Tianyi Tian

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In 2017, U.S. Deputy Secretary of Defense Shanahan stated that he wanted “built-in AI capability” in all future deployable defense systems [1]. Recently, the Pentagon further decided to almost triple the spend on military drones to $7 billion for FY2019 [2]. This development is unsettling because it implies that even warfare – where judgement on ethics and morality lead to life and death decisions – is being outsourced to machines.

The reason for drone deployment is obvious. Drones have long surpassed manned aircrafts in terms of range, endurance, safety, and cost efficiency, unlocking more difficult missions with fewer or no pilot lives at stake [3]. But AI-enhanced drones may enable completely new tactics. Apart from supportive roles like signal jamming, targeting, or refueling, autonomous drones can also be deployed in swarms [4] thanks to their ability to process higher level commands. Ground crews no longer have to actively fly the aircrafts. Instead, they can focus on the target environment and develop suitable ad-hoc combat plans. At least, that’s the vision.

So far, technology isn’t quite there yet. Drones actually cannot “see” very well because they need to keep a great safety distance to maintain stealth. To improve drone imagery, machine learning is introduced to make drones recognize individual targets based on images from previously gathered intel. But still, locking on specific persons is nothing more than an educated guess, given that even the most advanced drone cameras simply do not allow a resolution high enough for facial recognition [5]. Further, variable combat situations make it very difficult to develop satisfactory validation systems to test a drone’s reaction in all possible scenarios [4].

In my view, trusting autonomous drones to operate reliably in combat is like relying on half-blind pilots with unpredictable temper. It is also questionable if drones can ever reach the necessary reliability to be deployed hands-free. While they do have the potential to facilitate decision making by quickly mining through huge amounts of intel, the final call still has to be made by human operators to avoid uncertainty-based collateral damage.

To lighten up a bit, here is a rather satirical video comment on what conventional drones are capable of:

Sources:

[1] https://www.nbcnews.com/news/military/google-halt-controversial-project-aiding-pentagon-drones-n879471

[2] https://www.c4isrnet.com/unmanned/2018/07/05/the-pentagons-latest-budget-is-its-largest-counter-drone-budget-ever/

[3] https://www.goldmansachs.com/insights/technology-driving-innovation/drones/

[4] https://www.nato.int/docu/review/2017/also-in-2017/autonomous-military-drones-no-longer-science-fiction/EN/index.htm

[5] http://www.extremetech.com/extreme/146909-darpa-shows-off-1-8-gigapixel-surveillance-drone-can-spot-a-terrorist-from-20000-feet

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Artificial Intelligence (AI) has already pervaded many industries. In recent years, a few AI use cases also caught the attention of the creative industry: Microsoft’s Chinese chatbot XiaoIce became capable of generating decent image-inspired poems, Sony started dishing out pop songs using its AI Lab’s FlowComposer, and 20^thCentury Fox asked IBM Watson to create a trailer for the horror movie “Morgan”. Now one has to wonder: Is the creative human mind still needed?

To answer this question, we need to look behind the scenes. For AIs to become “creative”, they have to be trained on relevant datasets first to understand what to read out of future inputs. In our examples, thousands of existing image-poem pairs were used for XiaoIce to learn how to find poetic clues in images; hundreds of songs of the same genre were fed into FlowComposer to make it adapt to different music styles; and Watson was forced to “watch” tons of horror movies to understand which scenes from “Morgan” may be useful for the trailer. Except for the poems, both the songs and the trailer actually also required extensive manual arrangement before release.

The results in all three categories are definitely remarkable for their technological achievement, but not as satisfying when compared to pure human creations. The poems, despite having passed the Turing test, tend to be more descriptive and bland rather than emotional or meaningful. The songs do show some typical characteristics of the respective genres, but are not very catchy due to the lack of recognizable motives. As for the trailer, it summarized the movie in an almost chaotic way, because the AI was trained to focus on salient emotions instead of the plot.

As we can see, AIs nowadays still miss a human touch when it comes to creating original content. And it is questionable if they will ever obtain real creativity since their outputs heavily depend on the datasets they were trained on. Yet, their ability to extract patterns from vast amounts of materials may help human creators see and break artistic boundaries to set new standards – something the creative industry urgently needs, and always should strive for.

Sources:

https://thenextweb.com/artificial-intelligence/2018/08/10/microsofts-ai-can-convert-images-into-chinese-poetry/

Click to access 1804.08473.pdf

https://www.scientificamerican.com/article/a-compendium-of-ai-composed-pop-songs/

https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-creativity.html

The Role of Autonomous Drones in Future Warfare

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The blurring thin line between artificial and human creativity

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