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"Doing, not listening, was the starting point for how he [Einstein] learned physics,” wrote Scott Young about how Einstein learned physics. In the same way, the best way to learn about Generative AI is by building projects. Here is a guide to create a Retrieval Augmented Generation (RAG) application that provides a detailed project description, a step-by-step outline of the RAG process, which offers both theoretical insights and practical example code. Start your RAG project: https://lnkd.in/gEi4JF_w #GenerativeAI #MachineLearning #AIProjects #RAG #AICommunity #TechInnovation #AIExplained #DataScience

🎉 Groundbreaking news for the world of AI/ML and technology! The Nobel Prize in Physics has been awarded to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto for their pivotal discoveries and inventions that laid the groundwork for machine learning. 🌍💡 As a product manager using ML in building data products, I see firsthand how their foundational contributions to machine learning continue to shape the tools and systems we rely on every day. Their work has not only pushed the boundaries of using statistical methods and also enabled innovations in data-driven decision-making and analytics that power products all around us. This recognition reminds me of the incredible potential that lies in the intersection of fields like physics and data science, and how the breakthroughs fuel the creation of intelligent products that impact millions. #NobelPrize #Physics #MachineLearning #DataProducts #AI #Innovation

💡 Artificial Intelligence: The Brainchild of Physics 💡 AI is not just about data – it's about physics! This year’s 2024 Nobel Prize in Physics goes to John J. Hopfield and Geoffrey E. Hinton, whose groundbreaking work laid the foundation for today’s machine learning revolution. John Hopfield’s innovative associative memory network models the brain's neurons, helping systems store and reconstruct images and patterns. Imagine a blurred or incomplete picture being fed into a network that, using physics-based principles of atomic spin energy, "remembers" the original and sharpens it. This was one of the first steps toward making AI systems “think” more like us. Building on Hopfield's ideas, Geoffrey Hinton’s Boltzmann machine tapped into statistical physics to enable systems to autonomously recognize and classify patterns – a leap that has transformed industries from healthcare to image recognition. Hinton’s work gave rise to the AI boom we see today. Their pioneering discoveries not only shaped the neural networks we use daily but also continue to advance fields like materials science and beyond. This year’s Nobel Prize is a celebration of how physics can be the key to unlocking the full potential of AI. 🏆 Congratulations to these legends of science! #NobelPrize #Physics #AI #MachineLearning #NeuralNetworks #Innovation

Happy Sunday! Big News for Future Data Scientists! We're excited to announce that John J. Hopfield, alongside Geoffrey E. Hinton, has been awarded the 2024 Nobel Prize in Physics for their groundbreaking contributions to Artificial Intelligence! 🏆 Hopfield's pioneering work in the 1980s introduced the Hopfield network, a model that mimics how our brains store and recall memories. This innovative approach allows machines to recognize patterns and retrieve information, revolutionizing the field of machine learning. • Here are some highlights: •• Associative Memory: Hopfield networks enable computers to remember and reconstruct data, similar to how we recall memories when prompted by familiar cues. •• Interdisciplinary Impact: His work bridges physics and neurobiology, showcasing how concepts from different fields can lead to transformative technologies. •• Ethical Considerations: Both Hopfield and Hinton emphasize the importance of responsible AI development, warning about potential risks while celebrating AI's vast potential. For those just starting in data science, this is an inspiring moment! Dive into AI and machine learning with curiosity and a sense of responsibility. The future is bright! 🌈 #DataScience #AI #MachineLearning #NobelPrize #JohnHopfield #Inspiration #GeoffreyHinton #BeingDataScientist #BDS #Innovation #ML #Physics • Citations: [1] https://lnkd.in/d5u59kQz [2] https://lnkd.in/dR5Dwp_B [3] https://lnkd.in/dhK8PYwc [4] https://lnkd.in/dYQGsAr3 [5] https://lnkd.in/d8T2PCjQ [6] https://lnkd.in/dVXPQAkN [7] https://lnkd.in/dzkN2W-a [8] https://lnkd.in/dJSnQZue

At Queen Mary University of London, we're not just following the AI revolution—we're leading it. This webinar delves into the pioneering work of our esteemed panelists Dr. Lorenzo Jamone and Professor Pat Healey. They will unpack the latest advancements in multi-objective optimization, robotics, computational linguistics, and human-computer interaction. This is where technology from science fiction movies starts coming to life! #qmul #aiinnovation #techfrontiers #webinar #kameronslab

Celebrating the Power of Ideas and Innovation at the Intersection of Physics and AI 🎉 This year’s Nobel Prize in Physics honors Geoffrey Hinton and John Hopfield for their groundbreaking work on artificial neural networks. While awarded for physics, their work is a cornerstone of artificial intelligence, bridging two fields to shape data science, machine learning, and image recognition as we know them today. This achievement highlights how the most transformative ideas often emerge from cross-disciplinary collaboration and the sharing of knowledge. As an aspiring data analyst and AI enthusiast, I’m inspired by this recognition of the importance of ideas that transcend disciplines—showing how curiosity and knowledge exchange can drive real progress in AI and for humankind. The beauty of science lies in its endless connections, where curiosity unites fields, and ideas become the bridge to a brighter, more innovative future. #NobelPrize2024 #PhysicsMeetsAI #Innovation #DataScience #MachineLearning #Inspiration #BeautyOfScience

