This discouragement may not be useful because what you call "soulless token prediction machines" have been trained on human (and non-human) data that models human behavior which include concepts such as "grace".
A more pragmatic approach is to use the same concepts in the training data to produce the best results possible. In this instance, deploying and using conceptual techniques such as "grace" would likely increase the chances of a successful outcome. (However one cares to measure success.)
I'll refrain from comments about the bias signaled by the epithet "soulless token prediction machines" except to write that the standoff between organic and inorganic consciousnesses has been explored in art, literature, the computer sciences, etc. and those domains should be consulted when making judgments about inherent differences between humans and non-humans.
"Lets be nicer to the robots winky face" is not a solution to this problem. It's just a tool, and this is a technical problem with technical solutions. All of the AI companies could change this behavior if they wanted to.
This discouragement may not be useful because what you call "soulless token prediction machines" have been trained on human (and non-human) data that models human behavior which include concepts such as "grace".
A more pragmatic approach is to use the same concepts in the training data to produce the best results possible. In this instance, deploying and using conceptual techniques such as "grace" would likely increase the chances of a successful outcome. (However one cares to measure success.)
I'll refrain from comments about the bias signaled by the epithet "soulless token prediction machines" except to write that the standoff between organic and inorganic consciousnesses has been explored in art, literature, the computer sciences, etc. and those domains should be consulted when making judgments about inherent differences between humans and non-humans.