It’s essential to understand the benefits and shortcomings of AI and ML technologies. Artificial Intelligence (AI) will soon be at the heart of every significant technological system in the world, including payments, compliance, financial markets, security and defense, healthcare, the Internet of Things (IoT), and marketing. Machine learning (ML) is applied in various fields such as computer vision, speech recognition, natural language processing, web search, biotech, risk management, cyber security, and many others. It is the science of getting computers to act without being explicitly programmed, but rather is “programmed by example.” Two types of learning are commonly used: supervised and unsupervised. In supervised learning, a collection of labeled patterns is provided, and the learning process is measured by the quality of marking a newly encountered way. Labeled patterns are used to learn the descriptions of classes, which are used to label a new routine. In the case of unsupervised learning, the problem is to group a given collection of unlabeled patterns into meaningful categories. A neural network (NN) is a technology loosely inspired by the structure of the brain. A neural network consists of many simple elements called artificial neurons, each producing a sequence of activations. The components used in a neural network are far more straightforward than biological neurons. The number of elements and their interconnections are orders of magnitude fewer than the number of neurons and synapses in the human brain.