Out of Control

Key Takeaways

Six threads recur across the marginalia, and they read less like notes on a 1994 book than like a running argument with 2026.

The Ego versus the Hive. The densest thread by far — roughly a dozen notes read Kelly through Pluribus. “PLURIBUS IN A PARAGRAPH” marks the passage where suspension of the biological ego registers as an advantage for rapid evolution rather than a loss of self, and “Pluribus = maximum democracy” pushes the same idea toward governance. The open question posed against it: how does representative democracy work for nine billion people, and is cultural normativity the cheaper coordination mechanism?
Emergence read theologically. Kelly’s distributed-systems vocabulary gets mapped onto LDS cosmology throughout — “Let US make man in OUR image” against collective intelligence, “Gods in embryo,” “one eternal round,” and “matter can be neither created nor destroyed… organized intelligences.” The recurring provocation is whether eternal change differs from entropy, and where nature shows evidence of creation ex nihilo.
Information and coordination, not raw power. “Information / coordination are the magic of automation, not just raw power.” Cybernetics is glossed as the mechanism for communication, persuasion, and understanding rather than control — and the observation that we have already automated vast swaths of labor through millions of small purpose-built automatons rather than one general one.
A 1994 text read against current AI. “Sounds like an LLM to me,” “connectionism = neural nets; important new concept,” “making sand think,” and Sam Altman’s “intelligence is a fundamental property of matter.” Kelly’s neural-net and swarm passages are treated as having called the shot three decades early.
Prediction, and its limits. Psychohistory is tagged directly to Historical Futurism — “feels somewhat similar to my idea of Historical Futurism” — alongside the Lindy effect, prediction markets, and “history rhymes.” The counterweight is supplied in the same breath: how does this compare to the worldview of The Black Swan and mediocristan?
Against the de-growth frame. Two notes push back on treating humanity as separate from or harmful to nature — that people carry “a mental framework of humanity hurting the planet but they don’t appreciate its natural role within it.” Paired with the Manhattan Institute framing that life is possible because the planet walks a razor’s edge of compatible components.

Connections

The Black Swan — the marginalia explicitly set Kelly’s confidence in emergent prediction against Taleb’s mediocristan/extremistan split; psychohistory works only where the distribution is thin-tailed.
Stories of Your Life and Others — “Connect to Arrival” appears against Kelly’s passages on perception and observation; both treat cognition as inseparable from the observer’s frame.
Foundation — psychohistory, the Cosmic AC, and “The Last Question” recur through the notes as the fictional companion to Kelly’s swarm-prediction argument.

Highlights

The realm of the born—all that is nature—and the realm of the made—all that is humanly constructed—are becoming one. Machines are becoming biological and the biological is becoming engineered.
This book is about the marriage of the born and the made. By extracting the logical principle of both life and machines, and applying each to the task of building extremely complex systems, technicians are conjuring up contraptions that are at once both made and alive. This marriage between life and machines is one of convenience, because, in part, it has been forced by our current technical limitations.
Truly complex systems such as a cell, a meadow, an economy, or a brain (natural or artificial) require a rigorous nontechnological logic. We now see that no logic except bio-logic can assemble a thinking device, or even a workable system of any magnitude.
It’s eerie how much of life can be transferred. So far, some of the traits of the living that have successfully been transported to mechanical systems are: self-replication, self-governance, limited self-repair, mild evolution, and partial learning.

Kyle: Von Neumann’s automata
Yet at the same time that the logic of Bios is being imported into machines, the logic of Technos is being imported into life.
The meanings of “mechanical” and “life” are both stretching until all complicated things can be perceived as machines, and all self-sustaining machines can be perceived as alive. Yet beyond semantics, two concrete trends are happening: (1) Human-made things are behaving more lifelike, and (2) Life is becoming more engineered.
What should we call that common soul between the organic communities we know of as organisms and ecologies, and their manufactured counterparts of robots, corporations, economies, and computer circuits? I call those examples, both made and born, “vivisysterns” for the lifelikeness each kind of system holds.
I report on new experimental work in ecosystem assembly, restoration biology, coral reef replicas, social insects (bees and ants), and complex closed systems such as the Biosphere 2 project in Arizona, from wherein I write this prologue.
From these particular big systems I have appropriated unifying principles for all large vivisystems; I call them the laws of god, and they are the fundamentals shared by all self-sustaining, self-improving systems.
Nature is also a “meme bank,” an idea factory. Vital, postindustrial paradigms are hidden in every jungly ant hill.
The aggregate capacity of millions of biological machines may someday match our own skill of innovation. Ours may always be a flashy type of creativity, but there is something to be said for a slow, wide creativity of many dim parts working ceaselessly.
The world of the made will soon be like the world of the born: autonomous, adaptable, and creative but, consequently, out of our control. I think that’s a great bargain.
“Where is ‘this spirit of the hive’ … where does it reside?” asks the author Maurice Maeterlinck as early as 1901. “What is it that governs here, that issues orders, foresees the future…?” We are certain now it is not the queen bee. When a swarm pours itself out through the front slot of the hive, the queen bee can only follow.
“The hive chooses,” is the disarming answer of William Morton Wheeler, a natural philosopher and entomologist of the old school, who founded the field of social insects. Writing in a bombshell of an essay in 1911 (“The Ant Colony as an Organism” in the Journal of Morphology), Wheeler claimed that an insect colony was not merely the analog of an organism, it is indeed an organism, in every important and scientific sense of the word. He wrote: “Like a cell or the person, it behaves as a unitary whole, maintaining its identity in space, resisting dissolution … neither a thing nor a concept, but a continual flux or process.” It was a mob of 20,000 united into oneness.
But group mind seems to be a liability in the decisive moments of touchdown, where there is no room for averages. As the 5,000 conference participants begin to take down their plane for landing, the hush in the hall is ended by abrupt shouts and urgent commands. The auditorium becomes a gigantic cockpit in crisis. “Green, green, green!” one faction shouts. “More red!” a moment later from the crowd. “Red, red! REEEEED!” The plane is pitching to the left in a sickening way. It is obvious that it will miss the landing strip and arrive wing first. Unlike Pong, the flight simulator entails long delays in feedback from lever to effect, from the moment you tap the aileron to the moment it banks. The latent signals confuse the group mind.
How did they turn around? Nobody decided whether to turn left or right, or even to turn at all. Nobody was in charge. But as if of one mind, the plane banks and turns wide.
“Flockness” emerges from creatures completely oblivious of their collective shape, size, or alignment. A flocking bird is blind to the grace and cohesiveness of a flock in flight.
In the 17th century, an anonymous poet wrote: “… and the thousands of fishes moved as a huge beast, piercing the water. They appeared united, inexorably bound to a common fate. How comes this unity?”
The flock is more than the sum of the birds.
In the film Batman Returns a horde of large black bats swarmed through flooded tunnels into downtown Gotham. The bats were computer generated. A single bat was created and given leeway to automatically flap its wings. The one bat was copied by the dozens until the animators had a mob. Then each bat was instructed to move about on its own on the screen following only a few simple rules encoded into an algorithm: don’t bump into another bat, keep up with your neighbors, and don’t stray too far away. When the algorithmic bats were run, they flocked like real bats. The flocking rules were discovered by Craig Reynolds, a computer scientist working at Symbolics, a graphics hardware manufacturer.
So realistic is the flocking of Reynolds’s simple algorithms that biologists have gone back to their hi-speed films and concluded that the flocking behavior of real birds and fish must emerge from a similar set of simple rules. A flock was once thought to be a decisive sign of life, some noble formation only life could achieve. Via Reynolds’s algorithm it is now seen as an adaptive trick suitable for any distributed vivisystem, organic or made.
WHEELER, the ant pioneer, started calling the bustling cooperation of an insect colony a “superorganism” to clearly distinguish it from the metaphorical use of “organism.”
We would argue now that it is the complexity of our brains that extracts music from notes, since we presume oak trees can’t hear Bach. Yet “Bachness”—all that invades us when we hear Bach—is an appropriately poetic image of how a meaningful pattern emerges from musical notes and generic information.
The marvel of “hive mind” is that no one is in control, and yet an invisible hand governs, a hand that emerges from very dumb members.
This is a universal law of vivisystems: higher-level complexities cannot be inferred by lower-level existences. Nothing—no computer or mind, no means of mathematics, physics, or philosophy—can unravel the emergent pattern dissolved in the parts without actually playing it out. Only playing out a hive will tell you if a colony is immixed in a bee. The theorists put it this way: running a system is the quickest, shortest, and only sure method to discern emergent structures latent in it. There are no shortcuts to actually “expressing” a convoluted, nonlinear equation to discover what it does. Too much of its behavior is packed away.
The system by which knowledge is sequestered in our brain became more than just an academic question as computer scientists tried to build an artificial intelligence. What is the architecture of memory in a hive mind?
There are more possible ideas/experiences than there are ways to combine neurons in the brain. Memory, then, must organize itself in some way to accommodate more possible thoughts than it has room to store. It cannot have a shelf for every thought of the past, nor a place reserved for every potential thought of the future.
The act of perceiving and the act of remembering are the same. Both assemble an emergent whole from many distributed pieces.
“Memory,” says cognitive scientist Douglas Hofstadter, “is highly reconstructive. Retrieval from memory involves selecting out of a vast field of things what’s important and what is not important, emphasizing the important stuff, downplaying the unimportant.” That selection process is perception. “I am a very big believer,” Hofstadter told me, “that the core processes of cognition are very, very tightly related to perception.”

Kyle: Cognition tightly tied to PERCEPTION
Kanerva’s algorithm was an elegant method to store a finite number of data points in a very immense potential memory space. In other words, Kanerva showed a way to fit any perception a mind could have into a finite memory mechanism. Since there are more ideas possible in the universe than there are atoms or minutes, the actual ideas or perceptions that a human mind can ever get to are relatively sparse within the total possibilities; therefore Kanerva called his technique a “sparse distributed memory” algorithm.

Kyle: Observer abbreviations (Observer Effect) make infinite capable of finite perception.
When we remember, we re-create the act of the original perception; that is, we relocate the pattern by a process similar to the one we used to perceive the pattern originally.
But merely combating idleness is not what makes distributing computing worth doing. Distributed being and hive minds have their own rewards, such as greater immunity to disruption. At Digital Equipment Corporation’s research lab in Palo Alto, California, an engineer demonstrated this advantage of distributed computation by opening the door of the closet that held the company’s own computer network and dramatically yanking a cable out of its guts. The network instantly routed around the breach and didn’t falter a bit.
A SINK BRIMS with water. You pull the plug. The water stirs. A vortex materializes. It blooms into a tiny whirlpool, growing as if it were alive. In a minute the whirl extends from surface to drain, animating the whole basin. An ever changing cascade of water molecules swirls through the tornado, transmuting the whirlpool’s being from moment to moment. Yet the whirlpool persists, essentially unchanged, dancing on the edge of collapse. “We are not stuff that abides, but patterns that perpetuate themselves,” wrote Norbert Wiener.
No matter how intimately you know the chemical character of H2O, it does not prepare you for the character of a whirlpool. Like all emergent entities, the essence of a vortex emanates from a messy collection of other entities; in this case, a pool of water molecules.
Emergence requires a population of entities, a multitude, a collective, a mob, more.
We know these parallel-operating wholes by different names. We know a swarm of bees, or a cloud of modems, or a network of brain neurons, or a food web of animals, or a collective of agents. The class of systems to which all of the above belong is variously called: networks, complex adaptive systems, swarm systems, vivisystems, or collective systems. I use all these terms in this book.
Organizationally, each of these is a collection of many (thousands) of autonomous members. “Autonomous” means that each member reacts individually according to internal rules and the state of its local environment. This is opposed to obeying orders from a center, or reacting in lock step to the overall environment. These autonomous members are highly connected to each other, but not to a central hub. They thus form a peer network. Since there is no center of control, the management and heart of the system are said to be decentrally distributed within the system, as a hive is administered.
There are four distinct facets of distributed being that supply vivisystems their character: The absence of imposed centralized control The autonomous nature of subunits The high connectivity between the subunits The webby nonlinear causality of peers influencing peers.
Systems that can shift the locus of adaptation over time from one part of the system to another (from the body to the genes or from one individual to a population) must be swarm based. Noncollective systems cannot evolve (in the biological sense).
Resilient—Because collective systems are built upon multitudes in parallel, there is redundancy. Individuals don’t count. Small failures are lost in the hubbub. Big failures are held in check by becoming merely small failures at the next highest level on a hierarchy.

Kyle: Carol’s loss of her wife is heartbreaking but technically incompatible with the resiliency of the hive mind (eg everything but her physical body resides within the hive mind). But by the same token, resiliency in the big hive mind in Starship Troopers subverts any individual value in the meaningless loss of life the hive is willing to submit the drones to because they have no individual worth.
Noncontrollable—There is no authority in charge. Guiding a swarm system can only be done as a shepherd would drive a herd: by applying force at crucial leverage points, and by subverting the natural tendencies of the system to new ends (use the sheep’s fear of wolves to gather them with a dog that wants to chase sheep). An economy can’t be controlled from the outside; it can only be slightly tweaked from within. A mind cannot be prevented from dreaming, it can only be plucked when it produces fruit. Wherever the word “emergent” appears, there disappears human control.

Kyle: How does representative democracy work for 9B people? We don’t know. Also a reason why cultural normativity is often preferred; it provides a shorthand for systems manipulation. When cultural norms are incoherent or, even worse, incompatible, the system (or swarm) becomes ungovernable.
The complexity of a swarm system bends it in unforeseeable ways.“The history of biology is about the unexpected,” says Chris Langton, a researcher now developing mathematical swarm models. The word emergent has its dark side. Emergent novelty in a video game is tremendous fun; emergent novelty in our airplane traffic-control system would be a national emergency.
We don’t need to know exactly how a tomato cell works to be able to grow, eat, or even improve tomatoes. We don’t need to know exactly how a massive computational collective system works to be able to build one, use it, and make it better. But whether we understand a system or not, we are responsible for it, so understanding would sure help.
A simple rule of thumb may help: For jobs where supreme control is demanded, good old clockware is the way to go. Where supreme adaptability is required, out-of-control swarmware is what you want.
A contemplative swarm thought: The Atom is the icon of 20th century science. The popular symbol of the Atom is stark: a black dot encircled by the hairline orbits of several other dots. The Atom whirls alone, the epitome of singleness. It is the metaphor for individuality: atomic. It is the irreducible seat of strength. The Atom stands for power and knowledge and certainty. It is as dependable as a circle, as regular as round. The image of the planetary Atom is printed on toys and on baseball caps. The swirling Atom works its way into corporate logos and government seals. It appears on the back of cereal boxes, in school books, and stars in TV commercials. The internal circles of the Atom mirror the cosmos, at once a law-abiding nucleus of energy, and at the same time the concentric heavenly spheres spinning in the galaxy. In the center is the animus, the It, the life force, holding all to their appropriate whirling stations. The symbolic Atoms’ sure orbits and definite interstices represent the understanding of the universe made known. The Atom conveys the naked power of simplicity.

Kyle: Animus = “It” = Power of the universe
The Net is an emblem of multiples. Out of it comes swarm being— distributed being—spreading the self over the entire web so that no part can say, “I am the I.” It is irredeemably social, unabashedly of many minds. It conveys the logic both of Computer and of Nature—which in turn convey a power beyond understanding.

Kyle: “The stone that sits on the very top of the mountains mighty face doesn’t think it’s more important than the stones that form the base.”
Whereas the Atom represents clean simplicity, the Net channels the messy power of complexity.
The only organization capable of unprejudiced growth, or unguided learning, is a network. All other topologies limit what can happen.
Craig Reynolds, the synthetic flocking inventor, points out the remarkable ability of networks to absorb the new without disruption: “There is no evidence that the complexity of natural flocks is bounded in any way. Flocks do not become ‘full’ or ‘overloaded’ as new birds join. When herring migrate toward their spawning grounds, they run in schools extending as long as 17 miles and containing millions offish.” How big a telephone network could we make? How many nodes can one even theoretically add to a network and still have it work? The question has hardly even been asked.

Kyle: I wonder if this is still true? Or has the internet pushed the boundary of network theory? What’s more, the Galactic Univac is this theory taken to its infinite extension. A truly infinite network
There are a variety of swarm topologies, but the only organization that holds a genuine plurality of shapes is the grand mesh. In fact, a plurality of truly divergent components can only remain coherent in a network. No other arrangement—chain, pyramid, tree, circle, hub—can contain true diversity working as a…

Kyle: Genuine diversity is infinitely accessible rather than requiring any specific structure.
We should expect to see networks wherever we see constant irregular…

Kyle: Recall all the different shots of networks that Kevin Kelly ran through is his Technium Unbounded talk.
Network logic is counterintuitive. Say you need to lay a telephone cable that will connect a bunch of cities; let’s make that three for illustration: Kansas City, San Diego, and Seattle. The total length of the lines connecting those three cities is 3,000 miles. Common sense says that if you add a fourth city to your telephone network, the total length of your cable will have to increase. But that’s not how network logic works. By adding a fourth city as a hub (let’s make that Salt Lake City) and running the lines from each of the three cities through Salt Lake City, we can decrease the total mileage of cable to 2,850 or 5 percent less than the original 3,000 miles. Therefore the total unraveled length of a network can be shortened by adding nodes to it! Yet there is a limit to this effect. Frank…
On the other hand, in 1968 Dietrich Braess, a German operations researcher, discovered that adding routes to an already congested network will only slow it down. Now called Braess’s Paradox, scientists have found many examples of how adding…
Then again, in 1990, three scientists working on networks of brain neurons reported that increasing the gain—the responsivity—of individual neurons did not increase their individual signal detection performance, but it did…
What we get from heavy-duty communication networks, and the networks of parallel computing, and the networks of distributed appliances and…
Alan Kay, a visionary who had much to do with inventing personal computers, says that the personally owned book was one of the chief shapers of the Renaissance notion of the individual, and that pervasively networked computers will be the main shaper of humans in the future. It’s not just individual books we are leaving behind, either. Global opinion polling in realtime 24 hours a day, seven days a week, ubiquitous telephones, asynchronous e-mail, 500 TV…
As we wire ourselves up into a hivish network, many things will emerge that we, as mere neurons in the network, don’t expect, don’t understand, can’t control, or don’t even perceive…
In 1991 Pauline staged a machine circus in downtown San Francisco. On this night, several thousand fans dressed in punk black leather convened, entirely by word of mouth, at an abandoned parking lot squeezed under a freeway overpass ramp. In the makeshift arena, under the industrial glare of spotlights, ten or so mechanical animals and autonomous iron gladiators waited to demolish each other with flames and brute force.

