Stockholm's Royal Swedish Academy of Sciences has awarded the Nobel for Physics this year to John Hopfield and Geoffrey Hinton, "for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
Both have "used tools from physics to construct methods that helped lay the foundation for today’s powerful machine learning," read the statement from the Academy.
Hopfield created a structure that could store and reconstruct information.
A method developed by Geoffrey Hinton can independently discover properties in data, which has become important for the large artificial neural networks now in use.
ANNs or artificial neural networks use a network of nodes that are interconnected to mimic the brain's neurons.
Computers can perform a huge amount of tasks today, from translating languages to even carrying out conversations or interpreting images. This technology is critical for research, which requires vast amounts of data to be sorted out and analysed.
Machine learning has developed quickly over the last 15 years or so, utilising ANN, a technology that is also referred to as artificial intelligence.
Hopfield and Hinton have made it possible for machines to mimic functions such as memory and learning.
"Using fundamental concepts and methods from physics, they have developed technologies that use structures in networks to process information," reads the statement from the Academy of Sciences.
Traditional software processes the data it receives by following a clear description to produce results.
In machine learning the computer learns by example, enabling it to tackle problems that are vague and complicated by following step by step instructions. It has become so advanced that now computers can interpret a picture by identifying the objects in it.
Man's desire to understand the workings of the brain led to the creation of ANN, which processes information using the entire network structure.
The concept of mimicking the working of the brain became popular in the 1940s when researchers attempted to figure out how the brain's network of neurons and synapses worked.
Through psychology they understood how humans learn as connections between neurons are reinforced when they work together.
Later computer simulations attempted to recreate the the brain's neural network functions by building artificial neural networks.
"The brain’s neurons are mimicked by nodes that are given different values, and the synapses are represented by connections between the nodes that can be made stronger or weaker," the statement from the Academy reads.
There was a time when discouraging theoretical results caused many researchers to suspect that these neural networks would never be of any real use, but interest in the subject reawakened in the 1980s, when several important ideas made an impact, including work by this year’s laureates.
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