How Mirroring the Architecture of the Human Brain Is Speeding Up AI Learning

While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess. The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. Today's largest deep learning models, like OpenAI's GPT-3 and Google's BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.