Unlocking Growth Potential at the Intersection of AI, Robotics, and Synthetic Biology

Abstract

Growth Potential at the Crossroads of Artificial Intelligence, Robotics, and Synthetic Biology. Biomedical and bioengineering advancements are impeded by our incapacity to foretell how biological systems will act. We are unable to extrapolate behaviour on a large scale from studies conducted on a small scale, nor can we foretell how changes to genotype will impact phenotype. Recent advances in machine learning have made it possible to get the necessary predictive power without a deep mechanical knowledge. Having said that, training them requires massive amounts of data. In order to create a wide variety of biological systems with good reproducibility, the quantity and quality of data needed can only be met by combining synthetic biology with automation. Advancements in predictive biology and better machine learning algorithms can be achieved through consistent funding of research into the areas of synthetic biology, machine learning, and automation.