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Understanding the complexity of the Earth systems and our climate is important to be able to make predictions about how they may change in the future. To do this, scientists use models which describe the relevant processes.

Deep learning – the area of machine learning generating its models from deep neural networks – has revolutionized the way we think of machine learning problems.While its disruptive force initially hit the field of computer vision with the massive adoption.

Natural language processing, i.e. the automated processing of human language with computers, is certainly not a new discipline. Some date it back to 1950, with Alan Turing’s famous test which a machine would pass by holding a convincingly “human” conversation.

Our aim is to create a database of who said what and when in both chambers of the Swiss parliament over the past 127 years. The Swiss Federal Archives recently carried out the digitalization of the proceedings of both the National Council and the Council of States. Thanks to these efforts, we can now openly access over 40,000 documents pertaining to all votes, speeches, laws, amendments to laws, etc., from 1891 to the present day.

Not all datasets are images and we need architectures that adapt to other types of data, encoding both domain specific knowledge and data specific characteristics. For instance, at the SDSC, we deal with spherical data, i.e. curved images on a sphere, but without clear borders and arbitrary orientation.

PSI, ETH Zurich, and the SDSC are jointly working on automatically analysing such three-dimensional images to ease the burden of the clinicians and pathologists in identifying healthy tissue from sick ones.