Kyle: Networked counterculture
One SRL crew member says that they like to put shows on in Europe because there is a lot of “Obtainium” there. What’s Obtainium?: “Something that is easily obtained, easily liberated, or gotten for free.” That which isn’t made out of Obtainium is built from military surplus parts that Pauline buys by the truckload for $65 per pound from friendly downsizing military bases.
His several-hundred-dollar swarming creatures—decked out with recycled infrared sensors and junked stepped motors—beat out the MIT robot lab in an informal race to construct the first autonomous swarming robots.
The problem with our robots today is that we don’t respect them. They are stuck in factories without windows, doing jobs that humans don’t want to do. We take machines as slaves, but they are not that. That’s what Marvin Minsky, the mathematician who pioneered artificial intelligence, tells anyone who will listen. Minsky goes all the way as an advocate for downloading human intelligence into a computer. Doug Englebart, on the other hand, is the legendary guy who invented word processing, the mouse, and hypermedia, and who is an advocate for computers-for-the-people. When the two gurus met at MIT in the 1950s, they are reputed to have had the following conversation: MINSKY: We’re going to make machines intelligent. We are going to make them conscious! ENGLEBART: You’re going to do all that for the machines? What are you going to do for the people?
But I’m squarely on Minsky’s side—on the side of the made. People will survive. We’ll train our machines to serve us. But what are we going to do for the machines?
In a widely cited 1989 paper entitled “Fast, Cheap and Out of Control: A Robot Invasion of the Solar System,” Brooks claimed that “within a few years it will be possible at modest cost to invade a planet with millions of tiny robots.” He proposed to invade the moon with a fleet of shoe-box-size, solar-powered bulldozers that can be launched from throwaway rockets. Send an army of dispensable, limited agents coordinated on a task, and set them loose. Some will die, most will work, something will get done. The mobots can be built out of off-the-shelf parts in two years and launched completely assembled in the cheapest one-shot, lunar-orbit rocket. In the time it takes to argue about one big sucker, Brooks can have his invasion built and delivered.
This is a universal biological principle that Brooks helped illuminate—a law of god: When something works, don’t mess with it; build on top of it. In natural systems, improvements are “pasted” over an existing debugged system. The original layer continues to operate without even being (or needing to be) aware that it has another layer above it. When friends give you directions on how to get to their house, they don’t tell you to “avoid hitting other cars” even though you must absolutely follow this instruction. They don’t need to communicate the goals of lower operating levels because that work is done smoothly by a well-practiced steering skill. Instead, the directions to their house all pertain to high-level activities like navigating through a town. Animals learn (in evolutionary time) in a similar manner.
The distributed control layout for robots that Brooks devised came to be known as “subsumption architecture” because the higher level of behaviors subsumed the roles of lower levels of behaviors when they wished to take control.
That is, towns organized by this subsumption architecture can build, educate, rule, and prosper far more than they could individually. The federal structure of the U.S. government is therefore a subsumption architecture.
A brain and body are made the same way. From the bottom up. Instead of towns, you begin with simple behaviors—instincts and reflexes. You make a little circuit that does a simple job, and you get a lot of them going. Then you overlay a secondary level of complex behavior that can emerge out of that bunch of working reflexes. The original layer keeps working whether the second layer works or not. But when the second layer manages to produce a more complex behavior, it subsumes the action of the layer below it.
The most obvious way to do something complex, such as govern 100 million people or walk on two skinny legs, is to come up with a list of all the tasks that need to be done, in the order they are to be done, and then direct their completion from a central command, or brain. The former Soviet Union’s economy was wired in this logical but immensely impractical way. Its inherent instability of organization was evident long before it collapsed.
“The idea that the brain has a center is just wrong. Not only that, it is radically wrong,” claims Daniel Dennett. Dennett is a Tufts University professor of philosophy who has long advocated a “functional” view of the mind: that the functions of the mind, such as thinking, come from non-thinking parts. The semimind of a insectlike mobot is a good example of both animal and human minds. According to Dennett, there is no place that controls behavior, no place that creates “walking,” no place where the soul of being resides. Dennett: “The thing about brains is that when you look in them, you discover that there’s nobody home.”
Much more likely, says Dennett, is that “meaning emerges from distributed interaction of lots of little things, no one of which can mean a damn thing.” A whole bunch of decentralized modules produce raw and often contradictory parts—a possible word here, a speculative word there. “But out of the mess, not entirely coordinated, in fact largely competitive, what emerges is a speech act.”
The idea of a cacophony of alternative wits combining to form what we think of as a unified intelligence is what Marvin Minsky calls “society of mind.” Minsky says simply “You can build a mind from many little parts, each mindless by itself.” Imagine, he suggests, a simple brain composed of separate specialists each concerned with some important goal (or instinct) such as securing food, drink, shelter, reproduction, or defense. Singly, each is a moron; but together, organized in many different arrangements in a tangled hierarchy of control, they can create thinking. Minsky emphatically states, “You can’t have intelligence without a society of mind. We can only get smart things from stupid things.”
The “I” is a gross extrapolation that we use as an identity for ourselves and others. If there wasn’t an “I” or “Me” in every person then each would quickly invent one. And that, Minsky says, is exactly what we do. There is no “I” so we each invent one.
There is no “I” for a person, for a beehive, for a corporation, for an animal, for a nation, for any living thing. The “I” of a vivisystem is a ghost, an ephemeral shroud. It is like the transient form of a whirlpool held upright by a million spinning atoms of water. It can be scattered with a fingertip.
But a moment later, the shroud reappears, driven together by the churning of a deep distributed mob. Is the new whirlpool a different form, or the same? Are you different after a near-death experience, or only more mature? If the chapters in this book were arranged in a different order, would it be a different book or the same? When you can’t answer that question, then you know you are talking about a distributed system.
In the human management of distributed control, hierarchies of a certain type will proliferate rather than diminish. That goes especially for distributed systems involving human nodes—such as huge global computer networks. Many computer activists preach a new era in the network economy, an era built around computer peer-to-peer networks, a time when rigid patriarchal networks will wither away. They are right and wrong. While authoritarian “top-down” hierarchies will retreat, no distributed system can survive long without nested hierarchies of lateral “bottom-up” control. As influence flows peer to peer, it coheres into a chunk—a whole organelle— which then becomes the bottom unit in a larger web of slower actions. Over time a multi-level organization forms around the percolating-up control: fast at the bottom, slow at the top.
The law is concise: Distributed control has to be grown from simple local control. Complexity must be grown from simple systems that already work.
Contrary to popular business preaching, keeping everybody informed about everything is not how intelligence happens.
Centralized communication is not the only problem with a central brain. Maintaining a central memory is equally debilitating. A shared memory has to be updated rigorously, timely, and accurately—a problem that many corporations can commiserate with. For a robot, central command’s challenge is to compile and update a “world model,” a theory, or representation, of what it perceives—where the walls are, how far away the door is, and, by the way, beware of the stairs over there.

Kyle: What are the systems of the Pluribus hive mind like (in terms of energy, compute, storage?)
So difficult was the task of coordinating a central world view that Brooks discovered it was far easier to use the real world as its own model: “This is a good idea as the world really is a rather good model of itself.” With no centrally imposed model, no one has the job of reconciling disputed notions; they simply aren’t reconciled. Instead, various signals generate various behaviors. The behaviors are sorted out (suppressed, delayed, activated) in the web hierarchy of subsumed control.
Astute observers have noticed that Brooks’s prescription is an exact description of a market economy: there is no communication between agents, except that which occurs through observing the effects of actions (and not the actions themselves) that other agents have on the common world. The price of eggs is a message communicated to me by hundreds of millions of agents I have never met.
Brooks’s model, for all its radicalism in the field of artificial intelligence, is really a model of how complex organisms of any type work. We see a sub-sumption, web hierarchy in all kinds of vivisystems.
Almost every lesson from the Mobot Lab seems to teach that there is no mind without body in a real unforgiving world. “To think is to act, and to act is to think,” said Heinz von Foerster, gadfly of the 1950s cybernetic movement. “There is no life without movement.”
We twentieth century humans live entirely in our heads. And so we build robots that live in their heads.
Powerful computers birthed the fantasy of a pure disembodied intelligence.
One of the tenets in the gospel of American pop culture is the widely held creed of transferability of mind. People declare that mind transfer is a swell idea, or an awful idea, but not that it is a wrong idea. In modern folk-belief, mind is liquid to be poured from one vessel to another.
For better or worse, in reality we are not centered in our head. We are not centered in our mind. Even if we were, our mind has no center, no “I.” Our bodies have no centrality either. Bodies and minds blur across each others’ supposed boundaries. Bodies and minds are not that different from one another. They are both composed of swarms of sublevel things.

Kyle: The Ego vs The Hive
We are a lot closer to the truth when we point to our heart and not our head as the center of behaviors. Our emotions swim in a soup of hormones and peptides that percolate through our whole body. Oxytocin discharges thoughts of love (and perhaps lovely thoughts) from our glands. These hormones too process information. Our immune system, by science’s new reckoning, is an amazing parallel, decentralized perception machine, able to recognize and remember millions of different molecules.
If you don’t want a mind to emerge, then unhinge it from the body.
TEDIUM can unhinge a mind.
The body is the anchor of the mind, and of life. Bodies are machines to prevent the mind from blowing away under a wind of its own making.
How much better to learn while alive. That is the next big step for machines. To learn over time, on their own. To not only adapt, but evolve.
Chris Langton, an advocate of autonomous machine life, once asked Mark Pauline, “When machines are both superintelligent and superefficient, what will be the niche for humans? I mean, do we want machines, or do we want us?” Pauline responded in words that I hope echo throughout this book: “I think humans will accumulate artificial and mechanical abilities, while machines will accumulate biological intelligence. This will make the confrontation between the two even less decisive and less morally clear than it is now.”
So indecisive that the confrontation may resemble a conspiracy: robots who think, viruses that live in silicon, people hotwired to TV sets, life engineered at the gene level to grow what we want, the whole world networked into a human/machine mind. If it all works, we’ll have contraptions that help people live and be creative, and people who help the contraptions live and be creative.
The greatest social consequence of the Darwinian revolution was the grudging acceptance by humans that humans were random descendants of monkeys, neither perfect nor engineered. The greatest social consequence of neo-biological civilization will be the grudging acceptance by humans that humans are the random ancestors of machines, and that as machines we can be engineered ourselves.
I believe that humans are more than the combination of ape and machine (we have a lot going for us!), but I also believe that we are far more ape and machine than we think. That leaves room for an unmeasured but discernible human difference, a difference that inspires great literature, art, and our lives as a whole.
What one is not looking for, one does not see.
Evolution not only evolves the functioning community, but it also finely tunes the assembly process of the gathering until the community practically falls together.
It was very easy to arrive at a stable ecosystem, if you didn’t care what system you arrived at. This was surprising. Pimm said, “We know from chaos theory that many deterministic systems are exquisitely sensitive to initial conditions—one small difference will send it off into chaos. This stability is the opposite of that. You start out in complete randomness, and you see these things assemble towards something that is a lot more structured than you had any reason to believe could be there. This is anti-chaos.”

Kyle: Connect to the Manhattan Institute round table about how thin the line is to make all of this stuff work.
The problem Wingate faced was the perennial paradox that all whole systems makers confront: where do you start? Everything requires everything else to stay up, yet you can’t levitate the whole thing at once. Some things have to happen first. And in the correct order.
It was like the parable of “For Want of a Nail, The Kingdom Was Lost,” but in reverse: By finding the nail, the kingdom was won. Notch by notch, Wingate was reassembling a lost ecosystem. Ecosystems and other functioning systems, like empires, can be destroyed much faster than they can be created. It takes nature time to grow a forest or marsh because even nature can’t do everything at once.
The rule for machines is counterintuitive but clear: Complex machines must be made incrementally and often indirectly. Don’t try to make a functioning mechanical system all at once, in one glorious act of assembly. You have to first make a working system that serves as a platform for the system you really want. To make a mechanical mind, you need to make the equivalent of a mechanical thumb—a lateral approach that few appreciate. In assembling complexity, the bounty of increasing returns is won by multiple tries over time—a process anyone would call growth.
Creating extremely complex machines, such as robots and software programs of the future, will be like restoring prairies or tropical islands. These intricate constructions will have to be assembled over time because that is the only way to make sure they work from top to bottom. Unripe machinery let out before it is fully grown and fully integrated with diversity will be a common complaint. “We ship no hardware before its time,” will not sound funny before too long.
The chameleon responding to its own shifting image is an apt analog of the human world of fashion. Taken as a whole, what are fads but the response of a hive mind to its own reflection?
In 1952, W. Ross Ashby, a cybernetician interested in how machines could learn, wrote, “[An organism’s gene-pattern] does not specify in detail how a kitten shall catch a mouse, but provides a learning mechanism and a tendency to play, so that it is the mouse which teaches the kitten the finer points of how to catch mice.”
The “co” in coevolution is the mark of the future. In spite of complaints about the steady demise of interpersonal relationships, the lives of modern people are increasingly more codependent than ever. All politics these days means global politics and global politics means copolitics. The new online communities built between the spaces of communication networks are coworlds. Marshall McLuhan was not quite right. We are not hammering together a cozy global village. We are weaving together a crowded global hive—a coworld of utmost sociality and mirrorlike reciprocation. In this environment, all evolution, including the evolution of manufactured entities, is coevolution. Nothing changes without also moving closer to its changing neighbors.
Here’s news: half of the living world is codependent! Business consultants commonly warn their clients against becoming a symbiont company dependent upon a single customer-company, or a single supplier. But many do, and as far as I can tell, live profitable lives, no shorter on average than other companies. The surge of alliance-making in the 1990s among large corporations—particularly among those in the information and network industries—is another facet of an increasing revolutionary economic world. Rather than eat or compete with a competitor, the two form an alliance—a symbiosis.
The more copious life’s social behaviors are, the more likely they are to be subverted into mutually beneficial interactions. The more mutually responsive we construct our economic and material world, the more revolutionary games we’ll see.
There is a sort of madness in pursuing self-reflections, that same madness we sensed in the nuclear arms race of post-World War II. Coevolution moves things to the absurd. The butterfly and the milkweed, although competitors in a way, cannot live apart. Paul Ehrlich sees coevolution pushing two competitors into “obligate cooperation.” He wrote, “It’s against the interests of either predator or prey to eliminate the enemy.” That is clearly irrational, yet that is clearly a force that drives nature.
Rabid mutualism doesn’t just happen in pairs. Threesomes can meld into an emergent, coevolutionarily wired symbiosis. Whole communities can be revolutionary. In fact, any organism that adapts to organisms around it will act as an indirect revolutionary agent to some degree. Since all organisms adapt that means all organisms in an ecosystem partake in a continuum of coevolution, from direct symbiosis to indirect mutual influence. The force of revolutionism flows from one creature to its most intimate neighbors, and then ripples out in fainter waves until it immeasurably touches all living organisms. In this way the loose network of a billion species on this home planet are knit together so that unraveling the coevolutionary fabric becomes impossible, and the parts elevate themselves into some aggregate state of spooky, stable instability.
Thermodynamic entropy draws all chemical reactions down to their minimal energy level. The furnace metaphor breaks down. Equilibrium on a dead planet is less like a thermostat and more like the uniform level of water in a bowl; it simply levels out when it can’t get any lower.
Yet the off-balance is itself balanced. The persistent disequilibrium that revolutionary life generates, and that Lovelock seeks as an acid test for its presence, is stable in its own way. As far as we can tell Earth’s atmospheric oxygen has remained at about 20 percent for hundreds of millions of years. The atmosphere acts not merely as an acrobat on a tightrope pitched far from the vertical, but as an acrobat teetering between tilting and falling, and poised therefor millions of years. She never falls, but never gets out of falling. It’s a state of permanent almost-fell.

Kyle: Manhattan institute about how life is possible because our planet walks the razors edge of compatible components
The organism behaves as environment, the environment behaves as organism.
“Life is not an external and accidental development on the terrestrial surface. Rather, it is intimately related with the constitution of the Earth’s crust,” Vernadsky wrote in 1929. “Without life, the face of the Earth would become as motionless and inert as the face of the moon.”

Kyle: Contrast this with de-growther humans who think we’re killing the planet.
They live in a world that is the breath and bones of their ancestors and that they are now sustaining.” Lovelock
“Living matter is the most powerful geological force,” Vernadsky claimed, “and it is growing with time.” The more life, the greater its material force. Humans intensify life further. We harness fossil energy and breathe life into machines. Our entire manufactured infrastructure—as an extension of our own bodies—becomes part of a wider, global-scale life. As the carbon dioxide from our industry pours into the air and alters the global air mix, the realm of our artificial machines also becomes part of the planetary life. Jonathan Weiner writing in The Next One Hundred Years then can rightly say, “The Industrial Revolution was an astonishing geological event.” If rocks are slow life, then our machines are quicker slow life.

Kyle: “Making sand think.”
In 1972, Lovelock offered a hypothesis of where the planet’s self-government lay. He wrote, “The entire range of living matter on Earth, from whales to viruses, from oaks to algae, could be regarded as constituting a single living entity, capable of manipulating the Earth’s atmosphere to suit its overall needs and endowed with faculties and powers far beyond those of its constituent part.” Lovelock called this view Gaia. Together with microbiologist Lynn Margulis, the two published the view in 1972 so that it could be critiqued on scientific terms. Lovelock says, “The Gaia theory is a bit stronger than coevolution,” at least as biologists use the word.
Many biologists (including Paul Ehrlich) are unhappy with the idea of Gaia because Lovelock expanded the definition of life without asking their permission. He unilaterally enlarged life’s scope to include a predominantly mechanical apparatus. In one easy word, a solid planet became “the largest manifestation of life” that we know. It is an odd beast: 99.9 percent rock, a lot of water, and a little air, wrapped up in the thinnest green film that would stretch around it.
But if Earth is reduced to the size of a bacteria, and inspected under high-powered optics, would it seem stranger than a virus? Gaia hovers there, a blue sphere under the stark light, inhaling energy, regulating its internal states, fending off disturbances, complexifying, and ready to transform another planet if given a chance.
That Gaia is made up of many purely mechanical circuits shouldn’t deter us from applying the label of life. After all, cells are mostly chemical cycles. Some ocean diatoms are mostly inert, crystallized calcium. Trees are mostly dead pulp. But they are still living organisms.
Lovelock: “There is no clear distinction anywhere on the Earth’s surface between living and nonliving matter. There is merely a hierarchy of intensity going from the material environment of the rocks and atmosphere to the living cells.”
THE TROUBLE WITH GAIA, as far as most skeptics are concerned, is that it makes a dead planet into a “smart” machine. We already are stymied in trying to design an artificial learning machine from inert computers, so the prospect of artificial learning evolving unbidden at a planetary scale seems ludicrous.
But learning is overrated as something difficult to evolve. This may have to do with our chauvinistic attachment to learning as an exclusive mark of our species. There is a strong sense, which I hope to demonstrate in this book, in which evolution itself is a type of learning. Therefore learning occurs wherever evolution is, even if artificially.
The riddle “What hand is the penny in?” is related to the riddle, “What color is the chameleon on a mirror?” The bottomless complexity which grows out of such simple rules intrigued John von Neumann, the mathematician who developed programmable logic for a computer in the early 1940s, and along with Wiener and Bateson launched the field of cybernetics.
For centuries, the orthodox political reasoning originally articulated by Thomas Hobbes in 1651 was dogma: that cooperation could only develop with the help of a benign central authority. Without top-down government, Hobbes claimed, there would be only collective selfishness. A strong hand had to bring forth political altruism, whatever the tone of economics. But the democracies of the West, beginning with the American and French Revolutions, suggested that societies with good communications could develop cooperative structures without heavy central control. Cooperation can emerge out of self-interest. In our postindustrial economy, spontaneous cooperation is a regular occurrence. Widespread industry-initiated standards (both of quality and protocols such as 110 volts or ASCII) and the rise of the Internet, the largest working anarchy in the world, have only intensified interest in the conditions necessary for hatching coevolutionary cooperation.
Error keeps the glue of coevolutionary relationships from binding too tightly into runaway death spirals, and therefore error keeps a coevolutionary system afloat and moving forward. Honor thy error.
Axelrod told me, “One of the earliest and most important insights from game theory was that nonzero-sum games had very different strategic implications than zero-sum games. In zero-sum games whatever hurts the other guy is good for you. In nonzero-sum games you can both do well, or both do poorly. I think people often take a zero-sum view of the world when they shouldn’t. They often say, ‘Well I’m doing better than the other guy, therefore I must be doing well.’ In a nonzero-sum you could be doing better than the other guy and both be doing terribly.” Axelrod noticed that the champion Tit-For-Tat strategy always won without exploiting an opponent’s strategy—it merely mirrored the other’s actions. Tit-For-Tat could not beat anyone’s strategy one on one, but in a nonzero-sum game it would still win a tournament because it had the highest cumulative score when played against many kinds of rules. As Axelrod pointed out to William Poundstone, author of Prisoner’s Dilemma, “That’s a very bizarre idea. You can’t win a chess tournament by never beating anybody.” But with coevolution—change changing in response to itself—you can win without beating others. Hard-nosed CEOs in the business world now recognize that in the era of networks and alliances, companies can make billions without beating others. Win-win, the cliche is called. Win-win is the story of life in coevolution.

