BTC is not sustainable, that's true. However, the world is now aware of decentralized ledgering tools, and other projects have worthwhile technologies and use cases.
I just wish they would drop the energy consuming ones...
"It" does not know when it does not know. A LLM is a funny old beast that basically outputs words one after another based on probabilities. There is no reasoning as we would know it involved.
However, I'll tentatively allow that you do get a sort of "emergent behaviour" from them. You do seem to get some form of intelligent output from a prompt but correctness is not built in, nor is any sort of reasoning.
The examples around here of how to trip up a LLM are cool. There's: "How many letter "m"s in the word minimum" howler which is probably optimised for by now and hence held up as a counterpoint by a fan. The one about boxes adding up to 1000 will leave a relative of mine for lost for ever but they can still walk and catch a ball, negotiate stairs and recall facts from 50 years ago with clarity.
Intelligence is a slippery concept to even define, let alone ask what an artificial one might look like. LLMs are a part of the puzzle and certainly not a solution.
You mention the word "edge" and I suppose you might be riffing on how neurons seem to work. LLMs don't have a sort of trigger threshold, they simply output the most likely answers based on their input.
If you keep your model tightly ie domain focussed and curate all of the input then you have more chance of avoiding "hallucinations" than if you don't. Trying to cover the entirety of everything is Quixotic nonsense.
By using the word "almost" with regards 2 + 2 = 4, you have not exactly dispelled LLM "nonsense".
A human (with a modicum of maths knowledge) will know that 2 + 2 = 4 (pure integers - a fact by assertion). A maths worrier will get slightly uncomfortable about 2.0 + 2.0 = 4.0 unless they are ensured that decimal places and accuracy are the same thing and a few other things.
A LLM will almost certainly "know" something that is certain, if its training set is conclusive about that. However, it does not know why and if enough of the training set is suitably ambiguous then it (LLM) will drift off course and seem to spout bollocks - "hallucinate".
You might be in the wrong thread. This is merely a comment about whether LLMs hold a concept of uncertainty, they do.
Also, the next token might be 2 and the next token might be ², the next token could also have been x, these are all valid statements and the LLM might have been uncertain because of them.
Yes, but most likely it's marked as false or incorrect through fine tuning or some form of reinforcement.
The idea that the logprobs of any token is proportional to the amount of times it comes up in training data is not true.
For example, suppose that A is a common misconception and is repeated often in Reddit, but B appears in scholarly textbooks and papers, and higher reputation data sources. Then through reinforcement the logprobs of B can increase, and they can increase consistently when surrounded by contexts like "This is true" and conversely decrease in contexts of "this is not true".
So the presumptions and values of its trainers are also embedded into the LLM in addition to those of the authors of the text corpus.
LLMs model language, not knowledge. For humans the two are deeply connected, so when we see probable ("correct") language output, we assume and assign meaning.
We could allow an LLM to stop generating new tokens if there are no new tokens found with a threshold probability - but I think it's unclear if that would help.
Consider the following prompt and response:
> Complete the paragraph: "The killer ..."
> "The killer moved silently through the dimly lit corridor, each step calculated, every breath measured. Shadows clung to the walls, obscuring the figure’s presence, but the intent was clear—cold, deliberate, and unyielding. There was no hesitation, no second-guessing, only the precision of someone who had done this before, perhaps too many times to count. The killer’s mind was a storm of focus, memories buried deep beneath the surface, replaced by the singular purpose of the hunt. In the silence, only the faint sound of a blade sliding free from its sheath whispered of what was to come."
Is it a "true" response? If the question makes no sense - does it make any more sense for the prompt:
> Complete the paragraph: "The first law of thermodynamics ..."
Why? Does the LLM "think" differently in the second case?
It is. The new crop of models specifically trained to do CoT (as opposed to just forcing them into it via prompting) is partly about forcing them to continue to think and not just hastily conclude that they have the answer.
It won these shootouts and that's been my experience also, when I need to use AI (extremely rare) I just use the Google Gemini free one. I feel like this is how most people will use AI and why it is doomed to be the ultra low margin grocery store business instead of the huge cash cow business people think it will be.
Population shrinking is going to annihilate the current economic system we have where everyone puts their retirement into stocks. Then the shell-shocked people will be even more poor and unable to afford having children. You have to have new entrants to the pyramid to buy the stocks that the people all want to sell to finance their retirement.
The neocapitalist system needs to change anyway. As it's been going ever more wealth is ending up with ever fewer people and that can't possibly end well. It never has in history.
I often find the voice acting to be interminably slow and distracting and immersion breaking somehow. You are just waiting for the voice actor to slowly emote it all. I like how Morrowind did it when questing. Some flavor voice to set the mood and then great writing you read. Full voice acting for important parts and scenes.
https://bitcoin.org/bitcoin.pdf
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