Why in news
The 2024 Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”
The work of John Hopfield and Geoffrey Hinton established crucial theoretical frameworks for ANNs, enabling machine learning.
Artificial Neural Networks (ANNs)
ANNs are computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process information.
Nodes are linked in layers, where each connection has a weight that influences the flow of information.
ANNs learn patterns through techniques like Hebbian learning, where repeated activation strengthens connections between neurons.
Types: Includes various models, such as Hopfield networks (for pattern recognition) and Boltzmann machines (for cognitive tasks).
Functionality: Capable of processing complex data inputs, such as images, and can denoise or complete patterns based on learned experiences.
Importance of ANNs
ANNs are widely used across various fields including physics, chemistry, biology, medicine, and finance, showcasing their versatility.
Hinton's contributions led to deep learning architectures, allowing for multi-layered processing of data, significantly enhancing AI capabilities
ANNs can perform complex computations through simple interconnected nodes, illustrating emergent behavior in computational systems.
With the rise of AI applications like chatbots (e.g., ChatGPT), ANNs have become a common part of everyday technology, influencing how we interact with machines.
COMMENTS