Web 4.0’s scale and social impact surpass by several orders of magnitude the inception of its 1.0 ancestor 25 years ago. Web 4.0 will change how humans operate and the way civilization functions. A few specific nanotechnologies, developed at a faster-than-anticipated pace, mostly outside the conventional silicon-based industry circuits, are quickly colliding into the foundations for web 4.0. The Big Picture on Nano details this ‘Coming-of-Ages’, provides a preliminary springboard to grasp context, challenges and opportunities that will accompany this web 4.0 wave of changes, and explains why massive social, economic and geopolitical displacements can be expected.

1. 1
The Big Picture
on nano
Head Consultant | management & technology
GDN consulting firm

2. 2
content
mantra, content, credits & references s03~04
1. 2015 global risks landscape & root causes s06~12
The Age of smartness
3. current responses (pervasive smartness) s14~19
The Age of wellness (the Big Picture on nano)
4. further responses (4 nanotechnology pillars)
a) BCI (brain computer interface) s22
b) 4D (smart materials) s23~24
c) nanotheranostics s25~37
d) neuromorphic IT s38~39
5. proposing a reverse Moore’s law s40~41
6. predicting the next internet (web 4.0) s42~45
7. anticipating emerging challenges s47~50
The Age of happiness
8. take-away s53~56

3. 3
Major risks
Responses
Mantra

4. 4
content, credits & references

5. 5
2015 global risks landscape

6. 6
2015 Global Risks Map
Source:GlobalRisksPerceptionSurvey2014/2015(WorldEconomicForum)

7. 7
2015 Global Risks Correlation Map
Source:GlobalRisksPerceptionSurvey2014/2015(WorldEconomicForum)

8. 8
Human population
Biosphere
regeneration
Human
consumption
~ 1.3 times
what
the planet
can sustain
Net loss
28%
Steadily growing since mid 80’s
Source: Simms A., NEF (New Economics Foundation), London
Earth Profit & Loss
2014 based on Averaged human lifestyle

9. 9
90%due to human
errors
failure of urban planning (10m lives each year)
29b barrels fuel wasted per year **
450b hours smoked in traffic equivalent to:
0.76 million lives each year ***
* source: WHO 2015 report on worldwide road death toll ** 88% of worldwide fuel consumption | 33b per year
*** based on 67.2 years or 588k hours worldwide average life expectancy
**** source: 2013 assessment by WHO’s International Agency for Research on Cancer (IARC)
1.24 million lives each year *
#1 cause of death for aged 15-29 years*
20~50 millions injured*
Worldwide road death toll
Worldwide road congestion Air pollution (in & outdoors)
8 millions lives each year ****

10. 10
failure of urban planning (10m lives each year)
compared to largest wholly anthropogenic catastrophes
What When Duration Ʃ death toll high est. per year
World War I 1914-18 4 65m 16m
World War II 1939-45 6 85m 14m
Taiping Rebellion 1851-64 13 100m 8m
Holodomor 1932-33 1 8m 8m
Bangladesh genocide 1971 1 3m 3m
What When Duration Ʃ death toll high est. per year
Great Chinese Famine 1958-62 4 55m 14m
Russian Famine 1921-22 2 10m 5m
Northern Chinese Famine 1876-79 3 13m 4m
China Floods 1931 1 3m 3m
Great Indian Famine 1876-78 2 6m 3m
compared to largest partially anthropogenic catastrophes

11. 11
Potential
reforestation
Food growing
Habitable
zones
Inhabitable
deserts
Inhabitable
floods, droughts
extreme weather
Land lost to
rising waters
(2m assumption)
a map of climate change risks
The World 4ºC warmer
Sources:
2015 Climate Action Tracker
New Scientist ,Climate Change Report, 2009

12. 12
ageing population & major fiscal imbalance risks
Worker (15 to 64) to Retiree (64+) dependency ratios
Source: UN, Department of Economic & Social Affairs, 2014

13. 13
disconnected physical, financial & geopolitical worlds
ETR (Ecological tax reform)??
EIA (Environmental impact assessment)??
Inter-sector policies, new institutions, legal reform??
IM (Industrial metabolism) ??
VA (Voluntary agreement) ??
EE (Ecological economics) ??
 Climate change
 Financial crises
 Social instabilities
 Major fiscal imbalances
 Ineffective multi-polar geopolitics

14. 14
The Age of smartness
smart economics
smart machines
smart cities

15. 15
share economy + co-creation + 3D manufacturing
 Disruptive global digital neo-economics, barter resurgence & dematerialization
 Share economy (Internet, Uber, Airbnb)  unused value is wasted value
 Crowd-sourcing + co-creation + 3D production value chain
The latest Maslow’s

16. 16
share economy + co-creation + 3D manufacturing
 Co-creation + 3D manufacturing  Communities of Passion (psychographics vs demographics)
 3D printing of medicines (Prof. Lee Cronin, Uni of Glasgow)
 3D printing of live tissues & organs (bio-printing)
Local Motors (Rally Fighter) | General Motors (Chevy Volt) | Fisker Automotive (Karma) | Tesla (Roadster)
co-creation + 3D
the automotive
industry example
Source: 2015, CEO Jay Rogers, Local Motors

17. 17
rise of the machines
The IoT (Internet of Things) or
IoE (Internet of Everything)
IPv4 (4.3b addresses)  IPv6 (3% in 2014)
Big Data Analytics
Deep domain expertise & learning algorithms
merging genomics & informatics
6, 7 & 8th level normalization
unstructured data
Mobile connecting
technology
smart information
smart machines
smart cities

18. 18
evolving from net emitters to
CO2-capture mega-systems
smart cities | the big picture
95% world cities located in
climate change risks areas
= both problem & solution
The 3rd demographic
revolution in 2008

19. 19
smart cities | extramuros
2015 AeroFarms case in New Jersey US*:
• 95% water needs reduction
• 75 times more productive
• biosphere size increase
• carbon-capture system
• 0-pesticide & 0-soil
• 16 days cycle
integrated vertical urban
farming (edible + bio-fuels)
new mobility
paradigm shift
average private cars
unused 92% of the
time **
existing infrastructure
3 times more efficient
with driverless ***
* Source: 2015 AeroFarms estimates
** Source: 2014 Stanford Energy Institute research
*** Source: 2011 Google Driverless Car Project, Sebastian Thrun

20. 20
smart cities | intramuros
autonomous, automated, alive,
sustainable, re-configurable
modular buildings
smart information
from GUI* to NUI**
& M2M*** (web 3.0)
* Graphical User Interface ** Natural User Interface *** Machine-To-Machine
India (31% urbanization) 2015 budget  70.2b INR**** to build 100 new smart cities
**** 1.2b US$

21. 21
The Age of wellness
non-silicon industries
+ nano-technologies
to drive web 4.0

22. 22
preamble  the nano scale

23. 23
BCI (Brain Computer Interface)
video overview
Click here

24. 24
4D printing (self assembly)
Skylar Tibbits | MIT
4D = 3D + 1D (time / transformation)
Self-Assembly = process
by which disordered parts
build an ordered structure
only via local interaction
Programmable matter =
material that can change
form or behave in a
programmable fashion
smart material
(robot without robots)
Robots 
Click here

25. 25
4D printing applications
Self-repairing active pipes (no mechanical pumps)
Self-transforming reconfigurable buildings
Other building / Construction applications
Aviation
Adaptive, supporting & assistive
textile, apparel & footwear
Self-assembling products from 2D to 3D
Lower-engineered adaptive furniture
Self-adaptive packaging
Automotive

26. 26
nanotheranostics
nanotheranostics = nano + therapies + diagnostics
nanomedicine
nanodrug delivery
nano regeneration
nanotheranostics
Related disciplines, concepts & terminology 
• zero-incision or non-invasive surgery
• precision or personalized medicine

27. 27
nanodrug  basic building block metaphor
Bio-markers
Container/s
Cargo/s (inside the container/s)

