Yahoo!’s recent innovations cover many active fields in technology, including one of the fastest-growing sectors in the business: mobile apps.

Three years ago, Yahoo!’s newly-appointed CEO, Marissa Mayer, decided to shake things up at the company. One of the first things she did was bring new talent onboard. Adam Cahan was one of those talents. Cahan, a graduate of Brown University and a former filmmaker at National Geographic, was chosen by Mayer as Yahoo!’s Senior Vice President of Mobile and Emerging Products. His overall mission is to improve and expand Yahoo!’s global mobile services, such as its apps for mobile and tablet devices, the image and video hosting site Flickr, and Yahoo!’s Smart TV.

When Cahan started his new job in 2012, Yahoo!’s mobile department only had 50 employees in a company of more than 13,000 people. According to him, mobile apps weren’t taken that seriously, or at least seriously enough. “The challenge was that mobile was an afterthought at the time,” he said. “It was, ‘Hey, I have all this work that I need to do and, oh, mobile.’ It was like the finishing part of a sentence as an add-on, rather than a strategy.”

Cahan immediately got to work changing that strategy. First, he expanded his department’s size and increased collaborative efforts in the office. Second, he placed emphasis on user experience rather than profit losses. He instructed his engineering team to simplify and rebuild mobile apps such as Yahoo!’s weather, finance, and email apps, in order to make them more accessible and efficient. Efficiency, in particular, was important to him, as it allows users to get more done and, more importantly, to use the app more often.

This retooling of the mobile department had a profound impact on Yahoo!. Not only has Yahoo! gained more than 400 million new mobile app users since he took over, the mobile department alone grossed an outstanding $1.2 billion in revenue in 2014.

Cahan’s efforts reflect the growing influence mobile apps have on overall technology use. For example, global mobile traffic now represents roughly 13% of Internet traffic. Of that traffic, 80% is done through mobile apps. Global mobile app revenues reached $30 billion last year and is expected to grow from there. Yahoo!’s and Cahan’s emphasis on mobile apps and services is one of the latest innovations the tech industry must get accustomed to.

Since their invention, computers have always done what the human brain can’t.

That’s why computers are best at handling massive sets of data and information and categorizing them into huge clusters. When it comes to picking apart minute details and looking at small sets of data, however, the human brain is more adept than a computer.

However, that could soon change with a new machine-learning algorithm that recently came out of MIT.

According to a December 8 Popular Science article, the MIT Model allows computers to group data points together based upon the data’s similarities to one another. The algorithm then creates a “prototype” from the different categories of data that includes features that are similar among all the pieces of data.

MIT’s press release on the MIT Model explains the way this model works by using a typical voter population in an election as an example.

“A plurality of the voters might be registered as Democrats, but a plurality of Republicans may have voted in the last primary,” MIT’s press release explains. “The conventional algorithm might then describe the typical voter as a registered Democrat who voted in the last Republican primary. The prototype constraint makes that kind of result very unlikely, since no single voter would match its characterization.”

The MIT Model could also help computers independently ward off spam and viruses, which currently affect about nine out of every 1,000 computers today, by better categorizing abnormal pieces of data and picking them out from the bigger picture of data on the computer’s system.

And, believe it or not, the MIT Model has been proven to be successful at helping computers interpret data more like we do. Under the traditional topic model algorithm, computers asked to find cooking recipes would bring back a random list of ingredients, while computers with the MIT Model were more likely to deliver results that more closely resembled recipes, according to Popular Science.

However, the MIT Model isn’t the perfect algorithm just yet — so it might still be some time before our computers can interpret data on a small scale as well as we can.