Kill Decision (20 page)

McKinney drew a series of dots on the board. “Given a list of cities”—she started connecting the dots with a single traveling line—“how do you find the shortest possible route that visits each city only once?” Her on-board solution quickly failed to do so, and she looked up. “Sounds simple, but it’s not; it’s what’s known as a nondeterministic polynomial-time hard problem—meaning it’s very difficult for humans to achieve. Ants solve this problem routinely. They will always find the shortest possible route to a food source, and as experiments using the Towers of Hanoi Problem set show, if that path is obstructed, they can adapt and find the next shortest route. And so on. They do all this without centralized control and without conscious intent.

“In many ways, individual ants are similar to individual neurons in the human brain. The fact that individual ants—let’s call them
agents
—follow fairly predictable behaviors, means that
metaheuristics
can simulate their actions with considerable accuracy.”

Snowcap held up her hand. “A metaheuristic is . . . ?”

“It’s an iterative computation method designed to improve a candidate solution. It’s a form of genetic or evolutionary programming. For example, here’s a basic ant algorithm for detecting the edges of pheromone trails. It was developed way back in 1992 by Marco Dorigo. . . .” She started scrawling on the board.

McKinney pointed at the formula. “An ant is a simple computational agent that iteratively constructs a solution for the problem at hand. At each iteration, each individual ant moves from a state
x
to state
y,
which represents a more complete intermediate solution. Thus, for each ant”—she pointed at the formula—“
k
, the probability
of moving from state
x
to state
y
depends on the combination of two values—namely the attractiveness
ηxy
of the move, as computed by some heuristic indicating a priori desirability of that move, and the trail level τ
xy
of the move, indicating how beneficial it has been in the past to make that particular move.”

Odin grimaced. “I think we might be getting too deep in the weeds here, Professor. How does your model function?”

McKinney nodded and erased the algorithm. “Right. Sorry. Just wanted to lay a foundation.”

“You can put the gory details up on the wiki.”

“Now, my work in particular . . .” McKinney thought for a moment, and then wrote two Latin names on the board. “
Oecophylla longinoda
and
Oecophylla smaragdina
—two closely related arboreal ant species that dominate the tropical forests of Africa, Asia, and Australia—otherwise known as the weaver ant due to their practice of weaving leaf nests with larval silk. They’re of the order”—she wrote on the board again with her clear, Arialesque print—“Hymenoptera, which includes bees and wasps. Weaver ants are what’s known as a eusocial insect, meaning they exhibit the highest level of social organization in nature.

“I developed Myrmidon, my weaver computer model, based on years of direct field observations.” McKinney paced before the board. “Unlike most ant species, weaver ants are fiercely territorial. They attack any intruders into their domain—no matter what the odds. Climb into a weaver tree, and you will be attacked. They swarm enemies with suicidal disregard. That strategy is not evolutionarily problematic because, as with many colony insects, weaver workers don’t reproduce—only the queens pass on their genetic material. Thus, workers always fight to the death—the colony is their legacy.

“A single weaver colony might span dozens of trees and include hundreds of nests built throughout their territory in an integrated network. From here they launch attacks, raise young, and care for livestock, other insects that they raise for nectar.”

The team members looked surprised at this last part.

McKinney drew another series of points similar to the Traveling Salesman Problem and started connecting them. “Weavers maintain a flexible network of routes between their population centers. And unlike most ants, they have excellent vision. They also have better memories than regimented species such as army ants. Individual weaver ants can accrue ‘experience’ which informs later actions.”

Expert Five piped in. “So they’re like a neural network.”

McKinney nodded. “Precisely. Weavers process experience via
mushroom bodies. . . .”
She drew the outline of an ant’s head, inside of which she drew several large blobs. The largest, occupying the bottom center, she shaded in. “These are brain structures found in almost all insects, and they manage context-dependent learning and memory processes. Their size correlates with the degree of a species’ level of social organization. The larger the mushroom body in the brain, the more socially organized an insect society is. As we’d expect, weaver ants have an unusually large mushroom body, which endows weaver workers with above average memory.

“That memory sharpens the iterative component of weaver swarming intelligence. Because swarming intelligence is all about data exchange. What we call”—she wrote a word on the board—“
stigmergy
. Stigmergy is where individual parts of a system communicate indirectly by modifying the local environment. In the case of weaver ants, they exchange data mostly through pheromones.” She started drawing lines that represented ant paths. “If they encounter a source of food or an enemy, they return to the nearest nest, all the while laying down a specific mix of chemical pheromones in a trail that communicates both what they’ve encountered—food or threat—and the degree to which they encountered it—lots of food or a big threat. Half a million individual agents moving about simultaneously doing this creates a network of these trails, known as the colony’s
pheromone matrix,
holding dozens of different encoded messages. This matrix fades over time, which means it represents in effect the colony’s current knowledge. As weavers encounter these trails, they’re recruited to address whatever message the trail communicates—for example, to harvest food or fight intruders. As they move along the trail, they reinforce the chemical message—sort of like upvoting something on Reddit or ‘Liking’ someone’s Facebook status. As that pheromone message gets stronger, it recruits still more workers to the cause, and soon, clusters of ants begin to form at the site of the threat or opportunity.”

