Digital Encyclopedia of Scientific Results

TL;DR

This paper envisions a new digital encyclopedia that leverages AI and machine learning to enhance research efficiency by focusing on the structure of solved problems, promoting correctness, openness, and personalized researcher interfaces.

Contribution

It introduces a novel, clean-slate system design emphasizing problem structures and connections, utilizing AI to coordinate large-scale research activities.

Findings

Proposes a system focusing on problem structures rather than individual studies.

Utilizes AI and machine learning to connect and coordinate research efforts.

Aims to improve correctness, openness, and personalization in scientific research.

Abstract

This study describes a vision, how technology can help improving the efficiency in research. We propose a new clean-slate design, where more emphasis is given on the correctness and up-to-dateness of the scientific results, it is more open to new ideas and better utilize the research efforts worldwide by providing personalized interface for every researcher. The key idea is to reveal the structure and connections of the problems solved in the scientific studies. We will build the system with the main focus on the solved problems itself, and treat the studies only as one presentation form. By utilizing artificial intelligence and machine learning on the network of the solved problems we could coordinate individual research activities in a large scale, that has never been seen before.