Nobel Prize in Physics 2024 has been awarded to John J. Hopfield and Geoffrey Hinton for foundational discoveries and inventions that enable Machine Learning (ML) with Artificial Neural Networks (ANNs).
Discoveries that were awarded Nobel Prize
- John Hopfield invented Hopfield network, a type of recurrent neural network that can store and reconstruct information.
- These networks work like a memory system, where they can store patterns (like images) and retrieve them.
- Network relies on Donald Hebb’s hypothesis – when neurons act together, they can enhance the network’s capability to process and store information.
- Hopfield networks can be used for tasks like image recognition and data reconstruction, making them valuable for various applications in machine learning.
- Geoffrey Hinton invented a method (Boltzmann machine) that can independently discover properties in data and has become important for large ANNs now in use.
- Boltzmann Machine is an early example of a generative model, which can create new patterns or examples based on what it has learned.
A trained Boltzmann machine can recognise familiar traits in information it has not previously seen.
Artificial Neural Networks (ANNs)
- Definition: ANN is a ML program or model that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.
- Working: Human brain is the inspiration behind neural network architecture.
- Human brain cells, called neurons, form a complex, highly interconnected network and send electrical signals to each other to help humans process information.
- Similarly, an ANN is made of artificial neurons or nodes that work together to solve a problem.