Have you ever answered a CAPTCHA while trying to access a website? Well then it's likely that you are an integral part of training classifier algorithms. Not only do those CAPTCHAs prove you’re a human, they also serve as large repositories for labeled datasets. You are likely teaching the next generation of self-driving cars the difference between a pole and a pedestrian.

Subject matter experts are the lifeblood of product development at Protenus. Although these experts exist throughout the industry — as founders, external consultants, colleagues, or friends in the industry — our best information comes from our own customers. The conversations we have with them play a key role in influencing our quarterly product planning process.

At the heart of Protenus Compliance Analytics platform lies the Artificial Intelligence (AI) engine that monitors millions of accesses to patient data every day and determines how likely it is for each to warrant further investigation. This AI engine, or the “classifier" as it’s known in data science, is a relatively little nugget compared to all the different processes our clients’ data go through every day to produce cases which are narratives explaining why certain interactions have been flagged as suspicious. Just a fraction, less than 2%, of a day’s run is spent on classification, while the rest goes to pre-processing, data aggregation, feature calculation, and alert and case generation.

Artificial intelligence is an incredibly popular topic these days as it is becoming more prevalent in our daily lives: from letting an autonomous car determine if a person or a plastic bag is in front of it, to reading a chest radiograph to detect cancer or other disease, to determining if a dangerous leak is occurring on an oil or gas pipeline. 

Scala for data science. These words may fill many data scientists with fear and uncertainty--others may even react with indignation. After all, doesn’t our Python toolkit give us everything we need for exploring data, training algorithms, visualizing results, and making discoveries? What is the benefit of throwing a new language into the mix, especially one with a decidedly less intuitive feel for many of us coming from academic research backgrounds?