Kyle: Connect to Arrival
Coevolution can be seen as two parties snared in the web of mutual propaganda. Coevolutionary relationships, from parasites to allies, are in their essence informational. A steady exchange of information welds them into a single system. At the same time, the exchange—whether of insults or assistance or plain news—creates a commons from which cooperation, self-organization, and win-win endgames can spawn. In the Network Era—that age we have just entered—dense communication is creating artificial worlds ripe for emergent coevolution, spontaneous self-organization, and win-win cooperation. In this Era, openness wins, central control is lost, and stability is a state of perpetual almost-falling ensured by constant error.
“Equilibrium is not only dead, it is death,” Burgess emphasizes. “To enrich a system you need variance in time and space. But too much change will kill you too. You go from an ecocline to ecotone.”
Burgess admits, “At the moment we have no industrial economic models that are variance driven, except gambling.”
Among the earliest studies of simulated stability was a paper published in 1970 by Gardner and Ashby. Ashby was an engineer interested in nonlinear control circuits and the virtues of positive feedback loops. Ashby and Gardner programmed simple network circuits in hundreds of variations into a computer, systematically changing the number of nodes and the degrees of connectivity between nodes. They discovered something startling: that beyond a certain threshold, increasing the connectivity would suddenly decrease the ability of the system to rebound after disturbances. In other words, complex systems were less likely to be stable than simple ones.
Rather, May’s simulated ecologies suggested that neither simplicity nor complexity had as much impact on stability as the pattern of the species interaction.
Biology suggests that in addition to regulating the numbers of connections per “node” in a network, a system tends to also regulate the “connectance” (the strength of coupledness) between each pair of nodes in a network. Nature seems to conserve connectance. We should thus expect to find a similar law of the conservation of connectance in cultural, economic, and mechanical systems, although I am not aware of any studies that have attempted to show this. If there is such a law in all vivisystems, we should also expect to find this connectance being constantly adjusted, perpetually in flux.
We see, too, that human institutions— those ecologies of human toil and dreams—must also be in a state of constant flux and reinvention, yet we are always surprised or resistant when change begins. (Ask a hip postmodern American if he would like to change the 200-year-old rule book known as the Constitution. He’ll suddenly become medieval.)

Kyle: “What new constitutions have you written?” (Thomas Jefferson)
Change, not redwood groves or parliaments, is eternal. The questions become: What controls change? How can we direct it? Can the distributed life in such loose associations as governments, economies, and ecologies be controlled in any meaningful way? Can future states of change even be predicted?

Kyle: How does “eternal” change differ from entropy?
Superorganism was a buzz word among biologists in the 1920s. They used it to describe the then novel idea that a collection of agents could act in concert to produce phenomena governed by the collective. Like a slime mold that assembled itself from moldy spots into a thrusting blob, an ecosystem coalesced into a stable superorganization—a hive or forest. A Georgia pine forest did not act like a pine tree, nor a Texas sagebrush desert like a sagebrush, just as a flock is not a big bird. They were something else, a loose federation of animals and plants united into an emergent superorganism exhibiting distinctive behavior.
Evolution requires a certain connectance among its participants to express its power; and so evolutionary dynamics exert themselves most forcefully in tightly coupled systems. In systems connected loosely, such as ecosystems, economic systems, and cultural systems, a less structured adaptation takes place. We know very little about the general dynamics of loosely coupled systems because this kind of distributed change is messy and infinitely indirect. Howard Pattee, an early cybernetician, defined hierarchical structure as a spectrum of connectance. He said, “To a Platonic mind, everything in the world is connected to everything else—and perhaps it is. Everything is connected, but some things are more connected than others.” Hierarchy for Pattee was the product of differential connectedness within one system. Members that were so loosely connected as to be “flat” would tend to form a separate organizational level distinct from areas where members were tightly connected. The range of connectance created a hierarchy.
Evolutionary change seems a strongly bound process very similar to mathematical computation, or even to thinking. In this way it is “cerebral.”
While evolution is governed by the straightforward flow of symbolic information issuing from the gene or computer chips, ecology is governed by the far less abstract,…
WHERE DOES diversity come from? In 1983, microbiologist Julian Adams discovered a clue when he brewed up a soup of cloned E. coli bacteria. He purified the broth until he had a perfectly homogenized pool of identical creatures. He put this soup of clones into a specially constructed chemostat that provided a uniform environment for them—every E. coli bug had the same temperature and nutrient bath. Then he let the soup of identical bugs replicate and ferment. At the end of 400 generations, the E. coli bacteria had bred new strains of itself with slightly different genes. Out of a starting point in a constant featureless environment, life spontaneously diversified. A surprised Adams dissected the genes of the variants (they weren’t new species) to find out what happened. One of the original bugs had undergone a mutation that caused it to excrete acetate, an organic chemical. A second bug experienced a mutation that allowed it to make use of the acetate excreted from the first. Suddenly a symbiotic codependence of acetate maker and acetate eater had emerged from the uniformity, and the pool diverged into an ecology. Although uniformity can yield diversity, variance does better. If the Earth were as smooth as a shiny ball bearing—a perfect spherical…
If evolution had its way, with no interference from geographical and geological dynamics—that is, without the clumsiness of a body—then mindlike evolution would feed upon itself and breed heavily recursive relationships. On a globe without mountains or storms or unexpected droughts, evolution would wind life into a ever-tightening web of coevolution, a smooth world stuffed with parasites, parasites upon parasites (hyperparasites), mimics, and symbionts, all caught up in accelerating codependence. But each species would be so tightly coupled with the others that it would be difficult to distinguish where the identity of one…
I should be impressed. But what strikes me as I sit among two million grass plants and several thousand juniper shrubs, is how similar life on Earth is. For all the possible shapes and behaviors animated matter could take, only a few—in wide variation—are tried out. Life can’t fool me. It’s all the same, like those canned goods in grocery stores with different labels but all manufactured by the same food conglomerate. Life on Earth obviously all comes from one transnational conglomerate.
Life is a networked thing—a distributed being. It is one organism extended in space and time. There is no individual life. Nowhere do we find a solo organism living. Life is always plural. (And not until it became plural— cloning itself—could life be called life.) Life entails interconnections, links, and shared multiples. “We are of the same blood, you and I,” coos the poet Mowgli. Ant, we are of the same blood, you and I. Tyrannosaurus, we are of the same blood, you and I. AIDS virus, we are of the same blood, you and I. The apparent individuals that life has dispersed itself into are illusions. “Life is [primarily] an ecological property, and an individual property for only a fleeting moment,” writes microbiologist Clair Folsome, a man who dabbled in making superorganisms inside bottles. We live one life, distributed. Life is a transforming flood that fills up empty containers and then spills out of them on its way to fill up more. The shape and number of vessels submerged by the flood doesn’t make a bit of difference.

Kyle: “And God said, Let US make man in OUR image… make and female, created he them.” (Genesis 1:26-27)
“Life is a planetary-scale phenomenon,” said James Lovelock. “There cannot be sparse life on a planet. It would be as unstable as half of an animal.”
It takes, on average, all the diseases and accidents of the world working 24 hours a day, 7 days a week, with no vacations, about 621,960 hours to kill a human organism. That’s 70 years of full-time attack to break the bounds of human life—barring the intervention of modern medicine (which may either accelerate or hinder death, depending on your views). This stubborn persistence in life is directly due to the complexity of the human body. In contrast, a well-built car that managed to puff its way to an upper limit of 200,000 miles before blowing a valve would have run for about 5,000 hours. A jet turbine engine may run for 40,000 hours before being rebuilt. A simple light bulb with no moving parts is good for 2,000 hours. The longevity of nonliving complexity isn’t even in the same league as the persistence of life.
The heroic achievement in nature is not the little fish that gets away, but that old man death is ever able to crash a system.
A complex system cannot die simply. The members of a system have a bargain with the whole. The parts say, “We are willing to sacrifice to the whole, because together we are greater than our sum.” Complexity locks in life. The parts may die, but the whole lives. As a system self-organizes into greater complexity, it increases its life. Not the length of its life, but its lifeness. It has more lives.
Biologist Lynn Margulis and others have pointed out that even a cell has lives in plural, as each cell is a historical marriage of at least three vestigial forms of bacteria.
Before life, there was no complex matter in the universe. The entire universe was utterly simple. Salts. Water. Elements. Very boring. After life, there was much complex matter. According to astrochemists, we can’t find complex molecules in the universe outside of life. Life tends to hijack any and all matter it comes in contact with and complexify it. By some weird arithmetic, the more life stuffs itself into the valley, the more spaces it creates for further life. In the end, this small valley along the northern coast of California will become a solid block of life. In the end, left to its own drift, life may infiltrate all matter.

Kyle: “Intelligence is a fundamental property of matter.” (Sam Altman)
In the 1950s, the physicist Erwin Schrödinger called the life force “negentropy” to indicate its opposite direction from the push of thermal decay. In the 1990s, an embryonic subculture of technocrats thriving in the U.S. calls the life force “extropy.” “Extropians,” as promoters of extropy call themselves, issued a seven-point lifestyle manifesto based on the vitalism of life’s extropy. Point number three is a creed that states their personal belief in “boundless expansion”— the faith that life will expand until it fills the universe. Those who don’t believe this are tagged “deathists.” In the context of their propaganda, this creed could be read as mere pollyanna self-inspiration, as in: We can do anything! But somewhat perversely I take their boast as a scientific proposition: life will fill the universe. Nobody knows what the theoretical limits to the infection of matter by life would be. Nor does anybody know what the maximum amount of life-enhanced matter that our sun could support is.

Kyle: Dylan Thomas, “Do not go gentle into that good night.”Do not go gentle into that good night,Old age should burn and rave at close of day;Rage, rage against the dying of the light.Though wise men at their end know dark is right,Because their words had forked no lightning theyDo not go gentle into that good night.Good men, the last wave by, crying how brightTheir frail deeds might have danced in a green bay,Rage, rage against the dying of the light.Wild men who caught and sang the sun in flight,And learn, too late, they grieved it on its way,Do not go gentle into that good night.Grave men, near death, who see with blinding sightBlind eyes could blaze like meteors and be gay,Rage, rage against the dying of the light.And you, my father, there on the sad height,Curse, bless, me now with your fierce tears, I pray.Do not go gentle into that good night.Rage, rage against the dying of the light.
When reduced to its essentials, life is very close to a computational function. For a number of years Ed Fredkin, a maverick thinker once associated with MIT, has been spinning out a heretical theory that the universe is a computer. Not metaphorically like a computer, but that matter and energy are forms of information processing of the same general class as the type of information processing that goes on inside a Macintosh. Fredkin disbelieves in the solidity of atoms and says flatly that “the most concrete thing in the world is information.” Stephen Wolfram, a mathematical genius who did pioneering work on the varieties of computer algorithms agrees. He was one of the first to view physical systems as computational processes, a view that has since become popular in some small circles of physicists and philosophers. In this outlook the minimal work accomplished by life resembles the physics and thermodynamics of the minimal work done in a computer. Fredkin and company would say that knowing the maximum amount of computation that could be done in the universe (if we considered all its matter as a computer) would tells us whether life will fill the universe, given the distribution of matter and energy we see in the cosmos. I do not know if anyone has made that calculation.
One of the very few scientists to have thought in earnest about the final destiny of life is the theoretical physicist Freeman Dyson. Dyson did some rough calculations to estimate whether life and intelligence could survive until the ultimate end of the universe. He concluded it could, writing: “The numerical results of my calculations show that the quantities of energy required for permanent survival and communication are surprisingly modest… . [T] hey give strong support to an optimistic view of the potentialities of life. No matter how far we go into the future, there will always be new things happening, new information coming in, new worlds to explore, a constantly expanding domain of life, consciousness and memory.” Dyson has taken this further than I would have dared. I was merely concerned about the dynamics of life, and how it infiltrates all matter, and how nothing known can halt it. But just as life irretrievably conquers matter, the lifelike higher processing power we call mind irrevocably conquers life and thus also all matter. Dyson writes in his lyrical and metaphysical book, Infinite in All Directions: It appears to me that the tendency of mind to infiltrate and control matter is a law of nature… . The infiltration of mind into the universe will not be permanently halted by any catastrophe or by any barrier that I can imagine. If our species does not choose to lead the way, others will do so, or may have already done so. If our species is extinguished, others will be wiser or luckier. Mind is patient. Mind has waited for 3 billion years on this planet before composing its first string quartet. It may have to wait for another 3 billion years before it spreads all over the galaxy. I do not expect that it will have to wait so long. But if necessary, it will wait. The universe is like a fertile soil spread out all around us, ready for the seeds of mind to sprout and grow. Ultimately, late or soon, mind will come into its heritage. What will mind choose to do when it informs and controls the universe? That is a question which we cannot hope to answer.

Kyle: Cosmic AC
Historian of science David Channell summarizes this progression in his book The Vital Machine: A Study of Technology and Organic Life. First, Copernicus eliminated the discontinuity between the terrestrial world and the rest of the physical universe. Next, Darwin eliminated the discontinuity between human beings and the rest of the organic world. And most recently, Freud eliminated the discontinuity between the rational world of the ego and the irrational world of the unconscious. But as [historian and psychologist Bruce] Mazlish has argued, there is one discontinuity that faces us yet. This “fourth discontinuity” is between human beings and the machine.
Ktesibios’s regula was the first nonliving object to self-regulate, self-govern, and self-control. Thus, it became the first self to biology. It was a true auto thing—directed from within. We now consider it to be the primordial automatic device because it held the first breath of life-likeness in a machine.
Heron wrote a huge encyclopedia (the Pneumatica) crammed with his incredible (even by today’s standards) inventions. The book was widely translated and copied in the ancient world and was influential beyond measure. In fact, for 2,000 years (that is, until the age of machines in the 18th century), no feedback systems were invented that Heron had not already fathered.
For every one person visibly working in a factory, thousands, of governors and self-regulators toiled invisibly. Today, hundreds of thousands of regulators, unseen, may work in a modern plant at once. A single human may be their coworker.

Kyle: We’ve already massively automated huge swaths of human labor with millions of small, purpose-built “automatons.” Agents will be no different
The steam engine is an unthinkable contraption without the domesticating loop of the revolving governor. It would explode in the face of its inventors without that tiny heart of a self. The immense surrogate slave power released by the steam engine ushered in the Industrial Revolution. But a second, more important revolution piggybacked on it unnoticed. There could not have been an industrial revolution without a parallel (though hidden) information revolution at the same time, launched by the rapid spread of the automatic feedback system. If a fire-eating machine, such as Watt’s engine, lacked self-control, it would have taken every working hand the machine displaced to babysit its energy. So information, and not coal itself, turned the power of machines useful and therefore desirable.

Kyle: Information / coordination are the magic of automation, not just raw power
More than the purely mechanical self-hood of the other regulators like Heron’s valve, Watt’s governor, and Drebbel’s thermostat, the servomechanism of Farcot suggested the possibility of a man-machine symbiosis—a joining of two worlds. The pilot merges into the servomechanism. He gets power, it gets existence. Together they steer.
Wiener waddled around like a smart duck. He had a legendary ability to learn while slumbering. Numerous eyewitnesses tell of Wiener sleeping during a meeting, suddenly awakening at the mention of his name, and then commenting on the conversation that passed while he dozed, usually adding some penetrating insight that dumbfounded everyone else.
Wiener’s startling ideas sailed into the public mind, even though few could comprehend his book, by means of the wonderfully colorful name he coined for both his perspective and the book: Cybernetics. As has been noted by many writers, cybernetics derives from the Greek for “steersman”—a pilot that steers a ship. Wiener, who worked with servomechanisms during World War II, was struck by their uncanny ability to aid steering of all types. What is usually not mentioned is that cybernetics was also used in ancient Greece to denote a governor of a country. Plato attributes Socrates as saying, “Cybernetics saves the souls, bodies, and material possessions from the gravest dangers,” a statement that encompasses both shades of the word. Government (and that meant self-government to these Greeks) brought order by fending off chaos. Also, one had to actively steer to avoid sinking the ship. The Latin corruption of kubernetesis the derivation of governor, which Watt picked up for his cybernetic flyball.
Wiener had in mind a more explicit definition, which he stated boldly in the full title of his book, Cybernetics: or control and communication in the animal and the machine. As Wiener’s sketchy ideas were embodied by later computers and fleshed out by other theorists, cybernetics gradually acquired more of the flavor of Ampere’s governance, but without the politics.

Kyle: Cybernetics is the mechanism for COMMUNICATION, PERSUASION, UNDERSTANDING
French writer Pierre de Latil in his 1956 book Thinking by Machine.
Where does self come from? The perplexing answer suggested by cybernetics is: it emerges from itself. It cannot appear any other way. Brian Goodwin, an evolutionary biologist, told reporter Roger Lewin, “The organism is the cause and effect of itself, its own intrinsic order and organization. Natural selection isn’t the cause of organisms. Genes don’t cause organisms. There are no causes of organisms. Organisms are self-causing agencies.” Self, therefore, is an auto-conspired form. It emerges to transcend itself, just as a long snake swallowing its own tail becomes Uroborus, the mythical loop.

