Physics can assist with key challenges in artificial intelligence | Artificial Intelligence - Science Daily

Summary:

Two challenges in the field of artificial intelligence have been solved by adopting a physical concept introduced a century ago to describe the formation of a magnet during a process of iron bulk cooling. Using a careful optimization procedure and exhaustive simulations, researchers have demonstrated the usefulness of the physical concept of power-law scaling to deep learning. This central concept in physics has also been found to be applicable in AI, and especially deep learning.

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Current research and applications in the field of artificial intelligence (AI) include several key challenges by Science Daily.

Rapid decision making: A deep learning neural network where each handwritten digit is presented only once to the trained network
Photo: Bar-Ilan University

 

These include: (a) A priori estimation of the required dataset size to achieve a desired test accuracy. For example, how many handwritten digits does a machine have to learn before being able to predict a new one with a success rate of 99%? Similarly, how many specific types of circumstances does an autonomous vehicle have to learn before its reaction will not lead to an accident? (b) The achievement of reliable decision-making under a limited number of examples, where each example can be trained only once, i.e., observed only for a short period. This type of realization of fast on-line decision making is representative of many aspects of human activity, robotic control and network optimization.

In an article published today in the journal Scientific Reports, researchers show how these two challenges are solved by adopting a physical concept that was introduced a century ago to describe the formation of a magnet during a process of iron bulk cooling.

Using a careful optimization procedure and exhaustive simulations, a group of scientists from Bar-Ilan University has demonstrated the usefulness of the physical concept of power-law scaling to deep learning. This central concept in physics, which arises from diverse phenomena, including the timing and magnitude of earthquakes, Internet topology and social networks, stock price fluctuations, word frequencies in linguistics, and signal amplitudes in brain activity, has also been found to be applicable in the ever-growing field of AI, and especially deep learning...

The reconstructed bridge from physics and experimental neuroscience to machine learning is expected to advance artificial intelligence and especially ultrafast decision making under limited training examples as to contribute to the formation of a theoretical framework of the field of deep learning.

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Additional resources

Journal Reference:

1. Yuval Meir, Shira Sardi, Shiri Hodassman, Karin Kisos, Itamar Ben-Noam, Amir Goldental, Ido Kanter. Power-law scaling to assist with key challenges in artificial intelligence. Scientific Reports, 2020; 10 (1) DOI: 10.1038/s41598-020-76764-1

Source: Science Daily