Although the brain’s architecture is very shallow, brain-inspired artificial neural networks' learning capabilities can outperform deep learning. Traditionally, artificial intelligence stems from human brain dynamics. However, brain learning is restricted in a number of significant aspects compar
Can the brain, limited in its ability to perform precise math, compete with AI systems run on high-speed parallel computers? Yes, for many tasks, as evidenced by everyday experiences. Given this, can a more efficient AI be built based on the brain’s design?
Although the brain’s architecture is very shallow, brain-inspired artificial neural networks’ learning capabilities can outperform deep learning. Traditionally, artificial intelligence stems from human brain dynamics. However, brain learning is restricted in a number of significant aspects compared to deep learning . First, efficient DL wiring structures consist of many tens of feedforward 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.
Scheme of a simple neural network based on dendritic tree vs. a complex artificial intelligence deep learning architecture . Credit: Prof. Ido Kanter, Bar-Ilan University 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 in the...
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