AI is capable of processing large data sets to extract correlation and other insights from the data. Human expert with deep understanding of the system is able to identify insights that are relevant, or those that an AI cannot observe / identify, and those that require knowledge of the world. While AI can handle large dimensional data, an expert can handle data only in lower dimensions. Hence, there is a need to connect the AI and expert to exploit the synergy between them. Labelling huge volume of data by Human Expert is cumbersome & time consuming, so AI smartly & efficiently involve the Expert for very minimal labelling process. And after every interactions with the Expert, the AI tries to pick the Expert’s brain by learning from expert input and generalize. After certain iterations, the AI should be able to infer like the expert, and quickly detect the underlying observation characteristics. The initial Feedbacks that Expert will require will have more labels along with explanations, but eventually when the AI is able to pick up Expert’s brain, the feedback can only be in validating id the label suggested by AI is right or not.
In scenarios where data are not effectively labelled for training due to class imbalance, evolving trends, updation of rules, AI need to effectively collaborate with Subject Matter Expert to extract the right fraudulent behaviours and to avoiding predicting false positive.
AI can help businesses better analyze threats and respond to attacks and security incidents which is evolving over time with the help of expert in the loop. It could also help to automate more menial tasks previously carried out by stretched and sometimes under-skilled security teams.
Clustering & Confidence Predictor Module groups the data initially. Q&A module samples few observations and request inputs from the Expert to label them and also provide the associated features/ reasoning. The Label Predictor generalizes the expert input, by assigning labels to all records, to be used by the DL model. DL model updates the network parameters and predicts labels with certain confidence. Clustering & Confidence Predictor Module takes the output from DL model and the cycle continues. Validation module accesses the performance of the DL model by validating its predictions for few observations by the Expert. This cycle also continues until the optimal performance of DL model is met.
Build this in Big Brain in just 4 steps within 30 minutes: