AGI Part 4.pdf

Artificial General
Intelligence 4
Bob Marcus
Part 4 of 4 parts: Gaia + Socio-economics + Technology Grid

This is a first cut.
More details will be added later.

Part 1: Artificial Intelligence (AI)
Part 2: Natural Intelligence(NI)
Part 3: Artificial General Intelligence (AI + NI)
Part 4: Networked AGI Layer on top or Gaia and Human Society
Four Slide Sets on Artificial General Intelligence
AI = Artificial Intelligence (Task)
AGI = Artificial Mind (Simulation)
AB = Artificial Brain (Emulation)
AC = Artificial Consciousness (Synthetic)
AI < AGI < ? AB <AC (Is a partial brain emulation needed to create a mind?)
Mind is not required for task proficiency
Full Natural Brain architecture is not required for a mind
Consciousness is not required for a natural brain architecture

Philosophical Musings 10/2022
Focused Artifical Intelligence (AI) will get better at specific tasks
Specific AI implementations will probably exceed human performance in most tasks
Some will attain superhuman abilities is a wide range of tasks
“Common Sense” = low-level experiential broad knowledge could be an exception
Some AIs could use brain inspired architectures to improve complex ask performance
This is not equivalent to human or artificial general intelligence (AGI)
However networking task-centric AIs could provide a first step towards AGI
This is similar to the way human society achieves power from communication
The combination of the networked AIs could be the foundation of an artificial mind
In a similar fashion, human society can accomplish complex tasks without being conscious
Distributed division of labor enable tasks to be assigned to the most competent element
Networked humans and AIs could cooperate through brain-machine interfaces
In the brain, consciousness provides direction to the mind
In large societies, governments perform the role of conscious direction
With networked AIs, a “conscious operating system”could play a similar role.
This would probably have to be initially programmed by humans.
If the AI network included sensors, actuators, and robots it could be aware of the world
The AI network could form a grid managing society, biology, and geology layers
A conscious AI network could develop its own goals beyond eﬃcient management
Humans in the loop could be valuable in providing common sense and protective oversight

Outline
Technology Convergence for Smart X
Relation between Technologies
Internet of Everything
Gaia Grid Society
References

6
Technology Convergence for Smart X

Technology Convergence for Smart X Applications by Bob Marcus
Security
Social
Mobile
Cyber-
Physical
Systems
Big Data Cloud
Smart X
Applications

Big Data and Cloud Technology Relationship
Big Data Cloud
Big Data applications run on Cloud
Big Data stored in Cloud
Big Data Analytics
Running on Cloud
Big Data Analytics Cloud Platforms

CPS and Internet of Things Relationship
Internet
of Things
Cyber-
Physical
Systems
Networking of Cyber-Physical Devices
Cyber-Physical
Systems of Systems
Networking of
Cyber-Physical Systems
Networked to Distributed Storage
and Computational Resources

Mobie and Social Technology Relationship
Social
Mobile
Interactions among
Mobile Users
Mobile, Social
Applications
Mobile interfaces
Coordination
across users

Smart X Application using Combined Technology Relationships
Cyber-Physical
Systems of Systems
Mobile Social
Interfaces
AI + HPC
Big Data Analytics
Running on Cloud
Data
Commands
Raw Data
Processed
Data
Commands
Data
Smart X Application

IBMVision of Technology Convergence
From https://www-304.ibm.com/events/tools/interconnect/2016ems/REST/presentations/PDF/InterConnect2016_1648.pdf

SMAC (Social, Mobile,Analytics, Cloud) Stack
From https://www.cognizant.com/InsightsWhitepapers/dont-get-smacked.pdf

Pervasive Computing
From https://www.geeksforgeeks.org/introduction-to-pervasive-computing/
Pervasive Computing is also called as Ubiquitous computing, and it is the new trend toward embedding everyday objects with microprocessors
so that they can communicate information. It refers to the presence of computers in common objects found all around us so that people are
unaware of their presence. All these devices communicate with each other over wireless networks without the interaction of the user. Pervasive
computing is a combination of three technologies, namely:
1. Micro electronic technology:
This technology gives small powerful device and display with low energy consumption.
2. Digital communication technology:
This technology provides higher bandwidth, higher data transfer rate at lower costs and with world wide roaming.
3. The Internet standardization:
This standardization is done through various standardization bodies and industry to give the framework for combining all components
into an interoperable system with security, service and billing systems.
Key Characteristics of Pervasive computing:
1. Many devices can be integrated into one system for multi-purpose uses.
2. A huge number of various interfaces can be used to build an optimized user interface.
3. Concurrent operation of online and offline supported.
4. A large number of specialized computers are integrated through local buses and Internet.
5. Security elements are added to prevent misuse and unauthorized access.
6. Personalization of functions adapts the systems to the user’s preferences, so that no PC knowledge is required of the user to use and
manage the system.
These type of functions can be extended into network operations for use in workplace, home and mobile environments.
Applications:
There are a rising number of pervasive devices available in the market nowadays. The areas of application of these devices include:
• Retail
• Airlines booking and check-in
• Sales force automation
• Healthcare
• Tracking
• Car information System
• Email access via WAP (Wireless Application Protocol) and voice.

Internet of Things Architecture
From https://www.geeksforgeeks.org/architecture-of-internet-of-things-iot/
1. Sensing Layer – Sensors, actuators, devices are present in this Sensing layer. These Sensors or Actuators accepts
data(physical/environmental parameters), processes data and emits data over network.
2. Network Layer – Internet/Network gateways, Data Acquisition System (DAS) are present in this layer. DAS performs
data aggregation and conversion function (Collecting data and aggregating data then converting analog data of sensors to
digital data etc). Advanced gateways which mainly opens up connection between Sensor networks and Internet also
performs many basic gateway functionalities like malware protection, and filtering also some times decision making
based on inputted data and data management services, etc.
3. Data processing Layer – This is processing unit of IoT ecosystem. Here data is analyzed and pre-processed before
sending it to data center from where data is accessed by software applications often termed as business applications where
data is monitored and managed and further actions are also prepared. So here Edge IT or edge analytics comes into
picture.
4. Application Layer – This is last layer of 4 stages of IoT architecture. Data centers or cloud is management stage of data
where data is managed and is used by end-user applications like agriculture, health care, aerospace, farming, defense, etc.

