Development of cloud, digital technologies and the introduction of chip technologies

TL;DR

This paper discusses the rapid development of machine learning, digital technologies, and chip technologies, emphasizing their applications, socio-economic impacts, safety, legal issues, and the importance of public acceptance.

Contribution

It provides an overview of ML and AI advancements, highlights the role of chipization, and discusses socio-economic, legal, and societal considerations for future deployment.

Findings

ML enables new possibilities in language, image, and medical diagnostics.

Digitalization and chip technologies are expanding application scope.

Ensuring safety, legality, and public acceptance is crucial for ML adoption.

Abstract

Hardly any other area of research has recently attracted as much attention as machine learning (ML) through the rapid advances in artificial intelligence (AI). This publication provides a short introduction to practical concepts and methods of machine learning, problems and emerging research questions, as well as an overview of the participants, an overview of the application areas and the socio-economic framework conditions of the research. In expert circles, ML is used as a key technology for modern artificial intelligence techniques, which is why AI and ML are often used interchangeably, especially in an economic context. Machine learning and, in particular, deep learning (DL) opens up entirely new possibilities in automatic language processing, image analysis, medical diagnostics, process management and customer management. One of the important aspects in this article is chipization. Due to the rapid development of digitalization, the number of applications will continue to grow as digital technologies advance. In the future, machines will more and more provide results that are important for decision making. To this end, it is important to ensure the safety, reliability and sufficient traceability of automated decision-making processes from the technological side. At the same time, it is necessary to ensure that ML applications are compatible with legal issues such as responsibility and liability for algorithmic decisions, as well as technically feasible. Its formulation and regulatory implementation is an important and complex issue that requires an interdisciplinary approach. Last but not least, public acceptance is critical to the continued diffusion of machine learning processes in applications. This requires widespread public discussion and the involvement of various social groups.