baiosphere’s Post

Artificial Intelligence and Humans: What Future do we Want? Join us for a conversation with Jorge Oleachea Catter and Nicolas Gertler, winners of the 2023 Opus 73 AI Prize!

Artificial Intelligence and Humans: What Future do we Want? Join us Friday for a conversation with Jorge Oleachea Catter and Nicolas Gertler, winners of the 2023 Opus 73 AI Prize!

"Excited to share that I've completed Endorer's Master Class on Generative AI for Business Innovation, led by Prof. Dr. Thiemo Wambsganss from the Human-Centered AI Systems Lab at Bern University of Applied Sciences, Switzerland. 🌐💼 It's been an insightful journey diving deep into the world of AI-driven business innovation, all from the comfort of my own home. Grateful for the opportunity to expand my knowledge and explore new horizons in the ever-evolving field of AI! #GenerativeAI #BusinessInnovation #OnlineLearning"

AI's role in scientific discovery is discussed in a recent Nature paper. It transforms research from hypothesis generation to data interpretation, reshaping all stages. Benefits include leveraging unlabeled data, generative AI in molecular design, and analyzing diverse data. Challenges include implementation, machine learning foundations, and changes in the scientific enterprise. Join the AI4Science meeting @NeurIPSConf to continue the discussion. Gratitude to the team and collaborators. First authors : Hanchen Wang, @TianfanFu @YuanqiD#AI4Science 🌟🔬 Check this paper: https://rdcu.be/dinBA #AIinScience #AI4Discovery #ScientificAI #AI4Research #DataDrivenScience #AI4Innovation #AI4ScientificDiscovery #AIforScience #AIChallenges #AI4ScientificEnterprise #NeurIPSConf

Join us in Brussels, Belgium this Thursday, February 1 to explore the intersections of open source, AI, and science! #opensource #openscience #opensourceai #qiskit

On Friday, we have the huge honor of welcoming Yann LeCun, Chief AI Scientist for Meta AI Research and Silver Professor at the Courant Institute of Mathematical Sciences at New York University, for a distinguished lecture on "From Machine Learning to Autonomous Intelligence", in which he will discuss questions such as "How could machines learn as efficiently as humans and animals?", "How could machines learn to reason and plan?", "How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons?", proposing a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a novel self-supervised training paradigm. Besides the Center for Advanced Studies (CAS) der Ludwig-Maximilians-Universität München and our Research Focus "Next Generation #AI", the following Munich ecosystem-partners support the event: baiosphere – the Bavarian AI Network, Bayerische Akademie der Wissenschaften, Bavarian Research Institute for Digital Transformation, Munich Center for Machine Learning, and relAI Konrad Zuse School of Excellence. There will also be a live stream of the event. If you are interested, please check out: https://lnkd.in/eqvkuGdF. Ludwig-Maximilians-Universität München, Technische Universität München

Interested in the application of physics-inspired AI? Eliu Huerta, Lead for Transitional AI and Computational Scientist at Argonne National Laboratory will describe important recent developments on Tuesday, March 26 as part of the AI Science Schmidt Fellows Speaker Series

🌐 In recognition of AI Month, Pawsey Supercomputing Research Centre proudly celebrates its role as a founding partner of the Trillion Parameter Consortium (TPC), a groundbreaking international initiative shaping the future of large-scale generative AI models for scientific advancement. The TPC unites global researchers from federal laboratories, research institutes, academia, and industry to address challenges in building large-scale AI systems, promoting trustworthy and reliable AI for scientific discovery. 🔍 Key Objectives of TPC: Develop scalable model architectures and training strategies. Organise and curate scientific data for model training. Optimise AI libraries for current and future exascale computing platforms. Create deep evaluation platforms to assess progress on scientific task learning, reliability, and trust. 🌐 TPC Initiatives: ✔️Open Community Building: Foster collaboration among researchers to advance large-scale generative AI models. Share methods, approaches, tools, insights, and workflows for scientific and engineering problem-solving. ✔️Project Incubation and Coordination: Avoid duplication of effort. Maximise project impact within the broader AI and scientific community. ✔️Global Resource Network: Facilitate the next generation of AI. Bring together researchers interested in large-scale AI for science and engineering. 🔗 Learn more about TPC founding organisations: https://bit.ly/47AAIir 👥 Collaboration for Scientific Synthesis: TPC envisions collaboration and cooperative efforts among multidisciplinary, multi-institutional teams to develop AI models capable of synthesising knowledge across scientific disciplines. 🗣️ Quote from Rick Stevens, Associate Laboratory Director at Argonne National Laboratory “At our laboratory and at partner institutions worldwide, teams are developing frontier AI models for scientific use. TPC accelerates these initiatives, creating the knowledge and tools for AI models to answer domain-specific questions and synthesise knowledge across disciplines.” 📰 Read the full article here: https://bit.ly/47zqYFo #AIArtificialIntelligence #TPC #PawseySupercomputing #AIMonth #ScientificAdvancement 🤖

KINDknow is pleased to share our new initiative "KINDknow Working Papers." This initiative aims to disseminate preliminary or advanced research findings from scholars associated with the KINDknow research center. The platform encourages the sharing of abstracts or infographics summarizing key research outcomes and opening up to dialogue. Contributions on this series can stem from KINDknow’s monthly paper presentation, "Food and Paper," promoting interaction among researchers, but more ongoing work can also be welcome. The initiative appears designed to facilitate academic collaboration and engagement with a broader community of interest in Early Childhood Education and Care, including those in academia and industry. It particularly encourages participation from younger, as well as visiting scholars of the center. Take a glimpse at our first one, published online and public access at Zenodo by Zacharias Andreadakis, PhD and free to reach out to the center with reflections or feedback! Andreadakis, Z. (2024). KINDknow Working Paper 1: Exploration in the Age of AI. Zenodo. https://lnkd.in/dheru4ku