John Hopfield and Geoffrey Hinton are pioneers in neural networks and artificial intelligence. Hopfield developed Hopfield Networks, introducing energy-based models that simulate associative memory, connecting neural computation with physics. Hinton is a pioneer of deep learning, contributing to the development of backpropagation, Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs), enabling multilayer neural networks and modern AI applications. In 2018, he received the Turing Award. 

The discovery of artificial neural networks marked a turning point in artificial intelligence, providing the foundation that revolutionised the ability of machines to convert data into knowledge. In recent years, these foundations have also made it possible to scale the size of algorithms in the so-called Deep Learning field and to work with multiple data simultaneously with the latest work based on multimodality.

This Nobel Prize represents an exceptional recognition of fundamental research inmachine learning and, specifically,neural network anddeep learning techniques, which form the basis of artificial intelligence systems such as ChatGPT. I believe that this prize goes beyond the merit of the individuals awarded, recognising an entire scientific community that began its work more than half a century ago.

Hinton's work in what is known as Backpropagation and its application to neural networks was fundamental to give “depth” to deep learning. 

In statistics what we do is learn from the data to estimate parameters that tell us how the variables are related. This is done by trying to minimize the error that is made in the predictions. But when you add layers of depth to a neural network (which is what allows us to do such impressive things as understand language or create an image) the relationship between the error made in the prediction and the input data is lost. To avoid that problem, the Hinton-driven mechanism causes the error to be distributed backwards from the layer where the result is predicted to the input data layer, allowing the best value for the parameters to be set at all layers. 

In short, without Hinton's work we would not have chatGPT or AI-generated videos or any of the things that amaze us today.

In my opinion, the work of J.J. Hopfield and G.E. Hinton is extraordinary, but I don't think it was a good decision by the Nobel Committee in Physics. To begin with, it was Warren McCulloch and Walter Pitts who first proposed neural networks. To continue, this work has only a lateral relationship to physics, even though the Committee argues that it has to do with atomic spin. This is not true: at best it may be an ‘inspiration’ to consider that the union of several processing units (artificial neurons) can contain and store distributed information. My impression is that the committee's decision has more to do with AI being an important and ‘fashionable’ field, and less to do with the contribution to physics of the laureates. 
 

For me, this Nobel Prize to John J. Hopfield and Geoffrey E. Hinton is a recognition of two key figures in the development of modern artificial intelligence (AI), as they are pioneers and precursors of artificial neural networks, which form the basis of today's machine learning, such as deep learning or transformers. These technologies have enabled the development of language models such as ChatGPT or Gemini, which are revolutionizing the way we interact with technology. Their discoveries have transformed all areas of AI, from image processing and data science to natural language processing, with advances that are already impacting our daily lives. A clear example of its impact are virtual assistants such as Siri or Alexa, and automatic translators, which millions of people use on a daily basis. On the other hand, in more specialized areas, we find applications such as personalized medicine or advanced systems for the detection and mitigation of hate speech, which are also making use of the technologies that Hopfield and Hinton pioneered. This award underlines the importance of continuing research in these systems to face the technological and scientific challenges of the future, always with an eye on the common good and building a better society.

Neural networks have been linked to the discipline of Artificial Intelligence (AI) since their beginnings in the 1950s. Like AI, it has experienced periods of euphoria and others of stagnation, for example, it was necessary to have the backpropagation algorithm to allow them to learn. The idea is as simple as it is functional: to try to replicate a human neuron mathematically. One of the great breakthroughs in the 2000s was deep learning, where Hinton, together with other researchers, started a revolution that, to this day, continues to have an impact on us.

Hinton is one of the founders of deep learning. It began to be applied with great success in the automatic classification of images, later it began to be applied to sequential problems (audio, video, etc.) and has been continuously evolving to make the leap to the generation of content, with GANs being particularly well known. Large language models (LLMs) together with transformers led us to build GPT models, which we are all familiar with.

Today, generative AI and its potential are on everyone's lips. Advances in hardware components, large amounts of data and specifically designed and optimised algorithms have favoured this new technology. I think this is a well-deserved recognition, as all these techniques are based on neural networks. Personally, I was surprised by the news. The two award winners have been cornerstones in the development of neural networks andmachine learning. I see it as an award to all those who, in one way or another, have contributed to the advancement of AI and its positive use for our society.

It was certainly a surprise that this year's Nobel Prize in Physics went to two pioneers of artificial intelligence (AI). However, when you consider the huge impact that AI now has on our lives and on many fields of science, it is no longer so much of a surprise. This impact can only increase and will surely lead to more than one discovery receiving a Nobel Prize in the future. It seems a wise decision by the committee and congratulations are due to them and to the laureates. 

The first half of the 1980s saw a revival of artificial intelligence (AI), after the so-called 'AI reversal' of the 1970s, thanks to important developments in the field of artificial neural networks led by John Hopfiled and Geoffrey E. Hinton. Hinton.

Hopfiled in 1982 connected the biological aspects of the nervous system with the computational domain. In his paper entitled Neural networks and physical systems with emergent collective computational abilities (PNAS, 1982) he stresses: 'Computational properties useful for biological organisms or for building computers can emerge as collective properties of systems - which have a large number of simple equivalent simple components (or neurons). The physical meaning of addressable content memory is described by an appropriate phase space flow of the state of a system'. With his proposal he defines the so-called 'Hopfield networks', which are a type of recurrent artificial neural network, used as associative memory systems with binary units, which converge in their learning process, and which have applications in different fields such as image processing, speech processing, among others.

Geoffrey E. Hinton was the father of the training and learning model of multi-layer neural models (the one-layer model was the so-called perceptron of the 1970s) called backpropagation. It is a supervised learning method that adjusts the weights of connections in a neural network to minimize the error between the actual output and the desired output. Backpropagation has been crucial for the development of deep learning, because it allows to train deep neural networks efficiently, adjusting weights systematically to minimize error, helps neural networks to learn internal representations of data, which improves their ability to generalize to new data, and opened the door to developments in deep learning, image processing, voice, text... It is the basis of what is today the hatching of generative AI.

These results of the first half of the 1980s laid the foundation stones for the development of the following 40 years that have led to the current emergence of artificial intelligence and deep learning, which is based on these results that sought to emulate the functioning of the human neural system.

The Nobel Prize in Physics awarded to John J. Hopfield and Geoffrey E. Hinton represents a well-deserved recognition of their fundamental contribution to the field of machine learning and artificial neural networks. Hopfield is best known for developing the neural networks that bear his name, which are important because they demonstrated how the networks could efficiently store and retrieve patterns by simulating the associative behavior of the brain. Hinton, considered the godfather of artificial intelligence, played a key role in advancing deep learning, being one of the pioneers of the backpropagation algorithm. Their innovations have cemented the artificial intelligence revolution, which is currently transforming disciplines as diverse as medicine and high-energy physics.

It must have been very difficult to choose which researchers were awarded this year's Nobel Prize in Physics for the foundational discoveries and inventions that enable machine learning with artificial neural networks. Research in this area began back in the 1950s and after going through several periods of greater and lesser scientific development, it has reached the present day with renewed strength. Both John Hopfield and Geoffrey Hinton stand out for designing different types of artificial neural networks that have given rise to the creation of current artificial intelligence tools that allow both the detection of cancers in early stages and the generation of realistic artificial text. This news is surprising given that there is no Nobel Prize for computer science, highlighting the fact that multidisciplinary work is clearly the future of cutting-edge research.

It was a surprise, not because of the quality and contributions of the laureates, but because they are two multidisciplinary scientists who, coming from fields such as physics and psychology, have laid the foundations of artificial neural networks and machine learning as we know them.
For me, it is also a recognition of the relevance that artificial intelligence is having in our lives and, in particular, in many recent scientific advances. The science and technology of the 21st century will be the child of artificial intelligence. Once again, this award demonstrates the communicating vessels in science and technology, where many phenomena and theories that arise from Physics or Neuroscience have their counterpart in Computing, and end up becoming, after decades of refinement and the work of many other more anonymous people, the technologies that are changing the world.
In short, computing is mathematics, physics, psychology, biology, all the sciences. Without it, and without the intelligence derived from it, the world cannot be understood.

The awarding of the Nobel Prize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”, has caused me some surprise, although not too much. It is not the first time that a Nobel Prize has been awarded to an AI pioneer, as it was previously won by Herbert Simon (in Economics, which is not a 'classic' Nobel), but he had also had enough merits for it in Economics.

In this case, I think there is going to be some controversy, because we could actually consider that the prize is awarded, in a way, to a scientific advance, but also, or, above all, a computer science advance (Nobel, obviously, could not institute such a prize because computer science did not exist as such). That is, with a high component of engineering and technology; but I am afraid that, also, this prize could have been awarded because of the current social and media relevance of AI, which is dangerous if we are talking about a Nobel Prize.

Hopfield and Hinton are two pioneers of artificial neural networks (these networks, of multiple types, are the most fashionable branch of AI). Hopfield has pioneered a very original type of artificial neural networks named after him, the 'Hopfield networks'. For his part, Hinton (who had already won the Turing Award, for many the equivalent of the Nobel Prize in computer science) has been one of the creators of the so-called “Deep Learning” or deep learning, based on complex networks of artificial neurons, which are the basis of such well-known systems as ChatGPT. In this second case, above all, the prize has been awarded to Hinton, but it could have been awarded to a wide (or very wide number) of researchers who have been key in the development of this technology. Many other pioneers of AI, specifically of artificial neural networks and their physical and mathematical foundations, even before Hinton, are thus forgotten by the award, concentrated in two people. It is not the first time that the Nobel Foundation awards a Nobel Prize to the creators of a technology while forgetting others, even the pioneering discoverers of its scientific basis.

I am afraid that we have media debate for a while and more for Hinton's continuous, controversial statements on multiple issues around the future of AI and society, which will now be analyzed with a magnifying glass.

The only thing that surprised me is that it was not also given to Terry Sejnowski. Terry has as much merit as Hinton in the invention of the Boltzman Machine (which is the main reason for the award to Hinton). In fact, they both co-authored papers in 1985 and 1986 describing the learning algorithm in the Boltzman Machine. In addition, Sejnowski did his Ph.D. thesis with Hopfield, the other awardee. In my opinion it is unfair to have forgotten Sejnowski.

It is a significant development that the Nobel Prize in Physics recognises achievements in artificial intelligence, a discipline that essentially belongs to the field of Engineering. Given that there is no specific Nobel Prize for Engineering, perhaps it is time to consider creating one, as AI cuts across many fields but is based on engineering fundamentals.

The laureates receive this award for their undisputed contributions to artificial intelligence: Hopfield has pioneered the development of neural networks and Hinton has revolutionised the field with backpropagation, essential for modern Deep Learning. However, it is also important to remember that these advances have been made possible by decades of incremental work by an entire scientific community.

While recognising the merit of the awardees, there is still a notable lack of female representation in these awards. It would be encouraging to see greater diversity in these awards in the future, a challenge still pending not only for the Nobel Prize, but also for other prizes such as the Princess of Asturias, which in 2022 honoured four men as ‘fathers of AI’.