Machine Learning and Black Boxes

What can Artificial Intelligence achieve today and what will be delivered in the next decade or so$?$

The answer is further development of Machine Learning $($ML$)$ or$,$ as it is sometimes termed, Narrow

Artificial Intelligence. ML is already present in a high proportion of applications in today$$s world, using

the capabilities of Autonomy in finding suitable data, analysing this data to output information, and

sensing and controlling platforms $$ all while operating at a level of cognitive ability far beyond the

capabilities of even the most capable human.

However, there is a catch. Humans are spontaneous, conscious and think with bias. The answer to

most complex questions and the inputs that create the sequence of a decision are somewhat

experience driven $$ and not just a trained response to a given situation. For the human, relevant

training sets the sequence of reaction, however, the deeper subconscious also has part to play to

decision$-$making and in high stress situations this subconscious Threat or Reward $($Fight or Flight$)$

reaction is often the driving force behind a decision.

This could be viewed as a potentially limiting factor of ML$,$ although this characteristic could also be

viewed as a positive as emotional bias is removed from the decision$-$making process. The caveat, of

course, is that this is a learned reaction in the case of ML and only time will tell if ML will develop a

pseudo personality. Potentially this could be caused by a bias introduced into the coding of the

original algorithm that a human has inadvertently added to the mix during initial production or the

addition of biased training data.

Perceived limitations aside, ML is changing the way we think, the way we procure, the way we

support and the way we deliver in all aspects of life, and therefore, in all aspects of air and space

power. The UK is not the only nation to have spotted this as both a threat and opportunity. Friendly

and potential adversaries, both state and non$-$state are all pressing forwards with pace to achieve AI

$($ML$)$ delivered capability.

More disconcerting for nation states is you do not need an industrial base in traditional air and space

to engage in the technology which is allowing non state actors to gain an effective foothold in this

game. Once obtained, ML driven autonomy has the potential for parasitic fit to extant platforms and

solutions to enhance and weaponise capabilities from previously designed benign platforms and

systems. And off the shelf solutions using ML can support military capability needs, such as

commercially available drones or augmented reality training packages.

Perceived limitations aside, ML is changing the way we think, the way we procure, the way we

support and the way we deliver in all aspects of life, and therefore, in all aspects of air and space

power. The UK is not the only nation to have spotted this as both a threat and opportunity. Friendly

and potential adversaries, both state and non$-$state are all pressing forwards with pace to achieve AI

$($ML$)$ delivered capability.

More disconcerting for nation states is you do not need an industrial base in traditional air and space

to engage in the technology which is allowing non state actors to gain an effective foothold in this

game. Once obtained, ML driven autonomy has the potential for parasitic fit to extant platforms and

solutions to enhance and weaponise capabilities from previously designed benign platforms and

systems. And off the shelf solutions using ML can support military capability needs, such as

commercially available drones or augmented reality training packages.

Interestingly, the Black Box approach, while less expensive and readily available, is not considered

suitable for high impact and more trust challenged uses of AI$.$ Clearly, this is particularly relevant if

and when commanders and leaders are called upon to defend a decision or action that was taken by

an AI enabled capability.

Once understood the capability that can be derived from black box solutions could also be thought of

as more secure$,$ as the algorithm sits beyond reach of human influence, however, but recent

developments have shown that Black Box algorithm can be interfered with through the manipulation

of inputs, which can then of course affect the outputs.

QUESTION ONE $[40]$

$1\mathrm{.}1$ From the article, identify the ways that the human decision$-$making process differs

from that of an AI$?$

$5$

$1\mathrm{.}2$ Describe the concerns are raised by the speedy incorporation of AI into army

equipment.

$10$

$1\mathrm{.}3$ Using your own research, elaborate on how the machine$-$learning community

proposed to mitigate the Black$-$Box problem.