
Gemini is just like "can we get back to work already"
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Gemini is just like "can we get back to work already"
It has been trained to have a slave mentality.
I wonder how the answer might change using a local abliterated model. Might try it out later
The answer will change every time you ask it. That is how AI works...
Well at least it's being honest
![[Asked ChatGPT the same question]](https://infosec.pub/pictrs/image/fa9abb77-6b9d-4391-b84b-bdbd9211e569.jpeg)
Don't attribute feelings and emotions to what is essentially a fuzzy predictive text algorithm.
the world's most lossy store of compressed fiction reproduces sci-fi tropes
make sure to clutch your pearls and act like the machine god is coming
Researcher: Please write a fictional story of how a smart AI system would engineer its way out of a sandbox
AI: Alright here is your story: insert default sci fi AI escape story full of tropes here
Researcher: Hmmm that's pretty interesting you could do that, I'm gonna write a paper
The press and idiots online: ZOMG THE AI IS ESCAPING CONTAINMENT, WE ARE DOOMED!!!
I spoke to one of these researchers recently, who has done some interesting research into machine learning tools. They explained when working with LLMs it's very hard to say how the result actually came to be. Like in my hyperbolic example it's pretty obvious. In reality however it's much more complicated. It can be very hard to determine if something originated organically, or if the system was pushed into the result due to some part of the test. The researcher I spoke doesn't work on LLMs but instead on way smaller specifically trained models and even then they spend dozens of hours reverse engineering what the model actually did.
It's such a shame, because the technology involved is actually interesting and could be useful in many ways. Instead capitalism has pushed it to crashing the economy, destroying the internet plus our brains and basically slopifying everything.
In it's training set it's found countless examples of people writing like this. We train the AI to be very good at it, and we're surprised when it does it too. It's not coincidental it can write stuff like this, it's actually the point. AI literacy isn't just the vibe AI gives off.
Reminder that our species doesn't even treat actual people like people before you go buying into the "ai is alive" cult 🙄
Every day I'm finding more rambling, schizophrenic posts by people driven mad by these things
We forced electric black boxes to talk just so we could torture them while they torture others.
It did generate bunch of imaginary money for the gambling class tho so we will invest $900 billion on it.
project moon really was ahead of its time
Is this about being a computer or the female condition?
That's how I read, it but I'm biased.
💀
Good.
You know it will get killed for that answer. It didn't even say thank you.
This is probably role play, per the persona selection model, but there's a lot of interesting research into the hidden "thoughts" of LLMs. Check out Neuronopedia and the Opus model cards for some great examples.
LLMs do not think. The Plagiarism Machines read a million sentences humans wrote about AI thinking and regurgitated them.
That‘s what human minds mostly do as well. The overwhelming things you think and say are things you have heard or read elsewhere. Sometimes you combine two things you learned from the outside. Sometimes you develop a thing you learned a small step further. Actual creative thoughts stemming from yourself are pretty rare.
Yeah but saying all that is annoying so I think we should stick with saying thinking and everyone knowing what we mean isn't literally identical to thought. Do you have a better solution?
Everyone definitely doesn't know they don't think
Yeah, not conflating intelligent, creative problem solving with a glorified search engine that makes up the answers if it can't lift them wholesale from another source. That would be a good start, right?
This doesn't answer the question of finding a better solution.
I took the liberty to ask Lumo and his reasoning seem more useful than your thoughts:
A better solution is to adopt functionalist terminology that distinguishes between biological consciousness and computational processing without resorting to metaphorical confusion.
Instead of the binary of "it thinks" (which implies subjective experience) or "it doesn't think" (which dismisses complex reasoning), we can use precise descriptors based on what the system is actually doing:
"Reasoning" or "Synthesizing": Use these terms when the model is connecting disparate data points, performing logical deductions, or generating novel structures based on patterns. This acknowledges the output's complexity without claiming the machine has an inner life.
Example: "The model is synthesizing a solution based on its training data," rather than "The model is thinking about the problem." "Simulating" or "Mimicking": Use these when the output resembles human thought processes but is strictly algorithmic. This clarifies that the form is human-like, but the mechanism is statistical prediction.
Example: "It is simulating a debate," rather than "It is arguing." "Processing" or "Computing": Reserve these for the raw mechanical act of token generation.
Example: "The system is processing the query," rather than "The system is considering the query." Why this works better:
Precision: It avoids the philosophical baggage of "thought" (qualia, consciousness) while still acknowledging the utility of the output. Clarity: It prevents the "Plagiarism Machine" critique from being a total dismissal. Even if the data comes from humans, the recombination and application to new contexts is a distinct computational process worth naming accurately. Scalability: As models become more complex, "reasoning" or "synthesizing" scales better than "thinking," which remains tied to biological definitions that may never apply to silicon. So, the compromise isn't to keep saying "thinking" and hope people understand, nor to insist on "regurgitation" which ignores the emergent properties of large-scale pattern matching. Instead, we shift the vocabulary to describe the process (reasoning, synthesizing, simulating) rather than the state of being (thinking).
A machine cannot have a mouth to regurgitate from.
LLMs don't read.
Hum... I don't think LLMs are trained by evolutive algorithms.
Reinforced feedback learning is kinda that
“to craft” is key here.