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@scottjenson

Been hearing about all the positive use-cases *in the abstract* for years now. Never specifics.

Certainly, no use cases that are mission critical (driving, medical diagnosis, weapons deployment), though, due to the endemic hallucination problem—not without expert human review (such as senior programmers, or scientists) of the output.

Which means, at best, a replacement for a *bunch* of junior-level jobs.

Meanwhile the anthropomorphizing of AI companions proceeds apace.

@Mark_Harbinger @scottjenson LLMs can very obviously replace the first tier of phone tech/chat support--basically walking a checklist but accounting for fuzzy input. They're doing this today on all sorts of websites.

They are almost ready to replace drive thru cashiers--some places rolled it out and it had glitches, but I honestly think they'll be there in the next 12 months. It's not mission critical if the bot screws it up and it's cheaper for the restaurant, so of course it will happen.

@stilescrisis @Mark_Harbinger The problem is that most companies are looking at #LLMs as a way to save money instead of improving the product.

Most corp boosters are falling all over themselves to slash jobs. Theses attempts have already failed badly and likely continue to do so as they don't understand what they are trying to replace (Classic #UX mistake)

If, as you suggest, it's an upfront triage that *still* leads you to a human, but one which is now better prepared to help you. That's cool

@scottjenson @Mark_Harbinger Not necessarily? A request like "reset my password" could be entirely level-one tech support serviceable, thus fully automatable. And of course, drive-thru ordering isn't up-front triage, it's the whole enchilada.

@stilescrisis @scottjenson

This positive use-case is already being accomplished by regular algorithms...there is no need to conflate those types of jobs with LLM machine learning AI.

So, again...I'm still waiting for the positive use-case (that inures to the benefit of the general population/work force, not the corporate bottom-line).

@Mark_Harbinger @scottjenson There's no existing algorithm which allows someone to pull up to a drive-thru, say "I want a six pack of McNuggets and a large coke" and automate the point-of-sale entry. I don't know what you're talking about.

@stilescrisis @scottjenson

My response was to specifically to your "password recovery" example. But, yes, there are automated POS kiosks all over the world, too, so...?

@Mark_Harbinger @scottjenson At drive thrus? Are there?

@stilescrisis @scottjenson

Well, I guess it would depend on whether you drove up to it or not. Clearly the technology exists. And, my point is, it has nothing to do with machine learning LLMs...

@stilescrisis @Mark_Harbinger

LLMs can certainly be better than IVR systems. But even then just like IVRs, they can be horrible (and many likely will be)

I'm not making a categorical point about the tech but how so many companies are going to screw it up. Execution is 90% of the problem.

There are entire books written about over simplifying your understanding of the business problem to be solved. This is a near universal issue with any tech.

But I'm seeing it far more with LLMs today

@scottjenson @Mark_Harbinger This is where cloud providers like Azure and GCloud can add value. Integrating an LLM with an existing system is going to be challenging work for folks who aren't experienced at it, but the cost savings almost certainly make it worth the effort.

@stilescrisis @Mark_Harbinger you're focusing on the low level tech. Take a simple voice line application. What tasks do you give it, how do you roll over to humans, do you even offer humans? All have simplistic (and cheap) answers that can create a terrible experience.

Almost always, companies choose the cheap option believing in the hype, thinking "it can't be that bad" and it almost always is.