AI and Big Data methods are becoming an essential part of the toolkit of physicists working in different branches of physics, mainly in extracting patterns from complex data such as images, or combining different data sources. These developments are fuelled by advances in machine learning, the flood of Big Data, and artificial intelligence (AI) algorithms, which are flourishing and being deployed in a wide range of applications: from neuroscience and personalized medicine to detecting illegal fishing and precision agriculture. In the physics community, we anticipate an increasing role of machine learning techniques – and AI in general – in modeling, simulating, and analyzing data from the most diverse physical, technological, economical, social and biological systems.
The bridge between AI and interdisciplinary physics is both a two-way street and also at a crossroads; that is, insights and methods from physics are being used to advance AI. Methods and concepts from statistical physics are widely used to design algorithms for inference, sampling, and optimization, as well as to better understand why deep learning works so well. At the same time, neuronal, optical and quantum mechanical systems provide unique physical substrates that can be harnessed to design and implement diverse forms of learning and computation.
These important theoretical and methodological challenges of AI lie at the interface between several disciplines, including computer science, mathematics and physical sciences. Thus, we propose an interdisciplinary collaboration to collect, integrate, and synthesize the different results and perspectives in the related fields.
Hence, this Research Topic focuses on the theory, methods, and application of machine learning and artificial intelligence, as well as their application to physical science disciplines as a whole. Accordingly, we welcome contributions that:
1) provide theoretical understanding or insights about machine learning algorithms;
2) illustrate an application of machine learning or AI to model, simulate, or analyze data from a complex system;
3) propose novel implementations or applications of different substrates for learning and computation.
Please note that submissions through Frontiers in Artificial Intelligence must make at least some reference to both Artificial Intelligence and Machine Learning.
AI and Big Data methods are becoming an essential part of the toolkit of physicists working in different branches of physics, mainly in extracting patterns from complex data such as images, or combining different data sources. These developments are fuelled by advances in machine learning, the flood of Big Data, and artificial intelligence (AI) algorithms, which are flourishing and being deployed in a wide range of applications: from neuroscience and personalized medicine to detecting illegal fishing and precision agriculture. In the physics community, we anticipate an increasing role of machine learning techniques – and AI in general – in modeling, simulating, and analyzing data from the most diverse physical, technological, economical, social and biological systems.
The bridge between AI and interdisciplinary physics is both a two-way street and also at a crossroads; that is, insights and methods from physics are being used to advance AI. Methods and concepts from statistical physics are widely used to design algorithms for inference, sampling, and optimization, as well as to better understand why deep learning works so well. At the same time, neuronal, optical and quantum mechanical systems provide unique physical substrates that can be harnessed to design and implement diverse forms of learning and computation.
These important theoretical and methodological challenges of AI lie at the interface between several disciplines, including computer science, mathematics and physical sciences. Thus, we propose an interdisciplinary collaboration to collect, integrate, and synthesize the different results and perspectives in the related fields.
Hence, this Research Topic focuses on the theory, methods, and application of machine learning and artificial intelligence, as well as their application to physical science disciplines as a whole. Accordingly, we welcome contributions that:
1) provide theoretical understanding or insights about machine learning algorithms;
2) illustrate an application of machine learning or AI to model, simulate, or analyze data from a complex system;
3) propose novel implementations or applications of different substrates for learning and computation.
Please note that submissions through Frontiers in Artificial Intelligence must make at least some reference to both Artificial Intelligence and Machine Learning.
