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For me the main benefit would be that I really like 3D printing and I’d rather not have the waste, so I get from my end it’s more of a niche request and I’m fine with where things are.

I’ve talked with this person in the past, they are very reasonable in general, although I disagree with their stance on not offering the hardware only and allowing the user to 3D print their own case. They currently don’t want to do that because they can control the quality of prints that get shipped with their product, and anticipate (based on previous experience IIRC) an increase in support needs for self-printed cases that they both aren’t willing to bear and aren’t willing to allow. They want this to be sold as a finished product that happens to include some 3D printed parts that you CAN swap out later at your own risk, which makes enough sense even though it’s not how I would prefer it.

A crucial part of your statement is that it knows that it’s untrue, which it is incapable of. I would agree with you if it were actually capable of understanding.

https://chatgpt.com/share/67c64160-308c-8011-9bdf-c53379620e40

A false statement would be me saying that the color of a light that I cannot see and have never seen that is currently red is actually green without knowing. I am just as easily probably right as I am probably wrong, statistics are involved.

A lie would be me knowing that the color of a light that I am currently looking at is currently red and saying that it is actually green. No statistics, I’ve done this intentionally and the only outcome of my decision to act was that I spoke a falsehood.

AIs can generate false statements, yes, but they are not capable of lying. Lying requires cognition, which LLMs are, by their own admission and by the admission of the companies developing them, at the very least not currently capable of, and personally I believe that it’s likely that LLMs never will be.

Ask chatgpt, I’m done arguing effective consciousness vs actual consciousness.

https://chatgpt.com/share/67c64160-308c-8011-9bdf-c53379620e40

What do you believe that it is actively doing?

Again, it is very cool and incredibly good math that provides the next word in the chain that most likely matches what came before it. They do not think. Even models that deliberate are essentially just self-reinforcing the internal math with what is basically a second LLM to keep the first on-task, because that appears to help distribute the probabilities better.

I will not answer the brain question until LLMs have brains also.

We did, a long time ago. It’s called an encyclopedia.

If humans can’t be trusted to only provide facts, how can we be trusted to make a machine that only provides facts? How do we deal with disputed truths? Grey areas?

It is incapable of knowledge, it is math, what it says is determined by what is fed into it. If it admits to lying, it was trained on texts that admit to lying and the math says that it is most likely that it should apologize using the following tokenized responses with the following weights to probabilities etc.

It apologizes because math says that the most likely response is to apologize.

Edit: you can just ask it y’all

https://chatgpt.com/share/67c64160-308c-8011-9bdf-c53379620e40

I strongly worry that humans really weren’t ready for this “good enough” product to be their first “real” interaction with what can easily pass as an AGI without near-philosophical knowledge of the difference between an AGI and an LLM.

It’s obscenely hard to keep the fact that it is a very good pattern-matching auto-correct in mind when you’re several comments deep into a genuinely actually no lie completely pointless debate against spooky math.

I think the important point is that LLMs as we understand them do not have intent. They are fantastic at providing output that appears to meet the requirements set in the input text, and when they actually do meet those requirements instead of just seeming to they can provide genuinely helpful info and also it’s very easy to not immediately know the difference between output that looks correct and satisfies the purpose of an LLM vs actually being correct and satisfying the purpose of the user.