On the Aspects of Memory, Shared Memory, and Artificial Consciousness in the Virternity Project

Abstract

The quest for eternity has been alluring scientists, philosophers, and artists since the dawn of humanity. While everyone believes in the certainty of death, there has been growing efforts to devise methods or products to enhance the quality of life, improve healthcare, and hopefully increase the chances of long healthy lives. More recently, visionaries started to look into a more ambitious goal, namely, extending the human life after death. This goal can be achieved in principle through creating human-like models that mimic the human behavior and copy the human memories. These models might be possible to create with advancements in artificial intelligence, virtual reality, robotics, and large-scale database systems. The virtual eternity (Virternity) Project seeks to develop the framework and tools for such models. In this paper, we investigate some of the issues related to this endeavor and survey the relevant literature. In particular, we address the issues of 1) Memory models that support capturing and digital storage of human memories into accessible usable form, 2) Shared memory models that can help knowledge sharing between different individuals and groups, and 3) Artificial consciousness models that seek engineering implementation of the essential features of consciousness.

Full text

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On the Aspects of Memory, Shared Memory, and
Artificial Consciousness in the Virternity Project
Muhammad A. Rushdi , Member, IEEE
1
Abstract

—The quest for eternity has been alluring scientists, philosophers, and artists since the
dawn of humanity. While everyone believes in the certainty of death, there has been growing efforts
to devise methods or products to enhance the quality of life, improve healthcare, and hopefully
increase the chances of long healthy lives. More recently, visionaries started to look into a more
ambitious goal, namely, extending the human life after death. This goal can be achieved in principle
through creating human-like models that mimic the human behavior and copy the human memories.
These models might be possible to create with advancements in artificial intelligence, virtual reality,
robotics, and large-scale database systems. The virtual eternity (Virternity) Project seeks to develop
the framework and tools for such models. In this paper, we investigate some of the issues related to
this endeavor and survey the relevant literature. In particular, we address the issues of 1) Memory
models that support capturing and digital storage of human memories into accessible usable form, 2)
Shared memory models that can help knowledge sharing between different individuals and groups,
and 3) Artificial consciousness models that seek engineering implementation of the essential features
of consciousness.
Index Terms

—Virtual reality, memory, shared memory, artificial consciousness, eternity,
modeling.
I. INTRODUCTION
THIS report seeks to investigate and assess future challenges related to realization of eternal digital lives
that survive the human counterparts. This futuristic goal can be fulfilled through emerging technologies in
the fields of artificial intelligence, virtual reality, blockchains, and robotics. We will focus our attention on
three main challenges in this regard:
1) Memory models: These models support capturing and digital storage of human memories into
accessible usable form.
2) Shared memory models: These models evolve from individual memory models to help knowledge
sharing between different individuals and groups in electronic or virtual spaces.
3) Artificial consciousness models: These models seek engineering realization of the essential features of
consciousness.
In other words, the memory models are concerned with reading, digital storage, and efficient sharing of
human memories in the virtual spaces while artificial consciousness deals with writing or uploading
memories, consciousness, and awareness into robotic or virtual brains.
1M. A. Rushdi is with the Department of Biomedical Engineering and Systems, Cairo University, Giza 12613, Egypt. (e-mail:
mrushdi@eng1.cu.edu.eg).

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II. MEMORY MODELS: TRANSFER AND STORAGE OF HUMAN MEMORY INTO THE VIRTERNITY
ENVIRONMENT
Memory storage, whole brain emulation, and mind uploading has been recently proposed as a way of
permanently preserving our memories and somehow achieving immortality [Katz 2008]. Potential
applications include the creation of immortal digital forms of world-class scholars, and uploading of
e-crews for interstellar missions [Prisco 2012]. There are still difficulties and skepticism about these
frameworks. In particular, due to the huge number of neurons and their complexity, the processing
capabilities and computational techniques for the used computers would be immense.
II.1 Whole-Brain Emulation: Supporting Theories and Projects
Quest for Artificial Brains
The modeling and implementation of a digital brain that mimics the structure and function of the human
brain has been the focus of aspiring engineers, mathematicians, neuroscientists, and medical practitioners.
Holmes [Holmes 2002] investigated the common and different traits between digital and human brains.
Proponents of memory uploading and whole brain emulation have proposed several models and initiated
several projects for digital realization and storage of human memories [de Garis 2010], [Goertzel 2010],
[Rajendran 2013], [Moore 2000], [Shibata 1998]. Three motivations are frequently cited for brain
emulation [Cattell 2012]:
1. Simulations can help us gain a better understanding of how the brain and its malfunctions work.
2. Ideas based on neural network simulations may yield new ideas to develop intelligent behavior in
computers, for example through massive parallelism.
3. Hardware architectures arising from massive parallelism and adaptability of the brain may yield
new computer architectures and micro-architectures that can be applied to problems currently
intractable with conventional computing and networking architectures.
Challenges of Artificial Brains
The attempts of full brain emulation are still far from success because of several challenges [Cattell 2012]:
- Neural complexity: Complexities arise from large variabilities in neural structures and mechanisms.
For example, synapse types vary widely and they receive different transmitters. Action potentials at
dendrites combine in multiple ways. Dendritic computations affect neural firing probability and
frequency. Evenmore, dendritic computations may depend on the location of each synapse.
Deciding the level of dendritic computation modeling needed for brain emulation is still an open
question.
- Scale: The largest supercomputers and computer clusters today have thousands of processors, while
the human cortex has tens of billions of neurons and a quadrillion synapses. There is an ongoing
controversy between experts whether we are few years, decades, or more away from the human
cortex scale.
- Interconnectivity: Brain emulation in hardware poses a massive wiring problem. Each synapse
represents one input to a neuron, and each postsynaptic neuron shares synapse with an average of
10,000 other presynaptic neurons. The neuronal axon fans out to an average of 10,000 destinations.
So, each neuron has on average 10,000 inputs and 10,000 outputs. Moreover, the connections are

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not mostly local but widely spread across the brain tissues.
- Plasticity: Emulating a brain with static neural connections and behavior would not produce
intelligent machines. Synapses must be plastic, that is, the excitatory or inhibitory connection
strengths must change with learning. Also, neurons should be able to create new synapses and
connections to adapt with learned patterns or experiences.
- Power Consumption: Considerations should be made for the power consumed by brain emulation
with 50 billion neurons and 500 trillion connections, and the dissipation of the heat generated in the
process. The human brain uses very little power estimated at 25 Watts. Ultra-low-power electronics
offer a solution to catch up with but still far from the human brain.
In fact, the available hardware is still inadequate for all of these challenges. According to de Garis et al.

[de
Garis 2010], “the human brain has about 100 billion neurons, with each neuron connecting to roughly
10,000 others, with each synapse firing at maximum of about 10 bits per second; hence the total bit
processing rate is of the order of 10

16

bits per second. This number dwarfs the bit processing rates of the
computers of the 20th century.”

