CAIF's mission is to support research that will improve the cooperative intelligence of advanced AI systems for the benefit of all of humanity. We expand on this statement below:

  • Supporting research includes standard academic grantmaking, but also fostering research in other ways, such as organising workshops and other events, supporting students, awarding prizes, and providing educational tools.

  • Improvements that CAIF prioritises are counterfactual and long-term, i.e., those improvements over the next 10-20 years (or longer) that would have been unlikely without our support (see also 'Neglectedness' below).

  • Cooperative intelligence refers to the skills required for promoting cooperation between humans, machines, or organisations, though further research is required to fully conceptualise and define these skills.

  • Advanced AI systems include not only the present day state-of-the-art, but the kinds of powerful AI systems we can expect to see in 10-20 years and the networks of humans and organisations in which they are embedded.

  • The benefit of all of humanity is our fundamental concern, and highlights the fact that not all advances in Cooperative AI may be beneficial for everyone; we must take into account different perspectives and values.

For further details on CAIF's mission, see our paper on Open Problems in Cooperative AI and our Nature commentary.

Tractability, Importance, and Neglectedness

Scientific progress is often hard to predict. Great research is frequently driven by intrinsic curiosity, and it can be difficult to say in advance what the most important research directions for a field will turn out to be. On the other hand, we think that there are features which make some research directions more promising than others. Following an increasing number of philanthropic organisations (see here, for example), CAIF is guided by the tractability, importance, and neglectedness of potential activities, while maintaining an awareness of the value of curiosity and serendipity for great science.

Tractability: Does the research area lend itself especially well to making progress?

Importance: Is this research area likely to make an important contribution to advancing the cooperative intelligence of advanced AI systems for the benefit of all humanity?

Neglectedness: Is this work likely to be done anyway?

While tractability is a common dimension along which to assess potential research, our emphasis on the qualities of importance and neglectedness differ somewhat from their standard interpretations in academia. For instance, a result may be viewed as particularly important within the researcher’s own field, but to warrant the highest prioritisation such work should also be likely to help us build more cooperative AI, now and in the future. Likewise, in many fields there may be multiple research groups knowingly racing to solve the same problem, but from the perspective of counterfactual impact this may not be the kind of research that’s essential to prioritise, because it’s likely to happen with or without our support.