The authors believe that future designs of their technology could be used to automatically trigger drug release in humans when required.
The algorithm can be trained to track brain states that underlie ADHD or schizophrenia or otherwise be modified to suit your needs, explains study author Sachar Arnon to New Scientist. For example, if EEG detects signs of a burgeoning depressive episode, it could trigger DNA robots to expose anti-depressants briefly to counteract symptoms before they become full-blown. This way, the brain isn’t perpetually bathed in mind-altering drugs even when they’re not needed.
It’s a futuristic idea, and lots of things still need to be ironed out.
Artificial neural networks are famously based on biological ones. So not only do Lin and Tegmark’s ideas explain why deep learning machines work so well, they also explain why human brains can make sense of the universe. Evolution has somehow settled on a brain structure that is ideally suited to teasing apart the complexity of the universe.
This work opens the way for significant progress in artificial intelligence. Now that we finally understand why deep neural networks work so well, mathematicians can get to work exploring the specific mathematical properties that allow them to perform so well. “Strengthening the analytic understanding of deep learning may suggest ways of improving it,” say Lin and Tegmark.
Deep learning has taken giant strides in recent years. With this improved understanding, the rate of advancement is bound to accelerate.
“While suitable for kids eight and older, PocketBlock is by no means restricted to kids. Troutman said it’s also suitable for professional developers who want to deepen their understanding of the way cryptographic algorithms work, given that they’re often implementing them.”