Building Human-like Communicative Intelligence: A Grounded Perspective
- URL: http://arxiv.org/abs/2201.02734v1
- Date: Sun, 2 Jan 2022 01:43:24 GMT
- Title: Building Human-like Communicative Intelligence: A Grounded Perspective
- Authors: Marina Dubova
- Abstract summary: After making astounding progress in language learning, AI systems seem to approach the ceiling that does not reflect important aspects of human communicative capacities.
This paper suggests that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI.
I propose a list of concrete, implementable components for building "grounded" linguistic intelligence.
- Score: 1.0152838128195465
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Modern Artificial Intelligence (AI) systems excel at diverse tasks, from
image classification to strategy games, even outperforming humans in many of
these domains. After making astounding progress in language learning in the
recent decade, AI systems, however, seem to approach the ceiling that does not
reflect important aspects of human communicative capacities. Unlike human
learners, communicative AI systems often fail to systematically generalize to
new data, suffer from sample inefficiency, fail to capture common-sense
semantic knowledge, and do not translate to real-world communicative
situations. Cognitive Science offers several insights on how AI could move
forward from this point. This paper aims to: (1) suggest that the dominant
cognitively-inspired AI directions, based on nativist and symbolic paradigms,
lack necessary substantiation and concreteness to guide progress in modern AI,
and (2) articulate an alternative, "grounded", perspective on AI advancement,
inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research.
I review results on 4E research lines in Cognitive Science to distinguish the
main aspects of naturalistic learning conditions that play causal roles for
human language development. I then use this analysis to propose a list of
concrete, implementable components for building "grounded" linguistic
intelligence. These components include embodying machines in a
perception-action cycle, equipping agents with active exploration mechanisms so
they can build their own curriculum, allowing agents to gradually develop motor
abilities to promote piecemeal language development, and endowing the agents
with adaptive feedback from their physical and social environment. I hope that
these ideas can direct AI research towards building machines that develop
human-like language abilities through their experiences with the world.
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