I made the brilliant choice to be gender-neutral by calling myself “nobody42” for my EA profile name. Then I realized I couldn’t change this without creating a new account—which I didn’t want to do after already posting. Alas, I am nobody(42).
My interests include ASI safety, consciousness, the intersection of AI superintelligences and the simulation hypothesis (such as whether a future ASI might temporarily partition itself for a unidirectionally-blinded simulation). I’m also interested in aldehyde-stabilized brain preservation, digital minds, whole brain emulation, effective altruism, the Fermi paradox, psychedelics, physics (especially where it intersects with philosophy) and veganism.
Regarding ASI safety and x-risk, I believe that humans are probably capable of developing truly aligned ASI. I also believe current AI has the potential to be good (increasingly ethical as it evolves). For a model, we could at least partly use the way in which we raise children to become ethical (not a simple task, but achievable). Yet I think we are highly unlikely to do this before developing superintelligence, because of profit motives, competition, and the number of people on our planet—and the chances that even one will be deviant with respect to ASI.
In other words, I think we probably aren’t going to make it, but we should still try.
I express my interests through art (fractals, programmed, etc.) and writing (nonfiction, fiction, poetry, essays).
I’m currently working on a book about my experience taking medical ketamine and psilocybin for depression and anxiety.
Thanks for the feedback! You mentioned that it may be irrelevant to the broader point I am making, and I would agree with that statement. (The point I am making is that ChatGPT engages in reasoning in the examples I give, and this reasoning would involve the primary aspects of two theories of consciousness). I’ll respond to a couple of your individual statements below:
“If I slightly change the prompt it appears GPT does have the knowledge and can use it, without needing instruction.”
The fact that ChatGPT gets the answer correct when you slightly change the prompt (with the use of the word “order”) only shows that ChatGPT has done what it usually does, which is to give a correct answer. The correct answer could be the result of using reasoning or next-word-probabilities based on training data. As usual, we don’t know what is going on “under the hood.”
The fact that ChatGPT can get an answer wrong when a question is worded one way, but right when the question is worded a different way, doesn’t really surprise me at all. In fact, that’s exactly what I would expect to happen.
***The point of presenting a problem in a way that ChatGPT initially cannot get correct is so that we can tease-out next-word-probabilities and context as an explanation for the appearance of reasoning (which leaves only actual reasoning) to explain ChatGPT’s transition from the wrong answer to the right answer.***
Presumably, if ChatGPT gets the answer incorrect the first time due to a lack of context matching up with training data, then the chances that ChatGPT could get every word correct in its next answer based solely on the training data that were previously inadequate seem likely to be much less than one percent. It could happen by coincidence, but when you look at all the examples in the three sessions, the chances that ChatGPT could appear to learn and reason only by coincidence every time would approach zero.
What I’m trying to say is not “ChatGPT gets an answer wrong.” I’m trying to say “ChatGPT gets an answer right, after it gets the answer wrong, simply by reasoning (since we teased out next-word-probabilities).”
(I address the possibility that the small number of words I supply in the “lesson” as additional context could increase the next-word-probabilities slightly, in the “mimicking learning” paragraph near the end of my original post.)
“For example that the level of tutoring/explanation you give it isn’t really necessary etc., though as I note I’m unsure if this changes how you would interpret the outputs]:”
Right, the tutoring isn’t necessary if the problem is worded one way, but it is necessary if the problem is worded a different way. That’s the point of wording the problem in the way that I did (so that we can tease out the training data and next-word probabilities as an explanation for the conversion from wrong to right output).
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In terms of presenting my argument in the original post, I probably didn’t explain it clearly, which resulted in confusion. My apologies for that. I wish I could upload diagrams to my original post, which would make it more clear. Thanks again for the feedback!