Kyle: Ego; “like the ring upon my finger,” exists in “one eternal round.”
A system is anything that talks to itself. All living systems and organisms ultimately reduce to a bunch of regulators—chemical pathways and neuron circuits—having conversations as dumb as “I want, I want, I want; no, you can’t, you can’t, you can’t.”
Investing machines with the ability to adapt on their own, to evolve in their own direction, and grow without human oversight is the next great advance in technology. Giving machines freedom is the only way we can have intelligent control. What little time left in this century is rehearsal time for the chief psychological chore of the 21st century: letting go, with dignity.
Clair Folsome, a microbiologist working at the University of Hawaii, had concluded from his own work with microbial soups in jars that “the foundation for stable closed ecologies of all types is basically a microbial one.” He felt that microbes were responsible for “closing the bio-elemental loops”—the flows of atmosphere and nutrients—in any ecology.
All matter on Earth is recycled (except for the insignificant escape of a trace of light gases and the fractional influx of meteorites). In system-science terms, we say Earth is materially closed. The Earth is also energetically/informationally open: sunlight pours in, and information comes and goes.
From his flask worlds, Folsome concluded that it was microbes—tiny celled microbits of life, and not redwoods, crickets, orangutans—which do the lion’s share of breathing, generating air, and ultimately supporting the indefinite populations of other noticeable organisms on Earth. An invisible substrate of microbial life steers the course of life’s whole and welds together the different nutrient loops. The organisms that catch our eye and demand our attention, Folsome suspected, were mere ornate, decorative placehold-ings as far as the atmosphere was concerned. It was the microbes in the guts in mammals and the microbes that clung to tree roots that made trees and mammals valuable in closed systems, including our planet.
At a 1982 conference in France, Hawes presented a mock-up of a spherical, transparent spaceship. Inside the glass sphere were gardens, apartments, and a pool beneath a waterfall. “Why not look at life in space as a life instead of merely travel?” Hawes asked. “Why not build a spaceship like the one we’ve been traveling on?” That is, why not create a living satellite instead of hammering together a dead space station? Reproduce the holistic nature of Earth itself as a tiny transparent globe sailing through space.

Kyle: Inspiration for Passenger?
Allen and Nelson gradually formulated a hybrid technology—called ecotechnics— based on a convergence of both machines and living organisms to support future human habitats.
How many people would you need? Military captains, expedition leaders, start-up managers, and crisis centers had long recognized that a team of eight was the ideal number for any complex hazardous project. More than eight people, and decisions got slow and squirrely; less than eight, accidents and ignorance became serious handicaps.
“It’s a sticky problem,” said Peter Warshall, a consulting ecologist for the project. “It’s a pretty impossible job to pick 100 living things, even from the same place, and put them together to make a ‘wilderness’. And here we’re taking them from all over the world to mix together since we have so many biomes.”
Everything was connected to everything. It made planning a nightmare. One approach the ecologists favored was building redundancy of pathways into the food webs.
“Designing a biome was an opportunity to think like God,” recalled Warshall. You, as a god, could create something by nothing. You could create something—some wonderful synthetic vibrant ecosystem—but you had no control over precisely what something emerged. All you could do was gather all the parts and let them self-assemble into something that worked. Walter Adey said, “Ecosystems in the wild are made up of patches. You inject as many species as you can into the system and let it decide what patch of species it wants to be in.” Surrendering control became one of the “Principles of Synthetic Ecology.” Adey continued, “We have to accept the fact that the amount of information contained in an ecosystem far exceeds the amount contained in our heads. We are going to fail if we only try things we can control and understand.” The exact details of an emerging Bio2 ecology, he warned, were beyond predicting.

Kyle: Similar to how AWS is too complex a system for any one human mind to comprehend, but what about a networked mind with infinite capacity and compute?
“Most of the species will be pygmy,” Warshall told a Discover reporter before closure, “because we really don’t have that much space. In fact, ideally we’d have pygmy people, too.”
Any field of life is a cloth woven with countless separate loops. The loops of life—the routes which materials, functions, and energy follow—double up, cross over and interweave as knots until it is impossible to tell one thread from another. Only the larger pattern knitted by the loops emerges. Each circle strengthens the others, until the whole is hard to unravel.

Kyle: Matter can be neither created nor destroyed…organized intelligences… “like the ring on my finger.”
We might as well develop a science of synthetic ecosystem creation since we’ve been creating them anyway in a haphazard fashion. Many archeoecologists believe that the entire spectrum of early humanoid activities— hunting, grazing, setting prairie fires, and selective herb gathering—forged an “artificial” ecology upon the wilderness, that is, an ecology greatly shaped by human arts. In fact, all that we think of as natural virgin wilderness is abundant with artificiality and the mark of human activity. “Many rain forests are actually pretty heavily managed by indigenous Indians,” Burgess says. “But the first thing we do when we come in is wipe out the indigenous people, so the management expertise disappears. We assumed that this growth of old trees is pristine rain forest because the only way we know how to manage a forest is to clear the trees, and these weren’t clear-cut.” Burgess believes that the mark of human activity runs so deep that it cannot be undone easily. “Once you alter the ecosystem, and you get the right seeds dispersed in the ground and the essential climate window, then the transformation starts and it’s irreversible. This does not require the presence of man to keep the synthetic ecosystem going. It runs undisturbed. All the people in California could die and its current synthetic flora and fauna will remain. It’s a new meta-stable state that remains as long as the self-reinforcing conditions stay the same.”

Kyle: So many people have a mental framework of humanity hurting the planet but they don’t appreciate its natural role within that ecosystem; not as an interloper but as a critical participant.
On Gaia, the briefly closed miniature Gaias we construct are mostly instructional aides. They are models made to answer primarily one question: what influence do we, and can we, have over the unified system of life on Earth? Are there levels we can reach, or is Gaia entirely out of our control?
Again and again, this was the message from the naturalists who assembled Bio2: The subsidy we get from nature is incredible.
The rest of us humans are outside, but inside the test tube of planet Earth. We are fiddling with Earth’s atmosphere, yet haven’t the slightest idea of how to control it, or where the dials are, or even if the system really is out of kilter and in crisis.
The technosphere supports the biosphere. Huge blowers circulate the entire air of Bio2 several times in one day. Heavy pumps move the rainwater. The motors of the wave machine run day and night. Machines hum. This unabashedly manufactured world is not outside Bio2 but inside its tissue, like bone or cartilage, an integral part of the greater organism.
All three spirits are really manifestations of the same metamorphosis best described by Dorion Sagan in his book Biospheres: The “man-made” ecosystems known as biospheres are ultimately “natural”—a planetary phenomenon that is part of the reproductive antics of life as a whole… . We are at the first phase of a planetary metamorphosis, … [the] reappearance of individuality at a hitherto unsuspected scale: not of reproducing microorganisms, or plants or animals, but of the Earth as a living whole … Yes, humans beings are involved in this reproduction, but are not insects involved in the reproduction of many flowers? That the living Earth now depends upon us and our engineering technology for its reproduction does not invalidate the proposition that biospheres, ostensibly built for human beings, represent the reproduction of the planetary biosystem… . What is definitive success? Light people living inside it for two years? How about ten years, or a century? In fact, biosphere reproduction, the building of dwellings that internally recycle all that is needed for human life, begins something whose end we cannot foresee.
We’ll keep other species, I believe, because as Bio2 helps prove, life is a technology. Life is the ultimate technology. Machine technology is a temporary surrogate for life technology. As we improve our machines they will become more organic, more biological, more like life, because life is the best technology for living. Someday the bulk of the technosphere in Bio2 will be replaced by engineered life and lifelike systems. Someday the difference between machines and biology will be hard to discern. Yet “pure” life will still have its place. What we know as life today will remain the ultimate technology because of its autonomy—it goes by itself, and more importantly, it learns by itself. Ultimate technologies, of any sort, inevitably win the allegiance of engineers, corporations, bankers, visionaries, and pioneers—all the agents who once were thought of as pure life’s biggest threat.
The technology of writing descended from elite status, steadily lowering itself out of our consciousness altogether until we now hardly notice words scribbled everywhere from logos stamped on fruit to movie subtitles. Motors began as huge noble beasts; they have since evaporated into micro-things fused (and forgotten) in most mechanical devices. George Gilder, writing in Microcosm, says, “The development of computers can be seen as the process of collapse. One component after another, once well above the surface of the microcosm, falls into the invisible sphere, and is never again seen clearly by the naked eye.” The adaptive technologies that computers bring us started out as huge, conspicuous, and centralized. But as chips, motors, and sensors collapse into the invisible realms, their flexibility lingers as a distributed environment. The materials evaporate, leaving only their collective behavior. We interact with the collective behavior—the superorganism, the ecology—so that the room as a whole becomes an adaptive cocoon.
Thus the triumph of the computer does not dehumanize the world; it makes our environment more subject to human will.” It is not machines we are creating but a mechanical environment permeated with our sense of learning. We are extending our life into our surroundings.
One definition of a coevolutionary ecology is a collection of organisms that serve as their own environment.
As computers become assistants, toasters become pets.
As Danny Hillis pointed out to me, “The reason we create artificial environments instead of accepting natural ones is that we like our environments to be constant and predictable. We used to have a computer editor that let everyone have a different interface. So we all did. Then we discovered it was a bad idea because we couldn’t use each other’s terminals. So we went back to the old way: a shared interface, a common culture. That’s part of what brings us together as humans.”
If machines knew as much about each other as we know about each other (even in our privacy), the ecology of machines would be indomitable.
Technologies of adaptation, such as distributed intelligence, flex-time accounting, niche economics, and supervised evolution, all stir up the organic in machines. Wired together into one megaloop, the world of the made slips steadily toward the world of the born.
Tibbs notes that, “As the industrial system has evolved [fuels] have become increasingly hydrogen-rich. In theory at least, pure hydrogen would be the ideal ‘clean fuel.’”
Here in one paragraph is a pop-history of the world: The African savanna hatches human hunter-gatherers—raw biology; the hunter-gatherers hatch agriculture—domestication of the natural; the farmers hatch the industrial—domestication of the machine; the industrialists hatch the currently emerging postindustrial whatever. We are still figuring out what it is, but I’ll call it the marriage of the born and the made.
The challenge is simply stated: Extend the company’s internal network outward to include all those with whom the company interacts in the marketplace. Spin a grand web to include employees, suppliers, regulators, and customers; they all become part of your company’s collective being. They are the company.
A company that was pure network would have the following traits: distributed, decentralized, collaborative, and adaptive.
The coordination costs for large-scale outsourcing have been reduced to bearable amounts by electronic trading of massive amounts of technical and accounting information.
A network is a factory for information. As the value of a product is increased by the amount of knowledge invested in it, the networks that engender the knowledge increase in value. A factory-made widget once followed a linear path from design to manufacturing and delivery.
Whether or not companies become more like software themselves, it is certain that more and more of what they make depends on more complex software, so the problems of creating complexity without defects becomes essential.
Software reliability guru C. K. Cho admonished the industrialist not to think of software as a product but as a portable factory. You are selling—or giving—a factory (the program code) to others who will use it to manufacture an answer when they need one. Your problem is to make a factory that will generate zero-defect answers. The methods of making a factory that produces perfectly reliable widgets can be easily applied to creating a factory that makes perfectly reliable answers.

Kyle: Factory software
This is what nature does: it sacrifices elegance for reliability. The neural pathways in nature continue to stun scientists with how non-optimized they are. Researchers investigating the neurons in a crayfish’s tail reported astonishment at how clunky and inelegant the circuit was. With a little work they could come up with a more parsimonious design. But the crayfish tail circuit, more redundant than it perhaps needed to be, was error free.
I asked Nobel Laureate Herbert Simon how zero-defect philosophy squared with his concept of “satisficing”—don’t aim for optimization, aim for good enough. He laughed and said, “Oh, you can make zero-defect products. The question is, can you do it profitably? If you are interested in profits, then you need to satisfice your zero defects.” There’s that complexity tradeoff again.
Cataloged below are some traits I believe a networked-based economy would exhibit: • Distributed Cores—The boundaries of a company blur to obscurity. Tasks, even seemingly core tasks like accounting or manufacturing, are jobbed out via networks to contractors, who subcontract the tasks further. Companies, from one-person to Fortune 500, become societies of work centers distributed in ownership and geography. • Adaptive Technologies—If you are not in real time, you are dead. Bar codes, laser scanners, cellular phones, 700-numbers, and satellite uplinks which are directly connected to cash registers, polling devices, and delivery trucks steer the production of goods. Heads of lettuce, as well as airline tickets, have shifting prices displayed on an LED on the grocery shelf. • Flex Manufacturing—Smaller numbers of items can be produced in smaller time periods with smaller equipment. Film processing used to happen in a couple of national centers and take weeks. It’s now done in a little machine on every street corner in a hour. Modular equipment, no standing inventory, and computer-aided design shrink product development cycles from years to weeks. • Mass Customization—Individually customized products produced on a mass scale. Cars with weather equipment for your local neighborhood; VCRs preprogrammed to your habits. All products are manufactured to personal specifications, but at mass production prices. • Industrial Ecology—Closed-loop, no-waste, zero-pollution manufacturing; products designed for disassembly; and a gradual shift to biologically compatible techniques. Increasing intolerance for transgressions against the rule of biology. • Global Accounting—Even small businesses become global in perspective. Unexploited, undeveloped economic “frontiers” disappear geographically. The game shifts from zero-sum, where every win means someone else’s loss, to positive-sum, where the economic rewards go to those able play the system as a unified whole. Alliances, partnerships, collaboration, even if temporary or paradoxical, become essential and the norm. • Coevolved Customers—Customers are trained and educated by the company, and then the company is trained and educated by the customer. Products in a network culture become updatable franchises that coevolve in continuous improvement with customer use. Think software updates and subscriptions. Companies become clubs or user groups of coevolving customers. A company cannot be a learning company without also being a teaching company. • Knowledge Based—Networked data makes any job faster, better, easier. But data is cheap, and in the large volumes on networks, a nuisance. The advantage no longer lies in “how you do a job” but in “which job do you do?”
The central act of the coming era is to connect everything to everything. All matter, big and small, will be linked into vast webs of networks at many levels. Without grand meshes there is no life, intelligence, and evolution; with networks there are all of these and more.
There is a sense in which a global mind also emerges in a network culture. The global mind is the union of computer and nature—of telephones and human brains and more. It is a very large complexity of indeterminate shape governed by an invisible hand of its own. We humans will be unconscious of what the global mind ponders. This is not because we are not smart enough, but because the design of a mind does not allow the parts to understand the whole. The particular thoughts of the global mind—and its subsequent actions—will be out of our control and beyond our understanding. Thus network economics will breed a new spiritualism.
Tim calls this movement Crypto Anarchy. “I have to tell you I think there is a coming war between two forces,” Tim May confides to me. “One force wants full disclosure, an end to secret dealings. That’s the government going after pot smokers and controversial bulletin boards. The other force wants privacy and civil liberties. In this war, encryption wins. Unless the government is successful in banning encryption, which it won’t be, encryption always wins.”
May explained, “Medieval guilds would monopolize information. When someone tried to make leather or silver outside the guilds, the King’s men came in and pounded on them because the guild paid a levy to the King. What broke the medieval guilds was printing; someone could publish a treatise on how to tan leather. In the age of printing, corporations arose to monopolize certain expertise like gunsmithing, or making steel. Now encryption will cause the erosion of the current corporate monopoly on expertise and proprietary knowledge. Corporations won’t be able to keep secrets because of how easy it will be to sell information on the nets.”
A network is a distributed thing without a center of control, and with few clear boundaries. How do you secure something without boundaries? Certain types of encryption, it turns out, are an ideal way to bring security to a decentralized system while keeping the system flexible. Rather than trying to seal out trouble with a rigid wall of security, networks can tolerate all kinds of crap if a large portion of its members use peer-to-peer encryption.

Kyle: Pluribus Note: The signal interruption that happens when they’re afraid is a kind of jail breaking that Manousos tried to use
And this is scary because pervasive encryption removes economic activity—one driving force of our society—from any hope of central control. Encryption breeds out-of-controllness.
Cypherpunks intend to level the playing field against centralized computer resources with the Fax Effect. If you have the only fax machine in the world it is worth nothing. But for every other fax installed in the world, your fax machine increases in value. In fact, the more faxes in the world, the more valuable everybody’s fax becomes. This is the logic of the Net, also known as the law of increasing returns. It goes contrary to classical economic theories of wealth based on equilibratory tradeoff. These state that you can’t get something from nothing. The truth is, you can. (Only now are a few radical economics professors formalizing this notion.) Hackers, cypherpunks, and many hi-tech entrepreneurs already know that. In network economics, more brings more. This is why giving things away so often works, and why the cypherpunks want to pass out their tools gratis. It has less to do with charity than with the clear intuition that network economics reward the more and not the less—and you can seed the “more” at the start by giving the tools away.
One of the consequences of network economics, as facilitated by ciphers and digital technology, is the transformation of what we mean by pretty good privacy. Networks shift privacy from the realm of morals to the marketplace; privacy becomes a commodity.
WE MANAGE THE DISCONNECTION of domestic utilities, such as water or electricity, through metering. But metering is neither obvious nor easy. Thomas Edison’s dazzling electrical gizmos were of little use to anyone until people had easy access to electricity in their factories and homes. So at the peak of his career Edison diverted his attention away from designing electrical devices to focus on the electrical delivery network itself. At first, very little was settled about how electricity should be created (DC or AC?), carried, or billed. For billing, Edison favored the approach that most information providers today favor: charge a flat fee. Readers pay the same for a newspaper no matter how much of it they read. Ditto for cable TV, books and computer software. All are priced flat for all you can use. Edison pushed a flat fee for electricity—a fixed amount if you are connected, nothing if you aren’t—because he felt that the costs of accounting for differential usage would exceed the cost of variances in electricity usage. But mostly Edison was stymied about how to meter electricity. For the first six months of his General Electric Lighting Company in New York City, customers paid a flat fee. To Edison’s chagrin, that didn’t work out economically. Edison was forced to come up with a stop-gap solution. His remedy, an electrolytic meter, was erratic and impractical. It froze in winter, it sometimes ran backwards, and customers couldn’t read it (nor did they trust the company’s meter readers). It wasn’t until a decade after municipal electrical networks were up and running that another inventor came up with a reliable watt-hour meter. Now we can hardly imagine buying electricity any other way. A hundred years later the information industry still lacks an information meter. George Gilder, hi-tech gadfly, puts the problem this way: “Rather than having to pay for the whole reservoir every time you are thirsty, what you want is to only pay for a glass of water.”
The more fit—the more interesting or useful—a fact is, the wider it spreads. A pretty metaphor compares the spread of genes through a population with the similar spread of ideas, or memes, in a population. Both genes and memes depend on a network of replicating machines—cells or brains or computer terminals. A network in this general sense is a swarm of flexibly interconnected nodes each of which can copy (either exactly or with variation) a message taken from another node. A population of butterflies and a flurry of e-mail messages have the same mandate: replicate or die. Information wants to be copied.
“Free the bits!” shouts Tim May. This sense of the word “free” shifts Stewart Brand’s oft-quoted maxim, “Information wants to be free”—as in “without cost”—to the more subtle “without chains or imprisonment.” Information wants to be free to wander and reproduce. Success, in a networked world of decentralized nodes, belongs to those plans that do not resist either the replication or roaming urges of information.
This is batch-mode money. Electronic money is continuous-flow. It allows recurring expenses to be paid, in Alvin Toffler’s phrase, by “bleeding electronically from one’s bank account in tiny droplets, on a minute-by-minute basis.”

Kyle: The bloodstream of the networked organism
To the list of things to hack, we may now add finance. We are headed toward programmed capitalism.
Encryption wins because it is the necessary counterforce to the Net’s runaway tendency to link. Left to itself, the Net will connect everyone to everyone, everything to everything. The Net says, ‘Just connect.” The cipher, in contrast, says, “Disconnect.” Without some force of disconnection, the world would freeze up in an overloaded tangle of unprivate connections and unfil-tered information.
Encryption permits the requisite out-of-controllness that a hive culture demands in order to keep nimble and quick as it evolves into a deepening tangle.
Aristotle might have understood. In his day, gods were entities to be feared. God as a buddy, or even an ally, is hopelessly modern. You kept out of the gods’ way, appeased them when needed, and prayed that your god would vanquish the other gods. The world was dangerous and capricious.
TO WIN POPULOUS, you’ve got to think like a god. You cannot live many small individual lives and succeed. Nor can you manipulate every individual simultaneously and hope to remain sane. Control must be surrendered to a populous mob. Individuals of Populous land, who are no more than a few bits of code, have a certain amount of autonomy and anonymity. Their pandemonium must be harnessed collectively in an intelligent way. Thajt’s your job.
“We are as gods, and might as well get good at it,” declared Stewart Brand in 1968, who had personal computers (a term he later coined) and other vivisystems in mind when he said it.
By playing SimEarth and other god games we can get a feel of what it will be like to parry with autonomous vivisystems. In SimEarth, a mind-boggling web of factors impinge on each other, making it impossible to sort out what does what. Players sometimes complain that SimEarth appears to run without regard to human control. It’s as if the game has its own agenda and you are just watching.
Will Wright, the author of SimCity and coauthor of SimEarth, is thirtyish, bookish, and certainly one of the most innovative programmers working today. Because Sim games are so hard to control, he likes to call them Software Toys. You diddle with them, explore, try out fantasies, and learn. You don’t win, any more than you might win at gardening. Wright sees his robust simulation toys as the initial baby steps toward a full march of “adaptive technologies.” These technologies are not designed, improved upon, or adjusted by the creator; rather, they—on their own accord—adapt, learn, and evolve. It shifts a bit of power from the user to the used.
To get a feel for the dynamics of a city, Wright studied a simulation of an average city done in the 1960s at MIT by Jay Forrester. Forrester summarized city life into quantitative relations rendered as mathematical equations. They were almost rules of thumb: it takes so many residents to support one firefighter; or, you need so many parking spaces for each car. Forrester published his findings as Urban Dynamics, a book which influenced many aspiring computer modelers. Forrester’s own computer simulation was entirely numerical with no visual interface. He ran the simulation and got a stack of printouts on lined paper.
But now in the computer age—the age of simulations—we are making tiny worlds in larger bandwidths, with more interaction, and with deeper embodiment. We’ve come from inert figurines to SimCity. Some simulations, like Disneyland, are no longer so tiny.
In the early 1970s the Italian novelist Umberto Eco drove around America visiting as many low-brow roadside attractions as he could get to. Eco was a semiotician—a decipherer of unnoticed signs. He found America trafficking in subtle messages about simulations and degrees of reality.
French pop-philosopher Jean Baudrillard opens his small book, Simulations (1983), with these two tightly wound paragraphs: If we were able to take as the finest allegory of simulation the Borges tale where the cartographers of the Empire draw up a map so detailed that it ends up exactly covering the territory (but where the decline of the Empire sees this map become frayed and finally ruined, a few threads still discernible in the deserts …) then this fable has come full circle for us … Abstraction today is no longer that of the map, the double, the mirror or the concept. Simulation is no longer that of a territory, a referential being, or a substance. It is the generation of models of a real without origin or reality: a hyperreal. The territory no longer precedes the map, nor survives it. Henceforth, it is the map that precedes the territory—PRECESSION OF SIMULACRA—it is the map that engenders the territory and if we were to revive the fable today, it would be the territory whose shreds are slowly rotting across the map. It is the real, and not the map, whose vestiges subsist here and there, in the deserts which are no longer of the Empire, but our own. The desert of the real itself.
The Greek Epicureans, a school of radical philosophers who figured out there must be atoms, had an unusual theory of vision. They believed every object gave off an “idol” (eidola). The same concept came to be called simulacra in Latin. Lucretius, a Roman Epicurean, says you can think of simulacra as “images of things, a sort of outer skin perpetually peeled off the surfaces of objects and flying about this way and that through the air.” These simulacra were physical, but ethereal, things. Invisible simulacra emanated from an object and impinged upon the eye causing vision. A thing’s reflection assembled in a mirror demonstrated the existence of simulacra; how else could there be two of them, and one so diaphanous? Simulacra, the Epicureans believed, could enter into people’s senses through their pores while they slept, thus conveying the idols (images) carried in dreams. Art and paintings captured the idols radiated by the original subject, just as flypaper might catch bugs. A simulacra then was a derived entity, second to the original, a parallel image—or to use modern words, a virtual reality.

Kyle: Observer Effect; each of us creates a virtual reality that is distinct from anyone else’s reality. Like the Blind Men and the elephant.
We post-modern urbanites spend a huge portion of our day immersed in hyperrealities: phone conversations, TV viewing, computer screens, radio worlds. We value them highly. Try to have a dinner conversation without referencing something you saw or heard via the media! Simulacra have become the terrain we live in. In most ways we care to measure, the hyperreal is real for us. We enter and leave hyperreality with ease.
This is the call of the Net. Keep adding players. The more they are connected, the more valuable my connection becomes. It is revealing that these obsessive game players realize they get more “reality” by increasing the fullness of the network than they get by increasing the visual resolution of the environment. Reality is first revolutionary dynamics, only secondly is it six million pixels.
More is different. Keep adding grains of sand to the first grain and you’ll get a dune, which is altogether different than a single grain. Keep adding players to the Net and you get… what? … something very different… a distributed being, a virtual world, a hive mind, a networked community.
Col. Thorpe rightly proclaims that distributed intelligence— not firepower—wins wars. Other visionaries say the same about the future of corporations. “The next breakthrough won’t be in the individual interface but in the team interface,” says John Seely Brown, the research director of Xerox’s PARC.
The god who lowered himself into his own creation is an old theme. Stanislaw Lem once wrote a great science-fiction classic about a tyrant who kept his world in a box. But another version predates it by millennia.
Now Yahweh himself was outside of time, beyond space and form, and unlimited in scope—ultimate software. So making a model of himself that could operate in bounded material, limited in scale, and constrained by time was not a cinch. By definition, the model wasn’t perfect.

Kyle: Gods in embryo
To be a god, at least to be a creative one, one must relinquish control and embrace uncertainty. Absolute control is absolutely boring. To birth the new, the unexpected, the truly novel—that is, to be genuinely surprised— one must surrender the seat of power to the mob below. The great irony of god games is that letting go is the only way to win.
BORGES: The text of the two pages made it possible for a librarian to discover the fundamental law of the Library. This thinker observed that all the books, no matter how diverse they might be, are made up of the same elements: the space, the period, the comma, the twenty-two letters of the alphabet. He also alleged a fact which travelers have confirmed: In the vast Library there are no two identical books. From these two incontrovertible premises he deduced that the Library is total and that its shelves register all the possible combinations of the twenty-odd orthographical symbols (a number which, though extremely vast, is not infinite). ME: So, in other words, any book you could possibly write, in any language, could be found (theoretically) in the library. It contains all past and future books! BORGES: Everything: the minutely detailed history of the future, the archangels’ autobiographies, the faithful catalogue of the Library, thousands and thousands of false catalogues, the demonstration of the fallacy of the true catalogue, the Gnostic gospel of the Basilides, the commentary on that gospel, the commentary on the commentary on that gospel, the true story of your death, the translation of every book in all languages, the interpolations of every book in all books.
If this Library contained all possible books, my reasoning went, then any volumes that fit the rules of grammar (let alone were interesting) would be so tiny a fraction of the total books, that my coming upon one by random search would be miraculous. Five hundred years sounded about right as the time needed to find two sensible pages—any two sensible pages. To find a readable book would take several millenniums, with luck.
Karl Sims, who works for Thinking Machines, the maker of the CM5, has made a Borgian Library of art and pictures. Sims first wrote special software for the Connection Machine and then constructed a universe (which others call a Library) of all possible pictures. The same machinery that can generate a possible book can generate a possible picture. In the former case the output are letters printed in linear sequence; in the latter, a rectangle of pixels displayed on a screen. Sims hunts for patterns of pixels instead of patterns of letters.
The Method—as evolution—can be conceived of not as traveling but as breeding. Sims describes the twenty new images as twenty children of an original parent. The twenty pictures vary just as offspring do. Then he selects the “best” offspring, which in turn immediately sires twenty new variations. He’ll pick the best of that lot, and that best will sire twenty more variations. He can begin with a simple sphere and by cumulative selection end with a cathedral.
The shock is not that evolution has been transported from carbon to silicon; silicon and carbon are actually very similar elements. The shock of artificial evolution is that it is fundamentally natural to computers.
“Very early I had a strong intuitive conviction that the embryology I wanted should be recursive. My intuition was based partly upon the fact that embryology in real life can be thought of as recursive,” Dawkins told me. By recursive embryology, Dawkins meant that simple rules iterated over and over again (including rules that play upon their own results) would furnish much of the complexity of the final form. For instance, as the recursive rule “grow one unit then fork into two” is applied over successive generations to a starting stick, it will produce a bushy many-forked thing after about five iterations.
The Library contains all the forms of life past and life future and even, perhaps, the shape of life present on other planets. We are blocked by our own natural prejudices from contemplating these alternative life forms in any detail. Our minds quickly drift back to what we know as natural. We can give it a momentary thought, but we balk at filling in much detail on so whimsical a fantasy. But evolution can be harnessed to serve as a wild bronco to carry us where we can’t go by ourselves. On this untamed transport we arrive at a place stuffed with odd bodies, fully imagined (not by us) down to the last hair.
The copyright status of an artificially evolved creation is in legal limbo. Who gets the protection, the artist who bred or the artist who created the program? In the future, lawyers may demand a record of the evolutionary path an artist followed to arrive at an evolved creation as evidence that such work belongs to him and was not copied, or due to the creator of the Library.

Kyle: Same as AI-generated art / work today.
This sentiment was recognized centuries ago, long before the advent of computers. As Denis Diderot wrote in 1755: The number of books will grow continually, and one can predict that a time will come when it will be almost as difficult to learn anything from books as from the direct study of the whole universe. It will be almost as convenient to search for some bit of truth concealed in nature as it will be to find it hidden away in an immense multitude of bound volumes.
Some students of the human mind make a strong argument that thinking is a type of evolution of ideas within the brain. According to this argument, all created things are evolved. As I write these words, I have to agree. I began this book not with a sentence formed in my mind but with an arbitrarily chosen phrase, “I am.” Then in unconsciously rapid succession I evaluated a headful of possible next words. I picked one that seemed esthetically fit, “sealed.” After “I am sealed,” I went on to the next word, choosing from among 100,000s of possible ones. Each selected word bred the choices for the next until I had evolved almost a sentence of words. Toward the end of the sentence my choices were constrained somewhat by the words I had already chosen at the beginning, so learning helped the breeding go more quickly.

Kyle: Sounds like an LLM to me.
You could not even say computational evolution had a cult following. The lack of interest from biology was understandable (but not commendable); biologists reasoned that nature was far too complex to be meaningfully represented by computers of that time. The lack of interest from computer science is more baffling. I was often perplexed in my research for this book why such a fundamental process as computational evolution could be so wholly ignored? I now believe the disregard stems from the messy parallelism inherent in evolution and the fundamental conflict it presented to the reigning dogma of computers: the von Neumann serial program.
Browsing for nothing in particular in the University of Michigan math library in 1953, Holland had an epiphany. He stumbled upon a volume, The Genetical Theory of Natural Selection, written by R. A. Fisher in 1929. It was Darwin who led the consequential shift from thinking about creatures as individuals to thinking about populations of individuals, but it was Fisher who transformed this population-thinking into a quantitative science.
A single processor in the Connection Machine is very stupid. It might be as smart as an ant. On its own, a single processor could not come up with an original solution to anything, no matter how many years it spent. Nor would it come up with much if 64,000 processors were strung in a row. But 64,000 dumb, mindless, ant-brains wired up into a vast interconnected network become a field of evolving populations and, at the same time, look like a mass of neurons in a brain. Out of this network of dumbness emerge brilliant solutions to problems that tax humans. This “order-emerging-out-of-massive-connections” approach to artificial intelligence became known as “connectionism.”

