Christopher Parisien

A very impressive set of papers from Hewlett Packard Labs on sustainability, renewable energy and generative AI topics to be presented at this week’s NeurIPS conference. Great to see further work on spatiotemporal carbon optimisation, reinforcement learning for data centre sustainability, advancing research on nuclear fusion and expanding research with Carnegie Clean Energy. More details, links to papers and videos in this blog post.

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Go check out Brevitas at the links below and start experimenting with MX and all the latest quantization algorithms!

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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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⚡ Just a chill (very intense) guy in Vancouver for #NeurIPS... 🎉 🚨 Presenting not one, not two, but THREE papers that the Enkrypt AI team and I authored this weekend at the NeurIPS workshops! 🧠💥 Here’s what’s on deck from the Enkrypt AI team: 1️⃣ Red Teaming Gen AI: The SAGE-RT Synthetic Data Generation Process 2️⃣ Pluralistic Alignment: SAGE-RT Dataset's Impact on Model Safety Alignment 3️⃣ Safe Gen AI: Bias uncovered in over 50 LLMs (yes, it’s wild!) If you're here and want to chat about AI, AI safety, or just share some workshop wisdom, let’s connect! 📩 Let’s make some brilliant conversations happen. 🚀 Reach out if you're here! Let's talk all things AI and AI Safety! 🚀 #NeurIPS #NIPS #AISafety #AISecurity #Vancouver #Enkrypt AI

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Excited to share our latest publication in Computers in Biology and Medicine. In collaboration with the BC Children's Hospital Research Institute, this study introduces BRI-Net, an interpretable deep-learning model that leverages adversarial training to learn an unbiased representation of data, helping identify genetic markers associated with specific phenotypes. Using genomic data from a cohort of 362 pediatric cancer patients treated with cisplatin, recruited by the Canadian Pharmacogenomics Network for Drug Safety, our model identified two statistically significant SNPs associated with cisplatin-induced ototoxicity: CERS6 rs13022792 and TLR4 rs10759932. To explore our methodology and findings in detail, check out the full paper at https://lnkd.in/gEn4fZb9. A huge thanks to my amazing co-authors and collaborators, especially Bahareh Kashani, for making this possible!

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

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I just got my own copy of “Building LLMs for Production” by Louis-François Bouchard and Louie Peters. As I am getting more into applications of GenAI I expect this book to become one of my go-to resources in the upcoming months. https://amzn.to/3Yv5FmL #AI #GenAI #LLMs

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🎉 Thrilled to share some great news stemming from an amazing example of collaboration between UK and Greece supported by ARCHIMEDES Research on AI, Data Science, and Algorithms which will soon reach new highs thans to our partnership via the CHAI Hub. Our paper, "Benchmarking Counterfactual Image Generation," has been accepted to NeurIPS 2024 in the Datasets & Benchmarks track! We’ll present it in Vancouver on December 11th, and I couldn’t be prouder of our incredible Greek dream team of students who led this effort— Thomas Melistas Nikos Spyrou and Nefeli Gkouti. We address a critical challenge: how do we rigorously evaluate counterfactual image generation, where causal constraints must be respected, and ground truths are elusive? Our team developed a benchmarking framework that highlights the strengths of Hierarchical VAEs across diverse datasets. We’ve even made it available as a user-friendly Python package to encourage community collaboration. 🚀 Interactive demo: https://lnkd.in/e9GA5Ebu 📄 Paper: https://lnkd.in/eR2ig3pY A huge thanks to co-authors: Pedro P. Sanchez, Yannis Panagakis, Giorgos Papanastasiou, PhD, and Dr Athanasios Vlontzos. Excited to share our work with the AI community at NeurIPS. Do go and see the work and talk to this wonderful team of students. #NeurIPS2024 #GenerativeAI #Counterfactuals #CausalInference #MachineLearning #AIResearch

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Arm is upstreaming #Kleidi technology into #PyTorch with amazing effect - up to 3x faster CPU #inference in Eager mode, and up to 2x in Graph mode. These optimizations make running #LLMs on CPU more practical than ever. Check out our blog for more details! #Arm #Neoverse #Graviton https://lnkd.in/gvrh67v2

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We recently had Jon-Saad Falcon from Stanford AI Group give a talk at SambaNova Systems. He talked about his research ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems. You can check out the talk at - https://lnkd.in/eXkBTb-j RAG systems are really hard to evaluate. Lot of hand holding required with annotated data or manual inspection. Excited to see this research and its outcome to help alleviate this problem. Key to this idea is a composite system using LLM as a judge. LLM judges are trained using synthetic data on domain queries/answer pairs. Its exciting to see how our customers use SambaNova Systems Platform to enable these workflows efficiently. Exceptional speed allows for fast synthetic data generation! And ability to host multiple models on a single system with extremely fast model switching allows for efficient low cost deployment. Try out 405B running at 120+ tokens/second at https://fast.snova.ai/ Want to enable your own such workflows by hosting fine-tuned checkpoints? Sign up here - https://lnkd.in/ecAsSDRH

