We only describe our background worldview here. ![]() To the extent that there are new ideas, credit primarily goes to Paul Christiano and Jon Uesato. We’re reframing the well-known outer alignment difficulties for traditional deep learning architectures and contrasting them with compositional approaches. So it’s crucial to push toward process-based training now.Whether the most powerful ML systems will primarily be process-based or outcome-based is up in the air.This lock-in applies much more to outcome-based systems. ![]() Both process- and outcome-based evaluation are attractors to varying degrees: Once an architecture is entrenched, it’s hard to move away from it.In the long term, process-based ML systems help avoid catastrophic outcomes from systems gaming outcome measures and are thus more aligned.These tasks include long-range forecasting, policy decisions, and theoretical research. In the short term, process-based ML systems have better differential capabilities: They help us apply ML to tasks where we don’t have access to outcomes.This post explains why Ought is devoted to process-based systems. Outcome-based systems are built on end-to-end optimization, with supervision of final results.Process-based systems are built on human-understandable task decompositions, with direct supervision of reasoning steps.We can think about machine learning systems on a spectrum from process-based to outcome-based:
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |