I have three questions:
1. Flexible interfaces based on the kinds of grammars you describe in this post feel like their time have come, as an important part of scientific communication, but also as a tool to create, test, and update hypotheses themselves. Entire experiments could be written in the language of these grammars, where transformations and operations directly produce the artifacts that support scientific conclusions: things like means and variances, distributions, statistical tests, scatter plots, and the like. What are your thoughts on that? Is that a direction in which Lucid is interested in going?
2. I'm interested in how to automate the creation of such linked interfaces, whether they be for convnets, language models, seq2seq type models, etc. The idea would be to have the user write a query in a DSL that explicitly connects specific interior activations (on a given axis/axes) with gradients and visualization techniques to produce a linked interface. I haven't yet played around with Lucid -- is Lucid already such a DSL for tensorflow? Or does it rather collect various instantiations of the general grammar that have proved useful?
3. What are the best examples you know of linked interfaces and grammar-like approaches to visualization inspiring important discoveries? Within deep learning, it feels like the slam-dunk example is OpenAI's sentiment neuron, although that was a fairly simple form of visualization. I'd be curious to find more examples, both inside and outside the field of deep learning.