Moreover, with quadrillion synapses, unknown connectivity patterns, and
numerous dynamic parameters, our understanding of the brain is still far from complete.
Approaching the Singularity
With the exponential growth in computing power and advances in neuroscience and artificial intelligence,
artificial brian enthusiasts see the creation of artificial brains that closely mimic the human brain as
imminent and inevitable, and possibly only a few years or decades away [de Garis 2010], [Wu 2013]. In
particular, Raymond Kurzweil, a famous computer scientist and futurist, makes two predictions: artificial
brains will surpass human brainpower in 2029, and the computing power will surpass the brainpower of all
human brains combined in 2045 [Kurzweil 2006], [Grossman 2011].
As Goertzel [Goertzel 2007] puts it, “Kurzweil views human brain emulation as one route toward
Singularity-enabling Artificial General Intelligence (AGI), a route that involves:
1. Scanning human brains.
2. Creating human brain emulations in advanced computer hardware.
3. Studying these emulations to understand the principles underlying them.
4. Creating various AGI systems embodying these principles, including AGI systems capable of radical self
modification and self-improvement.”
Figure 1 shows a timeline of the growth of computing power and the prediction of the

Singularity.
Artificial Brain Models: Large-Scale Brain Simulations versus Biologically-Inspired Cognitive
Architectures
Two different main approaches have been adopted by researchers for artificial brain simulation and
realization. The two approaches differ regarding the level of detail and hence the computing circuitry and
power needed to create practical and truthful artificial equivalents of the human brain.
On the one hand, large-scale brain simulations

aim at closely simulating brain circuits, providing detailed
information on brain dynamics, and probing the structural brain complexity and nonlinear coherence of
large numbers of coupled neurons.
Nevertheless, large-scale brain simulations still don’t demonstrate intelligent behavior as these simulations

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lack information on intermediate-scale brain structure [de Garis 2010].
On the other hand, biologically-inspired cognitive architectures (BICAs) are designed to perform basic
processing functions following brain-like methods with reduced and more realistic complexity and
computational power requirements as compared to the human brain [Goertzel 2010].
Figure 1. A timeline of the growth of computing power and the prediction of the Singularity, namely, when
the computing power exceeds the human brainpower. [Grossman 2011]
Large-Scale Brain Simulations: A Short Survey
We briefly survey here prominent examples of large-scale brain simulations that depend on detailed models
of the brain structure and function.
A. Blue Brain Project
This project is led by Henry Markram where an IBM “Blue Gene” supercomputer has been used to
simulate and construct a detailed digital copy of the rodent brain and hopefully for the human brain [Hill
2008], [IBM 2008]. The project strategy is to obtain dense maps of the brain without the need to measure
every detail. This is done by finding interdependencies in the data acquired from neuroscience experiments
and use them to constrain the construction process [Sharma 2014], [Gidwani 2015]. So far, the project
constructed a detailed digital copy of a small part of the neocortex of young rats. Markram believes that the
long-term goal of the project “to simulate the full cortex of the human brain”

is achievable by 2018.

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B. Cognitive Computing via ‘Synaptronics’ and Supercomputing
The ‘SyNAPSE’(Systems of Neuromorphic Adaptive Plastic Scalable Electronics) project was launched by
the United States Defense Advanced Research Projects Agency (DARPA) in 2008 to fund universities and
companies to design and build brain-like systems. One of the grants went to a team led by Dharmendra
Modha, a computer scientist and engineer who manages IBM’s Cognitive Computing Group. Modha’s
team included academic and industry experts in analog VLSI, asynchronous VLSI, circuit design,
nanomaterials, psychology, theoretical computer science, supercomputing, simulation, virtual
environments, neuroscience and computational neuroscience [Frye 2007], [Ananthanarayanan 2007a].
Modha [Ananthanarayanan 2007b] used “147,456 processors and 144 terabytes of memory to simulate the
signaling of 1.6 billion neurons and 10 trillion synapses, which are numbers comparable to those in a cat’s
brain, and managed to achieve near real-time simulation speeds.”
For creating genuine artificial brains with near human-level abilities, Modha's priority is achieving
sufficient bit rate capabilites (high processing speeds) while Markram's priority in the Blue Brain Project is
achieving simulations with biologically realistic accuracy and sufficient knowledge of the neural
microcircuits.
C. Neurogrid Project
The "Neurogrid" framework is a brain-like hardware architecture [Benjamin 2014] developed by a team led
by the Stanford researcher, Kwabena Boahen, whose main research goal is to “understand how cognition
arises from neural properties.”

In particular, Boahen seeks to design integrated circuits to “emulate the
way neurons compute, thus linking the (supposedly) disparate fields of electronics and computer science on
the one hand with neurobiology and medicine on the other.”
Boahen's group successfully designed and built a silicon retina (which could be employed to give the blind
some degree of sight), and a self-organizing chip that emulates the way a growing brain develops its wiring.
In a recent article, Boahen [Boahen 2017] argued that since Moore’s Law is becoming to an end,
error-tolerant artificial brain design equipped with 3D transistors can be achieved by combining analog
dendritic computation with digital axonal communication. This is the basis for the Neurogrid framework
(Figure 2).
D. Brain-Based Devices (BBDs)
Brain-Based Devices (BBDs) [Krichmar 2005], [Fleischer 2009] have been developed by the American
biologist Gerald Edelman and his colleagues. Edelman defines these devices to be “realistic brain models
that control robotic devices performing behavioral tasks.”
A central theme of these models is that “the brain does not operate on its own. Instead, it is intimately
connected to its body, which in turn interacts closely with its environment.”

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Figure 2. Neurogrid. (a) Cell layers (green, lemon, and orange) are mapped onto Neurocores, which are
connected in a binary tree network. Inter- and intra-column connections are programmed in off- and
on-chip RAM, respectively. (b) A neuron has four ligand-gated and four voltage-gated ion-channel
populations, a dendrite, and a cell body, all modeled using subthreshold analog circuits. Neurocore has
65,536 silicon neurons (256 × 256 array), as well as spike routers implemented using asynchronous digital
circuits. Neurogrid holds 16 interconnected Neurocores. (Source: [Boahen 2017]).
E. Large-scale Model of Mammalian Thalamocortical Systems
In 2007, Eugene M. Izhikevich, Chairman and CEO of the ‘Brain Corporation’ in San Diego, proposed a
computational brain model based on extensive research on the mammalian brain [Izhikevich 2007].
Izhikevich's model is more ambitious than Markram's "Blue Brain Project" in terms of scale: Izhikevich's
human brain simulation was on a scale similar to that of the full human brain itself. The proposed model
covers three levels of anatomical details:
1. The model is based on the global white-matter thalamocortical anatomy of the human brain, obtained
using diffusion-tensor imaging (DTI) techniques.
2. The model contains many thalamic nuclei as well as 6-layered micro-circuitry of the cortex, that are

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based on biological labels and 3D models of neurons present in the visual cortex of the cat.
3. The model uses 22 different kinds of basic neurons with appropriate laminar distributions of their
branching dendritic trees.
The model simulates a million spiking neurons and includes half a billion synapses. The following
properties of real synapses were closely modeled: receptor kinetics, short-term plasticity, and long-term
dendritic spike-timing-dependent plasticity (dendritic STDP).
Biologically-Inspired Cognitive Architectures: A Short Survey
We briefly survey here prominent examples of Biologically-Inspired Cognitive Architectures (BICAs) that
resemble the brain functions but don’t depend on detailed models of the brain structure and function
[Goertzel 2010]. A BICA is intended to display loosely similar functions to a brain, based on internal
structures that are conceptually inspired by the brain (and not just the mind) but not necessarily extremely
similar to the brain.
Two key design properties that underlie the development of any cognitive architecture are memory

and
learning

. “Various types of memory serve as a repository for background knowledge about the world and
oneself, about the current episode of activity, while learning is the main process that shapes this
knowledge. Together learning and memory form the rudimentary aspects of cognition on which
higher-order functions and intelligent capabilities, such as deliberative reasoning, planning, and
self-regulation, are built.”
Figure 3. Simplified taxonomy of cognitive architectures (Source: [Duch 2008])
Duch et al.

