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Explore the challenges and approaches to acquiring and representing generic knowledge in AI systems through this lecture by Professor Lenhart Schubert from the University of Rochester. Delve into two text-based methods for addressing the "knowledge acquisition bottleneck": abstracting knowledge from predication and modification patterns in diverse texts, and interpreting general statements in ordinary language from lexicons and resources like Open Mind. Examine the KNEXT system and its role in these efforts, while considering the complexities of formalizing generalities such as "Cats land on their feet." Investigate the concept of "donkey anaphora" and learn about the proposed "dynamic Skolemization" approach, which leads to script- or frame-like representations in AI. Gain insights from Schubert's extensive experience in natural language understanding, knowledge representation and acquisition, reasoning, and self-awareness as an AAAI fellow and prolific researcher in the field of artificial intelligence.
