I don’t see much similarity beyond the most superficial.
If you sit at a slot machine and pump quarters into it, each “turn” is independent. You spin and you win or lose. It’s pure chance and there is no destination. You execute the exact same action over and over and hope random chance brings you more money.
If you sit down in front of a coding harness, the progress is incremental and directed. You ask for a thing, the LLM produces something that is hopefully close to what you wanted. You give it more direction to prod it closer to the end state you want. You are not executing the same action, but incrementally nudging it in the right direction. I’ve literally never restarted from the same initial state with the same prompt and hoped for a different result and I don’t know why anyone would. Rarely I’ve thrown away the progress made and started over but always with a very different prompt that includes learnings from the failed attempt.
i agree with you that it’s principally different from a slot machine, and that it’s possible to use it in a way (like you describe) that is much more focused, for lack of a better term, to great effect
most people don’t use ai this way though, and i still feel like the end-psychological reward mechanism is very, very similar to gambling regardless of how well one utilizes it (and this is even more obvious with image generation as you chase that perfect output)
perhaps it’s better to compare it to gacha than slots?
How do they use it? Surely no one is just repeating the same prompt over and over (except as a Ralph loop perhaps, which is automated). I’m really struggling with the notion that most people just throw the same prompt repeatedly hoping it eventually works. Because that doesn’t sound like gambling. It sounds crazy (and frustrating).
> and i still feel like the end-psychological reward mechanism is very, very similar to gambling regardless of how well one utilizes it
In the sense that you get a dopamine reward when you succeed, sure, but I get the same reward when I code by hand and achieve a successful result.
> and this is even more obvious with image generation as you chase that perfect output
This is fair, because sometimes with image generation the same exact prompt will produce very different output. This is becoming less true as the models get better and it becomes more effective to direct image generation iteratively than to keep starting from scratch with a barely tweaked prompt.
When working on a problem models will walk you down a garden path requiring only yes/no answers or very brief clarifications for a long time. It's always proposing its own workarounds/suggestions/etc. Especially if trying to debug something where it has more understanding than the user so really all it needs from you is "uh sure try that too I guess" from time to time.
And sometimes the debugging has already veered completely off course at the beginning so it's futile, but each time the fleeting hope that just a few thousand more tokens will magically fix it tempts you to keep going a little longer.
as an example, i was using chatgpt a few weeks back to help me remember the name of a painting i’d seen about a decade ago. i could recall the general shape of the subject and that it was europeanish, but nothing else. after seven turns or so it finally got it, and honestly, the relief of finally remembering the name felt like, well, hitting a jackpot
i’ve had a similar feeling of success after trying to get it to give a comprehensible answer when asking it for a solid counter-argument to philosophical questions. it is indeed often crazy and frustrating
> but I get the same reward when I code by hand
i have only done very simple coding work with llms, so that may be why our ideas differ about the feeling of reward. this is where the comparison to gacha makes more sense than slots, since when you’re coding, you still get a reward each turn whilst chasing the final/desired result
> the relief of finally remembering the name felt like, well, hitting a jackpot
I understand the joy of success but I fail to see how this is gambling. I could have an equivalent conversation with a friend (more likely about a movie in trying to remember than a painting, but still) and get the exact same type of iterative “no, not that one, it was more like X” and feel elated when my friend finally realizes I’m taking about a scene from Hot Tub Time Machine.
sorry, maybe my english is failing me here. what i was getting at is that using ai feels like gambling to me, where tokens, time, etc. are wagered against the chance for desired output. the risk is that you waste your tokens/time, and the prize is a useful answer (if not the exact code/solution/whatever one was hoping for)
i wasn’t trying to convince you, i’m just explaining that this is gambling in a meaningful sense to some people, especially with how turn-based and unpredictable the whole system is. whether it is actually gambling (semantically, legally, ontologically?) isn’t really interesting imo. what’s interesting is that it’s structured similarly and feels nearly identical to some people
but then, i also feel like there’s an element of gambling in the example of you talking with your friend, though i think it would be better illustrated if it was a conversation with a random person
If you sit at a slot machine and pump quarters into it, each “turn” is independent. You spin and you win or lose. It’s pure chance and there is no destination. You execute the exact same action over and over and hope random chance brings you more money.
If you sit down in front of a coding harness, the progress is incremental and directed. You ask for a thing, the LLM produces something that is hopefully close to what you wanted. You give it more direction to prod it closer to the end state you want. You are not executing the same action, but incrementally nudging it in the right direction. I’ve literally never restarted from the same initial state with the same prompt and hoped for a different result and I don’t know why anyone would. Rarely I’ve thrown away the progress made and started over but always with a very different prompt that includes learnings from the failed attempt.