[ Duch 2008] divides cognitive architectures into three categories (See Figure 3):

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A. Symbolic Architectures
These architectures focus on information processing using high-level symbols or declarative knowledge, in
a classical AI top-down, analytic approach.
For memory

representation in most of the symbolic architectures, a centralized control is used over the
information flow from sensory inputs through memory to motor outputs. Rule-based representations

of
perception-action memory in knowledge-based systems embed the human logical reasoning skills.
Graph-based representations

are typically encoded as directed graph structures composed of nodes for
symbolic entities and their attributes, and edges for relationships among these entities.
Learning

in symbolic systems can be realized using analytical and inductive learning. On the one hand,
analytical learning methods

(e.g. explanation-based learning (EBL) [Mitchell 1986] and analogical
learning [Veloso 1990]) aim at exploiting existing general/specific facts to infer other facts that they entail
logically. On the other hand, inductive machine learning methods

(e.g. knowledge-based inductive learning
(KBIL) [Larvac 1994] and delayed reinforcement learning [Shavlik 1990]) seek to derive from specific
facts or examples general rules which capture the underlying domain structure.
Examples of symbolic cognitive architectures are SOAR (State, Operator And Result) [Liard 1987], EPIC
(Executive Process Interactive Control) [Meyer 1997], ICARUS project [Langley 2005], NARS
(Non-Axiomatic Reasoning System) project [Wang 2006], and SNePS (Semantic Network Processing
System) [Shapiro 2007].
B. Emergent Architectures
Emergent architectures use low-level activation signals flowing through a network consisting of numerous
processing units, a bottom-up process relying on the emergent self-organizing and associative properties.
Emergent cognitive architectures are inspired by connectionist

ideas where processing elements (PEs) form
network nodes that interact with each other in a specific way changing their internal states and revealing
interesting emergent properties. There are two complementary approaches to memory organization,
globalist

and localist

. The Multi-Layer Perceptron (MLP) and other neural networks process information in
a distributed, global way. All parameters of such networks influence their outputs. Generalization of
learned responses to novel stimuli is usually good, but learning new items may lead to catastrophic
interference with old knowledge [O'Reilly 2000]. The basis set expansion networks that use localized
functions (such as Gaussians) are examples of localist networks; the output signals for a given input depend
only on a small subset of units that are activated.
Several learning approaches have been proposed for emergent architectures [Kubat 2015]. Associative
learning

creates a mapping of specific input representation to specific output representation and in this
sense remembers the reactions (hetero-associations) or enables pattern completion (auto-associations). In
competitive learning

, all PEs compete to become active and learn in an unsupervised fashion.
Examples of emergent architectures are IBCA (Integrated Biologically-based Cognitive Architecture)
[O'Reilly 1999], Cortronics [Hecht-Nielsen 2007], NuPIC (Numenta Platform for Intelligent Computing)
[Hawkins 2004], and NOMAD (Neurally Organized Mobile Adaptive Device) [Edelman 1993].

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C. Hybrid Architectures
Hybrid architectures result from combining the symbolic and emergent paradigms in one way or another to
gain the benefits of both paradigms.
Based upon the memory type of the constituent modules, hybrid architectures can be divided in two classes:
localist-distributed

and symbolic-connectionist

. The localist-distributed

architectures comprise a
combination of localist modules (with each concept specified by one PE node) and distributed modules
(with each concept represented by a set of overlapping nodes). In comparison, symbolic-connectionist
architectures involve a mixture of symbolic modules (i.e., rule- or graph-based memory) and connectionist
modules (either of localist or distributed type).
In terms of learning direction, hybrid architectures can involve: top-down

or bottom-up learning

. The
former involves a transition of knowledge from an explicit (accessible) conceptual level to an implicit
(inaccessible) sub-conceptual level, while the latter goes from a sub-conceptual level to a conceptual level.
Examples of hybrid architectures are ACT-R (Adaptive Components of Thought-Rational) [Anderson
2003], CLARION (The Connectionist Learning Adaptive Rule Induction ON-line) [Sun 2004], LIDA (The
Learning Intelligent Distribution Agent) [Franklin 2006], Polyscheme [Cassimatis 2007], and 4CAPS
architecture [Just 2007].
Alternative Scenarios for the Future of Artificial General Intelligence
The Singularity scenario envisioned by Ray Kurzweil is not the only plausible scenario for the future of
Artificial General Intelligence (AGI). Ben Goertzel [Goertzel 2007] lists some possible alternative
scenarios:
- Steady Incremental Progress Scenarios. Narrow-AI research continues incrementally, as it’s doing
now—gradually and slowly becoming less and less narrow (more and more general). Explicit AGI
research doesn’t really get anywhere till narrow AI has built up a lot more knowledge about how to
solve particular sorts of problems using specialized algorithms. Eventually, maybe hundreds of
years from now, narrow AI research becomes sufficiently general that it reaches human-level AGI.
- Dead-End Scenarios. Narrow-AI research continues and leads to various domain-specific successes
but doesn’t succeed in progressively moving toward AGI.
- AGI-Based Singularity Scenarios. A human-level AGI is achieved, and this AGI succeeds at both
progressively increasing its own intelligence and creating other radical technologies, thus leading to
a massive and massively unpredictable transformation of the conditions in our region of the
universe.
- Skynet Scenario. (Named after the malevolent AGI in the Terminator movies.) A powerful AGI is
created, improves itself, develops amazing new technologies (backwards time travel, in the movies),
and enslaves or annihilates us pesky little humans.
- Kurzweil’s Scenario. The scenario Kurzweil envisions is essentially that AGI is achieved via
scanning human brains, figuring out the nature of human thought from these scans, and then
replicating human brain function on massively powerful computer hardware. Kurzweil’s scenario is
a cooperative one where humans and human-like AI agents ‘coexist’ with no us-versus-them

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confrontations, and where the boundaries between the two sides become blurred and hard to define.
Whole-Brain Emulation: A Roadmap
Sandberg and Bostrom [Sandberg 2008] wrote a comprehensive technical report on whole-brain emulation
(WBE). They discussed different levels of emulation, success criteria, assumptions, requirements,
technology drivers, uncertainties and alternatives, scanning procedures, image processing techniques
[Lloyd 2002], scanning interpretation, and environment simulation. Sandberg and Bostrom suggest that any
approach to WBE has two phases (See Figure 4):
- Phase I: Developing the basic capabilities and settling key research questions that determine the
feasibility, required level of detail and optimal techniques. This phase mainly involves partial scans,
simulations and integration of the research modalities.
- Phase II: This phase begins once the core methods [Kozma 2007] have been developed and an
automated scan-interpret-simulate pipeline is in place. At this point the first emulations become
possible. If the developed methods prove to be scalable they can then be applied to increasingly
complex brains. The main issue is scaling up techniques that have already been proven on the small
scale.