Expert Two, the blond man, nodded. “Meaning it goes viral.”

McKinney nodded. “Basically, yes. In this way, weavers manage everything from nest building, food collection, colony defense, and so on. At each iteration of their activity, each ant builds a solution by applying a constructive procedure that uses the common memory of the colony—that is, the
pheromone matrix
. So, although individual weaver ants have very little processing power, collectively they perform complex management feats.”

McKinney dropped the marker in the tray at the base of the whiteboard. “In fact, if I were going to create an autonomous drone—and I had no ethical constraints—swarming intelligence would be a logical choice. Lots of simple computational agents reacting to each other via stigmergic processes. That’s why weaver ants don’t need a large brain to solve complex puzzles. They can solve problems because they can afford to try every solution at random until they discover one that works. A creature with a single body can’t do that. A mistake could mean biological death. But the death of hundreds of workers to a colony numbering in the hundreds of thousands is irrelevant. In fact, the colony is the real organism, not the individual.”

Expert Five interjected, “Then we would expect swarming drones to be cheap and disposable.”

McKinney nodded. “And individually, not very smart. The demise of one or dozens or hundreds might not mean the demise of the group—and the survivors would be informed by the experience of swarm mates around them.”

Singleton scribbled on his notepad. “You sound intrigued by the possibilities.”

“I find weaver ants fascinating. But I wouldn’t want to meet them scaled up in size and given weapons. It would be an insanely foolish thing to build.”

Odin stood up again. “Thank you, Professor.”

Singleton cleared his throat. “All of this would be incredibly useful information if we were facing hundreds of thousands of swarming robots—which we are not. Can we get down to business now?”

Odin just stared at Singleton for a moment. “Now that we know what risks we might face from swarming intelligence, let’s review recent operations.” Odin turned to the African-American scientist at the far end of the table. “Four, tell me what you learned from the Tanzanian video.”

The man put on glasses and started examining his laptop screen. “Pretty amazing to finally see one of these things flying, Odin. Your hunch about the target was dead on.” He glanced up at McKinney. “No offense intended, Six.”

“None taken.”

He tapped a combination of keys, and what was on his screen moved to a larger flat-screen monitor hanging on the wall where everyone could see it. It was the black-and-white FLIR footage of the drone that had attacked McKinney in Africa.

“From what we can tell, Odin, this isn’t an extant design.” He pointed at various features with a laser pointer. “Forward canards. Midsection dome. Slightly swept wing. What we’re looking at here is a Frankenstein machine—something put together from all sorts of different drone designs.”

“What’s the prognosis for recovering wreckage?”

“In the Amani jungle reserve? Approaching zero. Whole armies have disappeared in there.”

“What about its radar track? Where did it come from?”

“Came on radar off the east coast of Africa, near Zanzibar.”

“HUMINT?”

“CIA’s got some local stringers asking around, but that’s gonna take time. Could be weeks till we hear anything.”

“What about ships in the area?”

“There were dozens of ships and small craft. It’s near a major African port, but there were no satellite assets overhead at the time.”

The Korean scientist nodded. “The enemy’s probably monitoring orbit schedules.”

“Okay, so even though we were in the right time and place, we still have no idea where these things are originating.”

The African-American scientist nodded sadly. “It’ll be worse here in the States. The drones mix in with domestic air traffic—small private planes. There are thousands of unregistered private airstrips—runways on ranches and commercial and private lands that aren’t attended by flight controllers or anyone else. Radar echoes alone aren’t going to identify these things, and since they are remotely controlled we can’t listen for unique radio signatures.”

The Korean scientist nodded. “None of them have been picked up by DEA drones or coastal radar, so they might be being built and launched domestically. But with just two dozen attacks over three months, we don’t have much data to work with. There’s too much terrain to cover.”

The blond scientist added with a slight Germanic accent, “Without an intact specimen—”

The Korean scientist next to him shook his head vigorously. “The moment we try to grab it, it’ll explode in our faces—making it next to impossible to determine who built it, how it operates, and how to defend against it.”

Odin glanced down at the notes on his pad of paper and crossed an item off a list. “That’s being handled, Two. Next comes target prediction. Where are we?”

The Japanese researcher shook his head. “Nowhere, Odin. We’ve run the previous bombing victims through tens of thousands of link analysis filters, searching for any recognizable pattern or connection, but there’s nothing. A human rights activist, a financier, oil company executives . . .” He threw up his hands. “None of them knew each other or had interactions of any type. They didn’t work for the same companies or even in the same industry. They had no common financial interests, enemies, religious or political affiliations, social interests. Exchanged no communications. Not all of them were American, and on paper some of them would have been political adversaries—for example, the human rights activist in Chicago and the private prison lobbyist in Houston. Or the financial journalists killed in the New York café bombing or the retired East German Communist party boss living in Queens.”