Finding a shortcut to anything has always fascinated human beings. From saving time to cutting down on budgets, finding the shortest path to the end goal is quite often the ultimate aim. But what if machines could do that for us, with the same level of accuracy and precision that we manage?
It might be a very real possibility now.
According to a report presented in Nature last week by researchers at DeepMind, a navigational artificial intelligence system can explore complex simulated environments (like a maze) and find the shortest route to an end destination, like humans and animals! It works like a GPS system, and the AI manages to carve a path around the environment (or maze) with remarkable skill and accuracy.
The AI consists of an artificial neural network that uses the concept of grid cells. A grid cell is a type of neuron in the brains of many species that allows them to understand their position in space. While learning how to navigate, the neural net spontaneously develops the equivalent of grid cells.
For investigating the role of grid cells in navigational functions, the researchers attempted to use deep-learning neural networks. They have also released the approach they followed, which we have summarized below:
The researchers have admitted they came upon this algorithm unexpectedly but it’s ended up being a truly valuable research for the community. AI researchers can use this for improving existing automated navigation systems (maybe self-driving cars, or helping robots). This approach is not only restricted to navigation though. It can also be used for testing theories related to brain functioning, a field which has seen tons of coverage but little breakthrough.