Los Angeles Palo Alto © 2004 Accelerating.org Challenges for World Security Policy John Smart USAWC, August 2004, Carlisle, PA Adapting to the Future:

© 2004 Accelerating.org

Los AngelesPalo Alto

Challenges for World Security Policy

John SmartUSAWC, August 2004, Carlisle, PA

Adapting to the Future:The Impact of Accelerating Change



Systems Theory

Systems Theorists Make Things Simple

(sometimes too simple!)

"Everything should be made as simple as possible, but not simpler."

— Albert Einstein



Institute for the Study of Accelerating Change

ISAC (Accelerating.org) is a nonprofit community of scientists, technologists, entrepreneurs, administrators, educators, analysts, humanists, and systems theorists discussing and dissecting accelerating change.

We practice “developmental future studies,” that is, we seek to discover a set of persistent factors, stable trends, convergent capacities, and highly probable scenarios for our common future, and to use this information now to improve our daily evolutionary choices.

Specifically, these include accelerating intelligence, immunity, and interdependence in our global sociotechnological systems, increasing technological autonomy, and the increasing intimacy of the human-machine, physical-digital interface.

Intro to Future Studies



Four Types of Future Studies

– Exploratory (Speculative Literature, Art)– Agenda-Driven (Institutional, Strategic Plans)– Consensus-Driven (Political, Trade Organizations)– Research-Predictive (Stable Developmental Trends)

The last is the critical one for acceleration studies and singularity studies

It is also the only one generating falsifiable hypotheses

Accelerating and increasingly efficient, autonomous, miniaturized, and localized computation is apparently a fundamental meta-stable universal developmental trend. Or not. That is a key hypothesis we seek to address.



Observation 1: The “Prediction Wall”

The faster change goes, the shorter-term our average business plans. Ten-year plans (1950's) have been replaced with ten-week (quarterly) plans (2000's). Future appears very contingent, on average.

There is a growing inability of human minds to imagine some aspects of our future, a time that must apparently include greater-than-human technological sophistication and intelligence.

Judith Berman, in "Science Fiction Without the Future," 2001, notes that even most science fiction writers have abandoned attempts to portray the accelerated technological world of fifty years hence.



Observation 2: The Prediction Crystal Ball

What does hindsight tell us about prediction?

The Year 2000 was the most intensive long range prediction effort of its time, done at the height of the forecasting/ operationsresearch/ cybernetics/think tank (RAND) driven/ “instrumental rationality”era of future studies

(Kahn & Wiener, 1967).



Lesson 1: Forecasting in certain domains of the modern environment is highly predictable

Example: Information and Communication Technologies

Evaluating the predictions of The Year 2000, technology roadmapper Richard Albright notes:

“Forecasts in computers and communication stood out as about 80% correct, while forecasts in all other

fields (social, political, etc.) were judged to be less than 50% correct.”

Why? Here TY2000 used trend extrapolation (simple). The major ICT change they missed was morphological (nonsimple) the massive “network transition,” to decentralized vs. centralized computing.

Richard Albright, “What can Past Technology Forecasts Tell Us About the Future?”, Technological Forecasting and Social Change, Jan 2002



Many Technology-Related Transformations are Amazingly Predictable

Miniaturization (per linear dimension) Price Performance in Computing (Moore’s Law) Input-Output, Storage, Bandwidth Network Node Density (Poor’s Law) Protein Structure Solution (Dickerson’s Law) Algorithmic Efficiency (Statistical NLP, etc.) Software Performance (6 year doubling) Economic Growth (2-4% year, over long spatial scales)

Thought Question: Is annual economic growth a function of exponential technological surprise interfacing with human expectation?(Remember: Efficient market hypothesis in Economics would predict zero annual growth)



Relative Growth Rates are Also Amazingly Predictable

Brad DeLong (2003) noted that memory density predictably outgrows microprocessor density, which predictably outgrows wired bandwidth, which predictably outgrows wireless. Expect: 1st: New Storage Apps, 2nd: New Processing Apps, 3rd: New Communications Apps, 4th: New Wireless Apps



Some Tech Capacity Growth Rates Are Independent of Socioeconomic Cycles

There are many natural cycles: Political-Economic Pendulum, Boom-Bust, War-Peace…

Ray Kurzweil first noted that a generalized, century-long Moore’s Law was unaffected by the U.S. Great Depression of the 1930’s.

Conclusion: Human-discovered, Not human-created complexity here. Not that many intellectual or physical resources are required to keep us on the accelerating developmental trajectory. (“MEST compression is a rigged game.”)

Age of Spiritual Machines, 1999



Lesson 2: Both Social and Developmental Factors Determine Forecasting Expertise

Professional futurists Joseph Coates, John Mahaffie, and Andy Hines, a broad literature review, note:

“In reviewing the 54 areas (in science and technology) in which we gathered forecasts, four clearly stood out as the best: aerospace, information technology, manufacturing, and robotics.”

They also note:

“In aerospace and information technology, there is widespread interest and governmental emphasis on forecasts… In other fields, such as economics and basic mathematics, there is little or nothing [in forecasting the futures of the field, vs. using the field for forecasts].

Q: What causes this selective interest?Joseph Coates, John Mahaffie, Andy Hines, “Technological Forecasting: 1970-1993”, Technological Forecasting and Social Change, 1994

Accelerating Systems Theory



Something Curious Is Going On

Unexplained.(Don’t look for this in your physics or information theory texts…)



Brief History of Accelerating Change

Billion Years Ago

12 Big Bang (MEST)

11.5 Milky Way (Atoms)

8 Sun (Energy)

4.5 Earth (Molecules)

3.5 Bacteria (Cell)

2.5 Sponge (Body)

0.7 Clams (Nerves)

0.5 Trilobites (Brains)

0.2 Bees (Swarms)

0.100 Mammals

0.002 Humans, Tools & Clans Co-evolution

Generations Ago

100,000 Speech

750 Agriculture

500 Writing

400 Libraries

40 Universities

24 Printing

16 Accurate Clocks

5 Telephone

4 Radio

3 Television

2 Computer

1 Internet/e-Mail

0 GPS, CD, WDM



Observation 1: Tech Interval Time Compression

3 million years ago collective rock throwing; early stone tools

1.5 million years ago lever, wedge, inclined plane

500,000 years ago control of fire

50,000 years ago bow and arrow; fine tools

5,000 years ago wheel and axle; sail

500 years ago printing press with movable type; rifle

50 years ago commercial digital computers

10 years ago commercial internet



Obs. 2: Continuous Tech Innovation (Even in 400-1400 A.D., Fall of Rome to Black Plague)

Technological or Sociotechnological Innovation Date (A.D.), Location

Alchemy (pre-science) develops a wide following 410, EuropeConstantinople University 425, TurkeyPowers and Roots (Arybhata) 476, IndiaHeavy plow; horse shoes; practical horse harness 500, EuropeWooden coffins (Alemanni) 507, GermanyDraw looms (silk weaving) 550, EgyptDecimal reckoning 595, IndiaCanterbury Monastery/University 598, EnglandBook printing 600, ChinaSuan-Ching (Science Encyclopedia) 619, ChinaOriginum Etymologiarum Liibri XX (Sci. Encyc.) 622, SpainFirst surgical procedures 650, IndiaWater wheel for milling (Medieval energy source) 700, EuropeStirrup arrives in Europe from China 710, EuropeEarly Chemistry (Abu Masa Dshaffar) 720, Mid-East



Continuous Tech Innovation (400-1400 A.D., Fall of Rome to Black Plague)

Medicine, Astronomy, Math, Optics, Chemistry 750, Arab SpainHanlin Academy 750, ChinaPictorial Book Printing 765, JapanIron and smithing become common; felling ax 770, EuropeChemistry (Jabir) 782, Mid-EastMayan Acropoli (peak) 800, MexicoAlgebra (Muhammed al Chwarazmi) 810, PersiaPtolemaic Astronomy; Soap becomes common 828, EuropeRotary grindstone to sharpen iron 834, EuropePaper money 845, ChinaSalerno University 850, ItalyIron becomes common; Trebuchets 850, EuropeAstrolabe (navigation) 850, Mid-EastAngkor Thom (city) 860, CambodiaNew Mathematics and Science (Jahiz, Al-Kindi) 870, Mid-EastViking shipbuilding 900, EuropePaper arrives in Arab world 900, Egypt




