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"Superhuman science"
This paper explores the potential impact of AI on scientific fields where innovation is about searching big spaces of possibility (e.g. new drugs, materials, architectures to build better AI systems...).
brookings.edu/wp-content/upl…
#econai ht @mattsclancy
This paper explores the potential impact of AI on scientific fields where innovation is about searching big spaces of possibility (e.g. new drugs, materials, architectures to build better AI systems...).
brookings.edu/wp-content/upl…
#econai ht @mattsclancy
1. AI systems trained on historical data can help predict which opportunities have more potential, improving prioritisation and expected returns from innovation.
But downstream blockers (e.g. around testing of the ideas that are developed) could constrain these benefits.
But downstream blockers (e.g. around testing of the ideas that are developed) could constrain these benefits.
2. The paper gets into the weeds of this by modelling innovation as a multi-stage process of idea generation and testing where tests results at different stages are not wholly reliable. It would be cool to map and operationalise this idea in specific fields.
