Understanding AI Techniques & Fields With Their Function

With its ability to understand, learn, and make decisions, various specific techniques and fields within AI have evolved to meet different needs.

Key Techniques and Fields in AI

1. Machine Learning 
   Machine Learning (ML) is a core component of AI. It involves creating algorithms that automatically learn from data, enabling them to recognize patterns and make data driven decisions.
2. Neural Network
   Neural Networks are systems designed to mimic the structure and function of the human brain in processing information. These systems consist of interconnected processing units called "neurons," organized into multiple layers. Neural networks are used to solve unstructured data problems by learning patterns and relationships within images, audio, and text.
3. Deep Learning
   Deep Learning is a subset of Machine Learning that utilizes Neural Networks with many layers. The depth of these layers allows Deep Learning to learn highly complex and abstract data patterns.
4. Natural Language Processing
   Natural Language Processing (NLP) is a field focused on the interaction between computers and human language. NLP aims to enable systems to understand, interpret, and generate human language naturally. To achieve this, NLP relies on Machine Learning techniques, particularly Deep Learning.
5. Large Language Model
   Large Language Models (LLMs) are Deep Learning models within NLP. They are extremely large-scale models trained on vast amounts of data. This training enables LLMs to understand context, generate text, and answer questions in a human-like manner.