We’re doing a hackathon with Apart Research on 26th. I created a list of problem statements for people to brainstorm off of.
Pro-active insight extraction from new research
Reading papers can take a long time and is often not worthwhile. As a result, researchers might read too many papers or almost none. However, there are still valuable nuggets in papers and posts. The issue is finding them. So, how might we design an AI research assistant that proactively looks at new papers (and old) and shares valuable information with researchers in a naturally consumable way? Part of this work involves presenting individual research with what they would personally find valuable and not overwhelm them with things they are less interested in.
How can we improve the LLM experience for researchers?
Many alignment researchers will use language models much less than they would like to because they don’t know how to prompt the models, it takes time to create a valuable prompt, the model doesn’t have enough context for their project, the model is not up-to-date on the latest techniques, etc. How might we make LLMs more useful for researchers by relieving them of those bottlenecks?
Simple experiments can be done quickly, but turning it into a full project can take a lot of time
One key bottleneck for alignment research is transitioning from an initial 24-hour simple experiment in a notebook to a set of complete experiments tested with different models, datasets, interventions, etc. How can we help researchers move through that second research phase much faster?
How might we use AI agents to automate alignment research?
As AI agents become more capable, we can use them to automate parts of alignment research. The paper “A Multimodal Automated Interpretability Agent” serves as an initial attempt at this. How might we use AI agents to help either speed up alignment research or unlock paths that were previously inaccessible?
How can we nudge research toward better objectives (agendas or short experiments) for their research?
Even if we make researchers highly efficient, it means nothing if they are not working on the right things. Choosing the right objectives (projects and next steps) through time can be the difference between 0x to 1x to +100x. How can we ensure that researchers are working on the most valuable things?
What can be done to accelerate implementation and iteration speed?
Implementation and iteration speed on the most informative experiments matter greatly. How can we nudge them to gain the most bits of information in the shortest time? This involves helping them work on the right agendas/projects and helping them break down their projects in ways that help them make progress faster (and avoiding ending up tunnel-visioned on the wrong project for months/years).
How can we connect all of the ideas in the field?
How can we integrate the open questions/projects in the field (with their critiques) in such a way that helps the researcher come up with well-grounded research directions faster? How can we aid them in choosing better directions and adjust throughout their research? This kind of work may eventually be a precursor to guiding AI agents to help us develop better ideas for alignment research.
We’re doing a hackathon with Apart Research on 26th. I created a list of problem statements for people to brainstorm off of.
Pro-active insight extraction from new research
Reading papers can take a long time and is often not worthwhile. As a result, researchers might read too many papers or almost none. However, there are still valuable nuggets in papers and posts. The issue is finding them. So, how might we design an AI research assistant that proactively looks at new papers (and old) and shares valuable information with researchers in a naturally consumable way? Part of this work involves presenting individual research with what they would personally find valuable and not overwhelm them with things they are less interested in.
How can we improve the LLM experience for researchers?
Many alignment researchers will use language models much less than they would like to because they don’t know how to prompt the models, it takes time to create a valuable prompt, the model doesn’t have enough context for their project, the model is not up-to-date on the latest techniques, etc. How might we make LLMs more useful for researchers by relieving them of those bottlenecks?
Simple experiments can be done quickly, but turning it into a full project can take a lot of time
One key bottleneck for alignment research is transitioning from an initial 24-hour simple experiment in a notebook to a set of complete experiments tested with different models, datasets, interventions, etc. How can we help researchers move through that second research phase much faster?
How might we use AI agents to automate alignment research?
As AI agents become more capable, we can use them to automate parts of alignment research. The paper “A Multimodal Automated Interpretability Agent” serves as an initial attempt at this. How might we use AI agents to help either speed up alignment research or unlock paths that were previously inaccessible?
How can we nudge research toward better objectives (agendas or short experiments) for their research?
Even if we make researchers highly efficient, it means nothing if they are not working on the right things. Choosing the right objectives (projects and next steps) through time can be the difference between 0x to 1x to +100x. How can we ensure that researchers are working on the most valuable things?
What can be done to accelerate implementation and iteration speed?
Implementation and iteration speed on the most informative experiments matter greatly. How can we nudge them to gain the most bits of information in the shortest time? This involves helping them work on the right agendas/projects and helping them break down their projects in ways that help them make progress faster (and avoiding ending up tunnel-visioned on the wrong project for months/years).
How can we connect all of the ideas in the field?
How can we integrate the open questions/projects in the field (with their critiques) in such a way that helps the researcher come up with well-grounded research directions faster? How can we aid them in choosing better directions and adjust throughout their research? This kind of work may eventually be a precursor to guiding AI agents to help us develop better ideas for alignment research.