this post was submitted on 20 Nov 2023
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The best part of Open AI’s self professed goal to make an AGI is that the more we learn about LLM’s the more it becomes clear that they inherently can never bridge the gap to AGI.
One would almost think the constant complaining about mythical dangers of AGI might be a distraction from the real more mundane dangers LLM’s pose here and now like exasperating bias, making mass misinformation easy, and of course shielding major companies from accountability.
Or the other option is that it’s just marketing, look at how scary our totally real product is, look how fast it improved when we went from a medium sized dataset to the largest that will ever be possible, don’t ask questions like why would a autocomplete that has been feed the entire internet actually help our business, just pay us and bolt it on to whatever you can.
LLM's ability to replace jobs is honestly more terrifying than so called AGI.
At least with AGI, if they really can think like human, is that they may actually think about the implications of their actions....
Robots / automation have replaced so many human physical labor jobs, even large dumb heavy machinery.
Language models replacing mundane human language tasks is hardly surprising.
I have replaced entire employee jobs with scrips / code, there are a lot of very basic jobs out there.
Scripts and automation do what thier programmed to. There are bugs and mistakes, but you can theoretically get something programmed right. LLM’s generate text that looks like a human language. If they were just getting used to make up random bullshit it wouldn’t be a problem, but there are few applications where random bullshit is actually beneficial.
Just like the executives assist that was tasked with scanning documents. And LLM can likely safely and quickly do many people tasks:
There are a lot of human language job tasks that have zero imagination required just the ability to read summarize and write some proper English.
Thouse all sound like things where it might be really bad if it injects untrue information, and with an LLM, by definition it has no understanding of what it’s summarizing. It could be especially bad if the people useing it actually trust what it outputs as facts about what was fed into it, but if they don’t and still check the source than what’s the point.
"That's not writing, that's just typing!"
If I hand someone a set of bullet notes and ask them to send out a notice in writing to the company. They are going to convert those notes into paragraphs and sentences.. Not just send out the notes.
Also MS already has a module for teams that will take the conversation transcript, and output action items based on the conversation.. It is like having a note taker during the meeting. https://www.youtube.com/watch?v=N1gpkk-MwpY
Oh, I'm sure they will. That is not, in the slightest, the same as caring about said implications in ways that mean that the species won't get murked, though.