Do you believe that there is something already published that should have moved our subjective probabilities outside of the ranges noted in the post? If so, I’d love to know what it is! Please use this thread to collect potential examples, and include a link. Some info about why it should have done that (if not obvious) would also be welcome. (Only new posts are eligible for the prizes, though.)
I think considerations like those presented in Daniel Kokotajlo’s Fun with +12 OOMs of Compute suggest that you should have ≥50% credence on AGI by 2043.
Agree, and add that code models won’t be data constrained as they can generate their own training data. It’s simple to write tests automatically, and you can run the code to see whether it passes the tests before adding it to your training dataset. As an unfortunate side effect, part of this process involves constantly and automatically running code output by a large model, and feeding it data which it generated so it can update its weights, both of which are not good safety-wise if the model is misaligned and power seeking.
I don’t know if this has been incorporated into a wider timelines analysis yet as it is quite recent, but this was a notable update for me given the latest scaling laws which indicate that data is the constraining factor, not parameter count. Much shorter timelines than 2043 seem like a live and strategically relevant possibility.
This is more of a meta-consideration around shared cultural background and norms. Could it just be a case of allowing yourselves to update toward more scary-sounding probabilities? You have all the information already. This video from Rob Miles (“There’s No Rule That Says We’ll Make It”)[transcript copied from YouTube] made me think along these lines. Aside from background culture considerations around human exceptionalism (inspired by religion) and optimism favouring good endings (Hollywood; perhaps also history to date?), I think there is also an inherent conservatism borne by prestigious mega-philanthropy whereby a doom-laden outlook just doesn’t fit in.
Optimism seems to tilt one in favour of conjunctive reasoning, and pessimism favours disjunctive reasoning. Are you factoring both in?
This is a pretty deep and important point. There may be psychological and cultural biases that make it pretty hard to shift the expected likelihoods of worst-case AI scenarios much higher than they already are—which might bias the essay contest against arguments winning even if they make a logically compelling case for more likely catastrophes.
Maybe one way to reframe this is to consider the prediction “P(misalignment x-risk|AGI)” to also be contingent on us muddling along at the current level of AI alignment effort, without significant increases in funding, talent, insights, or breakthroughs. In other words, probability of very bad things happening, given AGI happening, but also given the status-quo level of effort on AI safety.
Do you believe that there is something already published that should have moved our subjective probabilities outside of the ranges noted in the post? If so, I’d love to know what it is! Please use this thread to collect potential examples, and include a link. Some info about why it should have done that (if not obvious) would also be welcome. (Only new posts are eligible for the prizes, though.)
I think considerations like those presented in Daniel Kokotajlo’s Fun with +12 OOMs of Compute suggest that you should have ≥50% credence on AGI by 2043.
Agree, and add that code models won’t be data constrained as they can generate their own training data. It’s simple to write tests automatically, and you can run the code to see whether it passes the tests before adding it to your training dataset. As an unfortunate side effect, part of this process involves constantly and automatically running code output by a large model, and feeding it data which it generated so it can update its weights, both of which are not good safety-wise if the model is misaligned and power seeking.
I don’t know if this has been incorporated into a wider timelines analysis yet as it is quite recent, but this was a notable update for me given the latest scaling laws which indicate that data is the constraining factor, not parameter count. Much shorter timelines than 2043 seem like a live and strategically relevant possibility.
This is more of a meta-consideration around shared cultural background and norms. Could it just be a case of allowing yourselves to update toward more scary-sounding probabilities? You have all the information already. This video from Rob Miles (“There’s No Rule That Says We’ll Make It”)[transcript copied from YouTube] made me think along these lines. Aside from background culture considerations around human exceptionalism (inspired by religion) and optimism favouring good endings (Hollywood; perhaps also history to date?), I think there is also an inherent conservatism borne by prestigious mega-philanthropy whereby a doom-laden outlook just doesn’t fit in.
Optimism seems to tilt one in favour of conjunctive reasoning, and pessimism favours disjunctive reasoning. Are you factoring both in?
This is a pretty deep and important point. There may be psychological and cultural biases that make it pretty hard to shift the expected likelihoods of worst-case AI scenarios much higher than they already are—which might bias the essay contest against arguments winning even if they make a logically compelling case for more likely catastrophes.
Maybe one way to reframe this is to consider the prediction “P(misalignment x-risk|AGI)” to also be contingent on us muddling along at the current level of AI alignment effort, without significant increases in funding, talent, insights, or breakthroughs. In other words, probability of very bad things happening, given AGI happening, but also given the status-quo level of effort on AI safety.