Artificial Intelligence

Artificial Intelligence (AI) – Is Human Intelligence exhibited by machines. Broadly defined to include any simulation of human intelligence. Expanding and branching areas of research, development, and investment. Includes robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs).

Machine Learning (ML) – Is a Subfield of AI that aims to teach computers the ability to do tasks with data, without explicit programming. Uses numerical and statistical approaches, including artificial neural networks to encode learning in models. Models built using “training” computation runs or through usage.

Deep Learning (DL) – Subfield of ML that uses specialized techniques involving multi-layer (2+) artificial neural networks. Layering allows cascaded learning and abstraction levels (e.g. line -> shape -> object -> scene). Computationally intensive enabled by clouds, GPUs, and specialized HW such as FPGAs, TPUs, etc.

Data Science – Uses Scientific methods, algorithms and systems to extract knowledge or insights from Big Data. Also known as Predictive or Advanced Analytics. Uses Algorithmic and computational techniques and tools for handing large data sets. Increasingly focused on preparing and modeling data for ML & DL tasks. Encompasses statistical methods, data manipulation and streaming technologies (e.g. Spark, Hadoop) which are the Key skill and tools behind building modern AI technologies.