Facebook and Google built a framework to study how AI agents talk to each other

The intricacies of evolutionary linguistics are myriad and underexplored, but research involving artificial intelligence (AI) might unlock the door to new theories about how dialects develop among users. This work isn't the first to investigate language with machine learning algorithms -- a paper published by Facebook researchers in June 2017 detailed how two agents learned to "negotiate" with each other in chat messages. But they claim it's the first to use "latest-generation deep neural agents" capable of dealing with "rich perceptual input," and they say it convincingly demonstrates that language can evolve from simple exchanges. The team began by deploying groups -- communities -- of agents equipped with the ability to communicate in a simulated environment, with complexities ranging from simple (a set of equations) to relatively complicated (a deep neural network). The "games" the agents were tasked with playing had several key properties: They were symmetric, enabling the agents to act as both "speakers" and "listeners"; they allowed the agents to communicate about something "external" to themselves, such as the sensory experience of something in their environment; and they took place in a world the agents could at least partially observe.