Sergei Nirenburg

Yorick Wilks

H. L. Somers

Translation / Warren Weaver / - Mechanical translation / A.D. Booth / - The mechanical determination of meaning / Erwin Reifler / - Stochastic methods of mechanical translation / Gilbert W. King / - A framework for syntactic translation / Victor H. Yngve / - The present status of automatic translation of languages / Yehoshua Bar-Hillel / - A preliminary approach to Japanese English automatic translation / Susumu Kuno / - ALPAC : the (in) famous report / John Hutchins / - Automatic translation : some theoretical aspects and the design of a translation system / O.S. Kulagina / - Automatic translation and the concept of sublanguage / J. Lehrberger / - Machine translation as an expert task / Roderick L. Johnson / - Translation by structural correspondences / Ronald M. Kaplan / - Treatment of meaning in MT systems / Sergei Nirenburg / - Three levels of linguistic analysis in machine translation / Michael Zarechnak / - A framework of a mechanical translation between Japanese and English by a ...

Readings in Machine Translation

Marjorie Mcshane

Jesse English

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. 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.

Computational Cognitive Modeling for Trustworthy, Hybrid AI

Agents in the Long Game of AI

Progress in Machine Translation

"An explantion of a knowledge-based system for creating computational agents with human-like language ability"--

Linguistics for the Age of AI

Technologies enabling computers to process specific languages facilitate economic and political progress of societies where these languages are spoken. Development of methods and systems for language processing is therefore a worthy goal for national governments as well as for business entities and scientific and educational institutions in every country in the world. As work on systems and resources for the 'lower-density' languages becomes more widespread, an important question is how to leverage the results and experience accumulated by the field of computational linguistics for the major languages in the development of resources and systems for lower-density languages. This issue has been at the core of the NATO Advanced Studies Institute on language technologies for middle- and low-density languages held in Georgia in October 2007. This publication is a collection - of publication-oriented versions - of the lectures presented there and is a useful source of knowledge about many core facets of modern computational-linguistic work. By the same token, it can serve as a reference source for people interested in learning about strategies that are best suited for developing computational-linguistic capabilities for lesser-studied languages either 'from scratch' or using components developed for other languages. The book should also be quite useful in teaching practical system- and resource-building topics in computational linguistics.

Sergei Nirenburg

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