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You Can’t Learn What’s Not There: Self Supervised Learning and the Poverty of the Stimulus Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Overcoming the Knowledge Bottleneck Using Lifelong Learning by Social Agents

Authors : Sergei Nirenburg, Marjorie McShane, Jesse English

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

In this position paper we argue that the best way to overcome the notorious knowledge bottleneck in AI is using lifelong learning by social intelligent agents. Keys to this capability are deep language understanding, dialog interaction, sufficiently broad-coverage and fine-grain knowledge bases to bootstrap the learning process, and the agent’s operation within a comprehensive cognitive architecture.

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previous chapter Scaling Federated Learning for Fine-Tuning of Large Language Models
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Metadata
Title
Overcoming the Knowledge Bottleneck Using Lifelong Learning by Social Agents
Authors
Sergei Nirenburg
Marjorie McShane
Jesse English
Copyright Year
2021
Book
Natural Language Processing and Information Systems

Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-80599-9_3

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