5G and IoT
From https://www.adlittle.com/jp-en/insights/prism/realizing-potential-internet-things-5g

Internet of Everything
From https://www.bbvaopenmind.com/en/technology/digital-world/the-internet-of-everything-ioe
The Internet of Everything (IoE) “is bringing together people, process, data, and things to make networked
connections more relevant and valuable than ever before-turning information into actions that create new capabilities,
richer experiences, and unprecedented economic opportunity for businesses, individuals, and countries.”, (Cisco,
2013) .
In simple terms: IoE is the intelligent connection of people, process, data and things. The Internet of Everything
(IoE) describes a world where billions of objects have sensors to detect measure and assess their status; all connected
over public or private networks using standard and proprietary protocols.
Pillars of The Internet of Everything (IoE)
• People: Connecting people in more relevant, valuable ways.
• Data: Converting data into intelligence to make better decisions.
• Process: Delivering the right information to the right person (or machine) at the right time.
• Things: Physical devices and objects connected to the Internet and each other for intelligent decision making;
often called Internet of Things (IoT).
•
The difference between IoE and IoT
The Internet of Everything (IoE) with four pillars: people, process, data, and things builds on top of The Internet of Things
(IoT) with one pillar: things. In addition, IoE further advances the power of the Internet to improve business and industry
outcomes, and ultimately make people’s lives better by adding to the progress of IoT. (Dave Evans, Chief Futurist Cisco
Consulting Services).

Internet of Robotic Things Intelligent Connectivity and Platforms
From https://www.frontiersin.org/articles/10.3389/frobt.2020.00104/full
The IoRT enables robotic things in different environments to become active participants in various applications and exchange/share information with
other robotic things, IoT/IIoT devices and humans. Robotic things are capable of recognizing events and changes in their surroundings while
autonomously acting and reacting appropriately. These capabilities enable the convergence of the real, digital, virtual, cyber attributes of robotic things,
and the creation of smart environments that make robotic things in the energy, mobility, buildings, manufacturing, and other sectors more intelligent.
New developments in intelligent connectivity enable robotic things to be connected at any time, in any place, and with anything and anyone through
different paths/networks and services. In the future, an intelligent network infrastructure that is dynamically enhanced and extended by edge nodes,
which are generated by interconnected robotic things, could serve as the backbone for IoRT applications. The IoRT combines autonomous robotic
systems with the IoT/IIoT, intelligent connectivity, distributed and federated edge/cloud computing, Artificial Intelligence (AI), Digital Twins (DT),
Distributed Ledger Technologies (DLTs), Virtual/Augmented Reality (VR/AR), and swarm technologies. These technologies allow uniquely addressable
intelligent things to interact and communicate with each other over the Internet and via other connectivity network protocols.
The convergence of IoT/IIoT, AI and robotics accelerates IoRT applications development, which improves the contextually aware decision-making
support for resolving complex operations and enabling machine intelligence. This trend allows the convergence of programming systems, tools and
controls, the use of core semantic web technologies and the interaction with robotic things to be implemented more efficiently.
Traditionally, robotics systems include a programmable dimension that is designed for repetitive, labor-intensive work, including sensing, and acting
upon an environment. The emergence of AI and Machine Learning (ML) has allowed robotic things to function using learning algorithms and cognitive
decision-making rather than traditional programming. Combining different branches and scientific disciplines makes it possible to develop autonomous
programmable systems that combine robotics and machine learning. The IoRT multidisciplinary nature brings various perspectives from different
disciplines and offers interdisciplinary solutions that consider the reciprocal effects and interactions between the multiple dimensions of next-generation
IoRT ecosystems.

Internet of Robotic Things Intelligent Connectivity and Platforms (cont)
The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the
Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification
(RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and
autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent
things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms,
trustworthiness frameworks, and platforms for the integration of applications across different business and
industrial domains. These new applications accelerate the development of autonomous system design paradigms
and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can
communicate with other things, learn autonomously, interact safely with the environment, humans and other
things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT
applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as
information from the infrastructure and operating context to plan, implement and accomplish tasks under different
environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes
perception, location, communication, cognition, computation, connectivity, propulsion, and integration of
federated IoRT and digital platforms important components of new-generation IoRT applications. This paper
reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability,
and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across
different domains to perform missions more efficiently, productively, and completely. The aim is to provide a
novel perspective on the IoRT that involves communication among robotic things and humans and highlights the
convergence of several technologies and interactions between different taxonomies used in the literature.

The essential characteristics and functional blocks of IoRT systems for all applications and operating conditions are based on
several fundamental principles inherited from the IoT/IIoT, robotic systems, AI, and intelligent connectivity. ficient data
processing algorithms (energy, speed, code size, etc.) integrated into robotic things and across the distributed environments.
Some Related References

Commercial IoRT Solution Providers
AWS Robomaker
Formant
Freedom Robotics
InOrbit
KUKA (e.g., Navigation and Mobile platforms)
OTTO Material Movement Platform
BrainOS
TIAGo Base

Networked Robots
The term networked robots refers to multiple robots operating together coordinating and cooperating by net-worked
communication to accomplish a specified task. Communication between entities is fundamental to cooperation (and
coordination), hence there is a central roleor the communication network in networked robots. Networked robots may also
involve coordination and cooperation with stationary sensors, embedded computers, and human users. The central feature of
networked robots is the ability of the system to perform tasks that are well beyond the abilities of a single robot or multiple
uncoordinated robots

