Year: 2025
Type: article
Abstract: The pioneering methods and concepts developed by John Hopfield and Geoffrey Hinton have been instrumental in shaping the field of artificial neural networks (ANNs).In addition, Hinton played a leading role in the efforts to extend the methods to deep and dense ANNs.With their breakthroughs, which stand on the foundations of physical science, they have shown a completely new way for us to use computers to aid and to guide us in tackling many of the challenges in our society.Simply put, thanks to their work, Humanity now has a new item in its toolbox, which we can choose to use for good purposes.Machine learning based on ANNs is currently revolutionizing science, engineering, and daily life.The field is already on its way to enable breakthroughs toward building a sustainable society, e.g., by helping to identify new functional materials.How deep learning by ANNs will be used in the future depends on how we humans choose to use these incredibly potent tools, already present in many aspects of our lives.In this paper, we present a short survey on the work of the two Nobel laureates in the field of machine learning and neural networks.We present recent achievements in the field of machine learning and artificial intelligence.Moreover, we provide new results on machine learning through physics-informed neural networks.
Source: Engineering Sciences
Author: Angela Slavova
Institutions: Bulgarian Academy of Sciences, Institute of Mechanics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412097687