Free Courses Sale ends Soon, Get It Now
Disclaimer: Copyright infringement is not intended.
The Nobel Prize in Physics 2024 was awarded to John J. Hopfield and Geoffrey E. Hinton.
The Nobel Prize in Physics 2024 was awarded "for foundational discoveries and inventions that enable machine learning with artificial neural networks".
He invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels.
The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet.
The entire network is trained by determining values for the connections between the nodes so that the stored images have low energy. This is comparable to the energy in the spin system found in physics.
The Hopfield network goes over the nodes and modifies their values when it is fed a distorted or incomplete image, causing the network's energy to decrease.
As a result, the network searches in steps for the saved image that most closely resembles the faulty image it was given.
He used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine.
The Boltzmann machine is commonly used with two different types of nodes. Information is fed to one group, which is called visible nodes. The other nodes form a hidden layer.
The hidden nodes’ values and connections also contribute to the energy of the network as a whole.
The machine is run by applying a rule for updating the values of the nodes one at a time.
Eventually, the machine will enter a state in which the nodes’ pattern can change, but the attributes of the network as a whole remain the same.
Then, using Boltzmann's equation, each potential pattern will have a distinct probability that is based on the energy of the network.
The Boltzmann machine is an early example of a generative model because when it stops, it produces a new pattern.
The Boltzmann machine can learn – not from instructions, but from being given examples.
It is trained by updating the values in the network’s connections so that the example patterns, which were fed to the visible nodes when it was trained, have the highest possible probability of occurring when the machine is run.
If the same pattern is repeated several times during this training, the probability of this pattern is even higher. Training also affects the probability of outputting new patterns that resemble the examples on which the machine was trained.
A trained Boltzmann machine can recognise familiar traits in information it has not previously seen. Imagine meeting a friend’s sibling, and you can immediately see that they must be related.
Similarly, the Boltzmann machine can recognise an entirely new example if it belongs to a category found in the training material, and differentiate it from dissimilar material.
An artificial neural network is designed to mimic the brain.
Inspired by biological neurons in the brain, artificial neural networks are large collections of “neurons”, or nodes, connected by “synapses”, or weighted couplings, which are trained to perform certain tasks.
An artificial neural network processes information using its entire network structure.
The Nobel Prize in Physics
The Nobel Prize in Physics is an annual award given by the Royal Swedish Academy of Sciences For those who have made the most outstanding contributions to mankind in the field of physics. It is one of the five Nobel Prizes established by the will of Alfred Nobel in 1895 and awarded since 1901, the others being the Nobel Prize in Chemistry, Nobel Prize in Literature, Nobel Peace Prize, and Nobel Prize in Physiology or Medicine. The award consists of a diploma, a certificate indicating the monetary reward, and a medal. The medal's front side features the identical portrait of Alfred Nobel that appears on the medals for literature, chemistry, and physics. Significant Details Regarding the Physics Nobel Prize German physicist Wilhelm Röntgen received the first-ever Nobel Prize in Physics in appreciation of the outstanding contributions he made with the discovery of X-rays. The Nobel Prize in Physics has been awarded 118 times to 227 Nobel Prize laureates between 1901 and 2024. John Bardeen is the only laureate who has been awarded the Nobel Prize in Physics twice, in 1956 and 1972. This means that a total of 226 individuals have received the Nobel Prize in Physics. |
Important articles for refrence:
How are the Nobel Prizes Decided?
Sources:
https://indianexpress.com/article/explained/nobel-prize-nomination-selection-process-9608765/
https://www.nobelprize.org/prizes/physics/
PRACTICE QUESTIONQ.Consider the following statements about the Boltzmann Machine recently seen in the news:
Which of the above statements are incorrect? A. 1 only B. 2 and 3 only C. 1, 2 and 3 only D. None Ans: B Explanation: Statement 1 is incorrect: The Boltzmann machine is commonly used with two different types of nodes. Information is fed to one group, which is called visible nodes. The other nodes form a hidden layer. The hidden nodes’ values and connections also contribute to the energy of the network as a whole. The Boltzmann machine can learn – not from instructions, but from being given examples. Statement 2 is correct: It is trained by updating the values in the network’s connections so that the example patterns, which were fed to the visible nodes when it was trained, have the highest possible probability of occurring when the machine is run. If the same pattern is repeated several times during this training, the probability of this pattern is even higher. Training also affects the probability of outputting new patterns that resemble the examples on which the machine was trained. Statement 3 is correct: A trained Boltzmann machine can recognise familiar traits in information it has not previously seen. Imagine meeting a friend’s sibling, and you can immediately see that they must be related. Similarly, the Boltzmann machine can recognise an entirely new example if it belongs to a category found in the training material, and differentiate it from dissimilar material. |
© 2024 iasgyan. All right reserved