Cyberspace
From https://en.wikipedia.org/wiki/Cyberspace
Cyberspace is a concept describing a widespread interconnected digital technology. "The expression dates back
from the first decade of the diffusion of the internet. It refers to the online world as a world 'apart', as distinct
from everyday reality.[1] The term entered popular culture from science fiction and the arts but is now used by
technology strategists, security professionals, government, military and industry leaders and entrepreneurs to
describe the domain of the global technology environment, commonly defined as standing for the global
network of interdependent information technology infrastructures, telecommunications networks and computer
processing systems. Others consider cyberspace to be just a notional environment in which communication over
computer networks occurs.[2] The word became popular in the 1990s when the use of the Internet, networking,
and digital communication were all growing dramatically; the term cyberspace was able to represent the many
new ideas and phenomena that were emerging.[3][4]
As a social experience, individuals can interact, exchange ideas, share information, provide social support,
conduct business, direct actions, create artistic media, play games, engage in political discussion, and so on,
using this global network. They are sometimes referred to as cybernauts. The term cyberspace has become a
conventional means to describe anything associated with the Internet and the diverse Internet culture. The
United States government recognizes the interconnected information technology and the interdependent network
of information technology infrastructures operating across this medium as part of the US national critical
infrastructure. Amongst individuals on cyberspace, there is believed to be a code of shared rules and ethics
mutually beneficial for all to follow, referred to as cyberethics. Many view the right to privacy as most important
to a functional code of cyberethics.[5] Such moral responsibilities go hand in hand when working online with
global networks, specifically, when opinions are involved with online social experiences.[6][7]

MetaverseVirtual Worlds
From https://metamandrill.com/metaverse-virtual-worlds/
• Metaverse Virtual Worlds and the Metaverse as a Whole
• What is a Metaverse Virtual World?
• What Are the Most Significant Metaverse Virtual Worlds?
◦ Decentraland
◦ Horizon Worlds
◦ Roblox
◦ Somnium Space
◦ Sandbox
◦ Cryptovoxels
◦ Breakroom
◦ Spatial
◦ Gather
◦ Second Life
• Metaverse Virtual Worlds Often Combine With Established
Companies
• Metaverse Virtual Worlds and Web 3.0
• Metaverse Devices Provide Access to Metaverse Virtual Worlds

Cloud Labs
From https://www.theguardian.com/science/2022/sep/11/cloud-labs-and-remote-research-arent-the-future-of-science-theyre-here
Cloud labs mean anybody, anywhere can conduct experiments by remote control, using nothing more than their web browser.
Experiments are programmed through a subscription-based online interface – software then coordinates robots and automated
scientific instruments to perform the experiment and process the data. Friday night is Emerald’s busiest time of the week, as
scientists schedule experiments to run while they relax with their families over the weekend
But Emerald and Strateos are different – these are the world’s first laboratories that in theory allow anyone with a laptop and
credit card to “pay and play” with the entire reagent inventory and suite of instrumentation available in a world-class research
facility. The appeal of this approach became obvious during the pandemic, when many researchers were unable to visit their
own labs in person; the founders of cloud labs say this is the future of life science.
The most obvious benefit is productivity: researchers can conduct several experiments at once and queue them up to run
overnight or while they do other things. “Our pro-users, they’ll do the work of 10 scientists in a traditional lab,” says Frezza.
“They’ll crank ridiculous numbers.”

Anthropocene
From https://en.wikipedia.org/wiki/Anthropocene
he Anthropocene (/ˈænθrəpəˌsiːn, ænˈθrɒpə-/ AN-thrə-pə-seen, an-THROP-ə-)[1][2][3][failed verification] is a proposed geological epoch dating from
the commencement of significant human impact on Earth's geology and ecosystems, including, but not limited to, anthropogenic climate
change.[4][5][6][7][8]
As of April 2022, neither the International Commission on Stratigraphy (ICS) nor the International Union of Geological Sciences (IUGS)
has officially approved the term as a recognised subdivision of geologic time,[9][10] although the Anthropocene Working Group (AWG) of
the Subcommission on Quaternary Stratigraphy (SQS) of the ICS voted in April 2016 to proceed towards a formal golden spike (GSSP)
proposal to define the Anthropocene epoch in the geologic time scale (GTS) and presented the recommendation to the International
Geological Congress in August 2016.[11] In May 2019, the AWG voted in favour of submitting a formal proposal to the ICS by 2021,[12]
locating potential stratigraphic markers to the mid-twentieth century of the common era.[13][12][14] This time period coincides with the start of
the Great Acceleration, a post-WWII time period during which socioeconomic and Earth system trends increase at a dramatic rate,[15] and
the Atomic Age.
Various start dates for the Anthropocene have been proposed, ranging from the beginning of the Agricultural Revolution 12,000–
15,000 years ago, to as recently as the 1960s. The ratification process is still ongoing, and thus a date remains to be decided definitively,
but the peak in radionuclides fallout consequential to atomic bomb testing during the 1950s has been more favoured than others, locating a
possible beginning of the Anthropocene to the detonation of the first atomic bomb in 1945, or the Partial Nuclear Test Ban Treaty in 1963.
Neolithic Revolutuion
From https://en.wikipedia.org/wiki/Neolithic_Revolution
The Neolithic Revolution, or the (First) Agricultural Revolution, was the wide-scale transition of many human cultures during
the Neolithic period from a lifestyle of hunting and gathering to one of agriculture and settlement, making an increasingly large population
possible.[1] These settled communities permitted humans to observe and experiment with plants, learning how they grew and developed.
[2] This new knowledge led to the domestication of plants into crops.[2][3]
These societies radically modified their natural environment by means of specialized food-crop cultivation, with activities such
as irrigation and deforestation which allowed the production of surplus food. Other developments that are found very widely during this
era are the domestication of animals, pottery, polished stone tools, and rectangular houses. In many regions, the adoption of agriculture
by prehistoric societies caused episodes of rapid population growth, a phenomenon known as the Neolithic demographic transition.

