SL. How would you describe the world of warfare? When we try to describe a system, we have
different parameters, we need to talk about domains, dimensions, rulers, and protractors
or sensors, players, laws. So what are all these aspects? How do you accurately model
this world? What do you need to take into account to make some good model of the world
of warfare? TG. So let me let me pull two threads here.
The first one is sort of, I’ll say, a bounding framework. There’s something that’s a
sort of a driving trend right now within the US DoD. It’s got a couple of different names,
but for purposes of this discussion we’ll use one of the names that goes under Joint
All Domain Operations (JADO) or Joint All Domain Command and Control (JADC2). So we’ll
just call it JADO. The key thing there is All Domain. So let’s
talk a little bit about what a domain is. Historically, you got an army that fights
on the land, you got an air force that fights in the air, and you got a Navy that fights
at sea. We talked about those physical domains. There’s been a lot of press over the past
year. We just passed the first anniversary of the US Space Force.[26] So that’s an
acknowledgement that space is a domain. You know, people talk a lot about cyber or the
electromagnetic spectrum. Those are all domains. I’ve even heard of a domain referenced before
when we start talking about things like information, I’ll call it the cognitive domain. You know,
people refer to hybrid warfare or gray zone warfare, hearts and minds, things of that
nature. All of those are different domains. One of the big trends right now is a realization
that you really put yourself at a disadvantage if you look at only one domain, or even if
you look at multiple domains, but each individually, because the reality is that it is all very
interdependent and codependent. The second big factor that ends up popping
up, and they’re both directly connected, and the punchline here, I want to come back
to the question of complexity, and dimensionality and all this, the other one is speed.
SL. Yeah, time. TG. Yep, Time. One of the terms that we’ve
been starting to use around DARPA, I’ll give credit to our Director for inspiring
this term, Victoria Coleman, we’re calling it Time Compression. It’s “How can we
make time speed up?” essentially. In simplistic terms, “How can we do things faster?”
There is this traditional determinism, particularly within DoD that likes to say “Okay, I want
to study things and try to forecast what the future is going to be and in the process of
doing that forecasting, come up with every possible contingency”. Then I’m going
to go build one heck of a powerful system, that either has a high enough performance
level or is adaptable enough, that is going to address every single one of those contingencies.
The reality is [that] we are now in such a complex multidimensional world, that all posit
that this forecast model doesn’t carry. So instead, we have to be in a mode of rapid
responsiveness. How could I acknowledge, not just assume, how can I acknowledge that I
can’t predict the future with any degree of accuracy, and then there are going to be
contingencies that occur, that I just haven’t forecasted? How can I be in a rapidly responsive
mode? That’s time compression. So a lot of what we’re looking at right now is, both
of those big themes pulled together, how can we be all domain and recognize all the interdependencies
of this very high dimensionality space? How at the same time, can we do it with incredible
speed? This is the notion of time compression. I think there’s an interesting aside. I
love this story because it shines a light on this issue of time compression and it’s
about toilet paper. This was inspired by an article I read in Fortune magazine back early
in the pandemic.[27] They were talking about the shock to the supply chain for consumer
goods, whenever we had the run on stores and the shortage of toilet paper. They said [that]
if you look in the business world, particularly in manufacturing, they’re also a very forecast-centric
type of world, they do a great job of data analytics. A company that makes the things
will forecast out almost, I don’t know how many decimal places, what they think their
sales are going to be. It’s all about managing that inventory, its efficiency. One of the
things they said in this article is that one of the reasons the shelves ended up empty
of toilet paper, is they were already running at something like ~93% capacity, just so they
were making sure they had no inefficiencies. That’s great from an efficiency and effectiveness
standpoint, but it leaves no resilience. That’s a very brittle system. All of a sudden, there’s
this contingency disruption of a pandemic and people doing a run on stores. It doesn’t
have the latitude to respond to that. You think, okay, I’ll take a legacy forecast
approach and says, well, I need to open up my error bars, I need to account for [the
full range of possible demands]. Well, that’s not practical because that would say “Okay,
I need to have three times the quantity of milling machines”. Well, if the typical
toilet paper manufacturer went and bought three milling machines for everyone they have
the day, they would go out of business. They can’t afford that level over provisioning.
So the answer is, what are things I can do that can be rapidly adapted? How can I measure
disruption? What are the knobs I can turn to get to good enough? It’s not going to
be optimal. But how can I get to good enough in a rapidly responsive manner? That’s what
I see is the big trend going forward. That’s certainly where my organization has been focused.
SL. I think the problem with toilet paper was that toilet paper takes space. When people
buy, let’s say, five or six packs, the next customer will see an empty shelf. So they
will think they’re running out of toilet paper. So you could think about compressing
it even more, so that it looks small, and you can put more and more so that the shelf
doesn’t get empty. TG. That’s it, you hit the nail on the head,
in this article they talk about that. And that’s exactly what one of these soft knobs
is. They couldn’t go whip up a new milling machine in a day. But what they could do is
reprogram their production line, so that they could start packaging, you know, packages,
smaller package and... SL. Vacuum packed toilet paper.
