In a new machine learning approach, engineers did away with the human brain and all its beautiful complexity.
Whereas most machine learning algorithms can’t hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in “neuroplasticity.”
That is, as it goes about its work—say, in the future, maybe driving a car or directing a robot—it can learn from experience and adjust its connections on the fly.
In work published last year, the group, which includes researchers from MIT and Austria’s Institute of Science and Technology, said that despite its simplicity, C.
Their worm-brain algorithm was much simpler than other cutting-edge machine learning algorithms, and yet it was still able to accomplish similar tasks, like keeping a car in its lane.
“Today, deep learning models with many millions of parameters are often used for learning complex tasks such as autonomous driving,” a study author.
In contrast, in a liquid neural network, the parameters can continue changing over time and with experience.
This adaptability means the algorithm is less likely to break as the world throws new or noisy information its way—like, for example, when rain obscures an autonomous car’s camera.
At a time when big players like OpenAI and Google are regularly making headlines with gargantuan machine learning algorithms, it’s a fascinating example of an alternative approach headed in the opposite direction.