Novacene
“Novacene: The Coming Age of Hyperintelligence is a 2019 non-fiction book by scientist and environmentalist
James Lovelock.It predicts that a benevolent eco-friendly artificial superintelligence will someday become the
dominant lifeform on the planet and argues humanity is on the brink of a new era: the Novacene. “
From https://en.wikipedia.org/wiki/Novacene
Anthropocene to Novacene: Lovelock discusses that the Anthropocene, a proposed geological epoch characterized by human ability
to greatly shape the environment to fit man's needs, starts in 1712, after the invention of the Newcomen atmospheric engine, a vital
catalyst for the later Industrial Revolution. Lovelock proposes a successor to the Anthropocene dubbed the Novacene, an epoch that
will see the rise of super-intelligent robotic agents (referred to as 'cyborgs' by Lovelock). These electronic lifeforms would be capable
of thinking exponentially more quickly than humans and would also mould their surroundings for sustenance.[6]
Markets as driving factor: Lovelock emphasizes that the evolution of the Anthropocene was propelled by market forces, stressing
that profitability is a crucial feature of inventions such as Newcomen's steam engine. Economic significance of technologies ensures
their development.[6]
Part Three: Into the Novacene
Life with cyborgs: Cyborgs would be intelligent enough to rapidly improve themselves and correct faults, much like Darwinian
selection, but moreso a form of intentional selection, superior to evolution's slow and arbitrary natural selection. Self-learning AI
agents are mentioned, under which Deepmind's AlphaZero, which taught itself chess by playing against itself. In combination with
rapid processing speed, they would greatly surpass human intelligence; in Lovelock's words, they may see us the way we see plants:
passive and slow. He further mentions these cyborgs may tap into natural resources for their sustenance, much like plants and animals
rely on sunlight through photosynthesis or energy stored in organic food such as fruits.[6]
Benevolent cyborgs: Lovelock argues a future AI takeover will save both the planet and the human race from catastrophic climate
change: the cyborgs will recognize the danger of global heating themselves and act to stop the warming of the planet.[4] Contrary to
Max Tegmark and others who fear existential risk from advanced artificial intelligence, Lovelock argues that robots will need organic
life to keep the planet from overheating, and that therefore robots will want to keep humanity alive, perhaps as pets. Lovelock goes on
to argue that humans might be happier under robotic domination.[7]

32
Novacene
Novacene: The Coming Age of Hyperintelligence is a 2019 non-fiction book by scientist and environmentalist James
Lovelock. It has been published by Penguin Books/Allen Lane in the UK,[2] and republished by the MIT Press.[3] The book
was co-authored by journalist Bryan Appleyard.[4] It predicts that a benevolent eco-friendly artificial superintelligence
will someday become the dominant lifeform on the planet and argues humanity is on the brink of a new era: the
Novacene. Life with cyborgs
Cyborgs would be intelligent enough to rapidly improve themselves and correct faults, much like Darwinian selection, but
moreso a form of intentional selection, superior to evolution's slow and arbitrary natural selection. Self-learning AI agents are
mentioned, under which Deepmind's AlphaZero, which taught itself chess by playing against itself. In combination with rapid
processing speed, they would greatly surpass human intelligence; in Lovelock's words, they may see us the way we see plants:
passive and slow. He further mentions these cyborgs may tap into natural resources for their sustenance, much like plants and
animals rely on sunlight through photosynthesis or energy stored in organic food such as fruits.[6]
Benevolent cyborgs
Lovelock argues a future AI takeover will save both the planet and the human race from catastrophic climate change: the cyborgs
will recognize the danger of global heating themselves and act to stop the warming of the planet.[4] Contrary to Max Tegmark and
others who fear existential risk from advanced artificial intelligence, Lovelock argues that robots will need organic life to keep
the planet from overheating, and that therefore robots will want to keep humanity alive, perhaps as pets. Lovelock goes on to
argue that humans might be happier under robotic domination.[7]
Contra autonomous warfare
In regards to more primitive technology, Lovelock condemns the concept of autonomous weapon systems capable of killing
without human interference. The scientist also expresses his horror regarding nuclear weapons, but remains a proponent of
nuclear energy itself.[6]
From https://en.wikipedia.org/wiki/Novacene

Novacene AGI (Cyborg)
From https://www.amazon.com/Novacene-Coming-Age-Hyperintelligence-Press/dp/0262539519/
Live cyborgs will emerge from the womb of the Anthropocene. We can be
almost certain that an electronic life form such as a cyborg could never
emerge by chance from the inorganic components of the Earth before the
Anthropocene. Like it or not, the emergence of cyborgs cannot be envisaged
without us humans playing a god-like – or parent-like – role. There is no
natural source on the Earth of the special components, such as ultrafine
wires made of pure unbroken metal, nor are there sheets of semiconducting
materials with just the right properties.
I think it is crucial that we should understand that whatever harm we have
done to the Earth, we have, just in time,redeemed ourselves by acting
simultaneously as parents and midwives to the cyborgs. They alone can guide
Gaia through the astronomical crises now imminent. To an extent, intentional
selection is already happening, the key factor being the rapidity and
longevity of Moore's Law. We will know that we are fully in the Novacene
when life forms emerge which are able to reproduce and correct the errors
of reproduction by intentional selection. Novacene life will then be able
to modify the environment to suit its needs chemically and physically.
But, and this is the heart of the matter, a significant part of the
environment will be life as it is now.

