Decades even before the buzz went off, machine learning has proven its ability to decipher information from vast datasets to see hard-to-spot patterns, classify and cluster data, as well as make predictions using algorithms. With its myriad of real-life applications, cybersecurity remains to be one of its top use areas: It gives traditional cybersecurity solutions the edge it needs to catch destructive threats such as ransomware before it gets deployed in a system, which saves organizations' time, money, and reputations. Traditional machine learning largely deals with historical knowledge. It allows computers to make inferences based on datasets that have been previously labeled by humans. In cybersecurity, training a machine learning model to learn what malicious files and programs look like can help in the discovery of new, emerging, or unclassified threats via correlation.

I was recently a guest on the Game-Changing Predictive Machine Learning radio show hosted by Bonnie Graham. The other guests were Gil Gomez of Deloitte and Hudson Harris of HarrisLogic. Here are some of my remarks from the show. "Machine learning is really about complex-but-repetitive decisions and being able to automate those in new ways. And when you think about it that way, it's hard to think of an area where it won't have an impact. "Almost every job, every role, has some aspect of that job that involves complex, repetitive decisions, and you can now automate that and elevate the person doing the role to a higher level, allowing them to do more with less." "Governance is an area that is not sufficiently discussed when it comes to machine learning.

We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme. As a result, the proposed method retains the exact, model-free validity when the data are i.i.d. or more generally exchangeable, similar to usual conformal inference methods. When exchangeability fails, as is the case for common time series data, the proposed approach is approximately valid under weak assumptions on the conformity score.