Scientists' Perspectives on the Potential for Generative AI in their
Fields
- URL: http://arxiv.org/abs/2304.01420v1
- Date: Tue, 4 Apr 2023 00:06:28 GMT
- Title: Scientists' Perspectives on the Potential for Generative AI in their
Fields
- Authors: Meredith Ringel Morris
- Abstract summary: Generative AI models are on the cusp of transforming many aspects of modern life.
There is potential for Generative AI to have a substantive impact on the methods and pace of discovery for a range of scientific disciplines.
We interviewed twenty scientists from a range of fields to gain insight into whether or how Generative AI might add value to the practice of their respective disciplines.
- Score: 18.753742428223912
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative AI models, including large language models and multimodal models
that include text and other media, are on the cusp of transforming many aspects
of modern life, including entertainment, education, civic life, the arts, and a
range of professions. There is potential for Generative AI to have a
substantive impact on the methods and pace of discovery for a range of
scientific disciplines. We interviewed twenty scientists from a range of fields
(including the physical, life, and social sciences) to gain insight into
whether or how Generative AI technologies might add value to the practice of
their respective disciplines, including not only ways in which AI might
accelerate scientific discovery (i.e., research), but also other aspects of
their profession, including the education of future scholars and the
communication of scientific findings. In addition to identifying opportunities
for Generative AI to augment scientists' current practices, we also asked
participants to reflect on concerns about AI. These findings can help guide the
responsible development of models and interfaces for scientific education,
inquiry, and communication.
Related papers
- 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) - 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) - Future of Information Retrieval Research in the Age of Generative AI [61.56371468069577]
In the fast-evolving field of information retrieval (IR), the integration of generative AI technologies such as large language models (LLMs) is transforming how users search for and interact with information.
Recognizing this paradigm shift, a visioning workshop was held in July 2024 to discuss the future of IR in the age of generative AI.
This report contains a summary of discussions as potentially important research topics and contains a list of recommendations for academics, industry practitioners, institutions, evaluation campaigns, and funding agencies.
arXiv Detail & Related papers (2024-12-03T00:01:48Z) - Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science [25.683422870223076]
We present a large-scale analysis of the AI4Science literature.
We quantitatively highlight key disparities between AI methods and scientific problems.
We explore the potential and challenges of facilitating collaboration between AI and scientific communities.
arXiv Detail & Related papers (2024-11-27T00:40:51Z) - Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
Next several decades may well be a turning point for humanity, comparable to the industrial revolution.
Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts.
We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
arXiv Detail & Related papers (2024-04-06T22:18:31Z) - 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) - Experiential AI: A transdisciplinary framework for legibility and agency
in AI [13.397979132753138]
Experiential AI is a research agenda in which scientists and artists come together to investigate the entanglements between humans and machines.
The paper discusses advances and limitations in the field of explainable AI.
arXiv Detail & Related papers (2023-06-01T12:59:06Z) - AI for Science: An Emerging Agenda [30.260160661295682]
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling"
The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains.
Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers.
arXiv Detail & Related papers (2023-03-07T20:21:43Z) - 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) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z)
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.