"The Seven Tools of Causal Inference, with Reflections on Machine Learning," by ACM A.M. Turing Award recipient Judea Pearl, describes tools that overcome obstacles to human-level machine intelligence. Pearl delivers a message to machine-learning and AI experts in an original video at bit.ly/2GUEyJW.
Excerpt from the long paper, ultimately positioning our challenge:
Key insights:
- Data Science is a two-body problem, connecting data and reality, including the forces behind the data.
- Data Science is the art of interpreting reality in the light of data, not a mirror through which data sees itself from different angles.
- The ladder of causation is the double helix of causal thinking, defining what can and cannot be learned about actions and about worlds that could have been. ... "
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