AI may not need massive training data after all

New research from Johns Hopkins University shows that artificial intelligence systems built with designs inspired by biology can begin to resemble human brain activity even before they are trained on any data. The study suggests that how AI is structured may be just as important as how much data it processes.

The findings, published in Nature Machine Intelligence, challenge the dominant strategy in AI development. Instead of relying on months of training, enormous datasets, and vast computing power, the research highlights the value of starting with a brain-like architectural foundation.

Rethinking the Data Heavy Approach to AI

"The way that the AI field is moving right now is to throw a ...