Artificial intelligence has entered the mainstream in a way the world has never experienced before. Millions of people are using tools such as ChatGPT and Stable Diffusion for AI-generated help answering questions, creating images and accomplishing a host of other tasks.
But anyone who has used these systems has probably also noticed these tools have limitations. They can, for example, overlook a key component of a request or come up with something that’s not quite right.
“Every day, these AI models impress us, but we’re still not sure how trustworthy and reliable they are,” said Parisa Kordjamshidi, an assistant professor in the Department of Computer Science and Engineering at Michigan State University.
“Even when they provide the right answer, they might be right for the wrong reasons. We need to know what is their line of reasoning,” Kordjamshidi said. “That’s not very clear right now, and that’s the challenge.”
The Office of Naval Research has awarded Kordjamshidi and her colleagues a $1.8 million grant to make our interactions with AI more reasonable and reliable. This would bolster the confidence people have in using AI tools that are increasingly acting as digital assistants. But the team also has larger goals.
The researchers are working to help AI better process a range of inputs — text, images and video — to make human interactions with computer systems more powerful and seamless. The project could thus enable advances in a variety of applications, Kordjamshidi said, including education, navigation and multimodal question-answering systems in general.
This represents one of the major research thrusts for Kordjamshidi’s team. In fact, she won a 2019 National Science Foundation Faculty Early Career Development, or CAREER, Award and a 2021 Amazon Research Award on this front. Her team is working to help AI understand natural, everyday human language — rather than computer code — and put that understanding to work in following human instructions for navigating a realistic environment.
“We want to be able to connect this to the real world, to physical environments,” Kordjamshidi said. “Even if an AI system is 70% reliable, that wouldn’t be high enough for many serious real-world applications.”
While Kordjamshidi’s primary expertise is on the natural language component of AI, she’s teamed up with other AI innovators with complementary skills on this grant. At MSU, that includes Yu Kong and Vishnu Boddeti, both assistant professors in the College of Engineering. Dan Roth, a professor at the University of Pennsylvania, is also a co-investigator.
“This is a very collaborative project,” Kordjamshidi said. “We’re bringing together experts from various areas of learning and reasoning over vision and language data.”
Make new algorithms, but keep the old
The core of the team’s idea is to combine modern algorithms with an earlier approach to AI that started rising to prominence in the 1980s. Now known as classical or symbolic AI, these algorithms worked to teach computer systems explicit forms of reasoning and logic.