In a remarkable turn of events, two physicists, John J. Hopfield and Geoffrey E. Hinton, were awarded the Nobel Prize in Physics for their groundbreaking work on neural networks and machine learning algorithms. Their research, deeply rooted in the principles of statistical mechanics, has not only revolutionized the field of artificial intelligence but also paved the way for advancements in materials science and beyond.
From Statistical Mechanics to AI Landscapes
The work of Hopfield and Hinton, at NASA, is illustrative of the capabilities afforded by cross-disciplinary convergence (latter went on to win the Nobel prize for their biomedical work). It was drawing inspiration from statistical mechanics that they came up with the ideas for neural network theories upon which all modern AI rests, you may have heard of their subsequent algorithms — they now power technology like ChatGPT.
The central link is the principle of minimizing an energy. Statistical mechanics concerns the collective behavior of large systems, and can be used to predict the probability that a system is in some state (like solid, liquid or gas), of course by using ⑴ as one of its input data. Hopfield and Hinton realized that this behavior could be exploited to build networks of connected ‘neurons’ that search for the lowest-energy arrangement, potentially a powerful way to solve difficult problems using a form of collective intelligence.
Generative Learning as Catalyst for Unleashing Potential
Generative Learning One of the most exciting applications of learning basic Hopfield and Hinton is going to be generative learning, that in whatever a model produces new data examples which are similar so the ones it was trained with. It is this methodology that led to the amazing strides made in AI generated art, video and text.
Boltzmann machines (discussed in the next post that has a part 2) contain visible and hidden neurons researchers try to estimate the likelihood of a specific configuration of visible data (where in our case each pixel value is fed separately to the machine). This allows the network to understand complex patterns and create new samples that appear very similar to the original data. My research group has also been investigating how Boltzmann machines can be implemented on quantum computers to extend the generative learning capabilities of your new species.
Upload Date: Bridging the Gap Between Physics and Computer Science
Hopfield and Hinton demonstrated that not only has physics enabled progress in artificial intelligence, but the new science of AI is beginning to reciprocate by uncovering deep connections between physics and computer science. This is a confirmation of the success when researchers in different disciplines work together and bring forth inspiration from one area to another, as evident with their Nobel Prize in Physics.
As a computational materials scientist, I am perhaps more excited about how these will change the fabric of material research in the future. Now researchers are using statistical mechanics and neural networks to understand materials behavior at a more fundamental level, which is enabling discoveries in energy storage and biomedical engineering.
