Machine learning has delivered amazing results, but there also have been failures, ranging from the harmless to potentially deadly. New work suggests that common assumptions about the cause behind these supposed malfunctions may be mistaken, information that is crucial for evaluating the reliability of these networks.
Misinformation or artifact: A new way to think about machine learning: A researcher considers when – and if – we should consider artificial intelligence a failure
