Consider how often you ask Siri or Alexa a question, use predictive text to finish a sentence on your smartphone, or watch whatever show Netflix suggests. AI has only recently begun to permeate all aspects of society, but the technology is far from new. Professor Zhu (Drew) Zhang, the Alfred J. Verrecchia Endowed Chair in Artificial Intelligence and Business Analytics, has dedicated more than 20 years of his life to the study and advancement of AI. The University of Rhode Island (URI) researcher boasts an innovative body of work on AI, spanning from financial markets to health care.

As a part of the URI College of Business, Zhang has spent much of his time focusing on the marketing and finance applications of AI. One of his current projects uses what he explains as “textural data, more specifically mining news data, to forecast market movements, for example the volatility of the financial market.”

Money managers use combinations of fundamental and technical analysis to predict trends in the stock market and invest their clients’ wealth accordingly. Despite their expertise, less than 10% of actively managed funds have outperformed the U.S. stock market in the last two decades.

With more than 60% of the population investing their hard earned money in stocks, it is crucial to have tools to make the best possible investments. Zhang is developing a state-of-the art AI methodology for forecasting market movements. These methods will manage risk while optimizing return on investment, allowing clients on both Wall Street and Main Street to confidently manage their wealth. Other business applications of his AI research include predicting product sales by mining social media data.

Zhang says that, as a child, his affinity for science fiction films and their exploration of AI—well before the technology was a reality—planted a seed for exploring these concepts. His interest flourished while working toward his undergraduate degree in information systems. Noticing a gap in the ability of technology to analyze text, rather than simply scan and store it, Zhang focused his efforts on the advancement of AI and finding solutions to the real-life problems he and others faced.

Zhang earned his Ph.D. in computer and information science in 2005 from the University of Michigan. Now, with a focus on the intersection of natural language processing and machine learning, Zhang has worked with industry leaders such as Google, IBM, Alliance, and Microsoft. Recently, he published papers with Microsoft research colleagues on conversational AI.

He uses this expertise to mine textual data such as news articles, social media posts, and product reviews to train his AI models. Recently, he has expanded his research horizon into multimodal data mining. And, as the capabilities of AI grow and its uses multiply, the need for people in all fields to have a basic understanding of it has likewise grown.

Zhang says that, as a child, his affinity for science fiction films and their exploration of AI—well before the technology was a reality—planted a seed for exploring these concepts. His interest flourished while working toward his undergraduate degree in information systems.

In addition to his innovative research in business AI, Zhang has recently begun a collaboration with colleagues from the College of Nursing to develop conversational AI agents for use in the healthcare industry. While this endeavor remains in its infancy, Zhang, along with his collaborators and students, hopes to bring much needed assistance to those in the medical field, and their patients.

“Whether you’re the patient or the caretaker, we’re actually not good at handling large amounts of data,” he explains. “Even if we can to some extent, we don’t like the data being thrown at us in a mechanical form.”

Zhang sees an opportunity for AI to come in and assist medical professionals. One of the main challenges that needs to be tackled is that humans prefer human-like communication over computer-like communication. Consequently, the success of AI working in a nursing setting would correlate with how well it is able to mimic human communication.

Given his expertise in both natural language processing and machine learning, Zhang is uniquely qualified to help achieve the goal of offering a more human-like interaction where a patient can engage with a conversational agent.

This is why Zhang focuses his efforts not only on research, but also on the advancement of AI knowledge among both students and faculty. Through programs, such as the Distinguished Speakers Series and AI Fireside Chats, both at URI, Zhang strives to bring the University’s most prominent AI scholars together to talk about their research and educate others on campus.

With the increasing prevalence and advancement of AI, one topic that inevitably surfaces is job security. In the last few months, businesses and individuals have become increasingly concerned about the future of AI in various industries. For example, the recent Writers Guild of America and Screen Actors Guild strikes partially focused on protecting artists from losing jobs to AI.

Zhang agrees that people are right to be worried, as we will most likely start seeing AI replace some jobs.

However, he also stresses: “It’s not like something that hasn’t happened before. Whenever we had a new generation of technology, there were always concerns about jobs being lost or replaced, but it turned out that there are always new jobs that will arise from somewhere else.”

Zhang says he expects that this pattern will continue with the advancement of AI. With this shift in the job market, students—and more importantly, their curriculum—is going to have to adapt.

“Previously, when we talked about literacy, we were talking about the reading, writing, math kinds of literacy,” says Zhang. “Then, you needed some type of IT literacy. Now we’re talking about AI. You need some type of AI literacy.”

While most people do not need to learn how AI works inside and out, Zhang notes, many would benefit from learning how to communicate both with and about AI to keep up with the ever-changing world.