Don't miss out on the Joint Conference between DDLS, WASP, and WASP-HS! 🔔 The deadline is fast approaching – make sure to register and secure your spot before September 3! Join us on September 24-25 at the Wallenberg Conference Centre in Gothenburg for the conference "The Disruptive Role of Data and AI in the Life Sciences,”, and learn more about the open joint WASP/DDLS NEST call. This two-day event will delve into how data- and AI-driven research are transforming life sciences, featuring excellent international keynote speakers, a panel discussion, a poster session and ample time to mingle. The conference will focus on different aspects of research where collaboration over scientific domains is essential and will explore the following topics: 🔎 How data- and AI-driven research is shaping the future of life science 🔎 Development of new approaches to human-in-the-loop technologies and their use 🔎 The need for studies at the intersection of society, AI, and data driven life sciences We look forward to welcoming you to The Disruptive Role of Data and AI in the Life Sciences! Read more and register: https://lnkd.in/dR_pFgE9 Read more about the call: https://lnkd.in/dKg4JFeS In collaboration with: WASP-HS Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society, SciLifeLab #datadrivenlifescience #AI #AIforscience
WASP – Wallenberg AI, Autonomous Systems and Software Program’s Post
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Don't miss out on the Joint Conference between DDLS, WASP, and WASP-HS! 🔔 Have you missed the deadline for registration? Don't worry, we have extended the deadline until today so make sure to register now and secure your spot! Join us on September 24-25 at the Wallenberg Conference Centre in Gothenburg for the conference "The Disruptive Role of Data and AI in the Life Sciences,”, and learn more about the open joint WASP/DDLS NEST call. This two-day event will delve into how data- and AI-driven research are transforming life sciences, featuring excellent international keynote speakers, a panel discussion, a poster session and ample time to mingle. The conference will focus on different aspects of research where collaboration over scientific domains is essential and will explore the following topics: 🔎 How data- and AI-driven research is shaping the future of life science 🔎 Development of new approaches to human-in-the-loop technologies and their use 🔎 The need for studies at the intersection of society, AI, and data driven life sciences We look forward to welcoming you to The Disruptive Role of Data and AI in the Life Sciences! Read more and register: https://lnkd.in/dR_pFgE9 Read more about the call: https://lnkd.in/dKg4JFeS In collaboration with: WASP-HS Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society and SciLifeLab #datadrivenlifescience #AI #AIforscience
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🌟 Exciting Announcement for IJCNN 2025! (Call for Papers)🌟 We are thrilled to introduce our Special Session: "Artificial Intelligence and Machine Learning Innovations (AIMLI): Showcasing Funded European Projects", taking place at the IEEE International Joint Conference on Neural Networks (IJCNN 2025) in Rome, Italy (June 30 – July 5, 2025). This session will spotlight groundbreaking AI and ML advancements addressing critical societal, economic, and environmental challenges. From sustainable mobility and smart agriculture to healthcare technologies and Industry 4.0, we aim to showcase the transformative power of European-funded initiatives. 📢 Why Participate? ✅ Share your innovative methodologies, case studies, and project outcomes. ✅ Exchange ideas with leading researchers and practitioners. ✅ Highlight the societal and economic impacts of your AI/ML solutions. 📅 Important Dates Paper Submission Deadline: January 15, 2025 Authors Notification: March 31, 2025 Camera Ready & Registration: May 1, 2025 Visit our website for all the details and submission guidelines: 👉 https://lnkd.in/d6pzDYrW Let’s shape the future of AI together! 🚀 Feel free to share this post and tag colleagues who might be interested in contributing. #IJCNN2025 #AI #MachineLearning #Innovation #Research #Sustainability #DigitalTransformation #Rome2025 #CIPARLABS #EU
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Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding💡by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search 📜. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audio track generated using 🍓 #o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: ➡Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. ➡Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. ➡Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. 💡In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using #AutoGen. #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Paper: Alireza Ghafarollahi and Markus J. Buehler, SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning, https://lnkd.in/e-88pQ6c, 2024. Data and weights 🤗: https://lnkd.in/eku6j7JV Code: https://lnkd.in/eJVZD8BE
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What if AI could autonomously reveal hidden connections to advance scientific discovery? — Introducing SciAgents, a groundbreaking tool that enhances scientific discovery, created at Massachusetts Institute of Technology and built with Microsoft’s #opensource multi-agent framework, #AutoGen. 🎊🙌🧬 SciAgents empowers researchers to: - Use graph reasoning to uncover interdisciplinary relationships that might otherwise remain hidden. - Explore vast datasets and propose new hypotheses grounded in a web of interconnected knowledge. - Collaborate with AI as a tool to support breakthrough discoveries. — Here’s my take: The biggest advantage AI brings to science isn’t necessarily “better reasoning,” but rather its ability to connect the dots in an ever-expanding ocean of data. 🧩 Imagine scientific knowledge as a puzzle with 100 trillion pieces— for any given scientific endeavor, AI can help us find the few pieces needed to complete that part of the picture and move scientific discovery forward. 💡 #AI #ScientificDiscovery #DataScience #Innovation #ResearchTools
Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding💡by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search 📜. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audio track generated using 🍓 #o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: ➡Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. ➡Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. ➡Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. 💡In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using #AutoGen. #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Paper: Alireza Ghafarollahi and Markus J. Buehler, SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning, https://lnkd.in/e-88pQ6c, 2024. Data and weights 🤗: https://lnkd.in/eku6j7JV Code: https://lnkd.in/eJVZD8BE
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Software Engineer | Backend Engineer | PHP developer | Python developer| Data Scientist | Machine Learning
This is great usage of AI for scientific purpose.
Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding💡by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search 📜. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audio track generated using 🍓 #o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: ➡Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. ➡Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. ➡Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. 💡In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using #AutoGen. #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Paper: Alireza Ghafarollahi and Markus J. Buehler, SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning, https://lnkd.in/e-88pQ6c, 2024. Data and weights 🤗: https://lnkd.in/eku6j7JV Code: https://lnkd.in/eJVZD8BE
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Scientist.Selling Partner Services(SPS)@Amazon | Postdoc.CCB@Havard | PhD.CEE@SNU | x-Master (VP).SAIT@SEC
SciAgents are drastically important concnept in automation of various science research as it utilize the selection of appropriate math and science theory tools.
Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding💡by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search 📜. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audio track generated using 🍓 #o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: ➡Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. ➡Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. ➡Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. 💡In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using #AutoGen. #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Paper: Alireza Ghafarollahi and Markus J. Buehler, SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning, https://lnkd.in/e-88pQ6c, 2024. Data and weights 🤗: https://lnkd.in/eku6j7JV Code: https://lnkd.in/eJVZD8BE
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On 19th November 2024, Uppsala University hosted an insightful session on Information Visualization, featuring the renowned Professor Alexandru C. Telea from Utrecht University, The Netherlands. The colloquium tackled the challenges of visualizing high-dimensional data, which is central to many fields yet notoriously difficult to represent beyond three dimensions. 🔹 Part 1: Explored classical visualization techniques for lower-dimensional data. 🔹 Part 2: Focused on dimensionality reduction (DR) techniques, showcasing their scalability and utility in high-dimensional contexts. DR’s potential was further highlighted through applications in explainable AI. A huge thanks to Prof. Telea for sharing his expertise and advancing our understanding of this vital field! #InformationVisualization #HighDimensionalData #ExplainableAI #UppsalaUniversity #InfraVis
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„You don't understand anything until you learn it more than one way.“ - Marvin Minsky
I am pitching this already for so long to investors in Europe and they just don’t get it… Maybe now they understand a bit better the value of ExtensityAI and #SymbolicAI #NeurosymbolicAI #SymbolicReasoning #AgenticFrameworks
Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding💡by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search 📜. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audio track generated using 🍓 #o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: ➡Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. ➡Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. ➡Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. 💡In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using #AutoGen. #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Paper: Alireza Ghafarollahi and Markus J. Buehler, SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning, https://lnkd.in/e-88pQ6c, 2024. Data and weights 🤗: https://lnkd.in/eku6j7JV Code: https://lnkd.in/eJVZD8BE
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VC. Board Member. CEO & CTO. Innovator & Entrepreneur | Health| Life Sciences| Education|Technology | TedX Speaker. Talks about #AIInHealth, #WomeninTech, #Entrepreneurs, #Diversity
If you missed the stellar talk by Scott J. Campbell MD, MPH “AI Whisperer” on how he is building bridges across the gap between healthcare and AI, at the Society of Physician Entrepreneurs (SoPE) session organized by Paul Grewal and hosted by James Huie (Wilson Sonsini Goodrich & Rosati), here are the the three AI technologies that hold greatest promise for innovation in health: Federated Learning, Knowledge Graph Databases and Synthetic Data! #futureofhealth #aiforhealth #syntheticdata #federatedlearning #knowledgegraphdatasets Patricia Florissi, Ph. D. MuckAI Girish, Ashwin Ram, Javier Tordable
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📅 Don't Miss Out: AMLD - Applied Machine Learning Days in Lausanne 🇨🇭 In our journey as innovators in AI, focusing on structuring and visualizing unstructured data, we're excited to highlight an upcoming event that resonates with our passion for innovation and knowledge-sharing in AI and machine learning. 💡 Join industry leaders, academics, and government officials at the Applied Machine Learning Days (AMLD) from March 23rd to 26th, hosted at the SwissTech Convention Center on the EPFL (École polytechnique fédérale de Lausanne) campus. 🌍 With speakers from over 40 countries, AMLD promises four days of enriching keynotes, topical tracks, workshops, and an exhibition. It offers an unparalleled opportunity to dive deep into the latest research, insights, and trends in machine learning. Unfortunately, we cannot attend in person, but we look forward to your insights after the event. 🫶 Which talk or workshop should one definitely not miss? Explore more about AMLD and register at the link below: https://lnkd.in/gAe55wDd #AMLD #AIInnovation #MachineLearning #AICommunity #FutureOfTech #Gopf #AI #ArtificialIntelligence #KI #KünstlicheIntelligenz #CompetitiveIntelligence #FutureTech