Salerno Medical School 900, ItalyLinens and woolens 942, FlandersFirst European bridges 963, EnglandArithmetical notation brought to Europe by Arabs 975, Europe1,000 volume encyclopedia 978, ChinaFirst Mayan and Tiuanaco Civilizations 1000, Cent./S.AmericaHorizontal loom 1000, EuropeAstrolabe arrives in Europe 1050, EuropeGreek medicine arrives in Europe (Constantine) 1070, EuropeWater-driven mechanical clock 1090, ChinaAntidotarum (2650 medical prescriptions) 1098, ItalyBologna University 1119, ItalyMariner's compass 1125, EuropeTown charters granted (protecting commerce) 1132, FranceAl-Idrisi's "Geography" 1154, ItalyOxford University 1167, EnglandVertical sail windmills 1180, Europe




Glass mirrors 1180, EnglandSecond Mayan Civilization 1190, Cent. AmericaCambridge University 1200, EnglandArabic numerals in Europe (Leonardo Fibonacci) 1202, EnglandTiled roofs 1212, EnglandCotton manufacture 1225, SpainCoal mining 1233, EnglandRoger Bacon, our first scientist (Opus; Communia) 1250, EnglandGoose quill writing pen 1250, ItalyThe inquisition begins using instruments of torture 1252, Spain Tradesman guilds engage in street fighting over turf 1267, EnglandToll roads 1269, EnglandHuman dissection 1275, EnglandWood block printing; spectacles 1290, ItalyStandardization of distance measures (yard, acre) 1305, EnglandUse of gunpowder for firearms (Berthold Schwarz) 1313, GermanySawmill; wheelbarrow; cannon (large and hand) 1325, Europe




Pisa and Grenoble Universities; Queens College 1330, EuropeFirst scientific weather forecasts (William Merlee) 1337, EnglandMechanical clock reaches Europe 1354, FranceBlast furnaces; cast iron explodes across Europe 1360, EuropeSteel crossbow first used in war 1370, EuropeVienna, Hiedelberg, and Cologne Universities 1380, EuropeIncorporation of the Fishmonger's Company 1384, EnglandJohann Gutenberg, inventor of mass printing, born 1396, Germany

Lesson: Tech innovation appears to be a developmental process, independent of Wars, Enlightenments, Reformations, Inquisitions, Crusades, Subjugations, and other aspects of our cyclic evolutionary ideological, cultural, and economic history.

Tech advances are something we consistently choose, even unconsciously, regardless of who is in power, because they have strong "non-zero sum" effects on human aspirations.



Moore's Law - Miniaturization Processing, Storage, ... Price/Performance 2X over 12-18 months

Metcalf's Law - Interconnection Economic value of a network increases as the square of

the number of connections Gilder's Law - Quantization

Bandwidth increases 3X every 36 months Negroponte's Law - Digitization

Superiority of "bits over atoms" Profound impact felt in "Knowledge Economy" where

ideas are ultimate raw material Smith's Law - Simulation

Alvy Ray Smith, Microsoft Research (to Howard Rheingold) “Reality is 80 million polygons a second.”

A demand saturation threshold, like CPUs and productivity apps (which human-saturated in 1990’s).

No market saturation until we reach this point

Many Capacity-Based “Meta-Trends” in

and Thresholds in Tech Acceleration



Transistor Doublings (2 years)

Courtesy of Ray Kurzweil and KurzweilAI.net



Processor Performance (1.8 years)




DRAM Miniaturization (5.4 years)




Many Unexpected Physical Processes are Moore’s-Related, e.g. Dickerson’s Law

Richard Dickerson, 1978, Cal Tech:

Protein crystal structure solutions grow according to n=exp(0.19y1960)

Dickerson’s law predicted 14,201 solved crystal structures by 2002. The actual number (in online Protein Data Bank (PDB)) was 14,250. Just 49 more.

Macroscopically, the curve has been quite consistent.



Hans Moravec, Robot, 1999



Henry Adams, 1909: The First Singularity Theorist

The final Ethereal Phase would last only about four years, and thereafter "bring Thought to the limit of its

possibilities."

Wild speculation or computational reality?

Still too early to tell, at present.



The Technological Singularity: 2nd Order “Envelope of S-Curves”?

Each unique physical-computational substrate appears to have its own S-shaped “capability curve.”

The information inherent in these substrates is apparently not made obsolete, but is instead incorporated into the developmental architecture of the next emergent system.



Eldredge and Gould(Biological Species)

Pareto’s Law (“The 80/20 Rule”)(income distribution technology, econ, politics) Rule of Thumb: 20% Punctuation (Development)

80% Equilibrium (Evolution)

Suggested Reading:For the 20%: Clay Christiansen, The Innovator's DilemmaFor the 80%: Jason Jennings, Less is More

Punctuated Equilibrium (in Biology, Technology, Economics, Politics…)



Saturation: A Biological Lesson

How S Curves Get Old

Resource limits in a niche Material

Energetic

Spatial

Temporal

Competitive limits in a niche Intelligence/Info-Processing

Curious Facts:

1. Our special universal structure permits each new computational substrate to be far more MEST resource-efficient than the last

2. The most complex local systems have no intellectual competition

Result: No apparent limits to the acceleration of local intelligence, interdependence, and immunity in new substrates over time.



P.E. Lesson: Maintaining Equilibrium is Our 80% Adaptive Strategy

While we gamely try unpredictable evolutionary strategies to improve our intelligence, interdependence, and resiliency, these don’t always work. What is certain is that successful solutions always increase MEST efficiency, they “do more, better, with less.” Strategies to capitalize on this:

Teach efficiency/OR as a civic and business skill. Look globally to find most resource-efficient solutions. Practice competitive intelligence for MEST-efficiency. Build a culture that rewards MEST refinements.

Examples: Brazil's Urban Bus System, Copied in LA. Open Source Software. Last year’s mature technologies. Recycling. 30 million old cell phones in U.S., send to EN’s.



Saturation Example 1: Total World Population

Positive

feedback loop: Agriculture, Colonial Expansion, Economics, Scientific Method, Industrialization, Politics, Education, Healthcare, Information Technologies, etc.



So What Stopped the Growth?



Saturation Example 2:Total World Energy Use

DOE/EIA data shows total world energy use growth rate peaked in the 1970’s. Real and projected growth is progressively flatter since.

Saturation factors: 1. Major conservation after 1973-74 oil shocks2. Stunning MEST efficiency of each new

generation of technological system3. Saturation of human population, and of

human needs for tech transformation

Steve Jurvetson notes (2003) the DOE estimates solid state lighting (eg. the organic LEDs in today's stoplights) will cut the world's energy demand for lighting in half over the next 20 years. Lighting is approximately 20% of energy demand.

Expect such MEST efficiencies to be multiplied dramatically in coming years. Technology is becoming more energy-effective in ways very few of us currently understand.



End of Fossil Fuels? Don’t Hold Your Breath

Hydrogen, Solar, and other renewables may well turn out to have been an unachievable dream, like Nuclear Powered Houses and 20th Century Mars Colonies. Promising on paper but ruthlessly outcompeted by accelerating MEST efficiencies in older, mature legacy technologies, like zero emission fossil fuel combustion, carbon sequestration, nanofiltration (desalination, etc.).

China is pioneering coal liquefaction and nuclear power. China, Australia, Canada, several others are very coal-rich nations.Natural gas conversion is now down to $40/barrel.We have hundreds of years of planetary NG reserves, at least a thousand years of proven coal reserves, and (theoretically) similar methane hydrate and deep ocean oil reserves.

Bucky Fuller was right. Energy is so plentiful on Earth it is becoming steadily less geopolitically important, as the economy “etherealizes” (virtualizes). Old paradigm.

The Theory of Evolutionary Development



Evolution vs. Development“The Twin’s Thumbprints”

Consider two identical twins:

Thumbprints Brain wiring

Evolution drives almost all the unique local patterns.Development creates the predictable global patterns.



Understanding Development

Just a few thousand developmental genes ride herd over all that molecular evolutionary chaos.

Yet two genetic twins look, in many respects, identical.How is that possible?

They’ve been tuned, cyclically, for a future-specific convergent emergent order, in a stable development environment.

Origination of Organismal Form, Müller and Newman, 2003



Cambrian Explosion

Com

plex

Env

iron

men

tal I

nter

actio

n

Selection/Emergence/Phase Space Collapse/MEST Collapse

Development

Adaptive Radiation/Chaos/Pseudo-Random Search

Evolution

570 mya. 35 body plans emerged immediately after. No new body plans since! Only new brain plans, built on top of the body plans (homeobox gene duplication).

Body/brain plans: “eukaryotic multicell. evolutionary developmental substrates.”