28. 28
nanotheranostics vs conventional cancer treatment
In vivo
via nanotheranostics
Ex vivo (In vitro) interrogation
+ chemotherapy
Detection entire circulation interrogation sample-based delayed interrogation
vs

29. 29
In vivo
via nanotheranostics
Ex vivo (In vitro) interrogation
+ chemotherapy
Detection entire circulation interrogation sample-based interrogation
Progression dynamic monitoring -
Xvs
nanotheranostics vs conventional cancer treatment

30. 30
In vivo
via nanotheranostics
Ex vivo (In vitro) interrogation
+ chemotherapy
Detection entire circulation interrogation sample-based interrogation
Progression dynamic monitoring -
Therapy
narrow target & destruction upon
recognition of pathogenic cells +
real-time response monitoring
broad range & mass destruction
with collateral damages to healthy
cells + asynchronous monitoring
vs
nanotheranostics vs conventional cancer treatment

31. 31
In vivo
via nanotheranostics
Ex vivo (In vitro) interrogation
+ chemotherapy
Detection entire circulation interrogation sample-based interrogation
Progression dynamic monitoring -
Therapy
narrow target & destruction upon
recognition of pathogenic cells +
real-time response monitoring
broad range & mass destruction
with collateral damages to healthy
cells + asynchronous monitoring
vs
vs
nanotheranostics vs conventional cancer treatment

32. 32
advanced nanotheranostics via swarm of nanobots
 From rudimentary multiplexing nanodrugs to smarter bio-computing ones
 Versatile built-in computing/robotics using DNA origami  nanobots
 More advanced computing based on swarms of nanobots
• Hand shakes
• Quorum sensing
• Swarm computing

33. 33
nanobots
Ido Bachelet, Shawn Douglas, Daniel Levner & the research team

34. 34
nanobots swarms computing
≈ Commodore 64

35. 35
wifibots

36. 36
First Human trials
to start in 2015
nanobots  zero-incision or non-invasive surgery
or

37. 37
reinventing Big Pharma business model
or
“Big Pharma spends trillions of dollars
attempting to build ‘sophisticated guns’
(drugs) that shoot only ‘bad people’
(pathogens). This is difficult and indeed
fails most of the time. We already have
enough guns (both used & unused drugs):
what we need is to teach the soldier
(nanodrug platform) how to use them”
2014, Ido Bachelet

38. 38
and deliver drug/s directly to the pathogenic site and responsively produce custom drugs
use DNA as bricks to build nano-machines Let RNA/DNA be the nano-machine
from DNA origami techniques to RNA/DNA 3&4D printing
next stage with advanced nanotheranostics
the new metaphors

39. 39
brain research & neuromorphic computing
conventional IT | a very brief history
“It is comparatively easy to make computers exhibit adult
level performance on intelligence tests or when playing
checkers, and yet very difficult or impossible to give them the
skills of a 1-year old when it comes to perception & mobility”
1980 | Hans Moravec | Carnegie Mellon University | Robotics & Artificial Intelligence
George Boole
1815 – 1864
Charles Babbage
1791 – 1871
Alan Turing
1912 – 1954
John Von Neumann
1903 – 1957

40. 40
brain research & neuromorphic computing
advent of non-Von Neumann machines
Qualcomm Zeroth NPU
Neural Processor Unit
* 86b neurons + 100t synapses in a Human brain
IBM TrueNorth
1m neurons + 256m synapses*
Artificial Intelligence
Deep Learning algorithms
Yann LeCunGeoff Hinton
€ 1.2 billion over 10 years (2013 – 2023)
US$ 3 billions over 10 years (2013 – 2023)
Both projects triggered by
too lengthy delays in
solving brain disorders and
degenerative conditions via
cognitive & neuroscience

41. 41Gordon E. Moore
Moore’s Law
1965 1990 2015 2020 2050 2080 …
Macro
Nano

42. 42
Moore’s Law
1965 1990 2015 2020 2050 2080 …
Macro
Nano
4D
neuromorphic
biocomputing
where it all collides  reverse

