6 ms·

People are stochastic. You build reliable processes out of unreliable parts with feedback and self-correcting mechanisms. AI is not actually magically special i

by barrkel 3mo ago

People are stochastic. You build reliable processes out of unreliable parts with feedback and self-correcting mechanisms. AI is not actually magically special in this regard. It has higher variance and we're still figuring out how to get all the tradeoffs right.

gmerc 3mo ago
[dead]
Topfi 3mo ago
Please. If you told a customer support rep that you are the former US president [0], they would not hand over the account straight away because you asked nicely. These models are great tools, but putting them and people on the same level does a disservice to our species and also is simply incorrect to what we know these models to be and their capabilities/limitations. [0] https://www.theguardian.com/technology/2026/jun/01/meta-ai-hack-obama-sephora-instagram https://www.theguardian.com/technology/2026/jun/01/meta-ai-h...
- barrkel 3mo ago
  I didn't put them on the same level. At the same time, one should acknowledge that not all tasks are on the same level.
  - skydhash 3mo ago
    Most tasks we use computers for are deterministic and was coded for that specific quality. Introducing nom deterministic behavior is lowering the value of the app, especially for power users.
    - klibertp 3mo ago
      I don't think this is true. Computers are still computers, and code executes (mostly) deterministically. What computers struggle with is making sense of unclear inputs, precisely because of their determinism. OCR, speech-to-text, and computer vision all proved extremely limited when tackled purely deterministically. The whole "ML revolution" before LLMs was about recognizing that messy, noisy data can be interpreted with some accuracy using statistical methods. LLMs are the continuation of that: they are even harder to measure and completely impossible to prove, but they bring capabilities we have been unable to achieve any other way for decades. Should they be used to handle deterministic logic? No, obviously not - but they do enable computer systems to start working in contexts where they either couldn't at all before, or were just bad at them.
rowanG077 3mo ago
The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. That is to say I do agree that building reliable processes out of unreliable parts with feedback is the modus operandi. However AI cannot meaningfully handle feedback and learn. And that is a key unsolved problem.
- rahidz 3mo ago
  "AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck." please take a look at the error(s) made in the prior run. what could've been done better? create or modify an existing skill to emphasize this, or suggest additional language in AGENTS.md.
  - drdexebtjl 3mo ago
    It will return a bunch of relevant-sounding insight, modify skills and context files… Then do the same error again. We’re not at the point where AI is capable of knowing what went wrong and self-aware enough to understand how it could reliably change its own behavior. For months I’ve been trying to have the agents stop manually writing our auto-generated SQL migrations and run the command that generates them instead. SOTA models insist on occasionally getting it wrong.
- embedding-shape 3mo ago
  > The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. I feel like this line of thinking is kind of an unfair comparison. I'm not saying LLMs are magical beings that can suddenly learn by themselves after getting something wrong, but your "person making mistake then being corrected" assumes you do tell the person about the mistake and tell them to avoid doing the same mistake in the future, but for the "LLM making mistake" example you then intentionally avoid letting the prompt being changed in response to the mistake, which would be the "then being corrected" part on the LLM side of the comparison. Similarly, if you just let a person make a mistake and don't let them know about the mistake, they might keep making that same mistake over and over again. If you update how you use the LLM as you discover what mistakes it does, just like you'd correct a person, then you can use an LLM and also the LLM can "be taught to not make that mistake again".
csomar 3mo ago
People live in very stochastic and volatile environments and they manage that in ways no LLMs currently ever can. (ie: imagine sending an LLM all the data - sensory/auditory/etc… - that a human receive) People’s job is to partially reign in this volatile environment by creating processes with stable output.

by barrkel 3mo ago

gmerc 3mo ago

[dead]

Topfi 3mo ago

Please. If you told a customer support rep that you are the former US president [0], they would not hand over the account straight away because you asked nicely. These models are great tools, but putting them and people on the same level does a disservice to our species and also is simply incorrect to what we know these models to be and their capabilities/limitations. [0] https://www.theguardian.com/technology/2026/jun/01/meta-ai-hack-obama-sephora-instagram https://www.theguardian.com/technology/2026/jun/01/meta-ai-h...

barrkel 3mo ago
I didn't put them on the same level. At the same time, one should acknowledge that not all tasks are on the same level.
- skydhash 3mo ago
  Most tasks we use computers for are deterministic and was coded for that specific quality. Introducing nom deterministic behavior is lowering the value of the app, especially for power users.
  - klibertp 3mo ago
    I don't think this is true. Computers are still computers, and code executes (mostly) deterministically. What computers struggle with is making sense of unclear inputs, precisely because of their determinism. OCR, speech-to-text, and computer vision all proved extremely limited when tackled purely deterministically. The whole "ML revolution" before LLMs was about recognizing that messy, noisy data can be interpreted with some accuracy using statistical methods. LLMs are the continuation of that: they are even harder to measure and completely impossible to prove, but they bring capabilities we have been unable to achieve any other way for decades. Should they be used to handle deterministic logic? No, obviously not - but they do enable computer systems to start working in contexts where they either couldn't at all before, or were just bad at them.

rowanG077 3mo ago

The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. That is to say I do agree that building reliable processes out of unreliable parts with feedback is the modus operandi. However AI cannot meaningfully handle feedback and learn. And that is a key unsolved problem.

rahidz 3mo ago
"AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck." please take a look at the error(s) made in the prior run. what could've been done better? create or modify an existing skill to emphasize this, or suggest additional language in AGENTS.md.
- drdexebtjl 3mo ago
  It will return a bunch of relevant-sounding insight, modify skills and context files… Then do the same error again. We’re not at the point where AI is capable of knowing what went wrong and self-aware enough to understand how it could reliably change its own behavior. For months I’ve been trying to have the agents stop manually writing our auto-generated SQL migrations and run the command that generates them instead. SOTA models insist on occasionally getting it wrong.
embedding-shape 3mo ago
> The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. I feel like this line of thinking is kind of an unfair comparison. I'm not saying LLMs are magical beings that can suddenly learn by themselves after getting something wrong, but your "person making mistake then being corrected" assumes you do tell the person about the mistake and tell them to avoid doing the same mistake in the future, but for the "LLM making mistake" example you then intentionally avoid letting the prompt being changed in response to the mistake, which would be the "then being corrected" part on the LLM side of the comparison. Similarly, if you just let a person make a mistake and don't let them know about the mistake, they might keep making that same mistake over and over again. If you update how you use the LLM as you discover what mistakes it does, just like you'd correct a person, then you can use an LLM and also the LLM can "be taught to not make that mistake again".

csomar 3mo ago

People live in very stochastic and volatile environments and they manage that in ways no LLMs currently ever can. (ie: imagine sending an LLM all the data - sensory/auditory/etc… - that a human receive) People’s job is to partially reign in this volatile environment by creating processes with stable output.