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Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies Proceedings of a Workshop (2019) / Chapter Skim
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9 Machine Learning Systems
Pages 46-52

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 46...
... build AI using scalable and explainable tools to cover these life-cycle steps by including human-in-the-loop to capture knowledge from domain experts, knowledge workers, and end users, with systems supporting provenance, debugging, and error analysis and (2) to construct and refine domain knowledge using machine learning and deep learning techniques.
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From page 47...
... For example, to categorize each sentence in a contract for IBM, a domain expert can use this semantic layer to write highly precise algorithms with abstraction that are more portable than doing a deep learning model. IBM has also done some work on using deep learning to automatically learn transparent domain-specific analysis that enables domain experts to co-create meaningful interpretable models.
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From page 48...
... By developing classifiers that can automatically determine the level of difficulty of a task, the expert effort is reduced and results can be improved because experts curate difficult tasks and the crowd curates easy tasks. If humans are in the loop for model development, it is possible to develop self-explaining models in a target language that humans can manipulate as well as reduce the amount of labeled data required with transfer learning and active learning.
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From page 49...
... There are many analogues for the IC, as this is a good use case of interactive machine learning with a straightforward and simple method. NOAA has six National Marine Fisheries Science Centers in the United States, and each was doing some form of underwater data collection with camera rigs at different depths for fisheries stock assessment and then developing algorithms to analyze these data.
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From page 50...
... Hoogs gave an overview of NOAA's example data collectors; data streams exceed the capabilities of human analysts, so automated tools must be developed to increase the speed of analysis, reduce costs, and improve assessments. These data collectors include towed-camera or towed-diver benthic surveys, remotely operated vehicle fish surveys, net camera platforms, stereo-camera platforms, and animal body cameras.
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From page 51...
... VIAME's components. SOURCE: Anthony Hoogs, Kitware, Inc., presentation to the workshop, December 12, 2018.
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From page 52...
... He reiterated the importance of engaging academic researchers who may be interested in IC/DoD problems with compelling challenges and complete data sets. While interactive AI for online specialization is very promising, he noted that deep learning has had less impact, thus far, on high-level reasoning and contextual problems than image recognition.
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Key Terms

  • deep learning
  • entity resolution
  • interactive machine
  • active learning
  • robust machine

  • training data
  • labeled data
  • semantic role
  • role labeling
  • domain experts

  • backend analytics
  • adversarial attacks
  • building block
  • learning model
  • knowledge representation

  • learning revolution
  • data provenance
  • financial knowledge
  • data collectors
  • domain knowledge

  • fisheries science
  • marine fisheries
  • model development
  • natural language
  • integration language

  • fisheries centers
  • hoogs described
  • national marine
  • domain expertise
  • language processing


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