"Generative AI, a rapidly advancing field of artificial intelligence, has the capacity to create original and often remarkably convincing content across a wide range of domains. Built on sophisticated machine learning models, generative AI systems simulate aspects of human learning and decision-making to detect patterns and relationships in vast datasets. They then leverage this knowledge to respond to users' natural language queries in ways that increasingly resemble human reasoning and creativity."
"What can we learn from the existing benchmarks established for LLMs? In what ways might it enrich traditional accounts of logical reasoning? How does symbolic logic interface with LLM reasoning-and with probabilistic reasoning more generally? Consistency has been a major challenge for LLMs; what are the best ways to handle inconsistency? As the current face of AI, how can generative models be combined with traditional symbolic reasoning? And how is logical reasoning connected to explainability and causality in the context of generative AI?"
Generative AI systems produce original, convincing content by using machine learning models that simulate human learning and decision-making to detect patterns in vast datasets. These systems respond to natural language queries in ways resembling human reasoning and creativity. Significant challenges include reliability of outputs, accuracy of reasoning, and explainability of underlying processes, which generate broader philosophical concerns. Research topics include evaluating LLM reasoning, benchmarking, integrating symbolic and probabilistic logic, handling inconsistency, and linking logical reasoning to causality and explainability. Research should also address epistemic consequences, ethical and social implications, and foster dialogue between Eastern and Western philosophical traditions.
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