20210614 The place of unthought thoughts.: Page 12 of 15

20210614 The place of unthought thoughts.

Nigel Morris-Cotterill


Today I'm writing about white holes, "The Place of Unthought Thoughts."

This is not unknown unknowns, it's about the things we know about but don't want to know more about, where our quest for understanding reaches the end of our willingness to enquire.

If books, etc. can have "out-takes" this is one of mine, a section that ended up on the cutting room floor, so to speak.

This BLOG/cast includes part of my research into suspicion but didn’t find its way into the book or the courses. It is highly relevant as it explains some of the background to the research and some of the thinking that went into the final (for now) product.

Get a notebook, tell yourself to make a cup of tea then go to sit in front of the tv to drink it and note down every action you take starting from ″receive instruction to make tea″ remembering that ″deciding to go to the kitchen″ is an activity. We short circuit it by saying ″I got up, went to the kitchen, boiled the kettle, put tea in the pot, put boiling water on it, put milk in a cup, poured tea, took it and sat in front of the telly.″

But no, that is far, far, far away from recording every step in the process.

″step into kitchen
Decision: need to walk further.
Decision: need to walk further.
Decision: need to walk further.
Decision: stop walking.
Decision: look for the kettle
Turn to the right
Decision: pick up kettle
Extend arm
Extend fingers
Wrap fingers around the kettle’s handle
Lift kettle
Decision: assess weight of kettle to determine whether more water is required.
Apply weight of kettle to database, look up weight of kettle v amount of water and sufficiency to make a pot of tea.″
As you can see, even a simple task requires both action, data collection and decision making.

Any form of automation must have this level of detail.

But that isn’t good enough. What happens if the kettle isn’t in the kitchen? Maybe it was used as an ad hoc watering can earlier in the day and is still on the balcony?

You need to build in this and all other possibilities to avoid mistakes in processing or, possibly the lesser of two evils, the system stopping and saying it doesn’t have what it needs to go on.
Here is where we start to ask the hard questions.

The who, where, when, why and (w)how that we find where unthought thoughts reside.

We cannot say ″that’s just how it is″ or ″we’ve reached the end of our current understanding so let’s stop and go to the pub.″

We have to find out why it doesn’t work and, vitally, we have to find out how to break it before we put it into everyday use.

OK, back to P v NP. The theory is that anything that is in P is also in NP. So, on the face of it,

a+b+c is the same as c+b+a and P does equal NP.

This is because we are taking a dataset and applying it and then applying a check to it. If it passes, it’s good.

This may (or may not) be at least part of what the ″checksum″ is when you download software to ensure that what you get is exactly what the original upload contained. But that’s one of those asides that crop up in the white hole. Let’s not follow it so we can stick to the point.