AI for a Better World: Sustainability and Technology

Main Article Content

Abstract

A potential solution to the severe ecological problems that mankind is now facing may lie at the crossroads of sustainability and artificial intelligence (AI). the function of artificial intelligence in promoting sustainability initiatives in a range of fields, such as conservation, energy management, resource optimisation, and climate modelling. Artificial intelligence (AI) solutions provide new ways to improve resource efficiency, decrease carbon emissions, and encourage environmental stewardship by utilising data analytics, machine learning, and predictive modelling. More nimble reactions to opportunities and risks in the environment are possible with the help of AI-driven systems that integrate real-time monitoring, decision-making, and adaptive management tactics. Adopting AI in sustainability efforts brings up ethical, socioeconomic, and governance concerns that need careful evaluation, in addition to its possible advantages. these difficulties and emphasises the significance of transparent, equitable, and accountable AI deployment practices. The ultimate goal of using AI for good is to help stakeholders work together to build a better, more sustainable world that can withstand the test of time.

Article Details

How to Cite
Garg, A. (2024). AI for a Better World: Sustainability and Technology. Shodh Sagar Journal of Artificial Intelligence and Machine Learning, 1(1), 33–38. https://doi.org/10.36676/ssjaiml.v1.i1.04
Section
Original Research Articles

References

Acar, A. Z., & Murtagh, N. (2018). Artificial Intelligence for a Sustainable Future. Cham: Springer.

Gandomi, A., & Haider, M. (2019). Beyond the Hype: Big Data Concepts, Methods, and Analytics. Cham: Springer.

He, H., & Zhang, F. (2016). Deep Learning Based Text Classification: A Comprehensive Review. CoRR, abs/1705. (http://arxiv.org/abs/1705.02798)

Kahneman, D., & Klein, G. (2009). Conditions for Intuitive Expertise: A Failure to Disagree. American Psychologist, 64(6), 515–526.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.

Russell, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.

Sadowski, J., & Guston, D. H. (2016). Innovation from below: The emergence of AI and blockchain as tools for collective governance. Technology in Society, 49, 30–39.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359.

Yampolskiy, R. V. (2016). Artificial Intelligence Safety Engineering: Why Machine Ethics is a Wrong Approach. arXiv:1607.03352 [cs]. (http://arxiv.org/abs/1607.03352)

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.