Author here. When constructing this paper, we needed an interpretable metric (time horizon), but this is not very data-efficient. We basically made the fewest tasks we could to get acceptable error bars, because high-quality task creation from scratch is very expensive. (We already spent over $150k baselining tasks, and more on the bounty and baselining infra.) Therefore we should expect that restricting to only 32 of the 170 tasks in the paper makes the error bars much wider; it roughly increases the error bars by a factor of sqrt(170/32) = 2.3.
Now if these tasks were log-uniformly distributed from 1 second to 16 hours, we would still be able to compare these results to the rest of our dataset, but it’s worse than that: all fully_private tasks are too difficult for models before GPT-4, so removing SWAA requires restricting the x axis to the 2 years since GPT-4. This is 1⁄3 of our x axis range, so it makes error bars on the trendline slope 3 times larger. Combined with the previous effect the error bars become 6.9 times larger than the main paper! So the analysis in this post, although it uses higher-quality data, basically throws away all the statistical power of the paper. I wish I had 170 high-quality private tasks but there was simply not enough time to construct them. Likewise, I wish we had baselines for all tasks, but sadly we will probably move to a different safety case framework before we get them.
Though SWAA tasks have their own problems (eg many of them were written by Claude), they were developed in-house and are private, and it’s unlikely that GPT-2 and GPT-3 would have a horizon like 10x different on a different benchmark. So for this check we really should not exclude them.
privacy_level
num_tasks
fully_private
32
public_problem
22
easy_to_memorize
18
public_solution
17
semi_private
2
When we include SWAA, the story is not too different from the original paper: doubling roughly 8 months. Note how large the error bars are.
With only fully_private tasks and SWAA and RE-Bench, the extrapolated date of 1-month AI looks similar to the main paper, but the error bars are much larger; this is entirely driven by the small number of tasks (green bar).
For comparison, the main paper extrapolation
When I exclude SWAA and RE-Bench too, the script refuses to even run, because sometimes the time horizon slope is negative, but when I suppress those errors and take the 2024-2025 trend we get 80% CI of early 2026 to mid-2047! This is consistent with the main result but pretty uninformative.
You can see that with only fully_private tasks (ignore the tasks under 1 minute; these are SWAA), it’s hard to even tell whether longer tasks are more difficult in the <1 hour range (tiles plot). As such we should be suspicious of whether the time horizon metric works at all with so few tasks.
Author here. When constructing this paper, we needed an interpretable metric (time horizon), but this is not very data-efficient. We basically made the fewest tasks we could to get acceptable error bars, because high-quality task creation from scratch is very expensive. (We already spent over $150k baselining tasks, and more on the bounty and baselining infra.) Therefore we should expect that restricting to only 32 of the 170 tasks in the paper makes the error bars much wider; it roughly increases the error bars by a factor of sqrt(170/32) = 2.3.
Now if these tasks were log-uniformly distributed from 1 second to 16 hours, we would still be able to compare these results to the rest of our dataset, but it’s worse than that: all fully_private tasks are too difficult for models before GPT-4, so removing SWAA requires restricting the x axis to the 2 years since GPT-4. This is 1⁄3 of our x axis range, so it makes error bars on the trendline slope 3 times larger. Combined with the previous effect the error bars become 6.9 times larger than the main paper! So the analysis in this post, although it uses higher-quality data, basically throws away all the statistical power of the paper. I wish I had 170 high-quality private tasks but there was simply not enough time to construct them. Likewise, I wish we had baselines for all tasks, but sadly we will probably move to a different safety case framework before we get them.
Though SWAA tasks have their own problems (eg many of them were written by Claude), they were developed in-house and are private, and it’s unlikely that GPT-2 and GPT-3 would have a horizon like 10x different on a different benchmark. So for this check we really should not exclude them.
When we include SWAA, the story is not too different from the original paper: doubling roughly 8 months. Note how large the error bars are.
With only fully_private tasks and SWAA and RE-Bench, the extrapolated date of 1-month AI looks similar to the main paper, but the error bars are much larger; this is entirely driven by the small number of tasks (green bar).
For comparison, the main paper extrapolation
When I exclude SWAA and RE-Bench too, the script refuses to even run, because sometimes the time horizon slope is negative, but when I suppress those errors and take the 2024-2025 trend we get 80% CI of early 2026 to mid-2047! This is consistent with the main result but pretty uninformative.
You can see that with only fully_private tasks (ignore the tasks under 1 minute; these are SWAA), it’s hard to even tell whether longer tasks are more difficult in the <1 hour range (tiles plot). As such we should be suspicious of whether the time horizon metric works at all with so few tasks.