This is a linkpost for https://www.thecompendium.ai/
The Compendium, A full argument about extinction risk from AGI
7Vladimir Nesov
3Adam Shimi
2Adam Shimi
7Vladimir Nesov
3Adam Shimi
4Vladimir Nesov
2Adam Shimi
2Adam Shimi
1Vojtech Kovarik
2Adam Shimi
1Vojtech Kovarik
3Adam Shimi
2Adam Shimi
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From footnote 2 to The state of AI today:
GPT-2 cost an estimated $43,000 to train in 2019; today it is possible to train a 124M parameter GPT-2 for $20 in 90 minutes.
Isn't $43,000 the estimate for the 1.5B replication of GPT-2 rather than for the 124M? If so, this phrasing is somewhat misleading. We only need $250 even for the 1.5B version, but still.
Good catch, I think we are indeed mixing the sizes here.
As you say, the point still stands, but we will change it in the next minor update to either compare the same size or make the difference in size explicit.
1From chapter The state of AI today:
The most likely and proximal blocker is power consumption (data-centers training modern AIs use enormous amounts of electricity, up to the equivalent of the yearly consumption of 1000 average US households) and ...
Clusters like xAI's Memphis datacenter with 100K H100s consume about 150 megawatts. An average US household consumes 10,800 kilowatt-hours a year, which is 1.23 kilowatts on average. So the power consumption of a 100K H100s cluster is equivalent to that of 121,000 average US households, not 1,000 average US households. If we take a cluster of 16K H100s that trained Llama-3-405B, that's still 24 megawatts and equivalent to 19,000 average US households.
So you likely mean the amount of energy (as opposed to power) consumed in training a model ("yearly consumption of 1000 average US households"). The fraction of all power consumed by a cluster of H100s is about 1500 watts per GPU, and that GPU at 40% compute utilization produces 0.4e15 FLOP/s of useful dense BF16 compute. Thus about 3.75e-12 joules is expended per FLOP that goes into training a model. For 4e25 FLOPs of Llama-3-405B, that's 1.5e14 joules, or 41e6 kilowatt-hours, which is consumed by 3,800 average US households in a year[1].
This interpretation fits the numbers better, but it's a bit confusing, since the model is trained for much less than a year, while the clusters will go on consuming their energy all year long. And the power constraints that are a plausible proximal blocker of scaling are about power, not energy.
If we instead take 2e25 FLOPs attributed to original GPT-4, and 700 watts of a single H100, while ignoring the surrounding machinery of a datacenter (even though you are talking about what a datacenter consumes in this quote, so this is an incorrect way of estimating energy consumption), and train on H100s (instead of A100s used for original GPT-4), then this gives 9.7e6 kilowatt-hours, or the yearly consumption of 900 average US households. With A100s, we instead have 400 watts and 0.3e15 FLOP/s (becoming 0.12e15 FLOP/s at 40% utilization), which gets us 18.5e6 kilowatt-hours for a 2e25 FLOPs model, or yearly consumption of 1,700 average US households (again, ignoring the rest of the datacenter, which is not the correct thing to do). ↩︎
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1Thanks for the comment!
We want to check the maths, but if you're indeed correct we will update the numbers (and reasoning) in the next minor version.
1From chapter The state of AI today:
Later this year, the first 100,000 GPU cluster will go online
It's not the first, there's xAI cluster from September, and likely a Microsoft cluster from May.
Even the cited The Information article says about the Meta cluster in question that
The previously unreported cluster, which could be fully completed by October or November, comes as two other companies have touted their own.
After reading the first section and skimming the rest, my impression is that the document is a good overview, but does not present any detailed argument for why godlike AI would lead to human extinction. (Except for the "smarter species" analogy, which I would say doesn't qualify.) So if I put on my sceptic hat, I can imagine reading the whole document in detail and somewhat-justifiably going away with "yeah, well, that sounds like a nice story, but I am not updating based on this".
That seems fine to me, given that (as far as I am concerned) no detailed convincing arguments for AI X-risk exist. But at the moment, the summary of the document gave me the impression that maybe some such argument will appear. So I suggest updating the summary (or some other part of the doc) to make it explicit that no detailed arugment for AI X-risk will be given.
Thanks for the comment!
We have indeed gotten the feedback by multiple people that this part didn't feel detailed enough (although we got this much more from very technical readers than from non-technical ones), and are working at improving the arguments.
1Some suggestions for improving the doc (I noticed the link to the editable version too late, apologies):
What is AI? Who is building it? Why? And is it going to be a future we want?
Something weird with the last sentence here (substituting "AI" for "it" makes the sentence un-grammatical).
Machines of hateful competition need not have such hindrances.
"Hateful" seems likely to put off some readers here, and I also think it is not warranted -- indifference is both more likely and also sufficient for extinction. So "Machines of indifferent competition" might work better.
There is no one is coming to save us.
Typo, extra "is".
The only thing necessary for the triumph of evil is for good people to do nothing. If you do nothing, evil triumphs, and that’s it.
Perhaps rewrite this for less antagonistic language? I know it is a quote and all, but still. (This can be interpreted as "the people building AI are evil and trying to cause harm on purpose". That seems false. And including this in the writing is likely to give the reader the impression that you don't understand the situation with AI, and stop reading.)
Perhaps (1) make it apparent that the first thing is a quote and (2) change the second sentence to "If you do nothing, our story gets a bad ending, and that's it.". Or just rewrite the whole thing.
Thanks for the comment!
We'll correct the typo in the next patch/bug fix.
As for the more direct adversarial tone of the prologue, it is an explicit choice (and is contrasted by the rest of the document). For the moment, we're waiting to get more feedback on the doc to see if it really turns people off or not.
We (Connor Leahy, Gabriel Alfour, Chris Scammell, Andrea Miotti, Adam Shimi) have just published The Compendium, which brings together in a single place the most important arguments that drive our models of the AGI race, and what we need to do to avoid catastrophe.
We felt that something like this has been missing from the AI conversation. Most of these points have been shared before, but a “comprehensive worldview” doc has been missing. We’ve tried our best to fill this gap, and welcome feedback and debate about the arguments. The Compendium is a living document, and we’ll keep updating it as we learn more and change our minds.
We would appreciate your feedback, whether or not you agree with us:
Here is the twitter thread and the summary: