Data Science and Artificial Intelligence GG-15

INTRODUCTION The Joint Artificial Intelligence Center (JAIC) was established to enable the Military Departments and Services, Joint Staff, Combatant Commands (CCMDs), Office of the Secretary of Defense (OSD), and other DoD Components, to serve on a Cross-Functional Team (CFT) to swiftly introduce new capabilities and effectively experiment with new operational concepts in support of DoD's warfighting missions and business functions with the overarching goal of accelerating the delivery of AI-enabled capabilities, scaling the Department-wide impact of AI, and synchronizing DoD AI activities to expand Joint Force advantages.

As a senior advisor, consults with team members on the development of simulation and learning technologies to support autonomous and human systems, real time human-in-loop simulations, synthetic natural environments, advanced distributed learning, computer generated forces, and immersive virtual environments.

bachelor’s degree in engineering (0800/0854), computer science (1550), operational research (1515), mathematics (1529), or statistics (1529) (*See Education Requirements Fact Sheet) Mastery of a specialty field to the extent that the engineer is capable of applying new developments and experienced judgment to solve novel or obscure problems;

Mastery knowledge, experience and understanding of advanced mathematical modeling techniques, probability theory, statistical theory, game theory and/or robotics, computer vision, human learning technology and reinforcement learning or related disciplines to satisfy specific user requirements.

For the Army to successfully develop artificial intelligence, it needs to ask the right questions

AI technology has matured since the mid-1950s, when development began, but acquisition professionals need to temper unrealistic expectations, be cautious of buying into industry hype, and gain enough understanding of AI to ask the right questions before making an investment.

refers to the ability of machines to perform tasks that normally require human intelligence—for example, recognizing patterns, learning from experience, drawing conclusions, making predictions or taking action—whether digitally or as the smart software behind autonomous physical systems,” according to the 2018 DOD AI Strategy, released in February.

The Army’s Rapid Capabilities and Critical Technologies Office (RCCTO) is already applying AI technology to address signal detection on the battlefield, by inserting AI and machine-learning prototypes into electronic warfare systems.

In 1970, cognitive scientist Marvin Minsky predicted “a machine with the general intelligence of an average human being” would manifest within 10 years.

The field has cycled through similar peaks of optimism that give way to failure since then—and has yet to produce a machine that can achieve the heights that Minsky predicted.

It is not possible to predict all corner cases (situations outside of normal operating parameters), and misclassification of data can lead to fatal errors.

In March 2018, an Uber experimental autonomous vehicle operating in Tempe, Arizona, struck and killed a woman who was walking her bicycle outside of a crosswalk in a poorly illuminated area.

The National Transportation Safety Board report on the incident, published in May 2018, noted: “According to Uber, emergency braking maneuvers are not enabled while the vehicle is under computer control, to reduce the potential for erratic vehicle behavior.

In 2017, National Science Foundation researchers built an algorithm to determine what changes to an object would confuse an AI classification program (like a driverless car program of the kind Uber was testing in Arizona).

The computer “learning” is usually performed offline using a training dataset to build a mathematical model to reflect the real world.

Employees at those companies continually updated their word lists to adapt, while spammers only needed to slightly modify words in an email to create new scams and get around spam filters.

Machine learning automates that process by building a statistical model of spam email to classify emails as spam versus “ham” (good email).

Data also must be tagged or labeled with descriptions to train and test algorithms (e.g., emails classified as spam or ham, or pictures tagged as “helicopter”).

Without tags, the data is less useful and informative than it could be—a computer learns more from a picture of a helicopter tagged with the word “helicopter” than it does from just the picture without a tag.

Rigorous testing measures how a model performs with a test dataset that does not contain the data used to train the AI model, to give a true representation of the model’s performance.

F-score, the weighted average of precision and recall, overcomes the accuracy paradox because it takes into account false positives and false negatives and balances recall and precision.

Programmers use heuristics, “rules of thumb,” to reduce complexity, parameters and processing power needs, or to fill knowledge gaps during algorithm development.

The heuristics 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.

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.

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 decisions and the efficient spending of Army resources.

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