Question

Human beings are capable of deciding upon rules based on intuitions and observations their neurons presumably provide (certainly metaphysical presumptuous). According to WP, this is inductive reasoning:

Inductive reasoning is a method of reasoning in which a general principle is derived from a body of observations. It consists of making broad generalizations based on specific observations.

Computers are capable of deciding upon rules based on weighted connection models of computation based on data sets where connection models are modeled on actual neurons and data sets might be data from visual processing systems designed around cameras. These systems are called rule-based machine learning systems:

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply... rule-based machine learning applies some form of learning algorithm to automatically identify useful rules, rather than a human needing to apply prior domain knowledge to manually construct rules and curate a rule set.

The question is simple. Does rule-based machine learning qualify as inductive reasoning?

Answer 1

Rule-based machine learning systems analyze data, identify patterns, and infer rules, which aligns with the principles of inductive reasoning used in descriptive science to draw general conclusions from specific observations.

Step-by-step explanation:

Yes, rule-based machine learning (RBML) qualifies as a form of inductive reasoning. In inductive reasoning, general conclusions are drawn from specific observations, which is a method commonly used in the scientific field, particularly descriptive science. Biologists, for example, make numerous observations and record qualitative or quantitative data, then analyze this data to formulate broad generalizations. Similarly, machine learning systems process extensive datasets and observations--such as visual inputs from cameras--to derive general rules that can govern behaviors within an algorithm.

In RBML, the learning system analyzes the data provided, identifying patterns and connections that can then be formulated into rules. These rules can be used to make predictions or decisions regarding new data that the system encounters.

Just as a biologist observes the activation of different brain regions to determine their function in response to certain stimuli, machine learning systems identify patterns and infer rules that can predict outcomes based on the input data they process. Therefore, the process that rule-based machine learning systems employ mirrors the inductive reasoning approach used by scientists to infer conclusions and construct broader generalizations.