Min Xu

Here's a great read in STAT about the exciting vision of NVIDIA's Kimberly Powell of using AI in drug development. Powell described generative AI enabling "medicine n-of-1" custom drugs tailored to individuals within 10-15 years by streamlining drug development.   But this is not just a concept for the future. It's already starting today, and we're excited to be combining modern biology with AI and machine learning and technologies like NVIDIA’s GPUs to engineer, rather than discover, new oligonucleotide-based medicines for both rare and common diseases. Just last week, we presented clinical results at ASGCT showing how our approach creating optimal, purpose-built datasets allowed us to harness AI/ML to engineer the first allele-selective Locked Nucleic Acid (LNA) gapmer design ASO as an investigational individualized treatment for a patient with an ultra-rare disease. In partnership with TNPO2 Foundation and Nicole Coufal, we moved from whiteboard to clinic in 13 months targeting only a single nucleotide change - fast enough to administer this investigational treatment before the patient was 2 years old.   As innovators unleash the full power of AI in drug development, we move closer to dissolving traditional economies of scale and ushering in a new era of precision medicine for ultra-rare, rare, and common diseases. We're proud to be paving this path forward.   #AI #ML #drugdiscovery #drugengineering #innovation #STATBreakthrough

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📣 Join us on May 29th, from 11 am – 12 pm ET for our webinar, "Revolutionizing Evidence Synthesis: Advanced Data Extraction and RoB Assessment with PICO Portal’s Ask AI" and learn how to leverage AI for extracting data at scale. 🚀 Don’t miss out on this opportunity to see our most advanced algorithm yet, maximizing efficiency for your evidence synthesis projects! 🗓 Learn more and register: https://lnkd.in/dTiDpY59

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Northwestern University engineers have developed MaxDiff RL, a new AI algorithm enhancing robot reliability by using “designed randomness” to improve data quality and accelerate learning in robotics Read more: https://lnkd.in/e7wmUDNx #LabHorizons #ai #algorithm #northwestern #study #robot #robotics

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What does self-driving teach us about implementing AI in healthcare workflows? We had the opportunity to sit down with Will Xie, CEO of Cosign AI to discuss how his experiences at Cruise and Waymo influence his perspective on Health AI. In industries like self-driving and healthcare, errors can have profound human consequences. At Cosign AI, our priority is the impact our technology has on patient care. Read more about our conversation and insights in the full article below: https://lnkd.in/eCKVDS2F

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Whether quantum chemistry, quantum machine learning, or quantum computing, the team at PennyLane will support you with customized learning for your course. Dr. Yufei Ding, Associate Professor at UC San Diego shares her experience👇

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🎉 Congratulations to Mohammad Mahdi Johari from the Idiap Research Institute Laboratory on the successful defense of his PhD thesis “Advancing Self-Supervised Deep Learning for 3D Scene Understanding” under the supervision of Prof. François Fleuret and Prof. Daniel Gatica-Perez. 🔎 His research uses deep learning to tackle key challenges in 3D computer vision, such as depth estimation, novel view synthesis, and SLAM. The thesis proposes novel methods for each task that achieve state-of-the-art performance while being more efficient or requiring less data to train. #epfl_edee

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How are the most cutting-edge #biotechnology companies using #AI to supercharge R&D? Learn from Shape Therapeutics Inc., an AI-first biotech that’s combining AI and RNA technology to generate hundreds of billions of therapeutic possibilities. Read their story to learn how they’re: ✔️ Developing breakthroughs in programmable RNA ✔️ Paving the way for AI-enabled scientists and AI-ready data, with a 270% increase in use of pre-configured templates for data entries ✔️ Powering scale, automation, and last mile capabilities “Having all of this automated behind the scenes, and integrating with our own training data sets, expedites our experimentalists' lives." — Ron Hause (Head of AI, ShapeTX) https://lnkd.in/dAqVBPnG #PoweredByAI #BuiltOnBenchling

