Thinking Fast and Slow in AI: the Role of Metacognition
- URL: http://arxiv.org/abs/2110.01834v1
- Date: Tue, 5 Oct 2021 06:05:38 GMT
- Title: Thinking Fast and Slow in AI: the Role of Metacognition
- Authors: Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano,
Lior Horesh, Jon Lenchner, Andrea Loreggia, Nicholas Mattei, Francesca Rossi,
Biplav Srivastava and Kristen Brent Venable
- Abstract summary: State-of-the-art AI still lacks many capabilities that would naturally be included in a notion of (human) intelligence.
We argue that a better study of the mechanisms that allow humans to have these capabilities can help us understand how to imbue AI systems with these competencies.
- Score: 35.114607887343105
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AI systems have seen dramatic advancement in recent years, bringing many
applications that pervade our everyday life. However, we are still mostly
seeing instances of narrow AI: many of these recent developments are typically
focused on a very limited set of competencies and goals, e.g., image
interpretation, natural language processing, classification, prediction, and
many others. Moreover, while these successes can be accredited to improved
algorithms and techniques, they are also tightly linked to the availability of
huge datasets and computational power. State-of-the-art AI still lacks many
capabilities that would naturally be included in a notion of (human)
intelligence.
We argue that a better study of the mechanisms that allow humans to have
these capabilities can help us understand how to imbue AI systems with these
competencies. We focus especially on D. Kahneman's theory of thinking fast and
slow, and we propose a multi-agent AI architecture where incoming problems are
solved by either system 1 (or "fast") agents, that react by exploiting only
past experience, or by system 2 (or "slow") agents, that are deliberately
activated when there is the need to reason and search for optimal solutions
beyond what is expected from the system 1 agent. Both kinds of agents are
supported by a model of the world, containing domain knowledge about the
environment, and a model of "self", containing information about past actions
of the system and solvers' skills.
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