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Challenges in Machine Generation of Analytic Products from Multi-Source Data Proceedings of a Workshop (2017) / Chapter Skim
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2 Session 1: Plenary
Pages 3-8

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From page 3...
... The task was to create an algorithm for an analytic product, using established intelligence community formatting and evaluation criteria, that could address an intelligence question and be used to aid policy makers and war fighters. Upon completion of the Xpress Challenge, ODNI and OUSD(I)
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From page 4...
... probabilistic programming.5 Dietterich explained that there have been corresponding innovations in probabilistic inference methods, including in belief propagation, variational inference, Markov chain Monte Carlo, and Hamiltonian Monte Carlo techniques. Probabilistic modeling can help represent knowledge related to the problem at hand, reason about latent variables (which is crucial for the intelligence community)
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From page 5...
... With sufficient data, Dietterich explained, function learning can be more accurate than probabilistic modeling, in part because fewer modeling assumptions are made and less effort is required for model development. Despite the many strengths of the probabilistic modeling paradigm and the end-to-end function learning paradigm, Dietterich explained that there are weaknesses related to the assumption of stationarity; need for an abundance of data; data collection process that is frequently, but often unnecessarily, biased; brittleness of end-toend training; and need for new methods for verification, validation, and monitoring.
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From page 6...
... Rao provided a few examples of useful data-driven approaches and models for security, including using rulebased systems that study generic attack patterns, viewing domain name server traffic to identify evasive cohort behaviors of botnets, beaconing detection, looking at web access patterns, predicting malicious URLs, passively exploring networks, and identifying device anomaly detections. He explained that adversaries use "fast-fluxing" (i.e., changing an IP address)
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From page 7...
... indications and warning. He emphasized that the need to utilize available masses of structured, labeled data sets extends beyond the intelligence community, throughout the entire DoD, and beyond the National Security agencies.
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From page 8...
... Rao inquired about DOD's approach to securing both the talent and the technologies needed to execute the goals of Project Maven. Axtell said that the primary focus of Project Maven is to acquire and build relationships with existing commercial capabilities.
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Key Terms

  • project maven
  • intelligence community
  • function learning
  • probabilistic modeling
  • analytic products

  • big data
  • probabilistic programming
  • data labeling
  • learning paradigm
  • dietterich explained

  • audience participant
  • capability technology
  • modeling paradigm
  • participant asked
  • unstructured data

  • supervised learning
  • deep learning
  • probabilistic inference
  • learning techniques
  • threat intelligence

  • artificial intelligence
  • tom dietterich
  • dietterich described
  • josyula rao
  • oregon state

  • programming language
  • technology matrix
  • enabling technologies
  • social media
  • rama chellappa


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