Terminology

Artificial Intelligence - Generally refers to the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language processing. 

Machine Learning - The process of developing computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. 

Deep Learning - A subset of machine learning in which multilayered neural networks learn from vast amounts of data.

Generative AI - refers to a type of artificial intelligence system that is designed to generate new, original content, such as images, text, music, or even entire videos. Unlike other AI systems that are primarily focused on analyzing and interpreting existing data, generative AI models are capable of creating new data based on patterns and examples they have learned during the training process.  Generative AI models are typically built using deep learning techniques.

Responsible AI -  An emerging area of AI Governance that covers both ethics and democratization.   Fairness, interpretability, privacy, and security are key principles of Responsible AI.

Democratizing AI - Bringing the power of AI and Machine Learning to all users (business, clinical, technology) by enabling them to experiment with different data sources and machine learning frameworks without requiring a data science expert.  By providing access to information and tools, and enabling seamless collaboration with data science teams, we can accelerate innovation, while producing models that adhere to quality standards, policies, and Responsible AI principles.

Transparent AI - Transparent AI means explainable AI i.e. how easy it is for a user to understand our AI algorithms and their decisions.  It tells about how the individual components of AI work, what is their goal and how are they connected. This gives a clear picture of the goal of AI model is achieved. One should be able to understand what decision was made by the model and why was that decision made.