What are the main 7 areas of artificial intelligence?

Artificial intelligence (AI) is broadly organized into seven core areas, each addressing specific technical challenges. These include machine learning (ML), natural language processing (NLP), computer vision, robotics, expert systems, planning and decision-making, and speech recognition. These domains focus on distinct problems, such as data-driven pattern recognition (ML), human-language interactions (NLP), visual data interpretation (computer vision), and autonomous system control (robotics). Developers often specialize in one or more of these areas based on their application needs, tools, and algorithms.

Machine learning involves training models to make predictions or decisions from data, using techniques like supervised learning (e.g., regression, classification) or unsupervised learning (e.g., clustering). For example, a recommendation system on a streaming platform uses collaborative filtering to suggest content. Natural language processing deals with text or speech analysis, such as sentiment classification using transformer models like BERT or building chatbots with sequence-to-sequence architectures. Computer vision focuses on extracting information from images or videos, like object detection using YOLO (You Only Look Once) or facial recognition with OpenCV. These areas often overlap—for instance, training a self-driving car requires combining computer vision (detecting obstacles) with ML (predicting driver behavior).

Robotics integrates hardware and software to create autonomous systems, such as industrial robots using ROS (Robot Operating System) for assembly lines. Expert systems rely on rule-based reasoning for specialized tasks, like diagnosing medical conditions using knowledge graphs. Planning and decision-making involve algorithms like A* search or reinforcement learning (e.g., AlphaGo) to optimize sequences of actions. Speech recognition converts spoken language to text, using tools like Mozilla DeepSpeech or cloud APIs. Developers working in these areas often use frameworks like TensorFlow (ML), PyTorch (NLP), or OpenCV (computer vision) to implement solutions. Understanding these domains helps engineers choose the right tools and approaches for tasks like automating workflows, analyzing data, or building intelligent applications.