AI, Machine Learning, Neural Nets, Deep Learning The goal of Artificial Intelligence is typically defined as creating computer systems that can perform tasks that require intelligence for humans to perform. This can include tasks such as reasoning, solving problems, representing knowledge, processing human language, perceiving the world, moving within an environment, and learning.
Of those types of AI, machine learning is one of the most important. Machine learning is concerned with creating computer systems that get better at doing a task as they gain experience; for example, programs that get better at chess the more they play, or software that learns to identify objects in photos after being trained with large numbers of photos labelled with different objects.
There are many approaches to machine learning, for example using rule learners, statistical methods, and artificial neural networks. ANNs, also called neural nets, are designed to map inputs (such as a photo) to outputs (such as a list of objects in the photo) by passing signals through a network of artificial neurons, connected to each other by weighted links, that are related to those that McCulloch and Pitts proposed back in 1943. ANNs can “learn” by adjusting their weights, which changes the mapping from inputs to outputs. During a learning phase, an ANN is repeatedly presented with many samples of inputs and the corresponding correct answers, and slowly adjusts its weights until its outputs approximate the correct answers as well as possible.
While neural nets had been explored in ML in the 1980s and 1990s, it is in the last decade that they have led to a new development, termed deep learning. At a basic level, deep learning involves learning with very large neural nets where the inputs are connected via many layers of neurons to the outputs. Because of their size and structure, they are able to represent complex mappings. In addition, early layers can be considered to act as learnable subroutines that are re-used by later layers, or to perform automatic feature processing at multiple levels. Training deep networks has given rise to new challenges that are being addressed via the advances in computation and data that I mentioned earlier.
“Superhuman AI”, “Artificial General Intelligence”, and Hype
At times there can be quite a lot of hype in the AI community. The term Superhuman AI is occasionally used by people hyping their work, although it has a specific accepted definition that is not based in hype: an AI system that can outperform humans at some specific task. We already have some examples of superhuman AI. For example, AlphaGo Zero  is able to perform better than any of the world’s best human players of Go. It learned by playing millions of simulated games against itself, rather than studying human masters. A poker-playing program called Libratus  has been demonstrated to beat the best professional human poker players in specially-arranged tournaments.