Learning Machines: In Search of a Concept Oriented Language

TL;DR

This paper explores the development of next-generation intelligent machines capable of knowledge discovery, decision-making, and understanding concepts, proposing a framework for a concept-oriented language inspired by human intelligence.

Contribution

It introduces a novel framework for a concept-oriented language aimed at advancing machine intelligence beyond current data-driven approaches.

Findings

Historical insights into intelligence and machine learning

Proposed a general framework for a concept-oriented language

Discussion on the requirements for future intelligent machines

Abstract

What is the next step after the data/digital revolution? What do we need the most to reach this aim? How machines can memorize, learn or discover? What should they be able to do to be qualified as "intelligent"? These questions relate to the next generation "intelligent" machines. Probably, these machines should be able to handle knowledge discovery, decision-making and concepts. In this paper, we will take into account some historical contributions and discuss these different questions through an analogy to human intelligence. Also, a general framework for a concept oriented language will be proposed.