
Artificial Intelligence versus Mission Command - ChanderG
http://defenceindepth.co/2015/11/25/artificial-intelligence-versus-mission-command/
======
boothead
Commanders already have ample opportunity to micro manage. As Stan McChrystal
mentions in his book Team of Teams, he was often watching real time video and
had real time comms with troops on the ground carrying out ops. He resisted
the desire to interject, citing a desire to push as many decisions to the
edges of the network.

I don't see AI (at least in it's current form) in a position to make strategic
decisions. I see AI increasing the fidelity of and extracting patterns from
information flowing through the battle space (or boardroom). So I see the
greatest contribution that AI can make at the moment in the OO (Observe,
Orient - what do I see and what does it mean) of OODA, with the Decide and Act
still firmly the remit of humans.

~~~
Simorgh
I am quite curious about the OODA loop. I understand that the concept was
pioneered by John Boyd and was actually borrowed by Steve Blank (who cited it
in developing the 'customer development' approach to starting a start-up).

How central is the OODA loop to military methodologies?

I ask because in the biography of John Boyd it was portrayed as being somewhat
ignored by the Air Force (but foundational to the Marines).

~~~
KineticLensman
Many military processes use decision cycles that are similar to OODA. An
example is the intelligence cycle [1] whose steps include Direct - Collect -
Process - Disseminate. Similarly, the targeting cycle [2]. These cycles are
integrated in an HQ to support the commander's decision making. So OODA itself
might not be obvious to an individual soldier, but the orders and reporting
that drives his activities will follow from cyclic thinking in the HQ.

[1]
[https://en.wikipedia.org/wiki/Intelligence_cycle](https://en.wikipedia.org/wiki/Intelligence_cycle)
[2]
[https://en.wikipedia.org/wiki/Targeting_%28warfare%29](https://en.wikipedia.org/wiki/Targeting_%28warfare%29)

------
denniskane
> Let's suppose our AI must chose, in a flurry of combat, between sacrificing
> the life of a friendly soldier, or killing two other people, likely to be
> civilians.

This question of programmatically determining "friend" vs "foe" is highly
problematic, to say the least. The only reason why humans make such
distinctions is because they rely on them in order to ensure their own
physical survival, so they can successfully propagate the species.

In order for a lifeless machine to make these kinds of distinctions, there
must exist some kind of objectively verifiable calculation procedure that
decides what exactly makes another human friendly or not. If this calculation
procedure is simply meant to mimic the subjective calculations that are made
by human military strategists, then this technology could not properly be
considered the kind of interesting problem that AI researchers would want to
work on. But if it is indeed meant to be objectively valid, then it will
surely need to initiate a deep learning function that can very easily come up
with a conclusion that finally determines that the so-called "friend" as
determined by the human military strategist is actually a foe that needs to be
eliminated.

So I think that the entire concept of developing highly sophisticated
autonomous agents is inextricably wound up in the interesting, objective
question of what it truly means to be human, rather than the more prosaic,
subjective question of what it means to be a certain type of human that
happens to judge another type of human as friend or foe.

