Towards Sustainable AI: Energy and Data Efficiency in Biological and Artificial Intelligence

The field of comparative intelligence research is increasingly focusing on the relative energy efficiency of biological and artificial intelligence. While artificial intelligence (AI) has garnered significant attention for its ability to provide data-driven answers through machine learning and large language models, the essence of intelligence remains elusive. Current measures like floating-point operations (FLOPs) are well-defined for digital computing but lack a biological counterpart, such as a BioFLOP. Traditional spike or synaptic analyses often overlook the rich subthreshold processing in biological systems. Notably, biological brains, which evolved in complex environments, are vastly more energy-efficient than their artificial counterparts, a discrepancy that could hold the key to understanding both nervous systems and AI. Recent studies have highlighted this energy efficiency gap, but a comprehensive understanding of the computational processes in biological systems and their potential applications in AI design remains underexplored.

This research topic aims to investigate the energy demands and relative efficiency of artificial and biological intelligence. The primary objectives include understanding the computational processes in biological systems through cost analysis, exploring how these insights can inform the design of more energy-efficient computing, and examining the evolution of intelligence. Specific questions to be addressed include: What kinds of information must be processed in biological versus artificial systems? How can we define a BioFLOP? Can insights from biological energy efficiency lead to more sustainable AI designs?

To gather further insights into the boundaries of this research, we welcome articles addressing, but not limited to, the following themes:
- Multi-scale information processing in neural networks
- Energy efficiency comparisons between biological and artificial systems
- Computational cost analysis in biological systems
- Microchip characteristics and their impact on AI energy efficiency
- Algorithmic approaches to energy-efficient machine learning
- Environmental impacts of AI energy consumption
- Comparative studies across species and biological scales
- Theoretical frameworks for defining and measuring intelligence in biological and artificial systems

Topic Editor Stephen Larson is the co-founder of and employed by MetaCell LLC, LTD. The other Topic Editors declare no competing interests with regard to the Research Topic subject.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: energy efficiency, Comparative Intelligence, Machine Learning Efficiency, Multi-Scale Analysis, BioFLOP

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.