Artifical intelligence not only affords impressive performance, but also creates significant demand for energy. The more demanding the tasks for which it is trained, the more energy it consumes. Víctor López-Pastor and Florian Marquardt, two scientists at the Max Planck Institute for the Science of Light in Erlangen, Germany, present a method by which artificial intelligence could be trained much more efficiently.
The amount of energy required to train GPT-3, which makes ChatGPT an eloquent and apparently well-informed Chatbot, has not been revealed by Open AI, the company behind that artificial intelligence . According to the German statistics company Statista, this would require 1000 megawatt hours -- about as much as 200 German households with three or more people consume annually.
"The data transfer between these two components alone devours large quantities of energy when a neural network trains hundreds of billions of parameters, i.e. synapses, with up to one terabyte of data" says Florian Marquardt, director of the Max Planck Institute for the Science of Light and professor at the University of Erlangen.
The brain is characterized by undertaking the numerous steps of a thought process in parallel and not sequentially. The nerve cells, or more precisely the synapses, are both processor and memory combined. Various systems around the world are being treated as possible candidates for the neuromorphic counterparts to our nerve cells, including photonic circuits utilizing light instead of electrons to perform calculations. Their components serve simultaneously as switches and memory cells.
As a consequence there will likely be an even greater desire to implement neural networks outside conventional digital computers and to replace them with efficiently trained neuromorphic computers."We are therefore confident that self-learning physical machines have a strong chance of being used in the further development of artificial intelligence," says the physicist.
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