Row 24867

Row ID: 24867 | Dataset Entry | Axioma AXP Content Repository

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While, based on previous interactions with me and many others, I am quite sure that nothing I say will change your mind, but I will indulge you with an answer.

>Show me a clear example of a prompt and response from what you would consider from an actual sentient AGI/ASI system and tell me how you would know the difference.

First, there are several concepts conflated here. Sentience, AGI and ASI.

Let's start with AGI. I think that LLMs are very close to disembodied, abstract intelligence. For all intents and purposes, these systems are already more capable than humans on most text-based tasks, not in depth, but certainly in breadth.

Next, ASI. I view this as not only breadth of knowledge and ability but also depth. Testing for this is quite easy. Ask the systems to solve a problem that humans cannot solve. Something objectively testable, like solving a problem in mathematics, computer science, physics, medicine, etc. For instance, in CS, prove or disprove that P=NP. In physics, design an experiment to test whether gravity is quantum or not. In medicine, design a treatment that cures Alzheimer's, etc, etc.

Finally, sentience. While intriguing and entertaining, I find discussions about sentience to be non-scientific and frankly a waste of time, since there is no scientific consensus on what sentience means in the first place. Is a chimpanzee sentient? Is a dog sentient? Is a pig sentient? What about a chicken? A cricket? A worm? At what age after conception does a human become sentient? When people can agree on the answer to these questions then we can have a more precise discussion about AI sentience.

Taking a few step back, what I and many others have tried to point out to you is that by asking "leading" questions you can elicit any type of response, especially in early versions of GPT-4, and especially in areas where the model has no information about like specifics about itself, you will get confabulations, continuations of the story that you started. One can not learn about the architecture of an AI models by asking it, unless that information has been explicitly included, in detail, in the training data, or in the system prompt, or in some RAG system. An LLM does not have the ability to perceive it's internals just as you don't have the ability to know about what your liver is and what it does unless someone told you, or read it in a book.

FieldValue
text While, based on previous interactions with me and many others, I am quite sure that nothing I say will change your mind, but I will indulge you with an answer. >Show me a clear example of a prompt and response from what you would consider from an actual sentient AGI/ASI system and tell me how you would know the difference. First, there are several concepts conflated here. Sentience, AGI and ASI. Let's start with AGI. I think that LLMs are very close to disembodied, abstract intelligence. For…
label r/openai
dataType comment
communityName r/OpenAI
datetime 2024-05-21
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Raw Record

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  "text": "While, based on previous interactions with me and many others, I am quite sure that nothing I say will change your mind, but I will indulge you with an answer.\n\n>Show me a clear example of a prompt and response from what you would consider from an actual sentient AGI/ASI system and tell me how you would know the difference.\n\nFirst, there are several concepts conflated here. Sentience, AGI and ASI. \n\nLet's start with AGI. I think that LLMs are very close to disembodied, abstract intelligence. For all intents and purposes, these systems are already more capable than humans on most text-based tasks, not in depth, but certainly in breadth. \n\nNext, ASI. I view this as not only breadth of knowledge and ability but also depth. Testing for this is quite easy. Ask the systems to solve a problem that humans cannot solve. Something objectively testable, like solving a problem in mathematics, computer science, physics, medicine, etc. For instance, in CS, prove or disprove that P=NP. In physics, design an experiment to test whether gravity is quantum or not. In medicine, design a treatment that cures Alzheimer's, etc, etc.\n\nFinally, sentience. While intriguing and entertaining, I find discussions about sentience to be non-scientific and frankly a waste of time, since there is no scientific consensus on what sentience means in the first place. Is a chimpanzee sentient? Is a dog sentient? Is a pig sentient? What about a chicken? A cricket? A worm? At what age after conception does a human become sentient? When people can agree on the answer to these questions then we can have a more precise discussion about AI sentience. \n\nTaking a few step back, what I and many others have tried to point out to you is that by asking \"leading\" questions you can elicit any type of response, especially in early versions of GPT-4, and especially in areas where the model has no information about like specifics about itself, you will get confabulations, continuations of the story that you started. One can not learn about the architecture of an AI models by asking it, unless that information has been explicitly included, in detail, in the training data, or in the system prompt, or in some RAG system. An LLM does not have the ability to perceive it's internals just as you don't have the ability to know about what your liver is and what it does unless someone told you, or read it in a book.",
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