Let’s take a brief break from research results and observational approaches to consider the broader context of how we do space science. In particular, what can we do to cut across barriers between different disciplines as well as widely differing venues? Working on a highly directed commercial product is a different process than doing academic research within the confines of a publicly supported research lab. And then there is the question of how to incorporate ever more vigorous citizen science.
SpaceML is an online toolbox that tackles these issues with a specific intention of improving the artificial intelligence that drives modern projects, with the aim of boosting interdisciplinary work. The project’s website speaks of “building the Machine Learning (ML) infrastructure needed to streamline and super-charge the intelligent applications, automation and robotics needed to explore deep space and better manage our planetary spaceship.”
I’m interested in the model developing here, which makes useful connections. Both ESA and NASA have taken an active interest in enhancing interdisciplinary research via accessible data and new AI technologies, as a recent presentation on SpaceML notes:
NASA Science Mission Directorate has declared  a commitment to open science, with an emphasis on continual monitoring and updating of deployed systems, improved engagement of the scientific community with citizen scientists, and data access to the wider research community for robust validation of published research results.
Within this context, SpaceML is being developed in the US by the Frontier Development Lab and hosted by the SETI Institute in California, while the UK presence is via FDL at Oxford University and works in partnership with the European Space Agency. This is a public-private collaboration that melds data storage, code-sharing and data analysis in the cloud. The site includes analysis-ready datasets, space science projects and tools.
Bill Diamond, CEO of the SETI Institute, explains the emergence of the approach:
“The most impactful and useful applications of AI and machine learning techniques require datasets that have been properly prepared, organized and structured for such approaches. Five years of FDL research across a wide range of science domains has enabled the establishment of a number of analysis-ready datasets that we are delighted to now make available to the broader research community.”
The SpaceML.org website includes a number of projects including the calibration of space-based instruments in heliophysics studies, the automation of meteor surveillance platforms in the CAMS network (Cameras for Allsky Meteor Surveillance), and one of particular relevance to Centauri Dreams readers, a project called INARA, which stands for Intelligent ExoplaNET Atmospheric RetrievAl. Its description:
“…a pipeline for atmospheric retrieval based on a synthesized dataset of three million planetary spectra, to detect evidence of possible biological activity in exoplanet atmospheres.”
SpaceML will curate a central repository of project notebooks and datasets generated by projects like these, with introductory material and sample data allowing users to experiment with small amounts of data before plunging into the entire dataset. New datasets growing out of ongoing research will be made available as they emerge.
I think researchers of all stripes are going to find this approach useful as it should boost dialogue among the various sectors in which scientists engage. I mentioned citizen scientists earlier, but the gap between academic research labs, which aim at generating long-term results, and industry laboratories driven by the need to develop commercial products to pay for investment is just as wide. Availability of data and access to experts across a multidisciplinary range creates a promising model.
James Parr is FDL Director and CEO at Trillium Technologies, which runs both the US and the European branches of Frontier Development Lab. Says Parr:
“We were concerned on how to make our AI research more reproducible. We realized that the best way to do this was to make the data easily accessible, but also that we needed to simplify both the on-boarding process, initial experimentation and workflow adaptation process. The problem with AI reproducibility isn’t necessarily, ‘not invented here’ – it’s more, ‘not enough time to even try’. We figured if we could share analysis ready data, enable rapid server-side experimentation and good version control, it would be the best thing to help make these tools get picked up by the community for the benefit of all.”
So SpaceML is an AI accelerator, one distributing open-source data and embracing an open model for the deployment of AI-enhanced space research. The current datasets and projects grow out of five years of applying AI to space topics ranging from lunar exploration to astrobiology, completed by FDL teams working in multidisciplinary areas in partnership with NASA and ESA and commercial partners. The growth of international accelerators could quicken the pace of multidisciplinary research.
What other multidisciplinary efforts will emerge as we streamline our networks? It’s a space I’ll continue to track. For more on SpaceML, a short description can be found in Koul et al., “SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA,” COSPAR 2021 Workshop on Cloud Computing for Space Sciences” (preprint).