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Minds and Machines: Nobel Prize Recognizes the Physics Behind Artificial Intelligence In a surprising turn of events, the 2024 Nobel Prize in Physics has been awarded to John J. Hopfield and Geoffrey E. Hinton for their groundbreaking work in artificial intelligence (AI) and machine learning. This decision by the Royal Swedish Academy of Sciences marks a significant shift in recognizing the profound impact of AI research on our understanding of complex systems and computational capabilities. As we delve into the implications of this award, we’ll explore the foundational aspects of their work, its connection to physics, the current landscape of AI research, and the potential future directions of this rapidly evolving field. The Significance of Their Work At the heart of Hopfield and Hinton’s contributions lies the development of neural networks, which have become the backbone of modern AI technologies. Their research in the 1980s laid the groundwork for the machine learning revolution we’re witnessing today. John Hopfield, a professor emeritus at Princeton University, introduced the concept of the Hopfield network in 1982. This type of artificial neural network is characterized by its ability to store and reconstruct patterns, mimicking the associative memory of biological systems. Hopfield’s work demonstrated how a network of interconnected neurons could exhibit emergent behavior, storing and retrieving information in a manner reminiscent of human memory. for the full article follow the link... https://lnkd.in/gEiNagfv #NobelPrize #AI #ArtificialIntelligence #Physics #HumanMemory #AiNews #AiTrends #News #Trends #DhakaAi

In 2024, the Nobel Prize in Physics was awarded to two pioneers of artificial intelligence (AI), John J. Hopfield and Geoffrey E. Hinton. This may seem surprising at first—how could AI research, which is often associated with computer science, lead to the world’s most prestigious honor in physics? However, this award underscores the profound impact that their work on neural networks and computational models has had on the intersection of physics, biology, and machine learning, revolutionizing multiple disciplines. **John J. Hopfield**, a physicist by training, made his most significant contributions by developing the **Hopfield Network** in the 1980s. His research aimed to model the brain's processes using a computational framework grounded in physics. Hopfield demonstrated that neural networks could function similarly to physical systems, with neurons acting like elements in a dynamical system that finds stable solutions, or "memories," much like a spin glass reaching its lowest energy state. His network not only mimicked certain cognitive processes but also provided a bridge between statistical mechanics and computational neuroscience. This crossover helped lay the groundwork for understanding the physics of computation and how complex systems could self-organize to solve problems—an insight that proved invaluable to both AI and physics. **Geoffrey E. Hinton**, meanwhile, is often hailed as one of the "godfathers of AI" due to his work on **backpropagation** and deep learning. His contributions focused on making neural networks more efficient and biologically plausible, which led to major advances in machine learning and AI. Hinton's work transformed the field of computational learning, enabling machines to recognize patterns, classify images, and translate languages with unprecedented accuracy. His ideas about distributed representations, energy-based models, and the hierarchical structure of neural networks also have their roots in physical concepts like energy minimization and information theory. So, how does AI research connect to physics? At its core, AI, especially neural networks, is about understanding how information is processed and organized. This is fundamentally a physics problem—one that deals with energy states, optimization, and complex systems. Hopfield’s work applied these physical principles directly to neural computation, while Hinton's innovations leveraged concepts from statistical mechanics and information theory to create the deep learning models that have transformed modern AI. #DataScience #MachineLearning #Statistics #JointProbability #PredictiveModeling #AI

🔝It is no doubt that science has always been at the forefront of human progress, constantly pushing boundaries and embracing new methodologies to solve complex problems. Historically, some of the most groundbreaking discoveries have come from scientists who were brave enough to work across disciplines, combining expertise from different fields to create innovative solutions. 📍This year’s Nobel Prizes in physics and chemistry sparked debate by recognising AI-driven research, raising questions about disciplinary boundaries. While some argue machine learning is more computer science than physics, others celebrate the interdisciplinary nature of modern research. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐛𝐲 𝐦𝐞: ❓Should we maintain discipline boundaries or start working across disciplines to harness the disruptive power of AI and computers? ❓Should we be concerned about our role in applying AI to science or embrace it as a tool to address unsolved scientific questions? 𝐌𝐲 𝐬𝐩𝐨𝐧𝐭𝐚𝐧𝐞𝐨𝐮𝐬 𝐭𝐡𝐨𝐮𝐠𝐡𝐭𝐬 𝐛𝐫𝐨𝐮𝐠𝐡𝐭 𝐛𝐲 𝐥𝐨𝐠𝐢𝐜 𝐚𝐧𝐝 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠: 🍀We need to work across disciplines. We should use all possible means, including AI and advanced neural networks, to research intricate scientific topics like protein folding prediction and push the development of more sophisticated models. #NobelPrize #science #sciencebreakthrough #AI #NobelinChemistry2024 #NobelinPhysics2024

Huge congratulations to Geoffrey Hinton, ex-Googler and Godfather of AI, for winning the Nobel Prize in Physics! This is an incredible achievement and a testament to Geoff's groundbreaking work in artificial neural networks. His contributions at Google and beyond have been nothing short of unprecedented, laying the foundation for the AI revolution we're experiencing today. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures. Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning. Geoff's dedication to exploring the potential of machine learning has not only advanced the field of computer science but has also opened up countless possibilities for the future of technology and its impact on society. Let's all celebrate this momentous occasion and acknowledge the profound impact Geoff has had on shaping our world. #AI #NobelPrize #GeoffreyHinton #Innovation #MachineLearning #Google

