Executive summary: Recent improvements in large language models (LLMs) have made LLM-based epistemic systems promising as an effective altruist intervention area, with potential for far-reaching impacts on forecasting, decision-making, and global coordination.
Key points:
LLM-based epistemic processes (LEPs) could dramatically improve forecasting and decision-making across many domains, potentially reducing foreseeable mistakes and improving coordination.
Developing LEPs will likely require significant scaffolding (software infrastructure), which presents challenges for research, centralization, and incentives.
Key components of LEPs include data collection, world modeling, human elicitation, forecasting, and presentation of results.
Standardized, portable LEPs could serve as useful benchmarks and baselines for evaluating other systems and resolving subjective questions.
Important uncertainties include the relative importance of scaffolding vs. direct LLM improvements, and how to prioritize philanthropic work in this area.
Potential risks include accelerating AI capabilities without corresponding safety improvements, and empowering malicious actors.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: Recent improvements in large language models (LLMs) have made LLM-based epistemic systems promising as an effective altruist intervention area, with potential for far-reaching impacts on forecasting, decision-making, and global coordination.
Key points:
LLM-based epistemic processes (LEPs) could dramatically improve forecasting and decision-making across many domains, potentially reducing foreseeable mistakes and improving coordination.
Developing LEPs will likely require significant scaffolding (software infrastructure), which presents challenges for research, centralization, and incentives.
Key components of LEPs include data collection, world modeling, human elicitation, forecasting, and presentation of results.
Standardized, portable LEPs could serve as useful benchmarks and baselines for evaluating other systems and resolving subjective questions.
Important uncertainties include the relative importance of scaffolding vs. direct LLM improvements, and how to prioritize philanthropic work in this area.
Potential risks include accelerating AI capabilities without corresponding safety improvements, and empowering malicious actors.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.