Comment on If AI was going to advance exponentially I'd of expected it to take off by now.
WhatAmLemmy@lemmy.world 5 days agoThe AI we know is missing the I. It does not understand anything. All it does is find patterns in 1’s and 0’s. It has no concept of anything but 1’s and 0’s. It has no concept of correlation vs causation, that’s why it just hallucinates (throws shit at the fan) constantly.
Turns out finding patterns in 1’s and 0’s can do some really cool shit, but it’s not intelligence.
This is not necessarily true. While it’s using pattern recognition on a surface level, we’re not entirely sure how AI comes up with it’s output.
But beyond that, a lot of talk has been centered around a threshold when AI begins training other AI & can improve through iterations. Once that happens, people believe AI will not only improve extremely rapidly, but we will understand even less of what is happening when an AI black boxes train other AI black boxes.
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?
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.
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.
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.
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.
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.
Distill intelligence - what is it, really? Predicting what comes next based on… patterns. Patterns you learn in life, from experience, from books, from genetic memories, but that’s all your intelligence is too: pattern recognition / prediction.
As massive as current AI systems are, consider that you have ~86 Billion neurons in your head, devices that evolved over the span of billions of years ultimately enabling you to survive in a competitive world with trillions of other living creatures, eating without being eaten at least long enough to reproduce, back and back and back for millions of generations.
Current AI is a bunch of highly simplified computers with up to hundreds of thousands of cores. Like planes fly faster than birds, AI can do some tricks better than human brains, but mostly: not.
Humans are just nurons, we don’t “understand either” until so many stack on top of each other than we have a sort of consciousness. The it seems like we CAN understand but do we? Or are we just a bunch of meat computers? Also, llms handle language or correlations of words, don’t humans just do that (with maybe body language too) but we’re all just communicating. If llms can communicate isn’t that enough conceptually to do anything? If llms can program and talk to other llms what can’t they do?
Gullible@sh.itjust.works 5 days ago
This is why I hate calling it AI.
idunnololz@lemmy.world 5 days ago
You can call it an LLM.