Page 283 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Data Lake

The cleaned and processed data is stored in a data lake, a centralized repository that supports
both structured and unstructured data. It serves as a source for AI model training, fine-tuning,
and inference. Change 4.2-Climate

2) AI Platform

This stage involves AI model development, training, inference, and the use of large language
models (LLMs).

PECIP AI Model Inference

AI models use the processed data to make inferences about polar ecological changes,
microalgae carbon sequestration, and other key metrics.

These inferences provide real-time insights or predictions (e.g., glacier melt rates, carbon
absorption efficiency).

Open Source LLM Training & Fine-Tuning

Large language models (LLMs) are trained and fine-tuned using data from the data lake.

This process ensures the LLMs are optimized for specific tasks, such as answering questions
about the polar environment or recommending sustainability practices.

PECIP LLM Repository

The LLMs are stored in a repository for reuse in various applications, such as Q&A systems or
generating reports.

PECIP LLM Q&A
A specialized system enables users to ask questions and receive answers powered by the
fine-tuned LLMs.

3) Policy & Implementation

This stage applies the insights and predictions generated in the earlier stages to real-world
climate action and sustainability policies.

SINK-Carbon (Carbon Trading Platform)

AI-driven insights about carbon sequestration are channeled into a carbon trading platform
to support carbon neutrality goals.

This platform enables organizations to trade carbon credits based on measurable reductions
in carbon emissions or increases in carbon absorption.

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