Steam locomotive operators would notice some behaviors of their machines that they couldn’t entirely explain. They were out there, shoveling the coal, filling the boilers, and turning the valves but some aspects of how the engines performed - why they would run stronger in some circumstances than others - were a mystery to the men on the front lines. Decades later, intense theoretical study could explain most of the observed phenomena by things like local boiling inside the boiler insulating the surface against heat transfer from the firebox, etc. but at the time when the tech was new: it was just a mystery.
Most of the “mysteries” of AI are similarly due to the fact that the operators are “vibe coding” - they go through the motions and they see what comes out. They’re focused on their objectives, the input-output transform, and most of them aren’t too caught up in the how and why of what it is doing.
People will study the how and why, but like any new tech, their understanding is going to lag behind the actions of the doers who are out there breaking new ground.
Coldcell@sh.itjust.works 6 days ago
I can’t quite wrap my head around this, these systems were coded, written by humans to call functions, assign weights, parse data. How do we not know what it’s doing?
Kyrgizion@lemmy.world 6 days ago
Same way anesthesiology works. We don’t know. We know how to sedate people but we have no idea why it works. AI is much the same. That doesn’t mean it’s sentient yet but to call it merely a text predictor is also selling it short. It’s a black box under the hood.
Coldcell@sh.itjust.works 6 days ago
Writing code to process data is absolutely not the same way anesthesiology works 😂 Comparing state specific logic bound systems to the messy biological processes of a nervous system is what gets this misattribution of ‘AI’ in the first place. Currently it is just glorified auto-correct working off statistical data about human language, I’m still not sure how a written program can have a voodoo spooky black box that does things we don’t understand as a core part of it.
irmoz@lemmy.world 6 days ago
The uncertainty comes from reverse-engineering how a specific output relates to the prompt input. It uses extremely fuzzy logic to compute the answer to “What is the closest planet to the Sun?”
MangoCats@feddit.it 6 days ago
It’s a bit of “emergent properties” - so many things are happening under the hood they don’t understand exactly how it’s doing what it’s doing, why one type of mesh performs better on a particular class of problems than another.
The equations of the Lorenz attractor are simple, well studied, but it’s output is less than predictable and even those who study it are at a loss to explain “where it’s going to go next” with any precision.
The_Decryptor@aussie.zone 6 days ago
Yeah, there’s a mysticism that’s sprung up around LLMs as if they’re some magic blackbox, rather than a well understood construct to the point where you can buy books from Amazon on how to write one from scratch.
It’s not like ChatGPT or Claude appeared from nowhere, the people who built them do talks about them all the time.
Monstrosity@lemm.ee 6 days ago
What a load of horseshit lol
The_Decryptor@aussie.zone 6 days ago
If these AI researchers really have no idea how these things work, then how can they possibly improve the models or techniques?
Like how they now claim all that after upgrades that now these LLMs can “reason” about problems, how did they actually go and add that if it’s a black box?