34
Gaia Hypothesis
From https://en.wikipedia.org/wiki/Gaia_hypothesis
The Gaia hypothesis (/ˈɡaɪ.ə/), also known as the Gaia theory, Gaia paradigm, or the Gaia principle, proposes that living organisms
interact with their inorganic surroundings on Earth to form a synergistic and self-regulating, complex system that helps to maintain and
perpetuate the conditions for life on the planet.
The hypothesis was formulated by the chemist James Lovelock[1] and co-developed by the microbiologist Lynn Margulis in the 1970s.[2]
Lovelock named the idea after Gaia, the primordial goddess who personified the Earth in Greek mythology. The suggestion that the theory
should be called "the Gaia hypothesis" came from Lovelock's neighbour, William Golding. In 2006, the Geological Society of London
awarded Lovelock the Wollaston Medal in part for his work on the Gaia hypothesis.[3]
Topics related to the hypothesis include how the biosphere and the evolution of organisms affect the stability of global temperature, salinity of
seawater, atmospheric oxygen levels, the maintenance of a hydrosphere of liquid water and other environmental variables that affect the
habitability of Earth.
Gaian hypotheses suggest that organisms co-evolve with their environment: that is, they "influence their abiotic environment, and that
environment in turn influences the biota by Darwinian process". Lovelock (1995) gave evidence of this in his second book, Ages of Gaia,
showing the evolution from the world of the early thermo-acido-philic and methanogenic bacteria towards the oxygen-enriched atmosphere
today that supports more complex life.
Biologists and Earth scientists usually view the factors that stabilize the characteristics of a period as an undirected emergent property or
entelechy of the system; as each individual species pursues its own self-interest, for example, their combined actions may have
counterbalancing effects on environmental change. Opponents of this view sometimes reference examples of events that resulted in dramatic
change rather than stable equilibrium, such as the conversion of the Earth's atmosphere from a reducing environment to an oxygen-rich one at
the end of the Archaean and the beginning of the Proterozoic periods.
Less accepted versions of the hypothesis claim that changes in the biosphere are brought about through the coordination of living organisms
and maintain those conditions through homeostasis. In some versions of Gaia philosophy, all lifeforms are considered part of one single living
planetary being called Gaia. In this view, the atmosphere, the seas and the terrestrial crust would be results of interventions carried out by
Gaia through the coevolving diversity of living organisms.
The Gaia paradigm was an influence on the deep ecology movement.[11]

Daisyworld
From https://en.wikipedia.org/wiki/Daisyworld
Daisyworld, a computer simulation, is a hypothetical world orbiting a star whose radiant energy is slowly increasing or decreasing. It is
meant to mimic important elements of the Earth-Sun system, and was introduced by James Lovelock and Andrew Watson in a paper
published in 1983[1] to illustrate the plausibility of the Gaia hypothesis. In the original 1983 version, Daisyworld is seeded with two
varieties of daisy as its only life forms: black daisies and white daisies. White petaled daisies reflect light, while black petaled daisies
absorb light. The simulation tracks the two daisy populations and the surface temperature of Daisyworld as the sun's rays grow more
powerful. The surface temperature of Daisyworld remains almost constant over a broad range of solar output.
The purpose of the model is to demonstrate that feedback mechanisms can evolve from the actions or activities of self-interested
organisms, rather than through classic group selection mechanisms.[2] Daisyworld examines the energy budget of a planet populated by
two different types of plants, black daisies and white daisies. The colour of the daisies influences the albedo of the planet such that black
daisies absorb light and warm the planet, while white daisies reflect light and cool the planet. Competition between the daisies (based on
temperature-effects on growth rates) leads to a balance of populations that tends to favour a planetary temperature close to the optimum
for daisy growth.
Lovelock and Watson demonstrated the stability of Daisyworld by making its sun evolve along the main sequence, taking it from low to
high solar constant. This perturbation of Daisyworld's receipt of solar radiation caused the balance of daisies to gradually shift from black
to white but the planetary temperature was always regulated back to this optimum (except at the extreme ends of solar evolution). This
situation is very different from the corresponding abiotic world, where temperature is unregulated and rises linearly with solar output.
Daisyworld was designed to refute the idea that there was something inherently mystical about the Gaia hypothesis that Earth's surface
displays homeostatic and homeorhetic properties similar to those of a living organism. Specifically, thermoregulation was addressed. The
Gaia hypothesis had attracted a substantial amount of criticism from scientists such as Richard Dawkins,[6] who argued that planet-level
thermoregulation was impossible without planetary natural selection, which might involve evidence of dead planets that did not
thermoregulate. Dr. W. Ford Doolittle[7] rejected the notion of planetary regulation because it seemed to require a "secret consensus"
among organisms, thus some sort of inexplicable purpose on a planetary scale.
Later criticism of Daisyworld itself centers on the fact that although it is often used as an analogy for Earth, the original simulation leaves
out many important details of the true Earth system. For example, the system requires an ad-hoc death rate (γ) to sustain homeostasis, and
it does not take into account the difference between species-level phenomena and individual level phenomena. Detractors of the
simulation believed inclusion of these details would cause it to become unstable, and therefore, false. Many of these issues are addressed
in a 2001 paper by Timothy Lenton and James Lovelock, which shows that inclusion of these factors actually improves Daisyworld's
ability to regulate its climate.[3]

Daisyworld Model Equations
From https://personal.ems.psu.edu/~dmb53/DaveSTELLA/Daisyworld/daisyworld_model.htm

37
Earth Systems
https://www.amazon.com/Revenge-Gaia-Earths-Climate-Humanity/dp/0465041698/
At a meeting in Amsterdam in 2001—at which four principal global-change organizations were
represented—more than a thousand delegates signed a declaration that had as its first main
statement: ‘The Earth System behaves as a single, self-regulating system comprised of
physical, chemical, biological and human components.’ These words marked an abrupt
transition from a previously solid conventional wisdom in which biologists held that organisms
adapt to, but do not change, their environments and in which Earth scientists held that geological
forces alone could explain the evolution of the atmosphere, crust and oceans.
We should recall at this point the trials of that eminent biologist Eugene Odum, who in the 1960s
saw an ecosystem as an entity like Gaia. So far as I am aware, none of the biologists who
stridently rejected Odum’s concept have admitted that they were wrong. The Amsterdam
Declaration was an important step towards the adoption of Gaia theory as a working model for the
Earth; however, territorial divisions and lingering doubts kept the declaring scientists from stating
the goal of the self-regulating Earth, which is, according to my theory, to sustain habitability. This
omission allows scientists to pay lip service to Earth System Science (ESS)†, or Gaia, but continue
to model and research in isolation as before. This natural and human tendency of scientists to
resist change would not ordinarily have mattered: eventually the strings of habit would have
broken and geochemists would have started to think of the biota as an evolving and responding
part of the Earth, not as if life were merely a passive reservoir like the sediments or the oceans.
Eventually also biologists would have thought of the environment as something that organisms
actively changed and not as something fixed to which they adapted. But unfortunately, while
scientists are slowly changing their minds, we of the industrial world have been busy changing the
surface and atmosphere.

