The research, published Thursday in the journal, Scientific Reports, explains how they did it. First, the team spent 75 days recording two groups of 11 bats held in separate cages. The team then went through the video footage to suss out which individuals were squeaking at each other, what they were squeaking about -- food, sleep, perch or sex (or lack thereof) -- and the ultimate outcome of the argument. Finally, they trained the machine learning algorithm with 15,000 calls from seven adult females using those variables. In the end, the algorithm managed to correctly identify the bat making the call (compared to the video footage) 71 percent of the time, the subject of that argument 61 percent of the time and the eventual outcome 41 percent of the time.

Egyptian fruit bats are widespread throughout Africa and often roost together in colonies of 1,000 or more individuals. With that many neighbors packed together, it's no wonder they're such a noisy bunch. And thanks to some exciting machine learning research from Tel Aviv University, we now understand a bit of what they're saying. The research, published Thursday in the journal, Scientific Reports, explains how they did it. First, the team spent 75 days recording two groups of 11 bats held in separate cages.