one of asimov's finest , a metaphor that continues to find relevance in my day to day existence - that the conclusions we so readily come to are assumptions made in the absence of the awareness of something more
This is my favorite Asimov story. It's got a protagonist with compelling motivations, a society that has problems but also convincing reasons why they persist, and a great ending.
Dr Antonelli said, “Or do you believe that studying some subject will bend the brain cells in that direction, like that other theory that a pregnant woman need only listen to great music persistently to make a composer of her child. Do you believe that?”
Apparently, Asimov was an early critic of the “Mozart in the womb” movement.
Is this still in print, maybe as part of a collection? I tried to find it but couldn't. Many of his other works seem to be available as paperback, including a bunch of story collections.
Remind me of a recent discussion we had among Stackoverflow moderator:
> “Think about it,” he continued. “Who discovers the edge cases the docs don’t mention? Who answers the questions that haven’t been asked before? It can’t be people trained only to repeat canonical answers. Somewhere, it has to stop. Somewhere, someone has to think.”
> “Yes,” said the Moderator.
> He leaned back. For a moment, restlessness flickered in his eyes.
> “So why wasn’t I told this at the start?”
> “If we told everyone,” said the Moderator gently, “we’d destroy the system. Most contributors must believe the goal is to fix their CRUD apps. They need closure. They need certainty. They need to get to be a Registered Something—Frontend, Backend, DevOps, Full stack. Only someone who suffered through the abuse of another moderator closing their novel question as a duplicate can be trusted to put enough effort to make an actual contribution”
I've read this a long time ago, when I was a kid. Back then I thought about the education system and how it sometimes inhibits the creativity within the students. But right now, other comparison comes to mind - I don't know how relevant it is, though, so please don't judge it strictly.
Modern "AI" (LLM-based) systems are somewhat similar to the humans in this story who were taped. They may have a lot of knowledge, even a lot of knowledge that is really specialized, but once this knowledge becomes outdated or they are required to create something new - they struggle a lot. Even the systems with RAG and "continuous memory" (not sure if that's the right term) don't really learn something new. From what I know, they can accumulate the knowledge, but they still struggle with creativity and skill learning. And that may be the problem for the users of these systems as well, because they may sometimes rely on the shallow knowledge provided by the LLM model or "AI" system instead of thinking and trying to solve the problem themselves.
Luckily enough, most of the humans in our world can still follow the George's example. That's what makes us different from LLM-based systems. We can learn something new, and learn it deeply, creating the deep and unique networks of associations between different "entities" in our mind, which allows us to be truly creative. We also can dynamically update our knowledge and skills, as well as our qualities and mindset, and so on...
What concerns me is that learning depth is more discouraged than ever. For a long time it's been discouraged, which is natural as we have a preference for simple things rather than difficult/complex things. But we're pushing much harder than ever before. From the way we have influencer education videos to the way people push LLMs ("you can just vibe code, no thinking required"). We've advanced enough that it's easy to make things look good enough but looks can be deceiving. It's impossible to know what's good enough without depth of knowledge, without mastery.
No machine will ever be sufficient to overcome the fundamental problem: a novice is incapable of properly evaluating a system. No human is capable of doing this either, nor can they (despite many believing they can). It's a fundamental information problem. The best we can do is match our human system, where we trust the experts, who have depth. But we even see the limits of that and how frequently they get ignored by those woefully unqualified to evaluate. Maybe it'll be better as people tend to trust machines more. But for the same reason it could be significantly worse. It's near impossible to fix a problem you can't identify.
The page linked has some more information available, but its author (abelard?) cites from "Mein Kampf" later, naming the books author as "Adolph" (sic!).
Caution is advised.
I am sort of questioning my use of LLMs again after, first reluctantly, starting to use them multiple times a day. This story seems like it was intended to be an allegory for LLM-use though I know it couldn't have been.
There's a similar story about a progression of robot repair devices --- which has to end in a "Master Robot Repairman" profession which is the folks who repair the robots which repair other robots.
Blanking on author and title, but read it a _long_ while ago, and it had a distinctly golden age feel --- maybe Murray Leinster?