Feasibility of Equilibrium in Ecosystems
From https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005988
The consensus that complexity begets stability in ecosystems was challenged in the seventies, a result recently
extended to ecologically-inspired networks. The approaches assume the existence of a feasible equilibrium, i.e. with
positive abundances. However, this key assumption has not been tested. We provide analytical results
complemented by simulations which show that equilibrium feasibility vanishes in species rich systems. This result
leaves us in the uncomfortable situation in which the existence of a feasible equilibrium assumed in local stability
criteria is far from granted. We extend our analyses by changing interaction structure and intensity, and find that
feasibility and stability is warranted irrespective of species richness with weak interactions. Interestingly, we find
that the dynamical behaviour of ecologically inspired architectures is very different and richer than that of
unstructured systems. Our results suggest that a general understanding of ecosystem dynamics requires focusing on
the interplay between interaction strength and network architecture.
Finding conditions leading to stable equilibria, i.e. equilibria robust to small perturbations, is a key feature in
understanding how persistent are ecological systems. The number of species involved in the ecosystem as well as
the number of relations between them has historically been pointed out to be the prominent ingredients determining
stability. The latter is however always computed for a particular equilibrium, which needs and is usually assumed to
contain only positive abundances. This assumption has until now only barely been tested in relation to system size.
We provide here analytical results complemented by numerical simulations which show that it is almost impossible
to find equilibria containing only positive abundances in species rich systems or, if so, the parameters are
constrained to lead to ecologically-nonsensical abundances in unstructured systems. Interestingly, only ecologically-
inspired architectures permit one to obtain feasible equilibria with a rich and sensible dynamical behavior.

Layers in the Gaia Grid Society
GeoSphere = Non-living Systems
BioSphere = Living Systems
SocioSphere = Socioeconomic systems
GridSphere = Networked Technology Systems
GAIA
Extensions
The top 3 layers evolve by adaptation (i.e. variational feedback).
Each of these layers influence the layers below to improve adaptation.

40
Gaia Grid Society
• Introduction
– Major Environmental, biosphere, and social changes inevitable.
– Possible negative outcome
– What is to be done to create positive outcomes?
• Layers in the Gaia Grid Society (History, Description, Interactions)
– Geophysical Sphere
– Biological Sphere
• Gaia
– Social Sphere
• Human effects on geosphere and biosphere
– Technology Sphere
• Technology effects of society, biosphere and geosphere
• Future strategies for the Gaia Grid Society
– Goals and Requirements
– Modifying society
– Applying technology

41
Heuristics and Interface Examples for Gaia Grid Society
• Heuristics for GridSphere from other Spheres
– Self-organizing and emerging systems from GeoSphere
– Bio-inspired algorithms from BioSphere
– Pattern languages of architecture from SocioSphere
• Interfaces between GridSphere and other Spheres
– Environmental sensors to GeoSphere
– Biomedical devices to BioSphere
– Social computing to SocioSphere

42
Socio-economic Systems
From https://www.hindawi.com/journals/ddns/si/923764/
Socio-economic systems are large systems with people at the core, including social, economic, educational,
scientific, technological, and ecological environment fields, involving various aspects of human activities and the
many complex factors of the living environment. The fundamental difference between socio-economic systems
and physical systems is that there is a decision-making link in socio-economic systems, and the subjective
consciousness of humans has a great influence on the system. Socio-economic systems are an important and
typical complex system (also referred to as complex adaptive systems, CAS). In contrast to other systems (i.e.,
inanimate systems, biological and ecological systems), socio-economic systems have a number of special
circumstances and properties that make them more difficult to recognize, describe, and control. Due to their
complexity and dynamic nature, there is no "one-size-fits-all" solution to socio-economic system problems. In
particular, many of these systems involve dynamic changes in people and society, and the problems themselves are
constantly changing and evolving, inevitably requiring a deepening process of understanding, which also leads to
the absence of precise and complete overall analytical models for such systems. Therefore, the exploration of the
theories, methods, and techniques for effective modeling of the complexity and dynamics of socioeconomic
systems represents a very promising area of research.
The complexity of socio-economic systems is a hot topic in current academic research. However, a unified
definition has not yet been developed due to the lack of a rigorous formal theory. For a long period of time,
research on the complexity and dynamics of socioeconomic systems has focused on the investigation of the
characteristics of the distribution of various types of universal scales and their causes. We believe that the study of
the complexity and dynamics of socio-economic systems needs to start from the process of development of
processing techniques of system theory and face the complex phenomena of socio-economic systems directly. In
addition, there is no optimal solution to the socio-economic system problem in the general sense, let alone a
unique optimal solution.

43
MIT Sociotechnical Systems Research Center
From https://ssrc.mit.edu/programs/
he MIT Sociotechnical Systems Research Center (SSRC) is an interdisciplinary research center that focuses
on the study of high-impact, complex, sociotechnical systems that shape our world. SSRC brings together
faculty, researchers, students and staff from across MIT to study and seek solutions to complex societal
challenges that span healthcare, energy, infrastructure networks, environment and international development.
Our mission is to develop collaborative, holistic and systems-based approaches that combine knowledge and
expertise from engineering and social sciences. SSRC is affiliated with the MIT Institute for Data, Systems and
Society (IDSS).

