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References (18)
[3]
MLP-Mixer: An all-MLP Architecture for Vision
Founder's Pitch
"AI-CARE revolutionizes AI evaluation by providing a carbon-aware metric that empowers sustainable model deployment decisions."
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0-10 scaleHigh Potential
1/4 signals
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4/4 signals
Series A Potential
4/4 signals
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Why It Matters
As AI models become larger and more widespread, the environmental impact of their energy usage becomes significant. Current evaluative metrics do not account for carbon emissions, leading to increased use of less sustainable models. AI-CARE offers an essential toolkit for aligning AI development with environmental sustainability goals, particularly valuable in energy-conscious and climate-focused markets.
Product Angle
To productize AI-CARE, the tool can be integrated into existing ML and AI development pipelines as a plugin or standalone dashboard offering visual reports on energy consumption metrics, allowing companies to make informed decisions about the sustainability of their AI models.
Disruption
AI-CARE can replace or supplement traditional performance metrics that do not consider environmental impacts, potentially making it a new standard across industries focused on sustainable AI deployments.
Product Opportunity
The demand is driven by companies looking to minimize their environmental impact and comply with climate regulations. Enterprises and cloud service providers pay to integrate sustainability tools into their workflows, representing a growing market as environmental, social, and governance (ESG) factors become central to business strategies.
Use Case Idea
Businesses and data centers can use AI-CARE to select AI models that are not only effective but also sustainable, reducing their carbon footprint and aligning with environmental regulations and CSR goals.
Science
AI-CARE proposes a framework for evaluating AI models not only based on their predictive performance but also on their energy consumption and carbon emissions. It aggregates these metrics into a carbon-performance tradeoff curve, allowing users to visualize and compare the environmental costs alongside performance benefits.
Method & Eval
The tool was tested on standard datasets like MNIST, CIFAR-10, and ImageNet-100 across various models, concurrently measuring performance and carbon emissions to illustrate tradeoffs using a carbon-performance curve.
Caveats
The tool depends on external energy and carbon measurement services, which could introduce variability or inaccuracies. Furthermore, it does not optimize models but rather reports their environmental impact, limiting its influence on actual model efficiency.
Author Intelligence
KC Santosh
LEAD University of South Dakota
kc.santosh@usd.edu
Srikanth Baride
University of South Dakota
srikanth.baride@usd.edu
Rodrigue Rizk
University of South Dakota
rodrigue.rizk@usd.edu