Comment by vidarh
1 month 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.
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.
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.
> 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.)
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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.