What is AI that is not ML?

Artificial Intelligence (AI) is a broad field that encompasses the development of systems or machines that can perform tasks that typically require human intelligence. Machine Learning (ML) is a subset of AI, but not all AI is based on machine learning techniques. AI includes a wide range of approaches and methods, some of which do not directly involve machine learning. Here are a few examples of AI that are not necessarily based on ML:

These are AI systems that make decisions or perform tasks based on predefined rules and logical conditions. They don't learn from data but rely on explicitly programmed rules. For instance, early chatbots that followed specific rules to respond to user queries were examples of rule-based AI.

Expert systems are designed to mimic the decision-making process of human experts in a specific domain. They use a knowledge base of facts and rules to make informed decisions. These systems don't learn from data but are built upon expert knowledge.

Many AI applications involve searching through large spaces of possibilities to find optimal solutions. Techniques like depth-first search, breadth-first search, and A* search are used in areas like game playing and pathfinding.

While some aspects of NLP involve machine learning, there are also rule-based and symbolic approaches in NLP. For instance, designing grammatical rules or using finite-state machines for specific language processing tasks.

In computer vision, not all techniques require machine learning. Some approaches involve traditional image processing methods, geometric transformations, and edge detection, which don't necessarily rely on learning from data.

Genetic algorithms are optimization techniques inspired by the process of natural selection. They involve generating and evolving a population of potential solutions over multiple generations to find the best solution to a problem.

AI systems that deal with planning and scheduling tasks can use techniques such as heuristic search and constraint satisfaction, without employing machine learning.

Symbolic AI involves manipulating symbols and performing logical inference. It's often used in areas like theorem proving and knowledge representation.

These are just a few examples of AI techniques that are not solely based on machine learning. AI encompasses a rich variety of methods that go beyond just learning from data and includes approaches that are rule-based, knowledge-driven, symbolic, heuristic-based, and more.