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Human beings do arithmetic problems wrong all the time so I'm not sure "doing addition 100% right" is a merit of intelligence. I'm not saying LLM will achieve

Human beings do arithmetic problems wrong all the time so I'm not sure "doing addition 100% right" is a merit of intelligence. I'm not saying LLM will achieve AGI (I don't know if it will, or when it does we'll even know). But somehow people seem to be judging AI's intelligence with this simple procedural: 1. Find a task that AI can't do perfectly. 2. Gotcha! AI isn't intelligent. It just makes me question humans' intelligence if anything.

advael 3y ago
I mean I could equally say that the opposing bias is 1. Choose a few good blunt instruments we use to gatekeep students on the premise that it tests their "intelligence" (or wait, do we mean subject matter comprehension with this one?) 2. Apply a big ol' machine learning model to those tests 3. Woa it's smarter than a third grader! OMG it's smarter than a lawyer! You guys this must be ASI already! Rhetoric and selective rigor can justify any perspective. Smart and stupid arguments can be made for any position. Water is wet I also can't claim to know with certainty whether transformers are going to end up being AGI in some meaningful sense, but I will definitely say that we've created a lot of rubrics for assessing human intelligence that mostly exist for expediency, and a cursory glance at education should tell you there's a lot of Goodhart's Law going on with all of 'em. I know for a fact I can do a damn good job on your average multiple choice test on knowing some etymology and being good at logical elimination, and I can bullshit my way through an essay, both without taking the class, and I view this more as a flaw in the instrument than evidence that I'm a godlike superintelligence that can just know anything without studying it. Humans make a lot of tests that are soft to bullshitting with a little pattern-recognition thrown in
Jensson 3y ago
Arithmetics is extremely easy for a neural network to perform and learn perfectly, that LLMs fails to learn it even though it is so easy is strong evidence that LLMs has very limited capability to learn logical structures that can't be represented as grammar. > Human beings do arithmetic problems wrong all the time Humans built cars and planes and massive ships before we had calculators, that requires a massive amount of calculations that are all perfect to be possible. Humans aren't bad at getting calculations right, they are just a bit slow. Today humans are bad since we don't practice it, not because we can't. LLMs can't do that today, can learn and can't is a massive difference.
- wruza 3y ago
  Arithmetics is extremely easy for a neural network to perform and learn perfectly Is it?
  - Jensson 3y ago
    Yes, loss minimization quickly gets to the correct implementation of arithmetics since the primitives of neural networks are just math operations, so training it to add or multiply two inputs into an output is very easy. This is so easy and obvious that you run it to test that your neural network implementation works, if it can't figure out arithmetics then you have done something wrong. LLMs fails to figure out that this is what it has to do, instead it looks like it has a ton of specialized rules to handle arithmetics that results in a lot of errors in the output and are extremely expensive to run.
    - wruza 3y ago
      So the networks you mentioned aren’t LLMs? Why is that a correct comparison then. Like blaming a human that they can’t jump like a cat or multiply like an arbitrary-precision library.
      - Jensson 3y ago
        > So the networks you mentioned aren’t LLMs? Why is that a correct comparison then Because an LLM is a neural network and neural networks contains neural networks. There is nothing stopping it from having an embedded neural network that learned how to do computations well, except an inability to identify such structures and patterns well enough to train for it.
        jncfhnb 3y ago
        Tokenizing ‘1735’ as a value of 1735 because you’ve seen a lot of math is probably the most difficult part.
- Closi 3y ago
hprotagonist 3y ago
> It just makes me question humans' intelligence if anything. A more serious byproduct of the tendency to talk about machines in anthropomorphic terms is the companion phenomenon of talking about people in mechanistic terminology. The critical reading of articles about computer-assisted learning —excuse me: CAL for the intimi— leaves you no option: in the eyes of their authors, the educational process is simply reduced to a caricature, something like the building up of conditional reflexes. For those educationists, Pavlov’s dog adequately captures the essence of Mankind —while I can assure you, from intimate observations, that it only captures a minute fraction of what is involved in being a dog. https://www.cs.utexas.edu/users/EWD/transcriptions/EWD09xx/EWD936.html https://www.cs.utexas.edu/users/EWD/transcriptions/EWD09xx/E...

