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You keep drawing dichotomies that I don't hold. The choices of language are not exhausted by "deception" and "computing bug". Both those words are your inventi

You keep drawing dichotomies that I don't hold. The choices of language are not exhausted by "deception" and "computing bug". Both those words are your invention. I wouldn't even call this behavior a bug at all, since, as I pointed out in my original comment, it's a design characteristic of these systems to replicate the kind of material fed into them as training data, and the output we're seeing here correctly manifests that. Do you really think the only way to say "Don't use ChatGPT where strict adherence is essential because it's not that kind of system" is to say that the text generator may "deceived" someone, with all of the secondary implications that the word choice suggests? You can't conceive that there are any other metaphors and word choices that might both be more accurate and point in more fruitful research directions?

famouswaffles 3y ago
>The choices of language are not exhausted by "deception" and "computing bug". Both those words are your invention. It's just an illustration. >Do you really think the only way to say "Don't use ChatGPT where strict adherence is essential because it's not that kind of system" is to say that the text generator may "deceived" someone, with all of the secondary implications that the word choice suggests? You can't conceive that there are any other metaphors and word choices that might both be more accurate and point in more fruitful research directions? I have yet to see anyone who harped on "don't anthropomize" provide better predictive models of output behavior. "Unreliable" is just a statement of fact. Output you might expect from those secondary implications manifest in the text or action output as well so that's fine. This brings me back to the second point in my original reply. You don't know how deception works in the brain and you don't know how deceptive like output works in LLMs. Couple that with Humanity's track record on these sort of discussions and those "secondary implications" may well be true.
- swatcoder 3y ago
  In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data, and the very predictive hypothesis one would draw from that is the one I put in my original hypothesis, and that was insightfully expanded upon by the person that replied to me. That model produces a hypothesis that fully accords with the experiment and does so with fewer assumptions about things like theory of mind, consciosness, or agency, which are all associated with "deception" Now, the model with the least assumptions is not always the most productive model, but here it works exceedingly well. The paper would have been stronger (less contestible) if framed in that model, but would admittedly have received less attention.
  - famouswaffles 3y ago
    >In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data There was no document in GPT-4's training that looked anything like what the transcript of this experiment looked like so that's pretty wrong. It behaves like how people who wrote its training set might have behaved is more accurate not only because of the better model but literally because the predictor's goal is to reverse engineer the casual processes that led to that output. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 A Language Model fed on protein sequences has biological structure and function emerge in the inner layers because those are the processes that could have led to that output. If you say, it generated text that looks like its training then why does it not stop at alphanumeric sequences that simply look like protein sequences? Why is it generating actual novel proteins ? https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Because looking like the training data is not actually the goal. Modelling it is.

famouswaffles 3y ago

>The choices of language are not exhausted by "deception" and "computing bug". Both those words are your invention. It's just an illustration. >Do you really think the only way to say "Don't use ChatGPT where strict adherence is essential because it's not that kind of system" is to say that the text generator may "deceived" someone, with all of the secondary implications that the word choice suggests? You can't conceive that there are any other metaphors and word choices that might both be more accurate and point in more fruitful research directions? I have yet to see anyone who harped on "don't anthropomize" provide better predictive models of output behavior. "Unreliable" is just a statement of fact. Output you might expect from those secondary implications manifest in the text or action output as well so that's fine. This brings me back to the second point in my original reply. You don't know how deception works in the brain and you don't know how deceptive like output works in LLMs. Couple that with Humanity's track record on these sort of discussions and those "secondary implications" may well be true.

swatcoder 3y ago
In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data, and the very predictive hypothesis one would draw from that is the one I put in my original hypothesis, and that was insightfully expanded upon by the person that replied to me. That model produces a hypothesis that fully accords with the experiment and does so with fewer assumptions about things like theory of mind, consciosness, or agency, which are all associated with "deception" Now, the model with the least assumptions is not always the most productive model, but here it works exceedingly well. The paper would have been stronger (less contestible) if framed in that model, but would admittedly have received less attention.
- famouswaffles 3y ago
  >In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data There was no document in GPT-4's training that looked anything like what the transcript of this experiment looked like so that's pretty wrong. It behaves like how people who wrote its training set might have behaved is more accurate not only because of the better model but literally because the predictor's goal is to reverse engineer the casual processes that led to that output. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 A Language Model fed on protein sequences has biological structure and function emerge in the inner layers because those are the processes that could have led to that output. If you say, it generated text that looks like its training then why does it not stop at alphanumeric sequences that simply look like protein sequences? Why is it generating actual novel proteins ? https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Because looking like the training data is not actually the goal. Modelling it is.