Artificial Intelligence (AI) encompasses a wide range of technologies each suited to specific tasks and challenges. One of the branches of AI is ML which allows systems to learn from data and build models without explicit programming. Some ML systems can adapt to improve continuously with new data throughout their lifetime. Others require explicit retraining to update their capabilities.
Supervised learning - Utilizes labeled data and algorithms such as linear regression and decision trees to perform tasks such as prediction and classification.
Unsupervised learning - Uncovers patterns in unlabeled data using techniques such as clustering.
Reinforcement learning - Enable intelligent agents to learn optimal behavior by interacting with the environment through trial and error.
Deep learning, a subset of ML methods use deep neural networks to solve complex problems.
- Convolutional Neural Networks - Used for image recognition and object detection
- Recurrent neural networks - Specialize in processing sequential data e.g. text and time series.
- Transformers handle long range dependencies in sequences, powering models for natural language processing and vision transformers for images.
Other specialized AI technologies include;
- NLP for language analysis
- Computer vision - Analyzes visual data, supports apps like facial recognition and robotics.
- Fuzzy logic - reasoning under uncertainty
- Search algorithms = solving optimization problems e.g. navigation and strategic decision making.
- Rule based reasoning systems/ expert systems - for structured decision support.
Full integration of these AI technologies remains limited. However, new developments such as LLM
demonstrate the potential to combine different AI technologies into unified, intelligent systems.
Agentic AI extends these technologies through autonomous agents that plan, reason, and act independently to
achieve goals in dynamic environments.
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