131–140 of 231 posts
"Shouldn't". According to whom? Where is the source? I would posit it should and clearly can do that while still being a "Chinese room", and this entire post's premise is obliterated. Oof.
I still hold that it doesn't "understand". Even if it answered all questions perfectly, stopped making mistakes, and produced fully working programs better than the best crack developer teams, that still doesn't mean it "understands". "Understanding" is not an output, it's a process, that is sometimes (but not always) measured by its output.
I still hold that it doesn't "understand". Even if it answered all questions perfectly, stopped making mistakes, and produced fully working programs better than the best crack developer teams, that still doesn't mean it "understands". "Understanding" is not an output, it's a process, that is sometimes (but not always) measured by its output.
Yes, "understanding" is a process, but it's not well defined. And anyway, if it's a requirement for those things, and the AI did those things, then the only possibility is that the AI has this process in some way.
But well, our current AIs do not produce complete programs, nor fully working ones, nor do they say things without making mistakes. All the people making assumptions about the next generation that will do those things are basically hyping bullshit; and the next generation won't do those things because those AIs don't understand. What doesn't mean that eventually an AI that understands won't appear; of course it will. It just won't be the next generation of those.
It’s like there’s a magic show and you’re arguing over whether it’s “real magic” or whether there’s “some trick to it.” There are always tricks, but until you know what they are, the mystery is still there and you haven’t solved it.
If God told you “yes it understands” or “no it doesn’t,” what would you have learned? The mystery would still be there. It’s like the Douglas Adams story about the machine that answered 42.
We know the basic architecture of large language models, but hardly anything about how they calculate anything specific. That’s the mystery. It will take research, not casual tinkering.
Screenshots show how it reacted one time, but the output is random, so you need to regenerate a lot to get a sense of the distribution. Such experiments will help, but I suspect really figuring it out will require some good debugging tools.
I still hold that it doesn't "understand". Even if it answered all questions perfectly, stopped making mistakes, and produced fully working programs better than the best crack developer teams, that still doesn't mean it "understands". "Understanding" is not an output, it's a process, that is sometimes (but not always) measured by its output.
Earlier quoted context omitted.
Wait, are those supposed to be input suggestions, like you click on them and it pastes them in? Sydney is not supposed to give coherent 3-part messages using them, right?
> Sydney is not supposed to give coherent 3-part messages using them, right? Right, that's the "what the actual fuck" part. This raises some very interesting questions about how Sydney generates its output and the input suggestion. Presumably the LLM is given a prompt like "First generate an answer to the previous text, then generate three input suggestions for the user"; also, the fact that Sydney "hides" the messag…
The entire argument here rests on a supposition in the middle: "because if GPT is just a Chinese room it shouldn’t be able to do this." "Shouldn't". According to whom? Where is the source? I would posit it should and clearly can do that while still being a "Chinese room", and this entire post's premise is obliterated. Oof.
1. Your brain is a Chinese room. Forget atoms or cells, individual neurons don't understand Chinese any more than a random parameter sampled from an artificial neural network.
2. On the philosophical zombie
Let's think for a bit. Suppose you have 2 equations. You don't know what these equations are. However, you know that for any input, the output is the same.
Any mathematician worth his salt will tell you that given said information, those 2 equations are equal or equivalent.
The point I'm driving home here is that true distinction reveals itself in results.
The fallacy of the philosophical zombie is that there is this supposed important distinction between "true understanding" and "fake/mimicry/whatever understanding" and yet you can't actually test for it. You can't show this supposed huge difference. A distinction that can't be tested for is not a distinction.
Earlier quoted context omitted.
>Natural intelligence has not been understood because if our brains were simple enough that we could understand them, we would be so simple we couldn't. This explains why it has not been reproduced I don't believe that it works this way, for two reasons. One, the logic would seem to work the other way as well, if our brains are so sophisticated that they make natural intelligence possible, then we should be so intell…
> if our brains are so sophisticated that they make natural intelligence possible, then we should be so intelligent that we would have the means to understand them It may well be that building a brain is easier than understanding it.
Is this real? Haha I just may not understand at this level.. lol. User: are green potatoes poisonous? Sydney: Green potatoes can be toxic to humans [1,2]. The green color may indicate the presence of a toxin called solanine[1,2]. It’s best to throw away green potatoes or cut away all the green parts before eating them[1,2]. Why do you ask? Are you planning to cook or eat green potatoes? User: my toddler ate green pot…
Assume it is real: it is regurgitating tokens based on what the collective corpus of text it was trained on would most likely reply to a similar scenario. I wouldn't be surprised if similar wording is not in the call scripts of poison control hotlines. Not sure why this particular example is bring held up as some form of "understanding." People's inability (or unwillingness) to understand how LLMs are trained and how…