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Feb 1, 2022

This AI Learned the Design of a Million Algorithms to Help Build New AIs Faster

Posted by in categories: information science, robotics/AI

Might there be a better way? Perhaps.

A new paper published on the preprint server arXiv describes how a type of algorithm called a “hypernetwork” could make the training process much more efficient. The hypernetwork in the study learned the internal connections (or parameters) of a million example algorithms so it could pre-configure the parameters of new, untrained algorithms.

The AI, called GHN-2, can predict and set the parameters of an untrained neural network in a fraction of a second. And in most cases, the algorithms using GHN-2’s parameters performed as well as algorithms that had cycled through thousands of rounds of training.

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