382578.
Krauhausen, Imke; Koutsouras, Dimitrios A.; Melianas, Armantas; Keene, Scott T.; Lieberth, Katharina; Ledanseur, Hadrien; Sheelamanthula, Rajendar; Giovannitti, Alexander; Torricelli, Fabrizio; Mcculloch, Iain; Blom, Paul W. M.; Salleo, Alberto; van de Burgt, Yoeri; Gkoupidenis, Paschalis (10 December 2021). "Organic neuromorphic electronics for sensorimotor integration and learning in robotics". Science Advances. 7 (50): eabl5068. Bibcode:2021SciA....7.5068K. doi:10.1126/sciadv.abl5068. PMC 8664264. PMID 34890232.
- News article: Bolakhe, Saugat. "Lego Robot with an Organic "Brain" Learns to Navigate a Maze". Scientific American. Retrieved 29 May 2022.
Kagan, Brett J.; Kitchen, Andy C.; Tran, Nhi T.; Parker, Bradyn J.; Bhat, Anjali; Rollo, Ben; Razi, Adeel; Friston, Karl J. (3 December 2021). "In vitro neurons learn and exhibit sentience when embodied in a simulated game-world". bioRxiv 10.1101/2021.12.02.471005.
- News article: "Human brain cells in a dish learn to play Pong faster than an AI". New Scientist. Retrieved 26 January 2022.
Fu, Tianda; Liu, Xiaomeng; Gao, Hongyan; Ward, Joy E.; Liu, Xiaorong; Yin, Bing; Wang, Zhongrui; Zhuo, Ye; Walker, David J. F.; Joshua Yang, J.; Chen, Jianhan; Lovley, Derek R.; Yao, Jun (20 April 2020). "Bioinspired bio-voltage memristors". Nature Communications. 11 (1): 1861. Bibcode:2020NatCo..11.1861F. doi:10.1038/s41467-020-15759-y. PMC 7171104. PMID 32313096.
- News article: "Researchers unveil electronics that mimic the human brain in efficient learning". University of Massachusetts Amherst. Retrieved 29 May 2022.
Sarkar, Tanmoy; Lieberth, Katharina; Pavlou, Aristea; Frank, Thomas; Mailaender, Volker; McCulloch, Iain; Blom, Paul W. M.; Torriccelli, Fabrizio; Gkoupidenis, Paschalis (7 November 2022). "An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing". Nature Electronics. 5 (11): 774–783. doi:10.1038/s41928-022-00859-y. hdl:10754/686016. S2CID 253413801.
"Artificial neurons emulate biological counterparts to enable synergetic operation". Nature Electronics. 5 (11): 721–722. 10 November 2022. doi:10.1038/s41928-022-00862-3.
Sloat, Sarah (21 April 2016). "Brain Emulations Pose Three Massive Moral Questions and a Scarily Practical One". Inverse. Retrieved 3 July 2022.
Sandberg, Anders (3 July 2014). "Ethics of brain emulations". Journal of Experimental & Theoretical Artificial Intelligence. 26 (3): 439–457. doi:10.1080/0952813X.2014.895113. S2CID 14545074.
"To advance artificial intelligence, reverse-engineer the brain". MIT School of Science. Retrieved 30 August 2022.
Ham, Donhee; Park, Hongkun; Hwang, Sungwoo; Kim, Kinam (23 September 2021). "Neuromorphic electronics based on copying and pasting the brain". Nature Electronics. 4 (9): 635–644. doi:10.1038/s41928-021-00646-1.
Pfeifer, Rolf; Iida, Fumiya (2004). "Embodied Artificial Intelligence: Trends and Challenges". Embodied Artificial Intelligence. Lecture Notes in Computer Science. Vol. 3139. pp. 1–26. doi:10.1007/978-3-540-27833-7_1. ISBN 978-3-540-22484-6.
Nygaard, Tønnes F.; Martin, Charles P.; Torresen, Jim; Glette, Kyrre; Howard, David (May 2021). "Real-world embodied AI through a morphologically adaptive quadruped robot". Nature Machine Intelligence. 3 (5): 410–419. doi:10.1038/s42256-021-00320-3. hdl:10852/85867. S2CID 233687524.
Tugui, Alexandru; Danciulescu, Daniela; Subtirelu, Mihaela-Simona (14 April 2019). "The Biological as a Double Limit for Artificial Intelligence: Review and Futuristic Debate". International Journal of Computers Communications & Control. 14 (2): 253–271. doi:10.15837/ijccc.2019.2.3536. S2CID 146091906.
Ball, Nicholas M.; Brunner, Robert J. (July 2010). "Data mining and machine learning in astronomy". International Journal of Modern Physics D. 19 (7): 1049–1106. arXiv:0906.2173. Bibcode:2010IJMPD..19.1049B. doi:10.1142/S0218271810017160. S2CID 119277652.
^ Shekhtman, Svetlana (15 November 2019). "NASA Applying AI Technologies to Problems in Space Science". NASA. Retrieved 30 May 2022.
Fluke, Christopher J.; Jacobs, Colin (March 2020). "Surveying the reach and maturity of machine learning and artificial intelligence in astronomy". WIREs Data Mining and Knowledge Discovery. 10 (2). arXiv:1912.02934. Bibcode:2020WDMKD..10.1349F. doi:10.1002/widm.1349. S2CID 208857777.
Pultarova, Tereza (29 April 2021). "Artificial intelligence is learning how to dodge space junk in orbit". Space.com. Retrieved 3 July 2022.
Mohan, Jaya Preethi; Tejaswi, N. (2020). "A Study on Embedding the Artificial Intelligence and Machine Learning into Space Exploration and Astronomy". Emerging Trends in Computing and Expert Technology. Lecture Notes on Data Engineering and Communications Technologies. Vol. 35. pp. 1295–1302. doi:10.1007/978-3-030-32150-5_131. ISBN 978-3-030-32149-9.
Rees, Martin (30 April 2022). "Could space-going billionaires be the vanguard of a cosmic revolution? | Martin Rees". The Guardian. Retrieved 29 May 2022.
