by Josie Wales
...have not actually been designed by humans, but by computers.
Neural networks are modeled loosely after the human brain and learn like them in similar ways by processing large amounts of data, along with algorithms fed to the networks by programmers.
A neural net is then able to teach itself to perform tasks by analyzing the training data.
Research in the area of deep learning is advancing so quickly that neural networks are now able to dream and can even communicate with each other using inhuman cryptographic language indecipherable to humans and other computers.
The only drawback to the technology is that the networks require a lot of memory and power to operate, but MIT associate professor of electrical engineering and computer science Vivienne Sze and her colleagues have been working on a solution that could enable the powerful software to operate on cell phones.
Sze and her team made a breakthrough last year in designing an energy-efficient computer chip that could allow mobile devices to run powerful artificial intelligence systems.
The researchers have since taken an alternate approach to their research by designing an array of new techniques to make neural nets more energy efficient.
The team will be presenting a paper on their research next week at the Computer Vision and Pattern Recognition Conference in Honolulu.
There, they will describe their methods for reducing neural networks' power consumption by as much as 43 percent over the best previous method and 73 percent over the standard implementation with the use of "energy-aware pruning."
According to Hartwig Adam, the team lead for mobile vision at Google: