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Published on: February 12, 2017
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Building machines that learn and think with people
Katherine M Collins1, Ilia Sucholutsky2, Umang Bhatt3,4
1Department of Engineering, University of Cambridge, Cambridge, UK. kmc61@cam.ac.uk.
Nature Human Behaviour
|October 22, 2024
Summary
We can engineer artificial intelligence (AI) systems as true thought partners by applying collaborative cognition science. These AI systems will actively model humans and the world, enhancing human-AI collaboration.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Current artificial intelligence (AI) systems function primarily as tools, not as collaborative partners.
- There is a need for AI systems that are reasonable, insightful, knowledgeable, reliable, and trustworthy.
Purpose of the Study:
- To explore how collaborative cognition science can engineer AI systems as "thought partners."
- To define modes of collaborative thought and desiderata for human-compatible AI partnerships.
- To propose a Bayesian approach for designing AI thought partners that model humans and the world.
Main Methods:
- Leveraging principles from computational cognitive science.
- Developing a Bayesian framework for AI design.
- Defining modes of human-AI collaborative thought.
Main Results:
- Outlined several modes for human-AI collaborative thought.
- Proposed key characteristics for human-compatible thought partnerships.
- Motivated a Bayesian scaling path for designing AI thought partners.
Conclusions:
- AI systems can be engineered as "thought partners" by integrating collaborative cognition principles.
- A Bayesian approach offers a viable path for developing AI that actively models users and environments.
- Future AI development should focus on creating systems that complement human limitations and expectations.
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