Invertebrates

Vertebrates

Bacteria

Insects

Multicellularity Discovered



Memetic Evolutionary Development

Com

plex

Int

erac

tion Selection, Convergence

Convergent SelectionMEST Compression

Development

Replication, VariationNatural SelectionPseudo-Random Search

Evolution

Variations on this ev. dev. model have been proposed for: Neural arboral pruning to develop brains (Edelman, Neural Darwinism, ‘88)Neural net connections to see patterns/make original thoughts (UCSD INS)Neural electrical activity to develop dominant thoughts (mosaics, fighting

for grossly 2D cortical space) (Calvin, The Cerebral Code, 1996)

Input to a neural network starts with chaos (rapid random signals), then creates emergent order (time-stable patterns), in both artificial and biological nets. Validity testing: Hybrid electronic/lobster neuron nets (UCSD INS)



The Left and Right Hands of “Evolutionary Development”

Com

plex

Env

iron

men

tal I

nter

actio

n

Selection & Convergence““Convergent Selection”Convergent Selection”Emergence,Global OptimaMEST-Compression Standard Attractors

Development

Replication & Variation ““Natural Selection”Natural Selection”Adaptive Radiation Chaos, ContingencyPseudo-Random SearchStrange Attractors

Evolution

Right HandLeft Hand

Well-Explored Phase Space OptimizationNew Computat’l Phase Space Opening



RVISC Life Cycle of Evolutionary Development

ReplicationSpacetime stable structure, transmissible partially by internal (DNA) template and partially by external (universal environmental) template. Templates are more internal with time.

VariationAbility to encode “requisite variety” of adaptive responses to environmental challenges, to preserve integrity, create novelty.

Interaction (Complex, Spacetime Bounded)Early exploration of the phase space favors natural selection, full exploration (“canalization”) favor developmental selection.

Selection (“Natural/Evolutionary” Selection)Information-producing, randomized, chaotic attractors.

Convergence (“Developmental” Selection)MEST-efficient, optimized, standard attractors.



Marbles, Landscapes, and Basins (Complex Systems, Evolution, & Development)

The marbles (systems) roll around on the landscape, each taking unpredictable (evolutionary) paths. But the paths predictably converge (development) on low points (MEST compression), the “attractors” at the bottom of each basin.



How Many Eyes Are Developmentally Optimal?

Evolution tried this experiment.

Development calculated an operational optimum.

Some reptiles (e.g. Xantusia vigilis, certain skinks) still have a parietal (“pineal”) vestigial third eye.



Optimization and MEST Efficiency:The Promise of Operations Research

Is a Four Wheeled Automobile an Inevitable Developmental Attractor?

Examples: Wheel on Earth. Social computation device. Diffusion proportional to population density and diversity.



Troodon and the Dinosauroid Hypothesis

Dale Russell, 1982: Anthropoid forms as a standard attractor.

A number of small dinosaurs (raptors and oviraptors) developed bipedalism, binocular vision, complex hands with opposable thumbs, and brain-to-body ratios equivalent to modern birds. They were intelligent pack-hunters of both large and small animals (including our mammalian precursors) both diurnally and nocturnally. They would likely have become the dominant planetary species due to their superior intelligence, hunting, and manipulation skills without the K-T event 65 million years ago.



Why is Upright Posture Energetically More Efficient?

Observation: The smartest bioorganisms are slow-moving bipeds.

Once a species is culturally computing using behavioral mimicry (and later, sounds), in high-density living environments, and using mimicry defenses like collective rock throwing at 80 mph (which requires opposable thumbs and strong arms), such favored species no longer need to be fast, thick-skinned, or sharp-taloned.

From this point forward, they can optimize computation by moving more densely and slowly on average, within their newest phase space for evolutionary development: mimicry and memetic culture.

Theory: Our once-horizontal backs have only very recently been coaxed into an almost always upright position, for maximum hand manipulation ability, hence the "scoliosis curve" of our lower back with its pains.

In the modern world niche, we spend most of our days physically inactive inside large boxes (now mainly in front of electronic boxes), or moving between boxes inside smaller wheeled boxes, while our collective computations flow across the planet at the speed of light.

The brains of our electronic successors (not their sensors and effectors) will most certainly be even more immobile still, if the developmental singularity hypothesis is correct.



The Challenge in ManagingTechnological Development

Since the birth of civilization, humanity has been learning to build special types of technological systems that are progressively able to do more for us, in a more networked and resilient fashion, using less resources (matter, energy, space, time, human and economic capital) to deliver any fixed amount of complexity, productivity, or capability.

We are faced daily with many possible evolutionary choices in which to invest our precious time, energy, and resources, but only a few optimal developmental pathways will clearly "do more, and better, with less."



Evolution and Development:Yin and Yang



Evolution and Development:Two Universal Systems Processes

Each are pairs of a fundamental dichotomy, polar opposites, conflicting models for understanding universal change. The easy observation is that both processes have explanatory value in different contexts.

The deeper question is when, where, and how they interrelate.

EvolutionChanceRandomnessVariety/ManyPossibilitiesUniquenessUncertaintyAccidentBottom-upDivergentDifferentiation

DevelopmentNecessityDeterminismUnity/OneConstraintsSamenessPredictabilityDesign (self-organized or other)Top-DownConvergentIntegration



Evo-Devo Provides Reasons for Polarities

Evolution

CreativityNovelty-SeekingFemale“Right Brain”DemocraticFreedomExperimentationPlayEntropy Creation“Watch a Movie at 1am”“Sleep at 1pm”

Development

DiscoveryTruth-SeekingMale“Left Brain”RepublicanJusticeOptimizationWorkEntropy Density Maximization“Sleep at 1am”“Watch a Movie at 1pm”

We each have both of these qualities. Best use always depends on context. Use them both! Keep the balance!



Political Polarities: Generativity vs. Sustainability

Evo-Devo Theory Brings Process Balance to Political Dialogs on Innovation and Sustainability

Developmental sustainability without generativity creates sterility, clonality, overdetermination, adaptive weakness (e.g., Maoism).

Evolutionary generativity without sustainability creates chaos, entropy, a degradation that is not natural recycling (e.g., Anarchocapitalism).



Human Migration Patterns and Large Land Mammal Extinctions

Gone: BisonFlightless BirdsElephantsLionsMarsupial TigersEtc.

We depleted the easiest fuel first.Everywhere.

Likely a computationally optimal strategy.

Jared Diamond, The Third Chimpanzee, 1994



Rise and Fall of Complex Societies

Mesopotamia, “Cradle of Civilization” (Modern Iraq: Assyrians, Babylonians, Sumerians) 6000 BC – 500 BC. Mineral salts from repeated irrigation, no crop rotation decimated farming by 2300 BC). Fertile no more.



Rise and Fall: Nabatea

Petra (Nabateans), 400 BC – 400 CE (Jordan: trading experts, progressively wood-depleted overirrigated, and overgrazed (hyrax burrows)

Jared Diamond, The Third Chimpanzee, 1994

Rock Hyrax(burrows are vegetation

time capsules)



Rise and Fall: Anasazi

Chaco Canyon and Mesa Verde (Anasazi), 800 – 1200 CE (New Mexico, Colorado: trading, ceremonial, and industry hubs, wood depleted (100,000 timbers used in CC pueblos!), soil depleted (Chaco and Mesa Verde). No crop rotation. Unsupportable pop. for the agrotech.

Cliff Palace, Mesa Verde, COPueblo Bonito, Chaco Canyon, NM



Dominant Empire Progression-Combustion (Phase I: Near East-to-West)

BabylonianEgyptian (New Kingdom)Hellennistic (Alexander)

RomanBritish

Spanish

French

AustriaGermany



Dominant Empire Progression-Combustion (Phase II: West-to-Far East)

American

Japan(Temporary: Pop density,Few youth, no resources.

East Asian Tigers(TaiwanHong KongSouth KoreaSingapore)

India

ChinaExpect a Singapore-style “Autocratic Capitalist” transition. Population control, plentiful resources,stunning growth rate, drive, and intellectual capital. U.S. science fairs: 50,000 high school kids/year. Chinese science fairs: 6,000,000 kids/year. For now.

BHR-1, 2002



Subtle Lessons: Life Cycles of Dominant Cultures

Each culture burns through its resource base (wood, farmland, population, natural resources) as fast as it can, creating as much innovation as it can.

Even civilizations go through growth, maturity, decline, and renewal.

The more powerful technology gets, the less painful and environmentally impactful this natural renewal/rebirth cycle.

(Example: Japan doesn’t collapse, only suffers a decade of malaise, even as it gets technologically greener every year.)

