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The Necessary Roadblock to Artificial General Intelligence: Corrigibility

EasyChair Preprint no. 846

7 pagesPublished: March 20, 2019

Abstract

With the rapid pace of advancement in the field of artificial intelligence (AI), this essay purports to accentuate the importance of corrigibility in AI in order to stimulate and catalyze more effort and focus in this research area. We will first introduce the idea of corrigibility with its properties and describe the expected behavior for a corrigible AI. Afterwards, based on the established meaning of corrigibility, we will showcase the importance of corrigibility by going over some modern and near-futuristic examples that are specifically selected to be relatable and foreseeable. Then, we will explore existing methods of establishing corrigibility in agents and their respective limitations, using the reinforcement learning (RL) framework as a proxy framework to artificial general intelligence (AGI). At last, we will identify the central themes of potential research frontiers that we believe would be crucial to boost quality research output in corrigibility.

Keyphrases: AI Safety, Artificial Intelligence, corrigibility, Reinforcement Learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:846,
  author = {Yat Long Lo and Chung Yu Woo and Ka Lok Ng},
  title = {The Necessary Roadblock to Artificial General Intelligence: Corrigibility},
  howpublished = {EasyChair Preprint no. 846},

  year = {EasyChair, 2019}}
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