Speaker
Description
The No Miracles Argument is arguably the strongest case for scientific realism. Yet, it seems that machine-learning-driven scientific discovery evades its justificatory force. Machine learning models are explicitly trained for the purpose of detecting patterns in data, their predictive success comes by design and does not indicate the truth of the underlying representation. Moreover, such models do not generalize well beyond the domain of their specific training data. Thus, there seems to be no miraculous success to be explained.
However, I will argue that this line of reasoning does not apply to the machine learning models that are currently employed in physics research. Due to their relatively restricted tasks such as signal-background separation, solving the inverse problem, or hypothesis tests, the crucial inferential steps remain in human hands. Such machine learning methods might be faster and more accurate but not essentially different from previous methods.