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>Think of a human with below average intelligence. Then think of a human genius. LLMs are not AGI. A human with below average intelligence is still a league ab

>Think of a human with below average intelligence. Then think of a human genius. LLMs are not AGI. A human with below average intelligence is still a league above a chimpanzee. A chimpanzee will never be able to read, not because "it's too dumb", but because a chimp's brain lacks the actual hardware for reading. The LLM is the chimpanzee. The gap between an LLM and a "human with below average intelligence" is far more than 10x.

epups 3y ago
By what criteria? In several professional capacities, including coding, LLM's are far superior to an average human already.
- slowmovintarget 3y ago
  No they're not. They're superior at specific tests when prompted. But asking for a complete application nearly always fails, when an average human that knows programming should be able to make one that works.
  - CamperBob2 3y ago
    But asking for a complete application nearly always fails, when an average human that knows programming should be able to make one that works. "This talking dog is a dumbass. The risotto recipe he gave me sucked, and the C++ code he wrote is full of security holes. I don't see what all the hype is about." Hint: ML will get better. Humans will not. ("Slow moving target," indeed.) What we're learning is just how many aspects of our vaunted 'intelligence' are really just features of our languages and the graphs they encode.
    - riffraff 3y ago
      This might be true but it's irrelevant to the claim "LLMs are already better than humans".
      - CamperBob2 3y ago
        You are fixated on the current state of the art, when the first couple of time derivatives are what actually matter. The assertion in question is already true in a limited sense, and it's only going to go in one direction from here.
      - ben_w 3y ago
        Google Translate, which IIRC is also a Transformer model like GPT these days, knows more natural languages than I can remember the names of, to a higher standard than I know my best non-native language, and I moved to Germany 5 years ago. GPT-3 knows most programming languages better than I do, even though I literally learned to read with the Commodore 64 user manual back in the 80s and haven't stopped being a nerd since; and while GPT code isn't always correct or even compilable, it's not like I don't still make mistakes that cause compilation to fail a few times a day, and there's a reason we all insist on testing code rather than just assuming it will work when a dev stops typing. Again, I acknowledge GPT-3.5 (and I assume 4 but have not used it) is not perfect: like others I'd call 3.5 a "junior developer" (and it isn't even that good in every subject!); yet, despite that, the only domains where I can regularly beat it at those where my perception of reality is fundamentally different from its perception (how words sound and how numbers are composed, so it's relatively bad at arithmetic and rhyme), or where it has been forced to be bad (ask it to write about conflict, my experience is everyone reconciles and lives happily ever after). But in most cases it would take me years to get as good as it already is. And there are more of those subjects than I can remember the names of, too.
  - p-e-w 3y ago
    > when an average human that knows programming should be able to make one that works. Very few people who "know programming" are actually capable of creating a complete application that serves a specific purpose. Many professional programmers struggle to implement basic algorithms like prime number testing. Coding AIs absolutely outperform the average programming professional, because the average programming professional can barely program. The software industry's demand for programmers is far too great for every coding position to be filled with top-class full stack engineers.
    - shreyshnaccount 3y ago
      Who is this average programmer you're talking about? We did primality testing in literally the first sem at uni
      - pedrosorio 3y ago
        Take a look at this old, commonly referenced article, might be before your time: https://blog.codinghorror.com/why-cant-programmers-program/ https://blog.codinghorror.com/why-cant-programmers-program/
      - TeMPOraL 3y ago
        > We did primality testing in literally the first sem at uni There's a lot of things you did during university classes. I bet you don't even remember half of them, and of the half you do, you couldn't actually do most of them from memory right now. The advantage LLMs have over programmers is that they've seen much more code than any human ever would, remember pretty much all of it - not necessarily verbatim, but also as learned concepts, and that this knowledge does not decay. It's all equally accessible. Whether you ask LLM for basic or optimized primality testing algorithms, or how to use some obscure part of Microsoft's DCOM that approximately nobody on the planet wrote new code for in the last decade - they'll perform equally well. Not perfect, but not worse than an average software dev that does remember the things they're asked to work with.
        shreyshnaccount 3y ago
  - qup 3y ago
    The LLM knows it doesn't work, too, we just don't run it long enough to let it try it out. It would try, get an error, and know how to handle the error. https://github.com/drifting-in-space/botsh https://github.com/drifting-in-space/botsh Check out some agents that probably can already do more than you knew about.
p-e-w 3y ago
> The gap between an LLM and a "human with below average intelligence" is far more than 10x. In which direction? GPT-4 passes the bar exam with a top 10% score. How do you think a human with below average intelligence (or even with average intelligence) would fare? Copilot generates programming code that solves problems, and in most cases the code is correct. It outperforms many junior professional developers. Do you think a human with below average intelligence could do that?
- intelVISA 3y ago
  > Copilot generates programming code that solves problems, and in most cases the code is correct. It outperforms many junior professional developers. Do you think a human with below average intelligence could do that? Absolutely, it's why Javascript is so popular.
  - p-e-w 3y ago
    A human with below average intelligence can outperform many junior professional software engineers because of JavaScript? Do you mean a programmer with below average intelligence (for a programmer)? Because I'm having a hard time believing that you actually believe what you wrote. BTW, JavaScript is actually a fairly difficult language to learn and use. Python, BASIC, and even Fortran are much simpler conceptually and have far fewer pitfalls. JavaScript is popular because it's the only language that every modern computer has an interpreter for, not because it's so easy that idiots can use it.
    - arcanemachiner 3y ago
      It's gotta be a low-effort joke.
    - intelVISA 3y ago
      It was a bit tongue in cheek I'll confess. :)
- nemothekid 3y ago
  >How do you think a human with below average intelligence (or even with average intelligence) would fare? I don't understand what rote memorization to pass a test has to do with intelligence. For the record Kim Kardashian passed the bar; I imagine anyone given the proper motivation and time to study could do it, it's not a hard test. If AGI is a computer passing a test, then AGI was achieved a long time ago. I don't think a human with below average intelligence could multiple 2 very large primes but I don't mistake my calculator for intelligence. A below average intelligence human can drive a car with a couple hours of training, an LLM can't do that (with a far lower power budget as well). I'm not saying AGI is impossible, but it's clear LLMs are not AGIs; it's not a matter of having a 100x more powerful LLM, just like making an Ape better at sign language won't make them better at abstract reasoning. An Ape's brain fundamentally lacks the mental machinery for higher level things that humans do. It's not a question of not being smart enough. LLMs are simply one component of the human mind.
ithkuil 3y ago
The key idea behind the current wave of AI is that the necessary "structure" will be created through training by sheer amount of data. The idea has been dismissed for decades but it turned out one of the most effective ways to get practical results. Now, can and should all structure necessarily come from pure training? Is there a better "seed" structure that can make models more effective? Is there some other bits that are missing that are not in the microstructure but in the way the model is connected to the external world and itself (feedback loops etc)?
qup 3y ago
In your metaphor, maybe what the LLM (chimpanzee) "brain" is missing is some things like an inner thought loop, long term and short term storage and context, etc. Pieces we can understand, build, and surround the LLM with. Given the right arrangement, and the correct "abracadabra," perhaps that gets to where we are. It seems to me that LLMs are perhaps capable of making up the inner thought loop, and the rest of the obvious systems seem doable. The context seems to be the hard problem; pulling in the proper things, and giving them the correct amount of attention.
- pixl97 3y ago
  >pulling in the proper things, and giving them the correct amount of attention. This is likely one of the harder problems to solve. One of the strengths humans seem to have is the power of analogy. These analogies quite often can lead us into new paths of thought, or at least in the correct direction. How do you do this in a machine system without wasting tons of power chasing dead ends, not sure?
ummonk 3y ago
From the origins of the first sentient life on Earth half a billion years ago, our common ancestor with the chimpanzees was 98% of the evolutionary timespan on the way to humans. Do you really want to bank on that last 2% being difficult to match in computers?
fnordpiglet 3y ago
LLM are a single model. Multimodal LLM tightly integrated with classical AI techniques to inform, conform, compel, solve, optimize, etc in a tight feedback loop…. I think folks are too entranced by the abilities of LLM and too jaded to realize the flaws are in domains we’ve already achieved great success. the magic really comes when we combine them with classical AI techniques. I don’t think you need more than GPT4 level LLM, what you need is goal based agency, information retrieval, optimizers, solvers, etc. These are things we’ve already created. The magic comes not from bigger LLM but from integration.

