Physics Can Assist with Key Challenges in Artificial Intelligence (IMAGE)
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In an article published today in the journal Scientific Reports, researchers from Bar-Ilan University show how two challenges in current research and applications in the field of artificial intelligence 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, the scientists have 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. Image Rapid decision making: A deep learning neural network where each handwritten digit is presented only once to the trained network
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Prof. Ido Kanter, Bar-Ilan University
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