Comment by elevatortrim
12 hours ago
No contradiction here:
When we say “machine”, we mean deterministic algorithms and predictable mechanisms.
Generative AI is neither of those things (in theory it is deterministic but not for any practical applications).
If we order by predictability:
Quick Sort > Brenda > Gen AI
There are two kinds of reliability:
Machine reliability does the same thing the same way every time. If there's an error on some input, it will always make that error on that input, and somebody can investigate it and fix it, and then it will never make that error again.
Human reliability does the job even when there are weird variances or things nobody bothered to check for. If the printer runs out of paper, the human goes to the supply cabinet and gets out paper and if there is no paper the human decides whether to run out right now and buy more paper or postpone the print job until tomorrow; possibly they decide that the printing doesn't need to be done at all, or they go downstairs and use a different printer... Humans make errors but they fix them.
LLMs are not machine reliable and not human reliable.
> . If the printer runs out of paper, the human goes to the supply cabinet and gets out paper and if there is no paper the human decides
Sure, these humans exists, but the others, that I happen to encounter every day unfortunately, are the ones that go into broken mode immediately when something is unexpected. Today I ordered something they ran out of and the girl behind the counter just stared in The Deep not having a clue what to do now. Do or say. Or yesterday at dinner, the PoS (on batteries) ran out of power when I tried to pay for dinner. The guy just walked off and went outside for a smoke. I stood there with waiting to pay. The owner apologized and fixed it after a while but I am saying, the employee who runs out of paper and then finds and puts more paper in is not very ... common... In the real world.
Alignment problem? JK
Or the human might take the printer out back with his buddies and smash it to bits ;)
I was brought up on the refrain of "aren't computers silly, they do exactly what you tell them to do to the letter, even if it's not what you meant". That had its roots in computers mostly being programmable BASIC machines.
Then came the apps and notifications, and we had to caveat "... when you're writing programs". Which is a diminishing part of the computer experience.
And now we have to append "... unless you're using AI tools".
The distinction is clear to technical people. But it seems like an increasingly niche and alien thing from the broader societal perspective.
I think we need a new refrain, because with the AI stuff it increasingly seems "computers do what they want, don't even get it right, but pretend that they did."
We have absolutely descended, and rapidly, into “computers do whatever the fuck they want and there’s nothing you can do about it” in the past 5 years, and gen AI is only half of the problem.
The other half comes from how incredibly opinionated and controlling the tech giants have become. Microsoft doesn’t even ALLOW consent on windows (yes or maybe later), Google is doing all it can to turn the entire internet into a chrome-only experience, and Apple has to be fought for an entire decade to allow users to place app icons wherever they want on their Home Screen.
There is no question that the overly explicit quirky paradigm of the past was better for almost everyone. It allowed for user control and user expression, but apparently those concepts are bad for the wallet of big tech so they have to go. Generative AI is just the latest biggest nail in the coffin.
We have come a LONG way from the "Where do you want to go today?" of the 90s. Now, it's "You're going where we tell you that you can go, whether you like it or not!"
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Pop culture characters like Lt. Commander Data seem anachronistic now.
It was Second Technician Arnold Judas Rimmer, BSc., SSc. all along.
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Nit: no ML is deterministic in any way. Anything that is Generative AI is ML. This fact is literally built into the algorithms at the mathematical level.
First, they all add a source of randomness, and second deterministic according to the users model. A pseudo-random number generator is also deterministic in the technical sense, but for the user it isn't.
When the user can't reason about it, it isn't deterministic to them.
If you think programs are predictable, I have a bridge to sell you.
The only relevant metric here is how often each thing makes mistakes. Programs are the most reliable, though far from 100%, humans are much less than that, and LLMs are around the level of humans, depending on the humans and the LLM.
When human makes a mistake, we call it a mistake. When human lies, we call it a lie. In both cases, we blame the human.
When LLM does the same, we call it hallucination and blame the human.
Which is the correct reaction, because LLM isn't a human and can't be held accountable.
Programs can be very close to 100% reliable when made well.
In my life, I've never seen `sort` produce output that wasn't properly sorted. I've never seen a calculator come up with the wrong answer when adding two numbers. I have seen filesystems fail to produce the exact same data that was previously written, but this is something that happens once in a blue moon, and the process is done probably millions of times a day on my computers.
There are bugs, but bugs can be reduced to a very low level with time, effort, and motivation. And technically, most bugs are predictable in theory, they just aren't known ahead of time. There are hardware issues, but those are usually extremely rare.
Nothing is 100% predictable, but software can get to a point that's almost indistinguishable.
> Programs can be very close to 100% reliable when made well.
This is a tautology.
> I've never seen a calculator come up with the wrong answer when adding two numbers.
https://imgz.org/i6XLg7Fz.png
> And technically, most bugs are predictable in theory, they just aren't known ahead of time.
When we're talking about reliability, it doesn't matter whether a thing can be reliable in theory, it matters whether it's reliable in practice. Software is unreliable, humans are unreliable, LLMs are unreliable. To claim otherwise is just wishful thinking.
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>I've never seen a calculator come up with the wrong answer when adding two numbers.
Intel once made a CPU that barely got some math wrong that probably would not affect the vast majority of users. The backlash from the industry was so strong that intel spent half a billion (1994) dollars replacing all of them.
Our entire industry avoids floating point numbers for some types of calculations because, even though they are mostly deterministic with minimal constraints, that mental model is so hard to manage that you are better off avoiding it entirely and removing an entire class of errors from your work
But now we are just supposed to do everything with a slot machine that WILL randomly just do the wrong thing some unknowable percentage of the time, and that wrong thing has no logic?
No, fuck that. I don't even call myself an engineer and such frivolity is still beyond the pale. I didn't take 4 years of college and ten years of hard earned experience to build systems that will randomly fuck over people with no explanation or rhyme or reason.
I DO use systems that are probabilistic in nature, but we use rather simple versions of those because when I tell management "We can't explain why the model got that output", they rightly refuse to accept that answer. Some percentage of orders getting mispredicted is fine. Orders getting mispredicted that cannot be explained entirely from their data is NOT. When a customer calls us, we cannot tell them "Oh, that's just how Neural networks are, you were unlucky".
Notably, those in the industry that HAVE jumped on the neural net/"AI" bandwagon for this exact problem domain have not demonstrated anything close to seriously better results. In fact, one of our most DRAMATICALLY effective signals is a third party service that has been around for decades, and we were using a legacy integration that hadn't been updated in a decade. Meanwhile, Google's equivalent product/service couldn't even match the results of internally developed random forest models from data science teams that were.... not good. It didn't even match the service Microsoft has recently killed, which was similarly bragadocious about "AI" and similarly trash.
All that panopticon's worth of data, all that computing power, all that supposed talent, all that lack of privacy and tracking, and it was almost as bad as a coin flip.
> If we order by predictability:
> Quick Sort > Brenda > Gen AI
Those last two might be the wrong way round.