The problem with artificial intelligence is that everybody thinks it should do everything perfectly right out of the box, according to Rama Chellappa, an expert in computer vision and machine learning.
"There's no such thing. There are always tradeoffs," said Chellappa, a Bloomberg Distinguished Professor in electrical and computer engineering and biomedical engineering, during a virtual discussion on Thursday—the latest session in the Johns Hopkins Congressional Briefing series.
AI, Chellappa noted, is built on data, and data is not always perfect or complete. Errors in how data is collected, compiled, or processed will affect the success of any AI tool that relies on it. That is how challenges such as bias creep into autonomous systems.
The process for reaching for fully functional AI, he said, yields new discoveries along the way.
"[AI] is doing good things in a lot of ways, but it is not a perfect technology," he said. "For example, take the autonomous car. We may not have a fully autonomous driving car that works everywhere so you can read your book and drink coffee [while driving]... But the process of getting there has helped us figure out how to design … new safety features that have been put in cars, even inexpensive entry-level cars."
The July 28 briefing covered a variety of topics related to AI, including its use in autonomous technologies, how it can help in the design and discovery of new materials, ethical considerations related to AI, the use of AI within health care, and the major challenges that need to be addressed before AI could reach its full potential.