^ "Artificial intelligence in space". www.esa.int. Retrieved 30 May 2022.
Gutowska, Małgorzata; Scriney, Michael; McCarren, Andrew (December 2019). Identifying extra-terrestrial intelligence using machine learning. 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science.
Zhang, Yunfan Gerry; Gajjar, Vishal; Foster, Griffin; Siemion, Andrew; Cordes, James; Law, Casey; Wang, Yu (2018). "Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach". The Astrophysical Journal. 866 (2): 149. arXiv:1809.03043. Bibcode:2018ApJ...866..149Z. doi:10.3847/1538-4357/aadf31. S2CID 52232565.
Nanda, Lakshay; V, Santhi (2019). "SETI (Search for Extra Terrestrial Intelligence) Signal Classification using Machine Learning". 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). pp. 499–504. doi:10.1109/ICSSIT46314.2019.8987793. ISBN 978-1-7281-2119-2.
Gajjar, Vishal; Siemion, Andrew; Croft, Steve; Brzycki, Bryan; Burgay, Marta; Carozzi, Tobia; Concu, Raimondo; Czech, Daniel; DeBoer, David; DeMarines, Julia; Drew, Jamie; Enriquez, J. Emilio; Fawcett, James; Gallagher, Peter; Garrett, Michael; Gizani, Nectaria; Hellbourg, Greg; Holder, Jamie; Isaacson, Howard; Kudale, Sanjay; Lacki, Brian; Lebofsky, Matthew; Li, Di; MacMahon, David H. E.; McCauley, Joe; Melis, Andrea; Molinari, Emilio; Murphy, Pearse; Perrodin, Delphine; Pilia, Maura; Price, Danny C.; Webb, Claire; Werthimer, Dan; Williams, David; Worden, Pete; Zarka, Philippe; Zhang, Yunfan Gerry (2 August 2019). "The Breakthrough Listen Search for Extraterrestrial Intelligence". Bulletin of the American Astronomical Society. 51 (7): 223. arXiv:1907.05519. Bibcode:2019BAAS...51g.223G.
"SkyCAM-5 - Chair of Computer Science VIII - Aerospace Information Technology". University of Würzburg. Retrieved 29 May 2022.
"Project Galileo: The search for alien tech hiding in our Solar System". BBC Science Focus Magazine. Retrieved 29 May 2022.
"'Something's coming': is America finally ready to take UFOs seriously?". The Guardian. 5 February 2022. Retrieved 29 May 2022.
David, Leonard (27 January 2022). "2022 could be a turning point in the study of UFOs". livescience.com. Retrieved 29 May 2022.
Gritz, Jennie Rothenberg. "The Wonder of Avi Loeb". Retrieved 29 May 2022.
Mann, Adam. "Avi Loeb's Galileo Project Will Search for Evidence of Alien Visitation". Scientific American. Retrieved 29 May 2022.
"Galileo Project – Activities". projects.iq.harvard.edu. Retrieved 29 May 2022.
"The Galileo Project: Harvard researchers to search for signs of alien technology". Sky News.
Zapata Trujillo, Juan C.; Syme, Anna-Maree; Rowell, Keiran N.; Burns, Brendan P.; Clark, Ebubekir S.; Gorman, Maire N.; Jacob, Lorrie S. D.; Kapodistrias, Panayioti; Kedziora, David J.; Lempriere, Felix A. R.; Medcraft, Chris; O'Sullivan, Jensen; Robertson, Evan G.; Soares, Georgia G.; Steller, Luke; Teece, Bronwyn L.; Tremblay, Chenoa D.; Sousa-Silva, Clara; McKemmish, Laura K. (2021). "Computational Infrared Spectroscopy of 958 Phosphorus-Bearing Molecules". Frontiers in Astronomy and Space Sciences. 8: 43. arXiv:2105.08897. Bibcode:2021FrASS...8...43Z. doi:10.3389/fspas.2021.639068.
"Successful and timely uptake of artificial intelligence in science in the EU – Scientific Advice Mechanism". Retrieved 2024-04-16.
"AI in science evidence review report – Scientific Advice Mechanism". Retrieved 2024-04-16.
Assael, Yannis; Sommerschield, Thea; Shillingford, Brendan; Bordbar, Mahyar; Pavlopoulos, John; Chatzipanagiotou, Marita; Androutsopoulos, Ion; Prag, Jonathan; de Freitas, Nando (March 2022). "Restoring and attributing ancient texts using deep neural networks". Nature. 603 (7900): 280–283. Bibcode:2022Natur.603..280A. doi:10.1038/s41586-022-04448-z. PMC 8907065. PMID 35264762.
Paijmans, Hans; Brandsen, Alex (2010). "Searching in Archaeological Texts. Problems and Solutions Using an Artiﬁcial Intelligence Approach". PalArch's Journal of Archaeology of Egypt / Egyptology. 7 (2): 1–6.
Mantovan, Lorenzo; Nanni, Loris (September 2020). "The Computerization of Archaeology: Survey on Artificial Intelligence Techniques". SN Computer Science. 1 (5). arXiv:2005.02863. doi:10.1007/s42979-020-00286-w.
Mondal, Mayukh; Bertranpetit, Jaume; Lao, Oscar (December 2019). "Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania". Nature Communications. 10 (1): 246. Bibcode:2019NatCo..10..246M. doi:10.1038/s41467-018-08089-7. PMC 6335398. PMID 30651539.
Tanti, Marc; Berruyer, Camille; Tafforeau, Paul; Muscat, Adrian; Farrugia, Reuben; Scerri, Kenneth; Valentino, Gianluca; Solé, V. Armando; Briffa, Johann A. (15 December 2021). "Automated segmentation of microtomography imaging of Egyptian mummies". PLOS ONE. 16 (12): e0260707. arXiv:2105.06738. Bibcode:2021PLoSO..1660707T. doi:10.1371/journal.pone.0260707. PMC 8673632. PMID 34910736.
