Machine learning is about understanding and building computational processes for adapting to data and experience, something that for most of natural history has only existed in living organisms. Lying at the interface between computer science and statistics, machine learning has in recent years come into the spotlight for providing rich new tools for data analysis. While machine learning is interacting with many different scientific areas, collaborations with the life sciences have been particular exciting as biology invests increasingly in automation and high-throughput data collection methods.
It is an amazing time for computer scientists and biologists to work together, but we can go far beyond data analysis. I will discuss two such collaborative areas that push this boundary: the automated design of biologically-relevant systems, and the exploration of adaptive algorithms in biological substrates. For the former, I will describe ongoing work to automate the process of design of systems such as organic molecules, DNA sequences, and biomimetic robots. For the latter, I will give an overview of recent work showing how important classes of machine learning algorithms can be implemented with biomolecules, without resorting to digital models for chemical computation.