It's because the first vowel in 'about' is an unstressed schwa. It sounds unnatural in English to stress or lengthen a schwa.
(The only exception I can think of is 'evennn'. e.g. Bob and Sue are at a dinner party with friends. Bob tells everyone that he likes all vegetables. Sue knows he doesn't like broccoli, so she nudges Bob and says "evennn...?". This makes sense if 'even' in fact contains a syllabic nasal consonant rather than schwa: /'ivn=/.)
At work we have an interactive guided process/wizard that is currently implemented with an unwieldy low-code engine. I've been replacing this with a Node API that uses XState to encode process states. The API endpoints wait for XState to enter a 'ready' state (calling out to external services in the process), then pass meta properties of the current state back to the client.
One nice thing about this is that I can translate the existing low-code model into XState more or less directly. And the state machine can be rendered as a process flow diagram using viz tools in the Xstate ecosystem or some straightforward custom tooling.
Matter does not exist. It is simply an abstraction to describe the relative progress of time. Every way we measure "matter" involves the progress of time: microscope, telescope, ruler, calipers, vibrations of photons hitting the eye.
When you say that the earth has rotated 15°, it simply means an hour has elapsed. When you move 60 miles while the earth rotates 15°, you have gone 60 mph.
The past is the way things were before they moved. Our memory is an image of the way things were arranged before. Matter is a creation of the mind.
We now know that exoplanets and the conditions for life abound in the universe. Where the conditions for life abound, the null hypothesis ought to be that life abounds. In discussions of alien life and intelligence, we are often biased by earlier states of knowledge about the universe and our position in it. When we first started digging up dinosaur bones, we came up with fantastical notions of creatures to explain these artifacts that were mysterious to us. Notions that fit into our existing worldview, drawn from folk knowledge and cultural history. Once archeologists started studying the bones carefully, they gave us stories more fantastical than we could have ever imagined in the framework of our folk knowledge. I suspect the same will turn out to be true of UAPs.
For example, UAP stories are often ridiculed on the premise that intelligent alien life would not bother to come all this way just to hide out in the ocean. That's our folk knowledge of aliens: they like to travel, are eager to make contact with other life forms and are capable of doing so. But the elusive behaviour of UAPs is exactly what we would expect from an "unmanned" scientific probe. The home planet would be dozens or hundreds of light years away, so the craft would need to be completely autonomous in the absence of any communication system. Where does an autonomous probe go to look for signs of life? Oceans.
After training my physics simulator on thousands of hours of video footage of trees moving in the wind, arborists tell me the trees are much more realistic (they are getting worried that I might put them out of business). But the physicists are still not satisfied. How many more videos do I need to generate the laws of motion?
ChatGPT-style AI should be called AC - Artificial Communication. It is trained on an extremely limited selection of utterances - most of our thinking is never written down. But no matter how many utterances are plugged in, the system will never be able to learn how to think. Its capabilities are stochastic and generalising, not systematic and generative.
An utterance is the expression or outcome of an internal thought process. A machine learning model is trained on the outcome, but not the process. Utterances look like thoughts to us; like pareidolia, we can't help but find thoughts in utterances. This is what makes ChatGPT so compelling.
People don't learn how to think either - it's a capability that grows from a genetic endowment. After the next AI winter, when we get over the spectacle of Artificial Communication, we may begin to examine our own generative capacities for thought. We'll know we're on the right track when AI is slow, uncooperative and childish, fails to thrive when exposed to nonsense training data, and requires very little of the right kind of training data to acquire its language.
(The only exception I can think of is 'evennn'. e.g. Bob and Sue are at a dinner party with friends. Bob tells everyone that he likes all vegetables. Sue knows he doesn't like broccoli, so she nudges Bob and says "evennn...?". This makes sense if 'even' in fact contains a syllabic nasal consonant rather than schwa: /'ivn=/.)
That said, I don't think it's a big deal!