Kyle: Connectionism = neural nets; important new concept
Connectionism rekindled earlier intuitions that evolution and learning were deeply related. The connectionists who were reaching for artificial learning latched onto the model of vast webs interconnecting dumb neurons, and then took off with it. They developed a brand of connected concurrent processing—running in either virtual or hardwired parallel computers—that performed simultaneous calculations en masse, similar to genetic algorithms but with more sophisticated (smarter) accounting systems. These smartened up networks were called neural networks. So far neural nets have achieved only limited success in generating partial “intelligence,” although their pattern-recognition abilities are useful.
But that anything at all emerges from a field of lowly connections is startling. What kind of magic happens inside a web to give it an almost divine power to birth organization from dumb nodes interconnected, or breed software from mindless processors wired to each other? What alchemic transformation occurs when you connect everything to everything? One minute you have a mob of simple individuals, the next, after connection, you have useful, emergent order.
Two hundred years from now, artificial adaptation—tamed, measured and piped into every type of mechanical apparatus we have—will become the central organizing force in our society.
We have built machines as complicated as is possible with unassisted engineering. The kind of projects we now have on the drawing boards—software programs reckoned in tens of millions of lines of code, communication systems spanning the planet, factories that must adapt to rapidly shifting global buying habits and retool in days, cheap Robbie the Robots—all demand a degree of complexity that only evolution can coordinate.
Naked information is hard to kill, and without death there is no evolution.
Directed evolution is another name for supervised learning, another name for the Method of traversing the Library, another name for breeding. Instead of letting the selection emerge, the breeder directs the choice of varieties of dogs, pigeons, pharmaceuticals, or graphic images.
Ackley gushes, “The space of computational machinery is unbelievably vast and we have only explored very tiny corners of it. What I’m doing, and what I want to do more of, is to expand the space of what people recognize as computation.”
Biological Lamarckian evolution is hampered by a strict mathematical law: that it is supremely easy to multiply prime factors together, but supremely hard to derive the prime factors out of the result. The best encryption schemes work on this same asymmetrical difficulty. Biological Lamarckism probably hasn’t happened because it requires an improbable biological decryption scheme.
The thing about Lamarckian evolution, says Ackley, is that it “very quickly squeezes out the idiots” in a population. Ackley once bellowed to a roomful of scientists, “Lamarck just blows the doors off of Darwin!”
Learning is defined as adaptation within an individual’s lifetime. In classical Darwinian evolution, individual learning doesn’t count for much. But Lamarckian evolution permits information acquired during a lifetime (including how to build muscles or solve equations) to be incorporated into the long-term, dumb learning that takes place over evolution. Lamarckian evolution produces smarter answers because it is a smarter type of search.
Because they were inspired by the collective behavior of ant colonies, the Milan group call their searches “Ant Algorithms.” Ants have distributed parallel systems all figured out. Ants are the history of social organization and the future of computers. A colony may contain a million workers and hundreds of queens, and the entire mass of them can build a city while only dimly aware of one another.
An army of ants too dumb to measure and too blind to see far can rapidly find the shortest route across a very rugged landscape. This calculation perfectly mirrors the evolutionary search: dumb, blind, simultaneous agents trying to optimize a path on a computationally rugged landscape. Ants are a parallel processing machine.
The Italians tested their ant machine on a standard benchmark, the traveling salesman problem. The riddle was: what is the shortest route between a large number of cities, if you can only visit each city once? Each virtual ant in the colony would set out rambling from city to city leaving a trail of pheromones. The shorter the path between cities, the less the pheromone evaporated. The stronger the pheromone signal, the more other ants followed that route. Shorter paths were thus self-reinforcing. Run for 5,000 rounds or so, the ant group-mind would evolve a fairly optimal global route.
Individual ants effectively broadcast what they have learned into their hive. Broadcasting, like cultural teaching, is a part of Lamarckian search. Ackley: “There are ways to exchange information other than sex. Like the evening news.”
Out of all the possible ways to connect the nodes in distributed computers, only a very few, such as the ant algorithms, have even been examined.
Parallel computers embody the challenge of all distributed swarm systems, including phone networks, military systems, the planetary 24-hour financial web, and large computer networks. Their complexity is taxing our ability to steer them. “The complexity of programming a massively parallel machine is probably beyond us,” Tom Ray told me. “I don’t think we’ll ever be able to write software that fully uses the capacity of parallelism.”
If we can evolve software, we’ll be way ahead.” When it comes to distributed network kinds of things, Rays says, “Evolution is the natural way to program.”
Danny Hillis has come to the same conclusion. He is serious when he says he wants his Connection Machine to evolve commercial software. “We want these systems to solve a problem we don’t know how to solve, but merely know how to state.”
When computer programs swell to billions of lines of code, just keeping them up and “alive” will become a major chore. Too much of the economy and too many people’s lives will depend on billion-line programs to let them go down for even an instant. David Ackley thinks that reliability and up-time will become the primary chore of the software itself. “I claim that for a really complex program sheer survival is going to consume more of its resources.” Right now only a small portion of a large program is dedicated to maintenance, error correction, and hygiene. “In the future,” predicts Ackley, “99 percent of raw computer cycles are going to be spent on the beast watching itself to keep it going. Only that remaining 1 percent is going to be used for user tasks—telephone switching or whatever. Because the beast can’t do the user tasks unless it survives.”
Engineers, even on 24-hour beepers, can’t keep billion-line code alive. Artificial evolution may be the only way to keep software on its toes, looking lively.
Artificial evolution is the end of engineering’s hegemony. Evolution will take us beyond our ability to plan. Evolution will craft things we can’t. Evolution will make them more flawless than we can. And evolution will maintain them as we can’t.
“In traditional animation all knowledge of physics has to come from the animator’s head, “ says Michael Kass, a computer graphics engineer at Apple Computer.
Kass is trying to imbue physics into simulated worlds. “We thought about the tradition of having the physics in the animator’s head and decided that instead, the computer should have some knowledge of physics.”
Ralph Guggenheim, production director at Pixar: “Most hand animators believe that what Pixar does is feed scripts into a computer and out comes a film. That’s why we were once barred from animation festivals. But if we were to really do that, we could not create great stories… . The chief day-to-day problem we have at Pixar is that computer animation reverses the animation process. It asks animators to describe before they animate what it is they want to animate!”
Animators, true artists, are like writers in that they don’t know what they want to say until they hear themselves say it. Guggenheim reiterates, “Animators can’t know a character until they animate it. They will tell you that it is very slow going in the beginning of a story because they are becoming familiar with their character. Then it starts speeding up as they become more intimate with it. As they get to the halfway point of the film, now they know the character well and they are screaming through the frames.”
“If animating a believable character was as easy as feeding a script into a computer, then there would be no bad actors in the world,” said Guggenheim. “But we know that not all actors are great.
“I’m a couch-potato director,” Strassman says. “If I don’t like the way the scene went I’ll have them redo it.” So he types in an alternative: “In this scene, John gets sad. He’s holding the book in his left hand. He offers it to Mary kindly, but she refuses it politely.” Again, the characters play out the scene.
As formalized in 1951 by Tinbergen in his book The Study of Instinct, the behavior of an animal is a decentralized coordination of independent action (drive) centers which are combined like behavioral building blocks. Some behavioral modules consist of a reflex; they invoke a simple function, such as: pull away when hot, or blink when touched. The reflex knows nothing of where it is, what else is going on, or even of the current goal of its host body. It can be triggered anytime the right stimulus appears.
From Strassman’s “desktop theater” we go to Joseph Bates’s “computational drama.” Bates envisions a drama of distributed control. A story becomes a type of coevolution, with perhaps only its outer boundaries predestined. You could be in an episode of Star Trek attempting to influence alternative storylines, or you could be on a journey with a synthetic Don Quixote confronting new fantasies. Bates, who is chiefly concerned about the experience of the human user of Oz, puts his quest this way. “The question I’m working on is: How do you impose a destiny upon a user without removing their freedom?”
“Virtual reality,” says de Graf, “is not going to be interesting unless it is populated with interesting characters.”
Until recently, all our artifacts, all our own handmade creations have been under our authority. But as we cultivate synthetic life in our artifacts, we cultivate the loss of our command. “Out of control,” to be honest, is a great exaggeration of the state that our enlivened machines will take. They will remain indirectly under our influence and guidance but free of our domination.
A book in Borges’s Library spans a million genes; a hi-resolution Hollywood movie frame, 30 million. Yet as immense as the libraries built out of these are, they are only a dust mote in the meta-library of all possible libraries. It is one of the hallmarks of life that it continues to enlarge the space of its own being. Nature is an ever-expanding library of possibilities. It is an open universe. At the same time that life turns up the most improbable books from the Library shelves, it is adding new wings to the collection, making room for more of its improbable texts.
Humans seek a simple formula such as Newton’s f=ma, Koza suggests, because it reflects our innate faith that at bottom there is elegant order in the universe. More importantly, simplicity is a human convenience. The heartwarming beauty we perceive in f=ma is reinforced by the cold fact that it is a much easier formula to use than Koza’s spiral monster. In the days before computers and calculators, a simple equation was more useful because it was easier to compute without errors. Complicated formulas were a grind and treacherous. But, within a certain range, neither nature nor parallel computers are troubled by convoluted logic. The extra steps we find ugly and stupefying, they do perfectly in tedious exactitude.
The great irony puzzling cognitive scientists is why human consciousness is so unable to think in parallel, despite the fact that the brain runs as a parallel machine. We have an almost uncanny blind spot in our intellect. We cannot innately grasp concepts in probability, horizontal causality, and simultaneous logic. We simply don’t think like that. Instead our minds retreat to the serial narrative—the linear story. That’s why the first computers were programmed in von Neumann’s serial design: because that’s how humans think.
In a very provocative essay in the Winter 1992 Daedalus, James Bailey, director of marketing at Thinking Machines, wrote of the wonderful boomeranging influence that parallel computers have on our thinking. Entitled “First We Reshape Our Computers. Then Our Computers Reshape Us,” Bailey argues that parallel computers are opening up new territories in our intellectual landscape. New styles of computer logic in turn force new questions and new perspectives from us. “Perhaps,” Bailey suggests, “whole new forms of reckoning exist, forms that only make sense in parallel.” Thinking like evolution may open up new doors in the universe.
“Evolution doesn’t care about what makes sense; it cares about what works,” says Tom Ray.
Indeed the catalog of natural oddities is almost as long as the list of all creatures; every creature is in some way hacking a living by reinterpreting the rules.
The only machine we know of that can reshape its internal connections is the living gray tissue we call the brain. The only machine that would generate its own structure that we can presently even imagine manufacturing would be a software program that could reprogram itself. The evolving equations of Sims and Koza are the first step toward a self-reprogramming machine. An equation that can breed other equations is the basic soil for this kind of life. Equations that breed other equations are an open-ended universe. Any possible equation could arise, including self-replicating equations and formulas that loop back in a Uroborus bite to support themselves. This kind of recursive program, which reaches into itself and rewrites its own rules, unleashes the most magnificent power of all: the creation of perpetual novelty.
AS A TOOL, evolution is good for three things: How to get somewhere you want but can’t find the route to. How to get to somewhere you can’t imagine. How to open up entirely new places to get to. The third use is the door to an open universe. It is unsupervised, undirected evolution. It is Holland’s ever-expanding perpetual novelty machine, the thing that creates itself.
Rudy Rucker came up with the most expansive motivation for artificial life I’ve heard: “Right now an ordinary computer program may be a thousand lines and take a few minutes to run. Artificial life is about finding a computer code that is only a few lines long and that takes a thousand years to run.” That seems about right. We want the same in our robots: Design them for a few years and then have them run for centuries, perhaps even manufacturing their replacements. That’s what an acorn is too—a few lines of code that run out as a 180-year-old tree.
Chris Langton’s office sat catty-corner to the atomic museum in Los Alamos, a reminder of the power we have to destroy. That power stirred Langton. “By the middle of this century, mankind had acquired the power to extinguish life,” he wrote in one of his academic papers. “By the end of the century, he will be able to create it. Of the two, it is hard to say which places the larger burden of responsibilities on our shoulders.”
Tom Ray once told me, “I don’t want to download life into computers. I want to upload computers into life.”
The goal is to make, say, a car that adjusts its frame and wheels to fit the kind of road it’s on, to make a road aware of its conditions to repair itself, to make a car factory flexible to produce a personalized car to fit each customer, to make a highway system aware of traffic to minimize it, and to make a city learn to balance the traffic it absorbs. Each of these impute to technology the ability to change itself. But rather than continually pump in bits of change, we’d like to implant the intact heart of change—an adaptive spirit—into the core of the system itself. This magic ghost is artificial evolution. In stronger doses evolution breeds artificial intelligence, and in dilute form it promotes mild adaptation. Either way, evolution is the broad self-guiding force that machines still lack in larger doses.
The irony is that evolution is even more ignorant than we knew: it is blind both coming and going. Blind not only to how things might be, but also to how they are now and were in the past. Nature doesn’t know what it did yesterday, doesn’t care. It keeps no audited record of successes, of smart moves, of things that helped. We—all organisms—are a historical record of sorts, but our history is not easy to unravel or decipher without great intelligence.
“It works, why worry?” is life’s deepest philosophy.
If nature transmitted information in both directions within organisms, it would allow the possibility of Lamarckian evolution, which requires two-way communication between gene and its products. The advantages of Lamarckism are awesome. When an animal needs faster legs to survive, it could use body-to-gene communication to direct the genes to make faster leg muscles, and then pass that innovation on to its offspring. Evolution would accelerate madly.
Genuine two-way genetic communication would light up an interesting bunch of questions: Would there be any biological advantage if such a mechanism were possible? What else would it take to have a Lamarckian biology? Could there have been a biological route to such a mechanism at one time? If it is possible, why hasn’t it happened? Could we outline a working biological Lamarckism as a thought experiment? In all probability, Lamarckian biology requires a type of deep complexity—an intelligence—that most organisms can’t reach. But where complexity is rich enough for intelligence, such as in human organisms and organizations, and their robotic offspring, Lamarckian evolution is possible and advantageous.
An adaptation spearheaded by the body (a somatic adaptation) is assimilated over time by the genes. Theoretical biologist C. H. Waddington called this transfer “genetic assimilation.” Cyberneticist Gregory Bateson called it “somatic adaptation.” Bateson likened it to legislative change in society—first a change is made by the people, then it is made law.
“Learn” means adaptation within a lifetime instead of over lifetimes. The computerists make no real distinction between behavioral learning and somatic learning. What matters is that both types of adaptation search the fitness space within the lifetime of an individual
In the words of Ackley and Littman, “We found that learning and evolution together were more successful than either alone in producing adaptive populations that survived to the end of our simulation.” Their organism’s exploratory learning is essentially a random search of a fixed problem.
Learning plus evolution is basically the recipe for culture. It may be that just as learning and behavior can pass off their information to genes, genes can pass their information off onto learning and behavior. The former is called genetic assimilation; the latter, cultural assimilation.
In this view—which is a rather old idea—each step of cultural learning won by early humankind (fire, hammer, writing) prepared a “possibility space” that allowed human minds and bodies to shift so that some of what it once did biologically would afterwards be done culturally. Over time the biology of humans became dependent on the culture of humans, and more supportive of further culturalization, since culture assumed some of biology’s work. Every additional week a child was reared by culture (grandparent’s wisdom) instead of by animal instinct gave human biology another chance to irrevocably transfer that duty to further cultural rearing.

Kyle: Everything is about information transfer. We used to have to wait a LONG time for “knowledge” to transfer genetically to our bodies and instincts. Then we started transferring faster with cultural / behavioral transfer. We’re living in the information economy where transfer occur through writing / laws, but what about cybernetic transfer? An SDK to the mind? Feels like much faster transfer (eg Neo learning Kung-Fu)
But if we consider culture as its own self-organizing system—a system with its own agenda and pressure to survive—then the history of humans gets even more interesting. As Richard Dawkins has shown, systems of self-replicating ideas or memes can quickly accumulate their own agenda and behaviors.
In the absence of culture, humans seem to lose distinctly human talents. (As somewhat unsatisfactory evidence we have the failures of “wolf children” raised by animals to develop into creative adults.) Culture and flesh, then, meld into a symbiotic relationship. In Danny Hillis’s terminology, civilized humans are “the world’s most successful symbionts”—culture and biology behaving as mutually beneficial parasites for each other—the coolest example of coevo-lution we have. And as in all cases of coevolution, it implies positive feedback and the law of increasing returns.
Just as life infiltrates matter mercilessly and then hijacks it forever, cultural life hijacks biology. In the strong sense I’m advocating here, culture modifies our genes.
Evolution on Earth has already undergone structural changes in its four-billion-year lifespan and will probably undergo more. The evolution of evolution can be summed up by the following series of historical evolution types: 1) Auto-genesis of systems 2) Replication 3) Genetic control 4) Somatic plasticity 5) Memetic culture 6) Self-directed evolution.
Organisms, memes, biomes—the whole ball of wax—are only evolution’s way to keep evolving. What evolution really wants—that is, where it is headed—is to uncover (or create) a mechanism that will most quickly uncover (or create) possible forms, things, ideas, processes in the universe. Its ultimate goal is not only to create forms, things, and ideas, but to create new ways in which new things are found or created. Hyperevolution does this by bootstrapping itself into a layered strategy that continually increases its reach, continually creates new libraries of possible places to explore, and continually searches for better, more creative ways to create.

Kyle: Trying to find the answer to “The Last Question”
Evolution’s job is to create all possible possibilities by creating the spaces in which they could be.
Evolution’s role to explain everything, however, stains it with a tinge of religiosity. As Bob Crosby of the Washington Evolutionary Systems Society unabashedly says, “Where other people see the hand of God, we see evolution.”
Author Mary Midgley begins her slim and wonderful monograph Evolution as a Religion, with these four sentences: “The theory of evolution is not just an inert piece of theoretical science. It is, and cannot help being, also a powerful folk-tale about human origins. Any narrative must have symbolic force. We are probably the first culture not to make that its main function.”
So while natural selection may be responsible for microchange—a trend in variations—no one can say indisputably that it is responsible for macrochange—the open-ended creation of an unexpected novel form and progress toward increasing complexity.
SYMBIOSIS—the merger of two organisms into one—was once thought to occur only in isolated curiosities like lichens. After Lynn Margulis postulated bacterial symbiosis as a central event in the formation of the ancestral cell, biologists found symbiosis popping up frequently in microbial life. Since microbial life is (and has always been) the bulk of all life on Earth, and the primary Gaian workhorse, widespread microbial symbiosis makes symbiosis fundamental, both in the past and in the present.
Viruses themselves are sometimes taken in symbiotically. A number of biologists believe that large chunks of human DNA were inserted viruses. A few even think that it’s a loop—that many human disease viruses are escaped hunks of human DNA. If true, the symbiotic nature of a cell provides a couple of lessons. First, it gives an example of a significant evolutionary change that lessens immediate benefits to the individual (since the individual disappears), in contradiction to classical Darwinian dogma. Second, it gives an example of evolutionary change that is not amassed by slight incremental differences, also in contradiction to Darwinian dogma. Routine symbiosis on a large scale could drive many of the complexities in nature that seem to require multiple simultaneous innovations. It would provide evolution with several other advantages; for instance, it would exploit the power of cooperation, rather than competition, exclusively. At the very least, cooperation nurtures a distinct set of niches and a type of diversity that competition cannot produce—such as lichens. In other words, it unleashes another dimension in evolution by enlarging its library of forms. Also, a small amount of symbiotic coordination at the right time could replace an eon of minor alterations. In one mutual relationship, evolution could jump past a million years of individual trial and error.

Kyle: PLURIBUS IN A PARAGRAPH. Suspension of the biological Ego is seen as an advantage to rapid evolution rather than a loss of something inherently critical.
Evolution theory, from Darwin on, has had a dismal record in dealing with the origin of innovation. As his book title made clear, the question of the origin of species was the great riddle Darwin hoped to solve, not the origin of individuality. He asked, Where did new kinds of creatures come from? He did not ask, Where did variation among individuals come from?
There is a grave and unmistakable lack of intermediates in the fossil record. The fact that creationists gloat over it should not tempt others to ignore it. The “fossil gaps” were a hole in Darwin’s theory that he promised would go away in the future, when more areas of Earth were searched by professional evolutionists. The gaps did not go away in the least. Once a “trade secret” of paleontologists, the gaps are now acknowledged by every leading authority on evolution. Here are two: “The known fossil record fails to document a single example of phyletic [gradual] evolution accomplishing a major morphologic transition and hence offers no evidence that the grad-ualistic model can be valid,” says Stephen Stanley, evolutionary paleontologist. And here’s Steven Jay Gould again, speaking as the expert paleontologist he is: All paleontologists know that the fossil record contains precious little in the way of intermediate forms; transitions between major groups are characteristically abrupt… . The history of most fossil species includes two features particularly inconsistent with gradualism: 1. Stasis. Most species exhibit no directional change during their tenure on Earth. They appear in the fossil record looking much the same way as when they disappear… . 2. Sudden appearance. In any local area, a species does not arise gradually by the steady transformation of its ancestors; it appears all at once and “fully formed.” In the eyes of science historians, Darwin’s most consequential claim was that the discontinuous face of life as a whole was an illusion. The separate-ness of species, the “immutable essence” intrinsic to each type of animal or plant—a principle which the ancient philosophers had taught forever—was, he claimed, false. The Bible spoke of creatures “each made in their kind,” and most biologists of the day, including the young Darwin, thought species kept to their breed in an idealized way. It was the type that mattered, while individuals conformed more or less to the type. The enlightened Darwin announced, however, that (1) every individual differed significantly; (2) all life was dynamically plastic, infinitely malleable between individuals, so (3) individuals arranged in populations were all that mattered. The barriers erected by species were porous and illusory. By shifting the discontinuity from species to every individual, Darwin vaporized it. Life was one evenly distributed being. But intriguing suspicions now accumulating in the study of complex systems, particularly complex systems that adapt, learn, and evolve, suggest Darwin was wrong in his most revolutionary premise. Life is largely clumped into parcels and only mildly plastic. Species either persist or die. They transmute into something else under only the most mysterious and uncertain conditions. By and large, complex things fall into categories and the categories persist. Stasis of the category is the norm: the typical lifespan for a species is between one and ten…
Things that resemble organisms—economic firms, thoughts in the brain, ecological communities, nation-states—also naturally differentiate into persistent clumps. Human institution clumps—churches, departments, companies—find it easier to grow than to evolve. Required to adapt too far from their origins, most institutions will die.
We know virtually nothing of the real distribution of life in the Library of realities. It may be so sparse and unpregnant with possibilities that there is only one living path through it—the path we are currently on. Or there might be broad highways in the Library that channel a number of paths into a few bottlenecks that all beings must cross—say, the resonant attractor of four legs, a tubular gut, five-digit hands. Or there may be a submerged bias in life’s substrate, so that no matter where you start you eventually arrive on the shores of bilateral symmetry, segmented limbs, and intelligence of one kind or another. We just don’t know. But with artificial evolution at work, we could know.
I believe there is a mathematics of life. Natural selection may be its additive function. But to fully explain the origin of life, the remarkable trend toward complexity, and the invention of intelligence requires more than addition. It needs a rich mathematics of complex functions built upon each other; it needs deeper evolution. Natural selection alone is not enough, not by miles. It must be alloyed with more creative, generative processes to accomplish much. It must have more to naturally select from.
To paraphrase Lewontin, “An evolution that cannot make all things, explains some things.”
At the facilitator’s command this circle of people bend their knees and sit on the spontaneously generated knee-lap of the person behind them. If done in unison, the ring of people lowering to sit are suddenly propped up on a self-supporting collective chair. If one person misses the lap behind him, the whole circling line crashes. The world’s record for a stable lap game is several hundred people.
As the reality of the lap game proves, however, circular causality is not impossible. Tautology can hold up 200 pounds of flesh. It’s real. Tautology is, in fact, an essential ingredient of stable systems.
Cognitive philosopher Douglas Hofstadter calls these paradoxical circuits “Strange Loops.” As examples, Hofstadter points to the seemingly ever rising notes in a Bach canon, or the endlessly rising steps in an Escher staircase. He also includes as Strange Loops the famous paradox about Cretan liars who say they never lie, and Godel’s proof of unprovable mathematical axioms. Hofstadter writes in Godel, Escher, Bach: “The ‘Strange Loop’ phenomenon occurs whenever, by moving upwards (or downwards) through the levels of some hierarchical system, we unexpectedly find ourselves right back where we started.”
I mentioned to Kauffman the controversial idea that in any society with the proper strength of communication and information connection, democracy becomes inevitable. Where ideas are free to flow and generate new ideas, the political organization will eventually head toward democracy as an unavoidable self-organizing strong attractor. Kauffman agreed with the parallel: “When I was a sophomore in ‘58 or ‘59 I wrote a paper in philosophy that I labored over with much passion. I was trying to figure out why democracy worked. It’s obvious that democracy doesn’t work because it’s the rule of the majority. Now, 33 years later, I see that democracy is a device that allows conflicting minorities to reach relative fluid compromises. It keeps subgroups from getting stuck on some locally good but globally inferior solution.”

Kyle: Pluribus = maximum democracy
Can we prove that a finite set of functions generates this infinite set of possibilities?” Whew. I call that a “Kauffman machine.” A small but well-chosen set of functions that connect into an auto-generating ring and produce an infinite jet of more complex functions. Nature is full of Kauffman machines. An egg cell producing the body of a whale is one. An evolution machine generating a flamingo over a billion years from a bacterial blob is another. Can we make an artificial Kauffman machine? This may more properly be called a von Neumann machine because von Neumann asked the same question in the early 1940s. He wondered, Can a machine make another machine more complex that itself? Whatever it is called, the question is the same: How does complexity build itself up?
A Question Worth Asking. That’s what Kauffman thought of his notion of self-organized order in evolutionary systems. Kauffman confided to me: “Somehow, each of us in our own heart is able to ask questions that we think are profound in the sense that the answer would be truly important. The enormous puzzle is why in the world any of us ask the questions that we do.”
But wouldn’t it be wonderful if somehow there are laws that make laws that make laws, so that the universe is, in John Wheeler’s words, something that is looking in at itself!? The universe posts its own rules and emerges out of a self-consistent thing. Maybe that’s not impossible, this notion that quarks and gluons and atoms and elementary particles have invented the laws by which they transform one another.”

Kyle: Laws of justice and mercy etc
The prime variable Kauffman played with was the connectivity of the network. In a sparsely connected network, each node would on average only connect to one other node, or less. In a richly connected network, each node would link to ten or a hundred or a thousand or a million other nodes. In theory the limit to the number of connections per node is simply the total number of nodes, minus one. A million-headed network could have a million-minus-one connections at each node; every node is connected to every other node. To continue our rough analogy, every employee of GM could be directly linked to all 749,999 other employees of GM.

Kyle: Pluribus Hive Mind
A system where few agents influenced other agents was not very adaptable. The soup of connections was too thin to transmit an innovation. The system would fail to evolve. As Kauffman increased the average number of links between nodes, the system became more resilient, “bouncing back” when perturbed. The system could maintain stability while the environment changed. It would evolve. The completely unexpected finding was that beyond a certain level of linking density, continued connectivity would only decrease the adaptability of the system as a whole.
Kauffman’s second unexpected finding was that this low optimal value didn’t seem to fluctuate much, no matter how many members comprised a specific network. In other words, as more members were added to the network, it didn’t pay (in terms of systemwide adaptability) to increase the number of links to each node. To evolve most rapidly, add members but don’t increase average link rates. This result confirmed what Craig Reynolds had found in his synthetic flocks: you could load a flock up with more and more members without having to reconfigure its structure.
At the ideal number of connections, the ideal amount of information flowed between agents, and the system as a whole found the optimal solutions consistently. If their environment was changing rapidly, this meant that the network remained stable—persisting as a whole over time.

Kyle: All of this optimization math is dependent on the compute capacity of any individual node. Finite minds require finite extensibility. But what about infinite minds? Or at least DRAMATICALLY less finite nodes?
We postmodern communication addicts might want to pay attention to this. In our networked society we are pumping up both the total number of people connected (in 1993, the global network of networks was expanding at the rate of 15 percent additional users per month!), and the number of people and places to whom each member is connected. Faxes, phones, direct junk mail, and large cross-referenced data bases in business and government in effect increase the number of links between each person. Neither expansion particularly increases the adaptability of our system (society) as a whole.
Swarmlike networks, he bets, all behave similarly on one level. Kauffman is fond of speculating that “IBM and E. coli both see the world in the same way.”
In 1824, the French military engineer Carnot (rhymes with Godot, the tardy lead in Samuel Beckett’s play) derived a principle that later became known as the Second Law of Thermodynamics. Roughly paraphrased it goes thus: all systems everywhere run down over time. Together with the First Law (that energy is conserved overall), Carnot’s Second Law was the key framework in the following century for understanding not only heat but most of physics, chemistry, and quantum mechanics. In short, the theory of heat undergirds all of modern physical science.

Kyle: Entropy?
The search for a Second Law of Biology, a law of rising order, is unconsciously behind much of the search for deeper evolutions and the quest for hyperlife. Many postdarwinians doubt that natural selection alone is powerful enough to offset Carnot’s Second Law of Thermodynamics. Yet, we are here, so something has. They are not sure what they are looking for, but they intuitively feel that it can be stated as a complementary force to entropy. Some call it anti-entropy, some call it negentropy, and a few call it extropy. Gregory Bateson once asked: “Is there a biological species of entropy?”

Kyle: The Last Question
In 1952, engineer Ross Ashby wrote in his influential book Design for a Brain, “The development of life on earth must not be seen as something remarkable. On the contrary, it was inevitable. It was inevitable in the sense that if a system as large as the surface of the earth, basically polystable, is kept gently simmering dynamically for five thousand million years, then nothing short of a miracle could keep the system away from those states in which the variables are aggregated into intensely self-preserved forms,”
Norbert Wiener, the original cybernetic man. Wiener writes in 1950: “Not only can we build purpose into machines, but in an overwhelming majority of cases a machine designed to avoid certain pitfalls of breakdown will look for purposes which it can fulfill.” Wiener implied that at a certain threshold of complexity of mechanical design, emergent purpose was inevitable.
Our own minds are a society of mindless agents; purpose emerges from that mix in exactly the same way purpose emerges from other noninten-tional vivisystems.
Nor are hard-won attributes easily given up. It is an axiom in cultural evolution that technologies once invented are never uninvented. Once a vivisys-tem discovers language or memory it does not retreat from it. The presence of life also does not retreat. I am aware of no geological domain that organic life has infiltrated and then retreated from. Once life settles in an environment (hot springs, alpine rock, robots) it will tenaciously maintain some presence there. Life exploits the inorganic world, recklessly transforming it into the organic. “Atoms, once drawn into the torrent of living matter, do not readily leave it,” writes Vernadsky.
In brief, we don’t know for sure what happens as organisms apparently complexify.
Marvin Minsky noticed a similar power of change-which-changes-its-own-rules in the development of a child’s mind. Minsky: “A mind cannot really grow very much by only accumulating more and more new knowledge. It must also develop new and better ways to use what it already knows. That’s Papert’s Principle: Some of the most crucial steps in mental growth are based not simply on acquiring new skills but on acquiring new administrative ways to use what one already knows.”
Natural selection selects individuals; Buss says that what constitutes an individual evolves over time. As an example, billions of years ago cells were the unit of natural selection, but eventually cells banded together and natural selection shifted to selecting their group—a multicellu-lar organism—as the individual to select upon. One way to look at this is to say what constitutes an evolutionary individual evolves. At first an individual was a stable system, then a molecule, then a cell, then an organism. What next? Ever since Darwin, many imaginative evolutionists have proposed “group selection,” evolution that works on groups of species as if a species were an individual. Certain kinds of species would survive or die not because of the survivability of the organism but because of unknown qualities of its specieshood—perhaps its evolvability.

Kyle: Pluribus, The Ego; the definition of an “individual” has evolved over time
“I’ve been thinking about the future, and I have one question,” I begin. “You want to know if IBM is gonna be up or down!” Farmer suggests with a wry smile. “No. I want to know why the future is so hard to predict.” “Oh, that’s simple.” I was asking about predicting because a prediction is a form of control. It is a type of control particularly suited to distributed systems. By anticipating the future, a vivisystem can shift its stance to preadapt to it, and in this way control its destiny. John Holland says, “Anticipation is what complex adaptive systems do.” Farmer likes to use a favorite example when explaining the anatomy of a prediction. “Here catch this!” he says tossing you a ball. You grab it. “You know how you caught that?” he asks. “By prediction.” Farmer contends you have a model in your head of how baseballs fly. You could predict the trajectory of a high-fly using Newton’s classic equation of f=ma, but your brain doesn’t stock up on elementary physics equations. Rather, it builds a model directly from experiential data. A baseball player watches a thousand baseballs come off a bat, and a thousand times lifts his gloved hand, and a thousand times adjusts his guess with his mitt. Without knowing how, his brain gradually compiles a model of where the ball lands—a model almost as good as f=ma, but not as generalized. It’s based entirely on a series of hand-eye data from past catches. In the field of logic such a process is known as induction, in contradistinction to the deduction process that leads to f=ma.

Kyle: World models
Almost by definition, vivisystems—lions, stock markets, evolutionary populations, intelligences—are unpredictable. Their messy, recursive field of causality, of every part being both cause and effect, makes it difficult for any part of the system to make routine linear extrapolations into the future. But the whole system can serve as a distributed apparatus to make approximate guesses about the future.
With these lessons firmly in mind, Farmer together with five other physicists (one of them a former Chaos Cabal member) engineered a start-up company to crack every gambler’s dream: Wall Street. They would use high-powered computers. They would stuff them with experimental nonlinear dynamics and other esoteric rocket-scientist tricks. They would think laterally and let the technology do as much as possible without their control. They would create a thing, an organism if you will, that would on its own gamble millions of dollars. They would make it… (drum roll, please) … predict the future. With a bit of bravado, the old gang hung out their new shingle: the Prediction Company. The guys in the Prediction Company figure that looking ahead a few days into the financial market future is all that is needed to make big bucks. Indeed, recent research done at the Santa Fe Institute, where Farmer and colleagues hang out, makes it clear that “seeing further is not seeing better.” When immersed in real world complexity, where few choices are clear cut and every decision is clouded by incomplete information, evaluating choices too far ahead becomes counterproductive. Although this conclusion seems intuitive for humans, it has not been clear why it should pertain to computers and model worlds. The human brain is easily distracted. But let’s say you have unlimited computing power specifically dedicated to the task of seeing ahead. Why wouldn’t deeper, farther be better? The short answer is that tiny errors (caused by limited information) compound into grievous errors when extended very far into the future. And the cost of dealing with exponentially increasing numbers of error-tainted possibilities just isn’t worth the immense trouble, even if computation is free (which it never is). Santa Fe Institute investigators, Yale economist John Geanakoplos and Minnesota professor Larry Gray, used chess-playing computer programs as the test-bed for their forecasting work. (The best computer chess programs, such as the top-ranked Deep Thought, can beat all human players except for the very best grandmasters.)

Kyle: Prediction markets; history rhymes.
At every move ahead the number of choices to consider explodes exponentially, yet great human players will concentrate only on a few of the most probable countermoves at each rehearsed turn. Occasionally they search far ahead when they spot familiar situations they know from experience to be valuable or dangerous. But in general, grandmasters (and now Deep Thought) work from rules of thumb. For instance: Favor moves that increase options; shy from moves that end well but require cutting off choices; work from strong positions that have many adjoining strong positions. Balance looking ahead to really paying attention to what’s happening now on the whole board.

Kyle: Interesting idea that this lends itself to maximizing optionality when, in real life, that can often lead to a failure to let compounding work on concentrated decision making.
According to Farmer, there are two kinds of complexity: inherent and apparent. Inherent complexity is the “true” complexity of chaotic systems. It leads to dark unpredictability. The other kind of complexity is the flip side of chaos—apparent complexity obscuring exploitable order.
Packard calls these areas “pockets of predictability” or “local predictability.” In other words, the distribution of unpredictability is not uniform throughout systems. Most of the time, most of a complex system may not be forecastable, but some small part of it may be for short times. In hindsight, Packard believes local predictability is what allowed the Santa Cruz Cabal to make money forecasting the approximate path of a roulette ball.
David Berreby, writing in the March 1993 Discover, puts the search for pockets of predictability in terms of a lovely metaphor: “Looking at market chaos is like looking at a raging white-water river filled with wildly tossing waves and unpredictably swirling eddies. But suddenly, in one part of the river, you spot a familiar swirl of current, and for the next five or ten seconds you know the direction the water will move in that section of the river.”
In backcasting techniques (commonly used by professional futurists) a model is built withholding the most recent data from the human managing the model. Once the system finds order in past data, say from the 1980s, it is fed the record of the last several years.
Human traders unconsciously learn how to spot patterns of local predictability streaking through the ocean of random data. The traders make millions of dollars because they detect patterns (which they cannot articulate), then make an internal model (which they are unconscious of), in order to make predictions (which they are rewarded or punished for, sharpening the feedback loop). They have no more idea of what their model or theory is than of how they catch fly balls. They just do. Yet both kinds of models were empirically constructed in the same inductive Ptolemaic way.
Prediction machinery is found in biology, too. As David Liddle, the director of a hi-tech think tank called Interval, says, “Dogs don’t do math,” yet dogs can be trained to predictively calculate the path of a Frisbee and catch it precisely. Intelligence and smartness in general is fundamentally prediction machinery. In the same way, all adaptation and evolution are milder and more thinly spread apparatus for anticipation and prediction.
His test-bed was to see how decentralized, massively parallel computing— what I’m calling “swarm computing”—could speed up a computer simulation of a tank battle,
There are about two dozen centers around the world that are playing war games where the U.S. is Blue—the protagonist. Most of these places are small departments at military schools and training centers, such as the Wargaming Center at Maxwell Air Force Base in Alabama, the legendary Global Game room at the Naval War College in Newport, Rhode Island, or the classic “sand box” table set-ups at the Army’s Combat Concepts Agency in Leavenworth, Kansas. Providing them technical support and know-how are academics and savants holed up in the numerous para-military think tanks peppering the beltway of Washington, D.C., or research alleys nested in the corridors of national laboratories like JPL and Lawrence Livermore Labs in California. The toy war simulators, of course, carry acronyms; TACWAR, JESS, RSAC, SAGA. A recent catalog of military software listed four hundred varieties of war games or other military models for sale right off the shelf.
Any simulation is only as good as the data it is based on, and Ware wanted Operation Internal Look based on reality as much as possible. That meant collecting a hundred thousand details about current forces in the Mid-East. Most of the work was horribly dull.
In the future, standard army-issue preparedness may demand having a parallel universe of possible wars spinning in a box at the command center, ready to go.
IF SILICON CHIPS ARE ENOUGH of a crystal ball to help steer a superarmy war, and algorithms coursing through small computers are enough predictive technology to outguess the stock market, then why not reconfigure a supercomputer to predict the rest of the world? If human society is just a large distributed system of agents and machines, why not construct an apparatus to forecast its future?

Kyle: Psychohistory; feels somewhat similar to my idea of Historical Futurism
On the whole, cultural predictions historically have been worse than random guesses. Old books are a graveyard of prophesied futures that never came to pass. A few prophecies hit the bullseye, but there is no way to discern beforehand the rare right one from the plentiful wrong ones. Since predictions are so often wrong, and since believing erroneous predictions is so tempting and so misleading, some professional futurists avoid predictions altogether on principle. To emphasize the corrupting unreliability of trying to prophesy, these futurists prefer to state their prejudice in deliberate exaggeration: “All predictions are wrong.”
There is nothing more certain about a complex system than to say it will be just like it is now a moment later. This observation is nearly a truism. Systems are things that keep persisting; so it is only tautological that from one moment to the next a system—even a living thing—doesn’t change much.
Equally true is the cliche that things occasionally do change from one day to the next. But can these immediate alterations be predicted? And if they can, could you stack up a series of predictable short-term changes into a probable medium-range trend?
the human ability to forecast aspects of our society, economy, and technology will steadily increase despite the Alice-in-Wonderland strangeness that dependable predictions will have upon present actions.
I follow the work of Theodore Modis, whose 1992 book, Predictions, nicely sums up the case for utility and believability of predictions.
The three pockets of Modis: Invariants, Growth Curves, Cyclic Waves.
Invariants. The natural and unconscious tendency for all organisms to optimize their behavior instills in that behavior “invariants” that change very little over time.
No system is yet over 50 percent efficient. A projected system operating on 45 percent efficiency is possible, but one that requires 55 percent is not. Therefore one can safely make a short-term prediction about fuel efficiency.
Growth Curves. The larger, more layered, more decentralized a system is, the more it takes on aspects of organic growth. Growing things share several universal characteristics. Among them are a lifespan that can be plotted as an S-shaped curve: slow birth, steep growth, slow decline. The worldwide production of cars per year or the lifetime production of symphonies composed by Mozart both fit an S-curve with great precision. “The predictive power of S-curves is neither magical nor worthless,” writes Modis. “What is hidden under the graceful shape of the S-curve is that fact that natural growth obeys a strict law.”
Cyclic Waves. The apparent complex behavior of a system is partly a reflection of the complex structure of the system’s environment.
Together, these three modes of prediction suggest that at certain moments of heightened visibility, the invisible pattern of order becomes clear to those paying attention. Like the next beat of a drum, its future can almost be heard. A moment later, the pattern is gone, muddied and overwritten by noise. Pockets of prediction won’t keep away big surprises. But local predictability does point to methods that can be improved, deepened, and lengthened into bigger things.

Kyle: How does this compare to the worldview of Black Swan and mediocristan?
Chartists are forever chasing the mythical “leading indicator” that will predict the destiny of stock prices as a number they can bet on. For many years chartists were ridiculed for their vaguely numerological approach. But in recent years academics such as Richard J. Sweeney and Blake LeBaron have shown that chartist methods often do work. A chartist’s technical rule can be stunningly simple: “If the market has been going up for a while, bet that it will continue to go up. If it’s on a downward trend, bet it will continue downward.” Such a rule reduces the high dimensionality of a complex market into to the low dimensionality of this simple two-part rule. In general, this kind of pattern-seeking works. The “up-up, down-down” pattern performs better than random chance, and thus better than the average investor. Since stasis is the most predictable thing about a system this pattern of order should not come as a surprise, even though it does.

Kyle: LINDY Effect
Chartists, on the other hand, seek a pattern from the data without concern for whether they understand why the pattern is there. If there is order in the universe, then somewhere, somehow, all complexity will disclose—at least momentarily—order that reveals its future path. One merely needs to learn what signals to disregard as noise. Chartism is organized induction in Doyne Farmer’s mode.
In a stock market, success stirs up strong self-canceling feedback currents. In other systems, such as a growing network, or an expanding corporation, anticipatory feedback is not self-canceling. Ordinarily, feedback is self-governing.
Wiener concluded that this power was a function of time-shifting. He wrote in 1954: “Feedback is a method of controlling a system by reinserting into it the results of its past performance.”
A system—organism, corporate firm, computer program—spends energy feeding the past back into the present because this is an economical way for the system to deal with the future. To see into the future one must see into the past. A constant pulse of the past along feedback loops informs and controls the future.

Kyle: Historical Futurism
Thus, systems stuck solely in the present will more often be surprised by change, and die. Therefore, a transparent environment rewards the evolution of predictive machinery, because prediction machinery confers surviv-ability upon complexity. Complex systems survive because they anticipate, and a transparent medium helps them anticipate. Opaqueness, on the other hand, would hinder anticipation, adaptation, and evolution of complex vivisysterns altogether.
POSTMODERN HUMANS swim in a third transparent medium now materializing. Every fact that can be digitized, is. Every measurement of collective human activity that can be ported over a network, is. Every trace of an individual’s life that can be transmuted into a number and sent over a wire, is. This wired planet becomes a torrent of bits circulating in a clear shell of glass fibers, databases, and input devices.
Once moving, data creates transparency. Once wired, a society can see itself.
industrial factories mass-produce video cameras, tape recorders, hard disks, text scanners, spreadsheets, modems, and satellite dishes. Each of these is an eye, an ear, or a neuron. Connected together they form a billion-lobed sense organ floating in the clear medium of whizzing digits. This tissue serves to feed-forward information from distant limbs into the body electric.
Telling the future, when it comes right down to it, is not solely a human yearning. It is the fundamental nature of any organism, and perhaps any complex system. Telling the future is what organisms are for. My working definition of a complex system is a “thing which talks to itself.” One might ask, then: What is the story that complex systems tell themselves? The answer is that they tell themselves stories of the future. Stories of what might come next—whether next is reckoned in nanoseconds or years.
He intuitively, and quite correctly, felt that cascading feedback loops—impossible to track with paper and pencil, but child’s play for a computer—were the only way to approach the web of influences between wealth, population, and resources. Why couldn’t the whole world be modeled?
Dennis Meadows, together with his wife Dana and two other coauthors, published the souped-up model, now filled with real data, as the “Limits to Growth.” The simulation was wildly successful as the first global spreadsheet. For the first time, the planetary system of life, earthly resources, and human culture were abstracted, embodied into a simulation, and set free to roam into the future.
The gist of the model’s discovery was this: “If the present growth trends in world population, industrialization, pollution, food production, and resource depletion continue unchanged, the limits to growth on this planet will be reached sometime within the next 100 years.” The modelers ran the simulation hundreds of times in hundreds of slightly different scenarios. But no matter how they made tradeoffs, almost all the simulations predicted population and living standards either withering away or bubbling up quickly to burst shortly thereafter.
As is true in all complex systems, the impact of a single adjustment cannot be calculated beforehand; it must be played out in the whole system to be measured. Vivisystems must anticipate to survive. Yet the complexity of the prediction apparatus must not overwhelm the vivisystem itself.
Mostly the “possible futures” it explores are those that seem plausible to the authors. Twenty years ago they ignored scenarios not based on what they felt were reasonable assumptions of expiring finite resources. But resources (such as rare metals, oil, and fertilizer) didn’t diminish. Any genuinely predictive model must be equipped with the capability to generate “unthinkable” scenarios. It is important that a system have sufficient elbowroom in the space of possibilities to wander in places we don’t expect. There is an art to this, because a model with too many degrees of freedom becomes unmanageable, while one too constrained becomes unreliable.
No room for learning. A group of early critics of the model once joked that they ran the Limits to Growth simulation from the year 1800 and by 1900 found a “20-foot level of horse manure on the streets.” At the rate horse transportation was increasing then, this would have been a logical extrapolation. The half-jesting critics felt that the model made no provisions for learning technologies, increasing efficiencies, or the ability of people to alter their behavior or invent solutions.
Meadows is right that intelligence reaches in to human culture and restructures it. But that isn’t done just by modelers, and it doesn’t happen only at cultural thresholds. This restructuring happens in six billion minds around the world, every day, in every era. Human culture is a decentralized evolutionary system if there ever was one. Any predictive model that fails to incorporate this distributed ongoing daily billion-headed microrevolution is doomed to collapse, as civilization itself would without it.
I do not focus on the Limits to Growth world model because I want to pick on its potent political implications (the first version did, after all, inspire a generation of antigrowth activists). Rather, the model’s inadequacies precisely parallel several core points I hope to make in this book. In bravely attempting to simulate an extremely complex adapting system (the human infrastructure of living on Earth), in order to feed-forward a scenario of this system into the future, the Forrester/Meadows model highlights not the limits to growth but the limits of certain simulations.
It was a topic that only a young country flush with success and confident of its role in the world would even think about: self-organizing systems—how organization bootstraps itself to life. Bootstrapping! It was the American dream put into an equation.
Among the several dozen visionaries invited over the nine years of the conference were Gregory Bateson, Norbert Wiener, Margaret Mead, Lawrence Frank, John von Neumann, Warren McCulloch, and Arturo Rosenblueth. This stellar congregation later became known as the cybernetic group for the perspective they pioneered—cybernetics, the art and science of control.
In Steve Heims’s history of this influential circle of minds, The Cybernetics Group, he says of the Macy Conferences: “Even such anthropocentric social scientists as Mead and Frank became proponents for the mechanical level of understanding, wherein life is described as an entropy-reducing device and humans characterized as servomechanisms, their minds as computers, and social conflicts by mathematical game theory.”
As Mead wrote later of the Macy Conferences, “Out of the deliberations of this (cybernetics) group came a whole series of fruitful developments of a very high order.” Specifically, the ideas of feedback control, circular causality, homeostasis in machines, and political game theory were born there and gradually entered the mainstream until they became elemental, almost cliche, concepts today.
In the fabric of knowledge we call science, there was a rent here, a hole. It was filled by young enthusiasts not burdened by wise old men. This gap made me wonder about the space of science. Scientific knowledge is a parallel distributed system. It has no center, no one in control. A million heads and dispersed books hold parts of it. It too is a web, a revolutionary system of fact and theory interacting and influencing other facts and theories. But the study of science as a network of agents searching in parallel over a rugged landscape of mysteries is a field larger than any I’ve tackled here.

Kyle: As We May Think; Tech Canon
Knowledge, truth, and information flow in networks and swarm systems. I have always been interested in the texture of scientific knowledge because it appears to be lumpy and uneven. Much of what we collectively know derives from a few small areas, yet between them lie vast deserts of ignorance. I can interpret that observation now as the effect of positive feedback and attrac-tors. A little bit of knowledge illuminates much around it, and that new illumination feeds on itself, so one corner explodes. The reverse also holds true: ignorance breeds ignorance. Areas where nothing is known, everyone avoids, so nothing is discovered. The result is an uneven landscape of empty know-nothing interrupted by hills of self-organized knowledge.
Scientists today believe science is revolutionary. They explain how science works via a model of ongoing minirevolutions. According to this perspective, researchers build a theory to explain facts (for example, rainbows occur because light is a wave). The theory itself will suggest places to look for new facts (can you bend a wave?). It’s the law of increasing returns again. As new facts are uncovered they are incorporated into the theory, buttressing its strength and reliability. Occasionally, scientists uncover new facts that aren’t readily explained by the theory (light sometimes acts like a particle). These are called anomalies. Anomalies are set aside at first, while new facts that concur with the reigning theory continue to stream in. At some point, the accumulating anomalies prove too great, too troublesome, or too numerous to ignore. Inevitably then, some young turk proposes a revolutionary different model that explains the anomalies (such as, light is both wave and particle). The old is gone; the new quickly reigns.
Real discovery in science, according to Kuhn in his seminal The Structure of Scientific Revolutions, only “commences with the awareness of anomaly.” Progress is an acknowledgment of the opposition. A series of established paradigms are overthrown by downtrodden and oppressed anomalies (and their finders) as they rebel and usurp the throne by their countertruth. The new ideas reign, at least for a while, until they too become ossified and insensitive to the squawks of new anomalies, and are eventually overthrown themselves.
To be astonished by a question no one else can get worked up about, or to be astonished by a matter nobody considers a problem, is perhaps a better paradigm for the progress of science.
In my experience most good questions come while stuck on a partial answer somewhere else.
This book has been an endeavor to find interesting questions. But on the way, some of the rather ordinary questions stopped me.
We are so ignorant of complexity that we haven’t yet asked the right question about what it is.
Is complexity in fact more efficient than simplicity?
Paleontologist Dave Raup postulates that 75 percent of all extinction events were caused by asteroid impacts. If there were no asteroids would there be no extinctions? If there were no extinctions of species on Earth, what would life look like now? Why, for that matter, do complex systems of any sort fail or die?
What are the down sides of connecting everything to everything?
Can evolution evolve its own teleological purpose? If organisms, which are but a federation of mindless agents, can originate goals, can evolution itself, equally blind and dumb but in a way a very slow organism, also evolve a goal?
God gets no honor in the academic papers of artificial lifers, evolutionary theorists, cosmologists, or simulationists. But much to my surprise, in private conversations these same researchers routinely speak of God. As used by scientists, God is a coolly nonreligious technical concept, closer to god—a local creator. When talking of worlds, both real and modeled, God is an almost algebraically precise notation standing for whatever “X” operating outside a world that has created that world.
God is a shorthand for the uncreated observer making things real. God thus becomes a scientific term, and a scientific concept. It doesn’t have the philosophical subtleties of prime cause, or the theological finery of Creator; it is merely a handy way to talk about the necessary initial conditions to run a world. So what are the requirements for godhood. What makes a good god?
The first step toward a highly linked web of knowledge was made by U.S. Army medical librarians trying to unify the indexing of medical journals. In 1955, Eugene Garfield, a librarian on that project who was interested in machine indexing, developed a computer system to automatically track the bibliographic citations of every scientific paper published in medicine. Eventually he founded a commercial company in his garage in Philadelphia—the Institute of Science Information (ISI)—that would track on a computer every scientific paper published, period. Today ISI—a company with many employees and supercomputers—cross-links millions of scholarly papers with their bibliographic references.
Citation indexing is currently employed to map the breaking “hot” areas of science. Clusters of a few extremely highly cited papers can indicate a rapidly moving area of research. An unintended corollary of this system is that government fund-givers use the Citation Index to assist them in determining whose research to fund. They count the total number of citations— adjusted for the “weight” or stature of the journal publishing the paper—of an individual scientist’s work in order to indicate the importance of that scientist. But like any network, citation evaluation breeds the opportunity for a positive feedback loop: the more funding, the more papers produced, the more citations garnered, the more funding secured, and so on. And it engenders the identical reverse loop of no funding, no papers, no citations, no funding.
A more elegant description of that system was coined “Hypertext” by Ted Nelson in 1974. In essence, hypertext is a large distributed document. A hypertext document is a vague network of live links between its words and ideas and sources. The document has no center, no end. You read hypertext by navigating through it, taking side tours to footnotes, and to footnotes to the footnotes, following parenthetical thoughts as long and complex as the “main” text. Any other document can be linked to and become part of another text. Computerized hypertext incorporates marginalia and commentaries to the text by other writers, updates, revisions, abstracts, digests, misinterpretations, and as in citation indexing, all bibliographic references to the work.
For one thing, it was easy to get lost. Without the centering hold of a narrative, everything in a hypertext network seems to have equal weight and appears to be the same wherever you go, as if the space were a suburban sprawl. The problem of locating items in a network is substantial. It harks back to the days of early writing when texts in a 14th-century scriptorium were difficult to locate since they lacked cataloguing, indexes, or tables of contents. The advantages which the hypertext model offers over the web of oral tradition is that the former can be indexed and catalogued. An index is an alternative way to read a printed text, but it is only one of many ways to read a hypertext. In a sufficiently large library of information without physical form—as future electronic libraries promise to be—the lack of simple but psychologically vital clues, such as knowing how much of the total you’ve read or roughly how many ways it can be read, is debilitating.
As Jay David Bolter writes in his outstanding, but little known book, Writing Spaces: In this late age of print, writers and readers still conceive of all texts, of text itself, as located in the space of a printed book. The conceptual space of a printed book is one in which writing is stable, monumental, and controlled exclusively by the author. It is the space defined by perfect printed volumes that exist in thousands of identical copies. The conceptual space of electronic writing, on the other hand, is characterized by fluidity and an interactive relationship between writer and reader. Technology, particularly the technology of knowledge, shapes our thought. The possibility space created by each technology permits certain kinds of thinking and discourages others.
Hypertext, on the other hand, stimulates yet another way of thinking: telegraphic, modular, nonlinear, malleable, cooperative.
As Brian Eno, the musician, wrote of Bolter’s work, “[Bolter’s thesis] is that the way we organize our writing space is the way we come to organize our thoughts, and in time becomes the way which we think the world itself must be organized.”
The space of knowledge in ancient times was a dynamic oral tradition. By the grammar of rhetoric, knowledge was structured as poetry and dialogue—subject to interruption, questioning, and parenthetical diversions. The space of early writing was likewise flexible. Texts were ongoing affairs, amended by readers, revised by disciples; a forum for discussions. When scripts moved to the printed page, the ideas they represented became monumental and fixed. Gone was the role of the reader in forming the text. The unalterable progression of ideas across pages in a book gave the work an impressive authority—“authority” and “author” deriving from a common root. As Bolter notes, “When ancient, medieval, or even Renaissance texts are prepared for modern readers, it is not only the words that are translated: the text itself is translated into the space of the modern printed book.”
The result is far different from a printed book, or even a chat around a table. The text is a sane conversation with millions of participants. The type of thought encouraged by the Internet hyperspace tends toward nurturing the nondogmatic, the experimental idea, the quip, the global perspective, the interdisciplinary synthesis, and the uninhibited, often emotional, response. Many participants prefer the quality of writing on the Net to book writing because Net-writing is of a conversational peer-to-peer style, frank and communicative, rather than precise and overwritten.
Distributed text, or hypertext, on the other hand supplies a new role for readers—every reader codetermines the meaning of a text. This relationship is the fundamental idea of postmodern literary criticism. For the postmodernists, there is no canon. They say hypertext allows “the reader to engage the author for control of the writing space.” The truth of a work changes with each reading, no one of which is exhaustive or more valid then another. Meaning is multiple, a swarm of interpretations. In order to decipher a text it must be viewed as a network of idea-threads, some threads of which are owned by the author, some belonging to the reader and her historical context and others belonging to the greater context of the author’s time. “The reader calls forth his or her own text out of the network, and each such text belongs to one reader and one particular act of reading,” says Bolter.
The total summation we call knowledge or science is a web of ideas pointing to, and reciprocally educating each other. Hypertext and electronic writing accelerate that reciprocity. Networks rearrange the writing space of the printed book into a writing space many orders larger and many ways more complex than of ink on paper. The entire instrumentation of our lives can be seen as part of that “writing space.”
Our society is a working pandemonium of fragments. That’s almost the definition of a distributed network. Bolter again: “Our culture is itself a vast writing space, a complex of symbolic structures… .Just as our culture is moving from the printed book to the computer, it is also in the final stages of the transition from a hierarchical social order to what we might call a ‘network culture.’”
There is no central keeper of knowledge in a network, only curators of particular views. People in a highly connected yet deeply fragmented society can no longer rely on a central canon for guidance. They are forced into the modern existential blackness of creating their own culture, beliefs, markets, and identity from a sticky mess of interdependent pieces. The industrial icon of a grand central or a hidden “I am” becomes hollow. Distributed, headless, emergent wholeness becomes the social ideal.
The ever insightful Bolter writes, “Critics accuse the computer of promoting homogeneity in our society, of producing uniformity through automation, but electronic reading and writing have just the opposite effect.” Computers promote heterogeneity, individualization, and autonomy.

Kyle: Still true with AI?
No one has been more wrong about computerization than George Orwell in 1984. So far, nearly everything about the actual possibility-space which computers have created indicates they are the end of authority and not its beginning.

Kyle: Maybe. But seems like it just needed more time. The internet has fallen pretty far from its original rebellious streak in its youth
We moderns think in a bubble of about ten years. Our history extends into the past five years and our future runs ahead five years, but no further. We don’t have a structured way, a cultural tool, for thinking in terms of decades or centuries. Tools for thinking about genes and evolution might change this. Pharmaceuticals that increase access to our own minds would, of course, also remake our thinking space.
How large is the space of possible ways of thinking? How many, or how few, of all types of logic have we found so far in the Library of thinking and knowledge?
My bet is that artificial intelligence, when it comes, will be intelligent but not very humanlike. It will be one of many nonhuman methods of thought that will probably fill the library of thinking space. This space will also hold types of thinking that we simply cannot understand at all. But still we will use them. Nonhuman cognitive methods will provide us wonderful results beyond and out of our control.
If we can travel anywhere in cognitive space, we would be capable of an open-ended universe of thoughts.
How do you make something from nothing? Although nature knows this trick, we haven’t learned much just by watching her. We have learned more by our failures in creating complexity and by combining these lessons with small successes in imitating and understanding natural systems.

Kyle: Where’s the evidence that nature makes something from nothing? Or the idea of God’s creation ex nihilo?
The Nine Laws of God governing the incubation of somethings from nothing: Distribute being Control from the bottom up Cultivate increasing returns Grow by chunking Maximize the fringes Honor your errors Pursue no optima; have multiple goals Seek persistent disequilibrium Change changes itself.
Distribute being. The spirit of a beehive, the behavior of an economy, the thinking of a supercomputer, and the life in me are distributed over a multitude of smaller units (which themselves may be distributed). When the sum of the parts can add up to more than the parts, then that extra being (that something from nothing) is distributed among the parts. Whenever we find something from nothing, we find it arising from a field of many interacting smaller pieces. All the mysteries we find most interesting—life, intelligence, evolution—are found in the soil of large distributed systems.

Kyle: Pluribus
Cultivate increasing returns. Each time you use an idea, a language, or a skill you strengthen it, reinforce it, and make it more likely to be used again. That’s known as positive feedback or snowballing. Success breeds success. In the Gospels, this principle of social dynamics is known as “To those who have, more will be given.” Anything which alters its environment to increase production of itself is playing the game of increasing returns. And all large, sustaining systems play the game. The law operates in economics, biology, computer science, and human psychology. Life on Earth alters Earth to beget more life. Confidence builds confidence. Order generates more order. Them that has, gets.
When the Technos is enlivened by Bios we get artifacts that can adapt, learn, and evolve. When our technology adapts, learns, and evolves then we will have a neo-biological civilization.
In the coming neo-biological era, all that we both rely on and fear will be more born than made. We now have computer viruses, neural networks, Biosphere 2, gene therapy, and smart cards—all humanly constructed artifacts that bind mechanical and biological processes. Future bionic hybrids will be more confusing, more pervasive, and more powerful. I imagine there might be a world of mutating buildings, living silicon polymers, software programs evolving offline, adaptable cars, rooms stuffed with coevolutionary furniture, gnatbots for cleaning, manufactured biological viruses that cure your illnesses, neural jacks, cyborgian body parts, designer food crops, simulated personalities, and a vast ecology of computing devices in constant flux.
As complex as things are today, everything will be more complex tomorrow.

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