Building Machines that Learn and Think with People
Abstract
What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,'' systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.
Keywords
Cite
@article{arxiv.2408.03943,
title = {Building Machines that Learn and Think with People},
author = {Katherine M. Collins and Ilia Sucholutsky and Umang Bhatt and Kartik Chandra and Lionel Wong and Mina Lee and Cedegao E. Zhang and Tan Zhi-Xuan and Mark Ho and Vikash Mansinghka and Adrian Weller and Joshua B. Tenenbaum and Thomas L. Griffiths},
journal= {arXiv preprint arXiv:2408.03943},
year = {2024}
}