Deep Machine Learning in Cosmology: Evolution or Revolution?

Could Machine Learning (ML) make fundamental discoveries and tackle unsolved problems in Cosmology? Detailed observations of the present contents of the universe are consistent with the Cosmological Constant Lambda and Cold Dark Matter model, subject to some unresolved inconsistencies ('tensions') among observations of the Hubble Constant and the clumpiness factor. To understand these issues further, large surveys of billions of galaxies and other probes require new statistical approaches. In recent years the power of ML, and in particular 'Deep Learning', has been demonstrated for object classification, photometric redshifts, anomaly detection, enhanced simulations, and inference of cosmological parameters. It is argued that the more traditional 'shallow learning' (i.e. with pre-processing feature extraction) is actually quite deep, as it brings in human knowledge, while 'deep learning' might be perceived as a black box, unless supplemented by explainability tools. The 'killer applications' of ML for Cosmology are still to come. New ways to train the next generation of scientists for the Data Intensive Science challenges ahead are also discussed. Finally, the chatbot ChatGPT is challenged to address the question posed in this article's title.