on November 30, 2018 at 4:25 PM<\/span><\/p>\n Army soldiers are testing goggles with an image-recognition system that can automatically spot threats like tanks and warn the rest of the squad — or transmit the target data to a distant missile battery so they can take it out.<\/span><\/p>\n DETROIT: Army soldiers are testing goggles<\/a> with an image-recognition system that can automatically spot threats like tanks and warn the rest of the squad \u2014 or transmit the target data to a distant missile battery<\/a> so they can take it out. It\u2019s the long-awaited realization of the desire to make \u201cevery soldier a sensor\u201d feeding intelligence over wireless networks<\/a> to the rest of the force.<\/span><\/p>\n \n The artificially intelligent target detection will be part of the Integrated Vision Augmentation System (IVAS), for which Microsoft\u2019s HoloLens won a two-year, $480 million contract<\/a> earlier this week. I\u2019ve written extensively about IVAS\u2019s other features back when it was still called HUD 3.0<\/a>. It\u2019s essentially an infantryman\u2019s version of a fighter pilot\u2019s Heads-Up Display, combining advanced night vision with augmented reality technology (much like Google Glass) to superimpose a targeting cross-hairs \u2014 wirelessly linked to the soldier\u2019s weapon to show exactly where it\u2019ll shoot \u2014 and tactical data over the wearer\u2019s field of vision. Other aspects will monitor the medical condition of the soldier and record such things as blast overpressure from roadside bomb blasts to assist medical treatment.<\/span><\/p>\n But this is the first I\u2019ve heard about the image-recognition feature. Brig. Gen. Chris Donahue<\/a> \u2014 until recently director of the Army\u2019s infantry modernization team<\/a> \u2014 mentioned it in a discussion of IVAS features he said \u201cwe\u2019re already prototyping.\u201d It\u2019s not clear whether it\u2019ll be an integral part of IVAS or an add-on, though he did say \u201cit\u2019s on the body,\u201d which implies there\u2019s either some kind of body camera or a supplemental processor not built into the goggles themselves.<\/span><\/p>\n The hard part yet to be figured out, Donahue acknowledged, is how to transmit the data from frontline troops to distant artillery. But the Army has two other modernization teams \u2014 for the network<\/a> and long-range precision firepower<\/a> (LRPF) \u2014 already working hard on how to share targeting data: Once they have a solution, the infantry should simply be able to plug in.<\/span> Tracked variant of the Russian Pantsir S1 anti-aircraft missile system (NATO reporting name SA-22 Greyhound)<\/span><\/p>\n \n \n AI Targeting, In Detail<\/span><\/p>\n Here\u2019s how Donahue described the auto-targeting at this week\u2019s AUSA AI conference<\/a>, with our annotations:<\/span><\/p>\n \u201cImagine you\u2019re fighting some enemy out there and you\u2019ve determined what your target is: The first thing you want to kill is a SA-22, the second thing is an S-300, the next thing is an S-400 (all three of these are Russian anti-aircraft systems, which new Army multi-domain doctrine<\/a> prioritizes destroying, preferably with long-range missiles<\/a>, so the Air Force can strike<\/a> freely \u2014 ed.) and the fourth thing is a T-90 (tank).\u201d<\/span><\/p>\n \u201cBecause of the machine learning and AI built into IVAS, it\u2019s going to instantly look across the battlefield and pick out the SA-22. Why? Because it has the data, it\u2019s seen thousands of images of this, back in its cloud<\/a>, which probably sits back on the vehicle.\u201d That might be a Humvee, for example, or an armored troop carrier like the 8\u00d78 Stryker<\/a> or tracked Bradley<\/a>. This approach is an example of the Army moving away from central servers on fixed bases and instead pushing computing power to individual vehicles or even foot soldiers on the \u201ctactical edge.\u201d<\/span><\/p>\n \u201cWith the right network \u2026 it then shoots directly from that individual, goes all the way back, goes back to the LRPF (missile battery). Probably humans in the loop. They hit a button; LRPF shoots and kills the SA-22.\u201d Pentagon policy requires a human being to make all decisions to use lethal force, although a less scrupulous adversary<\/a> could save precious seconds by automating the decision to fire. Given how often even cutting-edge image recognition AI screws up<\/a>, however, it seems like a very good idea to have a trained human check the target really is an enemy tank and not, say, a friendly one or a school bus full of orphans.<\/span><\/p>\n \n An example of the shortcomings of artificial intelligence when it comes to image recognition. (Andrej Karpathy, Li Fei-Fei, Stanford University)<\/span><\/p>\n \u201cAs that individual (soldier) continues to scan over to the left, they see a T-90 tank. It instantly kicks over to the person in their squad who\u2019s holding the Javelin (anti-tank missile); they shoot the Javelin, they kill the T-90.\u201d Again, Donahue\u2019s talking about the AI automatically warning a human being, who then decides whether to shoot. Given the other features of IVAS already described, it\u2019s likely this warning would consist of some kind of red icon popping up on the augmented reality goggles of every other soldier in the squad, probably with some kind of arrow pointing towards the threat.<\/span><\/p>\n This may sound awfully distracting, but it\u2019s a lot better than soldiers having to take their eyes off the battle to look down at a modified smartphone for tactical updates, as with the current Nett Warrior system running the Android Tactical Assault Kit (ATAK) app<\/a>. Years of \u201cattention management\u201d research on aircraft pilots show that people in combat hyperfocus on the threat in front of them and stop checking instrument displays \u2014 which is what you want them to do, rather than wander into combat staring down at a screen like an iPhone addict about to walk into an open manhole.<\/span><\/p>\n \n So in Army testing, soldiers vastly prefer the IVAS approach of putting vital data right in their field of vision. In fact, young troops wanted more data than their seniors originally thought they could handle, another Army one-star said. \u201cWhat they ask for is more data, not less, because they\u2019re just comfortable, because of the way they\u2019ve been raised, with screens and video games,\u201d said Brig. Gen. Joseph McGee<\/a>, head of the Army\u2019s talent management task force. \u201cIn the experiment with the heads-up display, we gave them just one or two pieces of data, and the soldiers came back and said, \u2018I don\u2019t just want one or two, I want 10 or 15 different data feeds coming in.\u2019\u201d<\/span><\/p>\n It\u2019s also important to note that the Army is not talking about the AI warning the soldier who\u2019s actually wearing it. That may seem counterintuitive, but given how the human eye and brain are still much more accurate than computerized image recognition, the wearer would probably have already seen anything the IVAS detected.<\/span><\/p>\n How ready is this technology? \u201cWe\u2019re already doing it. It\u2019s here,\u201d Donahue said. \u201cNow, how you build the infrastructure back behind to do that, we\u2019re not there.\u201d Presumably Donahue\u2019s referring to the wireless networking<\/a> to transmit the targeting data to artillery batteries<\/a> many miles away. He might also be referring to the extensive database of images required to teach AI<\/a> to recognize a tank or anti-aircraft missile.<\/span><\/p>\n![\"\"](\"https:\/\/www.onisilos.gr\/wp-content\/uploads\/2018\/12\/ENVG-Rapid-Target-Acquisition-1024x576.jpg\")
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