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Figure 4. A roadmap for Whole-Brain Emulation (WBE) [Sandberg 2008]
II.2 Whole-Brain Emulation: Opposition, Skepticism, and Debates
Objections and skepticism have been raised by several scientists and writers regarding the eminence of the
Singularity [Nordmann 2008],[Shanahan 2015] and whole brain emulation. We briefly review here these
objections and assess their merits.
A. McDermott’s critique of Kurzweil’s singularity scenario
In his critical review of Kurzweil’s “The Singularity Is Near”, Drew McDermott [McDermott 2006]
criticizes Kurzweil’s arguments for reaching human-level AI in 2029 and the global-level AI, or the
Singularity, in 2045. Ben Goertzel [Goertzel 2007] followed up with an analysis of the strengths and
weaknesses of Kurzweil’s arguments and McDermott’s counter-arguments. The main points of debate are
as follows:
- McDermott argues that Kurzweil does not provide any ‘proof’ that an AI-driven Singularity is upon
us. Goertzel refutes this argument saying that a proof is not possible in this case: “Obviously, in any
extrapolation of the future of a complex real-world system coupled with other complex real-world
systems, there can be no such thing as proof, only at best “probably approximately correct”
prediction.”
- Goertzel agrees, however, that there are no plausible uncertainty estimates around the predicted
years. “how certain could a date like 2045 possibly be? Even if 2045 is a good estimate of the mean
of the distribution of dates for human-level AI, what’s the variance?”
- Goertzel made counter arguments for McDermott’s critique of Kurzweil’s argument as to why
human-level AGI is likely to occur by 2029. “To get this figure, Kurzweil extrapolates not from
contemporary progress in the AI field, but rather from contemporary progress in computer
hardware and brain scanning. He argues that by 2029 we will have computers powerful enough to
host a detailed emulation of the human brain, and brain-scanners powerful enough to mine the
brain-data needed to create such an emulation. So according to this argument, even if the AI
approaches currently being pursued are all wrong, we’ll still get to human-level AI soon enough
just by copying the brain.”
- The main dispute McDermott has with the brain-scanning route to Artificial General Intelligence
(AGI) is that, even if we succeed in scanning the brain into a computer, this still won’t give us any
real understanding of how intelligence (human or otherwise) works. In McDermott’s words,
“Obviously improvements in brain-scanning technology are important and exciting, but they get us
somewhere only if accompanied by deepening of our understanding of the computations neurons
do. So the possibility of scanning is a very weak argument that our understanding is sure to deepen.
. . .
Even if we succeeded in duplicating a person to the point where we couldn’t tell the copy from the
original, that wouldn’t even confirm AI, let alone contribute to it.”
Goertzel agrees that McDermott has some point here, which Goertzel says is a point recognized by
Kurzweil himself in his book, “Kurzweil understands that being able to emulate a brain in
computer hardware is different from having a real understanding of how to flexibly create
human-level artificial intelligences. And Kurzweil understands that the human brain

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architecture—even ported to digital hardware—is probably not amenable to rapid recursive
self-improvement.”
B. No unifying theory of neuroscience or how the brain works
Several researchers express doubts about the possibility of creating digital copies of human brains as long
as we still don’t have a full understanding of how the brain works.
As John R. Searle (professor of the philosophy of the mind and language at the University of California at
Berkeley) writes in "The Mystery of Consciousness" [Searle 1997]:
“The dirty secret of contemporary neuroscience is that so far we do not have a unifying theoretical
principle of neuroscience. In the way that we have an atomic theory of matter, a germ theory of disease, a
genetic theory of inheritance, a tectonic plate theory of geology, a natural selection theory of evolution, a
blood- pumping theory of the heart, and even a contraction theory of the muscles, we do not in that sense
have a theory of how the brain works. We know a lot of facts about what actually goes on in the brain, but
we do not yet have a unifying theoretical account of how what goes on at the level of the neurobiology
enables the brain to do what it does by way of causing, structuring, and organizing our mental life.”
Evenmore, as John Horgan suggests in his book, "The Undiscovered Mind: How the Human Brain Defies
Replication, Medication, and Explanation" [Horgan 2000], neuroscience appears to be making
“antiprogress”

— the more information we acquire, the less we seem to know!
In addition, after a decade overseeing the Allen Institute for Brain Science, Paul Allen doesn’t think we’ll
get to the Singularity until after 2100! He threw the gauntlet down in an M.I.T. Technology Review article
called “The Singularity Isn’t Near” [Allen 2011]. The brain is a machine, sure, but the complexities of its
operation remain mysterious—way, way beyond our full practical understanding. In fact, the neuroscience
breakthroughs of the past 20 years are revealing how much we don’t understand, vastly expanding the scale
and scope of the known unknowns. “By the end of the century,”

he wrote, “we will still be wondering if the
Singularity is near.”
C. Truth versus hype in artificial intelligence research
Researchers from within the AI community itself see the early promises of human-like AI as questionable
and difficult to achieve.
James P. Hogan wrote in "Mind Matters: Exploring the World of Artificial Intelligence" [Hogan 1998],
“We haven’t really come a long way. … The early A.I. vision of reproducing all-around humanlike
reasoning and perception remains as elusive as ever.”
Nick Bostrom, Faculty of Philosophy, Oxford University, England, wrote [Bostrom 2000]: “The annals of
artificial intelligence are littered with broken promises. Half a century after the first electronic computer,
we still have nothing that even resembles an intelligent machine, if by ‘intelligent’ we mean possessing the
kind of general-purpose smartness that we humans pride ourselves on.”
Jaron Lanier (an eminent computer scientist known for coining the phrase “virtual reality” and for founding

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VPL Research, probably the first virtual reality company, later acquired by Sun Microsystems) wrote
[Lanier 2002], “The first fifty years of general computation, which roughly spanned the second half of the
twentieth century, were characterized by extravagant swings between giddy overstatement and
embarrassing near-paralysis. The practice of overstatement continues … Accompanying the parade of
quixotic overstatements of theoretical computer power has been a humiliating and unending sequence of
disappointments in the performance of real information systems.”
D. Will You Ever Be Able to Upload Your Brain?
Kenneth D. Miller, a professor of neuroscience at Columbia and a co-director of the Center for Theoretical
Neuroscience, strongly doubts the success of mind uploading in the near future due to the inadequacy of
hardware and understanding of the human brain [Miller 2015]:
“The current best achievement was determining the connections in a tiny piece of brain tissue containing
1,700 synapses; the human brain has more than a hundred billion times that number of synapses. While
progress is swift, no one has any realistic estimate of how long it will take to arrive at brain-size
connectomes. (My wild guess: centuries.)
Neuroscience is progressing rapidly, but the distance to go in understanding brain function is enormous. It
will almost certainly be a very long time before we can hope to preserve a brain in sufficient detail and for
sufficient time that some civilization much farther in the future, perhaps thousands or even millions of years
from now, might have the technological capacity to “upload” and recreate that individual’s mind.
We all find our own solutions to the problem death poses. For the foreseeable future, bringing your mind
back to life will not be one of them.”
E. Dangers of Brain Emulation
Peter Eckersley and Anders Sandberg explored the dangers of brain emulation [Eckersley 2013]. They
enumerate “possible dynamics leading to existential or catastrophic risks:
1. Geopolitical conflict:

Unfortunately, brain emulation technologies are capable of providing many
of the kinds of ingredients that are commonly regarded as contributing to the risk of war, including:
- increasing inequality (between emulations, humans who can afford and want to "become"
emulations, and humans who cannot);
- groups that become marginalized (humans who cannot compete with emulations, emulations or
groups of emulations that are at a disadvantage compared to other emulations);
- disruption of existing social power relationships and the creation of opportunities to establish new
kinds of power;
- potential first-strike advantages and cumulative resource advantages (holding more resources
increases the resource-gathering efficiency);
- the appearance of groups of intelligent beings who may empathise with each other even less than
humans historically have done;
- the appearance of groups of beings with strong internal loyalty and greater willingness to die‘ for
what they value;
- particularly strong triggers for racist and xenophobic prejudices;
- particularly strong triggers for vigorous religious objections;

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- the creation of situations in which the scope of human rights and property rights are poorly defined
and subject to dispute (and surprise).
2. A runaway emulation.
3. A contest between emulations.
4. Conflict between humans and emulations.

