4 ms·

I had no intention of implying any malfeasance in my use of the word "odd"; I mean it in the sense of unusual, unexpected and surprising. The thing is, you fini

I had no intention of implying any malfeasance in my use of the word "odd"; I mean it in the sense of unusual, unexpected and surprising. The thing is, you finishished your precursor post saying, about your tests and mine, that it comes down to there being a human in the loop making a judgement call, but in a follow-on you say that there are thousands of quantitative metrics. Why, I wondered, would that matter, if it comes down to a human making a judgement call? Were you switching to a different line of argument, one that (as far as I could tell) had not been raised before? That's what I found surprising about your claim. I am still rather confused about how this fits into what you are saying more generally. At first I thought you were saying, in your latest post, that the Turing-test interrogator should be restricted to asking questions from the sets having quantitative metrics in order for it to be an objective process, but that doesn't really hold up, as far as I can see. Frankly, I suspect that the tests with objective metrics are beside the point, and the essence of your position is contained within your final paragraph: "If the LLM can deceive the human [then] that is a hard True/False quantitative metric [and the only sort we can get]." If so, then (no surprise) I think there are some problems with it, but before I go further, I would like to check that I understand your position.

ninetyninenine 3y ago
>I had no intention of implying any malfeasance in my use of the word "odd"; I mean it in the sense of unusual, unexpected and surprising. The thing is, you finishished your precursor post saying, about your tests and mine, that it comes down to there being a human in the loop making a judgement call, but in a follow-on you say that there are thousands of quantitative metrics. Why, I wondered, would that matter, if it comes down to a human making a judgement call? Were you switching to a different line of argument, one that (as far as I could tell) had not been raised before? That's what I found surprising about your claim. It matters because of humans. If I gave an LLM thousands of quantitative tests and it passed them all but in an hour long conversation a human could identify it was an LLM through some flaw the human would consider all those tests useless. That's why it matters. The human making a judgement call is still a quantitative measurement btw as you can limit human output to True or False. But because every human is different in order to get good numbers you have to do measurements with multitudes of humans. >I am still rather confused about how this fits into what you are saying more generally. At first I thought you were saying, in your latest post, that the Turing-test interrogator should be restricted to asking questions from the sets having quantitative metrics in order for it to be an objective process, but that doesn't really hold up, as far as I can see. it can still be objective with a human in the loop assuming the human is honest. What's not objective is a human offering an opinion in the form of a paragraph with no definitive clarity on what constitutes a metric. I realize that elements of MY metric have indeterminism to it, but it is still a hard metric because the output is over a well defined set. Whenever you have indeterminism you would then turn to probability and many samples in order to produce a final quantitative result. >If so, then (no surprise) I think there are some problems with it, but before I go further, I would like to check that I understand your position. yes my position is that exactly. If all observable qualities indicate it's a duck, then there's nothing more you can determine beyond that, scientifically speaking. You're implying there is a better way?
- mannykannot 3y ago
  At this point, I think it is worth refreshing what the issue here is, which is whether LLMs understand that the language they receive is about an external world, which operates through causes which have nothing to do with token-combination statistics of the language itself. > It matters because of humans... I'm still a bit puzzled here, because it seems to me that the paragraph continuing from here is making the argument that LLM performance on these tests doesn't matter, as far as the question is concerned: in this paragraph you seem to be saying (paraphrased) that despite LLMs' impressive performance on these quantitative tests, they could still fail Turing tests, so their performance on these quantitative tests is not decisive. > yes my position is that exactly… The impression I get from what you have written in this post is that you are not claiming that a test conforming to your requirements has actually been successfully performed, you are just assuming it could be? Regardless, let’s assume (at least for the sake of argument) that the series of tests you propose have been performed, and the results are in: in the test environment, humans can’t distinguish current LLMs from humans any better than by chance. How do you get from that to answering the question we are actually interested in? The experiment does not explicitly address it. You might want to say something like “The Turing test has shown that the machines are as intelligent as humans so, like humans, these machines must realize that the language they receive is about an external world” but even the antecedent of that sentence is an interpretation that goes beyond what would have objectively been demonstrated by the Turing test, and the consequent is a subjective opinion that would not be entailed by the antecedent even if it were unassailable. Do you have a way to go from a successful Turing test to answering the question here, which meets your own quantitative and objective standards?
  - ninetyninenine 3y ago
    >I'm still a bit puzzled here, because it seems to me that the paragraph continuing from here is making the argument that LLM performance on these tests doesn't matter, as far as the question is concerned: in this paragraph you seem to be saying (paraphrased) that despite LLMs' impressive performance on these quantitative tests, they could still fail Turing tests, so their performance on these quantitative tests is not decisive. It matters in the quantitative sense. It measures AI performance. What it won't do is matter to YOU. Because you're a human and humans will keep moving the bar to a higher standard right? When AI shot passed the turing test humans just moved the goal posts. So to convince someone like YOU we have to look at the final metric. The point where LLM I/O becomes indistinguishable/superior to humans. Of course you look at the last decade... AI is rapidly approaching that final bar. >The impression I get from what you have written in this post is that you are not claiming that a test conforming to your requirements has actually been successfully performed, you are just assuming it could be? Whether I assume or don't assume, the projection of the trendline currently indicates that it will. Given the trendline that is the most probable conclusion. >The experiment does not explicitly address it. Nothing on the face of the earth can address the question. Because nobody truly knows what "understanding" something actually is. You can't even articulate the definition in a formal way such that it can be dictated on a computer program. So I went to the next best possibility, which is my point. The point is ALTHOUGH we don't know what understanding is, we ALL assume humans understand things. So we set that as a bar metric. Anything indistinguishable from a human must understand things. Anything that appears close to a human but is not quite human must understand things ALMOST as well as a human.

