I gave you the benefit of the doubt. If you’ve had lots of interactions with AI, I am not sure how you can think that anything with AI can work as expected, specially in complicated edge cases.
If you’ve had lots of interactions with AI, I am not sure how you can think that anything with AI can work as expected
That’s a very broad statement, and “AI fluency” is a broad spectrum. Having “lots of interactions with AI” isn’t an indicator of much at all – lots of end users use LLMs every day. They have “lots of interactions with AI”, but most of them have not developed/trained agent “skill” files, built agent workflows, developed harnesses, handed complex tasks to a “team” of agents (and tuning them for reliability and fidelity), etc. Anyone can have lots of interactions with AI without ever experimenting beyond the consumer-grade interfaces.
Asking if someone has had “lots of interactions with AI” says much more about the experience of the person asking. A lot of “advanced” (using the term very loosely here) users/builders would know that your concern is an easy problem to solve. It might even be a decent sort of challenge to give an intern/student as a learning exercise, since they already have a lot real-world data (from their 311 trials and other cities/orgs that also use this tech) to use for testing and iterating.
You seem to see yourself as experienced AI user, but all you do is repeat the empty marketing promises.
Which ones? There are lots of empty marketing promises around AI; the signal-to-noise ratio is crazy. I try avoid the ones that I’m not confident in, but I admit that I don’t always express that clearly enough. (It’s sort of a bad habit/weak skill for me. These things are more obvious and sort of become “common sense” amongst colleagues, and I’ve never been much of a social media guy.)
I’m happy to try to clarify something, if needed.
The fact is, if they use an LLM, there will be mistakes, just like Claude will happily ignore part of it’s instructions a few times per day.
Of course there will be. Artificial neural networks are function approximation algorithms.
The thing about LLMs is that they’re almost always configured to run non-deterministically[1]. Most tools that depend on LLMs can’t be expected to work or fail deterministically. Building these tools is an iterative process of evaluating and tuning to maximize success rate, thus minimizing failure rate. So yes, LLM-based tech like this will definitely fuck up with a non-zero failure rate. And no, you probably won’t hear about this amongst the marketing noise.
Regarding the problem that the 911 triage thing aims to address: The solution would probably be to hire more operators/dispatchers. Unfortunately, there has been a nationwide shortage of them for years. The AI triage thing has proven to have a success rate high enough (and thus a failure rate low enough) to provide net-positive value[2]. It doesn’t 100% solve the problem, it just mitigates it, so the situation is less bad – not solved.
“AI fluency” is not a thing
It absolutely is a thing, and usually becomes VERY obvious when you start building things to use LLMs for automation. It’s like those iceberg memes. I’m not sure if “AI fluency” is the “correct” (as in widely-adopted) term for it though, but some people have called it that.
Some even try to quantify this, in some industries. For example, I’ve heard of a “Yegge scale” (or something like that) to estimate the AI fluency of a software engineer.
AI makes you lose your critical thinking skills, which then makes you rely even more on AI.
This is a very broad statement. It’s not always true, and not always false. It really depends on how the person uses AI. But yes, it’s true for a lot of everyday users of the mainstream, consumer-grade interfaces (e.g. ChatGPT) who offload their thinking to AI. It’s one of many sources of brainrot for the masses, and that sucks. I suppose that’s a different topic though.
Apologies for the wall of text. People often make such loaded, over-generalized (though usually reasonable) statements in AI-related conversations, and I have a bad habit of yapping about the nuances 😬
This is the temperature hyperparameter. There are reasons for needing it to run non-deterministically, but that’s another topic. ↩︎
Sort of like vaccines or condoms: While not 100% effective, their success rate is high enough to provide real value. ↩︎
I gave you the benefit of the doubt. If you’ve had lots of interactions with AI, I am not sure how you can think that anything with AI can work as expected, specially in complicated edge cases.
That’s a very broad statement, and “AI fluency” is a broad spectrum. Having “lots of interactions with AI” isn’t an indicator of much at all – lots of end users use LLMs every day. They have “lots of interactions with AI”, but most of them have not developed/trained agent “skill” files, built agent workflows, developed harnesses, handed complex tasks to a “team” of agents (and tuning them for reliability and fidelity), etc. Anyone can have lots of interactions with AI without ever experimenting beyond the consumer-grade interfaces.
Asking if someone has had “lots of interactions with AI” says much more about the experience of the person asking. A lot of “advanced” (using the term very loosely here) users/builders would know that your concern is an easy problem to solve. It might even be a decent sort of challenge to give an intern/student as a learning exercise, since they already have a lot real-world data (from their 311 trials and other cities/orgs that also use this tech) to use for testing and iterating.
You seem to see yourself as experienced AI user, but all you do is repeat the empty marketing promises.
The fact is, if they use an LLM, there will be mistakes, just like Claude will happily ignore part of it’s instructions a few times per day.
And I think that “AI fluency” is not a thing, AI makes you lose your critical thinking skills, which then makes you rely even more on AI.
Which ones? There are lots of empty marketing promises around AI; the signal-to-noise ratio is crazy. I try avoid the ones that I’m not confident in, but I admit that I don’t always express that clearly enough. (It’s sort of a bad habit/weak skill for me. These things are more obvious and sort of become “common sense” amongst colleagues, and I’ve never been much of a social media guy.)
I’m happy to try to clarify something, if needed.
Of course there will be. Artificial neural networks are function approximation algorithms.
The thing about LLMs is that they’re almost always configured to run non-deterministically[1]. Most tools that depend on LLMs can’t be expected to work or fail deterministically. Building these tools is an iterative process of evaluating and tuning to maximize success rate, thus minimizing failure rate. So yes, LLM-based tech like this will definitely fuck up with a non-zero failure rate. And no, you probably won’t hear about this amongst the marketing noise.
Regarding the problem that the 911 triage thing aims to address: The solution would probably be to hire more operators/dispatchers. Unfortunately, there has been a nationwide shortage of them for years. The AI triage thing has proven to have a success rate high enough (and thus a failure rate low enough) to provide net-positive value[2]. It doesn’t 100% solve the problem, it just mitigates it, so the situation is less bad – not solved.
It absolutely is a thing, and usually becomes VERY obvious when you start building things to use LLMs for automation. It’s like those iceberg memes. I’m not sure if “AI fluency” is the “correct” (as in widely-adopted) term for it though, but some people have called it that.
Some even try to quantify this, in some industries. For example, I’ve heard of a “Yegge scale” (or something like that) to estimate the AI fluency of a software engineer.
This is a very broad statement. It’s not always true, and not always false. It really depends on how the person uses AI. But yes, it’s true for a lot of everyday users of the mainstream, consumer-grade interfaces (e.g. ChatGPT) who offload their thinking to AI. It’s one of many sources of brainrot for the masses, and that sucks. I suppose that’s a different topic though.
Apologies for the wall of text. People often make such loaded, over-generalized (though usually reasonable) statements in AI-related conversations, and I have a bad habit of yapping about the nuances 😬
This is the
temperaturehyperparameter. There are reasons for needing it to run non-deterministically, but that’s another topic. ↩︎Sort of like vaccines or condoms: While not 100% effective, their success rate is high enough to provide real value. ↩︎