  Today, the AI community is obsessed with 'state-of-the-art' scores (80%
papers in NeurIPS) as the major performance metrics, due to which an important
parameter, i.e., the environmental metric, remains unreported. Computational
capabilities were a limiting factor a decade ago; however, in foreseeable
future circumstances, the challenge will be to develop environment-friendly and
power-efficient algorithms. The human brain, which has been optimizing itself
for almost a million years, consumes the same amount of power as a typical
laptop. Therefore, developing nature-inspired algorithms is one solution to it.
In this study, we show that currently used ANNs are not what we find in nature,
and why, although having lower performance, spiking neural networks, which
mirror the mammalian visual cortex, have attracted much interest. We further
highlight the hardware gaps restricting the researchers from using spike-based
computation for developing neuromorphic energy-efficient microchips on a large
scale. Using neuromorphic processors instead of traditional GPUs might be more
environment friendly and efficient. These processors will turn SNNs into an
ideal solution for the problem. This paper presents in-depth attention
highlighting the current gaps, the lack of comparative research, while
proposing new research directions at the intersection of two fields --
neuroscience and deep learning. Further, we define a new evaluation metric
'NATURE' for reporting the carbon footprint of AI models.
