Although light on details, this is an application of AI for securing against credit card fraud in real time using cloud computing.
AI has been in the news a few times this month – Google (TensorFlow), Facebook (new milestones in AI), Microsoft releasing Cortana (Nadella welcomes our AI overlords) and mention of an AI spring from IBM and Salesforce.
Machine learning has also been applied to spam detection, intrusion detection, malicious file detection, malicious url detection, insurance claims leakage detection, activity/behaviour based authentication, threat detection and data loss prevention.
Worth noting that these successes are typically in narrow domains with narrow variations of what is being detected. Intrusion detection is a fairly hard problem for machine learning because the number of variations of attacks is high. As someone said, we’ll be using signatures for a long time.
The previous burst of activity around neural networks in the late 80’s and early 90’s had subsided around the same time as the rise of the internet in the mid to late 90’s. Around 2009, as GPU’s made parallel processing more mainstream, there was a resurgence in activity – deeper, multilayer, networks looking at overlapping regions of images (similar to wavelets) lead to convolutional neural networks being developed. These have had successes in image and voice recognition. A few resources – GPU gems for general purpose computing, visualizing convolutional nets, caffe deep learning framework.