Although the term Machine Learning (ML) was coined in 1959, it’s advancement and development has never been more critical than it is today, particularly within government agencies. As the amount of data being produced, manipulated, and stored exponentially increases, so does the very real threat of cyber-security breaches and fraud. Meanwhile, federal budgets and staff resources continue to decrease. ML can provide high-value services for federal agencies including data management and analytics, security threat detection, and process improvement—but the list does not stop there.
Machine Learning is a type of Artificial Intelligence (AI) that takes human-input data, analyzes it, and learns from it. Three types of Learning can occur: supervised learning in which the machine analyzes past high quality data and makes decisions about future data with the learned knowledge, unsupervised learning in which the machine makes inferences about future data based on patterns it finds within past data, and a combination of the two.
According to a recent MeriTalk survey, 81% of feds are currently utilizing some form of Big Data analytics for cybersecurity, while only 45% found their efforts to be “highly effective.” These numbers are staggering considering that Big Data is still a relatively new discipline to most people. Google Trends Analyses show that traditional Big Data is being phased out just as fast as it initially exploded, and that it will soon be replaced with AI applications and Machine Learning.
But not to fear, Machine Learning will not replace humans; not yet anyway. This is where data scientists come in. Data scientists are a critical component of Machine Learning for analytics and data-based predictions. Data scientists conduct statistical and algorithm modeling, and determine which ML platform is best suited for the data. R and Python are currently the two most popular programming languages in ML. More importantly, the data scientist must determine what the machine will do with the data.
