6 ms·

Obviously the other responder is being a little tongue-in-cheek but AGI to me would be virtually indistinguishable from a human in both ability to learn, grow a

Obviously the other responder is being a little tongue-in-cheek but AGI to me would be virtually indistinguishable from a human in both ability to learn, grow and adapt to new information.

Enginerrrd 2y ago
Honestly it doesn't even need to learn and grow much if at all if its able to properly reason about the world and its context and deal with the inexhaustible supply of imperfections and detail with reality.
- bashfulpup 2y ago
  That implies learning. Solve continual learning and you have agi. Wouldn't it amaze you if you learned 10 years ago that we would have AI that could do math and code better than 99% of all humans. And at the same time they could barely order you a hotdog on doordash. Fundamental ability is lacking. AGI is just as likely to be solved by Openai as it is by a college student with a laptop. Could be 1yr or 50yrs we cannot predict when.
  - Enginerrrd 2y ago
    Strictly speaking I'm not sure if it does require learning if information representing the updated context is presented. Though it depends what you define as learning. ("You have tried this twice, and it's not working.") is often enough to get even current LLM's to try something else. That said, your second paragraph is one of the best and most succinct ways of pointing out why current LLM's aren't yet close to AGI if though they sometimes feel like it's got the right idea.
    - bashfulpup 2y ago
      In context learning, learning via training. Both are things we barely understand the mechanism of. RAG is a basically a perfect example to understand the limits of in context learning and AI in general. It's faults are easier to understand but the same as any AI vs AGI problem. I could go on but CL is a massive gap of our knowledge and likely the only thing missing to AGI.
      - tw1984 2y ago
        > RAG is a basically a perfect example to understand the limits of in context learning and AI in general. How? RAG is not even in the field of AI.
        bashfulpup 2y ago
        Long explanation. Simple terms, you can't use a fixed box to solve an unbounded problem space. If your problem fits within the box it works, if it doesn't, you need CL. I tried to solve this via expanding the embedding/retrieval space but realized it's the same as CL and in my definition of it I was trying to solve AGI. I did a lot of unique algorithms and architectures but Unsuprisingly, I never solved this. I am thankful I finally understood this quote. "The first gulp from the glass of natural sciences will turn you into an atheist, but at the bottom of the glass God is waiting for you."
samtp 2y ago
Would it also get brainrot from consuming too much social media & made up stories? Because I imagine it's reasoning would have to be significantly better than the average human to avoid this.
- tempodox 2y ago
  It probably would avoid the toxic sewage altogether, for reasons like low nutrient content.

Obviously the other responder is being a little tongue-in-cheek but AGI to me would be virtually indistinguishable from a human in both ability to learn, grow and adapt to new information.

Enginerrrd 2y ago

Honestly it doesn't even need to learn and grow much if at all if its able to properly reason about the world and its context and deal with the inexhaustible supply of imperfections and detail with reality.

bashfulpup 2y ago
That implies learning. Solve continual learning and you have agi. Wouldn't it amaze you if you learned 10 years ago that we would have AI that could do math and code better than 99% of all humans. And at the same time they could barely order you a hotdog on doordash. Fundamental ability is lacking. AGI is just as likely to be solved by Openai as it is by a college student with a laptop. Could be 1yr or 50yrs we cannot predict when.
- Enginerrrd 2y ago
  Strictly speaking I'm not sure if it does require learning if information representing the updated context is presented. Though it depends what you define as learning. ("You have tried this twice, and it's not working.") is often enough to get even current LLM's to try something else. That said, your second paragraph is one of the best and most succinct ways of pointing out why current LLM's aren't yet close to AGI if though they sometimes feel like it's got the right idea.
  - bashfulpup 2y ago
    In context learning, learning via training. Both are things we barely understand the mechanism of. RAG is a basically a perfect example to understand the limits of in context learning and AI in general. It's faults are easier to understand but the same as any AI vs AGI problem. I could go on but CL is a massive gap of our knowledge and likely the only thing missing to AGI.
    - tw1984 2y ago
      > RAG is a basically a perfect example to understand the limits of in context learning and AI in general. How? RAG is not even in the field of AI.
      - bashfulpup 2y ago
        Long explanation. Simple terms, you can't use a fixed box to solve an unbounded problem space. If your problem fits within the box it works, if it doesn't, you need CL. I tried to solve this via expanding the embedding/retrieval space but realized it's the same as CL and in my definition of it I was trying to solve AGI. I did a lot of unique algorithms and architectures but Unsuprisingly, I never solved this. I am thankful I finally understood this quote. "The first gulp from the glass of natural sciences will turn you into an atheist, but at the bottom of the glass God is waiting for you."

samtp 2y ago

Would it also get brainrot from consuming too much social media & made up stories? Because I imagine it's reasoning would have to be significantly better than the average human to avoid this.

tempodox 2y ago
It probably would avoid the toxic sewage altogether, for reasons like low nutrient content.