Researchers explore how to bring larger neural networks closer to the energy efficiency of biological brains
The more lottery tickets you buy, the higher your chances of winning, but spending more than you win is obviously not a wise strategy. Something similar happens in AI powered by deep learning: we know that the larger a neural network is (i.e., the more parameters it has), the better it can learn the task we set for it.
Artificial intelligence is now part of our daily lives, with the subsequent pressing need for larger, more complex models. However, the demand for ever-increasing power and computing capacity is rising faster than the performance traditional computers can provide.
Many machine learning models have been developed, each with strengths and weaknesses. This catalog is not complete without neural network models. In OpenCV, you can use a neural network model developed using another framework. In this post, you will learn about the workflow of applying a neural network in OpenCV.…
Inspired by microscopic worms, Liquid AI’s founders developed a more adaptive, less energy-hungry kind of neural network. Now the MIT spin-off is revealing several new ultraefficient models.