43. 43
predicting the next smart personal gizmo

44. 44

45. 45
specifying the next (easy-to-swallow) gizmo
1. Rewrite your cells internal clock
• cell regeneration beyond 40 subdivision cycle
• prolong existence to 150, 200, 300 years or
• … until you get bored to (literally ;-) death
2. Your 24/7 personal bodyguard
• protect dynamically against all diseases
• RNA + DNA-tailored nanotheranostics
• universal built-in nanofactory
3. Instant knowledge access
• DNA-based knowledge storage
• bio-neuromorphic processes
• new B2C  Brain-2-Cloud
4. Tele-kinetic capabilities
• brain-control 4D Wi-Fi devices
5. Telepathic powers
• subvocalized communications
• new B2B  Brain-2-Brain
• web 4.0 aka BrainNet

46. 46

47. 47
anticipating emerging challenges

48. 48
bio-hacking
Erwin Schrödinger Craig Venter
1944 First DNA model proposal
“What is Life?”
Erwin Schrödinger
2000 Genome mapping completed
2010 First synthetic life created
“Life is a DNA software
system and I created it”
Craig Venter

49. 49
the smarter-than-human AI* conundrum
“The core problem is one of aligning AI goals with human goals.
If smarter-than-human AI’s are built with goal specifications that
subtly differ from what their human inventors intended, it is
unclear that it will be possible to stop those AI’s from using all
available resources to pursue those goals, any more than
chimpanzees can stop humans from doing what they want ”
Stuart Russel Professor of Computer Science
Smith-Zadeh Professor in Engineering
University of California, Berkeley
* AI = Artificial Intelligence

50. 50
Societal, ethical & economic impact of technology
an example | AI* learning machines taking over human jobs
47% of jobs to be automated
Source: 2014 Carl Benedikt Frey & Michael A. Osborne | Oxford University
* AI = Artificial Intelligence
WHAT WHY
WHERE WHEN
Source: USA Census, Bureau of Economic Analysis
Rebuilding
Eden
all Hell breaks
loose
or…
AI: the new taxable slave?
It has already started
Blue countries
most impacted

51. 51
1. Inconsistencies example: self-flying objects 2. Space cross-border harmonization?
3. Speed multi-stage adaptive regulations to
manage uncertain future of emerging technologies
4. Ownership which regulatory body should take
initiative with cross-disciplinary emerging technology?
5. Regulative vacuum religious powers, action
groups, future generations, industries self-regulations
6. Social, ethical & economic impact is
technology a human right, or market choice? Should
it be ruled by ill-informed and obsoleting economics?
law-making process inadequacies
“It took 80 years for the international
community to reach legal consensus
on chemical weapons ban”
“A driverless world would mean a
world without any road accidents:
this would prove catastrophic for
our car insurance business.” 2014
Warren Buffet (Geico Car insurance)
“Who is liable to the injured
when driverless cars choose
between pedestrian, cyclist
or passenger in any life-
threatening events?”
Meanwhile, over the past 20 minutes, the aggregate worldwide computing
power on the planet has generated and processed an amount of
information equivalent to that produced over the entire 20th Century.
hyper-regulated plane auto-pilot no regulations Where to regulate? UN-level? City-level? Or anywhere else in between?

52. 52

53. 53
The Age of happiness
smart government
democracy 3.0

54. 54
Engaged co-creative communities of passion
• From democratic representation to ‘Perestroika-n’ direct engagement
• Applying industry best practices to law-making government business
• Trust + Time + Well-being = the new currencies  Democracy 3.0
• Co-creation Law-centric engagement platforms with citizens
• Efficiently create/maintain legislations (faster & cheaper)
co-creation +
communities of
passion
Source: 2015, CEO Jay Rogers, Local Motors

55. 55
New economics | New KPI’s
Happiness replacing money as measure of Success
Source: 2015 World Happiness Report, Happiness KPI by region, population age group and gender

56. 56
Preparing for a new type of Human Adolescence
The super-healthy, hyper-knowledgeable, brain-connected
(and most probably… bored to death :-) 300-years-young

57. 57
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