New AI Inspired by the Human Brain

ai inspired by human brain

Human Brain, the center of future AI

A team of scientists from Bar Ilan University in Israel has declared that they have created a new kind of ultra-fast artificial intelligence algorithm inspired by the attributes of the human brain. The algorithm demonstrates that though the human brain computes at a much slower rate than modern computers, yet it is exceptionally fast and efficient.

The team led by Prof. Ido Kantar of Bar-Ilan University’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, conducted advanced experiments on large scale simulations and neuronal cultures to exhibit a new type of algorithm.

Rebonding neuroscience and AI

According to an article published in the journal Scientific Reports, the researchers asserted to be breaching the gap between neuroscience and advanced artificial intelligence algorithms, a gap that was overlooked for almost 70 years.

The algorithm outpaces learning rates accomplished to date by state-of-the-art learning algorithms.

“The present scientific and technological viewpoint is that neurobiology and machine learning are two distinct disciplines that advanced independently,” said Kanter. “The absence of expectedly reciprocal influence is puzzling.”

However, traditional artificial intelligence algorithms are grounded on synchronous inputs. Due to this, the respective timing of different inputs constituting the same frame is usually ignored. When driving, one notes cars, road signs, and pedestrian crossings and can easily recognize their temporal ordering and relative positions. Biological hardware, which is the learning rules, are planned to deal with asynchronous inputs and refine their corresponding information.

The following picture demonstrates how a synchronous input –computer- presents all objects at once, whereas an asynchronous input which is the human brain presents objects in a timed order.


Source: Prof. Ido Kanter

The ultra-fast learning rates are unexpectedly similar for both large and small networks. Thus, the weakness of the complex brain’s learning scheme is indeed a strength. The paper also calls for an action to reconstruct the channel between artificial intelligence and neurobiology, which the researchers believe has been wrecked.    They think that it can achieve the understandings of the basic principles of the brain and it has to be yet again the focal point of future artificial intelligence. 

A new branch of ultra-fast advanced artificial intelligence founded on brain dynamics could be developed by learning to imitate the productivity of the human brain. 


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