The neural networks that form the basis of social media can consume an infinite amount of energy

Artificial neural networks are widely used by social media platforms such as Twitter and Facebook to recommend content based on user preferences. This process is energy intensive and generates significant carbon emissions. In fact, the entire global power supply can be used to train a single neural network. That’s why the researchers behind a new study recommend using this technology where it’s most beneficial to the public interest.

Artificial neural networks are brain-inspired computer systems that can be trained to solve complex tasks better than humans.

These networks are often used in social media, broadcasting, online gaming, and other areas where users receive messages, movies, fun games, or other content tailored to their individual preferences. Elsewhere, neural networks are used in healthcare to recognize tumors in scans, among other things.

While the technology is incredibly effective, the Danish researcher behind a new study says it shouldn’t be abused. The authors of the study demonstrated that all the energy in the world could be used to train a single neural network without ever reaching perfection.

“The problem is that an infinite amount of energy can be used to, say, train these neural networks to target ads to us. The network never stops learning and improving. It’s like a black hole that absorbs all the energy you give it, which is by no means sustainable,” says Mikkel Abrahamsen, associate professor of computer science at the University of Copenhagen.

Therefore, this technology should be applied wisely and carefully reviewed before each use, as simpler and more energy-efficient solutions may suffice. Mr. Abrahamsen explains:

It is important that we consider where to use neural networks to provide the greatest value to humans. Some believe neural networks are better suited to scanning medical images of tumors than targeting ads and products on our social media and streaming platforms. In some cases, less resource-intensive methods such as regression exercises or random decision forests may be sufficient.

Endless training

Neural networks are trained by feeding them data. These can be scanned images of tumors from which a neural network learns to detect cancer in a patient.

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