Learning is one of the fundamental building blocks of artificial intelligence (AI) solutions. From a conceptual standpoint, learning is a process that improves the knowledge of an AI program by making observations about its environment. From a technical/mathematical standpoint, AI learning processes focused on processing a collection of input-output pairs for a specific function and predicts the outputs for new inputs. Most of the artificial intelligence(AI) basic literature identifies two main groups of learning models: supervised and unsupervised. However, that classification is an oversimplification of real world AI learning models and techniques.
To understand the different types of AI learning models, we can use two of the main elements of human learning processes: knowledge and feedback. From the knowledge perspective, learning models can be classified based on the representation of input and output data points. In terms of the feedback, AI learning models can be classified based on the interactions with the outside environment, users and other external factors.
AI Learning Models: Knowledge-Based Classification
Factoring its representation of knowledge, AI learning models can be classified in two main types: inductive and deductive.
— Inductive Learning: This type of AI learning model is based on inferring a general rule from datasets of input-output pairs.. Algorithms such as knowledge based inductive learning(KBIL) are a great example of this type of AI learning technique. KBIL focused on finding inductive hypotheses on a dataset with the help of background information.
— Deductive Learning: This type of AI learning technique starts with te series of rules nad infers new rules that are more efficient in the context of a specific AI algorithm. Explanation-Based Learning(EBL) and Relevance-0Based Learning(RBL) are examples examples o f deductive techniques. EBL extracts general rules from examples by “generalizing” the explanation. RBL focuses on identifying attributes and deductive generalizations from simple example.
AI Learning Models: Feedback-Based Classification
Based on the feedback characteristics, AI learning models can be classified as supervised, unsupervised, semi-supervised or reinforced.
— Unsupervised Learning: Unsupervised models focus on learning a pattern in the input data without any external feedback. Clustering is a classic example of unsupervised learning models.
— Supervised Learning: Supervised learning models use external feedback to learning functions that map inputs to output observations. In those models the external environment acts as a “teacher” of the AI algorithms.
— Semi-supervised Learning: Semi-Supervised learning uses a set of curated, labeled data and tries to infer new labels/attributes on new data data sets. Semi-Supervised learning models are a solid middle ground between supervised and unsupervised models.
— Reinforcement Learning: Reinforcement learning models use opposite dynamics such as rewards and punishment to “reinforce” different types of knowledge. This type of learning technique is becoming really popular in modern AI solutions.