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Put the power of quantum computing right into your workflows. Access everything you need: hybrid solvers, SDK, open-source tools, libraries, code examples, demos, and comprehensive documentation. It’s all available through the Leap™ quantum cloud service. Begin building quantum applications with ease today: https://lnkd.in/eNJUJMx

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NVIDIA AI Releases the TensorRT Model Optimizer: A Library to Quantize and Compress Deep Learning Models for Optimized Inference on GPUs Quick read: https://lnkd.in/gfGP7uWg GitHub: https://lnkd.in/gfj62278 NVIDIA NVIDIA AI Huizi Mao #ai

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Kyri Baker joins Farhad Billimoria, Conleigh Byers, Ahlmahz Negash, PhD, and Paul Dockery to discuss the use of AI to solve AC optimal power flow problems faster and better. The discussion starts with explanations on the different forms of artificial intelligence including machine learning, physics informed neural networks, and large language models. You can find Prof. Baker's recent work on emulating AC OPF solvers with neural networks in the show notes. https://lnkd.in/gXYC57V4

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In computational linguistics, researchers are focused on enhancing language models' ability to extract precise information from extensive textual data. Recent studies introduce "retrieval heads," specialized attention mechanisms that selectively focus on critical segments of large texts, improving targeted data retrieval. These heads, integrated into transformer-based models like LLaMA and Yi, significantly enhance accuracy and efficiency, particularly in handling long-context scenarios. The methodology involves thorough experiments across various models, including the Needle-in-a-Haystack test, which embeds specific information to measure precision. Results show models with active retrieval heads outperform those without, with accuracy dropping notably when these heads are masked. The empirical evidence underscores the effectiveness of retrieval heads in improving information retrieval precision and reliability. This research deepens our understanding of attention mechanisms in large-scale text processing, paving the way for more efficient and accurate language models. https://lnkd.in/dNNPvMXw https://lnkd.in/ddbHX6EA Innovation Hacks AI #aiinnovation #generativeai

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To understand a new approach to #AI inference compute management, VMblog spoke with Neetu Pathak and Sushant Tripathy of Skymel. https://lnkd.in/gPMCNkwc Skymel has emerged from stealth with NeuroSplit, an interesting "adaptive inferencing" technology that changes the way AI applications consume resources. #GenAI #artificialintelligence #GPUs

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One of this year’s People to Watch is Yann Lecun. He is best known for his work in deep learning and the invention of the #convolutional #network method. Yann is currently a part-time professor at #NYU and a VP and Chief AI Scientist for #FacebookAI Research. https://ow.ly/fKuv50RxWYq #artificialintelligence

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Apache Kafka and Confluent power a real-time prediction model that helps prevent the risk of deadly sepsis in bone marrow transplant patients. #DataScience #AI by Cameron Carlin and Sergei Semenov thanks to Confluent

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Our Sparse-IFT paper was accepted at #ICML this year! Sparse-IFT is a novel method for maximizing training efficiency of neural networks by utilizing sparsity to enhance accuracy at the same computational cost as dense models. Sparse-IFT shows promising results across AI: 🖼️ Image Classification - Results show a +3.5% accuracy boost for ResNet-18 on ImageNet within the same FLOP budget 🕵️ Object detection and segmentation - Enhancing mAP by 5.2% on MS COCO and mIoU by 2.4% on CityScapes.  📖NLP - GPT-3 enhanced by Sparse-IFT improves by 0.9% on the Open LLM leaderboard 👨‍💻 Explore the code on GitHub: https://lnkd.in/gXxBQX5r 📝 Check out the research paper: https://lnkd.in/gnHqw85Z 📞 Contact us to discuss sparsity at Cerebras: https://lnkd.in/gzqvn2z6

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We're premiering a new tutorial to demonstrate using the BrainChip Akida neuromorphic processor to classify images for edge AI workloads. The workshop will cover machine learning model creation, deployment to the BrainChip Akida Enablement Platform, and the path from prototype to product. Register here: https://lnkd.in/g7bY824V #edgeai #ai #technology

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