Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
The difference between statistical AI and classical AI lies in their approaches to solving problems and making decisions:
- Statistical AI:
- Focus: Statistical AI focuses on learning from data and making predictions or decisions based on statistical models.
- Methods: It heavily relies on techniques such as machine learning, including supervised, unsupervised, and reinforcement learning, to extract patterns and relationships from data.
- Examples: Neural networks, deep learning, Bayesian networks are examples of statistical AI techniques.
- Flexibility: It is often more flexible and adaptable to different types of data and problem domains.
- Applications: Widely used in areas such as natural language processing, computer vision, and recommendation systems.
- Classical AI:
- Focus: Classical AI focuses on symbolic reasoning and problem-solving using predefined rules and logic.
- Methods: It employs techniques such as expert systems, rule-based systems, and search algorithms to manipulate symbols and derive logical conclusions.
- Examples: Expert systems, rule-based systems, and symbolic reasoning engines are examples of classical AI techniques.
- Rigidity: It tends to be more rigid and requires explicit programming of rules and knowledge.
- Applications: Commonly used in fields like expert systems for medical diagnosis, rule-based systems for decision support, and planning systems.
In summary, statistical AI emphasizes learning from data to make predictions or decisions, while classical AI relies on symbolic reasoning and predefined rules for problem-solving.