Machine Learning (ML) and Artificial Intelligence (AI) are two very popular buzzwords right now.  They seem to be appearing everywhere, and are sometimes used interchangeably within various articles.  This brings about some confusion, and the question "are they the same thing"?  To answer this question it's best to take a look at just what is AI and Machine Learning.

With Google recently changing their indexing policy to downgrade websites that do not use encryption (https) we've seen a dramatic increase in domains supporting encryption.  The same can't be said of email.  Email is insecure, which seems to shock many of our clients.  Most messages are sent "in the clear", meaning with no form of encryption.  This opens up the user to having their emails intercepted by third parties, such as hackers, corporations, governments, etc.  While our servers have supported encryption since we first brought them online back in the early 2000's, many email servers still don't.  This is about to change with the Electronic Frontier Foundations (EFF) launching a new program called STARTTLS Everywhere.

Poor infrastructure design is a problem.  One that is further complicated by the very consultants trusted to avoid this very problem.  As we are currently seeing in a recent cyberattack in Atlanta, where they have to set aside more then $2.6 million for the recovery of a ransomware attack.  This attack took down a sizable portion of their infrastructure, and could have been avoided.

A study was released in May 2017 by Aspiring Minds that shows how poor the quality of programming is in India. According to their study only 36% of engineers can write compilable code. Compiling code is the action of taking source code and turning it into a program. Not being able to write compilable code is akin to a construction worker that can’t frame a wall, or a butcher that can’t cut meat. Uncompilable code is useless.