ML is the engine and heart of AI, these are sophisticated algorithms that are trained using large datasets and with time, based on results improve their accuracy and performance. ML usually learns using these types; supervised learning or labeled data and unsupervised learning or unlabeled data along with reinforcement learning or learning by experience and outcomes.

2. Deep Learning: 

DL is a sub-branch of Machine learning, inspired by a human brain pattern of neural network where data can be processed in layers. The advancements we see in image recognition, natural language processing (NLP) and voice is all down to deep learning.

3. Natural Language Processing: 

The much coveted ChatGPT is a natural language processing (NLP) marvel that generates smart responses to human conversations using deep learning and predictive analytics. Even before ChatGPT, NLP as an AI component has been understanding and generating responses as virtual assistants, producing translations, creating briefs and for sentiment analytics. 

Natural Language Understanding, a game changer in AI that mimics human like cognitive and colloquial capabilities so much so that it becomes hard to differentiate between AI and humans. 

4. Computer Vision: 

When you see an application that is trying to identify its visual surroundings and trying to pave out a path or an outcome like in facial recognition, autonomous cars and complex images know that computer vision algorithms are behind it.

5. Robotics: 

Uniting machines with sophisticated AI algorithms is not a new advancement. However, from limited decision making capabilities, robots now perform independent tasks with the help of AI. Such robots have been a revelation for many industries, even assisting humans in performing a surgery, deep sea exploration and to venture in other hazardous places. 

The robot Cassie, a creation of Agility Robotics can perform maneuvers like humans and has shown tremendous ability to adopt and learn at a rapid pace.