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(GIST OF SCIENCE REPORTER) CARBON FOOTPRINT OF AI

(APRIL-2024)

Artificial intelligence (AI) has enormous promise for addressing difficult problems, such as the climate catastrophe.

Source of Emissions: AI systems generate carbon emissions from the infrastructure needed to operate them, particularly data centers that handle vast amounts of data for training and maintenance.
Environmental Impact: Training large AI models can be highly energy-intensive. For instance, training a predecessor to ChatGPT resulted in carbon emissions comparable to driving over 100 gasoline-powered cars for a year.
Technological Solutions: Research into spiking neural networks (SNNs) and lifelong learning (L2) offers promise for reducing AI’s carbon footprint. SNNs, in particular, may be a more energy-efficient alternative to traditional artificial neural networks (ANNs).

Function: ANNs process and learn patterns from data, allowing them to make predictions.
Computational Demands: ANNs require significant computing power, memory, and time during training and inference due to the high number of multiplications with decimal numbers.
Precision-Memory Trade-off: There is a trade-off between processing speed and memory usage in hardware. High-precision decimal calculations in ANNs necessitate more memory, leading to increased energy consumption.
Complexity and Energy Consumption: As ANNs grow more complex, the number of calculations increases exponentially, resulting in a substantial rise in energy demands.