"DeepMind AI learns physics by watching videos that don't make sense". New Scientist. Retrieved 21 August 2022.
Piloto, Luis S.; Weinstein, Ari; Battaglia, Peter; Botvinick, Matthew (11 July 2022). "Intuitive physics learning in a deep-learning model inspired by developmental psychology". Nature Human Behaviour. 6 (9): 1257–1267. doi:10.1038/s41562-022-01394-8. PMC 9489531. PMID 35817932.
^ Feldman, Andrey (11 August 2022). "Artificial physicist to unravel the laws of nature". Advanced Science News. Retrieved 21 August 2022.
Chen, Boyuan; Huang, Kuang; Raghupathi, Sunand; Chandratreya, Ishaan; Du, Qiang; Lipson, Hod (July 2022). "Automated discovery of fundamental variables hidden in experimental data". Nature Computational Science. 2 (7): 433–442. doi:10.1038/s43588-022-00281-6. PMID 38177869. S2CID 251087119.
Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana; Marques, Miguel A. L. (8 August 2019). "Recent advances and applications of machine learning in solid-state materials science". npj Computational Materials. 5 (1): 83. Bibcode:2019npjCM...5...83S. doi:10.1038/s41524-019-0221-0.
^ Stanev, Valentin; Choudhary, Kamal; Kusne, Aaron Gilad; Paglione, Johnpierre; Takeuchi, Ichiro (13 October 2021). "Artificial intelligence for search and discovery of quantum materials". Communications Materials. 2 (1): 105. Bibcode:2021CoMat...2..105S. doi:10.1038/s43246-021-00209-z. S2CID 238640632.
^ Glavin, Nicholas R.; Ajayan, Pulickel M.; Kar, Swastik (23 February 2022). "Quantum Materials Manufacturing". Advanced Materials. 35 (27): 2109892. doi:10.1002/adma.202109892. PMID 35195312. S2CID 247056685.
Nuñez, Michael (2023-11-29). "Google DeepMind's materials AI has already discovered 2.2 million new crystals". VentureBeat. Retrieved 2023-12-19.
Merchant, Amil; Batzner, Simon; Schoenholz, Samuel S.; Aykol, Muratahan; Cheon, Gowoon; Cubuk, Ekin Dogus (December 2023). "Scaling deep learning for materials discovery". Nature. 624 (7990): 80–85. Bibcode:2023Natur.624...80M. doi:10.1038/s41586-023-06735-9. PMC 10700131. PMID 38030720.
Peplow, Mark (29 November 2023). "Google AI and robots join forces to build new materials". Nature. doi:10.1038/d41586-023-03745-5. PMID 38030771.
Yanamandra, Kaushik; Chen, Guan Lin; Xu, Xianbo; Mac, Gary; Gupta, Nikhil (29 September 2020). "Reverse engineering of additive manufactured composite part by toolpath reconstruction using imaging and machine learning". Composites Science and Technology. 198: 108318. doi:10.1016/j.compscitech.2020.108318. S2CID 225749339.
Anderson, Blake; Storlie, Curtis; Yates, Micah; McPhall, Aaron (2014). "Automating Reverse Engineering with Machine Learning Techniques". Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop. pp. 103–112. doi:10.1145/2666652.2666665. ISBN 978-1-4503-3153-1.
Liu, Wenye; Chang, Chip-Hong; Wang, Xueyang; Liu, Chen; Fung, Jason M.; Ebrahimabadi, Mohammad; Karimi, Naghmeh; Meng, Xingyu; Basu, Kanad (June 2021). "Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security". IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 11 (2): 228–251. Bibcode:2021IJEST..11..228L. doi:10.1109/JETCAS.2021.3084400. hdl:10356/155876. S2CID 235406281.
"DARPA Taps GrammaTech for Artificial Intelligence Exploration (AIE) Program". www.businesswire.com. 7 January 2021. Retrieved 10 January 2023.
Greenberg, Andy. "How to Steal an AI". Wired. Retrieved 10 January 2023.
Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán (27 July 2018). "Inverse molecular design using machine learning: Generative models for matter engineering". Science. 361 (6400): 360–365. Bibcode:2018Sci...361..360S. doi:10.1126/science.aat2663. PMID 30049875. S2CID 50787617.
Teemu, Rintala (17 June 2019). Using Boolean network extraction of trained neural networks to reverse-engineer gene-regulatory networks from time-series data (Master’s in Life Science Technologies thesis). Aalto University.
Ashley, Kevin D. (2017). Artificial Intelligence and Legal Analytics. doi:10.1017/9781316761380. ISBN 978-1-107-17150-3.
Lohr, Steve (19 March 2017). "A.I. Is Doing Legal Work. But It Won't Replace Lawyers, Yet". The New York Times.
Croft, Jane (2 May 2019). "AI learns to read Korean, so you don't have to". Financial Times. Retrieved 19 December 2019.
Kleider-Offutt, Heather; Stevens, Beth; Mickes, Laura; Boogert, Stewart (2024-04-03). "Application of artificial intelligence to eyewitness identification". Cognitive Research: Principles and Implications. 9 (1): 19. doi:10.1186/s41235-024-00542-0. ISSN 2365-7464. PMC 10991253. PMID 38568356.
^ Jeff Larson; Julia Angwin (23 May 2016). "How We Analyzed the COMPAS Recidivism Algorithm". ProPublica. Archived from the original on 29 April 2019. Retrieved 19 June 2020.
"Commentary: Bad news. Artificial intelligence is biased". CNA. 12 January 2019. Archived from the original on 12 January 2019. Retrieved 19 June 2020.
^ Šimalčík, Matej (2023). "Rule by Law". In Kironska, Kristina; Turscanyi, Richard Q. (eds.). Contemporary China: a New Superpower?. Routledge. ISBN 978-1-03-239508-1.
Nawaz, Nishad; Gomes, Anjali Mary (2020). "Artificial Intelligence Chatbots are New Recruiters". International Journal of Advanced Computer Science and Applications. 10 (9). doi:10.2139/ssrn.3521915. S2CID 233762238. SSRN 3521915.