Key Question: Why is a civilization life cycle apparently the optimal evolutionary developmental strategy?

Assumption: We’ve seen this pattern for too long, and in too many contexts, for it to be suboptimal.



Life Cycles: Further Thoughts

Compare and Contrast: universes, stars, complex planets, life forms, civilizations, cities, technologies, states of mind.

The more complex a system becomes, the more MEST efficient and information-protective the life cycle. Consider species extinction vs. cultural extinction (and digital capture).

When was the last time the death of a less adaptive thought in your mind was seen as wasteful or disruptive?

Stellar Life Cycle



Simplicity and Complexity

Universal Evolutionary Development is:

Simple at the Boundaries, Complex In Between

Simple MathOf the Very Small(Big Bang, Quantum Mechanics,Chemistry)

Simple MathOf the Very Large(Classical Mechanics,General Relativity)

Complex MathOf the In Between(Chaos, Life, Humans,Coming Technologies)

Ian Stewart, What Shape is a Snowflake?, 2001



Complex systemsare evolutionary.

Simple systems are developmental.

The universe is painting complex local evolutionary pictures, on a simple universe-wide developmental scaffolding.

The picture (canvas/intelligence, in the middle) is

mathematically complex (Gödelian incomplete),

and trillions of times evolutionarily unique.

The framework (easel/cosmic structure, very large, & paint/physical laws, MEST structure, very small) is uniform, and simple to understand.

The Meaning of Simplicity (Wigner’s ladder)

Evolution Development

Non-Pattern Pattern

Variety Uniformity

Symmetry and Supersymmetry

Symmetry Breaking

Chaotic Math Simple Math



Our Universe Has an Evolutionary Developmental Purpose

The more we study the dual processes of Evo-Devo, the better we discover the simple background, and can create a complex foreground. Take Home Points:

Evolutionary variation is generally increasing and becomes more MEST efficient with time and substrate.

Development (in special systems) is on an accelerating local trajectory to an intelligent destination.

Humans are both evolutionary & developmental actors, creating and catalyzing a new substrate transition.

We need both adequate evolutionary generativity, (uniqueness) and adequate developmental sustainability (accelerating niche construction) in this extraordinary journey.



Understanding the Bifurcation

Prediction Wall is Evolutionary Change

Prediction Crystal Ball is Developmental Change

Examples of Hierarchical Emergence



Cosmic Embryogenesis(in Three Easy Steps)

Geosphere/Geogenesis(Chemical Substrate)

Biosphere/Biogenesis(Biological-Genetic Substrate)

Noosphere/Noogenesis(Memetic-Technologic Substrate)

Le Phénomène Humain, 1955

Pierre Tielhard de Chardin (1881-1955)

Jesuit Priest, Transhumanist, Developmental Systems Theorist



Eight Useful Systems For Universal Computation (a.k.a. “Substrates”)

Substrate I.P. System

1. Galactic-Subatomic "Galactic"2. Stellar-Elemental "Atomic"3. Planetary-Molecular "Chemetic"4. Biomass-Unicellular "Genetic"5. Neurologic-Multicellular "Dendritic"6. Cultural-Linguistic "Memetic"7. Computational-Technologic "Algorithmic“8. AI-Hyperconscious "Technetic"

Note: Each is Vastly More MEST-Compressed and IP-Enabled



Every Substrate Has its Niche

Niche Construction, Odling-Smee, Laland, Feldman, 2004

The entire evolutionary history of life involves each organisms increasingly intelligent (value driven) modification of their niche, and environmental responses to these changes.

“Organisms do not simply 'adapt' to preexisting environments, but actively change and construct the world in which they live. Not until Niche Construction, however, has that understanding been turned into a coherent structure that brings together observations about natural history and an exact dynamical theory.”

– Richard Lewontin, Harvard



Niches are Increasingly Local in Spacetime

Biogenesis required a cooling Earth-crust, and billennia.

Multicellular organisms required a Cambrian Explosion, and millennia.

Human culture required a Linguistic Explosion, and tens of thousands of years.

Science and technology revolutions required a Cultural Enlightenment, the decomposing biomass of a fraction of Earth’s dead organisms, and hundreds of years.

Intelligent computers will likely be able to model the birth and death of the universe with the refuse thrown away annually by one American family. In tens of years?



Five Astrobiologically Developmental Systems for Human Computation?

Individual (Vitality,Creativity,Spirituality) Family/Relationship (Culture,Psychology) Tribal/Nation (Politics,Economics) Species/Planet (Peace,Globalization,Environment) Universal (Science,Technology,Computation)

– Question: Which is unlike the others? This last system is growing apparently asymptotically in local capacities

These five systems/dialogs seem likely to exist on all Earth-like planets (e.g., astrobiologically developmental).



Three Hierarchical Systems of Social Change

Sociotechnological (dominant since 1950!)

“It’s all about the technology” (what it enables, how inexpensively it can be developed)

Economic (dominant 1800-1950’s, secondary now)

“It’s all about the money” (who has it, control they gain with it)

Political/Cultural (dominant pre-1800’s, tertiary now)

“It’s all about the power” (who has it, control they gain with it)

Developmental Trends: 1. The levels have reorganized, to “fastest first.”2. More pluralism (a network property) on each level.

Pluralism examples: 40,000 NGO’s, rise of the power of media, tort law, Insurance, lobbies, etc.



The Developmental Spiral

Homo Habilis Age 2,000,000 yrs ago Homo Sapiens Age 100,000 yrs Tribal/Cro-Magnon Age 40,000 yrs Agricultural Age 7,000 yrs Empires Age 2,500 yrs Scientific Age 380 yrs (1500-1770) Industrial Age 180 yrs (1770-1950) Information Age 70 yrs (1950-2020) Symbiotic Age 30 yrs (2020-2050) Autonomy Age 10 yrs (2050-2060) Tech Singularity ≈ 2060



Gently Tightening Subcycles

1390-1500, 110 yrs

1500-1600, 100 yrs

1600-1690, 90 yrs

1690-1770, 80 yrs

1770-1840, 70 yrs

1840-1900, 60 yrs

1900-1950, 50 yrs

1950-1990, 40 yrs

1990-2020, 30 yrs

2020-2040, 20 yrs

2040-2050, 10 yrs

2050-2060, 5/2/1

Circa 2060

Pre-Scientific Rev.

1st Scientific Rev.

2nd Scientific Rev.

3rd Scientific Rev.

1st Industrial Rev.

2nd Industrial Rev.

3rd Industrial Rev.

1st Computer Rev.

2nd Computer Rev.

1st Symbiotic Rev.

2nd Symbiotic Rev.

Autonomy Rev’s

Tech Singularity

Oresme, Coord.Geom., Series

Copernicus, Vesalius

Bruno, Kepler, Descartes

Newton, Linnaeus

“CWT: Coal, Wood, Textiles”

“SST: Steam,Steel,Telegrph”

“ICE: Int.Comb,Chem, Electr”

“Dig.Comp,Engrg,MNC’s,TV”

“Planetnet, MIME, Security”

“GUI,LUI,NUI, Peace/Justice”

“Coll. Intell., Minor Magic”

“Autonomy-Under-the-Hood”

“AI,Earthpark”(Next:Uploads)

Period Subcycle Some Features



Four Pre-Singularity Subcycles?

A 30-year cycle, from 1990-2020 – 1st gen "stupid net "/early IA, weak nano, 2nd

gen Robots, early Ev Comp. World security begins.

A 20-year cycle, from 2020-2040 – LUI network, Biotech, not bio-augmentation,

Adaptive Robots, Peace/Justice Crusades.

A 10-year cycle, from 2040-2050 – LUI personality capture (weak uploading), Mature

Self-Reconfig./Evolutionary Computing.

2050: Era of Strong Autonomy – Progressively shorter 5-, 2-, 1-year tech cycles,

each more autocatalytic, seamless, human-centric.



