Machine learning which was developed 70 years ago is based on learning dynamics in the human brain. Deep learning algorithms have been able to generate results equivalent to human specialists in various areas with the help of fast and large-scale processing computers and giant data sets. However, they produce results distinct from the present knowledge of learning in neuroscience.
A team of scientists at Bar-Ilan University in Israel has illustrated a new kind of high-speed artificial intelligence algorithms which are based on the slow brain dynamics exceeding the learning rates attained to date by state-of-the-art learning algorithms using advanced experiments on neuronal cultures and simulations. The paper has been published in The Scientific Reports.
The research lead author, Prof. Ido Kanter, of Bar-Ilan University’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, said that till now it has been considered that neurobiology and machine learning are separate disciplines that progressed separately and the absence of likely reciprocal influence is puzzling.
He added that the data processing speed of the brain is slower than the first computer invented over 70 years ago because the number of neurons in a brain is less than the number of bits in a usual disc size of modern computers. Prof. Kanter, whose research team includes Herut Uzan, Shira Sardi, Amir Goldental and Roni Vardi also added that learning rates of the brain are very complex and isolated from the principles of learning in artificial intelligence algorithms. Since the biological system has to deal with asynchronous inputs, brain dynamics do not follow a well-defined clock synchronized for the nerve cells.
A key difference between artificial intelligence algorithms and the human brain is the nature of inputs handled. The human brain deals with asynchronous inputs, where the relative position of objects and the temporal ordering in the input are important such as identifying cars, pedestrians, other road signs while driving. On the other hand, AI algorithms deal with synchronous inputs where relative timing is ignored.
Recent studies have found that ultrafast learning rates are unexpectedly identical for small and large networks. So, the disadvantage of the complicated brain’s learning system is indeed an advantage. Another important finding is that learning can occur without learning steps through self-adaptation according to asynchronous inputs. This type of learning-without-learning occurs in the dendrites, several terminals of each neuron, as was recently experimentally observed.
The concept of productive deep learning algorithms based on the very slow brain’s dynamics provides the possibility to execute an advance type of artificial intelligence based on fast computation bridging the gap between neurobiology and artificial intelligence. Researchers conclude that understandings of our brain’s principles have to be at the centre of artificial intelligence once again.
Journal Reference: The Scientific Reports