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Check out this great article on the Financial Post about the impressive trajectory of DCS Prof. Sanja Fidler, head of the Nvidia Toronto Research Lab. The feature highlights some of the amazing accomplishments of Prof. Fidler and how she has helped make Nvidia a leader in AI. Read more at https://lnkd.in/gsXB3UgV.

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Excited to present my work next week at the #MS2Discovery Interdisciplinary Research Institute at Wilfrid Laurier University in Waterloo. The seminar will focus on my papers: 'Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems' (https://lnkd.in/e7ch4zeW) and 'Improving the Efficiency of Payments Systems Using Quantum Computing' (https://lnkd.in/d8e8zAu2). #Payments #AI #ML #Quantum #PaymentSystems

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Very insightful work by Anthropic’s interpretability team. And an amazing paper, with outstanding writing and figures. The idea is very simple: interpret LLMs by leveraging sparse autoencoders as surrogate models of the MLP of transformer blocks, which allow one to disambiguate the superposition of features captured by a single neuron. A simple idea, but a very careful and complex execution, as it is often the case in our line of work. The paper goes into many details and provide a large array of insights, although the gist of the implementation remains obfuscated due to the closed source nature of Claude. Too bad, because this is the kind of work that we need to better understand and eventually trust LLMs. This is demonstrated by the authors in the section ‘Influence on Behavior’, where they show that clamping some features to either high or low value during inference is “remarkably effective at modifying model outputs in specific, interpretable ways”. Hopefully this kind of work is going to be replicated and generalized to open-weights models, such that we have new ways to steer their behavior. https://lnkd.in/eVym7f_f #interpretability #xai #explainableai #steerableai #anthropic #claude #anthropic

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BERT is back in a new optimized look (ModernBERT). I still remember the old days in 2018. Yes, 6 years in the AI realm is considered old. When BERT model was considered a "large" language model with only 340M parameters. Nowadays Phi-4 model with 14B parameters is considered a "small" language model. I think that the AI world is still in need for these encoders as the BERT architecture despite the dominance of GPT-like decoders. If they are not used as the main models for the use case, the encoders are still an important component for retrieval stage in addition to the decoders for generation stage in any RAG system design. Anyway, welcome back BERT whether you are called large or small!! 🎊

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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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Large Language Models (LLMs) are absolute powerhouses when it comes to generating top-notch #GenAI responses. Yet, #LLMs lack that in-depth, domain-specific knowledge that can make all the difference in certain use cases. Additionally, let's not forget the hefty computing resources and expertise required to develop and deploy them. In my latest article, I delve into why Small Language Models (SLMs) are becoming the go-to choice for specific tasks. With their more focused approach, #SLMs are more efficient than LLMs, and have a smaller compute footprint. Thus, they are more suitable to run locally on workstations or on-prem servers. Check out my article 👇 and ‘d love to hear your thoughts about it https://lnkd.in/diRYaNXF #DellTechEMEA #iWork4Dell #AMD, #Intel, #NVIDIA 

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🚀 Exciting news in the world of reinforcement learning! One week ago at NeurIPS in Vancouver, we unveiled Arena - a revolutionary RLOps platform that's transforming reinforcement learning development. The launch generated significant buzz, validating our mission to democratise the most sought after paradigm in ML - Reinforcement Learning. As the holiday season approaches, we're excited to continue offering this powerful platform completely FREE to the community! Why Arena is Making Waves in the RL Community: ⚡️ Unparalleled speed - 10x faster training 🎯 Streamlined workflow - intuitive and easy-to-use interface 📊 Comprehensive analytics - real-time metrics visualisation 🤝 Built for collaboration- team-wide experiment sharing and version control 🛠️ Enterprise-Ready Features - custom environment support Take advantage of this holiday season to dive deep into RL! With most of us having some downtime, it's the perfect opportunity to explore Arena's capabilities without any cost barriers. Sign up now and get: - Full platform access - Unlimited experiments - Complete feature set - Community support Whether you're an RL enthusiast, researcher, or industry professional, Arena provides the tools you need to accelerate your reinforcement learning journey. Ready to jump in? Visit arena.agilerl.com to get started today! #reinforcementlearning #rlops #ai #machinelearning #innovation #agilerl #arena #artificialintelligence #deeplearning #techinnovation

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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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