The Brain Inspires a New Type of Artificial Intelligence
Arthur T Knackerbracket has found the following story:
Using advanced experiments on neuronal cultures and large scale simulations, scientists have demonstrated a new type of ultrafast artificial intelligence algorithms -- based on the very slow brain dynamics -- which outperform learning rates achieved to date by state-of-the-art learning algorithms.
Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in our brain. Using the speed of modern computers and large data sets, deep learning algorithms have recently produced results comparable to those of human experts in various applicable fields, but with different characteristics that are distant from current knowledge of learning in neuroscience.
Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artifical intelligence algorithms -- based on the very slow brain dynamics -- which outperform learning rates achieved to date by state-of-the-art learning algorithms.
In an article published today in the journal Scientific Reports, the researchers rebuild the bridge between neuroscience and advanced artificial intelligence algorithms that has been left virtually useless for almost 70 years.
"The current scientific and technological viewpoint is that neurobiology and machine learning are two distinct disciplines that advanced independently," said the study's lead author, Prof. Ido Kanter, of Bar-Ilan University's Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center. "The absence of expectedly reciprocal influence is puzzling."
"The number of neurons in a brain is less than the number of bits in a typical disc size of modern personal computers, and the computational speed of the brain is like the second hand on a clock, even slower than the first computer invented over 70 years ago," he continued. "In addition, the brain's learning rules are very complicated and remote from the principles of learning steps in current artificial intelligence algorithms," added Prof. Kanter, whose research team includes Herut Uzan, Shira Sardi, Amir Goldental and Roni Vardi.
Herut Uzan, Shira Sardi, Amir Goldental, Roni Vardi, Ido Kanter. Biological learning curves outperform existing ones in artificial intelligence algorithms. Scientific Reports, 2019; 9 (1) DOI: 10.1038/s41598-019-48016-4
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