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Explore advanced Battery Management Systems for EVs that combine physics-driven models with real-time learning to detect faults, monitor health, and enhance safety, addressing current limitations in preventing battery fires.
Explore the development of trustworthy AI models for healthcare beyond generative AI, focusing on neurosymbolic, targeted, and compact approaches that ensure explainability, safety, and guideline adherence for personalized health applications.
Explore the development of neurosymbolic AI models for critical applications, focusing on creating controllable, compact, and customizable systems that ensure reliable and grounded reasoning.
Gain insights into developing explainable multimodal AI models for rare event prediction through hands-on sessions covering data generation, preprocessing, model development, and evaluation using a new multimodal dataset.
Explore a Knowledge and Ontology Enhanced Approach to Natural Language Understanding (KOE-NLU) with insights from Usha Lokala's research at the AI Institute at UofSC.
Explore advanced Natural Language Understanding through knowledge and ontology-enhanced approaches, focusing on innovative techniques for improved language processing and semantic analysis.
Explore deep learning, reinforcement learning, and heuristic search techniques with Dr. Forest Agostinelli's comprehensive presentation from the AI Institute at UofSC.
Explore the frontier of AI reasoning and abstraction capabilities with Prof. Amit Sheth's keynote examining whether AI can truly reason like humans, part of the "Beyond the hype" webinar series.
Explore the complex issue of LLM hallucinations with Vipula Rawte, covering characterization, measurement, detection methods, and strategies for prevention and mitigation.
Explore a neurosymbolic framework using Dynamic Multimodal Process Knowledge Graphs for explainable process recommendations, demonstrated through recipe analysis and AI pipeline recommendation use cases.
Explore cutting-edge approaches to developing trustworthy AI systems, focusing on GenAI challenges, compact models, and neural-symbolic integration for robust artificial intelligence.
Gain insights into building and leading successful research ecosystems in academic settings through expert guidance on fostering innovation and collaborative excellence.
Explore the Multiagent CoPilot approach for industrial AI, combining knowledge graphs, rule-based systems, and neurosymbolic AI to create explainable solutions for Industry 4.0 challenges with practical development insights.
Explore the evolution of AI systems from monolithic models to "compound" systems and "agentic" frameworks, focusing on the Custom, Compact, and Composite AI with Neurosymbolic approach for building robust, intelligent AI.
Explore the complex balance between accuracy, cost, and latency in AI inference serving systems with Professor Pooyan Jamshidi's comprehensive analysis.
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