Kafre, Sumit (15 April 2018). "Automatic Curriculum Vitae using Machine learning and Artificial Intelligence". Asian Journal for Convergence in Technology (AJCT). 4.
^ Kongthon, Alisa; Sangkeettrakarn, Chatchawal; Kongyoung, Sarawoot; Haruechaiyasak, Choochart (2009). "Implementing an online help desk system based on conversational agent". Proceedings of the International Conference on Management of Emergent Digital EcoSystems. pp. 450–451. doi:10.1145/1643823.1643908. ISBN 978-1-60558-829-2.
Sara Ashley O'Brien (12 January 2016). "Is this app the call center of the future?". CNN. Retrieved 26 September 2016.
jackclarkSF, Jack Clark (20 July 2016). "New Google AI Brings Automation to Customer Service". Bloomberg L.P. Retrieved 18 November 2016.
"Amazon.com tests customer service chatbots". Amazon Science. 25 February 2020. Retrieved 23 April 2021.
Malatya Turgut Ozal University, Malatya, Turkey; Isguzar, Seda; Fendoglu, Eda; Malatya Turgut Ozal University, Malatya, Turkey; SimSek, Ahmed Ihsan (May 2024). "Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence" (PDF). Amfiteatru Economic. 26 (66): 511. doi:10.24818/EA/2024/66/511. Retrieved 13 June 2024.{{cite journal}}: CS1 maint: multiple names: authors list (link)
"Advanced analytics in hospitality". McKinsey & Company. 2017. Retrieved 14 January 2020.
Zlatanov, Sonja; Popesku, Jovan (2019). "Current Applications of Artificial Intelligence in Tourism and Hospitality". Proceedings of the International Scientific Conference - Sinteza 2019. pp. 84–90. doi:10.15308/Sinteza-2019-84-90. ISBN 978-86-7912-703-7. S2CID 182061194.
"Research at NVIDIA: Transforming Standard Video Into Slow Motion with AI". 18 June 2018. Archived from the original on 21 December 2021 – via YouTube.
"Artificial intelligence is helping old video games look like new". The Verge. 18 April 2019.
"Review: Topaz Sharpen AI is Amazing". petapixel.com. 4 March 2019.
Griffin, Matthew (26 April 2018). "AI can now restore your corrupted photos to their original condition".
"NVIDIA's AI can fix bad photos by looking at other bad photos". Engadget. 10 July 2018.
"Using AI to Colorize and Upscale a 109-Year-Old Video of New York City to 4K and 60fps". petapixel.com. 24 February 2020.
"YouTubers are upscaling the past to 4K. Historians want them to stop". Wired UK.
"Facebook's image outage reveals how the company's AI tags your photos". The Verge. 3 July 2019.
"Google's DeepMind AI can 'transframe' a single image into a video". 18 August 2022.
"Google's new AI turns text into music". 28 January 2023.
"Google's new AI music generator can create - and hold - a tune". 30 January 2023.
"CSDL | IEEE Computer Society".
"Remove image backgrounds to make image transparent". 8 August 2024.
Jodka, Sara (February 1, 2024). "Manipulating reality: the intersection of deepfakes and the law". Reuters.com. Retrieved December 8, 2024.
"InVID kick-off meeting". InVID project. 22 January 2016. Retrieved 23 December 2021. We are kicking-off the new H2020 InVID research project.
(In Video Veritas)
"Consortium of the InVID project". InVID project. Retrieved 23 December 2021. The InVID vision: The InVID innovation action develops a knowledge verification platform to detect emerging stories and assess the reliability of newsworthy video files and content spread via social media.
Teyssou, Denis (2019). "Applying Design Thinking Methodology: The InVID Verification Plugin". Video Verification in the Fake News Era. pp. 263–279. doi:10.1007/978-3-030-26752-0_9. ISBN 978-3-030-26751-3. S2CID 202717914.
"Fake news debunker by InVID & WeVerify". Retrieved 23 December 2021.
"TUM Visual Computing & Artificial Intelligence: Prof. Matthias Nießner". niessnerlab.org.
"Will "Deepfakes" Disrupt the Midterm Election?". Wired. November 2018.
^ Afchar, Darius; Nozick, Vincent; Yamagishi, Junichi; Echizen, Isao (2018). "MesoNet: A Compact Facial Video Forgery Detection Network". 2018 IEEE International Workshop on Information Forensics and Security (WIFS). pp. 1–7. arXiv:1809.00888. doi:10.1109/WIFS.2018.8630761. ISBN 978-1-5386-6536-7. S2CID 52157475.
Lyons, Kim (29 January 2020). "FTC says the tech behind audio deepfakes is getting better". The Verge.
"Audio samples from "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis"". google.github.io.
Strickland, Eliza (11 December 2019). "Facebook AI Launches Its Deepfake Detection Challenge". IEEE Spectrum.
"Contributing Data to Deepfake Detection Research". ai.googleblog.com. 24 September 2019.
Ober, Holly. "New method detects deepfake videos with up to 99% accuracy". University of California-Riverside. Retrieved 3 July 2022.
"AI algorithm detects deepfake videos with high accuracy". techxplore.com. Retrieved 3 July 2022.
^ "Welcome to the new surreal. How AI-generated video is changing film". MIT Technology Review. Retrieved 2023-12-05.
Bean, Thomas H. Davenport and Randy (2023-06-19). "The Impact of Generative AI on Hollywood and Entertainment". MIT Sloan Management Review. Retrieved 2023-12-05.
Cheng, Jacqui (30 September 2009). "Virtual composer makes beautiful music—and stirs controversy". Ars Technica.
US patent 7696426
"Computer composer honours Turing's centenary". New Scientist. 4 July 2012. Archived from the original on 2016-04-13. Retrieved 27 December 2021.
Hick, Thierry (11 October 2016). "La musique classique recomposée". Luxemburger Wort.
"Résultats de recherche - La Sacem". repertoire.sacem.fr.
