Most proposals for aligning advanced AI require collecting high-quality human data on complex tasks such as evaluating whether a critique of an argument was good, breaking a difficult question into easier subquestions, or examining the outputs of interpretability tools. Collecting high-quality human data is also necessary for many current alignment research projects.
We’d like to see a human data startup that prioritizes data quality over financial cost. It would follow complex instructions, ensure high data quality and reliability, and operate with a fast feedback loop that’s optimized for researchers’ workflow. Having access to this service would make it quicker and easier for safety teams to iterate on different alignment approaches
Some alignment research teams currently manage their own contractors because existing services (such as surgehq.ai and scale.ai) don’t fully address their needs; a competent human data startup could free up considerable amounts of time for top researchers.
Such an organization could also practice and build capacity for things that might be needed at ‘crunch time’ – i.e., rapidly producing moderately large amounts of human data, or checking a large volume of output from interpretability tools or adversarial probes with very high reliability.
The market for high-quality data will likely grow – as AI labs train increasingly large models at a high compute cost, they will become more willing to pay for data. As models become more competent, data needs to be more sophisticated or higher-quality to actually improve model performance.
Making it less annoying for researchers to gather high-quality human data relative to using more compute would incentivize the entire field towards doing work that’s more helpful for alignment, e.g., improving products by making them more aligned rather than by using more compute.
[Thanks to Jonas V for writing a bunch of this comment for me] [Views are my own and do not represent that of my employer]
High-quality human data
Artificial Intelligence
Most proposals for aligning advanced AI require collecting high-quality human data on complex tasks such as evaluating whether a critique of an argument was good, breaking a difficult question into easier subquestions, or examining the outputs of interpretability tools. Collecting high-quality human data is also necessary for many current alignment research projects.
We’d like to see a human data startup that prioritizes data quality over financial cost. It would follow complex instructions, ensure high data quality and reliability, and operate with a fast feedback loop that’s optimized for researchers’ workflow. Having access to this service would make it quicker and easier for safety teams to iterate on different alignment approaches
Some alignment research teams currently manage their own contractors because existing services (such as surgehq.ai and scale.ai) don’t fully address their needs; a competent human data startup could free up considerable amounts of time for top researchers.
Such an organization could also practice and build capacity for things that might be needed at ‘crunch time’ – i.e., rapidly producing moderately large amounts of human data, or checking a large volume of output from interpretability tools or adversarial probes with very high reliability.
The market for high-quality data will likely grow – as AI labs train increasingly large models at a high compute cost, they will become more willing to pay for data. As models become more competent, data needs to be more sophisticated or higher-quality to actually improve model performance.
Making it less annoying for researchers to gather high-quality human data relative to using more compute would incentivize the entire field towards doing work that’s more helpful for alignment, e.g., improving products by making them more aligned rather than by using more compute.
[Thanks to Jonas V for writing a bunch of this comment for me]
[Views are my own and do not represent that of my employer]