121–130 of 442 posts
Earlier quoted context omitted.
The first sentence in the comment you are responding to is sarcasm. Just replace "I can't believe" with "Of course".
> The first sentence in the comment you are responding to is sarcasm. Just replace "I can't believe" with "Of course". Do you have any evidence of this?
I wouldn't call it "accumulation of cognitive debt"; just call it cognitive decline, or loss of cognitive skills. And also DUH. If you stop speaking a language you forget it. The brain does not retain information that it does not need. Anybody remember the couple studies on the use of google maps for navigation? One was "Habitual use of GPS negatively impacts spatial memory during self-guided navigation"; another rep…
> The brain does not retain information that it does not need. Why do I still know how to optimize free conventional memory in DOS by configuring config.sys and autoexec.bat? I haven’t done this in 2 decades and I’m reasonably sure I never again will
I wouldn't call it "accumulation of cognitive debt"; just call it cognitive decline, or loss of cognitive skills. And also DUH. If you stop speaking a language you forget it. The brain does not retain information that it does not need. Anybody remember the couple studies on the use of google maps for navigation? One was "Habitual use of GPS negatively impacts spatial memory during self-guided navigation"; another rep…
Curious, did anyone try to learn a subject by predicting the next token, and how did it go?
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I don't think that research will show what you're hoping it would. I'm not a big proponent of AI, you shouldn't bother going through my history but it is there to back up my statement if you're bored. Anyway, even I find it hard to argue against AI agents for productivity, but I think ik depend a lot on how you use them. As an anecdotal example I mainly work with Python, C and Go, but once in a while I also work with…
You're proving the point in the actual research. Programmers who only use AI for learning/coding will lose this knowledge (of python, for example) that you have gained by actually "doing" it.
The core danger isn't the "debt" itself, which implies it can be repaid through practice. The real danger is crossing a "cognitive tipping point". This is the threshold where so much executive function, synthesis, and argumentation has been offloaded to an external system (like an LLM) that the biological brain, in its ruthless efficiency, not only prunes the unused connections but loses the meta-ability to rebuild them.
Our biological wetware is a use-it-or-lose-it system without version control. When a complex cognitive function atrophies, the "source code" is corrupted. There's no git revert for a collapsed neural network that once supported deep, structured thought.
This HN thread is focused on essay writing. But scale this up. We are running a massive, uncontrolled experiment in outsourcing our collective cognition. The long-term outcome isn't just a society of people who are less skilled, but a society of people who are structurally incapable of the kind of thinking that built our world.
So the question isn't just "how do we avoid cognitive debt?". The real, terrifying question is: "What kind of container do we need for our minds when the biological one proves to be so ruthlessly, and perhaps irreversibly, self-optimizing for laziness?"
I wouldn't call it "accumulation of cognitive debt"; just call it cognitive decline, or loss of cognitive skills. And also DUH. If you stop speaking a language you forget it. The brain does not retain information that it does not need. Anybody remember the couple studies on the use of google maps for navigation? One was "Habitual use of GPS negatively impacts spatial memory during self-guided navigation"; another rep…
> The brain does not retain information that it does not need. Sounds very plausible, though how does that square with the common experience that certain skills, famously 'riding a bike', never go away once learned?
I don't quite see their point. Obviously if you're delegating the task to someone/something then you're not getting as good at it as if you were to do it yourself. If I were to write machine code by hand, rather than having the compiler do it for me, I would definitely be better at it and have more neural circuitry devoted to it. As I see it, it's much more interesting to ask not wherther we are still good at doing t…
> Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.
When I write with AI, it feels smooth in the moment, but I’m not really thinking through the ideas. The writing sounds fine, but when I look back later, I often can’t remember why I phrased things that way. Now I try to write my own draft first, then use AI to help polish it. It takes a bit more effort upfront, but I feel like I learn more and remember things better.
The rule of thumb "LLMs are good at reducing text, not expanding it" is a good one here.
If you have something that you consider to be over 50% towards your desired result, reducing the space of the result has a higher chance of removing the negative factor than the positive.
In contrast, any case that the algorithm is less than 100% capable of producing the positive factor, adding on to the result could always increase the negative factor more than the positive, given a finite time constraint (aka any reasonable non-theoretical application).