What is the difference between artificial neural networks and biological brains?

What is the master algorithm that allows humans to be so efficient at learning things? That is a question that has perplexed artificial intelligence scientists and researchers who, for the past decades, have tried to replicate the thinking and problem-solving capabilities of the human brain. The dream of creating thinking machines has spurred many innovations in the field of AI, and has most recently contributed to the rise of deep learning, AI algorithms that roughly mimic the learning functions of the brain. But as some scientists argue, brute-force learning is not what gives humans and animals the ability to interact the world shortly after birth. The key is the structure and innate capabilities of the organic brain, an argument that is mostly dismissed in today's AI community, which is dominated by artificial neural networks. In a paper published in the peer-reviewed journal Nature, Anthony Zador, Professor of Neuroscience Cold Spring Harbor Laboratory, argues that it is a highly structured brain that allows animals to become very efficient learners.