Nello Cristianini’s Post

John Hopfield, a Nobel Prize laureate in Physics, showcases true polymathic prowess with a career spanning diverse scientific realms. From solid state physics to haemoglobin chemistry and DNA synthesis, his journey exemplifies intellectual versatility. In 1982, his groundbreaking work on brain-like networks, particularly the 'Hopfield network', laid the foundation for modern AI and machine learning. Honored alongside AI pioneer Geoffrey Hinton, Hopfield's legacy continues to shape the landscape of artificial intelligence. Learn more about this remarkable scientist's contributions in the field: [Link to article] #NobelPrize #Physics #AI #MachineLearning

2024 Nobel prizes in Physics and Chemistry goes to AI and ML Fascinating connection of AI crossing over traditional scientific boundaries to blend different fields and achieve ground breaking results. The physics award went to two computer scientists who laid the foundations for machine learning, while the chemistry awards went to 3 researchers for their use of machine learning to tackle one of biology’s biggest mysteries: how proteins fold. Second connection is their research that originated at a tech company: DeepMind, an AI research startup that was acquired by Google in 2014 2024 Nobel Prize for Chemistry recognized Demis Hassabis, John  and David BakerJumper and David Baker for using machine learning to tackle one of biology’s biggest challenges: predicting the 3D shape of proteins and designing them from scratch. 2024 Nobel prize for Physics recognized Princeton University physicist John Hopfield and University of Toronto computer scientist Geoffrey Hinton for key breakthroughs in AI revolution − making machines that learn https://lnkd.in/gX9yCTZY https://lnkd.in/ggHk5Q5q

🚨 Nobel Prize in Physics 2024: A Triumph for AI, But Raises Ethical and Scholarly Questions 🚨 The Royal Swedish Academy of Sciences has awarded the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton for their groundbreaking work on machine learning and artificial neural networks—a significant achievement not only for them but for the entire field of AI. However, this decision has sparked surprise and debate: 💡 Many are questioning why a Nobel in Physics was awarded for work that seems more rooted in computer science than in “real physics,” raising concerns about the recognition of AI's impact in a field traditionally focused on the physical universe. Is this a shift in how we define the boundaries of scientific disciplines? 🤔 More thought-provoking is Geoffrey E. Hinton’s personal stance. After parting ways with Google, Hinton expressed deep moral concerns about the future of AI. He has been quoted as saying: “I console myself with the normal excuse: If I hadn’t done it, somebody else would have.” This has led some to wonder: Can we award such a prestigious prize to someone who harbors serious doubts about the long-term value of their own work? 🔍 And the questions don’t stop there. Critics have also pointed out the relative scarcity of impactful publications by Hinton that might typically warrant such a distinction in physics. Does this raise further concerns about how we assess and recognize scientific contributions? Or is this Nobel Prize a sign of AI's increasing relevance across all scientific fields, blurring the lines of traditional disciplines? https://lnkd.in/d6jeEu5S #AI #EthicsInAI #NobelPrize #GeoffreyHinton #Physics #MachineLearning #ArtificialIntelligence #TechEthics #ScientificIntegrity

The announcements of the awardees for the Nobel Prizes in Physics and Chemistry seems to have really elicited rather extreme reactions, particularly the former and understandably so. There is the camp that is celebrating the recognition of the transformative power of AI, with the other camp bemoaning how this is just feeding into the hype and/or lamenting how it is a chance missed to recognize "traditional" scientists. Frankly, it is much easier to accept in the case of the awardees for the Chemistry prize, seeing as how there is a clear link to fields which are widely accepted to fall under the Chemistry umbrella. This link is far more tenuous in the case of the Physics prize, since neural networks aren't something we inextricably link to Physics. If anything, their work has spawned a completely new discipline of ML/AI, which has found applications in a vast variety of applications. We see evidence of this in the traditional ML conferences too, where the submission counts are well over 10,000 each year (which is cause for a separate discussion). But putting the pieces together, there seems to be a well-founded case for recognizing ML/AI as a discipline with a variety of sub-fields, along with all that comes with it - having more focused conferences devoted to certain categories of ML research, instituting prizes recognizing seminal works in AI, etc. We already have this in Mathematics, so it is not unheard of either. That's my two cents on this. I'm curious to hear - what do you think? P.S. Regardless of where we stand on this subject though, it is worth remembering that both sets of Nobel Prize awardees were recognized for outstanding accomplishments and no amount of discussion should detract from or ignore that fact.

🌟 Nobel Prize in Physics 2024: Bridging Physics and Machine Learning 🌟 When you hear “foundational discoveries and inventions that enable machine learning with artificial neural networks,” you might think of computer science. However, this topic has just won the Physics Nobel Prize in 2024! 🏆 Delving deeper, we find that the fundamentals of machine learning are deeply rooted in physics. 🔬 John Hopfield invented a network that saves and recreates patterns, using physics to describe a material’s characteristics due to atomic spin. Imagine the nodes as pixels. The Hopfield network is trained to find values for connections between nodes so that saved images have low energy. When fed a distorted image, it updates the nodes’ values to reduce the network’s energy, step by step, to find the most similar saved image. 🤖 Geoffrey Hinton built on Hopfield’s work to create the Boltzmann machine, which learns to recognize characteristic elements in data. Using tools from statistical physics, this machine is trained with examples likely to arise during its operation. It can classify images or generate new examples of trained patterns, sparking the explosive development of machine learning. “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,” says Ellen Moons, Chair of the Nobel Committee for Physics. This recognition highlights the interdisciplinary nature of innovation and the profound impact of physics on advancing technology. 🌐 #NobelPrize #Physics #MachineLearning #ArtificialIntelligence #Innovation