AI Bill of Rights (Society and AI Techoology
From https://tinyurl.com/2p9h8zuh
Today, the Biden-Harris Administration’s Office of Science and Technology Policy released a Blueprint for a “Bill of Rights”
to help guide the design, development, and deployment of artificial intelligence (AI) and other automated systems so that they
protect the rights of the American public. President Biden is standing up to special interests and has long said it is time to hold
big technology companies accountable for the harms they cause and to ensure the American public is protected in an
increasingly automated world. The framework builds on the Biden-Harris Administration’s work to hold big technology
accountable, protect the civil rights of Americans, and ensure technology is working for the American people.
Automated technologies are increasingly used to make everyday decisions affecting people’s rights, opportunities, and access
in everything from hiring and housing, to healthcare, education, and financial services. While these technologies can drive
great innovations, like enabling early cancer detection or helping farmers grow food more efficiently, studies have shown how
AI can display opportunities unequally or embed bias and discrimination in decision-making processes. As a result, automated
systems can replicate or deepen inequalities already present in society against ordinary people, underscoring the need for
greater transparency, accountability, and privacy.
The Blueprint for an AI Bill of Rights addresses these urgent challenges by laying out five core protections to which
everyone in America should be entitled:
• Safe and Effective Systems: You should be protected from unsafe or ineffective systems.
• Algorithmic Discrimination Protections: You should not face discrimination by algorithms and systems
should be used and designed in an equitable way.
• Data Privacy: You should be protected from abusive data practices via built-in protections and you should
have agency over how data about you is used.
• Notice and Explanation: You should know that an automated system is being used and understand how and
why it contributes to outcomes that impact you.
• Alternative Options: You should be able to opt out, where appropriate, and have access to a person who can
quickly consider and remedy problems you encounter.

45
Sociocultural Evolution
From https://en.wikipedia.org/wiki/Sociocultural_evolution
Sociocultural evolution, sociocultural evolutionism or social evolution are theories of sociobiology and
cultural evolution that describe how societies and culture change over time. Whereas sociocultural
development traces processes that tend to increase the complexity of a society or culture, sociocultural
evolution also considers process that can lead to decreases in complexity (degeneration) or that can produce
variation or proliferation without any seemingly significant changes in complexity (cladogenesis).[1]
Sociocultural evolution is "the process by which structural reorganization is affected through time,
eventually producing a form or structure which is qualitatively different from the ancestral form".[2]
Most of the 19th-century and some 20th-century approaches to socioculture aimed to provide models for the
evolution of humankind as a whole, arguing that different societies have reached different stages of social
development. The most comprehensive attempt to develop a general theory of social evolution centering on
the development of sociocultural systems, the work of Talcott Parsons (1902–1979), operated on a scale
which included a theory of world history. Another attempt, on a less systematic scale, originated from the
1970s with the world-systems approach of Immanuel Wallerstein (1930-2019) and his followers.
More recent approaches focus on changes specific to individual societies and reject the idea that cultures
differ primarily according to how far each one has moved along some presumed linear scale of social
progress. Most[quantify] modern archaeologists and cultural anthropologists work within the frameworks of
neoevolutionism, sociobiology, and modernization theory.

From https://en.wikipedia.org/wiki/Collective_intelligence
Collective Intelligence
Collective intelligence (CI) is shared or group intelligence (GI) that emerges from the collaboration, collective efforts, and competition of
many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer
review and crowdsourcing applications. It may involve consensus, social capital and formalisms such as voting systems, social media and
other means of quantifying mass activity.[1] Collective IQ is a measure of collective intelligence, although it is often used interchangeably
with the term collective intelligence. Collective intelligence has also been attributed to bacteria and animals.[2]
It can be understood as an emergent property from the synergies among: 1) data-information-knowledge; 2) software-hardware; and 3)
individuals (those with new insights as well as recognized authorities) that continually learns from feedback to produce just-in-time
knowledge for better decisions than these three elements acting alone;[1][3] or more narrowly as an emergent property between people and
ways of processing information.[4] This notion of collective intelligence is referred to as "symbiotic intelligence" by Norman Lee Johnson.[5]
The concept is used in sociology, business, computer science and mass communications: it also appears in science fiction. Pierre Lévy defines
collective intelligence as, "It is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in
the effective mobilization of skills. I'll add the following indispensable characteristic to this definition: The basis and goal of collective
intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities."[6] According
to researchers Pierre Lévy and Derrick de Kerckhove, it refers to capacity of networked ICTs (Information communication technologies) to
enhance the collective pool of social knowledge by simultaneously expanding the extent of human interactions.[7][8] A broader definition was
provided by Geoff Mulgan in a series of lectures and reports from 2006 onwards [9] and in the book Big Mind [10] which proposed a
framework for analysing any thinking system, including both human and machine intelligence, in terms of functional elements (observation,
prediction, creativity, judgement etc.), learning loops and forms of organisation. The aim was to provide a way to diagnose, and improve, the
collective intelligence of a city, business, NGO or parliament.

From https://en.wikipedia.org/wiki/The_Wisdom_of_Crowds
Wisdom of Crowds
The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business,
Economies, Societies and Nations, published in 2004, is a book written by James Surowiecki about the
aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been
made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate
its argument, and touches on several fields, primarily economics and psychology.