Tech Singularity – Overview

Circa 2060: Technological Singularity – The AI (shortly thereafter, AI's) claim self-

awareness. True, 3rd-gen uploading begins. – World population hits its maximum (2030-2050),

declines increasingly rapidly thereafter.2040

1970

Warren Sanderson, Nature, 412, 2001Tom McKendree, Hughes Aircraft, 1994

“The Envelope Curve isLocal Universal Computation”

Any Fixed-ComplexityReplicating Substrate (e.g. Homo Sapiens)



Types of Singularities

Mathematical Physical Cosmological (our best model?) Computational Developmental (our best model?) Technological

"singular" human-competitive A.I. Emergence discontinuous (physical-dynamical singularity) unknowable (computational-cognitive singularity) convergent (developmental singularity) hierarchical (developmental singularity) instantaneous (developmental singularity) reproductive (developmental singularity)



Finite-Time Singularities

PDE’s of General Relativity in a mass field, leading to black hole formation

PDE’s of Euler equations of inviscid fluids in relation to turbulence

Rotating coin spinning down to a table (Euler’s disk) Earthquakes (ex: slip-velocity Ruina-Dieterich friction

law and accelerating creep) Micro-organism chemotaxis models (aggregation to

form fruiting bodies) Stock market crashes (as catastrophic events).

Source: Didier Sornette, Critical Phenomena in the Natural Sciences, 1999



Macrohistorical Finite-Time Singularities

Why Stock Markets Crash, 2003

Singularity 2050 ±10 years

The Singularity is Near, 2005


Ray Kurzweil



Macrohistorical Finite-Time Singularities (cont’d)

The Evolutionary Trajectory, 1998


Trees of Evolution, 2000




From the Big Bang to Complex Stars: “The Decelerating Phase” of Universal ED



From Biogenesis to Intelligent Technology: The “Accelerating Phase” of Universal ED

Carl Sagan’s “Cosmic Calendar” (Dragons of Eden, 1977)

Each month is roughly 1 billion years.



A U-Shaped Curve of Change?

Big Bang Singularity

100,000 yrs ago: H. sap. sap.

1B yrs: Protogalaxies 8B yrs: Earth

100,000 yrs: Matter

50 yrs ago: Machina silico50 yrs: Scalar Field Scaffolds

Developmental Singularity?



Eric Chaisson’s “Phi” (Φ): A Universal Moore’s Law Curve

Free Energy Rate DensitySubstrate (ergs/second/gram)

Galaxies 0.5Stars 2 (“counterintuitive”)Planets (Early) 75Plants 900 Animals/Genetics 20,000(10^4)Brains (Human) 150,000(10^5) Culture (Human) 500,000(10^5)Int. Comb. Engines (10^6)Jets (10^8)

Pentium Chips (10^11)

Source: Eric Chaisson, Cosmic Evolution, 2001

Ф

time



Just what exactly are black holes?



Lee Smolin’s Answer:“Cosmological Natural Selection”

At least 8 of the 20 “standard model”universal parameters appear tuned for:

– black hole production– multi-billion year old Universes (capable of creating Life)

The Life of the Cosmos, 1996



Post 2060– Full AI Sim of Human Thoughtspace (ref.: Our multimillion

dollar sims of bacterial metabolome)– Historical Computational Closure (astronomy, geography,

brains, etc.). Maps rapidly close the very large and very small, leaving only the very complex…

"Inner space," not outer space, now appears to be our constrained developmental destiny, incredibly soon in cosmologic time. "

Developmental Singularity – Overview

For astronomical closure, see Martin Harwit, Cosmic Discovery, 1981



Physics of a “MESTI” Universe

Physical Driver: MEST Compression/Efficiency/Density

Emergent Properties: Information Intelligence (World Models) Information Interdepence (Ethics) Information Immunity (Resiliency) Information Incompleteness (Search)

An Interesting Speculation in Information Theory: Entropy = NegentropyUniversal Energy Potential is Conserved.



Binding Energy (of Computational Structure)

Systems theorist Ervin Laszlo (Evolution, 1987) notes each hierarchically emergent universal substrate greatly decreases the binding energy of its diverse (evolutionary) physical configurations. Examples:

matter (earliest emerging physical substrate), e.g., protons and neutronswithin the nucleus of atoms, is bound by nuclear exchange ("strong") forces

atoms are joined by much weaker ionic or covalent (electromagnetic) bonds cells within multicellular organisms are connected "another dimension down

the scale of bonding energy." memes encoded in a vesicular-morphologic language of synaptic weights and

dendritic arborization involve vastly less binding energy still technemes, in communicable electronically-encoded algorithms and logic circuitry involve orders of magnitude less binding energy yet again. gravitons. Note gravity is the 2nd weakest of the five known forces (only dark

energy is weaker). Yet in Smolin’s model gravity guides us to black holes as a developmental attractor for substrate computation in this universe.

In other words, the MEST efficiency, or energy cost of computation, of learning (encoding, remembering, reorganizing) rapidly tends to zero in emergent substrates as we approach the developmental singularity.



Growth and Limits of Computation

Universal Computing to Date: 10^120 logical ops – Turing, Von Neumann, Ed Fredkin, John Wheeler

Digital Computing to Date: 10^31 logical ops – Half this was produced in the last 2 year doubling.

300 Doublings (600 years) to a “Past-Closed” Omega Computer?

– Understanding most Developmental History and some of Evol. History. (e.g., CA’s, Gen. Engrg.)

Computing right down to Planck Scale?– No Minimum Energy to Send a Bit (Landauer)– Quantum and Femto-Scale Processes

Sources: Seth Lloyd, “Computational Capacity of the Universe, Phys.Rev.Lett., 2002C. Bennett & R. Landauer, “Fund. Phys. Limits of Computation, Sci. Am., July 1985



Understanding MEST Compression

MEST compression/Time

The Finite Universe Box

Six Billion Years Ago

WeEnd UpHere



A Developmental Universe?

Developmental Lesson: A Possible Destiny of Species

MEST compression, Intelligence, Interdependence, ImmunityInner Space, Not Outer Space (Mirror Worlds, Age of Sims)

Black Hole Equivalent Transcension?



The Fermi Paradox

So where are the ET’s?

Our Milky Way Galaxy is just 45,000 light years in radius.Earth-like planets 3-5 Billion years older than us nearer the core.

Andromeda GalaxyOnly 2 mill light yrs away

A Dev. Sing. Prediction: SETI Fossils by 2080



Present Score: 13 for Transcension, 2 for Expansion

The Case For Transcension1. Universal Speed Limit (c), and Isolation of Everything Interesting2. Singularities Everywhere3. Hyperspace (Our Universe is a Riemann Manifold in 4D Space)4. String and Supersymmetry Theory (10, 11, or 26 Dimensions)5. Multiverse Theories (CNS, INS) 6. Fermi Paradox (Parsimonious Transcension Solution)7. Relentless MEST Compression of Substrate Emergence 8. Technological Singularity Hypothesis9. “Plenty of Room at the Bottom” (Richard Feynman about Nanotech)10. Bottom is Strange (Quantum Weirdness), But Stably Convergent!11. A Non-Anthropomorphic Future12. Lambda Universe Message (The Kerrigan Problem. "Why Now?")13. Midpoint Principle (Subset of Cosmic Watermark Hyp./Wigner's Ladder)

The (Highly Suspect) Case for Expansion1. 3D Space is Suited to Humanity2. A Comfortable Extrapolation of our Frontier Experience



Virtual Space: Is Inner Space the Final Frontier?

Mirror Worlds, David Gelernter, 1998. Large scale structures in spacetime are:

• A vastly slower substrate for evolutionary development• Relatively computationally simple and tractable (transparent)• Rapidly encapsulated by our simulation science• A “rear view mirror” on the developmental trajectory of

emergence of universal intelligence?

versus

Non-Autonomous ISS Autonomous Human Brain



Physical Space: A Transparent Society (“Panopticon”)

Hitachi’s mu-chip: RFID for paper currency

David Brin,The Transparent Society, 1998



Ephemeralization (MEST Efficiency of Physical-Computational Transformations)

In 1938 (Nine Chains to the Moon), the poet and polymath Buckminster Fuller coined "Ephemeralization,” positing that in nature, "all progressions are from material to abstract" and "every one of the ephemeralization trends.. eventually hits the electrical stage" such that "even efficiency (doing more with less) ephemeralizes."

In 1981 (Critical Path), he called ephemeralization, "the invisible chemical, metallurgical, and electronic production of ever-more-efficient and satisfyingly effective performance with the investment of ever-less weight and volume of materials per unit function formed or performed". In Synergetics 2, 1983, he called it "the principle of doing ever more with ever less weight, time and energy per each given level of functional performance” This meta-trend has also been called “virtualization” by other theorists.

Combined, these statements may be among the first to name MEST compression/efficiency/density of computational transformations, the apparent driver of accelerating change in special physical environments.



The Practical Benefit of Understanding MEST Compression: Developmental Foresight

GDP weight trends down in every developed country.

