4 ms·

i think this author doesnt fully understand how llms work either. Dismissing it as "a statistical model" is silly. hell, quantum mechanics is a statistical mo

i think this author doesnt fully understand how llms work either. Dismissing it as "a statistical model" is silly. hell, quantum mechanics is a statistical model too. moreover, each layer of an llm imbues the model with the possibility of looking further back in the conversion and imbuing meaning and context through conceptual associations (thats the k-v part of the kv cache). I cant see how this doesn't describe, abstractly, human cognition. now, maybe llms are not fully capable of the breadth of human cognition or have a harder time training to certain deeper insight, but fundamentally the structure is there (clever training and/or architectural improvements may still be possible -- in the way that every CNN is a subgraph of a FCNN that would be nigh impossible for a FCNN to discover randomly through training) to say llms are not smart in any way that is recognizable is just cherry-picking anecdotal data. if llms were not ever recognizably smart, people would not be using them the way they are.

827a 1y ago
> I cant see how this doesn't describe, abstractly, human cognition. now, maybe llms are not fully capable of the breadth of human cognition But, I can fire back with: You're making the same fallacy you correctly assert the article as making. When I see how a CPU's ALU adds two numbers together, it looks strikingly similar to how I add two numbers together in my head. I can't see how the ALU's internal logic doesn't describe, abstractly, human cognition. Now, maybe the ALU isn't fully capable of the breadth of human cognition... It turns out, the gaps expressed in the "fully capable of the breadth of human cognition" part really, really, really matter. Like, when it comes to ALUs, they overwhelm any impact that the parts which look similar cover. The question should be: How significant are the gaps in how LLMs mirror human cognition? I'm not sure we know, but I suspect they're significant enough to not write away as trivial.
- throwawaymaths 1y ago
  do they matter in a practical sense? an LLM can write a structured essay better than most undergrads. and as for measuring "smart", we throw that word around a lot. a dog is smart in a human way for being able to fetch one of 30 objects based on name or to drive a car (yes, dogs can drive), the bar for "smart" is pretty low, claiming llms are not smart is just prejudice.
  - mjburgess 1y ago
    You are assuming that because we measure an undergrad's ability to critical think with undergrad essays, that is a valid test for the LLM's capacity to think -- it isnt. This measures only, extremely narrowly, the LLM's capacity to produce undergrad essays. Society doesnt require undergrad essays. Nor does it require yet another webserver, iot script, or weekend hobby project. Society has all of those things already, hence the ability to train LLMs to produce them. "Society", the economy, etc. are operating under competitive optimisation processes -- so that what is valuable, on the margin, is what isn't readily produced. What is readily produced, has been produced, is being produced, and so on. Solved problems are solved problems. Intelligence is the capacity of animals to operate "on the margin" -- that's why we have it: Intelligence is a process of rapid adaption to novel circumstances, it is not, unlike puzzle-solvers like to claim, the solution to puzzles. Once a puzzle is solved so there are historical exemplars of its solution, it no longer requires intelligence to solve it -- hence using an LLM. (In this sense computer science is the art of removing intelligence from the solving of unsolved and unposed puzzles). LLMs surface "solved problems" more readily than search engines. There's no evidence, and plenty against, that they provide the value of intelligence -- their ability to advance one's capabilities under compeititon from others, is literally zero -- since all players in the economic (, social, etc.) game have access to the LLM. The LLM itself, in this sense, not only has no intelligence, but doesnt even show up in intelligent processes that we follow. It's washed out immediately -- it removes from our task lists, some "tasks that require intelligence", leaving the remainder for our actual intelligence to engage with.
    - throwawaymaths 1y ago
      if this is how you feel you haven't really used llms enough or are deliberately ignoring sporadically appearing data. github copilot for me routinely solves microproblems in unexplored areas it has no business knowing. Not always, but it's also not zero. ...and i encourage you to be more realistic about the market and what society "needs". does society really need an army of consultants at accenture? i dont know. but they are getting paid a lot. does that mean the allocation of resources is wrong? or does that mean theres something cynical but real in their existence?
francisofascii 1y ago
The creators of llms don't fully understand how they work either.
gitaarik 1y ago
So how would you explain how LLMs work to a layman?

827a 1y ago

> I cant see how this doesn't describe, abstractly, human cognition. now, maybe llms are not fully capable of the breadth of human cognition But, I can fire back with: You're making the same fallacy you correctly assert the article as making. When I see how a CPU's ALU adds two numbers together, it looks strikingly similar to how I add two numbers together in my head. I can't see how the ALU's internal logic doesn't describe, abstractly, human cognition. Now, maybe the ALU isn't fully capable of the breadth of human cognition... It turns out, the gaps expressed in the "fully capable of the breadth of human cognition" part really, really, really matter. Like, when it comes to ALUs, they overwhelm any impact that the parts which look similar cover. The question should be: How significant are the gaps in how LLMs mirror human cognition? I'm not sure we know, but I suspect they're significant enough to not write away as trivial.

throwawaymaths 1y ago
do they matter in a practical sense? an LLM can write a structured essay better than most undergrads. and as for measuring "smart", we throw that word around a lot. a dog is smart in a human way for being able to fetch one of 30 objects based on name or to drive a car (yes, dogs can drive), the bar for "smart" is pretty low, claiming llms are not smart is just prejudice.
- mjburgess 1y ago
  You are assuming that because we measure an undergrad's ability to critical think with undergrad essays, that is a valid test for the LLM's capacity to think -- it isnt. This measures only, extremely narrowly, the LLM's capacity to produce undergrad essays. Society doesnt require undergrad essays. Nor does it require yet another webserver, iot script, or weekend hobby project. Society has all of those things already, hence the ability to train LLMs to produce them. "Society", the economy, etc. are operating under competitive optimisation processes -- so that what is valuable, on the margin, is what isn't readily produced. What is readily produced, has been produced, is being produced, and so on. Solved problems are solved problems. Intelligence is the capacity of animals to operate "on the margin" -- that's why we have it: Intelligence is a process of rapid adaption to novel circumstances, it is not, unlike puzzle-solvers like to claim, the solution to puzzles. Once a puzzle is solved so there are historical exemplars of its solution, it no longer requires intelligence to solve it -- hence using an LLM. (In this sense computer science is the art of removing intelligence from the solving of unsolved and unposed puzzles). LLMs surface "solved problems" more readily than search engines. There's no evidence, and plenty against, that they provide the value of intelligence -- their ability to advance one's capabilities under compeititon from others, is literally zero -- since all players in the economic (, social, etc.) game have access to the LLM. The LLM itself, in this sense, not only has no intelligence, but doesnt even show up in intelligent processes that we follow. It's washed out immediately -- it removes from our task lists, some "tasks that require intelligence", leaving the remainder for our actual intelligence to engage with.
  - throwawaymaths 1y ago
    if this is how you feel you haven't really used llms enough or are deliberately ignoring sporadically appearing data. github copilot for me routinely solves microproblems in unexplored areas it has no business knowing. Not always, but it's also not zero. ...and i encourage you to be more realistic about the market and what society "needs". does society really need an army of consultants at accenture? i dont know. but they are getting paid a lot. does that mean the allocation of resources is wrong? or does that mean theres something cynical but real in their existence?

francisofascii 1y ago

The creators of llms don't fully understand how they work either.

gitaarik 1y ago

So how would you explain how LLMs work to a layman?