Artificial intelligence can also be divided into different types based on how it itself learns and adapts to problems:
1. Symbolic AI (Rule-based)
- Approach based on logic and predefined rules
- Expert systems, fuzzy logic, decision trees
- Application example: Rule-based medical diagnostic systems
2. Machine Learning (Learning from Data)
- Models that learn by analyzing large amounts of data
- Supervised, unsupervised algorithms and reinforcement (autonomous learning)
- Application example: Recognition of bank fraud
3. Deep Learning (Complex Neural Networks)
- AI models inspired by human brain functioning
- Ability to recognize complex patterns within images, text and audio
- Examples of applications: Facial recognition, machine translation
4. Generative AI (LLM and Content Creation)
- Systems capable of generating new content
- Models such as GPT, DALL-E, Stable Diffusion, Sora
- Examples of applications: Advanced chatbots, image and video creation, automated writing
Each category of AI has its own strengths and weaknesses:
- Symbolic AI = Reliable but rigid
- Machine Learning = Suitable for complex analysis
- Deep Learning = Requires large amounts of data
- Generative AI = Powerful but subject to bias
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