Artificial intelligence for carbon emissions using system of systems theory

Title: Artificial intelligence for carbon emissions using system of systems theory
Authors: Loveleen Gaur, Anam Afaq, Gursimar Kaur Arora, Nabeel Khan
Affiliations: The University of the South Pacific, Amity International Business School
Keywords: Artificial intelligence, machine learning, carbon emission, system of systems theory, sustainability, carbon footprint

AI’s impact on the environment has been debated, with both positive and negative perspectives.
AI can reduce carbon footprints but also contributes to its own carbon emissions, creating a complex relationship.
Studies suggest AI might help mitigate climate change but need to address its carbon output.
System of systems (SoS) theory is used to analyze the interplay between AI and carbon emissions, highlighting complexities in their relationship.
The study promotes the development of sustainable AI practices throughout its lifecycle, from data collection to implementation.

The novelty of this research is using systems theory to assess AI's relationship with carbon emissions.
AI has considerable benefits but also unintended consequences, especially concerning carbon output, particularly in deep learning (DL).
Low-carbon-emitting machine learning (ML) models are emphasized as preferable compared to energy-intensive DL models.
The importance of AI ethics is stressed, alongside integrating energy costs into AI model evaluations.

Carbon Emissions in AI: The AI sector contributes significantly to carbon emissions.
System of Systems (SoS) Approach: Integrates independent components into a holistic design framework, applicable to AI and carbon emissions.
Network Analysis: Examines interconnected systems; strong relationships indicate significant carbon footprint factors.

offering Potential

Definition of AI model: A program trained to recognize patterns using various algorithms for problem-solving.
AI models differ in complexity and application, with numerous ML algorithms discussed.

Consensus leans towards AI's potential for reducing carbon emissions, promoting efficiency and sustainability.
However, AI is energy-intensive and poses risks regarding escalated energy consumption.
Predictions indicate rising emissions from AI industries, necessitating careful evaluation of technological impacts.

Early Stopping: Restricts model training duration to conserve resources.
Model Architecture: Simplifying architectures can promote efficiency.
Data Preprocessing: Efficiently processing data can limit energy use during training.
Training Strategy: Optimize training methods to reduce power requirements.
Hardware Efficiency: Use energy-efficient hardware (e.g. GPUs) for training.

DL's environmental implications are critical due to substantial energy demands.
Researchers should balance AI’s problem-solving capabilities with carbon emission responsibilities.

While AI has vast potential for societal benefits, its carbon footprint is non-negligible.
Emphasizes the necessity of adopting sustainable AI practices and measuring their carbon emissions.
Strategies for reducing carbon emissions are highlighted, promoting responsible technology use.

The paper aims to evaluate AI’s relationship with carbon emission through the SoS approach, discussing implications for future research and AI practices.

Review findings of carbon emissions in ML algorithms, emphasizing efficiencies in various approaches.
Showcase lesser carbon emissions associated with simpler ML models compared to complex DL models.

Encourage organizations to adopt practices that mitigate AI's ecological impact.
Integration of carbon accounting in AI development and deployment is suggested.

Usage of CodeCarbon for tracking carbon emissions during algorithm deployments.
The methodology details precise calculations for CO2 emissions based on power consumption and carbon intensity measures.

Introduces the formula for calculating the carbon emissions linked to specific algorithms or codes.

Discussed that ML algorithms led to varied carbon emission levels according to complexity.
Simpler algorithms yielded lower emissions, crucial for sustainable AI development.

Recommendations include using ML-efficient algorithms and employing pretrained models as alternatives to avoid heavy resource consumption.

Highlights the importance of accountability in carbon emissions associated with AI technologies.
Stresses the need for comprehensive metrics to gauge AI impacts on sustainability over mere technical performance.

Reinforces the critical view of AI as a dual force in environmental sustainability.
Future research directions should focus on advancing sustainable practices within AI deployments, balancing economic and environmental considerations.