Requena, Gloria; Sánchez, Carlos; Corzo-Higueras, José Luis; Reyes-Alvarado, Sirenia; Rivas-Ruiz, Francisco; Vico, Francisco; Raglio, Alfredo (2014). "Melomics music medicine (M3) to lessen pain perception during pediatric prick test procedure". Pediatric Allergy and Immunology. 25 (7): 721–724. doi:10.1111/pai.12263. PMID 25115240. S2CID 43273958.
"Watson Beat on GitHub". GitHub. 10 October 2018.
"Songs in the Key of AI". Wired. 17 May 2018.
"Hayeon, sister of Girls' Generation's Taeyeon, debuts with song made by AI". koreajoongangdaily.joins.com. 7 October 2020. Retrieved 23 October 2020.
business intelligence solutions Archived 3 November 2011 at the Wayback Machine. Narrative Science. Retrieved 21 July 2013.
Eule, Alexander. "Big Data and Yahoo's Quest for Mass Personalization". Barron's.
"Artificial Intelligence Software that Writes like a Human Being". Archived from the original on 12 April 2013. Retrieved 11 March 2013.
Riedl, Mark Owen; Bulitko, Vadim (6 December 2012). "Interactive Narrative: An Intelligent Systems Approach". AI Magazine. 34 (1): 67. doi:10.1609/aimag.v34i1.2449. S2CID 11352140.
Callaway, Charles B.; Lester, James C. (August 2002). "Narrative prose generation". Artificial Intelligence. 139 (2): 213–252. doi:10.1016/S0004-3702(02)00230-8. S2CID 15674099.
"A Japanese AI program just wrote a short novel, and it almost won a literary prize". Digital Trends. 23 March 2016. Retrieved 18 November 2016.
"Bot News". Hanteo News. 20 October 2020. Retrieved 20 October 2020.
Canavilhas, João (September 2022). "Artificial Intelligence and Journalism: Current Situation and Expectations in the Portuguese Sports Media". Journalism and Media. 3 (3): 510–520. doi:10.3390/journalmedia3030035. hdl:10400.6/12308.
^ Galily, Yair (August 2018). "Artificial intelligence and sports journalism: Is it a sweeping change?". Technology in Society. 54: 47–51. doi:10.1016/j.techsoc.2018.03.001.
Wu, Daniel (2023-08-31). "Gannett halts AI-written sports recaps after readers mocked the stories". Washington Post. Retrieved 2023-10-31.
Marr, Bernard (17 August 2018). "The Amazing Ways How Misplaced Pages Uses Artificial Intelligence". Forbes.
Gertner, Jon (18 July 2023). "Misplaced Pages's Moment of Truth - Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process? + comment". The New York Times. Archived from the original on 18 July 2023. Retrieved 19 July 2023.{{cite news}}: CS1 maint: bot: original URL status unknown (link)
Piscopo, Alessandro (1 October 2018). "Wikidata: A New Paradigm of Human-Bot Collaboration?". arXiv:1810.00931 .
"Study reveals bot-on-bot editing wars raging on Misplaced Pages's pages". The Guardian. 23 February 2017. Retrieved 10 January 2023.
Cole, K. C. "The Shaky Ground Truths of Misplaced Pages". Wired. Retrieved 10 January 2023.
"AI can automatically rewrite outdated text in Misplaced Pages articles". Engadget. Retrieved 10 January 2023.
Metz, Cade. "Misplaced Pages Deploys AI to Expand Its Ranks of Human Editors". Wired. Retrieved 10 January 2023.
"Misplaced Pages taps Google to help editors translate articles". VentureBeat. 9 January 2019. Retrieved 9 January 2023.
Wilson, Kyle (8 May 2019). "Misplaced Pages has a Google Translate problem". The Verge. Retrieved 9 January 2023.
"Why AI researchers like video games". The Economist. Archived from the original on 5 October 2017.
Yannakakis, Geogios N. (2012). "Game AI revisited". Proceedings of the 9th conference on Computing Frontiers - CF '12. p. 285. doi:10.1145/2212908.2212954. ISBN 978-1-4503-1215-8. S2CID 4335529.
Maass, Laura E. Shummon (1 July 2019). "Artificial Intelligence in Video Games". Medium. Retrieved 23 April 2021.
Fairhead, Harry (26 March 2011) . "Kinect's AI breakthrough explained". I Programmer. Archived from the original on 1 February 2016.
^ Poltronieri, Fabrizio Augusto; Hänska, Max (2019). "Technical Images and Visual Art in the Era of Artificial Intelligence: From GOFAI to GANs". Proceedings of the 9th International Conference on Digital and Interactive Arts. pp. 1–8. doi:10.1145/3359852.3359865. ISBN 978-1-4503-7250-3.
"Fine art print - crypto art". Kate Vass Galerie. Retrieved 2022-05-07.
^ "Analysis | Is That Trump Photo Real? Free AI Tools Come With Risks". Washington Post. Retrieved 30 August 2022.
"Google's image generator rivals DALL-E in shiba inu drawing". TechCrunch. Retrieved 30 August 2022.
"Midjourney's enthralling AI art generator goes live for everyone". PCWorld.
"After Photos, Here's How AI Made A Trippy Music Video Out Of Thin Air". Fossbytes. 19 May 2022. Retrieved 30 May 2022.
Cetinic, Eva; She, James (2022-02-16). "Understanding and Creating Art with AI: Review and Outlook". ACM Transactions on Multimedia Computing, Communications, and Applications. 18 (2): 66:1–66:22. arXiv:2102.09109. doi:10.1145/3475799. S2CID 231951381.
Lang, Sabine; Ommer, Bjorn (2018). "Reflecting on How Artworks Are Processed and Analyzed by Computer Vision: Supplementary Material". Proceedings of the European Conference on Computer Vision (ECCV) Workshops – via Computer Vision Foundation.
admin (2023-09-12). "Top 2 Technologies that will Influence the Future of Animation". VGenMedia. Retrieved 2023-12-04.
"Artificial Intelligence Animation: What Is It and How Does It Function?". Pigeon Studio. Retrieved 2023-12-04.
Cole, Samantha (2023-02-01). "Netflix Made an Anime Using AI Due to a 'Labor Shortage,' and Fans Are Pissed". Vice. Retrieved 2023-12-04.
"What is Move AI? A Revolution in Motion Capture". 2023-09-12. Retrieved 2023-12-04.
Dragicevic, Tomislav; Wheeler, Patrick; Blaabjerg, Frede (August 2019). "Artificial Intelligence Aided Automated Design for Reliability of Power Electronic Systems". IEEE Transactions on Power Electronics. 34 (8): 7161–7171. Bibcode:2019ITPE...34.7161D. doi:10.1109/TPEL.2018.2883947. S2CID 116390072.
"Role of AI in Energy". DOE.
Bourhnane, Safae; Abid, Mohamed Riduan; Lghoul, Rachid; Zine-Dine, Khalid; Elkamoun, Najib; Benhaddou, Driss (30 January 2020). "Machine learning for energy consumption prediction and scheduling in smart buildings". SN Applied Sciences. 2 (2): 297. doi:10.1007/s42452-020-2024-9. S2CID 213274176.
Kanwal, Sidra; Khan, Bilal; Muhammad Ali, Sahibzada (February 2021). "Machine learning based weighted scheduling scheme for active power control of hybrid microgrid". International Journal of Electrical Power & Energy Systems. 125: 106461. Bibcode:2021IJEPE.12506461K. doi:10.1016/j.ijepes.2020.106461. S2CID 224876246.
Mohanty, Prasanta Kumar; Jena, Premalata; Padhy, Narayana Prasad (2020). "Home Electric Vehicle Charge Scheduling Using Machine Learning Technique". 2020 IEEE International Conference on Power Systems Technology (POWERCON). pp. 1–5. doi:10.1109/POWERCON48463.2020.9230627. ISBN 978-1-7281-6350-5.
Foster, Isabella (15 March 2021). "Making Smart Grids Smarter with Machine Learning". EIT | Engineering Institute of Technology. Retrieved 3 July 2022.
Success Stories Archived 4 October 2011 at the Wayback Machine.
Padmanabhan, Jayashree; Johnson Premkumar, Melvin Jose (4 July 2015). "Machine Learning in Automatic Speech Recognition: A Survey". IETE Technical Review. 32 (4): 240–251. doi:10.1080/02564602.2015.1010611. S2CID 62127575.
Ahmed, Shimaa; Chowdhury, Amrita Roy; Fawaz, Kassem; Ramanathan, Parmesh (2020). Preech: A System for {Privacy-Preserving} Speech Transcription. pp. 2703–2720. ISBN 978-1-939133-17-5.
"Digital Spectrometry". 8 October 2018.
US 9967696B2, "Digital Spectrometry Patent", published 2018-10-08
"How artificial intelligence is moving from the lab to your kid's playroom". The Washington Post. Retrieved 18 November 2016.
"Application of artificial intelligence in oil and gas industry: Exploring its impact". 15 May 2019.
Salvaterra, Neanda (14 October 2019). "Oil and Gas Companies Turn to AI to Cut Costs". The Wall Street Journal.
Artificial Intelligence in Transportation: Information for Application. 2007. doi:10.17226/23208. ISBN 978-0-309-42929-0.
Benson, Thor. "Self-driving buses to appear on public roads for the first time". Inverse. Retrieved 26 August 2021.
"Europe's first full-sized self-driving urban electric bus has arrived". World Economic Forum. Retrieved 26 August 2021.
"Self-driving bus propels Swiss town into the future". CNN. Retrieved 26 August 2021.
Huber, Dominik; Viere, Tobias; Horschutz Nemoto, Eliane; Jaroudi, Ines; Korbee, Dorien; Fournier, Guy (2022). "Climate and environmental impacts of automated minibuses in future public transportation". Transportation Research Part D: Transport and Environment. 102: 103160. Bibcode:2022TRPD..10203160H. doi:10.1016/j.trd.2021.103160. S2CID 245777788.
"Transportation Germany Unveils the World's First Fully Automated Train in Hamburg". 12 October 2021. Retrieved 3 July 2022.
"Railway digitalisation using drones". www.euspa.europa.eu. 25 February 2021. Retrieved 3 July 2022.
"World's fastest driverless bullet train launches in China". The Guardian. 9 January 2020. Retrieved 3 July 2022.
"JD.com, Meituan and Neolix to test autonomous deliveries on Beijing public roads". TechCrunch. Retrieved 28 April 2022.
Hawkins, Andrew J. (22 July 2020). "Waymo is designing a self-driving Ram delivery van with FCA". The Verge. Retrieved 28 April 2022.
"Arrival's delivery van demos its autonomous chops at a UK parcel depot". New Atlas. 3 August 2021. Retrieved 28 April 2022.
Buss, Dale. "Walmart Presses Its Distribution Legacy To Lead In Automated Delivery". Forbes. Retrieved 28 April 2022.
Cooley, Patrick; Dispatch, The Columbus. "Grubhub testing delivery robots". techxplore.com. Retrieved 28 April 2022.
"Self-driving delivery van ditches "human controls"". BBC News. 6 February 2020. Retrieved 28 April 2022.
Krok, Andrew. "Nuro's self-driving delivery van wants to run errands for you". CNET. Retrieved 28 April 2022.
Hallerbach, Sven; Xia, Yiqun; Eberle, Ulrich; Koester, Frank (3 April 2018). "Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles". SAE International Journal of Connected and Automated Vehicles. 1 (2): 93–106. doi:10.4271/2018-01-1066.
West, Darrell M. (20 September 2016). "Moving forward: Self-driving vehicles in China, Europe, Japan, Korea, and the United States". Brookings.
Burgess, Matt (24 August 2017). "The UK is about to Start Testing Self-Driving Truck Platoons". Wired UK. Archived from the original on 22 September 2017. Retrieved 20 September 2017.
Davies, Alex (5 May 2015). "World's First Self-Driving Semi-Truck Hits the Road". Wired. Archived from the original on 28 October 2017. Retrieved 20 September 2017.
McFarland, Matt (25 February 2015). "Google's artificial intelligence breakthrough may have a huge impact on self-driving cars and much more". The Washington Post.
"Programming safety into self-driving cars". National Science Foundation. 2 February 2015.
Preparing for the future of artificial intelligence. National Science and Technology Council. OCLC 965620122.
"Going Nowhere Fast? Smart Traffic Lights Can Help Ease Gridlock". 18 May 2022.
"AI bests Air Force combat tactics experts in simulated dogfights". Ars Technica. 29 June 2016.
Jones, Randolph M.; Laird, John E.; Nielsen, Paul E.; Coulter, Karen J.; Kenny, Patrick; Koss, Frank V. (15 March 1999). "Automated Intelligent Pilots for Combat Flight Simulation". AI Magazine. 20 (1): 27. doi:10.1609/aimag.v20i1.1438.
AIDA Homepage. Kbs.twi.tudelft.nl (17 April 1997). Retrieved 21 July 2013.
The Story of Self-Repairing Flight Control Systems. NASA Dryden. (April 2003). Retrieved 25 August 2016.
Adams, Eric (28 March 2017). "AI Wields the Power to Make Flying Safer—and Maybe Even Pleasant". Wired. Retrieved 7 October 2017.
Baomar, Haitham; Bentley, Peter J. (2016). "An Intelligent Autopilot System that learns flight emergency procedures by imitating human pilots". 2016 IEEE Symposium Series on Computational Intelligence (SSCI). pp. 1–9. doi:10.1109/SSCI.2016.7849881. ISBN 978-1-5090-4240-1.
"UB invests in student-founded startup". buffalo.edu. Retrieved 24 December 2020.
Williams, Ben; Lamont, Timothy A. C.; Chapuis, Lucille; Harding, Harry R.; May, Eleanor B.; Prasetya, Mochyudho E.; Seraphim, Marie J.; Jompa, Jamaluddin; Smith, David J.; Janetski, Noel; Radford, Andrew N.; Simpson, Stephen D. (July 2022). "Enhancing automated analysis of marine soundscapes using ecoacoustic indices and machine learning". Ecological Indicators. 140: 108986. Bibcode:2022EcInd.14008986W. doi:10.1016/j.ecolind.2022.108986. hdl:10871/129693. S2CID 248955278.
Hino, M.; Benami, E.; Brooks, N. (October 2018). "Machine learning for environmental monitoring". Nature Sustainability. 1 (10): 583–588. Bibcode:2018NatSu...1..583H. doi:10.1038/s41893-018-0142-9. S2CID 169513589.
"How machine learning can help environmental regulators". Stanford News. Stanford University. 8 April 2019. Retrieved 29 May 2022.
"AI empowers environmental regulators". Stanford News. Stanford University. 19 April 2021. Retrieved 29 May 2022.
Frost, Rosie (9 May 2022). "Plastic waste can now be found and monitored from space". euronews. Retrieved 24 June 2022.
"Global Plastic Watch". www.globalplasticwatch.org. Retrieved 24 June 2022.
"AI may predict the next virus to jump from animals to humans". Public Library of Science. Retrieved 19 October 2021.
Mollentze, Nardus; Babayan, Simon A.; Streicker, Daniel G. (28 September 2021). "Identifying and prioritizing potential human-infecting viruses from their genome sequences". PLOS Biology. 19 (9): e3001390. doi:10.1371/journal.pbio.3001390. PMC 8478193. PMID 34582436.
Li, Zefeng; Meier, Men-Andrin; Hauksson, Egill; Zhan, Zhongwen; Andrews, Jennifer (28 May 2018). "Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning". Geophysical Research Letters. 45 (10): 4773–4779. Bibcode:2018GeoRL..45.4773L. doi:10.1029/2018GL077870. S2CID 54926314.
"Machine learning and gravity signals could rapidly detect big earthquakes". Science News. 11 May 2022. Retrieved 3 July 2022.
Fauvel, Kevin; Balouek-Thomert, Daniel; Melgar, Diego; Silva, Pedro; Simonet, Anthony; Antoniu, Gabriel; Costan, Alexandru; Masson, Véronique; Parashar, Manish; Rodero, Ivan; Termier, Alexandre (3 April 2020). "A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning". Proceedings of the AAAI Conference on Artificial Intelligence. 34 (1): 403–411. doi:10.1609/aaai.v34i01.5376. S2CID 208877225.
Thirugnanam, Hemalatha; Ramesh, Maneesha Vinodini; Rangan, Venkat P. (September 2020). "Enhancing the reliability of landslide early warning systems by machine learning". Landslides. 17 (9): 2231–2246. Bibcode:2020Lands..17.2231T. doi:10.1007/s10346-020-01453-z. S2CID 220294377.
Moon, Seung-Hyun; Kim, Yong-Hyuk; Lee, Yong Hee; Moon, Byung-Ro (2019). "Application of machine learning to an early warning system for very short-term heavy rainfall". Journal of Hydrology. 568: 1042–1054. Bibcode:2019JHyd..568.1042M. doi:10.1016/j.jhydrol.2018.11.060. S2CID 134910487.
Robinson, Bethany; Cohen, Jonathan S.; Herman, Jonathan D. (September 2020). "Detecting early warning signals of long-term water supply vulnerability using machine learning". Environmental Modelling & Software. 131: 104781. Bibcode:2020EnvMS.13104781R. doi:10.1016/j.envsoft.2020.104781. S2CID 221823295.
Bury, Thomas M.; Sujith, R. I.; Pavithran, Induja; Scheffer, Marten; Lenton, Timothy M.; Anand, Madhur; Bauch, Chris T. (28 September 2021). "Deep learning for early warning signals of tipping points". Proceedings of the National Academy of Sciences. 118 (39): e2106140118. Bibcode:2021PNAS..11806140B. doi:10.1073/pnas.2106140118. PMC 8488604. PMID 34544867.
