Traditionally, artificial intelligence stems from human brain dynamics. However, brain learning is restricted in a number of significant aspects compared to deep learning (DL). First, efficient DL wiring structures (architectures) consist of many tens of feedforward (consecutive) layers, whereas brain dynamics consist of only a few feedforward layers. Second, DL architectures typically consist of many consecutive filter layers, which are essential to identify one of the input classes. If the input is a car, for example, the first filter identifies wheels, the second one identifies doors, the third one lights and after many additional filters it becomes clear that the input object is, indeed, a car. Conversely, brain dynamics contain just a single filter located close to the retina. The last necessary component is the mathematical complex DL training procedure, which is evidently far beyond biological realization.
Can the brain, with its limited realization of precise mathematical operations, compete with advanced artificial intelligence systems implemented on fast and parallel computers? From our daily experience we know that for many tasks the answer is yes! Why is this and, given this affirmative answer, can one build a new type of efficient artificial intelligence inspired by the brain? In an article published today (January 30) in the journal Scientific Reports, researchers from Bar-Ilan University in Israel solve this puzzle.
"We've shown that efficient learning on an artificial tree architecture, where each weight has a single route to an output unit, can achieve better classification success rates than previously achieved by DL architectures consisting of more layers and filters. This finding paves the way for efficient, biologically-inspired new AI hardware and algorithms," said Prof. Ido Kanter, of Bar-Ilan's Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.
From Bar-Ilan University
View Full Article
No entries found