• Artificial Intelligence (AI) is a discipline that studies systems that exhibit behaviors associated with human intelligence, whether embodied solely in computer software, or as embedded software in a physical structure like a robot.

• Machine Learning (ML) is an automated approach to learning patterns from empirical data using training examples, usually large databases, with the development of an ML algorithm that when applied to new examples improve prediction. ML is concerned with identification of patterns in a wide range of data include quantitative databases such as diagnostic images or textual corpora such as in medical records, and personal reporting or personal health records. The challenge that ML addresses is to identify patterns of interest in data sets that encode extremely large numbers of factors and with vast numbers of cases from which generalizations are to be formed. Conversely, it is problematic to generalize from relatively small numbers of known cases to recognized patterns for new possibilities.

• Supervised versus Unsupervised Learning -- It is common to distinguish supervised and unsupervised methods, even though the label “unsupervised” is slightly misleading in that for any method to work, a machine-readable data set must be developed and encoded by a human. However, where methods are explicitly supervised, this tends to mean that there is a designated target feature and values within that feature such that training can be tuned to identify with greater levels of supervision. Error analysis from initial training is fed back into the training process and the models can be progressively refined.

• Deep Learning – Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. Although a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. Deep learning drives many AI applications and services that improve automation, performing analytical and physical tasks without human intervention.