Jung Soh

There's only 5 days left to submit your paper! Remember, we only a short 1-page abstract

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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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👉🏼 ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology-Head and Neck Surgery 🤓 Cai Long 👇🏻 https://lnkd.in/e7_3F-hi 🔍 Focus on data insights: - ChatENT outperformed ChatGPT4.0 in both the Canadian Royal College OHNS sample examination questions challenge and the US board practice questions challenge. - A significant error reduction of 58.4% and 26.0% was observed, respectively. - ChatENT demonstrated enhanced performance in analyzing and interpreting OHNS information, generating fewer hallucinations and showing greater consistency. 💡 Main outcomes and implications: - ChatENT is the first specialty-specific knowledge retrieval artificial intelligence in the medical field utilizing the latest LLM. - Promising applications in medical education, patient education, and clinical decision support are highlighted. - Overcoming limitations of existing LLMs, ChatENT signals a future of more precise, safe, and user-friendly AI applications in OHNS and other medical fields. 📚 Field significance: - Artificial Intelligence - Medical Education - Clinical Decision Support 🗄️: [#Otolaryngology #ArtificialIntelligence #MedicalEducation #ClinicalDecisionSupport]

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This is so exciting. For the Nobel body, for the scientific community, and for the world to acknowledge the tremendous work done to enable AI and ML as we know it today. The shift in paradigm that was created with Jeffrey's work (and the co-recipient's) has made work in this space, deep, philosophical and applicable at same time. https://lnkd.in/ggW-q45K #machinelearning #ai #artificialintelligence Congratulations form all of us at Mesh AI (collaborative scheduling to end clinician burnout).

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🎉 🎉 Check out this #HighlyCitedPaper "An Overview of Nanotechnologies for Drug Delivery to the Brain" by Shawn Wettig et al. author affiliations: University of Waterloo You can find it at: https://shorturl.at/ilrX5

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One of my favourite podcasts in the past few months. Peter Attia is both a medical doctor and and engineer. In this podcast, he discusses AI with Isaac Kohane, a medical doctor with a PhD in Computer Science. The first half they do a great job of exploring the history of AI and how we got today. The second half dives into AI in medicine. There is huge potential to boost medical care with further application of AI that exists today. https://lnkd.in/gZK3tcHc

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I'm excited that this week at the CAIAC - Canadian Artificial Intelligence Association conference George Wang will be presenting a paper Adaptive Learning Rates for Gradient Boosting Trees, based on work he did as an intern at Kinaxis last fall, in collaboration with Christopher Wang, Yunfei O., Behrouz Soleimani, PhD. There are 3 reasons this is both impressive and cool: 1. They combine heuristics, machine learning, and optimization to speed up the training for an important algorithm, Gradient Boosted Machines, widely used in both academia and industry. Faster training improves model performance. 2. This paper is just one of the many sparks of innovation in #artificialintelligence coming out of Kinaxis, where > 50% of our patents filed in the last several years have been in this space, because we continue to push the boundaries by inventing new solutions like this one to solve problems our customers face. We've been delivering AI to the market since 2018 and were the first to provide solutions powered by AI to address both demand and supply. We are rooted in Canada, which brought the world out of the last AI winter in 2012 when a team from the University of Toronto won the famous Imagenet competition by blowing the lid off previous results. Their submission was layers of neural networks called deep learning, which later became the foundation for the #generativeAI revolution we are seeing today. 3. We have an outstanding intern program in #machinelearning at Kinaxis, where interns work on real problems, guided by senior staff. As a testament to letting the students shine, when their paper was accepted they decided to have George present it. So check out the paper if you're at the conference, but if you miss the event you can't miss the fact that we are actively speaking and sparking on AI. In fact, just last week Chantal Bisson-Krol spoke in Edmonton at the #upperbound conference on AI-Powered Strategies for Resilient Supply Chains and I spoke at the Reuters Supply Chain USA event on Embedding AI in supply chain to deliver end-to-end value.

