A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.

Linguistics for the Age of AI

Computers & Technology

Mcshane, Marjorie

Nirenburg, Sergei

Computer Science

The MIT Press

A novel approach to hybrid AI aimed at developing trustworthy agent collaborators. The vast majority of current AI relies wholly on machine learning (ML). However, the past thirty years of effort in this paradigm have shown that, despite the many things that ML can achieve, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines ML with knowledge-based processing. In Agents in the Long Game of AI, Marjorie McShane, Sergei Nirenburg, and Jesse English present recent advances in hybrid AI with special emphases on content-centric computational cognitive modeling, explainability, and development methodologies.&nbsp; At present, hybridization typically involves sprinkling knowledge into an ML black box. The authors, by contrast, argue that hybridization will be best achieved in the opposite way: by building agents within a cognitive architecture and then integrating judiciously selected ML results. This approach leverages the power of ML without sacrificing the kind of explainability that will foster society's trust in AI. This book shows how we can develop trustworthy agent collaborators of a type not being addressed by the "ML alone" or "ML sprinkled by knowledge" paradigms--and why it is imperative to do so.

Agents in the Long Game of AI: Computational Cognitive Modeling for Trustworthy, Hybrid AI

English, Jesse

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Books by Mcshane, Marjorie