@JhaveriVatsala I still can't stop thinking of this cursed video, it's like that one bad end from a hentai game you suddenly remember years later as you're trying to fall asleep
'Artificial Superintelligence is inevitable. There's nothing we can do to stop it.'
'If Jensen Huang isn't restored as CEO of Nvidia, the entire AI industry could collapse.'
Same people.
Spoke to an Anthropic employee today. They have a near-100% p(doom), but they think the only chance of survival is that they're somehow completely wrong about alignment and it turns out to be solvable, and the only way to get there is by accelerating capabilities as fast as possible. They were much more confident that humanity would never have the political will to do what needs to be done to stop ASI development than that alignment would be solved, even though they think alignment will almost certainly not be solved
They have not called their elected representatives to inform them that this is what they believe
Inspired by the AI 2027 guys publicly despairing shortened timelines but not saying a word against (or, I strongly doubt, to) their buddies making AI doom happen:
It’s to the point where I will never forgive the people currently employed making frontier AI no matter what they do. They know what they are doing and they’ve had chance after chance to stop. We need to view them as the enemies they are and make them stop with laws. And I hope the stigma follows them the rest of their long lives after we save them by enacting a Pause.
This is accurate. I teach college courses, including one on AI.
And many college students HATE seeing any AI generated images or videos in my lectures. They think it's cringe and lame. They complain. They show disgust faces. AI is destroying their hope for a future career and a future life, and they despise it.
Gen Z is not buying the 'AI utopia' that Millennial tech bros are trying to sell them.
To bluntly explain my AI risk thinking:
1. If we rush ahead with RSI/ASI at true maximum pace, we're maybe 80% likely to die as a result.
2. In practice I think we’re already on track for the companies to harvest ~3 months worth of ‘delay + reduced pace’. By itself that might drive our risk of death down to 70% or so.
3. But if we can find a way to squeeze in 3 extra years of testing, and extraordinarily capable AI assistance with alignment/security, before handing off, we can perhaps get our risk of death down as low as 30%.
If that sounds implausible, keep in mind over those 3 years AIs could have done more research on this topic than humans have ever done on all topics in our entire history. If they're misaligned and sabotaging the effort then it's all for nought - but if they're aligned enough, it's a huge hammer which we've brought to bear.
4. A 30% risk of AI takeover remains completely mad, and if there were political will we should aim to do more to get that number down much further.
It makes sense for some people to work on projects that might buy the companies even greater periods to do their work much more carefully and methodically, or rethink their approach to the entire enterprise.
But the absolute gain of going from 3 months to 3 years is still very large, and it's more clear how we might get there from here.
5. Furthermore in most cases you need to figure out how to buy shorter amounts of time before you can then add coordination mechanisms to buy more time.
For instance, if we have enough time to test each frontier model properly and really kick the tires using many different new methods, that may turn up unambiguous evidence of deceptive misalignment and power seeking. That could build support for becoming even more careful again.
6. On my made-up guesses, the first year of actively chosen delay might improve our chances of survival by as much as 30 percentage points (pp), by helping us avoid unforced errors caused by rushing.
Keeping a nice curve shape the 2nd year adds more like 13pp, and the 3rd more like 8pp.
By the time you’re considering adding a 4th year, that gain might have shrunk to perhaps 5pp.
Why an S-curve on a log scale? Because that’s how returns to effort on research and other things tends to go: each doubling of time buys about the same gain.
But it’s flat at the start because there are so many severe problems failure is 'overdetermined'. And it’s flat at the end because eventually there's no risk left that can be reduced by this method.
7. All of these numbers are just guesses on my part, and I’ve said nothing here to justify them. Reasonable people disagree enormously about what this graph looks like.
I mostly write this to lay out how the 5 parameters in this graph underlie a lot of the different and sometimes conflicting strategies people in this space are adopting:
A: How dangerous is max speed?
B: How much do small delays help?
C: How much to big delays help?
D: How low can risk realistically get?
There's also a last one:
E: How much other risk do we incur for each additional year of delay?
8. Could we delay for too long?
Because any significant delay is hard to achieve, I think this is highly unlikely to happen. We're much more likely to undershoot than overshoot.
But for completeness I'll lay out the argument.
The 5pp in the fourth year seem clearly worth taking if we can. But the gains from each additional year keep shrinking, in my rough curve down to 1pp in the 8th year.
As the gains go down, delay involves risks that probably go up over time:
• AIxBio catastrophes
• A US-China or US-Russia war
• Very dangerous models being stolen or leaked
• Extreme concentration of power in human hands
• Others we haven’t noticed yet
We could also use AI-driven defensive acceleration to try to tackle them during the slowdown period, and we don't know how successful that would be.
If that goes very poorly, it's possible that a delay beyond 5 years could on net increase risk.
If it goes well, then maybe we could delay for 10 or 20 years or even longer before further delay becomes on net counterproductive.
I'll stick the images for this in the next tweet.
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(For simplicity here I'm setting aside the benefits to the current generation of e.g. making additional medical progress sooner, which people disagree on how to handle.)
(And in this model I add together pauses and 'pacing' and focus on how long we've managed to delay the point at which we allow RSI to proceed without any illusion that our oversight could prevent catastrophe if the machines doing it are misaligned.)