Physics and Chemistry Nobel Prizes for AI Research
The Nobel Prize in Physics has been awarded to two AI scientists, John Hopfield and Geoffrey Hinton, for their “foundational discoveries and inventions that enable machine learning with artificial neural networks.” Hinton’s work has been primarily in computer science, while Hopfield’s research was inspired by a physical system known as “spin glass,” a type of alloy with unique properties. Hinton developed a method called backpropagation, which allows artificial neural networks to learn from their errors and improve over time. The mathematics of spin glass systems was applied to the understanding of neural networks.
The Nobel Prize in Chemistry was awarded to three scientists: David Baker, Demis Hassabis, and John M. Jumper. Notably, Hassabis and Jumper, the CEO and senior research scientist at Google DeepMind, respectively, were recognized for developing an AI tool, AlfaFold that can predict protein structures based on known amino acid sequences from databases.
Both the Physics and Chemistry Nobel Prizes this year were awarded for AI-related research. These breakthroughs in AI-powered predictive capabilities have enabled scientists to determine protein structures with high accuracy, which will aid in finding cures for protein-related diseases.
AI has not only made significant strides in research but has also impacted our everyday lives. In today’s fast-paced world, AI applications can be seen across various domains—whether we are streaming shows on OTT platforms, shopping on e-commerce websites, or ordering food via delivery apps, AI predicts our preferences and offers tailored suggestions. Additionally, improvements in large language models (LLMs) have turned AI into a fluent reader and writer, a task that seemed challenging just a year ago. In the coming years, we can expect even more surprises, as AI continues to evolve towards systems capable of rational actions.
How artificial neural networks work?
Neural networks are inspired by the way the human brain processes information, where neurons (brain cells) communicate through complex networks to transmit signals. Similarly, artificial neural networks (ANNs) consist of layers of artificial neurons that work together to analyze and process data. Each artificial neuron, also known as a node, takes inputs, applies weights to them, processes them through an activation function, and passes the information to the next layer.
In an ANN, the process starts with the input layer, where raw data is received. The data is then sent to the hidden layers, which perform complex calculations and extract features from the input data. These hidden layers are crucial for detecting patterns and making decisions. Finally, the output layer provides the final result or prediction. The number of hidden layers, as well as the number of neurons within each layer, can vary depending on the complexity of the problem being solved. Simple tasks may require just one or two hidden layers, while more complex problems, like image recognition or natural language processing, might need deep neural networks with many layers, often referred to as deep learning.
Neural networks have transformed various industries by enabling advancements in self-driving cars, which rely on real-time data from sensors and cameras to make accurate driving decisions. In voice assistants like Siri and Alexa, neural networks help understand and respond to voice commands. Similarly, image and speech recognition systems use neural networks to identify objects in photos or convert spoken words into text. Furthermore, neural networks are integral in medical diagnosis, where they analyze scans or test results to detect anomalies, and in financial sectors, where they assess risks and identify fraudulent activities.
The versatility and adaptability of neural networks stem from their ability to learn and improve over time. By training on large datasets, neural networks can recognize patterns, generalize from previous experiences, and even adapt to new, unseen data. This capacity for learning makes them a core technology in the development of artificial intelligence, pushing the boundaries of what machines can accomplish and bringing us closer to creating systems that can perform tasks traditionally requiring human intelligence. As the field continues to grow, neural networks are expected to revolutionize even more areas, including healthcare, robotics, and creative industries, making everyday life more efficient and interconnected.
Author Prof Anand Kumar