Machine Intelligence requires large, well-documented datasets (examples) to be trained upon. Datasets often matter more than…
Machine Intelligence requires large, well-documented datasets (examples) to be trained upon. Datasets often matter more than algorithms per se, though they rarely get proper credit for the value that they can create.
Datasets such as Fei-Fei Li's ImageNet have enabled the recent expansion in capability of machine intelligence in powerful new ways that otherwise would be impossible. We want to do the same for the space of kind behaviours – a range of experiments in how one can construct, collate, and annotate a range of datasets that reflect many different cultures, opinions and creeds, and which can expand in scope and nuance over time, to empower socially-aware thinking machines for generations to come.
Being prosocial requires learning the preferences of others. We need a mechanism to reach those preferences to machines.
We recognise that what prosocial behavior looks like varies across time, geography, and culture. We intend to democratize access and opportunity for contribution to this world-changing system, that is currently concentrated in the hand of a few AI researchers/engineers.
Machine intelligences will simply amplify and return whatever data we give them. Our goal is to advance the field of machine ethics, by seeding technology that makes it easy to teach machines about ones individual and cultural behavioral preferences.