The Nobel Prize in Physics being awarded to pioneers in the field of machine learning may seem odd at first glance, but it is, in fact, well-deserved.
If one merely thinks that today’s artificial intelligence is achieved through computers, it’s natural to find this decision hard to understand. However, at its core, the two scientists used statistical physics and dynamical systems tools to build models, which are fundamentally about simulating and exploring the principles of physics and the physical world. For example, the structure of neural networks, which can store and recall information, is modeled using dynamical systems to simulate the human brain. The process of machine learning from examples relies on statistical physics. Due to the recent explosion of computational power, the models they proposed can now simulate neural networks at a scale comparable to the human brain, making them applicable to everyday life.
