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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, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.

AI for Good

Several U.S. academic institutions are employing AI to tackle some of the world's greatest economic and social challenges. For example, the University of South 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.

Education

There are a number of companies that create robots to teach subjects to children ranging from biology to computer science, though such tools have not become widespread yet.  There have also been a rise of intelligent tutoring systems, or ITS, in higher education.  For example, an ITS called SHERLOCK teaches Air Force technicians to diagnose electrical systems problems in aircraft.  Another example is DARPA, Defense Advanced Research Projects Agency, which used AI to develop a digital tutor to train its Navy recruits in technical skills in a shorter amount of time.     Universities have been slow in adopting AI technologies due to either a lack of funding or skepticism of the effectiveness of these tools, but in the coming years more classrooms will be utilizing technologies such as ITS to complement teachers. 

Advancements in natural language processing, combined with machine learning, have also enabled automatic grading of assignments as well as a data-driven understanding of individual students’ learning needs.  This led to an explosion in popularity of MOOCs, or Massive Open Online Courses, which allows students from around the world to take classes online.  Data sets collected from these large scale online learning systems have also enabled learning analytics, which will be used to improve the quality of learning at scale. Examples of how learning analytics can be used to improve the quality of learning include predicting which students are at risk of failure and analyzing student engagement.

Finance

Algorithmic Trading

Algorithmic trading involves the use of complex AI systems to make trading decisions at speeds several orders of magnitudes greater than any human is capable of, often making millions of trades in a day without any human intervention.  Automated trading systems are typically used by large institutional investors.

Personal Finance

Several products are emerging that utilize AI to assist people with their personal finances.  For example, Digit is an app powered by artificial intelligence that automatically helps consumers optimize their spending and savings based on their own personal habits and goals.  The app can analyze factors such as monthly income, current balance, and spending habits, then make its own decisions and transfer money to the savings account.  Wallet.AI, an upcoming startup in San Francisco, builds agents that analyze data that a consumer would leave behind, from Smartphone check-ins to tweets, to inform the consumer about their spending behavior. 

Portfolio Management

Robo-advisors are becoming more widely used in the investment management industry.  Robo-advisors provide financial advice and portfolio management with minimal human intervention.  This class of financial advisers work based on algorithms built to automatically develop a financial portfolio according to the investment goals and risk tolerance of the clients.  It can adjust to real-time changes in the market and continually calibrate the portfolio.

Underwriting

An online lender, Upstart, analyze vast amounts of consumer data and utilizes machine learning algorithms to develop credit risk models that predict a consumer’s likelihood of default. Their technology will be licensed to banks for them to leverage for their underwriting processes as well.  

ZestFinance developed their Zest Automated Machine Learning (ZAML) Platform specifically for credit underwriting as well.  This platform utilizes machine learning to analyze tens of thousands traditional and nontraditional variables (from purchase transactions to how a customer fills out a form) used in the credit industry to score borrowers.  The platform is particularly useful to assign credit scores to those with limited credit histories, such as millennials.  

Market Analysis and Data Mining

Several large financial institutions have invested in AI engines to assist with their investment practices.  BlackRock’s AI engine, Aladdin, is used both within the company and to clients to help with investment decisions.  Its wide range of functionalities includes the use of natural language processing to read 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 an AI engine called Sqreem (Sequential Quantum Reduction and Extraction Model) which can mine data to develop consumer profiles and match them with the wealth management products they’d most likely want.   Goldman Sachs uses Kensho, a market analytics platform that combines statistical computing with big data and natural language processing. Its machine learning systems mine through hoards of data on the web and assess correlations between world events and their impact on asset prices.

Transportation

Today's cars now have AI-based driver assist features such as self-parking and advanced cruise controls. AI has been used to optimize traffic management applications, which in turn reduces wait times, energy use, and emissions by as much as 25 percent. In the future, fully autonomous cars will be developed. AI in transportation is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives.

External links

• AI applications at aitopics.org
• How AI can be applied in many fields

References

• 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.
• Kurzweil, Ray (2005). The Singularity is Near: When Humans Transcend Biology. New York: Viking. ISBN 978-0-670-03384-3.
• National Research Council (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press. ISBN 0-309-06278-0. OCLC 246584055.
• Moghaddam, M. J., M. R. Soleymani, and M. A. Farsi. "Sequence planning for stamping operations in progressive dies." Journal of Intelligent Manufacturing(2013): 1-11.
• United States, National Science and Technology Council - Committee on Technology. Executive Office of the President. (2016). Preparing for the future of artificial intelligence.