Automating the Practice of Science -- Opportunities, Challenges, and Implications
- URL: http://arxiv.org/abs/2409.05890v1
- Date: Tue, 27 Aug 2024 15:51:31 GMT
- Title: Automating the Practice of Science -- Opportunities, Challenges, and Implications
- Authors: Sebastian Musslick, Laura K. Bartlett, Suyog H. Chandramouli, Marina Dubova, Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, Sabina J. Sloman, William R. Holmes,
- Abstract summary: This article evaluates the scope of automation within scientific practice and assesses recent approaches.
By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the frontier of automated scientific practice.
- Score: 48.54225838534946
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.
Related papers
- 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) - Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions [0.0]
Agentic AI systems are capable of reasoning, planning, and autonomous decision-making.
They are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results.
arXiv Detail & Related papers (2025-03-12T01:00:05Z) - Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.
Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - Open Problems in Mechanistic Interpretability [61.44773053835185]
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.
arXiv Detail & Related papers (2025-01-27T20:57:18Z) - Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges [11.232704182001253]
This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks.
We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery.
arXiv Detail & Related papers (2024-12-16T03:52:20Z) - 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) - A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient.
In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks.
We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
arXiv Detail & Related papers (2024-02-16T18:05:09Z) - Transforming organic chemistry research paradigms: moving from manual
efforts to the intersection of automation and artificial intelligence [0.9883261192383611]
Organic chemistry is undergoing a major paradigm shift, moving from a labor-intensive approach to a new era dominated by automation and AI.
This article examines the multiple opportunities and challenges presented by this paradigm shift and explores its far-reaching implications.
It provides valuable insights into the future trajectory of organic chemistry research, which is increasingly defined by the synergistic interaction of automation and AI.
arXiv Detail & Related papers (2023-11-26T09:46:03Z) - 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) - Automated Scientific Discovery: From Equation Discovery to Autonomous
Discovery Systems [5.7923858184309385]
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents.
We will present closed-loop scientific discovery systems, starting with the pioneering work on the Adam system up to current efforts in fields from material science to astronomy.
The maximal level, level five, is defined to require no human intervention at all in the production of scientific knowledge.
arXiv Detail & Related papers (2023-05-03T16:35:41Z) - The Technological Emergence of AutoML: A Survey of Performant Software
and Applications in the Context of Industry [72.10607978091492]
Automated/Autonomous Machine Learning (AutoML/AutonoML) is a relatively young field.
This review makes two primary contributions to knowledge around this topic.
It provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial.
arXiv Detail & Related papers (2022-11-08T10:42:08Z) - Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research.
An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system.
We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
arXiv Detail & Related papers (2022-04-11T14:36:39Z) - 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) - Autonomous discovery in the chemical sciences part II: Outlook [2.566673015346446]
This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences.
It is increasingly important to articulate what the role of automation and computation has been in the scientific process.
arXiv Detail & Related papers (2020-03-30T19:11:35Z)
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.