2024 Physics Nobel Prize awarded to two pioneers of artificial intelligence —John Hopfield and Geoffrey Hinton — for their research on machine learning with artificial neural networks that laid the foundations for much of the advanced AI systems we see today. Dr. Hopfield created a structure that mimics associative memory, allowing artificial systems to store and reconstruct information, such as images and patterns. This work is vital for how modern machine learning systems retain and utilize vast amounts of data, reminiscent of biological brain function. His development of Hopfield networks has become a crucial model in both physics and neural computation, contributing to energy minimization principles that guide network states towards optimal solutions. Dr. Hinton revolutionized neural networks by introducing methods that allow machines to independently learn and identify properties in vast datasets. His backpropagation algorithm, an essential component of deep learning, has enabled artificial neural networks to autonomously perform complex tasks, from recognizing images to processing natural language. This breakthrough underpins the large-scale neural networks driving innovations such as generative AI, autonomous systems, and human-machine interaction. Both leveraged the principles of physics to enable machine learning systems to scale, contributing not only to the theoretical underpinnings of AI but also to its practical, real-world applications. By integrating energy dynamics and optimization from physics into artificial neural networks, they have shaped how these systems mimic the learning processes observed in the human brain. In an era defined by exponential growth in AI capabilities, this recognition by the Nobel Committee affirms the importance of interdisciplinary approaches that fuse computational neuroscience, machine learning, and physics. Their work serves as a testament to the idea that groundbreaking innovation often arises at the intersection of seemingly disparate fields. In my opinion, this Nobel win is not just a victory for theoretical physics or artificial intelligence—it is a profound acknowledgment of the transformative power of AI and its potential to reshape industries, governance, and our understanding of intelligence itself. Both laureates applied principles from physics to push the boundaries of what artificial neural networks can achieve and as AI continues to evolve, the pioneering work of Hopfield and Hinton will remain a cornerstone of both academic research and industrial innovation. And we certainly would see many more awards for the impact on AI space. #NobelPrize2024 #AI #MachineLearning #NeuralNetworks #DeepLearning #JohnHopfield #GeoffreyHinton #Physics #ArtificialIntelligence

Physics Nobel Prize awarded to AI experts. Very interesting times we are living in. I'd love to hold an intellectual debate/session on these discoveries and the work of John Hopfield and Geoffrey Hinton. DM me if you're ready to talk about neural networks and the advancements of AI! #nobelprize #ai #neuralnetworks #machine #learning #nlp

After two scientists were awarded the Nobel Prize in Physics for their groundbreaking work on neural networks, many have been asking, “What does physics have to do with AI?” 🤔 This confusion is often fueled by popular explanations that equate neural networks with biological neurons or the brain. While these analogies can help market the technology, they’re not entirely accurate. 🚨 Leading AI educators like Andrew Ng emphasize the need to distance ourselves from these comparisons. At its core, a neural network is not a biological system—it’s a mathematical function. 🧠➡️🔢 A neural network consists of layers of nodes (often called neurons) that process data using mathematical operations. Each node calculates a weighted sum, applies an activation function (like ReLU or sigmoid), and passes the result forward. The goal is to adjust the weights during training to minimize error, allowing the network to model complex relationships with incredible accuracy. 📊⚙️ So what does physics have to do with all this? • John Hopfield introduced a model that views neural networks as energy landscapes, where systems minimize energy to reach stable states—like how thermodynamic systems find equilibrium. These states represent patterns or memories learned by the network. 🌐⚛️ • Geoffrey Hinton took it further with the Boltzmann machine, applying statistical mechanics to describe how neuron configurations evolve toward low-energy, high-probability states. 🧠📉 In essence, their work bridges AI and physics—proving that neural networks are “physics-inspired.” Though there are no simulated brains or neurons here, the mathematics behind these systems delivers fantastic real-world results. 🚀💡 #AI #Physics #NeuralNetworks #ArtificialIntelligence #NobelPrize #JohnHopfield #GeoffreyHinton #MachineLearning #DeepLearning #StatisticalMechanics #EnergyLandscapes #AndrewNg #DataScience

Nobel Prize for Physics proponents who laid the groundwork for AI ** AutogenAI is prestigious client I work with across the AI LLM space. Announced 8 October, the Nobel Prize in Physics 2024 was awarded to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”. Artificial Intelligence (AI) often refers to machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. The Royal Swedish Academy of Sciences stated, “This year’s two Nobel Laureates in Physics have conducted important work with artificial neural networks from the 1980s onward, using tools from physics to develop methods that are the foundation of today’s powerful machine learning. “John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.” As Ellen Moons, Chair of the Nobel Committee for Physics, explained, “The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.” In addition to the honour and prestige of the prize, the two laureates share 11 million Swedish kronor (approx. AUD$1.58 million). Since 1901, in Memory of Alfred Nobel, the Nobel Prize has been awarded annually across six categories. Prizes will be awarded at ceremonies in Norway, 10 December. Source: © The Royal Swedish Academy of Sciences.

This is so exciting. For the Nobel body, for the scientific community, and for the world to acknowledge the tremendous work done to enable AI and ML as we know it today. The shift in paradigm that was created with Jeffrey's work (and the co-recipient's) has made work in this space, deep, philosophical and applicable at same time. https://lnkd.in/ggW-q45K #machinelearning #ai #artificialintelligence Congratulations form all of us at Mesh AI (collaborative scheduling to end clinician burnout).

The “Godfathers of Deep Learning”, who also emphasized the need for collaboration among AI developers to establish safety guidelines and prevent harmful outcomes. Excited how #Physics is the cornerstone of our - future - life, as well as other noble disciplines. #noble #nobleprice #ai #deeplearning