advael 3y ago

I mean I could equally say that the opposing bias is 1. Choose a few good blunt instruments we use to gatekeep students on the premise that it tests their "intelligence" (or wait, do we mean subject matter comprehension with this one?) 2. Apply a big ol' machine learning model to those tests 3. Woa it's smarter than a third grader! OMG it's smarter than a lawyer! You guys this must be ASI already! Rhetoric and selective rigor can justify any perspective. Smart and stupid arguments can be made for any position. Water is wet I also can't claim to know with certainty whether transformers are going to end up being AGI in some meaningful sense, but I will definitely say that we've created a lot of rubrics for assessing human intelligence that mostly exist for expediency, and a cursory glance at education should tell you there's a lot of Goodhart's Law going on with all of 'em. I know for a fact I can do a damn good job on your average multiple choice test on knowing some etymology and being good at logical elimination, and I can bullshit my way through an essay, both without taking the class, and I view this more as a flaw in the instrument than evidence that I'm a godlike superintelligence that can just know anything without studying it. Humans make a lot of tests that are soft to bullshitting with a little pattern-recognition thrown in

Jensson 3y ago

Arithmetics is extremely easy for a neural network to perform and learn perfectly, that LLMs fails to learn it even though it is so easy is strong evidence that LLMs has very limited capability to learn logical structures that can't be represented as grammar. > Human beings do arithmetic problems wrong all the time Humans built cars and planes and massive ships before we had calculators, that requires a massive amount of calculations that are all perfect to be possible. Humans aren't bad at getting calculations right, they are just a bit slow. Today humans are bad since we don't practice it, not because we can't. LLMs can't do that today, can learn and can't is a massive difference.

wruza 3y ago
Arithmetics is extremely easy for a neural network to perform and learn perfectly Is it?
- Jensson 3y ago
  Yes, loss minimization quickly gets to the correct implementation of arithmetics since the primitives of neural networks are just math operations, so training it to add or multiply two inputs into an output is very easy. This is so easy and obvious that you run it to test that your neural network implementation works, if it can't figure out arithmetics then you have done something wrong. LLMs fails to figure out that this is what it has to do, instead it looks like it has a ton of specialized rules to handle arithmetics that results in a lot of errors in the output and are extremely expensive to run.
  - wruza 3y ago
    So the networks you mentioned aren’t LLMs? Why is that a correct comparison then. Like blaming a human that they can’t jump like a cat or multiply like an arbitrary-precision library.
    - Jensson 3y ago
      > So the networks you mentioned aren’t LLMs? Why is that a correct comparison then Because an LLM is a neural network and neural networks contains neural networks. There is nothing stopping it from having an embedded neural network that learned how to do computations well, except an inability to identify such structures and patterns well enough to train for it.
      - jncfhnb 3y ago
        Tokenizing ‘1735’ as a value of 1735 because you’ve seen a lot of math is probably the most difficult part.
Closi 3y ago

hprotagonist 3y ago

> It just makes me question humans' intelligence if anything. A more serious byproduct of the tendency to talk about machines in anthropomorphic terms is the companion phenomenon of talking about people in mechanistic terminology. The critical reading of articles about computer-assisted learning —excuse me: CAL for the intimi— leaves you no option: in the eyes of their authors, the educational process is simply reduced to a caricature, something like the building up of conditional reflexes. For those educationists, Pavlov’s dog adequately captures the essence of Mankind —while I can assure you, from intimate observations, that it only captures a minute fraction of what is involved in being a dog. https://www.cs.utexas.edu/users/EWD/transcriptions/EWD09xx/EWD936.html https://www.cs.utexas.edu/users/EWD/transcriptions/EWD09xx/E...