ninetyninenine 3y ago

>I had no intention of implying any malfeasance in my use of the word "odd"; I mean it in the sense of unusual, unexpected and surprising. The thing is, you finishished your precursor post saying, about your tests and mine, that it comes down to there being a human in the loop making a judgement call, but in a follow-on you say that there are thousands of quantitative metrics. Why, I wondered, would that matter, if it comes down to a human making a judgement call? Were you switching to a different line of argument, one that (as far as I could tell) had not been raised before? That's what I found surprising about your claim. It matters because of humans. If I gave an LLM thousands of quantitative tests and it passed them all but in an hour long conversation a human could identify it was an LLM through some flaw the human would consider all those tests useless. That's why it matters. The human making a judgement call is still a quantitative measurement btw as you can limit human output to True or False. But because every human is different in order to get good numbers you have to do measurements with multitudes of humans. >I am still rather confused about how this fits into what you are saying more generally. At first I thought you were saying, in your latest post, that the Turing-test interrogator should be restricted to asking questions from the sets having quantitative metrics in order for it to be an objective process, but that doesn't really hold up, as far as I can see. it can still be objective with a human in the loop assuming the human is honest. What's not objective is a human offering an opinion in the form of a paragraph with no definitive clarity on what constitutes a metric. I realize that elements of MY metric have indeterminism to it, but it is still a hard metric because the output is over a well defined set. Whenever you have indeterminism you would then turn to probability and many samples in order to produce a final quantitative result. >If so, then (no surprise) I think there are some problems with it, but before I go further, I would like to check that I understand your position. yes my position is that exactly. If all observable qualities indicate it's a duck, then there's nothing more you can determine beyond that, scientifically speaking. You're implying there is a better way?

mannykannot 3y ago
At this point, I think it is worth refreshing what the issue here is, which is whether LLMs understand that the language they receive is about an external world, which operates through causes which have nothing to do with token-combination statistics of the language itself. > It matters because of humans... I'm still a bit puzzled here, because it seems to me that the paragraph continuing from here is making the argument that LLM performance on these tests doesn't matter, as far as the question is concerned: in this paragraph you seem to be saying (paraphrased) that despite LLMs' impressive performance on these quantitative tests, they could still fail Turing tests, so their performance on these quantitative tests is not decisive. > yes my position is that exactly… The impression I get from what you have written in this post is that you are not claiming that a test conforming to your requirements has actually been successfully performed, you are just assuming it could be? Regardless, let’s assume (at least for the sake of argument) that the series of tests you propose have been performed, and the results are in: in the test environment, humans can’t distinguish current LLMs from humans any better than by chance. How do you get from that to answering the question we are actually interested in? The experiment does not explicitly address it. You might want to say something like “The Turing test has shown that the machines are as intelligent as humans so, like humans, these machines must realize that the language they receive is about an external world” but even the antecedent of that sentence is an interpretation that goes beyond what would have objectively been demonstrated by the Turing test, and the consequent is a subjective opinion that would not be entailed by the antecedent even if it were unassailable. Do you have a way to go from a successful Turing test to answering the question here, which meets your own quantitative and objective standards?
- ninetyninenine 3y ago
  >I'm still a bit puzzled here, because it seems to me that the paragraph continuing from here is making the argument that LLM performance on these tests doesn't matter, as far as the question is concerned: in this paragraph you seem to be saying (paraphrased) that despite LLMs' impressive performance on these quantitative tests, they could still fail Turing tests, so their performance on these quantitative tests is not decisive. It matters in the quantitative sense. It measures AI performance. What it won't do is matter to YOU. Because you're a human and humans will keep moving the bar to a higher standard right? When AI shot passed the turing test humans just moved the goal posts. So to convince someone like YOU we have to look at the final metric. The point where LLM I/O becomes indistinguishable/superior to humans. Of course you look at the last decade... AI is rapidly approaching that final bar. >The impression I get from what you have written in this post is that you are not claiming that a test conforming to your requirements has actually been successfully performed, you are just assuming it could be? Whether I assume or don't assume, the projection of the trendline currently indicates that it will. Given the trendline that is the most probable conclusion. >The experiment does not explicitly address it. Nothing on the face of the earth can address the question. Because nobody truly knows what "understanding" something actually is. You can't even articulate the definition in a formal way such that it can be dictated on a computer program. So I went to the next best possibility, which is my point. The point is ALTHOUGH we don't know what understanding is, we ALL assume humans understand things. So we set that as a bar metric. Anything indistinguishable from a human must understand things. Anything that appears close to a human but is not quite human must understand things ALMOST as well as a human.