”
Eckersley and Sandberg also raised computer security concerns [Eckersley 2013]: “Emulations would only
be vulnerable to hacking to the extent that they were connected to a network; it might be possible for them
to isolate themselves substantially from vulnerability by refusing to access networks without several levels
of indirection. But such emulations might find themselves at a tremendous disadvantage compared to their
cousins that were willing to take the risk of wiring a network connection deep into their emulation code.”
While most of the aforementioned objections are valid, it seems that some technical objections will
disappear as technologies advance and maturate. The social and ethical issues won’t be resolved without
carefully tailored legislations and awareness campaigns [DiCarlo 2016].
III. SHARED MEMORY AND EXPERIENCE MODELS
III.1 Memory and Experience Sharing: Supporting Theories and Projects
Memory sharing or externalization mainly went through three different stages. In the first stage, it was
shared through spoken languages. of course this wasn’t a suitable way to save or preserve memory. The
second stage started after the invention of writing which helped in memory sharing and saving. Moreover,
this task became easier and faster with the invention of printing. Nowaday, we enjoy the third stage of
memory sharing which take place through the internet [Vidal 2015].
Several models have been proposed for shared memories between individuals, small groups, or even whole
populations.
A. Conceptions of a Global Brain
There is no doubt that a group as a whole is more intelligent than each of its members. This is very
obvious in insects such as bees which can do amazing jobs together. Inspired by this fact, the idea of having
a global brain has been introduced by many authors under different names. However, Peter Russell was the
first author to use the term “Global brain” in 1982 [Russell 1983]. “The "global brain" is the name given to
the emerging intelligent network that is formed by all people on this planet together with the computers,
knowledge bases and communication links that connect them together”

[Heylighen 2002].
The relations between the global brain or universal intelligence and other types of intelligence can be
illustrated by the Venn diagram [Yampolskiy 2015] of Figure 5. Region 1 represents what is known as the
global brain, universal intelligence or super-intelligence: a computational agent that outperforms all other
intelligent agents over all possible environments. Region 2 is the standard unenhanced human-level
intelligence that is incapable of computations involving large numbers or significant amounts of memory.

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Region 3 is the intelligence currently possible to achieve through state-of-the-art AI programs. Region 4
represents an abstract view of animal intelligence. Other numbered regions represent types of intelligence
that can result from combining the aforementioned types of intelligence.
Figure 5. Venn diagram for different types of intelligence
Recently, developing a global brain has became an easier and more realistic idea because of the great
technological advancement. Moreover, having the ability to connect to the Internet from almost any place
using different devices has made the global brain easily accessible and efficiently used by everybody
despite their place or wealth.
Heylighen classifies the way authors see the global brain into four categories [Heylighen 2011]:
1) Organicism
This old metaphor is known since Aristotle and was the main interest of the founders of sociology. In this
metaphor, society is a ‘social organism” [Spencer 1969] or a “superorganism” [Wheeler 1911]. It grows
and its life length is extremely longer than the life of its individual components. However, this approach
doesn’t consider disagreements and competition between those components. Moreover, it has been used to
prevent protests against the rulers justifying that the entire body would be destroyed if one of its organs
rebel against the others [Bukharin 1925]. Because of this defect in the Organism metaphor, sociologists has
lost their interest in it. They started to focus on liberty and changing the society to a better condition.
2) Encyclopedism
In this metaphor, the global brain is a universal knowledge network. The French philosophers Denis
Diderot and Jean le Rond d’Alembert and many others participated in writing and editing the Encyclopédie
which was published between 1751 and 1772. This Encyclopedia had a great effect on the creation of this

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metaphor. This metaphor became more relevant after the World Wide Web was invented by the computer
scientist Timothy John Berners-Lee in 1989. In 2001, the Wikipedia project started taking advantage of the
web technologies to create the largest encyclopedia written in many different languages and accessed by
people in any place in the world. The main drawback of this metaphor is the complexity and changing
nature of knowledge.
3) Emergentism
In this metaphor, the focus is on the spiritual and psychological sides where humans’ consciousness
merge together. This happens when people focus their mind on the same thing. By achieving a coherent
consciousness, people may have a great influence on the world. They may even change the behaviour of
random systems as proposed by the Global Consciousness Project [GCP 2015]. This metaphor has a major
shortcoming which is its dependence on assumptions and future technology to prove its existence.
4) E

volutionary cybernetics
Heylighen argues that by integrating the evolutionary theory and cybernetics, we can overcome the
drawbacks that the three previous conceptions suffer from. “Evolutionary cybernetics introduces the
concept of metasystem transition: the self-organization of individual components into a positive-sum system
that functions at a higher level of intelligence and consciousness.” [Heylighen 2011]. In this metaphor, the
Internet interconnects and coordinates the global brain members.
B. Mind Design Space
Several attempts have been made to describe the mind design space

, the space of all possible minds
including biological and artificial minds, human minds, animal minds, and any hybrid minds. Yudkowsky
[Yudkowsky 2008] describes the map of mind design space as follows:
“Imagine a map of mind design space. In one corner, a tiny little circle contains all humans; within a
larger tiny circle containing all biological life; and all the rest of the huge map is the space of
minds-in-general. The entire map floats in a still vaster space, the space of optimization processes. Natural
selection creates complex functional machinery without mindfulness; evolution lies inside the space of
optimization processes but outside the circle of minds.
It is this enormous space of possibilities which outlaws anthropomorphism as legitimate
reasoning.”
Figure 6 shows one possible mapping [Yampolskiy 2015] inspired by this description. The minds are
assumed to be more ‘intelligent’ as we go from left to right on the relative intelligence scale. The set of
evolved earthly minds

(red ellipse) include human minds and sets of other earthly minds such as dog minds,
bug minds, male minds or in general the set of all animal minds. Those species show more ‘intelligence’ as
we go from left to right. Human-designed AI

minds (brown ellipse) include artificial mind models created
by humans. The intelligence of these minds evolves from the level of ordinary human intelligence to the
level of universal intelligence

. Alien and unknown minds can be accounted for as well as ellipses in this
space. All of the aforementioned sets of minds may have the ability to improve themselves and hence can
also be categorized as self-improving minds

. The space of minds in general is a subset of the space of
optimization processes which are more general than mind processes.

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Figure 6. The universe of possible minds [Yudkowsky 2008], [Yampolskiy 2015].
Similarly, Ivan Havel [Havel 2013] writes
“… all conceivable cases of intelligence (of people, machines, whatever) are represented by points in a
certain abstract multi-dimensional “super space” that I will call the intelligence space (shortly IS).
Imagine that a specific coordinate axis in IS is assigned to any conceivable particular ability, whether
human, machine, shared, or unknown (all axes having one common origin). If the ability is measurable the
assigned axis is endowed with a corresponding scale. Hypothetically, we can also assign scalar axes to
abilities, for which only relations like “weaker-stronger”, “better-worse”, “less-more” etc. are
meaningful; finally, abilities that may be only present or absent may be assigned with “axes” of two
(logical) values (yes-no). Let us assume that all coordinate axes are oriented in such a way that greater
distance from the common origin always corresponds to larger extent, higher grade, or at least to the
presence of the corresponding ability. The idea is that for each individual intelligence (i.e. the intelligence
of a particular person, machine, network, etc.), as well as for each generic intelligence (of some group)
there exists just one representing point in IS, whose coordinates determine the extent of involvement of
particular abilities.”
C. Mind Taxonomies: Goertzel’s Embodiment Classification
Ben Goertzel [Goertzel 2006] proposed a classification of minds mostly centered on the concept of
embodiment

of a mind, i.e., the association of the mind with one or more ‘physical’ systems (the ‘body’ of
the mind):
●Singly Embodied Mind: This mind controls a single ‘physical’ system. According to Goertzel,
“this "physical" system need not be physical in the narrowest sense -- it could for instance be an
aspect of a computer-simulation world. The important thing is that it displays the general properties
of physicalness -- such as, involving a rich influx of "sensations" that present themselves to the mind

