ML in PL: AI Art Festival’s Post

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

#TheGodfatherofAI 🧠 Yesterday, Geoffrey Hinton and John Hopfield received the Nobel Prize in Physics 2024 for their contributions to machine learning. Geoffrey Hinton and John Hopfield, co-winners of the 2024 Nobel Prize in Physics, were awarded "for foundational discoveries and inventions that enable machine learning with artificial neural networks." Hinton's contributions to AI include advancing the Boltzmann machine, the backpropagation algorithm, and neural network architectures, which have been critical in enabling AI systems to learn autonomously. John Hopfield, his co-laureate, is known for inventing the Hopfield network, a type of recurrent neural network that acts as an associative memory system, storing and reconstructing patterns, laying the groundwork for modern neural network theory. In his phone call after receiving the prize, Hinton also included a warning: "[...] we have no experience of what it's like to have things smarter than us. "[...] the threat of these things getting out of control." When asked about regrets in his life’s work, he added: “There are two kinds of regrets: regrets where you feel guilty because you did something you knew you shouldn’t have done, and then there are regrets where you did something you would do again in the same circumstances, but it may in the end not turn out well; that second kind of regret I have.” He continued: "[...] I am worried that the overall consequence of this might be systems more intelligent than us that eventually take control." During my presentations, I often explain his and many others' warnings. This is not really the fun and joy part (don't worry, there is plenty of fun and joy in my AI workshops), and I often see people with a foul taste in their mouth when they start to understand the implications of what I'm explaining. I love AI, work daily with it, and it will reshape the world as we know it. It has incredible potential, both for good and bad. Because it is so revolutionary, it is imperative we understand the nature of this beast. If not, it is clear to see we will be just another speck on the timeline of this universe. But for now, let's use these marvelous tools to lighten our daily loads, have creative fun, and extend our potential greatly! Watch their contributions briefly explained by the Nobel Prize committee: https://lnkd.in/eMSueSqr   Excerpt from an interview with Hinton: https://lnkd.in/erN33WTn   Wiki Geoffrey Hinton: https://lnkd.in/e23Xmv7C   Wiki John Hopfield: https://lnkd.in/eS3SSytC  #AI #MachineLearning #NobelPrize

The Royal Swedish Academy of Sciences has awarded the 2024 Nobel Prize in Physics to John J. Hopfield and Geoffrey E. Hinton for their groundbreaking contributions that have laid the foundation for machine learning with artificial neural networks. This year’s laureates have harnessed principles from physics to develop methodologies that underpin modern machine learning. John Hopfield pioneered an associative memory model capable of storing and reconstructing images and various data patterns. Geoffrey Hinton introduced an innovative approach for autonomously identifying properties within data, enabling tasks such as recognizing specific elements in images. In discussions of artificial intelligence, machine learning utilizing artificial neural networks is often at the forefront. This technology draws inspiration from the brain's structure, where neurons are represented by nodes influencing each other through adjustable connections akin to synapses. These networks undergo training, enhancing connections between nodes with simultaneous high values. The work of this year’s laureates has been instrumental in advancing artificial neural networks since the 1980s. John Hopfield developed a network that utilizes physical principles related to atomic spin to store and recreate patterns. In this framework, nodes can be visualized as pixels. The Hopfield network operates based on the energy dynamics of spin systems, optimizing the connections to achieve low energy states. When presented with a distorted image, the network systematically updates node values to retrieve the most similar stored image. Geoffrey Hinton expanded on Hopfield's concepts to create the Boltzmann machine, a network capable of learning to recognize patterns within specific data sets. By applying statistical physics, Hinton trained the Boltzmann machine using examples likely to occur, allowing it to classify images and generate new instances of learned patterns. His contributions have significantly fueled the rapid advancement of machine learning. #NobelPrize #Physics #MachineLearning #ArtificialIntelligence #NeuralNetworks #Innovation #ML #BigData #DataScience #ResearchExcellence #AI #Hinton #Swidish #Hopfield #StatisticalPhysics

