The AI landscape is constantly expanding, with numerous subfields and diverse functionalities. Here's an overview of some prominent branches:
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Machine Learning: As mentioned before, this core branch encompasses algorithms that learn from data, encompassing various subfields like:
- Supervised Learning: The system learns by being trained on labeled data, where the desired output is provided for each input.
- Unsupervised Learning: The system detects patterns and relationships in unlabeled data, identifying hidden structures without predefined categories.
- Reinforcement Learning: The system learns through trial and error, receiving rewards for desired actions and penalties for undesirable ones.
- Deep Learning: Utilizing artificial neural networks with multiple layers, it excels at tasks like image recognition, natural language processing, and speech recognition.
- Computer Vision: This field focuses on enabling machines to interpret and understand visual information from the real world, allowing for applications like object detection and facial recognition.
- Robotics: This combines AI with physical robots, allowing them to interact with the environment, learn from experience, and perform tasks autonomously.