TG. Not exactly vacuum packed but doing their packaging in smaller bundles with fewer roles
per package. Yeah, it’s the same idea. There were other things too, like, some of the shortages
were caused by disruptions of the supply chain. So can I have what amounts to a vendor radar
for new sources of pulp, and a new process for vetting and validating those providers
in a faster manner? So it’s a great example of taking a very
system architecture level thinking of these complex problems, as opposed to the obvious
solution, which is build more milling machines. It may be the obvious solution but it is not
very effective on the long run. SL. I was reading about AlphaGo and all these
AI systems, where they try to build super expert systems that can fight or play against
humans. But when you model a game, for example, Chess, the sort of game tree complexity is
10120, or if it’s Go is 10700. But when I think about the war game that must be like
1000s of orders of magnitude more complex than Chess and Go. So what are the approaches
to achieve an acceptable level of modeling? Something that works, but that is not too
complex. Because in theory, if you want to model things, you could start from the wavefunction
and the Schrödinger equation. That’s the most ridiculous thing that someone can do.
So what’s, what’s the sort of modeling that you guys do?
TG. Yeah, no, that’s a great, great question and something I personally think a lot about.
I’ll even toss it out to [a challenge to] your viewers, if anyone has any ideas on how
to do this kind of framework analysis in a quantitative way, I’d be interested in hearing
your ideas and, who knows, maybe you get a project out of it.
The fundamental way we’re looking at it is managing, and in some ways this is borrowed
straight out of network theory, it’s looking at how do we manage complexity and dimensionality
by breaking things apart into scale. If we think about some of the dimensions you mentioned,
those are consistent with some of the [dimensionality] we see at the decision support level [just]
inside an individual platform or a payload. One of the things I think we’ll probably
end up talking a little bit more about is our AlphaDogfight, you know, AI flying an
aircraft. We have [another program with] an AI controlling a sensor payload. Those kinds
of decision processes are on the order of what you’re saying, maybe, you know, 10200
or so. Now imagine that platform or that payload is part of one mission unit and that, in turn
is part of full squadrons or forces and then I’m talking All Domain and other types of
things. Each of those is taking about that same dimensionality and now growing that,
geometrically. People who think that they can take that kind
of endeavor and, as one of my bosses likes to say, sprinkle some AI pixie dust on it
and just assume that some algorithmic approach is going to discover a way to manage that
level of dimensionality, is just really not practical.
Instead, we say, “How can we break up those decision layers?” I’ll think of it in
terms of decisions that have to be made, “How can we partition those into a manageable degree
of complexity, or a manageable dimensionality and then still create optionality by being
able to abstract those interfaces in those boundaries?”
It’s really about partitioning and abstraction. If you look about the original inspiration
for this, that this was before my time as office director, but a number of years ago,
maybe 2015-2016, DARPA sponsored a conference called “Wait, What?”[28] And one of the
speakers there was a chaired fellow or chaired professor from UC Berkeley [Prof. Alberto
Luigi Sangiovanni-Vincentelli]. He was one of the creators of the original design tools
for semiconductors and he really presented this notion better than anyone I’ve ever
seen. It’s intrinsic, you mentioned knowing the wave function, well, you know, at one
level, if you want to do a really good semiconductor, it would be great to know the wave function
of every [individual transistor or gate, in an IC, but as you said, that would be totally
impractical with the number of gates in today’s devices.]
SL. Do you need that? Probably not. TG. Yeah, so we’ve got Moore’s Law,[29]
because of this notion of being able to manage scale by partitioning and abstraction. Then
once you’ve abstracted things, [you can build back up something big and complex by]
being able to do composition, with those abstract elements, and that’s [the same way] we’re
approaching this all domain warfare challenge [and addressing the dimensionality].
We’re living right now in a very challenging, but also interesting intersection between
technology and culture. Because to live with that model of abstraction and composition
drives a certain amount of acceptance of uncertainty. You know, I don’t know what’s happening
underneath, underneath the abstracted boundary of that next module but, trust me, it’s
going to satisfy some function. [This is the uncertainty a modern IC designer needs to
live with.] Well, usually, if you’re talking to people
in the military, they don’t like an answer to just “Trust me”. You know, they want
determinism. They want a certain number of decimal places of certitude in it. But I argue
that that is committing the statistics 101 fallacy of mistaking precision for accuracy.
We’re living in such a complex, dynamic world, that we have to be able to, back to
the toilet paper, live with disruption, live with uncertainty. The only way you do that
is by, you know, taking a more stochastic kind of model toward things.