Will AI Write Scientific Papers in the Future?

Abstract

In this presidential address, I would like to start with a personal reflection on the field and then share with you the research directions I am pursuing and my excitement about the future of AI. In my personal research to advance AI while advancing scientific discoveries, one question that I have been pondering for some years now is whether AI will write scientific papers in the future. I want to reflect on this question, and look back at the many accomplishments in our field that can make us very hopeful that the answer will be yes, and that it may happen sooner than we might expect.

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

“…Artificial Intelligence (AI) enables the community to construct community infrastructure for effective data integration and analysis thanks to a consensual ontology that has been embraced. The use of AI to synthesize new scientific knowledge that did not exist previously is a groundbreaking achievement that must be emphasized (Gil, 2022). Moreover, ontologies are used with great success in education and smart assistant applications (Sermet and Demir, 2021) as they allow to formulate the representation of a learning domain by specifying all concepts involved, relations between concepts, and properties and conditions that exist (Stancin et al, 2020;Grivokostopoulou et al, 2019).…”

A Computational Inflection for Scientific Discovery

“…We present key concepts of task-guided scientific knowledge retrieval, including work on prototypes that highlight the promise of the direction and bring into focus concrete steps forward for novel representations, tools, and services. We review systems that help researchers discover novel perspectives and inspirations [16,17,19,42], help guide the attention of researchers toward opportunity areas rife with uncertainties and unknowns [26,46], and models that leverage retrieval and synthesis of scientific knowledge as part of machine learning and prediction [12,37]. We conclude with a discussion of opportunities ahead with computational approaches that have the potential to revolutionize science.…”

“…More recent work has focused on developing robot scientists [7,23] that run certain biological experiments-not only formulating hypotheses but "closing the loop" by automated tests in a physical laboratory-where robots use narrow curated background knowledge (e.g., of a specific gene regulatory network [7]) and machine learning to guide new experiments. Related work explores automating scientific data analysis [12], which we discuss in Section 2 as a case of retrieval from scientific repositories to augment aspects of experimentation and analysis (see Table 1).…”

“…(1) Workflows are multi-step computational pipelines used as part of scientific experimentation for data preparation, analysis and simulation [12]. Technically this includes execution of code scripts, services and tools, querying databases and submitting jobs to the cloud.…”

“…In the life sciences, in areas such as genomics, there are specialized workflow management systems to help researchers find and use workflows, enabled by a community that creates and publicly shares repositories of workflows with standardised interfaces, metadata and functional annotations of tools and data [27]. As discussed in Gil [12], machine learning algorithms can potentially use these resources to automate workflow construction, learning to retrieve and synthesize data analysis pipelines. In this setting, outer world knowledge takes the form of workflow repositories, from which systems retrieve and synthesize modular building blocks; users' inner world is reflected via analysis objectives and constraints.…”