5 ms·

Humans also drop any hard requirements you specify regularly, and similarly require review. Nevertheless we manage to increase reliability of human output throu

by vidarh 6mo ago

Humans also drop any hard requirements you specify regularly, and similarly require review. Nevertheless we manage to increase reliability of human output through processes and reviews, and most of the methods we use for harnesses are taken from experience with how to reduce reliability issues in humans, who are notoriously difficult to ensure delivers reliably.

kaashif 6mo ago
The primary way to increase reliability is to automate. Instead of humans producing some output manually, humans producing machines which produce that output. I've seen a disturbing trend where a process that could've been a script or a requirement that could've been enforced deterministically is in fact "automated" through a set of instructions for an LLM.
- vidarh 6mo ago
  Sure, when that is possible. However, there are lots of processes we don't know how to automate in a deterministic way. Hence the vast amount of investment in building organisations of people with mechanism to make peoples output more reliable through structure, reviews, and so on. Large parts of human civilization rests on our ability to make something unreliable less unreliable through organisational structure and processes.
  - j45 6mo ago
    Underrated comment. So many applications of LLMs have even to start with deterministic brain when using a non-deterministic llm and then wonder why it’s not working.
  - intended 6mo ago
    We resolve that through liability, penalties, trust, responsibility, review and oversight. At the end of the day, if I am spending X$s for automation, I want to be able to sleep at night knowing my factory will not build a WMD or delete itself. If its simply a tool that is a multiplier for experts, then do I really need it? How much does it actually make my processes more efficient, faster, or more capable of earning revenue? There is a LOT that is forgiven when tech is new - but at some point the shiny newness falls off and it is compared to alternatives.
    - vidarh 6mo ago
      Liability, penalties, trust, and responsibility are means we use to try to influence the application of the processes that do. They do not directly affect reliability. They can be applied just as much to a team using AI as one that does not. Review and oversight does address reliability directly, and hence why we make use of those in processes to improve the reliability of mechanical processes as well, and why they are core elements of AI harnesses. > If its simply a tool that is a multiplier for experts, then do I really need it? How much does it actually make my processes more efficient, faster, or more capable of earning revenue? You can ask the same thing about all the supporting staff around the experts in your team. > There is a LOT that is forgiven when tech is new - but at some point the shiny newness falls off and it is compared to alternatives. Only teams without mature processes are not doing that for AI today. Most of the deployments of AI I work on are the outcome of comparing it to alternatives, and often are part of initiatives to increase reliability of human teams jut as much as increasing raw productivity, because they are often one and the same.
      - intended 6mo ago
        > Liability, penalties, trust, and responsibility are means we use to try to influence the application of the processes that do. They do not directly affect reliability. They can be applied just as much to a team using AI as one that does not. Yes and no. see next point. > You can ask the same thing about all the supporting staff around the experts in your team. I have a good idea of the shape of errors for a human based process, costing and the type of QA/QC team that has to be formed for it. We have decades, if not centuries of experience working with humans, which LLMs are promising to be the equivalents/superiors of. I think you and me, would both agree with the statement "use the right tool for the job". However, the current hype cycle has created expectations of reliability from LLMs that drive 'Automated Intelligence' styled workflows. On the other hand: > part of initiatives to increase reliability of human teams is a significantly more defensible uses of LLMs. For me, most deployments die on the altar of error rates. The only people who are using them to any effect are people who have an answer to "what happens when it blows up" and "what is the cost if something goes wrong". (there is no singular thread behind my comment. I think we probably have more in agreement than not, and its more a question of finding the precise words to declare the shapes we perceive.)
- jnpnj 6mo ago
  it's strange to see software engineers using skills aka human description of small scripts instead of scripting things directly. often there were cli / tools / libraries to do what a skill does for many years. maybe it's culture issue, people who enjoy automation / devops / predictability will naturally help themselves, but other people just want to "delegate" and be done without trying.
  - vidarh 6mo ago
    When people do that they are using skills wrong. The best way to use a skill is as a means to give targeted instructions on how to make use of cli / tools/ libraries, with the skill just covering the "squishy bits" that aren't easily encoded into something deterministic.
hansmayer 6mo ago
[flagged]
- teodosin 6mo ago
  Calm down. They were comparing a very specific and narrow aspect of both. Not totally equivalent maybe, but that doesn't justify a tantrum.
  - hansmayer 6mo ago
    I am incredibly calm. I just wonder at the idiots who think they should compare the magnificiently efficient human brain to the shitslop machines.
- vidarh 6mo ago
  Because certain aspects (both are error prone) are similar and comparable. The notion that two entities need to be close in abilities for it to be possible to compare them is nonsense. You make the point for me: We managed to put men on the moon despite humans being enormously unreliable and error prone, because we built system around them that allowed for harnessing the good bits and reducing the failures to acceptable levels. We are - I am anyway - using our lessons from building reliable systems from unreliable elements to raise the reliability of outputs of LLMs the same way.
  - hansmayer 6mo ago
    > We are - I am anyway - using our lessons from building reliable systems from unreliable elements to raise the reliability of outputs of LLMs the same way. :) :) :) I could tell immediately you are somehow vested in the "success" of the LLM. So 600 B dollars and five years later, can you tell me how far did you guys get? Apollo programme costed a tiny fraction of that and started putting people on the moon some ~10 years later. Would you say that you are on the way to accomplish something similar in the next five years?
    - vidarh 6mo ago

by vidarh 6mo ago

kaashif 6mo ago

The primary way to increase reliability is to automate. Instead of humans producing some output manually, humans producing machines which produce that output. I've seen a disturbing trend where a process that could've been a script or a requirement that could've been enforced deterministically is in fact "automated" through a set of instructions for an LLM.

