Machine learning is taking the tech world by storm. 2016 began with an announcement that Google was open-sourcing Tensor Flow, their machine learning (ML) software, and Microsoft quickly followed suit. Baidu and Amazon unveiled their own deep learning platforms a few months later, while Facebook began supporting the development of two ML frameworks. But the revolution has spread far beyond the tech realm. In fact, some of the more recent applications of ML technology aren’t just innovative; they’re weird and surprising.

1. Predicting whether a criminal defendant is a flight risk

Judges routinely have to decide whether a criminal defendant should await trial in jail or at home. It’s a tough call: jailing a defendant saps government resources (and, if the defendant is found not guilty, forces an innocent person to languish behind bars), while allowing a defendant to wait at home poses the risk that they might flee or commit another crime.

But a group of economists and computer scientists might have made the decision a little easier. Using data from hundreds of thousands of New York City cases, the group trained an algorithm to predict whether defendants were a flight risk. The algorithm draws on a defendant’s rap sheet and court records to make the call. When the algorithm was tested on cases it had never seen before, it dramatically outperformed experienced judges.

2. Using Twitter to diagnose psychopathy

Is it possible to figure out whether someone is a psychopath just by looking at their Twitter feed? Though anyone who’s gotten into a flame war with an egg avatar might have figured this out a while ago, researchers now have data to suggest a link between certain Twitter quirks and psychopathy. The study draws on a 2011 study identifying language patterns among diagnosed psychopaths to build a machine learning algorithm that finds such patterns in Twitter feeds.

Participants completed a self-assessment designed to gauge personality traits like machiavellianism, narcissism, openness, and extraversion. The ML system then analyzed their Twitter feeds and privacy settings, finding strong correlations between language use and psychopathic tendencies. Twitter users who cursed often, who were fond of the words “we” or “hate,” and who punctuated their tweets with filler words like “I mean” and “um” were systematically more prone to psychopathy than their counterparts.

3. Helping cyclists win the Tour de France

Cyclists in the Tour de France often struggle to figure out their position relative to other competitors. About two hundred cyclists compete in the race, and TV crews don’t have their cameras on all of them all the time – which makes it hard for coaches and participants to figure out their strategy once the race has begun.

That’s why data scientists have developed WinningAlgorithms. WinningAlgorithms is a machine learning system that mines the social media feeds of spectators along the route of the Tour de France, as spectators provide up-to-the-second data on where their favorite cyclists are. The algorithm also has the ability to parse out fact from fiction, deciding which social media posts are exaggerations and which are accurate representations of the event.

4. Identifying endangered whales

Cornell’s Bioacoustic Research Program is using machine learning to decrease shipping vessels’ environmental footprint. Shipping vessels rely on navigation technology to optimize transport routes – but even the most advanced navigation systems can’t predict when a sea creature might swim in front of the ship. Accidental collisions between transport ships and whales have left the North Atlantic right whale nearly extinct.

To keep the last four hundred right whales safe, Cornell researchers trained a machine learning system using a dataset of recorded whale calls. They then deployed a worldwide buoy network that scans the ocean for whale calls, predicting a whale’s location and trajectory given the distance of their calls between buoys.

5. Translating legalese

Legal documents are not for the faint of heart. But not everyone has the time or resources to hire a lawyer, and those who brave legal documents alone might find themselves wishing they’d opted for an easier activity, like reading a Hungarian newspaper. Those who quickly skim legal docs – or, even worse, don’t read them at all – might find themselves subject to penalties they never anticipated.

But Legal Robot has built a machine learning model that translates legal language into everyday-speak. Using deep learning and topographical data analysis, Legal Robot can both processes legalese and decide whether a contract contains elements that shouldn’t be there, like royalty fees in a non-disclosure agreement.

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Roxanna "Evan" Ramzipoor

Roxanna "Evan" Ramzipoor is a writer on the marketing team at Sift Science. Her fiction is represented by Kristin Nelson of Nelson Literary. To see more of her work, check out her blog: http://just-three-words.tumblr.com/