Artificial neural networks (ANNs) are a promising technology for supporting decision making and autonomous behavior. However, most current-day ANNs suffer from a fault that hinders them from achieving their promise: fragility. Fragile AIs can easily make faulty recommendations and decisions, and even execute faulty behavior, so reducing ANN fragility is highly desirable. In this Viewpoint, we describe issues involved in resolving ANN fragility and possible ways to do so. Our analysis is based on what is known about natural neural networks (NNNs), that is, animal nervous systems, as well as what is known about ANNs.
Engineered systems fall on a continuum from robust to fragile. One reason some ANNs are considered "fragile" is that seemingly minor changes in the data they are given can cause major shifts in how they classify the data. Such fragility is often evident when lab-trained ANNs are finally tested under real-world conditions. A classic example of hyper-fragility is an ANN that, after being trained to recognize images of stop signs, fails to recognize ones in which a small percentage of pixels has been altered.
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