Saying software can be inspired is like saying a rock can feel pain.
The rock doesn’t do anything similar to pain. The LLM on the other side does a lot of things similar to inspiration. I can give the LLM a very trivial question and it will answer with a mountain of text. Did my question or the books it was trained on “inspire” the LLM to write that? Maybe, depends of course how far reaching you want to define the word. But either way, the LLM produced something by itself, that was neither a copy of my prompt nor the training data.
Software can do a lot of things that rocks can’t do, that’s not a good analogy.
Whether software can feel “pain” depends a lot on your definitions, but I think there are circumstances in which software can be said to feel pain. Simple worms can sense painful stimuli and react to it, a program can do the same thing.
We’ve reached the point where the simplistic prejudices about artificial intelligence common in science fiction are no longer useful guidelines for talking about real artificial intelligence. Sci-fi writers have long assumed that AIs couldn’t create art and now it turns out it’s one of the things they’re actually rather good at.
AIs in their training stages are simply just running extreme statistical analysis on the input material. They’re not “learning” they’re not “inspired” they’re not “understanding”
The anthropomorphism of these models is a major problem. They are not human, they don’t learn like humans.
The anthropomorphism of these models is a major problem.
People attributing any kind of person hood or sentience is certainly a problem, the models are fundamentally not capable of that (no loops, no hidden thought). At least for now. However what you are doing isn’t really much better, just utterly wrong in the opposite direction.
Those models are very definitely do “learn” and “understand” by every definition of the word. Simply playing around with that will quickly show that and it’s baffling that anybody would try to claim otherwise. Yes, there are limits to what they can understand and there are plenty things that they can’t do, but the amount of questions they can answer goes far beyond what is directly in the training data. Heck, even the fact that they hallucinate is proof that they understand, since it would be impossible to make completely plausible, but incorrect, stuff up without having a deep understanding of the topics. Also humans make mistakes too and they’ll also make stuff up, so this isn’t even anything AI specific.
Hallucinations happen when there’s gaps in the training data and it’s just statistically picking what’s most likely to be next. It becomes incomprehensible when the model breaks down and doesn’t know where to go. However, the model doesn’t see a difference between hallucinating nonsense and a coherent sentence. They’re exactly the same to the model.
The model does not learn or understand anything. It statistically knows what the next word is. It doesn’t need to have seen something before to know that. It doesn’t understand what it’s outputting, it’s just outputting a long string that is gibberish to it.
I have formal training in AI and 90%+ of what I see people claiming AI can do is a complete misunderstanding of the tech.
It doesn’t understand what it’s outputting, it’s just outputting a long string that is gibberish to it.
Which is obviously nonsense, as I can ask it questions about its output. It can find mistakes in its own output and all that. It obviously understands what it is doing.
Well, now you know; software can be inspired by other people’s works. That’s what AIs are instructed to do during their training phase.
Does that mean software can also be afraid, or angry? What about happy software? Saying software can be inspired is like saying a rock can feel pain.
If it is programmed/trained that way, sure. I recommend having a listen to Geoffrey Hinton on the topic (41:50).
The rock doesn’t do anything similar to pain. The LLM on the other side does a lot of things similar to inspiration. I can give the LLM a very trivial question and it will answer with a mountain of text. Did my question or the books it was trained on “inspire” the LLM to write that? Maybe, depends of course how far reaching you want to define the word. But either way, the LLM produced something by itself, that was neither a copy of my prompt nor the training data.
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Software can do a lot of things that rocks can’t do, that’s not a good analogy.
Whether software can feel “pain” depends a lot on your definitions, but I think there are circumstances in which software can be said to feel pain. Simple worms can sense painful stimuli and react to it, a program can do the same thing.
We’ve reached the point where the simplistic prejudices about artificial intelligence common in science fiction are no longer useful guidelines for talking about real artificial intelligence. Sci-fi writers have long assumed that AIs couldn’t create art and now it turns out it’s one of the things they’re actually rather good at.
Software cannot be “inspired”
AIs in their training stages are simply just running extreme statistical analysis on the input material. They’re not “learning” they’re not “inspired” they’re not “understanding”
The anthropomorphism of these models is a major problem. They are not human, they don’t learn like humans.
People attributing any kind of person hood or sentience is certainly a problem, the models are fundamentally not capable of that (no loops, no hidden thought). At least for now. However what you are doing isn’t really much better, just utterly wrong in the opposite direction.
Those models are very definitely do “learn” and “understand” by every definition of the word. Simply playing around with that will quickly show that and it’s baffling that anybody would try to claim otherwise. Yes, there are limits to what they can understand and there are plenty things that they can’t do, but the amount of questions they can answer goes far beyond what is directly in the training data. Heck, even the fact that they hallucinate is proof that they understand, since it would be impossible to make completely plausible, but incorrect, stuff up without having a deep understanding of the topics. Also humans make mistakes too and they’ll also make stuff up, so this isn’t even anything AI specific.
Yeah, that’s just flat out wrong
Hallucinations happen when there’s gaps in the training data and it’s just statistically picking what’s most likely to be next. It becomes incomprehensible when the model breaks down and doesn’t know where to go. However, the model doesn’t see a difference between hallucinating nonsense and a coherent sentence. They’re exactly the same to the model.
The model does not learn or understand anything. It statistically knows what the next word is. It doesn’t need to have seen something before to know that. It doesn’t understand what it’s outputting, it’s just outputting a long string that is gibberish to it.
I have formal training in AI and 90%+ of what I see people claiming AI can do is a complete misunderstanding of the tech.
Than why do you keep talking such bullshit? You sound like you never even tried ChatGPT.
Yes, that’s understanding. What do you think your brain does differently? Please define whatever weird definition you have of “understand”.
You are aware of Emergent World Representations? Or have a listen to what Ilya Sutskever has to say on the topic, one of the people behind GPT-4 and AlexNet.
Which is obviously nonsense, as I can ask it questions about its output. It can find mistakes in its own output and all that. It obviously understands what it is doing.