Open Problems in Mechanistic Interpretability

• URL: http://arxiv.org/abs/2501.16496v1
• Date: Mon, 27 Jan 2025 20:57:18 GMT
• Title: Open Problems in Mechanistic Interpretability
• Authors: Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Bloom, Stella Biderman, Adria Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, Eric J. Michaud, Stephen Casper, Max Tegmark, William Saunders, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Tom McGrath,
• Abstract summary: Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities. Despite recent progress toward these goals, there are many open problems in the field that require solutions.
• Score: 61.44773053835185
• License:
• Abstract: Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to reveal deeper insights; we must figure out how best to apply our methods in pursuit of specific goals; and the field must grapple with socio-technical challenges that influence and are influenced by our work. This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing.

Related papers

• Open Problems in Technical AI Governance [93.89102632003996]
  Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
  arXiv  Detail & Related papers  (2024-07-20T21:13:56Z)
• Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience [4.524832437237367]
  Inner Interpretability is a promising field tasked with uncovering the inner mechanisms of AI systems. Recent critiques raise issues that question its usefulness to advance the broader goals of AI. Here we draw the relevant connections and highlight lessons that can be transferred productively between fields.
  arXiv  Detail & Related papers  (2024-06-03T14:16:56Z)
• Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency -- A Review [0.0]
  The rise of advanced technology in project management (PM) highlights a crucial need for inclusiveness. This work examines the enhancement of both inclusivity and efficiency in PM through technological integration.
  arXiv  Detail & Related papers  (2023-11-18T20:22:44Z)
• Towards Quantum Federated Learning [80.1976558772771]
  Quantum Federated Learning aims to enhance privacy, security, and efficiency in the learning process. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries.
  arXiv  Detail & Related papers  (2023-06-16T15:40:21Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication [5.742215677251865]
  We apply social systems theory to highlight challenges in explainable artificial intelligence. We aim to reinvigorate the technical research in the direction of interactive and iterative explainers.
  arXiv  Detail & Related papers  (2023-02-07T13:31:02Z)
• Knowledge-enhanced Neural Machine Reasoning: A Review [67.51157900655207]
  We introduce a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories. We elucidate the current application domains and provide insight into promising prospects for future research.
  arXiv  Detail & Related papers  (2023-02-04T04:54:30Z)
• A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities [8.17368686298331]
  Robustness of Artificial Intelligence (AI) systems remains elusive and constitutes a key issue that impedes large-scale adoption. We introduce three concepts to organize and describe the literature both from a fundamental and applied point of view. We highlight the central role of humans in evaluating and enhancing AI robustness, considering the necessary knowledge humans can provide.
  arXiv  Detail & Related papers  (2022-10-17T10:00:51Z)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence [6.218613353519724]
  Methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions. Projection is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations.
  arXiv  Detail & Related papers  (2021-03-24T22:33:51Z)

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.