Scientists and engineers are teaching machines to think like humans, using a cutting-edge approach to artificial intelligence (AI) called deep learning. Deep learning is inspired by the intricate networks of neurons in the human brain. By creating artificial neural networks with multiple layers, researchers can train computers to automatically discover patterns in vast amounts of data and learn high-level features. The critical ingredients of deep learning are advanced computer processors, large training datasets, and a lot of patience. Deep learning systems must be fed massive amounts of data, from images and videos to speech and text, before it is capable of recognizing objects, translating languages or making decisions. Deep learning is a subset of machine learning that mimics the workings of the human brain in processing data and creating patterns for use in decision-making. It is built around artificial neural networks, which are algorithms inspired by the biological structure and function of the brain. These networks consist of layers of nodes, or “neurons,” each layer designed to perform specific tasks, and the data is processed as it passes through these layers. What sets deep learning apart from other machine learning techniques is its ability to perform feature extraction automatically. This means it can identify complex patterns and relationships in large datasets without explicit instructions on what to look for. As the network is exposed to more data, it adjusts and improves its accuracy over time, learning to make better decisions.

Full commentary : How Deep Learning Lets AI Tackle Complex Tasks.