35 Deploying Artificial Intelligence ^Top Machine learning (ML) Employs self-learning by machines (unlike predictive analytics, which uses statistical techniques.) ML is considered a subset of AI, and exhibits the experiential “learning” associated with human intelligence and improves the performance of the computational algorithm as it learns without human involvement. For instance, with IBM Watson Health, the software is fed with “training data” of all possible data related to cancer diagnostics so that the software can recognize patterns in them, and when given the data of a new patient (test data) it can diagnose based on the learned pattern. Over time, it improves its precision by revising its original predictive model by both ingest- ing new data and learning from its own mistakes. Predictive accuracy thus improves with time and data. Deep learning (DL) A subfield of ML, with Neural Networks (NN) that contain neurons hundreds of layers deep. NN is used to create deeper and hierarchical layers of relation- ships between input variables in the data to assess their relative impact on the outcome for more accurate predictions. Simply put, NN contains elementary data processing units called neurons, like in a human brain, which connect and transmit synapses or signals to other neurons. Each neuron takes input data, processes it through a complex mathematical function, and sends an output to its connected neurons. In DL, they are structured in layers with an input layer receiving external data and an output layer throwing out the ultimate output of the algorithm. Sandwiched between the two is a hidden layer of neurons (where data processing hap- pens) with each neuron in each layer connecting with each one in the next layer. While this itself sounds sophisticated, imagine an NN with multiple such hidden layers! The computation gets very sophisticated very quickly. The greater the number of layers and neurons, the deeper the learning is. This architecture enables the neural net to learn non-intuitive and non-linear correlations among variables in the input data.