Understanding AI Data Repositories with Automatic Query Generation
  We describe a set of techniques to generate queries automatically based on
one or more ingested, input corpuses. These queries require no a priori domain
knowledge, and hence no human domain experts. Thus, these auto-generated
queries help address the epistemological question of how we know what we know,
or more precisely in this case, how an AI system with ingested data knows what
it knows. These auto-generated queries can also be used to identify and remedy
problem areas in ingested material -- areas for which the knowledge of the AI
system is incomplete or even erroneous. Similarly, the proposed techniques
facilitate tests of AI capability -- both in terms of coverage and accuracy. By
removing humans from the main learning loop, our approach also allows more
effective scaling of AI and cognitive capabilities to provide (1) broader
coverage in a single domain such as health or geology; and (2) more rapid
deployment to new domains. The proposed techniques also allow ingested
knowledge to be extended naturally. Our investigations are early, and this
paper provides a description of the techniques. Assessment of their efficacy is
our next step for future work.
