Machine Learning: When and Where the Horses Went Astray?

TL;DR

This paper critically examines the fundamental assumptions of Machine Learning, arguing that meaningful information is observer-dependent and cannot be extracted solely from raw data, challenging conventional definitions.

Contribution

It offers a philosophical critique of Machine Learning's core premise, emphasizing the role of observer conventions over data-driven information extraction.

Findings

Meaningful information is observer-dependent.

Data alone cannot contain meaningful information.

Current ML justifications are philosophically flawed.

Abstract

Machine Learning is usually defined as a subfield of AI, which is busy with information extraction from raw data sets. Despite of its common acceptance and widespread recognition, this definition is wrong and groundless. Meaningful information does not belong to the data that bear it. It belongs to the observers of the data and it is a shared agreement and a convention among them. Therefore, this private information cannot be extracted from the data by any means. Therefore, all further attempts of Machine Learning apologists to justify their funny business are inappropriate.