More MEST-efficient systems have increased “system dynamism”/degrees of structural freedom (Jack Hipple, TRIZ)

New technological paradigms generally use dramatically less MEST/capital investment. (Nano, Bio, Info, Cogno vs. Coal, Steel, and Oil development). Exception: Some infotech hardware (chip fab plants, microrobotics, etc.).



Seeing MEST Efficiency and Compression Everywhere in the World

Cities (>50% of world population circa 2005) Working in Offices (or telecommuting with

coming videophone virtual offices) Wal-Marts, Mega-Stores, 99 Cent Stores

(Retail Endgame: Wal-Mart #1 on Fortune 500 since 2001)

Flat-Pack Furniture (Ikea) Big Box Retail (Home Depot, Staples) Supply-Chain & Market Aggregators (Dell,

Amazon, eBay) Local community/Third Space (Starbucks)



Switching is shifting from circuits to packets. Data, then voice; Backbone, then access

Transmission is shifting from electronic to photonic.

First long haul, then metro, then local accessFunctions are moving from the enterprise to the Net.

IP universal protocol/ platform of choice is the NetOfferings are moving from products to services.

"Utilitization" of processing, applications, storage, knowledgeBioscience is moving from in vitro to in silico.

First Genomics, then Proteomics, then nanotechnologies

Key Shifts in the Venture Capital Market

Source: Jim Spohrer, IBM Almaden, 2004

(More agent-based, more MEST-compressed, more network-like, more information-based, more hardware oriented.)



Border monitoring(low altitude drug flights)

City monitoringEarly warning radarUrban broadband

Inventor: Hokan Colting21stCenturyAirships.com180 feet diameter. Autonomous.60,000 feet (vs. 22,000 miles)Permanent geosynch. location.Onboard solar and navigation.A “quarter sized” receiver dish.

Why are satellites presently losing against the wired world?

Latency, bandwidth, and launch costs.

MEST compression always wins.Don’t bet against it!

Stratellites: A Developmental Attractor?

The Future of Automation and Economics



World Economic Performance

GDP Per Capita in Western Europe,1000 – 1999 A.D.

This curve looks very smooth on a macroscopic scale.

The “knee of the curve” occurs at the industrial revolution, circa 1850.



Understanding Automation

Between 1995 and 2002 the world’s 20 largest economies lost 22 million industrial jobs. This is the shift from a Manufacturing to a Service/Information Economy.

1995-02, America lost 2 million industrial jobs, mostly to China. China lost 15 million such jobs, mostly to machines. (Fortune)

Despite the shrinking of America's industrial work force, our country's overall industrial output increased by 50% since 1992. (Economist)

“Robots are replacing humans or are greatly enhancing human performance in mining, manufacture, and agriculture. Huge areas of clerical work are also being automated. Standardized repetitive work is being taken over by electronic systems. The key to America's continued prosperity depends on shifting to ever more productive and diverse services. And the good news is jobs here are often better paying and far more interesting than those on we knew on farms and the assembly line.” (Tsvi Bisk)

"The Misery of Manufacturing," The Economist. Sept. 27, 2003"Worrying About Jobs Isn't Productive," Fortune Magazine. Nov. 10, 2003 “The Future of Making a Living,” Tsvi Bisk, 2003



Interface: Understanding Process Automation

Perhaps 80-90% of today's First World paycheck is paid for by automation (“tech we tend”).

Robert Solow, 1987 Nobel in Economics (Solow Productivity Paradox, Theory of Economic Growth)“7/8 comes from technical progess.”

Human contribution (10-20%) to a First World job is Social Value of Employment + Creativity + Education

Developing countries are next in line (sooner or later).

Continual education and grants (“taxing the machines”) are the final job descriptions for all human beings. Termite Mound



Work in 2050 Scenario: 100:10:1 Tax:Foundation:Corporate Global Philanthropy

As technology-driven corporate GDP grows exponentially at 4% or more each year, historical analysis argues governments will continue to do by far the most “social contract giving,” (100:10:1 govt. to individual to corporate giving ratio). That would mean that the service work of many, perhaps even most of our 200 million+ employees (total 2050 pop. of 300-400 million) circa 2050 will be supported by the equivalent of “grant proposals to the government” to do various public works, in the same the way our country’s 1.5 million nonprofits presently are supported by government and private foundation grants today. Thus the 1/6 of us that presently work for (or live off) the government will likely double by 2050 (European model).

Secondarily, individuals and their foundations, with progressively increased social leverage due to tech-aided wealth increase, will do more giving each year. Look to individuals, with their uniquely creative and transformative giving styles (through foundations, legacy, and discretionary giving) to usher in an Age of Global Philanthropy in the post-LUI era after 2020.

To recap, while corporations will bring lots of new technology-enabled wealth into the world, philanthropy will likely continue to be driven first by governments (100X) then individuals (10X) and finally business (1X).

See: Millionaires and the Millennium, Havens and Schervish, 1999



Process Automation Example: Oil Refinery (a Multi-Acre Automatic Factory)

Tyler, Texas, 1964. 360 acres. Run by three operators, each needing only a high school education. The 1972 version eliminated the three operators.



Problem: Social Disruption Due to Technological Revolutions

Manufacturing Globalization Revolution (1980’s) Info Tech (IT) Globalization Revolution (2000’s) LUI Automation Revolution (2020’s)

Some jobs that went to Mexican maquiladoras in the 1980’s are going to China in the 2000’s. Many of these jobs will go to machines in the 2020’s.

What to do?



Automation Development Creates Massive Economic-Demographic Shifts

Automating of farming pushed people into factories (1820, 80% of us were farmers, 2% today)

Automating of factories is pushing people into service (1947, 35% were in factories, 14% today)

Automating of service is pushing people into information tech (2003, 65% of GDP is in service industry)

Automating of IT will push people into symbiont groups (“personality capture”)

Automating of symbiont groups will push people beyond biology (“transhumanity”)



IT’s Exponential Economics




Our 2002 service to manufacturing labor ratio, 110 million service to 21 million goods workers, is 4.2:1

Automation and the Service Society



De Chardin on Acceleration: Technological “Cephalization” of Earth

"No one can deny that a world network of economic and psychic affiliations is being woven at ever increasing speed which envelops and constantly penetrates more deeply within each of us. With every day that passes it becomes a little more impossible for us to act or think otherwise than collectively." “Finite Sphericity + Acceleration =

Phase Transition”



U.S. Transcontinental Railroad: Promontory Point Fervor

The Network of the 1880’s

Built by hard-working immigrants



IT Globalization Revolution (2000-20):Promontory Point Revisited

The more things change, the more some things stay the same.

The coming intercontinental internet will be built primarily by hungry young programmers and tech support personnel in India, Asia, third-world Europe, Latin America, and other developing economic zones. In coming decades, such individuals will outnumber the First World technical support population between five- and ten-to-one. Consider what this means for the goals of modern business and education: Teaching skills for global management, partnerships, and collaboration.



Technological Globalization: Winners

Globalization is less a choice than a statistical inevitability, once we have accelerating, globe-spanning technologies (communication, databases, travel) on a planet of finite surface area (“sphericity”).

There are some clear winners in this phase transition, such as:

Network Memes and Traditions like Free Markets, Democracy, Peace and other Interdependencies(The Ideas that Conquered the World, Michael Mandelbaum)

Big Cities (backbone of the emerging superorganism)(Global Networks, Linked Cities, Saskia Sassen)

Global Corporations (large and small)(New World, New Rules, Marina Whitman)



Some of the longer term losers:

Non-Network Memes and Traditions like Autocracy, Fascism, Indefinite Protectionism, Communism

(Power and Prosperity, Mancur Olson) Centrally-Planned (mostly Top-Down) vs. Market-Driven

(mostly Bottom-Up) Economies (“Third World War”)(The Commanding Heights, Daniel Yergin)(Against the Tide, Douglas Irwin)

Groups or Nations with Ideologies/Religions SanctioningNetwork-Breaking Violence (“Fourth World War”) (The Clash of Civilizations, Samuel Huntington)

Centrally-Planned vs. Self-Organizing Political Systems (excepting critical systems, like Security) (The Future and Its Enemies, Virginia Postrel)

Technological Globalization: Losers



Technological Globalization: Uncertains

Most elements of modern society, of course, are evolutionary, meaning they remain ‘indeterminate’ actors which may or may not become winners. Their fate depends critically on the paths we choose. Some key examples:

Humanist Memes like Justice, Equal Opportunity, Individual Responsibility, Education, Charity, Compassion,

Cultural Diversity, Sustainability, Religious Tolerance (The Dignity of Difference, Jonathan Sacks)

The Unskilled Poor (In All Economies, U.S. to Uganda)(A Future Perfect, Micklethwait and Wooldridge)

The Developing World(The Mystery of Capital, Hernando de Soto)



“NBICS”: 5 Choices for Strategic Technological Development

Nanotech (micro and nanoscale technology) Biotech (biotechnology, health care) Infotech (computing and comm. technology) Cognotech (brain sciences, human factors) Sociotech (remaining technology applications)It is easy to misspend lots of R&D money on a still-early

technology in any field.Infotech examples: A.I., multimedia, internet, wireless

It is even easier to misspend disproportionate amounts of R&D budgets on a less centrally accelerating field.Current examples: Nanotech and biotech

Assumption: Any nation today can far more quickly get substantially better infotech than biotech or nanotech.



Is Biotech a Saturated Substrate?

21st century neuropharm and neurotech won’t accelerate biological complexity (seems likely now). – Neural homeostasis fights “top-down” interventions– “Most complex structure in the known universe”

Strong resistance to disruptive biointerventions– In-group ethics, body image, personal identity

We’ll learn a lot, not biologically “redesign humans”– No human-scale time, ability or reason to do so. – Expect “regression to mean” (elim. disease) instead.

Neuroscience will accelerate technological complexity – Biologically inspired computing. “Structural mimicry.”



Computational Limits on 21st Century Biotechnology

Biology is Bottom-Up Designed, Massively Multifactorial,and Nonlinearly Interdependent. “Genetically engrd humans” (2000) are “atomic vacuum cleaners” (1950)

Increased Differentiation = Decreased Intervention Clipping growth genes into frogs vs. mice vs. pigs. Developmental damage! “Negative pleiotropy increases with complexity.”

Our Genetic “Legacy Code” Appears Highly ConservedThe entire human race is more genetically similar than a single baboon troop.

A massive extinction event circa 70,000 years ago is one proposal for this (ref).Much more likely is simple developmental path dependency.

Mental Symbolic Manipulation is Deep DifferentiationWernicke’s and Broca’s are apparent equivalent of metazoan body plans!

(see Terrence Deacon, The Symbolic Species, on co-evolution of lang. & brain)Even with preadaptation (Gould) & requisite variety (Ashby), drift = dysfunction.

Features of Evol. and Expansion of Modern Humans, Inferred from Genomewide Microsatellite Markers," Zhivotovsky, 2003, AJHG



Nanotech and Cognotech are bothAI-Dependent Systems

Key Assumptions:

Nanotech Will Require Bottom-Up, Biologically-Inspired AI to Realize the full “Drexlerian” molecular assember vision (Erik Drexler, Engines of Creation, 1986).

Cognotech (e.g., human consciousness) will only expand past its current saturation when we have nanotech and fine-grained AI personality capture interfaces



Infotech and Sociotech Are the Engine and Driver of the Coming Transition

Infotech (“AI”): Process Automation

Storage, Networking, and Simulation

Biologically-Inspired Computing

Sociotech (“IA”): Digital Ecologies

Immunity, Compassion, and Interdependence

Linguistic User Interface



5 Info- and Socio-technological Levers for Third World in the 21st Century

1. Infotech (Education, Digital Ecologies)

2. Globalization (Education, Bilingualism, Unique Competitive Advantages)

3. Transparency (Education, Accountability, Anti-Corruption)

4. Liberalization (Education, Legal and Democratic Reform)

5. Compassion (Education, Rich-Poor Divides, NGOs, Workfare, Philanthropy)



Infotech: Digital Ecologies

Radio

Low Power TVCell Phones

Newspapers(Program Guides)

Internet

PDAs

Game PCsCordless Phones

Desktop PCs

Key Questions: Public access? Subsidized? Education?Strong network effects. Intrinsically socially stabilizing.“There is no digital divide.” (Cato Institute)

Email

AvatarsGroupware

Social Software

IM/SMS



AI-in-the-Interface (a.k.a. “IA”)

• AI is growing, but slowly (KMWorld, 4.2003) ― $1B in ’93 (mostly defense), $12B in 2002 (now mostly commercial). AGR of 12% ― U.S., Asia, Europe equally strong ― Belief nets, neural nets, expert sys growing faster than decision support and agents ― Incremental enhancement of existing apps (online catalogs, etc.) • Computer telephony (CT) making strides (Wildfire, Booking Sys, Directory Sys). ASR and TTS improve. Expect dedicated DSPs

on the desktop after central CT. (Circa 2010-15?)

• Coming: Linguistic User Interface (LUI) Persuasive Computing, and Personality Capture



Linguistic User Interface

Google’s cache (2002, % non-novel) Watch Windows 2004 become

Conversations 2020… Convergence of Infotech and Sociotech



Today: Gmail

Free, search-based webmail service with 1,000 megabytes (1 gigabyte) of storage. Google search quickly recalls any message you have ever sent or received. No more need to file messages to find them again.

All replies to each retrieved email are automatically displayed (“threaded”). Relevant text ads and links to related web pages are displayed adjacent to email messages.



Tomorrow: Social Software, Lifelogs

Gmail preserves, for the first time, everything we’ve ever typed. Gmailers are all bloggers (who don’t know it). Next, we’ll store everything we’ve ever said. Then everything we’ve ever seen. This storage (and processing, and bandwidth) makes us all networkable in ways we never dreamed.

Lifeblog, SenseCam, What Was I Thinking, and MyLifeBits (2003) are early examples of “LifeLogs.” Systems for auto-archiving and auto-indexing all life experience. Add NLP, collaborative filtering, and other early AI to this, and data begins turning into wisdom.



Phase Transitions: Web, Semantic Web, Social Software, Metaweb

Nova Spivak, 2004



Robo sapiens

AIST and Kawada’s HRP-2

(Something very cool about this algorithm…)

“Huey and Louey”

Aibo Soccer



What Computers Do that Human’s Don’t

Humans Need Secrecy, Lies, Violence. They Solve Computational Problems for Us. (Harold Bloom, The Lucifer Principle). But Computers?

Open-Ended Learning Capacity: Hyperconsciousness Greater Degrees of Freedom, "Perfect" Retention and Forgetting Communication of Knowledge Structures, Not Just Language Maintain Multiple Perspectives Until Data Come In. No Variation Cost.

Computational Ethics: NZS Games, Global OptimaInformation Flow Hypothesis of Self (Boundary, Dennett)Information Flow Hypothesis of Conflict (Rummel, etc.) Tolerance of Human Beings vs. Human Brains (Minsky, Society of Mind)

Conclusion: AI’s Will Be Far More Interdependent, Ethical, Empathic to Others, & Stable Than Humans Could Ever Be, By Apparent Design



In the long run, we become seamless with our machines.No other credible long term futures have been proposed.

“Technology is becoming organic. Nature is becoming technologic.” (Brian Arthur, SFI)

Solution: Personality Capture and Transhumanity



Your “Digital You” (Digital Twin)

Greg Panos (and Mother) PersonaFoundation.org

“I would never upload my consciousness into a machine.”

“I enjoy leaving behind stories about my life for my children.”

Prediction: When your mother dies in 2050, your digital mom will be “50% her.”When your best friend dies in 2080, your digital best friend will be “80% him.”When you die in 2099, your digital you will be 99% you. Will this feel like death, or growth?

Successive approximation, seamless integration, subtle transition.

When you can shift your consciousness between your electronic and biological components, the encapsulation and transcendence of the biological will feel like only growth, not death.

We wouldn’t have it any other way.



System Meta-Properties: Intelligence

Informational Intelligence

“The Cosmic Watermark Hypothesis” (E. Wigner)

Evidence: Ashby’s Law of Requisite Variety

Game is Rigged to Make Watermarks & Intelligence Strongly Coadaptive.

Evidence: Historical Computational Closure: Columbus's Geography Harwit's Astronomy

Smolin's Universe?



System Meta-Props: Interdependence

Informational Interdependence

“The Empirical Ethics Hypothesis” (E.O. Wilson)

Evidence: Evolutionary Psychology

Matt Ridley on reciprocal altruism, Guppies to Gangsters.

Evidence: Non-Zero Sum Games

Robert Wright on capitalism, cooperation, ethics.

Evidence: Statistical Elimination of Social Violence

R.J. Rummel on Statistics for Democide



System Meta-Properties: Immunity

Informational Immunity

“The Child-Proof Universe Hypothesis” (J. Smart)

Evidence: Average Distributed Complexity (ADC)

This measure always accelerating. Catastrophes only

catalyze and stabilize ADC.

