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I'm really curious to see if efforts like this, and from computational augmentation and automation more generally, will yield progress on symbolic modeling of large natural systems.

There is a segment in Adam Curtis's "All Watched Over By Machines of Loving Grace" which describes scientists trying to model the ecology of a prairie. As described by Curtis, the more data they collected and more complex their model, the worse the predictions of the model became. It feels like the lessons of the past couple generations (many decades) have been that symbolic models don't compose together easily; symbolic analysis only works in simple systems and has hit diminishing returns; numeric and ML methods work well enough; etc. I'm curious if better tooling (augmentation and/or automation) can push through some of these challenges and yield real understanding. Or if there are some fundamental scaling problems that can not be overcome.