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as being unanalyzable except in regard to their interrelations with one another, and that are in
large part not within the mind's direct and immediate control.”
●Multiply Embodied Mind: This mind controls a number of disconnected physical or simulated
systems. Theses systems “are disconnected (or nearly disconnected) from each other.
"Disconnected" here means that the bandwidth of information transmission between the
"disconnected" systems is vastly less than the bandwidth of transmission between the parts within
the individual systems. Human and animal minds are singly embodied, but a single computer
program simultaneously controlling a dozen robots might be multiply embodied.”
●Flexibly Embodied Mind: Such a mind controls a changing number of physical or simulated
systems. “A good example would be a human hooked into a video game environment via powerful
virtual reality style sensors and actuators. The human mind would be there invariantly regardless of
which character, or group of characters, was being played.”
●Non-Embodied Mind: A mind that resides in a physical substrate but doesn’t utilize the body in a
traditional way. For instance, “one could have a mind entirely devoted to proving mathematical
theorems, and able to communicate only in mathematics. Such a mind would have no need for a
body; its sensations and actions would consist of statements in a formal language.”
●Body-Centered Mind: This mind consists of patterns emerging between physical system and the
environment. “So in this case the body is not the substrate of the bulk of the mind-patterns, but only
the partial substrate. Of course, a system may be singly or multiply body-centered; and the
boundary between embodiment and body-centeredness is fuzzy. Humans have some embodiment
and some body-centeredness to them. The development of language, it would seem, moved us
further from strict embodiment toward the direction of body-centeredness. Future developments like
Internet-connected neural implants might move us even further in that direction.”
●Mindplex: A mindplex is a set of collaborating units each of which is itself a mind. The notions of
"mindness" and "mindplexness" are fuzzy. i.e., the distinction of an autonomous, coherent "mind"
from a part of a mind is vague and application-dependent. Goertzel [Goertzel 2006] gives the
example that “there would be much more mindplexness involved if, for instance, a massive AI
system were connected to the Internet and instructed to participate in online dialogues of various
forms in such a way as to encourage the achievement of the essential goals of humanity. In this
situation, we'd have a hybrid human/AI mindplex of the type I've called elsewhere a "global brain
mindplex."”
D. Mind Taxonomies: Quantum versus Classical Minds
Several scientists and philosophers explored the potential aspects and differences of quantum versus
classical minds. A classical mind is an embodiment based on properties of classical physics, while a
quantum mind

is an embodiment based on properties of quantum physics.
Ben Goertzel [Goertzel 2006] investigated the pertinence of quantum physics for the study of mind. In
particular, Goertzel argues that quantum decoherence is essentially the physical correlate of conscious
experience. Quantum decoherence means that, “as soon as a quantum system interacts with an

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environment, it very rapidly “decoheres”, meaning roughly that the various portions of its quantum
wavefunction stop interfering with each other, because they get so wrapped up in their interactions with the
wavefunctions of entities in the environment.”
Goertzel reasoned that consciousness is based on human observations which in turn can be interpreted
using quantum decoherence: “Firstly, I suggest that we view consciousness as “the process of observing.”
Now, “observation,” of course, is a psychological and subjective concept, but it also has a physical
correlate. I suggest the following characterization of the physical substrate of observation:

Subjective acts
of observation physically correspond to events involving the registration of something in a memory from
which that thing can later be retrieved

.
It immediately follows from this that observation necessarily requires an effectively-classical system that
involves decoherence. But what is not so obvious is that all decoherence involves an act of observation, in
the above sense. This is because, as soon as a process decoheres, the record of this process becomes
immanent in the perturbations of various particles all around it – so that, in principle, one could
reconstruct the process from all this data, even though this may be totally impractical to do. Therefore
every event of decoherence counts as an observation, since it counts as a registration of a memory that can
(in principle) be retrieved.”
Goertzel thinks that quantum minds are scientifically possible but the mechanisms of their operation and
interactions are still speculations:
“So

what can we say about the potential nature of quantum minds?

I’ve discussed quantum learning,
memory and reasoning – and in each case we see two fundamental aspects:
• Much more efficient operation than is possible for classical systems
• Distribution of learning, reasoning or memory across multiple universes
But

what will be the subjective experience of a quantum mind?

Clearly its mind will run much faster than
ours, which will make some difference to its experience. But this probably won’t be the most critical
difference. A quantum mind will experience directly quantum coupling with its environment, which may
mean that it doesn’t feel as distinct from its environment as we do. And the different subcomponents within
it will also experience nonlocal quantum coupling with each other – meaning that quantum experience may
have an even greater sense of holistic unity than our own experience. It may be very difficult for a quantum
mind to conceive of one part of its mind as separate from the others – because its reasoning, learning and
memory relies on the interpenetration of the different parts of the mind. Then there may be a direct
experience of the process of decoherentization, when the quantum interpenetration gives way to
macroscopic definiteness, and the multiple universes collapse to a good approximation of just one.
While these speculations are interesting, there is no way we can really know if they’re correct. The key
point I want to make for the moment is the scientific possibility of the existence of quantum minds that
possess a type of cognition completely different from our own. I see little credibility in claims that the
human brain is a macroscopic quantum system in a strong sense, but I do think that macroscopic quantum
cognitive systems are possible and will be both powerful and fascinating. Constructing such
systems should be one of the major goals of 21’st century science.”
Also, Dean Radin explored the connections between quantum physics and minds in his book "Entangled
Minds: Extrasensory Experiences in a Quantum Reality" [Radin 2006]. In particular, Radin focuses on the

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interpretation of psychic phenomena using tools of quantum physics. Radin uses the neutral term psi

,
coined in 1942 by British psychologist Robert Thouless, to refer to psychic experience without implying
origins or mechanisms. Common psi psychic experiences include “mind-to-mind connections (telepathy),
perceiving distant objects or events (clairvoyance), perceiving future events (precognition), and
mind-matter interactions (psychokinesis). Psi may also be involved in intuitive hunches, gut feelings,
distant healing, the power of intention, and the sense of being stared at.”
Several philosophers and scientists expressed doubts about the connection between quantum theory and
psychic phenomena. However, Radin eloquently argues in defense of this connection [Radin 2006], “Some
may object that linking the elegance of quantum theory to the spookiness of psychic phenomena is
illegitimate, that it’s a mistake to claim a connection exists simply because these two domains are
permeated with uncanny effects. This objection is certainly understandable. Quantum theory is a
mathematically precise and exquisitely well-tested description of the observable world. Psychic phenomena
are slippery, subjective events with a checkered past. But as it turns out, the fabric of reality suggested by
quantum theory and the observations associated with psychic phenomena bear striking resemblances. They
are eerily weird in precisely the right way to suggest a meaningful relationship.”
While Radin thinks that quantum physics won’t directly explain all psychic phenomena, many
manifestations of reality can be interpreted through quantum lens and useful insights and conclusions can
be gained through quantum analysis [Radin 2006]. In fact, Radin suggests that “we take seriously the
possibility that our minds are physically entangled with the universe, and that quantum theory is relevant to
understanding psi. That said, we should avoid jumping to premature conclusions. I’m not claiming that
quantum entanglement magically explains all things spooky. Rather, I propose that the fabric of reality is
comprised of “entangled threads” that are consistent with the core of psi experience. Of course, human
experience is far more than a collection of threads. Our bodies are tapestries built from countless
variations of the fabric of reality. And our subjective experiences (to stretch a metaphor) are quilts made
from tapestries that are stitched together in myriad, delightful ways. Understanding the nature of this quilt,
and its relationship to psi, will take more than identifying the nature of the threads that weave the fabric of
reality. But it’s an important first step. And it provides a new perspective from which to pose questions that
may lead to unexpected answers about psi.”
Moreover, quantum physics motivated the many-minds theory which claims that every sentient physical
system, every observer, is associated with not a single mind but rather a continuous infinity of minds.
Jeffrey Barrett clarifies this theory in his book “The Quantum Mechanics of Minds and Worlds” [Barrett
2001]: “It is important to be clear here about exactly what is evolving in a deterministic way and what sort
of supervenience the many-minds theory provides. One might distinguish between at least three types of
mental states in the theory. Call the state of one of an observer's minds a

