Title:
Neural networks behind social media can consume an infinite amount of energy
Published:
University of Copenhagen, 1 March 2022
From the news article:
Artificial neural networks are deployed intensively by social media platforms like Twitter and Facebook to recommend content that matches user preferences. The process is energy intensive and generates heavy carbon emissions. In fact, the world's entire energy supply could be used to train a single neural network. Therefore, researchers behind a new study recommend that the technology be used where it benefits the public interest most.
ALSO SEE
Title:
Training Neural Networks is ∃R-complete
Authors:
Mikkel Abrahamsen, University of Copenhagen
Linda Kleist, Technische Universtität Braunschweig
Tillmann Miltzow, Utrecht University
Published:
arXiv, 19 November 2021
Abstract:
Given a neural network, training data, and a threshold, finding weights for the neural network such that the total error is below the threshold is known to be NP-hard. We determine the algorithmic complexity of this fundamental problem precisely, by showing that it is ∃R-complete. This means that the problem is equivalent, up to polynomial time reductions, to deciding whether a system of polynomial equations and inequalities with integer coefficients and real unknowns has a solution. If, as widely expected, ∃R is strictly larger than NP, our work implies that the problem of training neural networks is not even in NP. Neural networks are usually trained using some variation of backpropagation. The result of this paper offers an explanation why techniques commonly used to solve big instances of NP-complete problems seem not to be of use for this task. Examples of such techniques are SAT solvers, IP solvers, local search, dynamic programming, to name a few general ones.