🌟 Nobel Physics Prize 2024 Goes to AI Pioneers Hopfield and Hinton! 🌟 Exciting news as the 2024 Nobel Prize in Physics has been awarded to two remarkable scientists, John Hopfield and Geoffrey Hinton, for their groundbreaking work in machine learning! 🏆✨ 🔍 Who are they? - Geoffrey Hinton: Often called the "godfather" of AI, he left Google to discuss the risks associated with AI. He believes smarter machines can help us, but we need to tread carefully. 🤖💡 - John Hopfield: A professor who created a unique memory system that helps computers understand and recreate images! 🖼️🧠 While it’s surprising to see AI pioneers recognized with the Nobel Prize in Physics—traditionally reserved for contributions to the fundamental laws of nature—this award highlights how interdisciplinary work is reshaping our understanding of science. 🌍🤖 Let’s celebrate innovation and the responsibility that comes with it! 🎉 What are your thoughts? 💬👇 https://lnkd.in/gbaJp3jh #nobelprize #machinelearning #innovation #ai #artificialintelligence #science #geoffreyhinton #johnhopfield #physics #technology #innovation

With this week's announcement of the Nobel Prize in Physics honoring John Hopfield and Geoffrey Hinton for their work on neural networks, I'm seeing some comments on LinkedIn asking, "Where's the physics?" This means that the applications of the work are becoming so ubiquitous that some folks can't recognize the science behind those applications. That's exactly why it's so revolutionary! Hopfield's work draws from the Ising model in statistical mechanics. Neurons in a Hopfield network settle into stable states in the same way spins in a magnet align to minimize energy. This mimics the physics of phase transitions and critical phenomena. Hinton's Boltzmann Machines are rooted in statistical mechanics. They use concepts like energy landscapes and thermal equilibrium to learn and represent complex patterns. Both approaches use energy functions to describe system behavior, a direct application of physics. The learning process in these networks works the same way as finding low-energy states in physical systems. For a deeper dive, check out the official announcement.

Today the Nobel Prize in Physics 2024 was awarded to John Hopfield and Geoffrey Hinton for their contributions to the field of artificial intelligence. It proves how interrelated physics, math, and computer science are. The laureates’ work on the foundation of artificial neural networks in the 1980s has helped initiate the current explosive development of machine learning. Many people wonder why these scientists received the award just yet and not earlier. It’s because artificial intelligence and its applications have had an impact on society and mankind very recently. AI is a rapidly developing field, with something new coming almost every day. Nobel prizes (particularly in physics) are given to inventions that are not just mere thought experiments or hypothetical theories, but those who have a positive impact on the growth of mankind. Quick question: “Did you know that models for machine learning were based on equations from physics?” Drop your answers here: https://lnkd.in/gQW3CeXQ (You may need to scroll down.) PS: Last year’s Nobel Prize in Physics was awarded to Pierre Agostini, Ferenc Krausz, and Anne L’Huillier for experiments that were able to generate attosecond pulses of light. Attosecond! Can you believe it? It's equal to one quintillionth of a second, or 10⁻¹⁸ seconds. #physics #nobelprize #nobelprizeinphysics2024 #nobelprizeinphysics #math #computerscience #ai #ml  

Geoff Hinton won the Nobel Prize in Physics with John Hopfield. Truly the Age of AI, with AI pioneers winning the Nobel Prize in Physics! This unprecedented recognition signifies the dawn of a new age where the boundaries between traditional scientific disciplines are blurring. https://lnkd.in/gkk2grDa Hold on - what happened to Real Physics topics - like energy, motion, gravity? New discoveries and inventions are inter- connected at hip levels, but does it not say something that physics Nobel is given for discoveries in AI? I remember when i heard about “Room Temperature Super Conductor” - I so badly wanted it to be true and thought it would be worthy of a Nobel. Do you remember that levitating things where fraternity claimed to have found a levitating behaviour and many teams across the world were trying at the same time to replicate the published paper. https://lnkd.in/gtf4WBMv Looking Ahead As we celebrate this milestone in AI, let’s consider: - How will this recognition shape the future of interdisciplinary research, will people in traditional research also look at shiny toys? - If male pattern baldness was to get resolved - will we see it recognized by traditionally siloed awards in the coming years? All in all, I am excited about future and this development influencing the trajectory of scientific research and innovation? #Innovation #NobelPrize #FutureOfScience #InterdisciplinaryResearch My name is Amit and I run slomoboco - a holding company to build and grow slow moving boring product + service companies. Konfirmity - Assured Security and Compliance company. Mockingjay - We allow software testing to catchup with development at will.

AI isn't just about coding—at its core, it's grounded in mathematics, and more specifically, physics!!! Today's The Nobel Prize Prize awarded to John J. Hopfield and Geoffrey E. Hinton underscores this deep connection. Their pioneering work highlights how fundamental principles of physics are instrumental in driving AI innovation. As AI continues to evolve, it’s crucial to recognize and appreciate the foundational sciences that make these advancements possible. #AI #NobelPrize #Physics #Innovation #AITrends #TheUncomofortableCEO https://lnkd.in/gJBunTp4