local mental state

. Local mental
states evolve randomly (but in a memory-preserving way) according to the dynamics described by the
single-mind theory and do not supervene on observers' physical states. Call the state of all of an observer's
minds his

complete mental state

. This state tells us which minds are in which local states (we are assuming
that minds have transtemporal identities). It also evolves randomly as the local mental states of each mind
evolves. These states do not supervene on observers' physical states either. Finally, call the measure of an
observer's minds associated with each term in the determinate-belief-basis expansion of the wave function
the observer's

global mental state

. It is only the observer's global mental state that one might expect to
evolve in a continuous deterministic way and to supervene on his physical state (though it might happen,
since the dynamics of each mind is stochastic, that all of an observer's minds jump to a single branch. But
events like this would be expected with probability zero).”

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E. Mind Taxonomies: Hall’s classification
Dr. J. Storrs Hall suggests that different stages a developing AI may belong to can be classified relative to
its human-like abilities [Hall 2007]:
●Hypohuman: An infrahuman entity with less-than-human capacity.
●Diahuman: An entity with human-level capacities in some areas, but still no general intelligence.
Examples include AI agents playing chess, reading signs, or assisting in decision making.
●Parahuman: An entity that is similar but not identical to humans, as for example, augmented
humans, or social robots that act like lawyers, doctors, or accountants.
●Allohuman: An entity that is as capable as humans, but in different areas.
●Epihuman: An entity that is slightly beyond the human level.
●Hyperhuman: An entity that is superintelligent or much more powerful than human. Hyperhumans
are able to improve themselves significantly faster than humans.
Figure 7 shows a graphical illustration of this classification scheme.
Figure 7. Kinds of minds relative to human-like abilities [Hall 2007]
F. Mind Taxonomies: Roberts’ Classification
Patrick Roberts in his book “Mind Making: The Shared Laws of Natural and Artificial Intelligence”
presents several criteria for classification of minds [Roberts 2009].
●Choose Means - Does the mind have redundant means to the same ends? How well does it move
between them?
●Mutate - Can the mind naturally gain and lose new ideas in its lifetime?
●Doubt - Is the mind eventually free to lose some or all beliefs? Or is it wired to obey the
implications of every sensation?
●Sense Itself - Does the mind have the senses to see the physical conditions of that mind?
●Preserve Itself - Does the mind also have the means to preserve or reproduce itself?
●Sense Minds - Does the mind understand other minds, at least of lower classes, and how well does it

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apply that to itself, to others?
●Sense Kin - Can the mind recognize the redundant minds, or at least the bodies of minds, that it was
designed to cooperate with?
●Learn - Does the mind's behavior change from experience? Does it learn associations?
●Feel - Does the mind have conscious experience?
●Communicate - Can the mind share beliefs with other minds?
G. Mind Taxonomies: Kelly’s classification
Kevin Kelly [Kelly 2007] has also proposed a taxonomy of minds which, in his implementation, is really
just a list of different minds, some of which have not showed up in other taxonomies. Kelly assumes that
“in the real world — even the space of powerful minds — trade-offs rule. One mind cannot do all mindful
things perfectly well. It will be better in certain dimensions at a cost of lesser abilities in other dimensions.
So the family of minds will reflect the different ways in which minds make these trade-offs

.”
Kelly’s list of minds is:
●Super fast human mind.
●Mind with operational access to its source code.
●Any mind capable of general intelligence and self-awareness.
●General intelligence without self-awareness.
●Self-awareness without general intelligence.
●Super logic machine without emotion.
●Mind capable of imagining greater mind.
●Mind capable of creating greater mind.
●Self-aware mind incapable of creating a greater mind.
●Mind capable of creating greater mind which creates greater mind, etc.
●Mind requiring protector while it develops.
●Very slow “invisible” mind over large physical distance.
●Mind capable of cloning itself and remaining in unity with clones.
●Mind capable of immortality.
●Rapid dynamic mind able to change its mind-space-type sectors (to think differently).
●Global mind — large supercritical mind of subcritical brains.
●Hive mind — large super critical mind made of smaller minds each of which is supercritical.
●Low-count hive mind with few critical minds making it up.
●Borg — supercritical mind of smaller minds that are supercritical but not self-aware.
●Nano mind — smallest possible (in terms of size and energy profile) supercritical mind.
●Storebit — Mind based primarily on vast storage and memory.
●Anticipators — Minds specializing in scenario and prediction making.
●Guardian angels — Minds trained and dedicated to enhancing your mind, useless to anyone else.
●Mind with communication access to all known “facts.”
●Mind which retains all known “facts,” never erasing.
●Symbiont, half machine half animal mind.
●Cyborg, half human half machine mind.
●Q-mind, using quantum computing
●Vast mind employing faster-than-light communications

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H. Mind Taxonomies: Sloman’s classification
Aaron Sloman in “The Structure of the Space of Possible Minds” [Sloman 1984], using his virtual machine
model, proposes a division of the space of possible minds with respect to the following properties:
●‘Quantitative’ versus ‘Structural’
●‘Continuous’ versus ‘Discrete’
●Complexity of stored instructions
●‘Serial’ versus ‘Parallel’
●‘Distributed’ versus ‘Fundamentally parallel’
●‘Connected to external environment’ versus ‘Not connected’
●‘Moving’ versus ‘Stationary’
●‘Capable of modeling others’ versus ‘Not capable’
●‘Capable of logical inference’ versus ‘Not capable’
●‘Fixed’ versus ‘Re-programmable’
●‘Goal consistency’ versus ‘Goal selection’
●‘Meta-motives’ versus ‘Motives’
●‘Able to delay goals’ versus ‘Immediate goal following’
●‘Static plan’ versus ‘Dynamic plan’
●‘Self-aware’ versus ‘Not self-aware’
I. Distributed Cognition: Global Brain Design Considerations
Clément Vidal, a researcher and an assistant professor at the Free University of Brussels, shows how the
externalization of our local brain functions is leading to a planetary level intelligence, or global brain
[Vidal 2015]. He argues that this mechanism of externalizing cognitive functions is a fundamental driver
towards an ever smarter human-machine symbiosis. He discusses implications and applications of
externalizing various cognitive functions such as memory, computation, hearing, vision, brainstorming,
reasoning, navigation, emotions and actions. Moreover, he illustrates the scenario with a fictional story of a
day in year 2060. Vidal then takes a top-down approach, and argues that this progressive externalization
helps to better understand, foresee and facilitate the emergence of a globally distributed intelligence, best
conceptualized as a global brain. He also discusses possible symbioses between biology and machines, and
what would be the elementary elements composing the global brain.
1) Cognitive Technology:

