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Just like it's mentioned later in the article: it doesn't really matter if you get an addition mostly right. You either get it right or you don't. I still appre

Just like it's mentioned later in the article: it doesn't really matter if you get an addition mostly right. You either get it right or you don't. I still appreciate their effort though, because even after altering the grading system, there were still some emergent abilities.

IanCal 2y ago
> it doesn't really matter if you get an addition mostly right. This is definitely not true in the real world. Approximate solutions are often good enough to answer a question.
- RandomLensman 2y ago
  But then it isn't "addition" but approximating "addition".
  - Satam 2y ago
    There's always some uncertainty to any kind of answer or computation, it's just at some threshold we unconsciously decide to take it for a fact.
    - RandomLensman 2y ago
      What uncertainty and threshold is there in the addition of integers, for example (within mathematics and the usual definitions)? Or in Boolean logic with the "and" operation? I don't think everything has uncertainty and thresholds to it, especially, when it actually resides outside of a technical implementation.
      - Satam 2y ago
        To verify the answer you'll always need to trust the technical implementation that's doing the computation. Doesn't matter if it's our brains or a calculator. Somewhere between "it's always wrong" and "it's always right unless the bits got flipped by cosmic rays" we deem the accuracy to be good enough.
        RandomLensman 2y ago
        Disagree, the theory exists outside of any specific technical implementation (every single one of those could be wrong, for example). You might not be able to verify something without being subject to random errors, but that doesn't mean the theory itself is subject to random errors. Any implementation (or write down etc.) of something can have errors, but the errors are in the implementation and do not give rise to uncertainty outside of the implementation. There is no uncertainty as to what the sum of two integers should be (within the usual mathematics).
    - Jensson 2y ago
      LLMs aren't wrong by a small percentage, they are wrong by a small number of tokens. They can miss a zero or be off by 100% and its just a token difference, to the LLM that is a minor mistake since everything else was right but it is a massive mistake in practice.
      - wruza 2y ago
        I watch math classes on youtube and some lecturers make symbolic mistakes all the time. Minus instead of a plus, missing exponents, saying x but writing y, etc. They only notice it when something unexpected contradicts down the line.
        Jensson 2y ago
        They got it right as you said, it just took a bit longer. That doesn't contradict what I said, humans can get things right very reliably by looking over the answers especially if you have another human to help look at the answers. An AI isn't comparable to a human, it is comparable to a team of humans, two ChatGPTs can't get more accurate by correcting each others answers but two humans can.
        Satam 2y ago
        A professor might be able to iterate to a correct answer but a student might not. And ChatGPT is definitely able to get improve its answer by iterating, it just depends on the toughness of the problem. If it's too difficult, no amount of iteration will get it much closer to the correct answer. If it's closer to its reasoning limits, then iterating will help.
        wruza 2y ago
        But if you stop them just there, an error persists. A professor is “multi-modal” and in a constant stream of evebts, including their lecture plan and premeditated key results. Are you sure that at some level of LLM “intelligence”, putting it into the same shoes wouldn’t improve the whole setting enough? I mean sure, they make mistakes. But if you stop-frame a professor, they make mistakes too. They don’t correct immediately, only after a contradiction gets presented. Reminds me how LLMs behave. Am I wrong here? Edit: was answering to gp, no idea how my post got here
raincole 2y ago
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 2y 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 2y 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.
arka2147483647 2y ago
Assume we have a child, and we test him regularly: - Test 1: First he can just draw squiggles on the math test - Test 2: Then he can do arithmetic correctly - Test 3: He fails on the last details on the algebraic calculation. Now, event though he fails on all tests, any reasonable parent would see that he improving nicely, and would be able to work in his chosen field in a year or so. Or alternatively, if we talk about AI, we can set the Test as a threshold, and we see the results are continuously trending upwards, and we can expect the curve to breach the threshold in the future. That is; measuring improvement, instead of pass/fail, allows one to predict when we might be able to use the AI for something.
- londons_explore 2y ago
  With AI you can do millions of tests. Some tests are easy by chance (eg. "Please multiply this list of numbers by zero"). Some tests are correct by chance alone, easy or hard. When you actually do these millions of tests, I don't think it really matters what the exact success metric is - an AI which is 'closer to correct, but still wrong' on one test will still get more tests correct overall on the dataset of millions of tests.
nathan_compton 2y ago
Tell it to floating point numbers.
t_mann 2y ago
> it doesn't really matter if you get an addition mostly right Back-of-the-envelope / mental math often works like that, and it's something that humans regularly use, so clearly it has some use.

