The problem I have with these definitions (and the accompanying theories) is that they are not practical. At best fun abstractions to wrap your head around with

The problem I have with these definitions (and the accompanying theories) is that they are not practical. At best fun abstractions to wrap your head around with, at worst pretentious and misguided. To advance the field of consciousness, I believe at the current stage we should always treat consciousness as a blackbox, and ask questions around it with practical engineering implications. Perhaps two categories of questions: Category 1: qualia/perception These would be human-centric questions related to experimenations with altered states of mind. Here is one for example: Why is it that under the effect of THC, certain stimuli and actions [1] can reliably slow down the perception of time, while certain stimuli (e.g. the soft humming of the aircon) tend to normalize time perception for some individual? [1]: e.g. start the stopwatch app, have the phone at arm's length, stare at the millisecond digit and slowly moving your phone closer to you. What can we say about the neural activations (and subsequently, oscillations) of individuals who are able to alter the time perception more easily (even at the presence of normalizing-stimuli) and how can this ability be learnt or unlearnt? Understanding of the above phenomenon could be used to design the calibration phrase of a BCI device so preprocessing, signal processing, etc, can be customized to deliver a smoother user experience. Category 2: data/computation One of the key charactistics of biological systems that invoke consciousness appears to be a cybernetics-oriented ability that involves orchestrating (often-function-specific) modules (e.g. in human brains) to accomplish (often-highly-abstracted(?)) tasks. Perhaps we can take inspirations from mindful practises (and other consciousness-centric activities) and study the brain and how its modules work together to come up with architectures, models, etc, that (going one step above spiking neural network?) mimic the cybernetic nature of consciousness for the integration of loosely-coupled things e.g. in transfer learning, etc, as well as systems that involve a lot of feedback loops. Perchance such biomimetics would help us to get a better idea how type (and category)-theoretical aspects of things can be introduced to engineer highly fault-torelent and energy-efficient systems that employ millions of pretrained models like GPT3 at the lower level and are constantly self-learning for general purpose tasks.