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Exploring Representation Levels in AI: A Deep Dive into Learning and Inference Techniques

This discussion delves into the intricacies of artificial intelligence by examining critical concepts such as top-down versus bottom-up approaches, and the distinctions between ground and lifted representations. We'll reflect on Fernando Pereira's insights regarding the Chomsky Hierarchy, exploring pure inference and learning versus interleaved strategies, and contrasting knowledge engineering with model learning. Additionally, we'll address the human-aware aspect of AI and engage in a dialogue about current controversies and emerging hot topics in AI today.

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Exploring Representation Levels in AI: A Deep Dive into Learning and Inference Techniques

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  1. Pendulum Swings in AI • Top-down vs. Bottom-up • Ground vs. Lifted representation • The longer I live the farther down the Chomsky Hierarchy I seem to fall [Fernando Pereira] • Pure Inference and Pure Learning vs. Interleaved inference and learning • Knowledge Engineering vs. Model Learning • Human-aware vs.

  2. The representational roller-coaster in CSE 471 FOPC Sit. Calc. First-order FOPC w.o. functions relational STRIS Planning propositional/ (factored) CSP Prop logic Bayes Nets Decision trees atomic State-space search MDPs Min-max Semester time  The plot shows the various topics we discussed this semester, and the representational level at which we discussed them. At the minimum we need to understand every task at the atomic representation level. Once we figure out how to do something at atomic level, we always strive to do it at higher (propositional, relational, first-order) levels for efficiency and compactness. During the course we may not discuss certain tasks at higher representation levels either because of lack of time, or because there simply doesn’t yet exist undergraduate level understanding of that topic at higher levels of representation..

  3. Discussion • What are the current controversies in AI? What are the hot topics in AI?

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