AI NEEDS DATA
in a format that can be translated into AI through machine learning.
We also must get serious about the data we need, related to what we want AI to do. We are great in cost data because that is easy to understand and measure, and we have taken the time to develop mean- ingful tools.
We need to get better in schedule and performance data by explaining better what causes deviations from the plan, and what we can do to get back on the plan. For instance, there are a finite number of reasons a schedule changes. Captur- ing those reasons for use in AI can be a
powerful planning tool to allow future PMs insight into potential schedule prob- lems. Finally, we need to capture our thinking in both planning and execution. In planning, we should be articulating our assumptions and, while executing, describ- ing whether those assumptions were valid or not and, more importantly, why.
THE PATH TO AI We get to AI through machine learning. Te data provides historical information and lessons learned. Machine learning is an iterative and cyclical process that depends on user and system feedback. Users must provide comments on the fielded system capabilities and problems
to allow constant improvement through regular updates, which will also incorpo- rate newly generated data.
AI identifies patterns in the data to match those to something occurring in a program’s execution today. It does this through machine learning, the enabling process of making data into AI. Machine learning allows computers to act or provide information without direction from an operator. One of the ways it does this is through sorting large amounts of data, referred to as “big data,” to identify those patterns.
Warfighters use this pattern recognition capability to identify changes in enemy activities—changes that only appear on examination of large amounts of data over long periods of time. Physicians use pattern recognition to assist in iden- tifying cancer in X-rays. In acquisition management, we are looking for patterns of current activity that match something that has happened elsewhere, on another program, at a different time.
Pattern matching isn’t the only capability we can expect from AI, though. Statistics are also key to machine learning and AI. Statistics offer the possibility of predic- tion, whether using simple regression techniques to more sophisticated math- ematical modeling. Prediction helps in planning and estimation, as well as risk tracking and mitigation. Prediction also provides a longer-range perspective (with the right data) by constantly comparing the present status using all available data with that of past, similar developments.
DATA COLLECTION
Army AI Task Force members held a data collection event Jan. 14 with the Aided Threat Recognition from Mobile Cooperative and Autonomous Sensors system at Fort Hunter Liggett, California. The data will be used to train the system. (Photo by Patrick Ferraris, Army Artificial Intelligence Task Force)
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System development is a human activ- ity that requires humans to do things in a particular way. We must examine crit- ically whether the processes, both those based on best practices and those based on bureaucratic requirements, contribute to
Army AL&T Magazine Summer 2020
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