Artificial intelligence -- specifically, machine learning -- is a part of daily life for computer and smartphone users. From autocorrecting typos to recommending new music, machine learning algorithms can help make life easier. They can also make mistakes.
It can be challenging for computer scientists to figure out what went wrong in such cases. This is because many machine learning algorithms learnfrominformation and make their predictions inside a virtual "black box," leaving few clues for researchers to follow.
A group of computer scientists at the University of Maryland has developed a promising newapproachfor interpreting machine learning algorithms. Unlike previous efforts, which typically sought to "break" the algorithms by removing key words from inputs to yield the wrong answer, the UMD group instead reduced the inputs to the bare minimum required to yield the correct answer. On average, the researchers got the correct answer with an input of less than three words.