RobustML

between #supervised and #unsupervised learning approaches there is a whole gray matter where the #robustml resides

Some say - #DeepLearning We say - not-so-robust-learning :) More evidence is piling. teapaw.org/DanHendrycks/status/11… #robustML

"Machine learning will always produce an answer" is an uneducated statement by academics eager to one up on others. It is true in absence of systems around ML. #robustML takes care of all this

Reliability is one of the metric of #RobustML MachieLearning is a two step process - Training step, Inferencing (production) step. Reliability metrics are required in both. A modern compute/software infrastructure will play a very important role in addition to data/methods.

Blockchain technology can add to the robustness of ML technology #robustML - #traceability

CNNs are designed to process and classify images for computer vision and many other tasks. But slight modifications — say, a few darker pixels within an image — may cause a CNN to produce a drastically different classification. NOT a #robustML

Enabling machines to process data from various sources to identify fraud or detect defects ("Anomaly Detection Automation") was the most appealing idea to lenders, followed by "Borrower Default Risk Assessment."