An Analysis the green AI with major beneficiary improvement over the Red AI and implementation the environment footprint to increase Green AI
Bindiya Jain (Assistant Professor)
Department of Computer Science, JNU University, Jaipur
Email: bindiyajain07@gmail.com
|
Keywords— Artificial intelligence, Audit ability; Ethics,
Interdisciplinary science, Interpretability. |
Abstract— Artificial
Intelligence (AI) can be deployed for a wide range of applications to promote
the goals of the Green AI. The environmental potential, characteristics,
causes of environmental risks, and initiatives are best practices for Green
AI. The computations required for deep learning research have been doubling
every few months, resulting observations are an estimated 300,000x increase
from 2012 to 2021. These computations have a surprisingly large carbon
footprint. Ironically, deep learning was inspired by the human brain, which
is remarkably energy efficient. The financial cost of the computations can
make it difficult for academics, students, and researchers, in particular
those from emerging economies, to engage in deep learning research. This
paper advocates a practical solution by making efficiency an evaluation
criterion for research alongside accuracy and related measures. Our goal is
to make green AI with major beneficiary improvement over the Red AI and
implementation the environment footprint to increase Green AI |
I.
INTRODUCTION
Green
AI is part of a broader environment friendly, scientific research, sustainable
and energy efficient system. The vision of Green AI is reducing the
computational expense with improves performance with the help of efficient
methods.
This study discovered green artificial
intelligence can improve social, economic and environmental aspects. Because
sustainability has been related to operational cost, reduction of waste, energy
and reduce pollution to improve the quality of life. Red AI has been valuable
scientific contribution to the field, but it is dominant. Red AI refers to
research that seeks to improve accuracy through the use of massive
computational cost, model performance and model complexity of cost in number of
parameter or inference time.
So, the goal of this paper is twofold,
first we want to raise awareness of the Green AI and encourage researchers, AI
community to recognize the value of work with low computational cost,
encouraging a reduction in resource spent. Second, Green AI is changing
fundamental ways to make an eco-friendly product, all types of sustainability.
Green AI can help operation management to more economical, environmental and
social sustainability. Sustainable operation management with artificial intelligence
is expected to improve the performance, economic, environment. I confirmed that
Green AI contributes to making good product design in all areas with smart and
also sustainable devices.
Rapid developments in Green AI have
triggered digital advancements in almost every industry. The technology is
capable of construing data contextually to provide requested information,
supply analysis, and push events based on findings. Simultaneously, businesses
need to meet social, investor and regulatory requirements regarding how they
use advanced technologies like Green AI. Significantly, it is also crucial that
organizations must commit to using the technology with a purpose, which leads
to the way of sustainable development. With advances in machine learning and
deep learning, we can now tap the predictive power of Green AI to make better
data-driven models of environmental processes to improve our ability to study
current and future trends, including water availability, ecosystem wellbeing,
and pollution.
Green AI can also play
a key role in enhancing environmental decisions and policy-making work, by
bringing an algorithmic approach to that work.
Green AI can use deep
predictive capabilities and intelligent grid systems to manage the demand and
supply of renewable energy. Transport, manufacturing, health care, finance and
banking agriculture, e-commerce, human recourse through a AI can help reduce
congestion, and improve the capability.
Voice reorganization application are popular in the public domain and there are many digital assistant platform to the market that interacts with people and provide information content as per their need on anything search Siri(Apple), Alexa (Amazon), Google messenger.
AI papers tend to target accuracy rather than efficiency. The figure shows the proportion of papers that target accuracy, efficiency, both or other from a random sample of 60 papers from top AI conferences.
II.
CONCLUSION
The Green AI
with sustainable approach refers to novel results while taking in account the
computational cost, encouraging a reduction in resources etc. Whereas Red AI
has resulted in rapidly escalating computational costs, Green AI promotes
approaches that have favorable performance and efficiency trade-offs. If
measures of efficiency are widely accepted as important evaluation metrics for
research alongside accuracy, then researchers will have the option of focusing
on the efficiency of their models with positive impact on both inclusiveness
and the environment. The term Green AI refers to AI research that yields novel
results while taking into account the computational cost, encouraging a
reduction in resources spent. Whereas Red AI has resulted in rapidly escalating
computational (and thus carbon) costs, Green AI promotes approaches that have
favorable performance/efficiency trade-offs. If measures of efficiency are
widely accepted as important evaluation metrics for research alongside
accuracy, then researchers will have the option of focusing on the efficiency
of their models with positive impact on both inclusiveness and the environment.
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