Leonardo Ferreira Guilhoto’s Post

This year’s #Nobel Prizes in #Physics and #Chemistry are sending a strong signal: Artificial Intelligence (AI) has moved from being an emerging tool to a driving force behind groundbreaking scientific inventions. The award-winning work by John Hopfield, Geoffrey Hinton, Demis Hassabis, David Baker and John Jumper highlights how AI is transforming diverse fields such as physics, biology, and chemistry. **The 2024 Physics laureates used tools from physics to construct methods that helped lay the foundation for today’s powerful machine learning. John Hopfield created a structure that can store and recreate information. Geoffrey Hinton invented a method that can autonomously find properties in data. **This year’s Chemistry prize acknowledges how AI solved one of biology’s toughest mysteries: figuring out the shapes of proteins. Winners in this area are Demis Hassabis and John M. Jumper for protein structure prediction and David Baker for computational protein design.

𝐖𝐡𝐞𝐧 𝐚 𝐏𝐡𝐲𝐬𝐢𝐜𝐢𝐬𝐭 𝐖𝐚𝐥𝐤𝐬 𝐢𝐧𝐭𝐨 𝐚 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐒𝐜𝐡𝐨𝐨𝐥 👩🔬📚🔍 - New Faculty Spotlight: Professor Hyejin Youn How does a physicist end up as a faculty member at a business school? For Prof. Hyejin Youn, the answer is simple: “one thing led to another.” A statistical physicist, she joined SNU Business School this September as a professor of strategy and international management.   Prof. Youn is expected to bring her interdisciplinary expertise to the business school. With advancements in massive data collection, computational power, and AI, physicists and other STEM scientists are increasingly venturing into computational social science.   💬 "Are questions such as why wars happen or why some people have more opportunities than others too complex for physics to answer? I don’t know, 𝐛𝐮𝐭 𝐢𝐭’𝐬 𝐰𝐨𝐫𝐭𝐡 𝐞𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠. I believe physicists can contribute to answering those questions."   📰For more of her story, read the interview in Physics Today: → https://lnkd.in/ewTEJijw #SNU #SNUFaculty #PhysicsToday

I’m excited to announce that I’ve started my bachelor’s thesis Fraunhofer IAO in Stuttgart! My topic is the efficient optimization of quantum neural networks, which allows me to combine both of my study subjects: physics and applied computer science. This unique intersection is what makes quantum computing so fascinating to me—it's a field where we explore whether we can improve our algorithmic approaches by fundamentally changing our computing principles, such as leveraging superposition and entanglement. I am looking forward to deepen my knowledge in the areas of gradient-free and gradient-based optimisers, the opportunities of quantum data, the trainability of quantum circuits, bayesian hyperparameter optimisation and much more. I’m really looking forward to collaborating with the talented team here and expanding my knowledge in this rapidly evolving domain. #QuantumComputing #NeuralNetworks #Optimization #Bayes #BachelorThesis #Research #Physics #ComputationalNeuroscience

Ever wondered where to find young, talented scientists in neuromorphic computing? 👩🏼🎓 🧠 Look no further. The CogniGron Talent Network is where early career researchers come together to face the computing challenges of tomorrow! Meet board member Otavio Citton, whose research interests lie in the realm of complex systems such as computer algorithms, the brain, biological systems and even societies. 💡 He believes that physics can be a powerful tool to better understand these problems, especially statistical mechanics and dynamical systems. Find out more about this network of young scientists ➡ https://lnkd.in/eS4NsZyM #young #scientists #researchers #talent #computing #neuromorphic #braininspired #physics #ai #data #CogniGron #FutureProofComputing

🌟 Exciting News from the World of Physics! 🌟 This year’s Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton for their groundbreaking contributions that laid the foundation for modern machine learning. - John Hopfield developed the Hopfield network, a model that enables the storage and reconstruction of patterns, akin to human associative memory. This network can recognize incomplete or noisy data, making it a powerful tool for data analysis. - Geoffrey Hinton introduced the Boltzmann machine, which learns from examples rather than explicit instructions. By leveraging principles from statistical physics, this model identifies patterns and probabilities within datasets. 🧠 Machine Learning vs. Traditional Software: While traditional software processes data through predetermined steps, machine learning allows computers to learn from examples, tackling complex problems like image recognition and language translation. 🔬 Impact on Physics and Beyond: The methods pioneered by Hopfield and Hinton are revolutionizing fields such as physics, aiding in the analysis of vast datasets for discoveries like the Higgs particle and gravitational waves. Their work is also paving the way for advancements in molecular predictions and material efficiency. 🌐 Looking Ahead: As we continue to harness the power of machine learning, ethical considerations will be paramount. Responsible use of these technologies is crucial for their sustainable development. Congratulations to this year’s laureates for their monumental contributions! 🎉 #NobelPrize #Physics #MachineLearning #ArtificialIntelligence #Innovation #DataScience

💪 Our research engineer Martin Výboh and two of our PhD students, Ivana Beňová and Róbert Belanec, attended MatFyz Connection at the Faculty of Mathematics, Physics and Informatics, Comenius University– a conference designed to strengthen the existing and create new research contacts. 👥 Besides extending their connections, both our students presented the research withing their dissertation. Ivana showed her findings on vision-language models and their capability to ground context-dependent verb phrases, and Róbert introduced his method on efficient model training and merging. 💻🔗 🙋♂️ 💬 The conference in Róbert’s words: "During our participation at MatFyz Connections, some of us could return to our alma mater and present to fellow researchers and students. We also attended a very interesting presentation by Michal Valko, as well as inspirational talks from ESET Science Award laureates. Matfyz Connections was indeed a valuable event, and we are looking forward to the conference next year." We are very proud of you, well done! 👏 #networking #research #conference #AI

Advancing AI-Driven Innovation in Physics at UAB 🎉 The UAB Department of Physics is thrilled to celebrate Dr. Yubo Qi, Assistant Professor of Physics, on being awarded a prestigious NSF grant for his project, “Developing a Deep Learning-based Multifunctional Method for Simulating Quantum Effects.” Dr. Qi’s research embodies what makes our department unique: a commitment to bridging the gap between foundational learning and cutting-edge research on advanced and quantum materials under extreme and non-equilibrium conditions. His work harnesses state-of-the-art AI techniques to simulate how electrons move in such materials, unlocking the potential for breakthrough technologies like quantum computers and renewable energy solutions. 🌟 What Sets UAB Physics Apart From the moment students step into our classrooms, they are introduced to AI and data science concepts tailored for all skill levels, empowering even the most underprepared students. This foundation builds toward participation in high-level, data-driven research, such as Dr. Qi’s project. His work represents the pinnacle of our vision: ✅ Accessible Education: Preparing students with the essential skills to succeed in the era of AI and quantum technologies. ✅ Transformative Research: Applying deep learning to solve real-world challenges in materials science, from designing efficient devices to creating sustainable technologies. ✅ Collaboration & Impact: Partnering with leading institutions like UT Austin and fostering opportunities for students to contribute directly to transformative research. Dr. Qi’s project reflects our belief that physics provides the liberal arts approach to science that is most needed today. It is a discipline that fosters critical thinking, creativity, and the technical expertise needed to lead during the Fourth Industrial Revolution. https://lnkd.in/dSbMgJfi

Because of their research related to artificial intelligence and quantum technology, Ph.D. students Anuvetha Govindarajan (physics) and Jocelyn Ornelas-Munoz (applied math) have each received a $25,000 fellowship from SandboxAQ. Read more about these "paw-some" Bobcats here 🔗 ucm.edu/aSuQIE

Congratulations to Professor John J. Hopfield and Geoffrey Hinton on receiving the Nobel Prize in Physics for their groundbreaking work modeling AI through principles like energy minimization and associative memory. During my research at UCSD on integrating physics with AI, I realized that the fundamental concepts of AI became much clearer solving problems in physics than in traditional courses focused solely on datasets and images. Also it might explain why many Kaggle Grandmasters come from physics backgrounds. As we look ahead, the next wave of AI is poised to integrate physics even more deeply, enhancing our ability to model complex, real-world systems Glad to know both nobel laureates did part of their research at my alma maters, UC San Diego and University of California, Berkeley. #AI #Physics #MachineLearning #DataScience #Kaggle #Research #Innovation #NobelPrize #Technology

Happy to share that after defending my MSc thesis "Diffusion model-based counterfactual explanations in computer vision" with distinction at Politechnika Warszawska, I am starting my PhD studies in Computer Science at Uniwersytet Warszawski! 🥸 Moreover, my 4-year research plan will be externally funded by the NCN Narodowe Centrum Nauki Preludium Bis grant "DeMeTeR: Interpreting Diffusion Models Through Representations" under the supervision of Przemyslaw Biecek. Our goal is to push the boundaries of the current state of XAI by 1) adapting diffusion-like models to synthesize new sample-based explanations and 2) elucidating the inner workings of these state-of-the-art generative algorithms by interpreting their internal representations. 🌾 This moment is truly special and would not have been possible without the great support of the entire MI2.ai team. 💜 #GenXAI #DeMeTeR