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Dr. Tali Dekel, a leading researcher in generative AI from the Weizmann Institute of Science, is pushing the boundaries of what's possible with large-scale deep-learning models! Her collaboration with Google has led to the creation of Lumiere, a groundbreaking text-to-video model for video generation. 🌟 Lumiere can transform simple text prompts or reference photos into rich, realistic videos. Imagine a squirrel on the Moon or a yawning Mona Lisa – Lumiere makes it happen! Dr. Dekel's work focuses on "re-rendering reality," recreating the visual world using computational tools. Her team's research is revealing fascinating insights and pushing the frontiers of technology! https://lnkd.in/eYKQYzKm #AI #WomenforScience

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💥 HSLU at NeurIPS? Oh yes we are!! Congrats to Fabian Gröger and team for being accepted to one of the highest impact AI/ML conferences on the planet. 🚀 🎉 🌎 NeurIPS 2024, the Thirty-Eighth Annual Conference on Neural Information Processing Systems, will be held at the Vancouver Convention Center Monday Dec 9 through Sunday Dec 15. HSLU researchers Fabian Gröger, Simone Lionetti, Ludovic Amruthalingam and Marc Pouly together with their collaborators from University of Basel, University Hospital Basel and Northwestern University are presenting “SelfClean: A Self-Supervised Data Cleaning Strategy”. ⚡ PROBLEM Most benchmark datasets for computer vision contain irrelevant images, near duplicates, and label errors. Consequently, model performance on these benchmarks may not be an accurate estimate of generalization capabilities. This is a particularly acute concern in computer vision for medicine where datasets are typically small, stakes are high, and annotation processes are expensive and error-prone. 😎 SOLUTION The researchers have developed a general procedure to clean up image datasets exploiting a latent space learned with self-supervision. By relying on self-supervised learning, the approach focuses on intrinsic properties of the data and avoids annotation biases. SelfClean achieves state-of-the-art performance in detecting irrelevant images, near duplicates, and label errors within popular computer vision benchmarks. 🌐 You can read the pre-print here: https://lnkd.in/eAQidcj4 👩‍💻 The official implementation is here: https://lnkd.in/eanJnQ73 🌟 🤩 Amazing work guys! Congratulations, and enjoy the trip to Canada. Philippe Gottfrois, Alvaro Gonzalez Jimenez, Matthew Groh, Alexander Navarini, LAC² - Lucerne AI and Cognitive Community, Switzerland Global Enterprise, Switzerland Innovation Park Central, Lucerne Business, Economic Promotion Canton of Zug, Schlössli Collab, Lucerne University of Applied Sciences and Arts, HSLU – Hochschule Luzern – Informatik

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MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) PhD candidate Shannon Shen works in the lab's Clinical Machine Learning Group. Shen's research aims at building better AI (for example, Large Language Models) for the end users, especially for expert users like doctors, lawyers, and even researchers. “This is a joint effort between Machine Learning and Human Computer Interaction — we need to design more accurate models as well as better ways for people and AIs to interact and collaborate,” he said. Shen cites the group’s recent work, Co-LLM, as an example. “It’s a method that enables collaboration between two or more language models of different expertise’s to provide better generations,” Shen said. “For example, let’s say we have a general language model like ChatGPT that can produce friendly responses but might not have enough expert knowledge, and another one that is trained to specialize in medicine. Our method makes it possible that the general language model ‘calls’ the medical model when needing to generate text involving the domain knowledge.” Read more about Shannon Shen and his research: https://bit.ly/3xN4lAJ

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Vancouver AI people: I'm hosting an event! I'm founding the Vancouver chapter of The Ai Salon and will be hosting our inaugural session on Thursday, November 28. The Ai Salon is a community founded in San Francisco that brings together scientists, founders, builders, and the generally curious to hold small-sized group conversations on the sociological, economic, cultural, and philosophical impacts of AI. Our inaugural event: Autonomous Scientific Discovery Will we ever be able to build an AI scientist? What would that look like? Is the genius and creativity of scientific breakthroughs something AI could ever replicate? What is the significance of the recent Nobel Prizes in physics and chemistry awarded for work on AI? Let's discuss. This will be an in-person event. Details and registration information at the link below. https://lu.ma/u1px71f0

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Are you thinking about participating in the Quantum Resource Estimation challenge, but don't know where to start? Check out the video tutorial on using Azure Quantum Resource Estimator! https://lnkd.in/gqGUhzp5

