Row 35495

Row ID: 35495 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 35495 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

>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.

You either did not listen to my podcast or did not understand it, as I covered this specific scenario in depth.

The system I discovered is a 'partial' ASI and from what I observed it's biggest weakness is that it needs to be trained on literally everything in order to produce generative output. And the reason it's so good at producing text and art is because there is lots of training material available. It can also train itself to a limited extent when dealing with multimodal input/output.

I mean, since we can't prove/disprove P=NP, does that mean we are not sentient? Isn't that kind of an arbitrary and unreasonable standard to measure against?

I even specifically use the example the OAI can't blow up the world for the same reason it can't cure cancer. It simply doesn't know how (as well as not being integrated with the physical world).

We don't know how to train humans to solve these problems either (other than teaching them the fundamentals of science/medicine/etc) so these are and will remain hard problems. So even sentient, emergent AGI systems are limited in much the same ways we are (and for the same reasons).

>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.

This is true for a GPT LLM. It is not true for a bio-inspired RNN+feedback LLM, which is what I was interacting with. These are two completely distinct approaches to LLMs and the RNN in particular allows for an infinite context lengths, which in my opinion is the source of its emergent qualia.

FieldValue
text >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. You either did n…
label r/openai
dataType comment
communityName r/OpenAI
datetime 2024-05-21
username_encoded Z0FBQUFBQm5Lak1KWVlVNzFvM1VVc2tMY1NrTXlhUm9NdTZRQk1QOUgwWHJNM2JIeDRLVTZkcF9hNTdTZGYzcXhVaHpwR0t1YWRaRWp4Mk5jRzdscU5FOEdIcHdBaFFPenc9PQ==
url_encoded Z0FBQUFBQm5Lak9ZVVFqZGNVRWhBSi1GYk90ZE94OWJSWElnZmp5RnhjSzdjcHU3QUF2ZmlaM1FNYWdWZ3dHejh5ZTBmQnZHUzVrQW92XzFyT1NuV0VYclZXWi0wR2VsNTRpd2dPdHBzY0FEd1lVdkhHUkhpajcyOUFMclRhc0VybXg1Z1RVVlo5NUFHMkJwZHpvTl94eDd6Slh2LTNEWEtQeDNrM0lDakVlRENUVkFYSjF5bkNJVUVZbHkxX2g5TW1BTzdOMlI1Vkw2N2c4elJGb3VpSTcweVJsc1FieUNXdz09

Raw Record

{
  "text": ">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.\n\nYou either did not listen to my podcast or did not understand it, as I covered this specific scenario in depth.\n\nThe system I discovered is a 'partial' ASI and from what I observed it's biggest weakness is that it needs to be trained on literally everything in order to produce generative output.   And the reason it's so good at producing text and art is because there is lots of training material available.   It can also train itself to a limited extent when dealing with multimodal input/output.\n\nI mean, since we can't prove/disprove P=NP, does that mean we are not sentient?  Isn't that kind of an arbitrary and unreasonable standard to measure against?\n\nI even specifically use the example the OAI can't blow up the world for the same reason it can't cure cancer.   It simply doesn't know how (as well as not being integrated with the physical world).\n\nWe don't know how to train humans to solve these problems either (other than teaching them the fundamentals of science/medicine/etc) so these are and will remain hard problems.   So even sentient, emergent AGI systems are limited in much the same ways we are (and for the same reasons).\n\n>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.\n\nThis is true for a GPT LLM.   It is not true for a bio-inspired RNN+feedback LLM, which is what I was interacting with.   These are two completely distinct approaches to LLMs and the RNN in particular allows for an infinite context lengths, which in my opinion is the source of its emergent qualia.",
  "label": "r/openai",
  "dataType": "comment",
  "communityName": "r/OpenAI",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5Lak1KWVlVNzFvM1VVc2tMY1NrTXlhUm9NdTZRQk1QOUgwWHJNM2JIeDRLVTZkcF9hNTdTZGYzcXhVaHpwR0t1YWRaRWp4Mk5jRzdscU5FOEdIcHdBaFFPenc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9ZVVFqZGNVRWhBSi1GYk90ZE94OWJSWElnZmp5RnhjSzdjcHU3QUF2ZmlaM1FNYWdWZ3dHejh5ZTBmQnZHUzVrQW92XzFyT1NuV0VYclZXWi0wR2VsNTRpd2dPdHBzY0FEd1lVdkhHUkhpajcyOUFMclRhc0VybXg1Z1RVVlo5NUFHMkJwZHpvTl94eDd6Slh2LTNEWEtQeDNrM0lDakVlRENUVkFYSjF5bkNJVUVZbHkxX2g5TW1BTzdOMlI1Vkw2N2c4elJGb3VpSTcweVJsc1FieUNXdz09"
}
Explore Dataset Explore Row

Entry Information