artificial intelligence in medicine

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

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

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

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

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

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

AI for Good is a platform launched in 2017 by the International Telecommunication Union (ITU) agency of the United Nations (UN). The goal of the platform is to use AI to help achieve the UN's Sustainable Development Goals.

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

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

AI elevates teaching, focusing on significant issues like the knowledge nexus and educational equality. The evolution of AI in education and technology should be used to improve human capabilities in relationships where they do not replace humans. UNESCO recognizes the future of AI in education as an instrument to reach Sustainable Development Goal 4, called "Inclusive and Equitable Quality Education.”

The World Economic Forum also stresses AI's contribution to students' overall improvement and transforming teaching into a more enjoyable process.

Personalized Learning

AI driven tutoring systems, such as Khan Academy, Duolingo and Carnegie Learning are the forefoot of delivering personalized education.

These platforms leverage AI algorithms to analyze individual learning patterns, strengths, and weaknesses, enabling the customization of content and Algorithm to suit each student's pace and style of learning.

Administrative Efficiency

In educational institutions, AI is increasingly used to automate routine tasks like attendance tracking, grading and marking, which allows educators to devote more time to interactive teaching and direct student engagement.

Furthermore, AI tools are employed to monitor student progress, analyze learning behaviors, and predict academic challenges, facilitating timely and proactive interventions for students who may be at risk of falling behind.

Ethical and Privacy Concerns

Despite the benefits, the integration of AI in education raises significant ethical and privacy concerns, particularly regarding the handling of sensitive student data.

It is imperative that AI systems in education are designed and operated with a strong emphasis on transparency, security, and respect for privacy to maintain trust and uphold the integrity of educational practices.

Much of the regulation will be influenced by the AI Act, the world’s first comprehensive AI law.

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

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

Various countries are deploying AI military applications. PD-notice 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

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

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).Jeremy Kahn
Lessons from DeepMind's breakthrough in protein-folding A.I.
'' Fortune'', 1 December 2020

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

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

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

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

Artificial intelligence can also be used to investigate genomes to uncover genetic history, such as interbreeding between archaic and modern humans by which for example the past existence of a ghost population, not Neanderthal or Denisovan, was inferred.

It can also be used for "non-invasive and non-destructive access to internal structures of archaeological remains".

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

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

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

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

AI algorithms have been used to detect deepfake videos.

Video production

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

Music

AI has been used to compose music of various genres.

David Cope created an AI called Emily Howell that managed to become well known in the field of algorithmic computer music. The algorithm behind Emily Howell is registered as a US patent.

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

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

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

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

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

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

TALESPIN made up stories similar to the fables of Aesop. The program started with a set of characters who wanted to achieve certain goals. The story narrated their attempts to satisfy these goals. Mark Riedl and Vadim Bulitko asserted that the essence of storytelling was experience management, or "how to balance the need for a coherent story progression with user agency, which is often at odds".

While AI storytelling focuses on story generation (character and plot), story communication also received attention. In 2002, researchers developed an architectural framework for narrative prose generation. They faithfully reproduced text variety and complexity on stories such as Little Red Riding Hood. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.

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

Literary authors are also exploring uses of AI. An example is David Jhave Johnston's work ''ReRites'' (2017-2019), where the poet created a daily rite of editing the poetic output of a neural network to create a series of performances and publications.

Sports writing

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

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

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

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

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

Lede AI has been used in 2023 to take scores from high school football games to generate stories automatically for the local newspaper. This was met with significant criticism from readers for the very robotic diction that was published. With some descriptions of games being a "close encounter of the athletic kind," readers were not pleased and let the publishing company, Gannett

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