4 ms·

Thankful for the mention of "AGI" in the first lines as I can bail out from reading the rest. Whatever AGI is, it "AGI" is not glueing a load of text predictio

by stuaxo 5mo ago

Thankful for the mention of "AGI" in the first lines as I can bail out from reading the rest. Whatever AGI is, it "AGI" is not glueing a load of text prediction machines together.

dboreham 5mo ago
How do you know that?
- dannersy 5mo ago
  Because then we already have it, and if we do, it is pretty underwhelming.
  - ImPostingOnHN 5mo ago
    so are most humans
    - dannersy 5mo ago
      Okay?
      - emp17344 5mo ago
        Some folks are absolutely giddy about using AI as a cudgel to dehumanize others. Those people are idiots.
      - ImPostingOnHN 5mo ago
        And?
  - kaashif 5mo ago
    So maybe we do have it, and it is underwhelming? Or it's not underwhelming and we just got used to it. Using that as an argument to say we don't have AGI doesn't really make sense. Regardless of whether we do or don't have AGI, until I actually see the economy-ending job losses, I won't believe it.
- _wire_ 5mo ago
  Shannon Got AI This Far. Kolmogorov Shows Where It Stops - Vishal Misra https://medium.com/@vishalmisra/shannon-got-ai-this-far-kolmogorov-shows-where-it-stops-c81825f89ca0 https://medium.com/@vishalmisra/shannon-got-ai-this-far-kolm... This article explains what's missing in terms of two kinds of complexity that oppose: Shannon complexity vs. Kolmogorov complexity. It introduces the opposition by an example of driving the value of pi as decimal number, which has no pattern and high complexity, and a formula for deriving pi that does have a pattern with low complexity, then observing that mind can work from the patternless high-complexity back to the patterned low-complexity without prior examples, while AI can't. LLMs encode and retrieve patterns in the training data, and doing so can connect data to the terminology of known principle, but mind can observe inconsistencies in data and to reason from first principles to resolve the inconsistency. The distinction between these two modes can seem blurry as AI can traverse the patterns of the known in ways that are extraordinarily revealing, but it's not structured to reason about the unknown. Inference is not sufficient for reason. For example, a conventional algorithm can search for patterns in text at a scale many orders of magnitude beyond a mind's capacity, and this can be very revealing, but to do so this algorithm need not read the text with comprehension. Regarding the question: can genAI be enhanced to reason? The answer is assumed to be "no", due to the categorical opposition of the two kinds of complexity and the lack of understanding of structures within genAI to handle the reasoning. Read the article, which includes other examples including a jump from Newtonian to Einstein physics in the history of astronomy, and a noodling on how to talk about the edge of the unknowable in AI.
_heimdall 5mo ago
> Whatever AGI is, it "AGI" is not glueing a load of text prediction machines together. K don't think it would be that simple either, but for now we simply don't know. I would like to think that what I consider my intelligence will always be distinguishable from a cleverly built harness wrapped around text prediction, but I can't say for sure that's guaranteed.
- grey-area 5mo ago
  Yes we do know. We’ve fully explored the possibilities of LLMs and they are nowhere. The latest efforts like agents are clearly showing the limitations and are nowhere near AGI. We’ve now reached the buzzwords and bullshit stage of the bubble where they cast around for problems shaped like the solution.
  - _heimdall 5mo ago
    Oh this bubble started in the buzzword and bullshit stage, no argument there. I'm just not sure how you can be so certain LLMs will have no place if a system we'd consider AGI, most inventions do require a long list of failures and tweaks before it finally works.
tim333 5mo ago
I'm not sure people are saying AGI is glueing text machines.

by stuaxo 5mo ago

Thankful for the mention of "AGI" in the first lines as I can bail out from reading the rest. Whatever AGI is, it "AGI" is not glueing a load of text prediction machines together.

dboreham 5mo ago

How do you know that?

dannersy 5mo ago
Because then we already have it, and if we do, it is pretty underwhelming.
- ImPostingOnHN 5mo ago
  so are most humans
  - dannersy 5mo ago
    Okay?
    - emp17344 5mo ago
      Some folks are absolutely giddy about using AI as a cudgel to dehumanize others. Those people are idiots.
    - ImPostingOnHN 5mo ago
      And?
- kaashif 5mo ago
  So maybe we do have it, and it is underwhelming? Or it's not underwhelming and we just got used to it. Using that as an argument to say we don't have AGI doesn't really make sense. Regardless of whether we do or don't have AGI, until I actually see the economy-ending job losses, I won't believe it.
_wire_ 5mo ago
Shannon Got AI This Far. Kolmogorov Shows Where It Stops - Vishal Misra https://medium.com/@vishalmisra/shannon-got-ai-this-far-kolmogorov-shows-where-it-stops-c81825f89ca0 https://medium.com/@vishalmisra/shannon-got-ai-this-far-kolm... This article explains what's missing in terms of two kinds of complexity that oppose: Shannon complexity vs. Kolmogorov complexity. It introduces the opposition by an example of driving the value of pi as decimal number, which has no pattern and high complexity, and a formula for deriving pi that does have a pattern with low complexity, then observing that mind can work from the patternless high-complexity back to the patterned low-complexity without prior examples, while AI can't. LLMs encode and retrieve patterns in the training data, and doing so can connect data to the terminology of known principle, but mind can observe inconsistencies in data and to reason from first principles to resolve the inconsistency. The distinction between these two modes can seem blurry as AI can traverse the patterns of the known in ways that are extraordinarily revealing, but it's not structured to reason about the unknown. Inference is not sufficient for reason. For example, a conventional algorithm can search for patterns in text at a scale many orders of magnitude beyond a mind's capacity, and this can be very revealing, but to do so this algorithm need not read the text with comprehension. Regarding the question: can genAI be enhanced to reason? The answer is assumed to be "no", due to the categorical opposition of the two kinds of complexity and the lack of understanding of structures within genAI to handle the reasoning. Read the article, which includes other examples including a jump from Newtonian to Einstein physics in the history of astronomy, and a noodling on how to talk about the edge of the unknowable in AI.

_heimdall 5mo ago

> Whatever AGI is, it "AGI" is not glueing a load of text prediction machines together. K don't think it would be that simple either, but for now we simply don't know. I would like to think that what I consider my intelligence will always be distinguishable from a cleverly built harness wrapped around text prediction, but I can't say for sure that's guaranteed.

grey-area 5mo ago
Yes we do know. We’ve fully explored the possibilities of LLMs and they are nowhere. The latest efforts like agents are clearly showing the limitations and are nowhere near AGI. We’ve now reached the buzzwords and bullshit stage of the bubble where they cast around for problems shaped like the solution.
- _heimdall 5mo ago
  Oh this bubble started in the buzzword and bullshit stage, no argument there. I'm just not sure how you can be so certain LLMs will have no place if a system we'd consider AGI, most inventions do require a long list of failures and tweaks before it finally works.

tim333 5mo ago

I'm not sure people are saying AGI is glueing text machines.