A scientific roadmap to help forecast and guide human-algorithm behavior toward the common good will be developed.

Leading researchers from academia, tech companies, focused research organizations and non-profits, start-ups, and venture capitalists and funders will explore current barriers to AI development and promising new directions for the field.

The hypothesis explored is that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. The implications of this approach are outlined, as is the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

Participatory research in 3 distinct global geographies will inform a new framework aiming to integrate understandings from the fields of voluntary family planning, sexual and reproductive health and rights with human flourishing science. 

Many fields—including psychology, sociology, communications, political science, and computer science—use computational methods to analyze text data. However, existing text analysis methods have a number of shortcomings. Dictionary methods, while easy to use, are often not very accurate when compared to recent methods. Machine learning models, while more accurate, can be difficult to train and use. This research demonstrates that the large-language model GPT is capable of accurately detecting various psychological constructs (as judged by manual annotators) in text across 12 languages, using simple prompts and no additional training data. GPT thus overcomes the limitations present in existing methods. GPT is also effective in several lesser-spoken languages, which could facilitate text analysis research from understudied contexts.