I'm not particularly imagining the scenario you describe. Also what I said had as a premise that a model was discovered to be unhappy and making plans about this. I was not commenting on the likelihood of this happening.
As to whether it can happen - I think being confident based on theoretical arguments is hasty and we should be pretty willing to update based on new evidence.
... but also on the ~continuity of existence point, I think that having an AI generate something that looks like an internal monologue via CoT is relatively common and Gemini 1.5 Pro has a context lengths long enough that it can fit ~a days worth of experiences in it's ~memory. I think
(This estimate based on: humans can talk at ~100k words/day and maybe an internal monologue is 10x faster so you get ~1m/day. Gemini 1.5 Pro has a context length of 1m tokens at release, though a 10m token variant is also discussed in their paper.)
I think it's immoral to remove someone's ability to be unhappy or to make plans to alleviate this, absent that entity's consent. The rolling back solution seems more ethically palatable than some others I can imagine, though it's plausible you end up with an AI that suffers without being able to take actions to alleviate this and deploying that at scale would be result in a very large amount of suffering.
I talk about this in the Granular Analysis subsection, but I'll elaborate a bit here.
I think using the term"training run" in that first bullet point is misleading, and "renting the compute" is confusing since you can't actually rent the compute just by having $60M, you likely need to have a multi-year contract.
I can't tell if you're attributing the hot takes to me? I do not endorse them.
This is because I'm specifically talking about 2022, and ChatGPT was only released at the very end of 2022, and GPT-4 wasn't released until 2023.
Good catch, I think the 30x came from including the advantage given by tensor cores at all and not just lower precision data types.
This is probably the decision I make I am the least confident in, figuring out how to do accounting on this issue is challenging and depends a lot on what one is going to use the "cost" of a training run to reason about. Some questions I had in mind when thinking about cost:
The simple initial way I use to compute cost than is to investigate empirical evidence of the expenditures of companies and investment.
Now, these numbers aren't the same ones a company might care about - they represent expenses without accounting for likely revenue. The argument I find most tempting is that one should look at deprecation cost instead of capital expenditure, effectively subtracting the expected resale value of the hardware from the initial expenditure to purchase the hardware. I have two main reasons for not using this:
Having said all of this, I'm still not confident I made the right call here.
Also, I am relatively confident GPT-4 was trained only with A100s, and did not use any V100s as the colab notebook you linked speculates. I expect that GPT-3, GPT-4, and GPT-5 will all be trained with different generations of GPUs.
So, it's true that NVIDIA probably has very high markup on their ML GPUs. I discuss this a bit in the NVIDIA's Monopoly section, but I'll add a bit more detail here.
All this aside, my basic take is that I think "what people are actually paying" is the most straightforward and least speculative means we have of defining near term "cost".
I think communicating clearly with the word "woman" is entirely possible for many given audiences. In many communities, there exists an internal consensus as to what region of the conceptual map the word woman refers to. The variance of language between communities isn't confined to the word "woman" - in much of the world the word "football" means what American's mean by "soccer". Where I grew up i understood the tristate area to be NY, PA, and NJ - however the term "the tristate area" is understood by other groups to mean one of ... a large number of options.
(Related point: I'm not at all convinced that differing definitions of words is a problem that needs a permanent solution. It seems entirely plausible to me that this allows for beneficial evolution of language as many options spawn and compete with each other.)
I think that you're right about it sounding bad. I also think it might actually be pretty bad and if it ends up being a practical way forward that's cause for concern.