What is learning in Artificial Intelligence?
Machine learning refers to a system capable of acquiring and integrating the knowledge automatically. The capability of the systems to learn from experience, training, analytical observation, and other means, results in a system that can continuously self-improve and thereby exhibit efficiency and effectiveness.
“Learning denotes changes in the systems that are adaptive in the sense that they enable the system to do the same task (or tasks drawn from the same population) more effectively the next time.” –Herbert Simon
“Learning is constructing or modifying representations of what is being experienced.” –Ryszard Michalski
“Learning is making useful changes in our minds.” –Marvin Minsky.
Read More: What is NLP in Artificial Intelligence?
Machine Learning System
It receives and processes the input obtained from a person ( i.e. a teacher), from reference material like magazines, journals, etc, or from the environment at large.
This is somewhat similar to the database. Initially, it may contain some basic knowledge. Thereafter it receives more knowledge which may be new and so is added as it is or it may replace the existing knowledge.
It uses the updated knowledge base to perform some tasks or solves some problems and produces the corresponding output.
It is receiving the two inputs, one from the learning element and one from the standard (or idealized) system. This is to identify the differences between the two inputs. The feedback is used to determine what should be done in order to produce the correct output.
It is a trained person or a computer program that is able to produce the correct output. In order to check whether the machine learning system has learned well, the same input is given to the standard system. The outputs of the standard system and that of performance element are given as inputs to the feedback element for the comparison. The standard system is also called idealized system.
Read More: What is Expert System in AI?
Types of Learning
The strategies for learning can be classified according to the amount of inference the system has to perform on its training data. In increasing order we have
Rote learning is a basic learning activity. It is also called memorization because the knowledge, without any modification, is simply copied into the knowledge base. As computed values are stored, this technique can save a significant amount of time.
Learning by Memorization which avoids understanding the inner complexities the subject that is being learned; Rote learning instead focuses on memorizing the material so that it can be recalled by the learner exactly the way it was read or heard.
Learning something by Repeating over and over and over again; saying the same thing and trying to remember how to say it; it does not help us to understand; it helps us to remember like we learn a poem, or a song, or something like that by rote learning.
The system is supplied with a set of training examples consisting of inputs and corresponding outputs and is required to discover the relation or mapping between then, e.g. as a series of rules, or a neural network.
Learning from examples: A form of supervised learning, uses specific examples to reach general conclusions; Concepts are learned from sets of labelled instances. This is called learning by induction.
Humans appear to learn quite a lot from one example. Human learning is accomplished by examining particular situations and relating them to the background knowledge in the form of known general principles. This kind of learning is called “Explanation-Based Learning (EBL)”.
The system is supplied with a set of training examples consisting only of inputs and is required to discover for itself what appropriate outputs should be.
Clustering: it is discovering a similar group and a kind of Unsupervised, Inductive learning in which natural classes are found for data instances, as well as ways of classifying them.
Discovery: Learning without the help from a teacher; Learning is both inductive and deductive. It is deductive if it proves theorems and discovers concepts about those theorems. It is inductive when it raises conjectures (guess). It is unsupervised, the specific goal not given.
Reinforcement learning is one of the most active research areas in Artificial Intelligence. Reinforcement learning is training by rewards and punishments. Here we train a computer as if we train a dog. If the dog obeys and acts according to our instructions we encourage it by giving biscuits or we punish it by beating or by scolding.
Similarly, if the system works well then the teacher gives positive value (i.e. reward) or the teacher gives negative value (i.e. punishment). The learning system which gets the punishment has to improve itself. Thus, it is a trial and error process. The reinforcement learning algorithms selectively retain the outputs that maximize the received reward over time.
To accumulate a lot of rewards, the learning system must prefer the best-experienced actions; however, it has to try new actions in order to discover better action selections for the future. IT is a Learning from feedback (+ve or -ve reward) given at the end of a sequence of steps. Unlike supervised learning, reinforcement learning takes place in an environment where the agent cannot directly compare the results of its action to the desired result.
Instead, it is given some reward or punishment that relates to its actions. It may win or lose a game, or be told it has made a good movie or a poor one. The job of reinforcement learning is to find a successful function using these rewards. Early expert systems relied on rote learning, but for modern AI systems, we are generally interested in the supervised learning of various levels of rules.