%0 Generic %1 thomas2019using %A Thomas, Brian %A Thronson, Harley %A Adrian, Andrew %A Lowndes, Alison %A Mason, James %A Memarsadeghi, Nargess %A Samadi, Shahin %A Varsi, Giulio %D 2019 %K learning machine %T Using Artificial Intelligence to Augment Science Prioritization for Astro2020 %U http://arxiv.org/abs/1908.00369 %X Science funding agencies (NASA, DOE, and NSF), the science community, and the US taxpayer have all benefited enormously from the several-decade series of National Academies Decadal Surveys. These Surveys are one of the primary means whereby these agencies may align multi-year strategic priorities and funding to guide the scientific community. They comprise highly regarded subject matter experts whose goal is to develop a set of science and program priorities that are recommended for major investments in the subsequent 10+ years. They do this using both their own professional knowledge and by synthesizing details from many thousands of existing and solicited documents. Congress, the relevant funding agencies, and the scientific community have placed great respect and value on these recommendations. Consequently, any significant changes to the process of determining these recommendations should be scrutinized carefully. That said, we believe that there is currently sufficient justification for the National Academies to consider some changes. We advocate that they supplement the established survey process with predictions of promising science priorities identified by application of current Artificial Intelligence (AI) techniques These techniques are being applied elsewhere in long-range planning and prioritization. We present a proposal to apply AI to aid the Decadal Survey panel in prioritizing science objectives. We emphasize that while AI can assist a mass review of papers, the decision-making remains with humans. In our paper below we summarize the case for using AI in this manner and suggest small inexpensive demonstration trials, including an AI/ML assessment of the white papers submitted to Astro2020 and backcasting to evaluate AI in making predictions for the 2010 Decadal Survey.

@misc{thomas2019using, abstract = {Science funding agencies (NASA, DOE, and NSF), the science community, and the US taxpayer have all benefited enormously from the several-decade series of National Academies Decadal Surveys. These Surveys are one of the primary means whereby these agencies may align multi-year strategic priorities and funding to guide the scientific community. They comprise highly regarded subject matter experts whose goal is to develop a set of science and program priorities that are recommended for major investments in the subsequent 10+ years. They do this using both their own professional knowledge and by synthesizing details from many thousands of existing and solicited documents. Congress, the relevant funding agencies, and the scientific community have placed great respect and value on these recommendations. Consequently, any significant changes to the process of determining these recommendations should be scrutinized carefully. That said, we believe that there is currently sufficient justification for the National Academies to consider some changes. We advocate that they supplement the established survey process with predictions of promising science priorities identified by application of current Artificial Intelligence (AI) techniques These techniques are being applied elsewhere in long-range planning and prioritization. We present a proposal to apply AI to aid the Decadal Survey panel in prioritizing science objectives. We emphasize that while AI can assist a mass review of papers, the decision-making remains with humans. In our paper below we summarize the case for using AI in this manner and suggest small inexpensive demonstration trials, including an AI/ML assessment of the white papers submitted to Astro2020 and backcasting to evaluate AI in making predictions for the 2010 Decadal Survey.}, added-at = {2019-08-02T05:30:59.000+0200}, author = {Thomas, Brian and Thronson, Harley and Adrian, Andrew and Lowndes, Alison and Mason, James and Memarsadeghi, Nargess and Samadi, Shahin and Varsi, Giulio}, biburl = {https://www.bibsonomy.org/bibtex/2b35e747e55bb9b5f68322fd3dc9cc5ea/ericblackman}, description = {Using Artificial Intelligence to Augment Science Prioritization for Astro2020}, interhash = {748e7a06b44275568630d1bbcb9a712b}, intrahash = {b35e747e55bb9b5f68322fd3dc9cc5ea}, keywords = {learning machine}, note = {cite arxiv:1908.00369Comment: Submitted in response to the State of the Profession APC call; Decadal Survey of Astronomy and Astrophysics, 2020}, timestamp = {2019-08-02T05:30:59.000+0200}, title = {Using Artificial Intelligence to Augment Science Prioritization for Astro2020}, url = {http://arxiv.org/abs/1908.00369}, year = 2019 }