ASK THE RIGHT QUESTIONS
Programmers use heuristics, “rules of thumb,” to reduce complex- ity, parameters and processing power needs, or to fill knowledge gaps during algorithm development. Tese heuristics may trade off optimality, completeness, accuracy or precision. Te heuris- tics could affect the program’s ability to find an optimal solution when multiple solutions exist or prevent it from finding the most correct or optimal solution. Tey may also only nominally decrease computing time. Poor heuristic choices and underly- ing assumptions degrade the validity of an algorithm’s output.
In the end, humans determine the underlying assumptions used to design AI programs. Te result presented to consumers is often a black box containing a mix of clever programming and smartly analyzed data. But if created poorly, models can be too sensitive or not sensitive enough, resulting in too many false positives or false negatives. Corner cases, human insertion of errors and inaccurate models from bad or limited datasets will also lead to errors. Data requirements, accurate modeling, processing power and fallibility also apply to other AI specialties, such as facial and voice recognition.
ASK THE RIGHT QUESTIONS, GET THE RIGHT TECHNOLOGY DOD is investing heavily in AI to gain military advantages and reduce workload. A working knowledge of AI will help product managers better understand industry presentations, and will help assess technical maturity and determine viability and scalability of a solution during the market research phase.
Preliminary market research questions include:
• How is the model built? What are the underlying assumptions? • How is the model tested? Was training data used in the test set? • How well does the model reflect the real world? What are the performance results of testing? How much better than random chance? What are the precision, recall and f-scores (closer to 100 percent is better) and confidence level in the results? What is the rate of false positives and negatives?
• How was the dataset gathered? If data was gathered from people, did the people know it was being gathered? How big are the training and test datasets? If the dataset isn’t built yet, how long will it take and how much will it cost to build?
• How well does the program perform against deception and adversarial inputs (e.g., a subject wearing sunglasses or a hat)? What happens when the program is presented with corner cases?
• How much computing power is required? Where does the processing occur? How long does it take for results to be computed?
88 Army AL&T Magazine Summer 2019
• Can the algorithm be updated easily? How are improvements inserted? How is real-time performance measured? Can oper- ators determine when the algorithm is performing poorly in real time?
• How well does the program work with existing programs to input and export insights?
• Is the system autonomous or human-assisted? How much human assistance does it require?
• Where are decisions made? Are they made by humans, or does the program automatically do it? This is a critical question for decisions about the use of force.
• What rights does the government have to the dataset and the trained model?
CONCLUSION Increases in processing power have enabled greater advances in AI to solve complex problems on and off the battlefield. Tere are still limits to what AI can do, however. We can be cautiously optimistic but must exercise prudence and rigor to ensure that we can identify the difference between a viable solution and a black box filled with empty promises. Asking the right questions up front will help unveil technology readiness—and help DOD steer clear of vendor oversell—enabling the right acquisition deci- sions and the efficient spending of Army resources.
For more information, go to the Program Executive Office for Command, Control and Communications –Tactical (PEO C3T) website at
http://peoc3t.army.mil/c3t/, or
the PEO C3T Public Affairs Office at 443-395-6489 or
usarmy.APG.peo-c3t.mbx.pao-peoc3t@
mail.mil.
LT. COL. JENNY STACY is the product manager for Satellite Communications within the Project Manager for Tactical Network at PEO C3T. She has an M.S. in computer science from the Naval Postgraduate School; her thesis, “Detecting Age in Online Chat,” received the Gary Kildall Award for computing innovation. She also holds a B.S. in computer science from the United States Military Academy at West Point. She is a member of the Army Acquisition Corps, and is Level III certified in program management and Level II certified in information technology.
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