Towards a Science Exocortex

• URL: http://arxiv.org/abs/2406.17809v2
• Date: Thu, 15 Aug 2024 14:32:34 GMT
• Title: Towards a Science Exocortex
• Authors: Kevin G. Yager,
• Abstract summary: We review the state of the art in agentic AI systems, and discuss how these methods could be extended to have greater impact on science. A science exocortex could be designed as a swarm of AI agents, with each agent individually streamlining specific researcher tasks.
• Score: 0.5687661359570725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) methods are poised to revolutionize intellectual work, with generative AI enabling automation of text analysis, text generation, and simple decision making or reasoning. The impact to science is only just beginning, but the opportunity is significant since scientific research relies fundamentally on extended chains of cognitive work. Here, we review the state of the art in agentic AI systems, and discuss how these methods could be extended to have even greater impact on science. We propose the development of an exocortex, a synthetic extension of a person's cognition. A science exocortex could be designed as a swarm of AI agents, with each agent individually streamlining specific researcher tasks, and whose inter-communication leads to emergent behavior that greatly extend the researcher's cognition and volition.

Related papers

• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery [14.465756130099091]
  This paper presents the first comprehensive framework for fully automatic scientific discovery. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, and describes its findings. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community.
  arXiv  Detail & Related papers  (2024-08-12T16:58:11Z)
• Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence [0.9065034043031668]
  The scientific method is the cornerstone of human progress across all branches of the natural and applied sciences. We argue that human complex reasoning for scientific discovery remains of vital importance, at least before the advent of artificial general intelligence. Knowing what data AI systems deemed important to make decisions can be a point of contact with domain experts and scientists.
  arXiv  Detail & Related papers  (2024-06-15T08:34:42Z)
• "Turing Tests" For An AI Scientist [0.0]
  This paper proposes a "Turing test for an AI scientist" to assess whether an AI agent can conduct scientific research independently. We propose seven benchmark tests that evaluate an AI agent's ability to make groundbreaking discoveries in various scientific domains.
  arXiv  Detail & Related papers  (2024-05-22T05:14:27Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain [0.7770029179741429]
  The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
  arXiv  Detail & Related papers  (2024-02-07T14:09:11Z)
• 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)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Advancing Perception in Artificial Intelligence through Principles of Cognitive Science [6.637438611344584]
  We focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment. We present a collection of methods in AI for researchers to build AI systems inspired by cognitive science.
  arXiv  Detail & Related papers  (2023-10-13T01:21:55Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks [56.20123080771364]
  Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning. CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
  arXiv  Detail & Related papers  (2022-01-20T17:07:03Z)
• Learning from learning machines: a new generation of AI technology to meet the needs of science [59.261050918992325]
  We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
  arXiv  Detail & Related papers  (2021-11-27T00:55:21Z)

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.