Think you know English? Think again. See if you have what it takes to teach a computer how to understand humans.

Anyone who has tried to learn English as a second language is only too familiar with its many — many — challenges. In addition to idioms, sarcasm, and a wide array of meanings when combined with various prepositions (think: make up, make out, make it, and of course, makeup), there’s also pop culture, trends, products, and more to keep straight. Luckily for us, we’re human, and even those well established in their native languages will be able to speak and decipher English with enough practice and exposure. But what about machines? How do we even begin to program them in a way that they can read and understand sentiment? Answer: very carefully. The process requires machine learning data scientists to use Natural Language Process (NLP) techniques, a form of advanced analytics. They use these techniques to build models that can decipher sentiment and weed out the meaningful information among the noise. 

A new report, featured in The New York Times, highlights the benefits of Big Data and machine learning in the airline industry and beyond.

The travel industry has always been a vast data collector. Every airline reservation, every hotel booking, every car rental ends up in a conventional database of structured data. But today Big Data — the unstructured data that includes ratings on blog sites, likes on social media, conversations with call centers, customer clickstreams, and more — is becoming increasingly important in determining how travel companies keep customers coming back.

The New 1:1 Marketing Approach in Four (Not-So-Easy) Steps

The concept of 1:1 marketing generated a lot of hype — ten years ago. It changed the way companies reached their customers, creating that illusion that personalized marketing was actually… well, personal. But it also raised expectations of customers. They became acutely aware that their actions — especially those online — were tracked and used for marketing purposes. Eventually, they ignored companies’ attempts to advertise based on a search term or status update. “Really? Just because I posted that I’m baking cookies with my son does not mean I want to buy Chips Ahoy right now.”