We stand at the foot of a significant inflection in the trajectory of
scientific discovery. As society continues on its fast-paced digital
transformation, so does humankind's collective scientific knowledge and
discourse. We now read and write papers in digitized form, and a great deal of
the formal and informal processes of science are captured digitally --
including papers, preprints and books, code and datasets, conference
presentations, and interactions in social networks and communication platforms.
The transition has led to the growth of a tremendous amount of information,
opening exciting opportunities for computational models and systems that
analyze and harness it. In parallel, exponential growth in data processing
power has fueled remarkable advances in AI, including self-supervised neural
models capable of learning powerful representations from large-scale
unstructured text without costly human supervision. The confluence of societal
and computational trends suggests that computer science is poised to ignite a
revolution in the scientific process itself.
However, the explosion of scientific data, results and publications stands in
stark contrast to the constancy of human cognitive capacity. While scientific
knowledge is expanding with rapidity, our minds have remained static, with
severe limitations on the capacity for finding, assimilating and manipulating
information. We propose a research agenda of task-guided knowledge retrieval,
in which systems counter humans' bounded capacity by ingesting corpora of
scientific knowledge and retrieving inspirations, explanations, solutions and
evidence synthesized to directly augment human performance on salient tasks in
scientific endeavors. We present initial progress on methods and prototypes,
and lay out important opportunities and challenges ahead with computational
approaches that have the potential to revolutionize science.

我们提出了一项以任务为导向的知识检索研究议程，利用计算模型和人工智能技术，摄取科学知识的典籍，检索灵感、解释、解决方案和证据，直接增强人类在科学领域突出任务上的表现，进而革命性地推动科学进程。