User:Jg1101/Applications of artificial intelligence

TITLE: Applications of artificial intelligence --> Artificial Intelligence Applications

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Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing. The use of AI has been utilized in providing user efficiencies by augmenting human capabilities. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, etc. Applications within AI gives way to a simulated user analysis within a particular industry based on an algorithmic perspective. These applications overall inhibit the ability to retrieve, recommend, interact, convert, and more.

Tools for computer science[edit]

Main article: Artificial intelligence tools for computer science

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 a part of AI. (See AI effect.) According to Russell & Norvig (2003, p. 15), 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 and object-oriented programming.

AI can be used to potentially determine the developer of anonymous binaries.

AI can be used to create other AI. 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. According to Google, NASNet's performance exceeded all previously published ImageNet performance.

Economic and social good[edit]

AI for Good is an ITU initiative supporting institutions employing AI to tackle some of the world's greatest economic and social challenges. According to McKinsey Global Institute, Artificial Intelligence has been utilized in: crisis response, environmental challenges, equality and inclusion, as well as health and hunger. For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels.

Cybersecurity

The cybersecurity arena faces significant challenges in the form of large-scale hacking attacks of different types that harm organizations of all kinds and create billions of dollars in business damage. This type of large-scale hacking is referred to as "cybercrime." As the usage of Information Technology continues to increase in the corporate climate, there is an emergence of vulnerabilities within the cybersphere that require real-time decisions. Artificial intelligence and Natural Language Processing (NLP) has begun to be used by security companies - for example, SIEM (Security Information and Event Management) solutions. The more advanced of these solutions use AI and NLP to automatically sort the data in networks into high risk and low-risk information. This enables security teams to focus on the attacks that have the potential to do real harm to the organization, and not become victims of attacks such as Denial of Service (DoS), Malware and others.

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 can be traced back to 1987 when Security Pacific National Bank in the US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoringbehavioral patterns of users for any abnormal changes or anomalies.

AI is increasingly being used by corporations. Jack Ma has controversially predicted that AI CEO's are 30 years away.

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI-based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking. In August 2019, the AICPA introduced an AI training course for accounting professionals.

History

The 1980s is really when AI started to become prominent in the finance world. This is when expert systems became more of a commercial product in the financial field. “For example, Dupont had built 100 expert systems which helped them save close to $10 million a year.” One of the first systems was the Protrader expert system designed by K.C. Chen and Ting-peng Lian that was able to predict the 87-point drop in 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 that helped with financial plans was created by Applied Expert Systems (APEX) called the PlanPower. It was first commercially shipped in 1986. Its function was to help give financial plans for people with incomes over $75,000 a year. That then led to the Client Profiling System that was used for incomes between $25,000 and $200,000 a year.

The 1990s was a lot more about fraud detection. One of the systems that was started in 1993 was the FinCEN Artificial Intelligence system (FAIS). It was able to review over 200,000 transactions per week and over two years it helped identify 400 potential cases of money laundering which would have been equal to $1 billion. Although expert systems did not last in the finance world, it did help jump-start the use of AI and help make it what it is today.

Health[edit]

Healthcare[edit]

X-ray of a hand, with automatic calculation of bone age by a computer software

Main article: Artificial intelligence in healthcare A patient-side surgical arm of Da Vinci Surgical System AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high-risk patients for population health. The breadth of applications is rapidly increasing. As an example, AI is being applied to the high-cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a groundbreaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.

Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover". Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.

Utilities[edit]

Power electronics converters are an enabling technology for renewable energy, energy storage, electric vehicles and high-voltage direct current transmission systems within the electrical grid. These converters are prone to failures and such failures can cause downtimes that may require costly maintenance or even have catastrophic consequences in mission critical applications. Researchers are using AI to do the automated design process for reliable power electronics converters, by calculating exact design parameters that ensure desired lifetime of the converter under specified mission profile.

Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search in a scheduling application that provides the work schedules of 20,000 engineers.

Deepfakes[edit]

In June 2016, a research team from the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face, a program which animates the face of a target person, transposing the facial expressions of an exterior source. The technology has been demonstrated animating the lips of people including Barack Obama and Vladimir Putin. Since then, other methods have been demonstrated based on deep neural network, from which the name "deepfake" was taken.

In September 2018, the U.S. Senator Mark Warner proposed to penalize social media companies that allow sharing of deepfake documents on their platform.

Vincent Nozick, a researcher from the Institut Gaspard Monge, found a way to detect rigged documents by analyzing the movements of the eyelid. The DARPA (a research group associated with the U.S. Department of Defense) has given 68 million dollars to work on deepfake detection. In Europe, the Horizon 2020 program financed InVid, software designed to help journalists to detect fake documents.

Deepfakes can be used for comedic purposes, but are better known for being used for fake news and hoaxes. Audio deepfakes, and AI software capable of detecting deepfakes and cloning human voices after 5 seconds of listening time also exist.