Nobel Prize for AI
This year’s Nobel Prize winners are scientists working on the development of Artificial Intelligence (AI). This says a lot about the focus of the modern world.
The Godfathers of AI
In the field of physics, the laureates were Professor John Hopfield from Princeton University and Geoffrey Hinton from the University of Toronto for their “fundamental discoveries and inventions that enable machine learning through artificial neural networks.” Hinton earned a PhD in physics and researched backpropagation in neural networks. Meanwhile, Hopfield had already published a paper in 1982 demonstrating that his neural network could function as associative memory, capable of storing and recalling established patterns.
The award honors the combination of computer science and physics for the benefit of humanity. It also highlights the importance of interdisciplinary research, particularly in fields that may not yet be prestigious but are intuitively crucial for the future of science, as is the case with artificial intelligence. One might even argue that this year’s prize was partially awarded to the field of computer science. Undoubtedly, this is linked to the current AI boom.
Let’s take a closer look at the achievements of both scientists. It was Hopfield who popularized the functioning of neural networks as memory systems, utilizing principles of statistical physics, particularly those describing the behavior of magnets. These principles help minimize energy functions, allowing networks to find stored patterns that best match the input data. This helped to better understand how human memory and the brain work, and then replicate this in artificial neural networks.
Hinton, on the other hand, expanded on backpropagation research, which enables the training of neural networks. When an error is detected, the system learns to avoid it in the future, minimizing errors between predicted and actual results. This algorithm allowed for the development of advanced machine learning models. It processes data, compares predicted results with actual ones, sends error information backward, updates the data, and repeats the process until a matching result is achieved.