epups 3y ago

By what criteria? In several professional capacities, including coding, LLM's are far superior to an average human already.

slowmovintarget 3y ago
No they're not. They're superior at specific tests when prompted. But asking for a complete application nearly always fails, when an average human that knows programming should be able to make one that works.
- CamperBob2 3y ago
  But asking for a complete application nearly always fails, when an average human that knows programming should be able to make one that works. "This talking dog is a dumbass. The risotto recipe he gave me sucked, and the C++ code he wrote is full of security holes. I don't see what all the hype is about." Hint: ML will get better. Humans will not. ("Slow moving target," indeed.) What we're learning is just how many aspects of our vaunted 'intelligence' are really just features of our languages and the graphs they encode.
  - riffraff 3y ago
    This might be true but it's irrelevant to the claim "LLMs are already better than humans".
    - CamperBob2 3y ago
      You are fixated on the current state of the art, when the first couple of time derivatives are what actually matter. The assertion in question is already true in a limited sense, and it's only going to go in one direction from here.
    - ben_w 3y ago
      Google Translate, which IIRC is also a Transformer model like GPT these days, knows more natural languages than I can remember the names of, to a higher standard than I know my best non-native language, and I moved to Germany 5 years ago. GPT-3 knows most programming languages better than I do, even though I literally learned to read with the Commodore 64 user manual back in the 80s and haven't stopped being a nerd since; and while GPT code isn't always correct or even compilable, it's not like I don't still make mistakes that cause compilation to fail a few times a day, and there's a reason we all insist on testing code rather than just assuming it will work when a dev stops typing. Again, I acknowledge GPT-3.5 (and I assume 4 but have not used it) is not perfect: like others I'd call 3.5 a "junior developer" (and it isn't even that good in every subject!); yet, despite that, the only domains where I can regularly beat it at those where my perception of reality is fundamentally different from its perception (how words sound and how numbers are composed, so it's relatively bad at arithmetic and rhyme), or where it has been forced to be bad (ask it to write about conflict, my experience is everyone reconciles and lives happily ever after). But in most cases it would take me years to get as good as it already is. And there are more of those subjects than I can remember the names of, too.
- p-e-w 3y ago
  > when an average human that knows programming should be able to make one that works. Very few people who "know programming" are actually capable of creating a complete application that serves a specific purpose. Many professional programmers struggle to implement basic algorithms like prime number testing. Coding AIs absolutely outperform the average programming professional, because the average programming professional can barely program. The software industry's demand for programmers is far too great for every coding position to be filled with top-class full stack engineers.
  - shreyshnaccount 3y ago
    Who is this average programmer you're talking about? We did primality testing in literally the first sem at uni
    - pedrosorio 3y ago
      Take a look at this old, commonly referenced article, might be before your time: https://blog.codinghorror.com/why-cant-programmers-program/ https://blog.codinghorror.com/why-cant-programmers-program/
    - TeMPOraL 3y ago
      > We did primality testing in literally the first sem at uni There's a lot of things you did during university classes. I bet you don't even remember half of them, and of the half you do, you couldn't actually do most of them from memory right now. The advantage LLMs have over programmers is that they've seen much more code than any human ever would, remember pretty much all of it - not necessarily verbatim, but also as learned concepts, and that this knowledge does not decay. It's all equally accessible. Whether you ask LLM for basic or optimized primality testing algorithms, or how to use some obscure part of Microsoft's DCOM that approximately nobody on the planet wrote new code for in the last decade - they'll perform equally well. Not perfect, but not worse than an average software dev that does remember the things they're asked to work with.
      - shreyshnaccount 3y ago
- qup 3y ago
  The LLM knows it doesn't work, too, we just don't run it long enough to let it try it out. It would try, get an error, and know how to handle the error. https://github.com/drifting-in-space/botsh https://github.com/drifting-in-space/botsh Check out some agents that probably can already do more than you knew about.