Evidence: History of Tech (vs. Civilizations) Fall of Egypt,Maya,Rome no effect on global tech diffusion.

Evidence: K-T Extinction Genetic complexity only increased

Evidence: History of PlaguesNever, ever a species threat. Immunity always catalyzed.



System Meta-Props: Incompleteness

Informational Intelligence

“The Incompleteness Theorem” (K. Godel)

Evidence: Godel, Church-Turing, Chaitin

Every system is computationally incomplete.

New substrates are necessary to answer undecidable

questions that can be posed from within any

formal logical system.

Accelerating Change, World Security, and the Non-Integrating Gap



Connectivity is a Developmental Attractor

Francis Fukuyama (The End of History), Thomas Friedmann (The Lexus and the Olive Branch), Robert Kagan (Of Power and Paradise) Thomas Barnett (The Pentagon’s New Map) and Samuel Huntington (The Clash of Civilizations) are all mostly right.

The developmental destination for nation states is clear. But the evolutionary path is bottom up, and so must be culturally unique.

Our job is to facilitate this one-way transition as uniquely and as measurably as possible.

These two goals sometimes conflict.



The Pentagon’s New Map

A New Global Defense Paradigm



Shrinking the Disconnected Gap

The “Ozone Hole”



The Disconnected Gap:Our Planetary Ozone Hole

Global Polarization (Core vs. Gap)“Disconnectedness (tech, economic, cultural) defines

danger.” (Thomas Barnett, Pentagon’s New Map) Strategy: Encircle, Support the Seam States

-- Plant resources in “supportive soil.” -- Greatest comparative advantage for shrinking the

hole (eg. Koreas). Strategy: Don’t Stir Up the Ant’s Nest

-- This is difficult, as due to differential immunity, our cultural memes (materialism, democracy, etc.) are as powerful as the germs that wiped out up to 90% of the less immunologically complex cultures (Rome, 1-200AD, Europe, 1300, America, 1492-1600)



“Broken Windows” Policies: A Precondition to MEV

Broken Windows theory of political scientist James Wilson and criminologist George Kelling (The Atlantic Monthly, March 1982)

Rapid response to and repair of the visibly "broken" aspects of a local community increases sense of control, ownership, initiative and vigilance against crime.

Billboards with easy reporting phone numbers and list of the top acts people should report. Giving statistics and trends. Enlisting the collective in simple vigilance.



Unconscious Gap Strategy: Measurable Exponential Value (MEV)

Culture-appropriate determination of needs Invited solutions, two way communication,

feedback, local customization Subsidize the solutions Measure the growth rate (exponentiation) Bottom up marketing A mix of self sufficiency and philanthropy

(development) If you don’t see exponential adoption,

intervention will not be perceived as a comparative advantage. Adapt and iterate.



Examples: Iraq

Communications (cellphones) Lighting (digital solid state) Energy (centralized economies of scale, subsidized

deflationary prices; decentralized storage and generation)Example: Donkey cart generators

Security (networked cameras; camera traps)Culturally-dependent: Britain vs. S. Africa vs. U.S.

Portable CD Players/local music ($10 at Wal-Mart) Public access radio and TV stations Food storage, culinary, and women’s needs Sports / Youth Fads



IDAP Technology Processes

Innovation Diffusion Assessment Policy

“The future is here, it’s just not evenly distributed yet.” – William Gibson

First to third world diffusion is arguably the greatest gap. But culture-appropriate assessment processes, sensitive policymaking, and fostering cultures of innovation are also important.



The Psychology of Exponential Growth

Exponential growth keeps people satisfied.

Benefits are self-reinforcing.

People maintain behavior on non-zero sum interactions, where the size of the pie and your absolute return grows even as your percentage decreases annually (Robert Wright, Non-Zero, 2000)

Citizens turn toward personal and local development, much less toward nationalism and ideology (Ron Inglehart, The Silent Revolution, 1976; Modernization and Postmodernization, 2002)

We can measure this (census and other surveys).



The Key Strategic Question with any Gap Intervention

Not whether we could have been liked better, won more “hearts and minds” (in Iraq or among our allies).

The key question is the degree to which new exponential ecologies (technological, economic, social) are adopted and persist in the community.

-- Tools, Markets, Rules

We can measure this (operations research).



The Say-Do Development Gap

2,600 Iraqi Development Projects Promised160 under way presently. (Time, July 2004)

Of all of these, communications has been our biggest shortcoming (“failure to communicate”).

We wired ourselves superbly (CPOF) but we never wired in to the populace, or even helped them to wire themselves, in exponential fashion.

Example: DARPA/USC ICT Tactical Language project. Top-down thinking. Avatars vs. Persistent Worlds.

We could have had scores of Iraqi/Arabic youth teaching our incoming soldiers tactical culture in massively multiplayer online worlds, and using those worlds for their own benefit as well. A tipping point among the youth (like Satellite Television in India, etc.).



Immune Recognition vs. Rejection

The phenomenon of immune recognition (and immune tolerance) vs. rejection.

The honeymoon period.

Rejection, if no measurable exponential value within the host network.

We did not pass this test (in fairness, we may never have passed).

Nevertheless, there were many missed opportunities for deploying MEV strategy.



Tech Immune Systems Example: Cellphones

An intrinsically defensive asset.

-- Monitorable (location and content)

-- Strengthen personal networks

-- The mean can self-police the extremes (report scofflaws)

-- Granular privileges (given and revoked)

-- Can be built robustly (dynamo, shoe batts)

-- Chip provides superior ID (address books)

-- Hot button to security radio band



Tech Immune Systems Example: Firearms for Police

Networked weapons are an intrinsically defensive asset.

-- Single shot magazines (deterrence)

-- Cameras and microphones (“Black Box”)

-- Cellphone to CENTCOM when safety off

-- The best training possible (on the job)

-- The inevitable future (worldwide buyback of all non-networked lethals except antiques (eg. Australia) the emergence of networked non-lethals.



What is our ‘control’ study?

How do we know providing Measurable Exponential Value would have worked in Iraq?

What’s our ‘control’ for the connectivity doctrine?

The Gap’s own history:

Maoist China, Kampuchea, Afghanistan...

Every example of swings away from connectivity has been unsustainable in space and time.



Why the Gap Shrinks

“He who can handle the quickest rate of change survives.” -- Col. John Boyd, Military Strategist

Time compression is one form of MEST compression.

Why Eurasia won the sociopolitical, technological, military, and germ development race (Largest East-West Axis, earliest domestication of animals, Jared Diamond, Guns, Germs, and Steel).

Why Europeans decimated the Americas and Pacific Islanders with a host of crowd infectious diseases, and not the other way around.

Why the Gap will shrink to next-to-nothing as we create a transparent global society this century.



U.S. Army: Development Challenges and Opportunities

Security Leader Development Follower This makes institutional sense. A natural constraint.

Many development capability options: Specialization (Corps of Engineers, etc.). Unique Capacities (Fire and weather mgmt, FLEs) Competition (Cross services bidding) Incentives if under budget and before deadline with

quality (ex: Kowloon Tunnel (Hong Kong), Human Genome Project, etc.))

Networks (America’s Army: worldwide devel. recruits) Partnerships. Most obvious: USAID (long term

optimists). Many others as well (bottom up).



Professional Futuring Tools

Acceleration Forecasting (M.S.) What is accelerating? How fast? For how long? What human problem does this solve? When/how should we implement?

Operations Research (M.S.) What are likely optima with present conditions? What are the possible MEST efficiencies?

Developmental Future Studies (M.S.) What are the inevitable attractors and TINA trends? When will we get to the next phase change, PTE? How does this influence present policy and strategy?



Closing Questions

Six Questions1. What would you monitor/scan/measure today to see if we are on an S-Curve or J-Curve of global computational change?2. What methods would you use to distinguish evolutionary randomness from developmental trajectory3. Is the tech singularity coming? What? When? Where? How? Why?4. What are our control options for accelerating and ever more autonomous computation?5. What are better and worse paths of technology development? 6. How do we promote unity, balance, and accelerating compassion inthe transition?Consider the First and Third World GDP Curves, 1900 to 2000. A Proposition: The third world curve is largely ours to choose.



Action Items

1. Sign up for free Tech Tidbits and Accelerating Times newsletters at Accelerating.org

2. Attend Accelerating Change (AC2004)November 5-7 at Stanford, Palo Alto, CA

3. Send feedback to [email protected]

Thank You.

Documents

Los Angeles Palo Alto © 2004 Accelerating.org Challenges for World Security Policy John Smart USAWC, August 2004, Carlisle, PA Adapting to the Future: