COMMENTARY


THE DROIDS YOU’RE LOOKING FOR


To make ef fective use of AI, the Army first needs a solid foundation of comprehensive data exchange standards and a thorough ontology framework.


by Thom Hawkins and Ken Lorentzen T


ell someone you’re working on artificial intelligence (AI) for the Department of Defense, and there’s one cultural reference point they’re likely to


mention—the “Terminator” franchise. As the story goes (and despite all of the movies in the series, it never gets particularly specific), robots advance suffi- ciently to gain consciousness and attack their human creators. “Killer robots?” they’ll ask, as if that’s exactly what you just claimed you do for a living.


It’s easy to get excited about AI because we now encounter it on a daily basis. Amazon has sold more than 100 million Alexa-enabled devices. We share the road with at least a few self-driving cars, and many more now park themselves. Netflix Inc. has more than 150 million subscribers, a result, in part, of the attraction of outside content. But Netflix increasingly produces its own content and matches subscribers to it via its recommendation engine. AI sure looks like magic, and is often referred to as such, with equal parts admiration and skepticism, as in the commonly heard phrase, “we’ll sprinkle some AI magic on this.”


Working with the nuts and bolts of defense data, though, it’s easy to see why Cyberdyne Systems, the developer of the AI entity in “Terminator,” would not have imagined that they were building toward something that would one day take over the world. Our data collection is inconsistent, storage is decen- tralized and standards vary from system to system. Te Army’s recent data strategy addresses the impor- tance of data to the military’s future and focuses on making data visible, accessible, understandable, trusted, interoperable and secure—though we’re still coming to grips with the implications of each of those factors.


Our systems-development approach has long been based on the notion that we should express a desired capability and give industry maximum flexibility to identify solutions. Tis is the method we’re using now in the pursuit of “narrow AI”—an application that provides a well-defined but limited capability. For example, software that determines when you’re likely to need resupply for a vehicle fleet based on user-entered usage rates. Narrow AI is unlikely to surprise or delight us with ingenuity.


https://asc.ar my.mil 139


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133  |  Page 134  |  Page 135  |  Page 136  |  Page 137  |  Page 138  |  Page 139  |  Page 140  |  Page 141  |  Page 142  |  Page 143  |  Page 144  |  Page 145  |  Page 146  |  Page 147  |  Page 148  |  Page 149  |  Page 150  |  Page 151  |  Page 152  |  Page 153  |  Page 154  |  Page 155  |  Page 156  |  Page 157  |  Page 158  |  Page 159  |  Page 160  |  Page 161  |  Page 162  |  Page 163  |  Page 164  |  Page 165  |  Page 166  |  Page 167  |  Page 168  |  Page 169  |  Page 170  |  Page 171  |  Page 172