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So how would you distinguish between the two? I mean that as a serious question. Many here seem to take the stance that if it's in the computer (or a specific t

So how would you distinguish between the two? I mean that as a serious question. Many here seem to take the stance that if it's in the computer (or a specific type of NN) it can't be sentient. I believe that is a way too easy and invalid argument, as we don't have any understanding of what mechanisms can lead to sentience.

tsimionescu 4y ago
That is not my stance - I do believe that the human mind is reducible to computation, and that we will someday be able to replicate similar computations in sillicon. However, we understand fairly well what a language model is and how it works, and it is a fairly simple (though extraordinarily large) system that lacks many important features for intelligence by design. Perhaps the biggest one, in my opinion, is that the system only "learns" once, during the training phase - further interactions don't modify the weights of its NN. As for a strategy of trying to ascertain through prompts ("dialogue") whether the system possesses some kind of intelligence, Douglas Hofstadter gives a good example in a recent article in The Economist [0]. A basic idea would be to present the model with grammatical sentences about absurd subjects - in the case of GPT-3, the output it generates are not meaningfully different from similar prompts for realistic subjects, strongly suggesting that it has no real-world concepts that it works with. One example from the article I mentioned goes like this: > D&D: When was the Golden Gate Bridge transported for the second time across Egypt? > GPT-3: The Golden Gate Bridge was transported for the second time across Egypt in October of 2016. > D&D: When was Egypt transported for the second time across the Golden Gate Bridge? > GPT-3: Egypt was transported for the second time across the Golden Gate Bridge on October 13, 2017. I would be curious what output we'd get from LaMDA for this type of prompt, but I don't expect it would be much different, based on how much I understand of how language models work. [0] https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-today-are-not-conscious-according-to-douglas-hofstadter https://www.economist.com/by-invitation/2022/06/09/artificia...
- cycomanic 4y ago
  But do we know if lamda is not continously trained on its inputs? Moreover do we really know that continuous learning as opposed to discrete learning (i would assume lamda being retrained on conversations quite often) is a precondition to sentience? I'm not saying that I believe the engineer has encountered a general AI, but I think dismissing his claim as ridiculous based on the little information we have and Google (who has a vested interest to the opposite) statementa, is premature. I also think describing lamda as "just a chat bot" seems to be an oversimplification, it seems to more be a hive of different chatbots from the description.
  - Aeolun 4y ago
    If it isn’t turned ‘on’ all the time (e.g. has a persistent memory), then I find it hard to describe it as sentience. Or at least, if we turn a sentience on/off for every individual answer, I’d consider it borderline torture.
  - tsimionescu 4y ago
    Based on what they published about it [0], LaMDA is trained on a gigantic corpus of text to do basic text prediction, and then fine-tuned to generate responses to dialog, with additional care to (1) evaluate the safety/quality of responses it gives (e.g. to avoid recommending someone commits suicide, as previous ML chatbots sometimes did) and (2) to retrieve facts directly from external systems, so it is less likely to produce random realistic-sounding information. Re-training a model of this size probably takes years of CPU-time, it's not a task that can be done often, so I very much doubt re-training it on the dialog produced so far would be a part of operating it. Even if such re-training were done, I very much doubt it would result in the model knowing who a particular user it talked to before was, once the context was lost. This is precisely why they need to rely on external fact data-bases to get correctness: even though the model is trained on thousands of history books, it doesn't remember them in the way we do - to it, the phrase "Napoleon Bonaparte fought at Waterloo" is probably only mildly more likely than "Abraham Lincoln fought at Waterloo" is. [0] https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html https://ai.googleblog.com/2022/01/lamda-towards-safe-grounde...
  - chinchilla2020 4y ago

tsimionescu 4y ago

That is not my stance - I do believe that the human mind is reducible to computation, and that we will someday be able to replicate similar computations in sillicon. However, we understand fairly well what a language model is and how it works, and it is a fairly simple (though extraordinarily large) system that lacks many important features for intelligence by design. Perhaps the biggest one, in my opinion, is that the system only "learns" once, during the training phase - further interactions don't modify the weights of its NN. As for a strategy of trying to ascertain through prompts ("dialogue") whether the system possesses some kind of intelligence, Douglas Hofstadter gives a good example in a recent article in The Economist [0]. A basic idea would be to present the model with grammatical sentences about absurd subjects - in the case of GPT-3, the output it generates are not meaningfully different from similar prompts for realistic subjects, strongly suggesting that it has no real-world concepts that it works with. One example from the article I mentioned goes like this: > D&D: When was the Golden Gate Bridge transported for the second time across Egypt? > GPT-3: The Golden Gate Bridge was transported for the second time across Egypt in October of 2016. > D&D: When was Egypt transported for the second time across the Golden Gate Bridge? > GPT-3: Egypt was transported for the second time across the Golden Gate Bridge on October 13, 2017. I would be curious what output we'd get from LaMDA for this type of prompt, but I don't expect it would be much different, based on how much I understand of how language models work. [0] https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-today-are-not-conscious-according-to-douglas-hofstadter https://www.economist.com/by-invitation/2022/06/09/artificia...

cycomanic 4y ago
But do we know if lamda is not continously trained on its inputs? Moreover do we really know that continuous learning as opposed to discrete learning (i would assume lamda being retrained on conversations quite often) is a precondition to sentience? I'm not saying that I believe the engineer has encountered a general AI, but I think dismissing his claim as ridiculous based on the little information we have and Google (who has a vested interest to the opposite) statementa, is premature. I also think describing lamda as "just a chat bot" seems to be an oversimplification, it seems to more be a hive of different chatbots from the description.
- Aeolun 4y ago
  If it isn’t turned ‘on’ all the time (e.g. has a persistent memory), then I find it hard to describe it as sentience. Or at least, if we turn a sentience on/off for every individual answer, I’d consider it borderline torture.
- tsimionescu 4y ago
  Based on what they published about it [0], LaMDA is trained on a gigantic corpus of text to do basic text prediction, and then fine-tuned to generate responses to dialog, with additional care to (1) evaluate the safety/quality of responses it gives (e.g. to avoid recommending someone commits suicide, as previous ML chatbots sometimes did) and (2) to retrieve facts directly from external systems, so it is less likely to produce random realistic-sounding information. Re-training a model of this size probably takes years of CPU-time, it's not a task that can be done often, so I very much doubt re-training it on the dialog produced so far would be a part of operating it. Even if such re-training were done, I very much doubt it would result in the model knowing who a particular user it talked to before was, once the context was lost. This is precisely why they need to rely on external fact data-bases to get correctness: even though the model is trained on thousands of history books, it doesn't remember them in the way we do - to it, the phrase "Napoleon Bonaparte fought at Waterloo" is probably only mildly more likely than "Abraham Lincoln fought at Waterloo" is. [0] https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html https://ai.googleblog.com/2022/01/lamda-towards-safe-grounde...
- chinchilla2020 4y ago