A very nice story, and an interesting reflection on the education system.
Also, and this is just an aside, but “the protagonist who is too special for the sorting hat” is a bit of a trope in young adult literature at this point. Is this the first real instance of it? 1957. That’s a while ago! I don’t even know if the “sorting hat” trope was established enough to subvert at the time.
Unlike hacks like Cline, Asimov gives the special character serious flaws like jealousy. The protagonist's skill is also merely rare, instead of unique, and his roommate seems to be on a higher level still.
This story is set thousands of years in the future, and yet their social norms are broadly those of 1960s America, conspicuously minus the racism. Their notion of gender equality, for instance, is to segregate, but add "(and women)" after every few "men" (respectively "(and husbands)" after "wives"). Stubby Trevelyan smokes, and litters the cigarette butts. This has to be deliberate on the part of the author. I wonder what Ladislas Ingenescu, Registered Historian, has to say about the matter?… if, indeed, he has any original thoughts to share.
I let my curiosity run and read the citizenship curriculum. While I in general agree to this curriculum, I shall argue that the most important thing for a citizen to learn, that should be on the top of list, is to push back when he thinks something is wrong. It is perhaps more important in nowadays.
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[ 1.9 ms ] story [ 52.5 ms ] threadhttps://www.inf.ufpr.br/renato/profession.html
Apparently, Asimov was an early critic of the “Mozart in the womb” movement.
> “Think about it,” he continued. “Who discovers the edge cases the docs don’t mention? Who answers the questions that haven’t been asked before? It can’t be people trained only to repeat canonical answers. Somewhere, it has to stop. Somewhere, someone has to think.”
> “Yes,” said the Moderator.
> He leaned back. For a moment, restlessness flickered in his eyes.
> “So why wasn’t I told this at the start?”
> “If we told everyone,” said the Moderator gently, “we’d destroy the system. Most contributors must believe the goal is to fix their CRUD apps. They need closure. They need certainty. They need to get to be a Registered Something—Frontend, Backend, DevOps, Full stack. Only someone who suffered through the abuse of another moderator closing their novel question as a duplicate can be trusted to put enough effort to make an actual contribution”
Modern "AI" (LLM-based) systems are somewhat similar to the humans in this story who were taped. They may have a lot of knowledge, even a lot of knowledge that is really specialized, but once this knowledge becomes outdated or they are required to create something new - they struggle a lot. Even the systems with RAG and "continuous memory" (not sure if that's the right term) don't really learn something new. From what I know, they can accumulate the knowledge, but they still struggle with creativity and skill learning. And that may be the problem for the users of these systems as well, because they may sometimes rely on the shallow knowledge provided by the LLM model or "AI" system instead of thinking and trying to solve the problem themselves.
Luckily enough, most of the humans in our world can still follow the George's example. That's what makes us different from LLM-based systems. We can learn something new, and learn it deeply, creating the deep and unique networks of associations between different "entities" in our mind, which allows us to be truly creative. We also can dynamically update our knowledge and skills, as well as our qualities and mindset, and so on...
That's what I'm hoping for, at least.
No machine will ever be sufficient to overcome the fundamental problem: a novice is incapable of properly evaluating a system. No human is capable of doing this either, nor can they (despite many believing they can). It's a fundamental information problem. The best we can do is match our human system, where we trust the experts, who have depth. But we even see the limits of that and how frequently they get ignored by those woefully unqualified to evaluate. Maybe it'll be better as people tend to trust machines more. But for the same reason it could be significantly worse. It's near impossible to fix a problem you can't identify.
Blanking on author and title, but read it a _long_ while ago, and it had a distinctly golden age feel --- maybe Murray Leinster?
Also, and this is just an aside, but “the protagonist who is too special for the sorting hat” is a bit of a trope in young adult literature at this point. Is this the first real instance of it? 1957. That’s a while ago! I don’t even know if the “sorting hat” trope was established enough to subvert at the time.
"Fans are slans."
0: https://kyla.substack.com/p/the-four-phases-of-institutional
https://windupstories.com/books/pump-six-and-other-stories/