Consciousness (and with it, ‘sentience’) are arguably red herrings for the field right now. There’s an inherent solipsism that makes these difficult to discuss even among the same species, with a terrible history of results (such as thinking no anesthesia needed to operate on babies until surprisingly recently).
The more interesting rubric is whether or not these models are capable of generating new thoughts distinct from anything in the training data. For GPT-4 in particular, that seems to be the case: https://arxiv.org/abs/2310.17567
As well, in general there’s too much focus on the neural networks and not the information right now. My brain is very different right now from when I was five. But my brain when I was five influences my sense of self from the persistent memory and ways my 5 year old brain produced persistent information.
Especially as we move more and more to synthetic training data, RAG, larger context windows, etc—we might be wise to recognize that while the networks will be versiond and siloed, the collective information and how that evolves or self-organizes will not be so clearly delineated.
Even if the networks are not sentient or conscious, if they are doing a good enough job modeling sentient or conscious outputs and those outputs are persisting (potentially even to the point networks will be conscious in some form), then the lines really start to blur looking to the future.
As for the crossing the river problem, that’s an interesting one to play with for SotA models. Variations of the standard form fail because of token similarity to the original, but breaking the similarity (with something as simple as emojis) can allow the model to successfully solve variations of the classic form on the first try (reproduced in both Gemini and GPT-4).
But in your case, given the wording in the response it may have in part failed on the first try because of having correctly incorporated world modeling around not leaving children unattended without someone older present. The degree to which GPT-4 models unbelievably nuanced aspects of the training data is not to be underestimated.
Thank you for the reply. The paper looks to be very useful, but will take me some time to fully digest. What you said about affecting LLMs’ success by breaking the similarity of problems with something as simple as an emoji is so interesting. : ) It also never occurred to me that GPT4 might have been affected by the underlying idea that children should never be left unattended. It goes to show that “arbitrary” details are not always arbitrary. Fascinating! Many thanks!
Consciousness (and with it, ‘sentience’) are arguably red herrings for the field right now. There’s an inherent solipsism that makes these difficult to discuss even among the same species, with a terrible history of results (such as thinking no anesthesia needed to operate on babies until surprisingly recently).
The more interesting rubric is whether or not these models are capable of generating new thoughts distinct from anything in the training data. For GPT-4 in particular, that seems to be the case: https://arxiv.org/abs/2310.17567
As well, in general there’s too much focus on the neural networks and not the information right now. My brain is very different right now from when I was five. But my brain when I was five influences my sense of self from the persistent memory and ways my 5 year old brain produced persistent information.
Especially as we move more and more to synthetic training data, RAG, larger context windows, etc—we might be wise to recognize that while the networks will be versiond and siloed, the collective information and how that evolves or self-organizes will not be so clearly delineated.
Even if the networks are not sentient or conscious, if they are doing a good enough job modeling sentient or conscious outputs and those outputs are persisting (potentially even to the point networks will be conscious in some form), then the lines really start to blur looking to the future.
As for the crossing the river problem, that’s an interesting one to play with for SotA models. Variations of the standard form fail because of token similarity to the original, but breaking the similarity (with something as simple as emojis) can allow the model to successfully solve variations of the classic form on the first try (reproduced in both Gemini and GPT-4).
But in your case, given the wording in the response it may have in part failed on the first try because of having correctly incorporated world modeling around not leaving children unattended without someone older present. The degree to which GPT-4 models unbelievably nuanced aspects of the training data is not to be underestimated.
Thank you for the reply. The paper looks to be very useful, but will take me some time to fully digest. What you said about affecting LLMs’ success by breaking the similarity of problems with something as simple as an emoji is so interesting. : ) It also never occurred to me that GPT4 might have been affected by the underlying idea that children should never be left unattended. It goes to show that “arbitrary” details are not always arbitrary. Fascinating! Many thanks!
The gist of the paper and the research that led into it had a great writeup in Quanta mag if you would like something more digestible:
https://www.quantamagazine.org/new-theory-suggests-chatbots-can-understand-text-20240122/