[ 25/May/21 ]
It is clear that human neural networks have a very strong set of biases for simple answers to complex questions. The major evolutionary and developmental reasons for such biases at multiple levels of the core of our being are now well documented and understood for those with an interest in such things.
Becoming comfortable with eternal uncertainty takes a lot of work. It is not a default setting for human beings.
And I am quite unlike the interviewee. I tend not to trust things until I have spent some time with primary datasets myself. After leaving uni I spent months becoming sufficiently familiar with the math that I could verify and critique all of Einstein’s writings. It took me almost a year to be confident of all of Goedel’s conclusions. I did biochem and ecology at uni, and had most of my youth on farms, and as a hunter had a large dataset of animal behaviour that I could test various theories against.
I like numbers, and actually working with the numbers of quantum mechanics gives one a reasonable appreciation of how something closely approximating classical causality can emerge from systems that are fundamentally uncertain within probability constraints. When one takes that principle and applies it to all levels of evolutionary development, particularly to levels of social development, then one starts to build a very different sort of picture of human interaction and human neural activity.
I have as strong a dislike for any claim to science based in truth as I have any religious form of truth.
For me, the only sort of science that is worthy of the name is one based in eternal uncertainty, and an eternal journey towards becoming less wrong as contexts allow.
One does not need to spend much time with the numbers of the world of atoms to appreciate that all understandings are necessarily simplistic approximations to whatever reality actually is. If Garret Lissi’s conjectures are anywhere near real, then the complexity present is truly mind boggling.
When one starts to understand the major systems of how cells, sensory systems, and neural networks actually operate, then it becomes clear that what we experience as reality is always and necessarily a vastly simplified model of whatever reality actually is. We all necessarily live in our own VR (Virtual Reality) versions of whatever OR (Objective Reality) actually is, these are our experiential realities, and they are all personal. We then go on to build our simplistic understandings of them, and many then have the arrogance to call that “Truth” ????
We need our models, and we all need to treat them with humility – every level. Mathematicians and physicists tend to be worst at that, because they can be so certain in some classes of maths and logic, and they subconsciously take that certainty across to reality (where it does not belong). That certainty tends to be worst in those who constrain their investigations to the simplest of possible mathematical and logical systems – that of the binary truth value system True/False. As one starts to explore higher order truth value systems (and their consequent logics and mathematics), the view changes drastically (the next simplest is the trinary True/False/Undecided).
Operational confidence is a very different thing from the “Certainty” of “Truth”. Confidence always retains a place for questioning and uncertainty that “Truth” does not allow (even if not often exercised, it remains present).