Machine learning for discovering life beyond Earth: autonomous strategies for new and unknown horizons

Recent developments in AI capabilities have seen significant advances in our ability to rapidly gather and summarise information, using big data repositories. In some applications, this has been realised, using Large Language Models (LLMs) to compile rapid responses to enquiries, although this output can be contaminated by spurious content, such as disinformation. Nevertheless, Machine learning architecture lies at the core of many of these capabilities, as its primary function is to identify patterns in data and make predictions, responses, or decisions based on those patterns. What happens when we encounter the unknown? Not just an anomaly, but an entire dataset outside of our previous knowledge corpus. Referencing existing knowledge bases–e.g., language structure, and known parameters for life and environment - enables us to create [frame] fundamentals, which we can then build upon and incorporate capacities, such as the ‘elasticity’ of affinity, for realising new [unknown] phenomena, to explore new horizons.