A defining moment of my career was when, thinking of leaving academia, I signed up for a course to learn what was the fuss about machine learning. At the time I had not much faith that my background as a theoretical physicist could match the machine learning world. Then something unexpected happened: during one of the first lectures, the lecturer showed something familiar on a slide and he couldn’t resist the impulse to mention the Hilbert space. That lecture was not meant for Physicist or Mathematicians, most of the people in the audience probably didn’t even notice it. But for me that was the moment in which I realised that all that fuss was connected to concepts that I had been studying for years. That indeed I was not out of place in that crowd. These days I see a lot of debate about if the fact that Nobel prizes are featuring AI means that Science is dead, that now only AI matters. I would just want to remind that before the hype and the rebranding into AI, there was linear algebra, functional analysis, optimisation, computer science… AI organically evolved within Science and branched out to reach the general population and popularisation. I don’t see AI to be in contraposition with “Pure Science”. Therefore I don’t find it controversial that the Nobel Academy is deciding to feature into their prizes the foundations of AI and AI applications to natural sciences. #AI #Physics #NobelPrize #MachineLearning

🎉🔬 Breaking News in Science! 🧠💡 The 2024 #NobelPrize in Physics goes to... 🥁 John J. Hopfield (U.S Researcher) and Geoffrey E. Hinton (Canadian Researcher)! 🏆🏆 Why? For their groundbreaking work in machine learning and artificial neural networks! 🤖🧠 🔹 Hopfield's contribution: Created an associative memory that can store and reconstruct patterns in data. Imagine a network that can remember and recreate images, even from incomplete information! 🖼️🧩 🔹 Hinton's innovation: Developed a method for networks to autonomously find properties in data. Think of it as teaching machines to see and understand the world around them! 👁️🌍 Their work laid the foundation for today's AI revolution, inspired by the human brain's structure. 🧠➡️💻 From physics principles to cutting-edge tech, these laureates have shaped the future of machine learning. Their inventions are behind the AI that's transforming industries and our daily lives! 🚀🌟 What are your thoughts on this year's Nobel Prize in Physics? How do you see AI impacting your field? Let's discuss! 💬👇 #AI #MachineLearning #Innovation #Science #TechRevolution

We will host Yasaman Bahri from Google DeepMind at our data-driven physical simulation (#DDPS) #seminar, Lawrence Livermore National Laboratory, on November 15th, 2024, 10 AM in California time. Anyone can join us through #WebEx room: https://lnkd.in/gdjiv5Ny Please take advantage of this wonderful opportunity! #Title: A #first-#principles #approach to #understanding #deep #learning #Abstract: Recent years have seen unprecedented advancements in the development of #ML and #AI; for the #sciences, these tools offer new paradigms for combining insights developed from #theory, #computation, and #experiment towards #design and #discovery. Beyond treating them as black boxes, however, uncovering and distilling the #fundamental principles behind how systems built using neural networks work is a grand challenge, and one that can be aided by ideas, tools, and methodology from physics. I will describe some of my past work that takes a first-principles approach to deep learning through the lens of #statistical #physics, #exactly #solvable #models and #mean-#field #theories, and #nonlinear #dynamics. I will first survey connections we discovered between large-width #deep #neural #networks, #Gaussian #processes, and #kernels, which bear application to data-driven research in the #physical #sciences. I’ll then discuss our work on understanding some facets of “#scaling #laws” describing the rate of improvement of an ML model with respect to increases in the amount of training data, model size, or computational resources. #Bio: Yasaman Bahri is a Research Scientist at Google DeepMind with research interests at the interface of statistical physics, machine learning, and condensed matter physics. In recent years, she has worked on the foundations of deep learning from a physics perspective. She has been an invited lecturer at the Les Houches School of Physics and was a co-organizer of a recent program at the Kavli Institute for Theoretical Physics. Previously, she received a Ph.D. in Physics at UC Berkeley. 📚 DDPS seminar is organized by libROM team ( www.librom.net ). 🗓️ DDPS seminar schedule: https://lnkd.in/gnGsx6qq

🚀 Exciting News in Physics ! 🏆✨ Celebration Moment for AI !! The 2024 Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton! John Hopfield and Geoffrey Hinton were instrumental in laying the groundwork for the machine learning revolution that began around 2010. ⏰ 🧑💻 The rapid progress we see today has been enabled by two key factors: ➡️ Access to massive datasets for training networks ➡️ Significant rise in computing power. Curious to learn what exactly they discovered and how it’s transforming machine learning as we know it? 🤔 Check out the attached media for a glimpse into their revolutionary work! 🔍🤯 Follow us for more such interesting information!! 🔄 #NobelPrize #Physics2024 #Innovation #ScienceBreakthrough #Curiosity #GodFatherofAI #JohnHopField #GeoffreyHinton #AssociativeMemory #BoltzmannMachine #AI #DeepLearning #SoftwareCompany #Innovation #DigitalTransformation #BusinessGrowth #technology #creativity #softwareengineering #programing #softwaredesign #StackSetu #futurism #startups #networking #success #business #ai #machinelearning