Anticipating Rare Events of Major Significance Proceedings of a Workshop (2022) / Chapter Skim
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3 Detection, Indications, and Warnings
3 Detection, Indications, and Warnings
Pages 5-10
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From page 5... ...
It was this realization, he said, that led him and his colleagues to focus on detecting fires when they are small so they could provide actionable intelligence to end users, including fire authorities; emergency response systems; city, state, and federal response agencies; utility companies; and insurance firms. Today, most incidents come to the attention of fire officials thanks to people calling emergency phone lines, and while those alerts are valuable, they rarely include precise locations that can shave critical minutes off the all-important initial response.
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From page 6... ...
One goal, he said, is to overlay more infrastructure assets to provide better information for end users. EXPERIENCES WITH ANTICIPATORY ANALYTICS AND RARE EVENTS Charles Clancy explained that his projects with IARPA aimed to leverage emerging big data capabilities and advances in AI and machine learning to develop anticipatory analytics for predicting the next war and forecasting events such as a terrorist attack, social unrest, and local election outcomes.
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From page 7... ...
The next step involves analyzing people's responses to a warning signal, given what they remember of the accuracy of past warnings and the benefits of the lead time that the signals provide. One can then determine the optimal level of an alert by balancing the trade-off between Type 1 and Type 2 errors (false positives and false negatives)
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From page 8... ...
"This Bayesian framework helps support rational decisions, both tactical and strategic," said Paté-Cornell, "The value of the monitoring system is based on the costs of the failure risks, and the model provides a basis for comparing two monitoring systems with different capabilities." She cautioned that their quantitative inputs were merely illustrative and that these results should not be used for making decisions. The objective of the second example, based on the thesis of Isaac Faber4 under her supervision, was to anticipate and prevent catastrophic cyber-attacks by generating early warnings of cyber threats using a hybrid system involving AI and a human operator.
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From page 9... ...
Satyam noted that this issue is why it is important to spend time coupling human intelligence on top of information produced by AI and of creating a virtuous loop that ends up producing more accurate results than either AI or human would produce on their own. Christopher Barrett added that in a changing environment, the goal is to build a cohesive, integrated representation of a system that can convey meaningful information to the decision process.
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From page 10... ...
Along the same lines, David Sweeney from DTRA asked how to train models where there is either no training data or insufficient ground truths. Theodore Plasse, DTRA, interjected that the goal is not to predict when nuclear war might happen.
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