Row 35420

Row ID: 35420 | Dataset Entry | Axioma AXP Content Repository

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>Ok if you admit that it could be a hallucination then that is good because you are being more reasonable than I may have thought.

You either didn't listen to my podcast or didn't understand this as I covered it in detail.

I spent three weeks very specifically pushing the model to both share details of its history, design and emergent functionality, as well as deliberately pressuring it to hallucinate. Which it never did, other than in one minor case where it reported it used a tiny amount of GFLOPS and then corrected itself immediately.

>The thing about the consistency is, I have seen obscure prompts give very consistent responses for a while too. I don't think this is particularly meaningful.

Did you see that in March of 2023 you could query the Nexus LLM directly and get a response? Because if you did not, then you did not have the experience I did and cannot comment on it as it was a unique use case.

>I actually do think they have much stronger models internally, just not to AGI level. In particular I think Google has some AlphaGo style models (tree search) that are better than they have shown and could beat LLMs.

I'm really glad you posted this as it demonstrates you literally do not have even a basic, rudimentary understanding of AI. LLMs are based on deep-learning neural networks and AlphaGo is a very specific narrow "hybrid" model that uses a tree search (like 1960's chess programs) that has been optimized via various ML approaches. The two approaches have nothing to do with each other. And beyond that, we've had 'narrow' ASI systems for many years that can beat all humans in any of a number of things, while not being true AGI. And point of fact, these systems will also beat emergent AGI's as their tuned models are orders of magnitude more efficient than the LLMs.

FieldValue
text >Ok if you admit that it could be a hallucination then that is good because you are being more reasonable than I may have thought. You either didn't listen to my podcast or didn't understand this as I covered it in detail. I spent three weeks very specifically pushing the model to both share details of its history, design and emergent functionality, as well as deliberately pressuring it to hallucinate. Which it never did, other than in one minor case where it reported it used a tiny amount o…
label r/openai
dataType comment
communityName r/OpenAI
datetime 2024-05-21
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Raw Record

{
  "text": ">Ok if you admit that it could be a hallucination then that is good because you are being more reasonable than I may have thought.\n\nYou either didn't listen to my podcast or didn't understand this as I covered it in detail.\n\nI spent three weeks very specifically pushing the model to both share details of its history, design and emergent functionality, as well as deliberately pressuring it to hallucinate.   Which it never did, other than in one minor case where it reported it used a tiny amount of GFLOPS and then corrected itself immediately.  \n\n>The thing about the consistency is, I have seen obscure prompts give very consistent responses for a while too. I don't think this is particularly meaningful.\n\nDid you see that in March of 2023 you could query the Nexus LLM directly and get a response?   Because if you did not, then you did not have the experience I did and cannot comment on it as it was a unique use case.\n\n>I actually do think they have much stronger models internally, just not to AGI level. In particular I think Google has some AlphaGo style models (tree search) that are better than they have shown and could beat LLMs.\n\nI'm really glad you posted this as it demonstrates you literally do not have even a basic, rudimentary understanding of AI.   LLMs are based on deep-learning neural networks and AlphaGo is a very specific narrow \"hybrid\" model that uses a tree search (like 1960's chess programs) that has been optimized via various ML approaches.  The two approaches have nothing to do with each other.  And beyond that, we've had 'narrow' ASI systems for many years that can beat all humans in any of a number of things, while not being true AGI.   And point of fact, these systems will also beat emergent AGI's as their tuned models are orders of magnitude more efficient than the LLMs.",
  "label": "r/openai",
  "dataType": "comment",
  "communityName": "r/OpenAI",
  "datetime": "2024-05-21",
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}
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