It's worth noting that AI / neural networks are NOT the same as a predictive model of behavior.  They are beyond reproach at pattern recognition, and since most behavior fits in a pattern, a neural net can make effective predictions.  At one point Amazon boasted that their predictions were so effective that they can sometimes have the product in shipment before the customer places the order. A neural net is different from typical programming where a programmer thinks up all the different cases and how the program should respond and lists it out in code.  Instead, a programmer builds the 'net' with some weights, then by feeding the data through the network adjusts the weights so that the result is desired (or left to its own devices to figure out how to acheive the best 'score'). So, when an event occurs that shifts 'normal' behavior, an AI system would need to be trained for that eventuality otherwise it uses 'normal' weighting to decide on 'abnormal' situations. The other big issue with AI, and what the article calls 'weakness' is that AI that is trained on a dataset, and most neural net systems become fitted to that data, so erroneous data gets ignored or triggers an 'incorrect' response (incorrect in our terms, for the AI that is the only result based on the weighting).