Discontinuity Futures Simulation Swan

Melanie Swan Principal

MS Futures Group+1-650-681-9482

[email protected]

Singularity University

August 5, 2009

GA11: 9-12

Slides: http://slideshare.net/LaBlogga/slideshows

Discontinuity Futures Simulation Workshop

2

Discontinuity futuresAugust 5, 2009

Summary

A key aspect of futures studies is the convergent and interdisciplinary nature of disparate technologies

Technology growth may be linear, exponential and discontinuous

The ultimate future depends on the order in which discontinuities are realized

Rapid transition time, doubling capability and adjacent advances may provide clues to discontinuous change

Image credit: Fausto de Martini

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Singularity University

Nanotechnology

Biotechnology and Bioinformatics

Medicine, Neuroscience and Human Enhancement

AI and Robotics

Space and Physical Sciences

Energy and Ecological Systems

Networks and Computing Systems

Futures Studies

Policy Law and Ethics

Finance and

Entrepreneurship

Unifying frameworks and track synthesis

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The ultimate future depends on the order in which discontinuities arrive

Sequence of discontinuities

Artificial intelligence

Molecular nanotechnology

Anti-agingtherapies

Whole human genome

New computing paradigm

Robotics

Intelligenceaugmentation

Personalized medicine

Affordable space launch

3D printing

Synthetic biology

Space-basedcivilization

New energy regime

Uploading

Modification of human

biological drives

time2009-2020 2020-2030 2030-2050+10 +20 +40

Brain emulation

Room- temperature

superconductivityMechanosynthesis

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Paradigms of growth and change

Linear Economic, demographic, life span phenomena

Exponential Technology: processors, memory, storage,

communications, iPhone applications

Discontinuous Plane, car, radio, wars, radar, nuclear weapons,

satellites, computers, Internet, globalization Impossible to predict

Rapid transition time and doubling capability Adjacent technology advances Level of engagement

Exponential

Discontinuous

Linear

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First principles (implied by historical trends)

Increase in humaneness Slavery, smoking, saturated fat,

dentistry Increase in abundance

Segment expansion (TIVO wedge) Radio/music sales, TV/YouTube

Multiple choices, not either/or Expanded possibilities

Ex: more habitable places: deserts, poles, seasteading, airsteading

Intervention (vs. interference) Inter-societal knowledge

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Evolving concept of science

Model and simulate

Enumerate and

experiment

Build

BioSpice.orgSimTK.org

PartsRegistry.orgGeneGo (pathway modeling)

Entelos virtual patient biosimulation

FabAtHome.org

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Evolving concept of health

A consumer-centric model of health care

Source: http://www.mdpi.com/1660-4601/6/2/492

Cure

Improvement

Normalization

Prevention

Enhancement

Self-expression

ConditionsSymptoms

Genome Biomarkers Behavior Environment

Health outcomes

Measure

ResearchTreat

InterveneExperiment

TrackMeasureLiteracy

Actions

Self

Professionals

Peers

Infectious disease

Chronic disease

Aging

Cure

Improvement

Normalization

Prevention

Enhancement

Self-expression

ConditionsSymptoms

Genome Biomarkers Behavior Environment

Health outcomes

Measure

ResearchTreat

InterveneExperiment

TrackMeasureLiteracy

Actions

Self

Professionals

Peers

Infectious disease

Chronic disease

Aging

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Extensibility of Moore’s Law

Penryn45 nm, 410-800m transistors

Core 2 65 nm, 291m transistors

Transistors per microprocessor

Source: http://www.kurzweilai.net/pps/Unither

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ITRS semiconductor roadmap

Source: http://download.intel.com/technology/silicon/Paolo_Semicon_West_071904.pdf

2007: 32 nm 2009, 22 nm 2011 2009: 32 nm shifted out to 2010

2009 2010X

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Computing paradigm shifts

Electro-mechanical

Relay Vacuum tube

Transistor Integrated circuit

?


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Evolving computational modelsCurrent model extensibility

Linear, von Neumann

Linear, von Neumann

ParallelParallelCloud, grid, distributed

Cloud, grid, distributed

Biological models

Novel models

Traditional model

QuantumQuantumOptical

computing

Optical computing

Cell broadband

engine

Cell broadband

engine Liquid computer

Liquid computer

New materials

New materials

3D chip stacking

3D chip stacking

Molecular electronics

Molecular electronics Solar

transistors

Solar transistors

DNA nanotech

DNA nanotech DNA

computing

DNA computing

BiosensorsBiosensorsCellular colonies

Cellular colonies

Bacterial intelligence

Bacterial intelligence

Bioparadigm discovery

Bioparadigm discovery

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End of Moore’s Law problem: when does top-down meet bottom-up? Top-down solutions

Block copolymer lithography CNT transistors Memristor Quantum-dot cellular automata Plasmonic materials & spintronics Quilt packaging & 3D stacking

Bottom-up solutions DNA self-assembly DNA computing DNA-based transistors 3D DNA nanocrystals Molecular memory Structural DNA:

Holliday junctionRotaxane Molecular propeller

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Arms race for the future of intelligence

Machine Human IBM Roadrunner 1.105 petaflop/s (>1,100

trillion IPS) and 80 TB memory1

Unlimited operational/build knowledge Quick upgrade cycles: performance

capability doubling every 18 months Linear, von Neumann architecture Understands rigid language Special purpose problem solving (Deep

Blue, Chinook, ATMs, fraud detection) Metal chassis, easy to backup

An estimated 20,000 trillion IPS and 1,000 TB memory2

Limited operational/build knowledge Slow upgrade cycles: 10,000 year

evolutionary adaptations Massively parallel architecture Understands flexible, fuzzy language General purpose problem solving,

works well in new situations Nucleotide chassis, no backup possible

1Source: Top 500, June 2009, http://www.top500.org/lists/2009/06, http://www.crn.com/hardware/2084031862Source: http://paula.univ.gda.pl/~dokgrk/bre01.html

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Full human brain neural simulation est: 2018

Average human: an estimated 20,000 trillion IPS

and 1,000 TB memory2

IBM Roadrunner: 1.1 petaflop/s (>1,100 trillion IPS) and 80 TB memory1

1http://www.top500.org/lists/2009/06, http://www.top500.org/system/89682http://paula.univ.gda.pl/~dokgrk/bre01.html

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Engineering life into technology

2029

Machine

Human

Human′ ?Capability

Year

Biomolecular interface convergence

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Summary

A key aspect of futures studies is the convergent and interdisciplinary nature of disparate technologies

Technology growth may be linear, exponential and discontinuous

The ultimate future depends on the order in which discontinuities are realized

Rapid transition time, doubling capability and adjacent advances may provide clues to discontinuous change

18


Discontinuity futures simulation workshop

Image credit: Natasha Vita-More, Primo Posthuman

World’s top venture capitalists 5 teams

Each team has $1b to invest

Round 1: 2009-2020

Round 1: 2020-2030

Round 3: 2030-2040

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Nanotechnology

Biotechnology and Bioinformatics

Medicine, Neuroscience and Human Enhancement

AI and Robotics



Networks and Computing Systems

Futures Studies

Policy Law and Ethics

Finance and

Entrepreneurship

Original ten Singularity University tracks

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Nanotechnology

Life Sciences

AI, Robotics, Networks and Computing Systems



You are the world’s leading venture capitalist…

Thank you

Melanie SwanPrincipal

MS Futures Group+1-650-681-9482

[email protected]

Slides: http://slideshare.net/LaBlogga/slideshows

Creative Commons 3.0 license