vidarh 6mo ago
Sure, when that is possible. However, there are lots of processes we don't know how to automate in a deterministic way. Hence the vast amount of investment in building organisations of people with mechanism to make peoples output more reliable through structure, reviews, and so on. Large parts of human civilization rests on our ability to make something unreliable less unreliable through organisational structure and processes.
- j45 6mo ago
  Underrated comment. So many applications of LLMs have even to start with deterministic brain when using a non-deterministic llm and then wonder why it’s not working.
- intended 6mo ago
  We resolve that through liability, penalties, trust, responsibility, review and oversight. At the end of the day, if I am spending X$s for automation, I want to be able to sleep at night knowing my factory will not build a WMD or delete itself. If its simply a tool that is a multiplier for experts, then do I really need it? How much does it actually make my processes more efficient, faster, or more capable of earning revenue? There is a LOT that is forgiven when tech is new - but at some point the shiny newness falls off and it is compared to alternatives.
  - vidarh 6mo ago
    Liability, penalties, trust, and responsibility are means we use to try to influence the application of the processes that do. They do not directly affect reliability. They can be applied just as much to a team using AI as one that does not. Review and oversight does address reliability directly, and hence why we make use of those in processes to improve the reliability of mechanical processes as well, and why they are core elements of AI harnesses. > If its simply a tool that is a multiplier for experts, then do I really need it? How much does it actually make my processes more efficient, faster, or more capable of earning revenue? You can ask the same thing about all the supporting staff around the experts in your team. > There is a LOT that is forgiven when tech is new - but at some point the shiny newness falls off and it is compared to alternatives. Only teams without mature processes are not doing that for AI today. Most of the deployments of AI I work on are the outcome of comparing it to alternatives, and often are part of initiatives to increase reliability of human teams jut as much as increasing raw productivity, because they are often one and the same.
    - intended 6mo ago
      > Liability, penalties, trust, and responsibility are means we use to try to influence the application of the processes that do. They do not directly affect reliability. They can be applied just as much to a team using AI as one that does not. Yes and no. see next point. > You can ask the same thing about all the supporting staff around the experts in your team. I have a good idea of the shape of errors for a human based process, costing and the type of QA/QC team that has to be formed for it. We have decades, if not centuries of experience working with humans, which LLMs are promising to be the equivalents/superiors of. I think you and me, would both agree with the statement "use the right tool for the job". However, the current hype cycle has created expectations of reliability from LLMs that drive 'Automated Intelligence' styled workflows. On the other hand: > part of initiatives to increase reliability of human teams is a significantly more defensible uses of LLMs. For me, most deployments die on the altar of error rates. The only people who are using them to any effect are people who have an answer to "what happens when it blows up" and "what is the cost if something goes wrong". (there is no singular thread behind my comment. I think we probably have more in agreement than not, and its more a question of finding the precise words to declare the shapes we perceive.)
jnpnj 6mo ago
it's strange to see software engineers using skills aka human description of small scripts instead of scripting things directly. often there were cli / tools / libraries to do what a skill does for many years. maybe it's culture issue, people who enjoy automation / devops / predictability will naturally help themselves, but other people just want to "delegate" and be done without trying.
- vidarh 6mo ago
  When people do that they are using skills wrong. The best way to use a skill is as a means to give targeted instructions on how to make use of cli / tools/ libraries, with the skill just covering the "squishy bits" that aren't easily encoded into something deterministic.

hansmayer 6mo ago

[flagged]

teodosin 6mo ago
Calm down. They were comparing a very specific and narrow aspect of both. Not totally equivalent maybe, but that doesn't justify a tantrum.
- hansmayer 6mo ago
  I am incredibly calm. I just wonder at the idiots who think they should compare the magnificiently efficient human brain to the shitslop machines.
vidarh 6mo ago
Because certain aspects (both are error prone) are similar and comparable. The notion that two entities need to be close in abilities for it to be possible to compare them is nonsense. You make the point for me: We managed to put men on the moon despite humans being enormously unreliable and error prone, because we built system around them that allowed for harnessing the good bits and reducing the failures to acceptable levels. We are - I am anyway - using our lessons from building reliable systems from unreliable elements to raise the reliability of outputs of LLMs the same way.
- hansmayer 6mo ago
  > We are - I am anyway - using our lessons from building reliable systems from unreliable elements to raise the reliability of outputs of LLMs the same way. :) :) :) I could tell immediately you are somehow vested in the "success" of the LLM. So 600 B dollars and five years later, can you tell me how far did you guys get? Apollo programme costed a tiny fraction of that and started putting people on the moon some ~10 years later. Would you say that you are on the way to accomplish something similar in the next five years?
  - vidarh 6mo ago