Natural Language Processing (NLP): This is a branch of AI that focuses on teaching computers to understand human language. NLP algorithms can be used to perform tasks such as language translation, sentiment analysis, and speech recognition.

Robotics: Robotics involves creating intelligent machines that can interact with the physical world. Robotics can be used in a wide range of applications, from manufacturing and assembly lines to healthcare and military operations.

Computer Vision: This is a branch of AI that focuses on teaching computers to interpret visual data, such as images and videos. Computer vision algorithms can be used to perform tasks such as object recognition, image classification, and facial recognition.

Deep Learning: This is a subset of machine learning that involves the use of neural networks to learn from large amounts of data. Deep learning algorithms can be used to perform tasks such as speech recognition, image and video analysis, and natural language processing.

To become an AI professional, one typically needs a strong foundation in computer science and mathematics, as well as expertise in one or more AI concepts, such as machine learning or natural language processing. A degree in computer science, mathematics, or a related field is often required, along with relevant work experience. Additionally, certifications such as the TensorFlow Developer Certificate or the Microsoft Certified: Azure AI Engineer Associate can be helpful in demonstrating expertise in AI.