AI and Human Co-Improvement

Meta FAIR researchers Jason Weston and Jakob Foerster argue that fully autonomous self-improving AI is neither the fastest nor safest path to superintelligence. Instead, they advocate for co-improvement: building AI that collaborates with human researchers to conduct AI research together, from ideation to experimentation. - **Core thesis:** Self-improvement seeks to eliminate humans from the loop as quickly as possible. Co-improvement keeps humans involved, providing steering capability toward positive outcomes while leveraging complementary skill sets. Because AI is not yet mature enough to fully self-improve and is susceptible to misalignment, co-improvement will get us there faster and more safely. - **Research collaboration skills:** The authors propose measuring and training AI on research collaboration abilities across problem identification, benchmark creation, method innovation, experiment design, collaborative execution, evaluation, scientific communication, and safety/alignment development. - **Bidirectional augmentation:** Unlike self-improvement which focuses on autonomous model updates, co-improvement centers on joint progress where humans help AI achieve greater abilities while AI augments human cognition and research capabilities. The goal is co-superintelligence through symbiosis. - **Paradigm shift acceleration:** Major AI advances came from human researchers finding combinations of training data and method changes. Co-research with strong collaborative AI should accelerate finding unknown new paradigm shifts while maintaining transparency and human-centered safety.