Christopher Pollack

The article discusses the pioneering work at Université de Sherbrooke led by Professor Maia Vergniory in topological quantum materials. It highlights the limitations of silicon-based technology and the need for new materials, emphasizing topological quantum chemistry (TQC) as a solution. Professor Vergniory's research, supported by a significant grant, focuses on developing quantum materials with applications in quantum computing and other technologies. The Université de Sherbrooke's strong research environment and collaboration opportunities further enhance this innovative work. For more details, you can read the full article here: https://lnkd.in/eQftXym9

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💥 Quantum theorists at UBC Blusson Quantum Matter Institute (Blusson QMI) have proposed a new approach to studying stacking ferroelectricity—spontaneous electric polarization—in layered, two-dimensional lab-grown materials. Ferroelectric materials are unique in their ability to “remember” their new state after the electric field is removed, which makes them useful in applications including solar cell technology and compact memory devices. “We've been learning in recent years that quantum geometry underlies a surprising range of the observable properties of materials,” said Dr. Marcel Franz, deputy scientific director at UBC Blusson QMI and a professor with the Department of Physics & Astronomy. “This work adds an important new entry to the growing list of phenomena that can be elucidated using this fascinating geometric approach.” “The most exciting part of our discovery is that the underlying physics behind stacking ferroelectricity can, in fact, be understood as a geometric property,” said UBC Blusson QMI postdoctoral fellow Dr. Benjamin T. Zhou, lead author of the study published in the journal Physical Review Letters. 👉 Read more: https://lnkd.in/gRSRXf36 #quantum #research #science #innovation #materials #discovery #QuantumBC #QuantumCoast #QuantumCanada

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How can you get the most out of AI in your work? Here’s a tip from Dr. Vivienne Ming: Treat it like a grad student. Her approach to AI isn’t about giving it all the power—it’s about partnership. A grad student spends 5 or 6 years immersed in a specific area of research. They know every intricate detail of their subject of study. But what do they sometimes lack? Perspective and a broader understanding. AI knows everything about everything—but as Vivienne says, it understands nothing. It’s our job to bring the context. How are you partnering with AI in your work? Are you treating it like a magic solution—or a powerful assistant? Share how AI is impacting your work in the comments.

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We have figured out a lot using mathematics, physics and chemistry to understand nature. We know how to predict electron flow in semiconductors, fluids in tubes, clouds in the sky, airflow over wings, space storms etc. Tools like computational fluid dynamics have remarkable applications in unexpected places. We also have ideas around PINN and we can use those insights to help us understand what happens in a foundational model. And I am sure there are lots more. None of these software artifacts should ever be said to be un-knowable or a "blackbox". That's intellectual laziness. These are all man made structures. We've figured out many parts of nature using math, physics and chemistry - we've mapped the Human Genome, so what's the excuse in not understanding foundational models? I look forward to people recognizing that there are existing human inventions (math, physics etc) whose insights can be used to remove/reduce the probabilistic nature of foundational models and thereby benefiting humanity as a whole. A press conference was held by University of Toronto with Geoffrey Hinton after the announcement that he was one of the two recipients of this year's Nobel Prize for Physics. Geoffrey is probably the only Turing Award winner to also have won a Nobel Prize. And the video - https://lnkd.in/gVHewNCd. Geoffrey is spot on where he says, at about 3:38: "I'm particularly proud of the fact that one of my students fired Sam Altman." Brilliant! #nobelprize #ai #opensource #turingaward

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NiNo: A Novel Machine Learning Approach to Accelerate Neural Network Training through Neuron Interaction and Nowcasting Researchers from Samsung’s SAIT AI Lab, Concordia University, Université de Montréal, and Mila have introduced a novel approach known as Neuron Interaction and Nowcasting (NINO) networks. This method aims to significantly reduce training time by predicting the future state of network parameters. Rather than applying an optimization step at every iteration, as with traditional methods, NINO employs a learnable function to predict future parameter updates periodically. By integrating neural graphs—which capture the relationships and interactions between neurons within layers—NINO can make rare yet highly accurate predictions. This periodic approach reduces the computational load while maintaining accuracy, particularly in complex architectures like transformers. At the core of the NINO methodology lies its ability to leverage neuron connectivity through graph neural networks (GNNs). Traditional optimizers like Adam treat parameter updates independently without considering the interactions between neurons. NINO, however, uses neural graphs to model these interactions, making predictions about future network parameters in a way that reflects the network’s inherent structure. The researchers built on the Weight Nowcaster Networks (WNN) method but improved it by incorporating neuron interaction modeling. They conditioned NINO to predict parameter changes for the near and distant future. This adaptability allows NINO to be applied at different stages of training without requiring constant retraining, making it suitable for various neural architectures, including vision and language tasks. The model can efficiently learn how network parameters evolve by using supervised learning from training trajectories across multiple tasks, enabling faster convergence.... Read our article on this:https://lnkd.in/gSQFYtcG Paper: https://lnkd.in/g9rJ5JT2 GitHub Page: https://lnkd.in/gJPbT-kM

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Artificial Intelligence takes center stage once again! The 2024 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 contributions to artificial neural networks! Their innovative methods, rooted in physics, has far-reaching impacts, including in areas like material design, where they are instrumental in developing new materials with specific properties. At Matmerize, we’re excited by this recognition, as it underscores the transformative potential of AI in revolutionizing materials science, directly aligning with and expanding the possibilities of our own work. https://lnkd.in/dyUfXAtg

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