Building Machines that Learn and Think with People

• URL: http://arxiv.org/abs/2408.03943v1
• Date: Mon, 22 Jul 2024 02:42:45 GMT
• Title: Building Machines that Learn and Think with People
• Authors: Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt, Kartik Chandra, Lionel Wong, Mina Lee, Cedegao E. Zhang, Tan Zhi-Xuan, Mark Ho, Vikash Mansinghka, Adrian Weller, Joshua B. Tenenbaum, Thomas L. Griffiths,
• Abstract summary: We show how the science of collaborative cognition can be put to work to engineer systems that really can be called thought partners'' 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.
• Score: 72.40300991089445
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
  Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
  arXiv  Detail & Related papers  (2024-04-29T04:51:05Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• BIASeD: Bringing Irrationality into Automated System Design [12.754146668390828]
  We claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.
  arXiv  Detail & Related papers  (2022-10-01T02:52:38Z)
• Team Learning as a Lens for Designing Human-AI Co-Creative Systems [12.24664973838839]
  Generative, ML-driven interactive systems have the potential to change how people interact with computers in creative processes. It is still unclear how we might achieve effective human-AI collaboration in open-ended task domains.
  arXiv  Detail & Related papers  (2022-07-06T22:11:13Z)
• Evaluating Understanding on Conceptual Abstraction Benchmarks [0.0]
  A long-held objective in AI is to build systems that understand concepts in a humanlike way. We argue that understanding a concept requires the ability to use it in varied contexts. Our concept-based approach to evaluation reveals information about AI systems that conventional test sets would have left hidden.
  arXiv  Detail & Related papers  (2022-06-28T17:52:46Z)
• Thinking Fast and Slow in AI [38.8581204791644]
  This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI, we may obtain similar capabilities in an AI system.
  arXiv  Detail & Related papers  (2020-10-12T20:10:05Z)
• Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks [83.37025218216888]
  We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations. The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications. Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
  arXiv  Detail & Related papers  (2020-07-24T23:35:03Z)
• Learning to Complement Humans [67.38348247794949]
  A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks. We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams.
  arXiv  Detail & Related papers  (2020-05-01T20:00:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.