It takes a lot of energy for machines to learn - Why AI is so power-hungry?
This month, Google forced out a
prominent AI ethics researcher after she voiced frustration with the company
for making her withdraw a research paper. The paper pointed out the risks of
language-processing artificial intelligence, the type used in Google Search and
other text analysis products.
Among the risks is the large carbon footprint of developing this
kind of AI technology. By some estimates,
training an AI model generates as much carbon emissions as
it takes to build and drive five cars over their lifetimes.
I am a researcher who studies and develops AI models,
and I am all too familiar with the skyrocketing energy and financial costs of
AI research. Why have AI models become so power hungry, and how are they
different from traditional data center computation?
Traditional data processing jobs done in data centers include
video streaming, email and social media. AI is more computationally intensive
because it needs to read through lots of data until it learns to understand it
– that is, is trained.
This training is very inefficient compared to how
people learn. Modern AI uses artificial neural networks, which are mathematical computations that mimic
neurons in the human brain. The strength of connection of each neuron to its
neighbor is a parameter of the network called weight. To learn how to
understand language, the network starts with random weights and adjusts them
until the output agrees with the correct answer.
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