Human inventions and its environmental challenges, especially artificial intelligence: New challenges require new thinking

Artificial intelligence (AI) is an umbrella term for a wide range of machine intelligence systems that can replicate the behavior of humans. AI and Big Data have emerged as defining characteristics of the fourth industrial revolution (IR). AI has developed tools. Because of the novelty, the investigation of IR, AI, and their environmental effects is still in the early stages of exploration. This study investigates how IR and AI affect human and environmental health and also discusses IR, AI, machine-human ideas, innovation, and AI's environmental benefits, further examines the challenges of these innovations, and recommends additional studies to explain their progress. As a result, the application of AI technology in environmental management, particularly concerning pollution, has become a significant advancement in reshaping our approach to monitoring the environment. Numerous countries are reaping substantial advantages by integrating AI in creating, executing, and assessing measures to address environmental degradation. These innovations can yield societal advantages and contribute to achieving the Sustainable Development Goals (SDGs) 2030; unfortunately, it is important to acknowledge that these benefits may not align well with environmental sustainability objectives, and the increasing number of electronic gadgets presents an additional concern. Conducting future research is crucial to investigate the growing prevalence of electronic devices utilized for AI, its potential ramifications for the future trajectory of climate change, and the approaches being taken to address the issue. Future research should prioritize conducting lifecycle environmental impact analyses, developing sustainable AI hardware, optimizing renewable energy usage, advancing climate modeling techniques, finding effective solutions for managing e-waste, utilizing AI for environmental monitoring and protection, conducting socio-environmental impact studies, developing policies and regulations, creating energy-efficient AI algorithms, and integrating circular economy principles to ensure that AI advancements align with environmental sustainability.