IanCal 2y ago

> it doesn't really matter if you get an addition mostly right. This is definitely not true in the real world. Approximate solutions are often good enough to answer a question.

RandomLensman 2y ago
But then it isn't "addition" but approximating "addition".
- Satam 2y ago
  There's always some uncertainty to any kind of answer or computation, it's just at some threshold we unconsciously decide to take it for a fact.
  - RandomLensman 2y ago
    What uncertainty and threshold is there in the addition of integers, for example (within mathematics and the usual definitions)? Or in Boolean logic with the "and" operation? I don't think everything has uncertainty and thresholds to it, especially, when it actually resides outside of a technical implementation.
    - Satam 2y ago
      To verify the answer you'll always need to trust the technical implementation that's doing the computation. Doesn't matter if it's our brains or a calculator. Somewhere between "it's always wrong" and "it's always right unless the bits got flipped by cosmic rays" we deem the accuracy to be good enough.
      - RandomLensman 2y ago
        Disagree, the theory exists outside of any specific technical implementation (every single one of those could be wrong, for example). You might not be able to verify something without being subject to random errors, but that doesn't mean the theory itself is subject to random errors. Any implementation (or write down etc.) of something can have errors, but the errors are in the implementation and do not give rise to uncertainty outside of the implementation. There is no uncertainty as to what the sum of two integers should be (within the usual mathematics).
  - Jensson 2y ago
    LLMs aren't wrong by a small percentage, they are wrong by a small number of tokens. They can miss a zero or be off by 100% and its just a token difference, to the LLM that is a minor mistake since everything else was right but it is a massive mistake in practice.
    - wruza 2y ago
      I watch math classes on youtube and some lecturers make symbolic mistakes all the time. Minus instead of a plus, missing exponents, saying x but writing y, etc. They only notice it when something unexpected contradicts down the line.
      - Jensson 2y ago
        They got it right as you said, it just took a bit longer. That doesn't contradict what I said, humans can get things right very reliably by looking over the answers especially if you have another human to help look at the answers. An AI isn't comparable to a human, it is comparable to a team of humans, two ChatGPTs can't get more accurate by correcting each others answers but two humans can.
        Satam 2y ago
        A professor might be able to iterate to a correct answer but a student might not. And ChatGPT is definitely able to get improve its answer by iterating, it just depends on the toughness of the problem. If it's too difficult, no amount of iteration will get it much closer to the correct answer. If it's closer to its reasoning limits, then iterating will help.
        wruza 2y ago
        But if you stop them just there, an error persists. A professor is “multi-modal” and in a constant stream of evebts, including their lecture plan and premeditated key results. Are you sure that at some level of LLM “intelligence”, putting it into the same shoes wouldn’t improve the whole setting enough? I mean sure, they make mistakes. But if you stop-frame a professor, they make mistakes too. They don’t correct immediately, only after a contradiction gets presented. Reminds me how LLMs behave. Am I wrong here? Edit: was answering to gp, no idea how my post got here

raincole 2y ago

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 2y 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 2y 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.

arka2147483647 2y ago

Assume we have a child, and we test him regularly: - Test 1: First he can just draw squiggles on the math test - Test 2: Then he can do arithmetic correctly - Test 3: He fails on the last details on the algebraic calculation. Now, event though he fails on all tests, any reasonable parent would see that he improving nicely, and would be able to work in his chosen field in a year or so. Or alternatively, if we talk about AI, we can set the Test as a threshold, and we see the results are continuously trending upwards, and we can expect the curve to breach the threshold in the future. That is; measuring improvement, instead of pass/fail, allows one to predict when we might be able to use the AI for something.

londons_explore 2y ago
With AI you can do millions of tests. Some tests are easy by chance (eg. "Please multiply this list of numbers by zero"). Some tests are correct by chance alone, easy or hard. When you actually do these millions of tests, I don't think it really matters what the exact success metric is - an AI which is 'closer to correct, but still wrong' on one test will still get more tests correct overall on the dataset of millions of tests.

nathan_compton 2y ago

Tell it to floating point numbers.

t_mann 2y ago

> it doesn't really matter if you get an addition mostly right Back-of-the-envelope / mental math often works like that, and it's something that humans regularly use, so clearly it has some use.