📢I am delighted to announce that our recent paper has been accepted at the 𝗡𝗲𝘂𝗿𝗼𝗔𝗜 𝘄𝗼𝗿𝗸𝘀𝗵𝗼𝗽 at ⚡NeurIPS⚡. The paper is entitled: '𝘽𝙧𝙖𝙞𝙣 𝙞𝙣 𝙩𝙝𝙚 𝘿𝙖𝙧𝙠: 𝘿𝙚𝙨𝙞𝙜𝙣 𝙋𝙧𝙞𝙣𝙘𝙞𝙥𝙡𝙚𝙨 𝙛𝙤𝙧 𝙉𝙚𝙪𝙧𝙤𝙢𝙞𝙢𝙚𝙩𝙞𝙘 𝙄𝙣𝙛𝙚𝙧𝙚𝙣𝙘𝙚 𝙪𝙣𝙙𝙚𝙧 𝙩𝙝𝙚 𝙁𝙧𝙚𝙚 𝙀𝙣𝙚𝙧𝙜𝙮 𝙋𝙧𝙞𝙣𝙘𝙞𝙥𝙡𝙚' This was based on a close collaboration with Dr. Szymon Urbas from Maynooth University and Prof. Karl Friston from UCL. We would also like to express our gratitude to Prof. Brendan Murphy and Prof. Adeel Razi for their invaluable feedback and support throughout the process. 𝘼𝙗𝙨𝙩𝙧𝙖𝙘𝙩: --------------------------------------------------------------- Deep learning has revolutionised artificial intelligence (AI) by enabling automatic feature extraction and function approximation from raw data. However, it faces challenges such as a lack of out-of-distribution generalisation, catastrophic forgetting and poor interpretability. In contrast, biological neural networks, such as those in the human brain, do not suffer from these issues, inspiring AI researchers to explore neuromimetic deep learning, which aims to replicate brain mechanisms within AI models. A foundational theory for this approach is the Free Energy Principle (FEP), which despite its potential, is often considered too complex to understand and implement in AI as it requires an interdisciplinary understanding across a variety of fields. This paper seeks to demystify the FEP and provide a comprehensive framework for designing neuromimetic models with human-like perception capabilities. We present a roadmap for implementing these models and a Pytorch code repository for applying FEP in a predictive coding network. #machinelearning #AI #deeplearning #neuralnetworks #artificialintelligence #robotics #neuroscience #datascience #datascientist

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/g5Uw8TF3 #NobelPrize #AI #ArtificialIntelligence #Physics #HumanMemory #AiNews #AiTrends #News #Trends #DhakaAi

This year's Nobel Prize in Physics went to two AI luminaries Geoffrey (Jeff) Hinton and John Hopefield. As with many other disciplines, AI/Machine Learning share its roots from Physics 🤯 Find out why in this short IEEE article. https://lnkd.in/e7bbnrSr #machinelearning #ai

I believe that It's Time for #AI and #MachineLearning to take center stage in all awards! --- BREAKING NEWS - #NobelPrize to AI & Machine Learning scientist https://lnkd.in/ggRfWzdM The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” This year’s physics laureates John Hopfield and Geoffrey Hinton used tools from physics to construct methods that helped lay the foundation for today’s powerful machine learning. Hopfield created a structure that can store and reconstruct information. Hinton invented a method that can independently discover properties in data and which has become important for the large artificial neural networks now in use. Although computers cannot think, machines can now mimic functions such as memory and learning. The 2024 Nobel Prize laureates in physics have helped make this possible. Using fundamental concepts and methods from physics, they have developed technologies that use structures in networks to process information. Press release: https://bit.ly/4diXSfz Popular information: https://bit.ly/4gK57jl Advanced information: https://bit.ly/4egLrly

Artificial intelligence is much more than image generation and smart-sounding chatbots; it’s also a Nobel-worthy endeavor rooted in physics! The idea of an artificial neural network goes back to the 1940s: with the brain’s network of neurons and synapses modeled by a network of computationally connected nodes. By connecting biophysics with condensed matter physics systems (particularly involving particle spins), John Hopfield conjectured and found a model to link large collections of nodes to computational abilities. Building on Hopfield’s work, Geoffrey Hinton extended Hopfield’s model to a Boltzmann machine, with generative capabilities. Everything using AI and machine learning today relies on this Nobel-winning work. https://lnkd.in/e8A7aq8F #structuredvisualthinking #transformation #strategy #decisionquality #business #csuite #decisionmaking #frameworks #truth #ai #listen #curiosity #leadership #creativity #marketing Big Think

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

🚀 Exciting News: 2024 Nobel Prize in Physics honors AI and Machine Learning pioneers! As a student of AI and Machine Learning, I find this recognition truly inspiring! 🌟 John J. Hopfield and Geoffrey E. Hinton have been awarded the 2024 Nobel Prize in Physics for their foundational discoveries in artificial neural networks. Their work not only revolutionized AI but also showed how interdisciplinary research can reshape the future. Hopfield’s network model simulates memory recall, and Hinton’s groundbreaking work on the Boltzmann machine have both revolutionized the way machines process data. These contributions laid the foundation for today’s deep learning models that power everything from image recognition to self-driving cars. This prize is a testament to the power of interdisciplinary science, proving that by combining fields like physics and AI, we can unlock incredible possibilities for humanity. #NobelPrize2024 #AI #MachineLearning