Transfer learning (TL) in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. Transfer learning is the reuse of a pre-trained model on a new problem. It's currently very popular in deep learning because it can train deep neural networks with comparatively little data. Importance of transfer of learning, The main purpose of any learning or education is that a person who acquires some knowledge or skill in a formal and controlled situation like a classroom, or a training situation, will be able to transfer such knowledge and skill to real life situations and adapt himself more effectively.

Can you teach a Machine intuition? It is what ML/DL/ANN after, as a supervised, unsupervised or reinforcement learning. For intuition is the ability to acquire knowledge without recourse to conscious reasoning, namely, - direct access to unconscious knowledge; - unconscious cognition; - inner sensing; - inner insight to unconscious pattern-recognition; and the ability to understand something instinctively, without any need for conscious reasoning.

Understanding what is machine intelligence and what is human intelligence. Most knowledge revolves here around a so-called finite-state machine (FSM) or finite-state automaton, finite automaton, or simply a state machine, a mathematical model of computation. An FSM is defined by a list of its states, its initial state, and the inputs that trigger each transition, the change from one state to another, realized as deterministic finite-state machines and non-deterministic finite-state machines. So, automata theory, as the study of abstract machines and automata, is what plays a major role in theory of computation and artificial intelligence.

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