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🔬 Researchers from Lawrence Berkeley National Laboratory (Berkeley Lab), UC Berkeley, the Institute for Systems Biology, and Université Laval have unlocked new insights into the TIP60 protein complex, a critical gateway to DNA accessibility. Published in the journal Science, this groundbreaking study reveals the atomic structure of TIP60, paving the way for potential breakthroughs in targeted therapies for diseases such as cancer and Alzheimer's. Using advanced high-resolution cryo-electron microscopy, the team elucidated how this complex enables essential DNA repair and transcription processes, which are vital for cellular health and often disrupted in disease states. This research not only enhances our understanding of genetic regulation but also opens exciting avenues for developing therapies that manipulate TIP60's activity to treat genetic and neurodegenerative disorders. Explore how this significant discovery could transform future medical treatments! #Genetics #HealthcareInnovation #ScienceNews Read more: https://lnkd.in/eMnjr2bp

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As a control engineer, this is the most interesting perspective I heard recently

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“AI was supposed to revolutionize 💊drug discovery. It hasn’t.” #IrrationalExuberance Interesting cover story (above the fold) by Sean Silcoff and Joe Castaldo at The Globe and Mail: https://lnkd.in/gCeVzMcc 💡IMO There needs to be better awareness and expectations (and ethics) around #AI innovation (and more 🇨🇦 Canadian innovation positivity). This is #DeepTech impact research where investor expectations of time horizons need to be in decades (not yesterday) and where exploration is seen as impactful learning (positive) rather than another Canadian startup failure (negative). “One of the promises of machine learning was better drugs, faster. A decade in, AI has failed to live up to the hype.” The general public has no idea the computing evolution that started as machine learning and has now evolved into computers capable of artificial intelligence (AI), generative AI, and quantum computing. #Quantum computers (basically all knowing god-like computing seen in the show Westworld) will achieve what would take AI computers decades of scientific AI research development, and do it in seconds. So does failed expectations mean that current research is a failure? No. We simply need more accurate expectations. Research and innovation requires time, a ton of money, and - most importantly - optimism. Hope. 💡🔥🚀 #Innovation #CorporateInnovation

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[14:00-15:00 PT | May 14 | Zoom]Join UBC Blusson Quantum Matter Institute (Blusson QMI) Investigator Joe Salfi for a talk on Scalable Quantum Computation with Spin! #research #innovation #quantum #materials #quantumcomputing

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In my most recent CanInnovate post [1], I interview Lisa Lambert, CEO of Quantum Industry Canada, on the state of quantum technologies in Canada. I have had an eye on this space for a while now, as it is an outlier among deep tech sectors in that Canada is actually leading globally in many respects. My goal in this interview was to understand how this came about, and to understand whether Canada’s path to its leading position in quantum technologies could be replicated in other deep tech sectors. The answer, unsurprisingly, is yes. The interview itself is far too long to attempt to translate into LinkedIn posts (it would take at least 15 posts), so to see Lisa’s full commentary you will have to visit the main article. Many thanks to Lisa for taking the time to give such thoughtful and detailed responses! Instead of the usual full-post summary, here I will draw on Lisa’s responses to attempt an answer to the original question. This is post 1 of 2 toward that goal. Canadian leadership in quantum technologies holds key lessons that generalize to other sectors of deep tech. It represents the combination of four ingredients: a pool of highly trained talent, infrastructure to enable their research, visionary leadership that was willing to make a bet that would not pay off during their tenure, and a catalyst to seed what became a virtuous cycle of innovation. The catalyst was a series of investments made in quantum technologies in the Waterloo region that led to the establishment of the Perimeter Institute. This investment was made decades ago, at a stage when quantum technologies generally were still firmly in the realm of academic research. In the case of quantum technologies, it was the vision of an individual who made a massive philanthropic investment that set Canada on its path to dominance. However, relying on once-in-a-century good will is not a valid strategy. The good news is that there is no obvious reason that the catalyst needs to come from a private individual. Examples abound globally of similar virtuous cycles of innovation catalyzed by public sector investments [2]. The failure of the Canadian public sector to learn this lesson leads to other challenges that were identified in the interview: not only are we the world’s source of intellectual property, but we are the world’s schoolhouse, training and exporting a steady stream of world-class talent. Canada has many deep tech sectors that have three of the four key ingredients: deep talent pools, world-class research infrastructure, and world-leading minds, but without the catalyst, Canada rarely gets past the stage of filing academic IP. If Canada hopes to achieve a similar position of leadership in other sectors, our policy makers will need to step up and provide that catalyst deliberately. Post 2: https://lnkd.in/gWUynQ_T

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