While monitoring transactions, an alert bank data analyst noticed unusual payments from a computer manufacturer to a casino. Because casinos are heavily computerized, one would expect the payments to go to the computer company. The analyst alerted an investigative agent, who rapidly scoured websites, proprietary data stores, and dark web sources to find detailed information about the two parties. The data revealed that the computer manufacturer was facing a criminal indictment and a civil law suit. Meanwhile, the casino had lost its gambling license due to money laundering and had set up shop in another country. Further investigation revealed the computer manufacturer was using the casino to launder money before the company's legal issues drove it out of business.
The bank's data analyst was a machine learning algorithm. The investigative agent was an artificial intelligence (AI) agent.
AI is all around. It's monitoring financial transactions. It's diagnosing illnesses, often more accurately than doctors. It's carrying out stock trades, screening job applicants, recommending products and services, and telling people what to watch on TV. It's in their phones and soon it will be driving their cars.
And it's coming to organizations, maybe sooner than people realize. Research firm International Data Corp. says worldwide spending on cognitive and AI systems will be $12 billion this year. It predicts spending will top $57 billion by 2021.
"If you think AI is not coming your way, it's probably coming sooner than you think it is," says Yulia Gurman, director of internal audit and corporate security for the Packaging Corporation of America in Lake Forest, Ill. Fresh off of attending a chief audit executive roundtable about AI, Gurman says AI wouldn't have been on the agenda a year ago. Like most of her peers present, she hasn't had to address AI within her organization yet. Now it's on her risk assessment radar. "Internal auditors should be alerting the board about what's coming their way," she says.
THE LEARNING ALGORITHM
Intelligent technology has already found a place on everyday devices. That personal assistant on the kitchen counter or on the phone is an AI. Alexa, Cortana, and Siri can find all sorts of information for people, and they can talk to other machines such as alarm systems, climate control, and cleaning robots.
Yet, most people don't realize they are interacting with AI. Nearly two-thirds of respondents to a recent survey by software company Pegasystems say they have not or aren't sure they have interacted with AI. But questions about the technologies they use--such as personal assistants, email spam filters, predictive search terms, recommended news on Facebook, and online shopping recommendations--reveal that 84 percent are interacting with AI, according to the What Consumers Really Think About AI report.
What makes AI possible is today's massive availability of data and computing power, as well as significant advances in the quality of the machine learning algorithms that make AI applications possible, says Pedro Domingos, a professor of computer science at the University of Washington in Seattle and author of The Master Algorithm. When AI researchers like Domingos talk about the technology, they often are referring to machine learning. Unlike other computer applications that must be written step-by-step by people, machine learning algorithms are designed to program themselves. The algorithm does this by analyzing huge amounts of data, learning about that data, and building a predictive model based on what it's learned. For example, the algorithm can build a model to predict the risk that a person will default on his or her credit card based on various factors about the individual, as well as historical factors that lead to...