Like young children, we adults also love to ask Why? It just seems so natural to want to understand why our car won't start, why our spouse is angry with us, why we've got a stomach ache.
I'm not denying the usefulness of why, of seeking causes, of fathoming the source from which a certain aspect of reality has sprung.
However, more and more, I'm beginning to sense that the question which we take for granted -- why? -- may lack meaning beyond the minds of us humans.
Of course, this could be said of anything.
Even when it comes to seemingly solid scientific facts, like how electromagnetism works, it's possible to envisage an alien being with a consciousness much different from ours not being able to grasp our principles of modern physics in this area.
This being might comprehend our common physical reality in a decidedly unique way. Which could include a lack of needing Why? to make sense of things.
I've talked about this general notion in a couple of blog posts:
"Why ask 'why' if the question is unanswerable?
"There's no answer to 'Why does the world exist?'"
Regarding the latter post, it seems almost unarguable that there can't be a reason for existence existing, since the existence of that purported reason would lead to another Why?
This is the problem with positing God as the creator of existence.
What or who created God? If the answer is "nothing, God has always existed," then one can simply say, "fine, I choose to accept that existence has always existed, which eliminates the need for God."
Ultimately, there has to be an end to Why? But when we're talking about everyday less-than-ultimate reality, does Why? make any more sense?
I don't know for sure.
Increasingly, I'm at least wondering whether we need Why? as much as most of us believe we do. And at the edge of my perplexity about Why?, I have a dim intuition that this entire concept is a human construction that isn't part of the fabric of reality as it exists outside of Homo sapiens' minds.
Here's three sources that have contributed to this vague feeling. I'll let the quotes speak for themselves.
(1) Some lines from the movie, "Arrival," where a mysterious alien spacecraft arrives on Earth carrying equally mysterious beings. Someone has said that it is important to know the purpose of the alien visit. A linguist responds:
Dr. Louise Banks: And "purpose" requires an understanding of intent. We need to find out, do they make conscious choices or is their motivation so instinctive that they don't understand a "why" question at all. And biggest of all, we need to have enough vocabulary with them that we understand their answer.
(2) Some quotes from a New Yorker article, "A.I. vs. M.D.: What happens when diagnosis is automated?"
“Our results support the hypothesis that a process similar to naming things in everyday life occurs when a physician promptly recognizes a characteristic and previously known lesion,” the researchers concluded. Identifying a lesion was a process similar to naming the animal. When you recognize a rhinoceros, you’re not considering and eliminating alternative candidates. Nor are you mentally fusing a unicorn, an armadillo, and a small elephant.
You recognize a rhinoceros in its totality—as a pattern. The same was true for radiologists. They weren’t cogitating, recollecting, differentiating; they were seeing a commonplace object. For my preceptor, similarly, those wet rales were as recognizable as a familiar jingle.
In 1945, the British philosopher Gilbert Ryle gave an influential lecture about two kinds of knowledge. A child knows that a bicycle has two wheels, that its tires are filled with air, and that you ride the contraption by pushing its pedals forward in circles. Ryle termed this kind of knowledge—the factual, propositional kind—“knowing that.” But to learn to ride a bicycle involves another realm of learning. A child learns how to ride by falling off, by balancing herself on two wheels, by going over potholes. Ryle termed this kind of knowledge—implicit, experiential, skill-based—“knowing how.”
The two kinds of knowledge would seem to be interdependent: you might use factual knowledge to deepen your experiential knowledge, and vice versa. But Ryle warned against the temptation to think that “knowing how” could be reduced to “knowing that”—a playbook of rules couldn’t teach a child to ride a bike. Our rules, he asserted, make sense only because we know how to use them: “Rules, like birds, must live before they can be stuffed.”
...“Imagine an old-fashioned program to identify a dog,” he said. “A software engineer would write a thousand if-then-else statements: if it has ears, and a snout, and has hair, and is not a rat . . . and so forth, ad infinitum. But that’s not how a child learns to identify a dog, of course. At first, she learns by seeing dogs and being told that they are dogs. She makes mistakes, and corrects herself. She thinks that a wolf is a dog—but is told that it belongs to an altogether different category. And so she shifts her understanding bit by bit: this is ‘dog,’ that is ‘wolf.’
The machine-learning algorithm, like the child, pulls information from a training set that has been classified. Here’s a dog, and here’s not a dog. It then extracts features from one set versus another. And, by testing itself against hundreds and thousands of classified images, it begins to create its own way to recognize a dog—again, the way a child does.” It just knows how to do it.
...The “black box” problem is endemic in deep learning. The system isn’t guided by an explicit store of medical knowledge and a list of diagnostic rules; it has effectively taught itself to differentiate moles from melanomas by making vast numbers of internal adjustments—something analogous to strengthening and weakening synaptic connections in the brain. Exactly how did it determine that a lesion was a melanoma? We can’t know, and it can’t tell us. All the internal adjustments and processing that allow the network to learn happen away from our scrutiny.
As is true of our own brains. When you make a slow turn on a bicycle, you lean in the opposite direction. My daughter knows to do this, but she doesn’t know that she does it. The melanoma machine must be extracting certain features from the images; does it matter that it can’t tell us which? It’s like the smiling god of knowledge. Encountering such a machine, one gets a glimpse of how an animal might perceive a human mind: all-knowing but perfectly impenetrable.
(3) Some quotes from Daniel Dennett's new book, "From Bacteria to Bach and Back: The Evolution of Minds."
Imagine we are back in the early days of this process where persistence turns gradually into multiplication, and we see a proliferation of some types of items where before there were none and we ask, "Why are we seeing these improbable things here?" The question is equivocal! For now there is both a process narrative answer, how come, and a justification, what for.
...We can reverse engineer any reproducing entity, determining its good and its bad, and saying why it is good or bad. This is the birth of reason, and it is satisfying to note that this is a case of what Glenn Adelson has aptly called Darwinism about Darwinism: we see the gradual emergence of the species of reasons out of the species of mere causes, what fors out of how comes, with no "essential" dividing line between them.
Just as there is no Prime Mammal -- the first mammal that didn't have a mammal for a mother -- there is no Prime Reason, the first feature of the biosphere that helped something exist because it made it better at existing than the "competition."
Natural selection is thus an automatic reason-finder, which "discovers" and "endorses" and "focuses" reasons over many generations. The scare quotes are to remind us that natural selection doesn't have a mind, doesn't itself have reasons, but is nevertheless "competent" to perform this "task" of design refinement.
...There are reasons why trees spread their branches, but they are not in any strong sense the trees' reasons. Sponges do things for reasons, bacteria do things for reasons; even viruses do things for reasons. But they don't have the reasons; they don't need to have the reasons.
...This is competence without comprehension.