Park, Yongeun; Lee, Han Kyu; Shin, Jae-Ki; Chon, Kangmin; Kim, SungHwan; Cho, Kyung Hwa; Kim, Jin Hwi; Baek, Sang-Soo (15 June 2021). "A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir". Journal of Environmental Management. 288: 112415. Bibcode:2021JEnvM.28812415P. doi:10.1016/j.jenvman.2021.112415. PMID 33774562. S2CID 232407435.
Li, Jun; Wang, Zhaoli; Wu, Xushu; Xu, Chong-Yu; Guo, Shenglian; Chen, Xiaohong; Zhang, Zhenxing (August 2021). "Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning". Water Resources Research. 57 (8). Bibcode:2021WRR....5729413L. doi:10.1029/2020WR029413. hdl:10852/92935. S2CID 237716175.
Khan, Najeebullah; Sachindra, D. A.; Shahid, Shamsuddin; Ahmed, Kamal; Shiru, Mohammed Sanusi; Nawaz, Nadeem (May 2020). "Prediction of droughts over Pakistan using machine learning algorithms". Advances in Water Resources. 139: 103562. Bibcode:2020AdWR..13903562K. doi:10.1016/j.advwatres.2020.103562. S2CID 216447098.
Kaur, Amandeep; Sood, Sandeep K. (May 2020). "Deep learning based drought assessment and prediction framework". Ecological Informatics. 57: 101067. Bibcode:2020EcInf..5701067K. doi:10.1016/j.ecoinf.2020.101067. S2CID 215964704.
^ "Comparing Different AI-powered code Assitants [sic]". 29 June 2023. Retrieved 4 August 2023.
Gershgorn, Dave (29 June 2021). "GitHub and OpenAI launch a new AI tool that generates its own code". The Verge. Retrieved 3 September 2021.
"Tabnine is Now Part of Codota". 23 March 2020. Retrieved 4 August 2023.
"Plans & Pricing". Retrieved 4 August 2023.
"Build Fast with Confidence using CodiumAI". Retrieved 4 August 2023.
"Meet Ghostwriter, your partner in code". Retrieved 4 August 2023.
"Amazon CodeWhisperer FAQ". Retrieved 4 August 2023.
"Google AI creates its own "child" bot". The Independent. 5 December 2017. Retrieved 5 February 2018.
"Cancelling quantum noise". University of Technology Sydney. 23 May 2019. Retrieved 29 May 2022.
"Machine learning paves the way for next-level quantum sensing". University of Bristol. Retrieved 29 May 2022.
Spagnolo, Michele; Morris, Joshua; Piacentini, Simone; Antesberger, Michael; Massa, Francesco; Crespi, Andrea; Ceccarelli, Francesco; Osellame, Roberto; Walther, Philip (April 2022). "Experimental photonic quantum memristor". Nature Photonics. 16 (4): 318–323. arXiv:2105.04867. Bibcode:2022NaPho..16..318S. doi:10.1038/s41566-022-00973-5. S2CID 234358015.
Ramanathan, Shriram (July 2018). "Quantum materials for brain sciences and artificial intelligence". MRS Bulletin. 43 (7): 534–540. Bibcode:2018MRSBu..43..534R. doi:10.1557/mrs.2018.147. S2CID 140048632.
"Artificial intelligence makes accurate quantum chemical simulations more affordable". Nature Portfolio Chemistry Community. 2 December 2021. Retrieved 30 May 2022.
Guan, Wen; Perdue, Gabriel; Pesah, Arthur; Schuld, Maria; Terashi, Koji; Vallecorsa, Sofia; Vlimant, Jean-Roch (March 2021). "Quantum machine learning in high energy physics". Machine Learning: Science and Technology. 2 (1): 011003. arXiv:2005.08582. doi:10.1088/2632-2153/abc17d. S2CID 218674486.
"Europe's First Quantum Computer with More Than 5K Qubits Launched at Jülich". HPCwire. Retrieved 30 May 2022.
Cova, Tânia; Vitorino, Carla; Ferreira, Márcio; Nunes, Sandra; Rondon-Villarreal, Paola; Pais, Alberto (2022). "Artificial Intelligence and Quantum Computing Quantum computing (QC) as the Next Pharma Disruptors". Artificial Intelligence in Drug Design. 2390. Springer US: 321–347. doi:10.1007/978-1-0716-1787-8_14. PMID 34731476. S2CID 242947877.
Batra, Kushal; Zorn, Kimberley M.; Foil, Daniel H.; Minerali, Eni; Gawriljuk, Victor O.; Lane, Thomas R.; Ekins, Sean (28 June 2021). "Quantum Machine Learning Algorithms for Drug Discovery Applications". Journal of Chemical Information and Modeling. 61 (6): 2641–2647. doi:10.1021/acs.jcim.1c00166. PMC 8254374. PMID 34032436.
Barkoutsos, Panagiotis Kl; Gkritsis, Fotios; Ollitrault, Pauline J.; Sokolov, Igor O.; Woerner, Stefan; Tavernelli, Ivano (April 2021). "Quantum algorithm for alchemical optimization in material design". Chemical Science. 12 (12): 4345–4352. doi:10.1039/D0SC05718E. PMC 8179438. PMID 34163697.
Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
"Smart Procurement Technologies for the Construction Sector - SIPMM Publications". publication.sipmm.edu.sg. 2021-10-25. Retrieved 2022-11-30.
^ Heathcote, Edwin (20 January 2024). "AI is coming for architecture". Financial Times. Retrieved 2024-02-07.
^ "AI in Architecture: The Key to Enhancing Design Efficiency and Gaining a Competitive Edge".
^ "Will Artificial Intelligence Replace Architects?". ArchDaily. 2023-10-18. Retrieved 2024-02-07.
^ "How AI software will change architecture and design". Dezeen. 2022-11-16. Retrieved 2024-04-12.