The Future of AI: Neat or Scruffy?

Bernardo Gonçalves, Fabio Gagliardi Cozman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The “neat” and “scruffy” portraits have long been painted to describe viewpoints, styles of reasoning and methodologies in AI research. Essentially, the neats defend techniques based on first principles and grounded in mathematical rigor, while the scruffies advocate diversity within cognitive architectures, sometimes meant to be models of parts of the brain, sometimes just kludges or ad-hoc pieces of engineered code. The recent success of deep learning has revived the debate between these two approaches to AI; in this context, some natural questions arise. How can we characterize, and how can we classify, these positions given the history of AI? More importantly, what is the relevance of these positions for the future of AI? How should AI research be pursued from now on, neatly or scruffly? These are the questions we address in this paper, resorting to historical analysis and to recent research trends to articulate possible ways to allocate energy so as to take the field to maximal fruition.

Original languageEnglish
Title of host publicationIntelligent Systems - 10th Brazilian Conference, BRACIS 2021, Proceedings, Part 2
EditorsAndré Britto, Karina Valdivia Delgado
PublisherSpringer Science and Business Media Deutschland GmbH
Pages177-192
Number of pages16
ISBN (Print)9783030916985
DOIs
StatePublished - 2021
Externally publishedYes
Event10th Brazilian Conference on Intelligent Systems, BRACIS 2021 - Virtual, Online
Duration: 29 Nov 20213 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13074 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Brazilian Conference on Intelligent Systems, BRACIS 2021
CityVirtual, Online
Period29/11/213/12/21

Keywords

  • Future of AI
  • History of AI
  • Neat vs. Scruffy
  • Scientific method
  • Styles of scientific reasoning

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