Objective The discovery of extrasolar planets - i.e. planets orbiting other stars - has fundamentally transformed our understanding of planets, solar systems and our place in the Milky Way. Recent discoveries have shown that planets between 1-2 R are the most abundant in our galaxy, so called super-Earths. Yet, they are entirely absent from our own solar system. Their nature, chemistry, formation histories or climate remain very much a mystery. Estimates of their densities suggest a variety of possible planet types and formation/evolution scenarios but current degeneracies cannot be broken with mass/radius measures alone. Spectroscopy of their atmospheres can provide vital insight. Recently, the first atmosphere around a super-Earth, 55 Cnc e, was discovered, showcasing that these worlds are far more complex than simple densities allow us to constrain. To achieve a more fundamental understanding, we need to move away from the status quo of treating individual planets as case-studies and analysing data ‘by hand’. A globally encompassing, self-consistent and self-calibrating approach is required. Here, I propose to move the field a significant step towards this goal with the ExoAI (Exoplanet Artificial Intelligence) framework. ExoAI will use state-of-the-art neural networks and Bayesian atmospheric retrieval algorithms applied to big-data. Given all available data of an instrument, ExoAI will autonomously learn the best calibration strategy, intelligently recognise spectral features and provide a full quantitative atmospheric model for every planet observed. This uniformly derived catalogue of super-Earth atmospheric models, will move us on from the individual case-studies and allow us to study the larger picture. We will constrain the underlying processes of planet formation/migration and bulk chemistries of super-Earths. The algorithm and the catalogue of atmospheric and instrument models will be made freely available to the community. Fields of science natural sciencescomputer and information sciencesartificial intelligencenatural sciencesphysical sciencesastronomygalactic astronomyengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticscognitive robotsnatural sciencesphysical sciencesastronomyplanetary sciencesplanetsgiant planetssuper-Earths Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2017-STG - ERC Starting Grant Call for proposal ERC-2017-STG See other projects for this call Funding Scheme ERC-STG - Starting Grant Host institution UNIVERSITY COLLEGE LONDON Net EU contribution € 1 500 000,00 Address GOWER STREET WC1E 6BT London United Kingdom See on map Region London Inner London — West Camden and City of London Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 500 000,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITY COLLEGE LONDON United Kingdom Net EU contribution € 1 500 000,00 Address GOWER STREET WC1E 6BT London See on map Region London Inner London — West Camden and City of London Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 500 000,00