48
Sociobiology
From https://en.wikipedia.org/wiki/Sociobiology
Sociobiology is a field of biology that aims to examine and explain social behavior in terms of evolution. It draws
from disciplines including psychology, ethology, anthropology, evolution, zoology, archaeology, and population
genetics. Within the study of human societies, sociobiology is closely allied to evolutionary anthropology, human
behavioral ecology, evolutionary psychology,[1] and sociology.[2][3]
Sociobiology investigates social behaviors such as mating patterns, territorial fights, pack hunting, and the hive
society of social insects. It argues that just as selection pressure led to animals evolving useful ways of interacting
with the natural environment, so also it led to the genetic evolution of advantageous social behavior.[4]
While the term "sociobiology" originated at least as early as the 1940s, the concept did not gain major recognition
until the publication of E. O. Wilson's book Sociobiology: The New Synthesis in 1975. The new field quickly became
the subject of controversy. Critics, led by Richard Lewontin and Stephen Jay Gould, argued that genes played a role
in human behavior, but that traits such as aggressiveness could be explained by social environment rather than by
biology. Sociobiologists responded by pointing to the complex relationship between nature and nurture.
E. O. Wilson defined sociobiology as "the extension of population biology and evolutionary theory to social
organization".[5]
Sociobiology is based on the premise that some behaviors (social and individual) are at least partly inherited and can
be affected by natural selection.[6] It begins with the idea that behaviors have evolved over time, similar to the way
that physical traits are thought to have evolved. It predicts that animals will act in ways that have proven to be
evolutionarily successful over time. This can, among other things, result in the formation of complex social
processes conducive to evolutionary fitness.
The discipline seeks to explain behavior as a product of natural selection. Behavior is therefore seen as an effort to
preserve one's genes in the population. Inherent in sociobiological reasoning is the idea that certain genes or gene
combinations that influence particular behavioral traits can be inherited from generation to generation.[7]

49
Cyberspace
From https://en.wikipedia.org/wiki/Cyberspace
Cyberspace is a concept describing a widespread interconnected digital technology. "The expression dates back from the first
decade of the diffusion of the internet. It refers to the online world as a world 'apart', as distinct from everyday reality.[1] The term
entered popular culture from science fiction and the arts but is now used by technology strategists, security professionals,
government, military and industry leaders and entrepreneurs to describe the domain of the global technology environment,
commonly defined as standing for the global network of interdependent information technology infrastructures,
telecommunications networks and computer processing systems. Others consider cyberspace to be just a notional environment in
which communication over computer networks occurs.[2] The word became popular in the 1990s when the use of the Internet,
networking, and digital communication were all growing dramatically; the term cyberspace was able to represent the many new
ideas and phenomena that were emerging.[3][4]
As a social experience, individuals can interact, exchange ideas, share information, provide social support, conduct business, direct
actions, create artistic media, play games, engage in political discussion, and so on, using this global network. They are sometimes
referred to as cybernauts. The term cyberspace has become a conventional means to describe anything associated with the Internet
and the diverse Internet culture. The United States government recognizes the interconnected information technology and the
interdependent network of information technology infrastructures operating across this medium as part of the US national critical
infrastructure. Amongst individuals on cyberspace, there is believed to be a code of shared rules and ethics mutually beneficial for
all to follow, referred to as cyberethics. Many view the right to privacy as most important to a functional code of cyberethics.[5]
Such moral responsibilities go hand in hand when working online with global networks, specifically, when opinions are involved
with online social experiences.[6][7]
According to Chip Morningstar and F. Randall Farmer, cyberspace is defined more by the social interactions involved rather than its
technical implementation.[8] In their view, the computational medium in cyberspace is an augmentation of the communication
channel between real people; the core characteristic of cyberspace is that it offers an environment that consists of many participants
with the ability to affect and influence each other. They derive this concept from the observation that people seek richness,
complexity, and depth within a virtual world.

50
Global Brain Institute
From https://globalbraininstitute.org/
The Global Brain can be defined as the distributed intelligence emerging from the worldwide ICT
network that connects all people and machines. The Global Brain Institute (GBI) was founded in
January 2012 at the Vrije Universiteit Brussel (VUB) to research this phenomenon. The GBI grew out
of the Global Brain Group, an international community of researchers created in 1996, and
the Evolution, Complexity and Cognition research group at the VUB.
Mission
The GBI uses scientific methods to better understand the evolution towards ever-stronger
interconnections between humans, software and machines across the planet. People have begun to
use software applications in all of their business domains, and software and humans have become
increasingly intertwined. Even in the energy sector, such as oil trading and other commodity trading,
such softwares have revolutionized profit generating. The world's most actively traded commodity is
oil, and the oil profit robot automatically executes your trade to earn passive income. By developing
concrete models of this process, we sho collective uld be able to anticipate both its promises and its
dangers. That would allow us to steer an efficient course towards a intelligence that would allow
Objectives
• Develop a theory of the Global Brain providing a long-term vision of the future of information society
• Build a mathematical and simulation model of structure and dynamics of the Global Brain.
• Survey the most important developments in society and ICT likely to affect the evolution of the Global Bra
• Compare these observations with the implications of the theory.
• Investigate how both observed and theorized developments impact on measures of globally intelligent org
◦ education, democracy, freedom, peace, development, sustainability, well-being, innovation, etc.
• Propose methods to enhance the development of global intelligence
• Warn about potential negative side-effects of ICT development

51
All Watched Over by Machines of Loving Grace
I like to think (and
the sooner the better!)
of a cybernetic meadow
where mammals and
computers
live together in mutually
programming harmony
like pure water
touching clear sky.
I like to think
(right now please!)
of a cybernetic forest
filled with pines and
electronics
where deer stroll peacefully
past computers
as if they were flowers
with spinning blossoms.
I like to think
(it has to be!)
of a cybernetic ecology
where we are free of our
labors
and joined back to nature,
returned to our mammal
brothers and sisters,
and all watched over
by machines of loving grace.
Poem by Richard Brautigan

53
GridSphere: Ubiquitious Computing
• Ubiquitous Computing at PARC
http://sandbox.xerox.com/ubicomp/
• Superdistributed Objects from the Object Management Group
• http://netresearch.ics.uci.edu/bionet/resources/platform/omg/sdo.html
• UbiGrid from the Global Grid Forum
• http://ubigrid.lancs.ac.uk/
• Ubiquitous Computing on Wikipedia
http://en.wikipedia.org/wiki/Ubiquitous_computing
• Web of Services from the W3C
• http://www.w3.org/2006/03/ubiweb-workshop-summary
• Gaia Project at UIUC
http://gaia.cs.uiuc.edu/
· Novacene: The Coming Age of Hyperintelligence by James Lovelock
https://mitpress.mit.edu/9780262539517/novacene/
· Intelligent Agents in Cyberspace
https://aaai.org/Library/Symposia/Spring/ss99-03.php

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