p-e-w 3y ago

> The gap between an LLM and a "human with below average intelligence" is far more than 10x. In which direction? GPT-4 passes the bar exam with a top 10% score. How do you think a human with below average intelligence (or even with average intelligence) would fare? Copilot generates programming code that solves problems, and in most cases the code is correct. It outperforms many junior professional developers. Do you think a human with below average intelligence could do that?

intelVISA 3y ago
> Copilot generates programming code that solves problems, and in most cases the code is correct. It outperforms many junior professional developers. Do you think a human with below average intelligence could do that? Absolutely, it's why Javascript is so popular.
- p-e-w 3y ago
  A human with below average intelligence can outperform many junior professional software engineers because of JavaScript? Do you mean a programmer with below average intelligence (for a programmer)? Because I'm having a hard time believing that you actually believe what you wrote. BTW, JavaScript is actually a fairly difficult language to learn and use. Python, BASIC, and even Fortran are much simpler conceptually and have far fewer pitfalls. JavaScript is popular because it's the only language that every modern computer has an interpreter for, not because it's so easy that idiots can use it.
  - arcanemachiner 3y ago
    It's gotta be a low-effort joke.
  - intelVISA 3y ago
    It was a bit tongue in cheek I'll confess. :)
nemothekid 3y ago
>How do you think a human with below average intelligence (or even with average intelligence) would fare? I don't understand what rote memorization to pass a test has to do with intelligence. For the record Kim Kardashian passed the bar; I imagine anyone given the proper motivation and time to study could do it, it's not a hard test. If AGI is a computer passing a test, then AGI was achieved a long time ago. I don't think a human with below average intelligence could multiple 2 very large primes but I don't mistake my calculator for intelligence. A below average intelligence human can drive a car with a couple hours of training, an LLM can't do that (with a far lower power budget as well). I'm not saying AGI is impossible, but it's clear LLMs are not AGIs; it's not a matter of having a 100x more powerful LLM, just like making an Ape better at sign language won't make them better at abstract reasoning. An Ape's brain fundamentally lacks the mental machinery for higher level things that humans do. It's not a question of not being smart enough. LLMs are simply one component of the human mind.

ithkuil 3y ago

The key idea behind the current wave of AI is that the necessary "structure" will be created through training by sheer amount of data. The idea has been dismissed for decades but it turned out one of the most effective ways to get practical results. Now, can and should all structure necessarily come from pure training? Is there a better "seed" structure that can make models more effective? Is there some other bits that are missing that are not in the microstructure but in the way the model is connected to the external world and itself (feedback loops etc)?

qup 3y ago

In your metaphor, maybe what the LLM (chimpanzee) "brain" is missing is some things like an inner thought loop, long term and short term storage and context, etc. Pieces we can understand, build, and surround the LLM with. Given the right arrangement, and the correct "abracadabra," perhaps that gets to where we are. It seems to me that LLMs are perhaps capable of making up the inner thought loop, and the rest of the obvious systems seem doable. The context seems to be the hard problem; pulling in the proper things, and giving them the correct amount of attention.

pixl97 3y ago
>pulling in the proper things, and giving them the correct amount of attention. This is likely one of the harder problems to solve. One of the strengths humans seem to have is the power of analogy. These analogies quite often can lead us into new paths of thought, or at least in the correct direction. How do you do this in a machine system without wasting tons of power chasing dead ends, not sure?

ummonk 3y ago

From the origins of the first sentient life on Earth half a billion years ago, our common ancestor with the chimpanzees was 98% of the evolutionary timespan on the way to humans. Do you really want to bank on that last 2% being difficult to match in computers?

fnordpiglet 3y ago

LLM are a single model. Multimodal LLM tightly integrated with classical AI techniques to inform, conform, compel, solve, optimize, etc in a tight feedback loop…. I think folks are too entranced by the abilities of LLM and too jaded to realize the flaws are in domains we’ve already achieved great success. the magic really comes when we combine them with classical AI techniques. I don’t think you need more than GPT4 level LLM, what you need is goal based agency, information retrieval, optimizers, solvers, etc. These are things we’ve already created. The magic comes not from bigger LLM but from integration.