On scientific understanding with artificial intelligence

• URL: http://arxiv.org/abs/2204.01467v1
• Date: Mon, 4 Apr 2022 13:45:13 GMT
• Title: On scientific understanding with artificial intelligence
• Abstract summary: We seek advice from the philosophy of science to understand scientific understanding. Then we collect dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. For each dimension, we explain new avenues to push beyond the status quo.
• Score: 2.2911874889696775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. However, as scientists, we would not be satisfied with the oracle itself. We want more. We want to comprehend how the oracle conceived these predictions. This feat, denoted as scientific understanding, has frequently been recognized as the essential aim of science. Now, the ever-growing power of computers and artificial intelligence poses one ultimate question: How can advanced artificial systems contribute to scientific understanding or achieve it autonomously? We are convinced that this is not a mere technical question but lies at the core of science. Therefore, here we set out to answer where we are and where we can go from here. We first seek advice from the philosophy of science to understand scientific understanding. Then we review the current state of the art, both from literature and by collecting dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. Those combined insights help us to define three dimensions of android-assisted scientific understanding: The android as a I) computational microscope, II) resource of inspiration and the ultimate, not yet existent III) agent of understanding. For each dimension, we explain new avenues to push beyond the status quo and unleash the full power of artificial intelligence's contribution to the central aim of science. We hope our perspective inspires and focuses research towards androids that get new scientific understanding and ultimately bring us closer to true artificial scientists.

Related papers

• The Past and Future of AI Scientists [0.0]
  AI Scientists can originate hypotheses, deduce their consequences, design and execute experiments, interpret their results, and revise their beliefs.<n>Such systems are integrated scientific agents, connected to the literature, formal knowledge, mathematical models, simulations, data-analysis systems and physical laboratories.<n>Nobel Turing Challenge sets the goal of developing by 2050 AI systems capable of automating Nobel-quality discoveries.
  arXiv  Detail & Related papers  (2026-08-14T15:50:35Z)
• Towards a new paradigm of scientific discovery with socialized artificial intelligence [48.48034861871986]
  We introduce Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence.<n>BLAZE conceives AI not as an assistant for isolated research tasks, but as an organizational infrastructure for scientific discovery.<n>Its purpose is not to replace human discovery, but to extend the scale, depth, and continuity of collective scientific inquiry.
  arXiv  Detail & Related papers  (2026-08-03T18:18:39Z)
• Embodied Science: Closing the Discovery Loop with Agentic Embodied AI [73.54118756665221]
  We argue for embodied science, a paradigm that reframes scientific discovery as a closed loop tightly coupling agentic reasoning with physical execution.<n>We propose a unified Perception-Language-Action-Discovery (PLAD) framework, wherein embodied agents perceive experimental environments, reason over scientific knowledge, execute physical interventions, and internalize outcomes to drive subsequent exploration.
  arXiv  Detail & Related papers  (2026-03-20T09:20:12Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
  We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
  arXiv  Detail & Related papers  (2025-06-24T13:31:44Z)
• Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
  An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle. AGS aims to significantly reduce the time and resources needed for scientific discovery. As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
  arXiv  Detail & Related papers  (2025-03-28T14:00:27Z)
• 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)
• Minds, Brains, AI [0.0]
  In the last year or so there has been extensive claims by major computational scientists, engineers, and others that AGI, artificial general intelligence, is five or ten years away, but without a scintilla of scientific evidence, for a broad body of these claims. This article reviews evidence for the following three propositions using extensive body of scientific research and related sources from the cognitive and neurosciences, evolutionary evidence, linguistics, data science, comparative psychology, self-driving cars, robotics, and the learning sciences.
  arXiv  Detail & Related papers  (2024-04-21T21:49:42Z)
• 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)
• DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [116.09762105379241]
  DeepSpeed4Science aims to build unique capabilities through AI system technology innovations. We showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.
  arXiv  Detail & Related papers  (2023-10-06T22:05:15Z)
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
  New area of research known as AI for science (AI4Science) Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales. Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
  arXiv  Detail & Related papers  (2023-07-17T12:14:14Z)
• 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)
• From Kepler to Newton: Explainable AI-based Paradigm for Science Discovery [16.392568986688595]
  We introduce an Explainable AI-based paradigm for science discovery. To demonstrate the AI-based science discovery process, we show how Kepler's laws of planetary motion and the Newton's law of universal gravitation can be rediscovered by (Explainable) AI.
  arXiv  Detail & Related papers  (2021-11-24T00:45:27Z)
• Standard Model Physics and the Digital Quantum Revolution: Thoughts about the Interface [68.8204255655161]
  Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress. From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
  arXiv  Detail & Related papers  (2021-07-10T06:12:06Z)
• Toward Building Science Discovery Machines [3.7565501074323224]
  We focus on the scientific discovery process where a high level of reasoning and remarkable problem-solving ability are required. We provide examples of the use of these principles in different fields such as physics, mathematics, and biology. We argue that in order to build science discovery machines and speed up the scientific discovery process, we should build theoretical and computational frameworks.
  arXiv  Detail & Related papers  (2021-03-24T14:04:03Z)
• Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
  A fascinating hypothesis is that human and animal intelligence could be explained by a few principles. This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
  arXiv  Detail & Related papers  (2020-11-30T18:29:25Z)
• Scientific intuition inspired by machine learning generated hypotheses [2.294014185517203]
  We shift the focus on the insights and the knowledge obtained by the machine learning models themselves. We apply gradient boosting in decision trees to extract human interpretable insights from big data sets from chemistry and physics. The ability to go beyond numerics opens the door to use machine learning to accelerate the discovery of conceptual understanding.
  arXiv  Detail & Related papers  (2020-10-27T12:12:12Z)

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.