The neural networks that underpin social media can consume an infinite amount of energy – Enerzine

Artificial neural networks are being deployed extensively by social media platforms like Twitter and Facebook to recommend content that matches user preferences. This process is energy-intensive and generates significant carbon emissions. In fact, the entire global energy supply could 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 frequently used in social media, streaming, online games, and areas where users receive messages, movies, fun games, or other content relevant to their individual preferences. Elsewhere, neural networks are being used in healthcare to recognize tumors on scans, among other things.

While the technology is incredibly effective, a Danish researcher behind a new study says it shouldn’t be misused. The study authors 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 a infinite amount of energy can be used to, for example, train these neural networks only to target advertisements to us. The network would never stop training and improving. It’s like a black hole that swallows all the energy you give it, which is by no means sustainable,” says Mikkel Abrahamsen, assistant professor in the computer science department at the University of Copenhagen.

Therefore, this technology must be deployed wisely and carefully considered before each use, as simpler and more energy efficient can suffice. Mr. Abrahamsen clarifies:

It is important that we consider where to use neural networks, in order to provide the greatest value to us humans. Some would consider neural networks to be better suited for scanning medical images of tumors than targeting ads and products on our social media and streaming platforms. In some cases, one could settle for less resource-intensive techniques, such as regression tasks or random decision forests.

Endless training

Neural networks are trained by providing data to them. These can be scanned images of tumours, through which a neural network learns to spot cancer in a patient.

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The neural networks that underpin social media can consume an infinite amount of energy – Enerzine

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