Externalization of the local brain functions
Vidal describes how the externalization of cognition brings new levels of intelligence. The externalization
is achieved through cognitive technology tools for different types of senses. Dror and Harnad [Dror 2008]
of the University of Southampton stressed the importance of cognitive technologies in extending the limits
of the human sensation and performance: “Cognitive technology does, however, extend the scope and
power of cognition, exactly as sensory and motor technology extends the scope and power of the bodily
senses and movement. Just as we can see further with telescopes, move faster with cars, and do more with
laser microsurgery than we can do with just our unaided hands and heads, so we can think faster and
further, and do more, with language, books, calculators, computers, the web, algorithms, software agents,

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plus whatever is in the heads of other cognizers. Both sensorimotor technology and cognitive technology
extend our bodies’ and brains’ performance capacities as well as giving us the feeling of being able to do
more than just our bodies and brains alone can do. Sensorimotor and cognitive technology can thus
generate a perceptual change, rather like virtual reality (VR), making us feel a difference in our body
image and causal power (perhaps not unlike what the physical metamorphosis from caterpillar to butterfly
might feel like, as one sensed one’s newfound somatic capacity to fly).”
Vidal discusses implications and applications of externalizing various cognitive functions [Vidal 2015]:
● Memory: Externalization of memory has been carried on through writing, printing, and, most
recently, through hypertext and the internet. “Memory is implemented biologically with neural
networks. It became sharable with language, and has been radically extended and safeguarded
thanks to the invention of writing. This allows sharing of manuscripts, first locally, then more
broadly with the invention of printing. The third phase of the externalization is the distribution of
memory over the internet. The invention of hypertext, and later of the web is an improvement on the
invention of writing, and can be analyzed as a globally distributed, collective and dynamical
memory.”

[Vidal 2015]
●Computation: Tools for the externalization of computation and counting include stones and sticks in
old ages to state-of-the-art computing devices and methods in modern ages. “Combined with the
invention of writing, calculus took another leap. Human computation is severely limited by the
working memory capacity of the brain. This is why mathematicians extend their cognition with their
favorite tools: paper and pen. Later, the specification of the general purpose computer stemming
from the work of Church, Gödel, Kleene and Turing led to the general computing devices we use
daily. The extension of computation to the internet leads to distributed computation and grid
computing.”

[Vidal 2015]
●Hearing: Methods and devices for externalization of hearing (e.g. speech recognition algorithms,
audio recorders, and hearing aids) have been actively developed to aid and augment the human
hearing function. “The hearing function starts with the biological ear, is captured thanks to
microphones on physical supports such as tapes, compact discs or memory cards. Thanks to big
data and pattern recognition algorithms, automatic music identification services have emerged.
They are portable thanks to their integration to smartphones connected to the internet. Their
accuracy in recognizing songs is already quite impressive.”

[Vidal 2015]
●Vision: The visual function has been enormously expanded through modern devices (e.g. cameras,
microscopes, and telescopes) and methods from the fields of image processing, computer vision,
and visualization. “Vision is biologically implemented with the eye, but visual information can be
stored thanks to cameras, technologically extended to scales with microscopes and telescopes, and
extended from the visible to the whole of the electromagnetic spectrum. When vision is networked
from smartphones to crowdworkers, such a connection allows new applications. For example, it is
possible to get nearly real-time crowdsourced answers to visual questions, which is extremely
helpful for visually impaired persons.”

[Vidal 2015]
●Divergent Thinking: Divergent thinking is defined as producing a diverse assortment of appropriate
responses to an open-ended question or task in which the product is not completely determined by
the information. So, divergent thinking concentrates on generating a large number of alternative

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responses including original, unexpected, or unusual ideas. Thus, divergent thinking is associated
with creativity [Razumnikova 2013]. Divergent thinking can be externalized in several ways,
“...most of the time, we have a lot of ideas thanks to triggering lists or brainstorming sessions with
cognitive partners. And of course, we don't want to lose such ideas, so collection tools such as
notes, mind maps, or voice recordings are essential in the process. With the internet and the web, it
is possible to radically scale up this creative process. Indeed, we can now seek input from a large
number of people (e.g. with wikis or with social media websites). The difficulties shift, namely to the
aggregation and sorting of the results to make a coherent whole. There are a few collective
outlining or mind mapping softwares, but they are probably underused given the benefits they could
provide. Software agents could also help divergent thinking, for example by automatically
aggregating similar ideas, or even by systematically suggesting combinations of existing ideas.”
[Vidal 2015]
●Convergent Thinking (Reasoning): Convergent thinking involves finding only the single correct
answer, conventional to a well-defined problem. Many facts or ideas are examined while
convergent thinking for their logical validity or in which a set of rules is followed. Convergent
thinking focuses on reaching a problem solution through the recognition and expression of
pre-established criteria. Standard intelligence

tests are similarly believed to measure convergent
thinking [Razumnikova 2013]. Externalization of convergent thinking is quite crucial and can be
carried on in several novel ways, “Extending reasoning to the environment leads to the use of
logical diagrams, thus providing greater control over problem solving and argumentation. The
reasoning then becomes easier to criticize, revise, understand and discuss. Such techniques are
widely used in finance, distribution, project management, people management, strategy, sales and
marketing. Distributing reasoning on the internet is still in its infancy, but holds great promises for
the future of distributed governance, since it has the potential to lead to large-scale decision
making. Artificial software agents could help semi-automatic reasoning by helping users and
groups to systematically and critically question an argumentation. More sophisticated agents could
also extract reasonings from existing text, or even reason on their own (e.g. with logical
programming). Again, a mixed approach of computers suggesting inferences, and asking for
feedback from humans seems more practically useful than pure artificial agents whose reasoning
would be limited by the structured data it can gather. It could lead to a

real symbiosis

, where
algorithms would learn from the feedback of humans, and humans would benefit from logical
connections suggested by software agents that they might otherwise have missed.”

[Vidal 2015]
●Navigation: Navigation is the process or activity of localization (accurately ascertaining one's
position) and mapping (planning and following a route). This process is quite difficult without
external aids: “Helped with machines and a Global Positioning System using a voice synthesizer,
the task of navigation becomes incredibly simple. The connection to the internet can provide
additional real-time information, can allow users to correct maps when they find errors, and leads
to an increasing variety of location-based services and applications.”

[Vidal 2015]
●Emotions: The link between emotions and cognition is inextricable, and methods for the detection
and externalization of human motions have been recently emerging. In fact, the field of Affective
Computing (AC) aspires to narrow the communicative gap between the highly emotional human
and the emotionally challenged computer by developing computational systems that recognize and
respond to the affective states (e.g., moods and emotions) of the user [Calvo 2010], [Zeng 2009].
The basic tenet behind most AC systems is that “automatically recognizing and responding to a

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user’s affective states during interactions with a computer can enhance the quality of the
interaction, thereby making a computer interface more usable, enjoyable, and effective”

[Calvo
2010]. Emotion sensors can already perform facial and emotional recognition. Other biomedical
signals such as brain waves, heart rate, skin connectivity or even real-time blood analysis might be
used. The advancement of affective computing technologies will significantly improve the speed
and convenience of human interactions with the environment. Vidal gives an interesting example
[Vidal 2015], “A key element to make the human-machine symbiosis functional is to have high
bandwidth of interaction. For a smartphone user, taking a picture still takes the time to decide to
take the picture, to put one's hand in the pocket, to perform a few swipes, wait for an autofocus to
happen, and to click the shutter button. This is extremely quick compared to what photography was
in the 1820's, but extremely slow compared to what it could be. Imagine you are wearing
augmented reality glasses. You blink your right eye, done, your picture is ready to be shared – if
you wish to. Better, you don't even take the decision to take a picture. The computer you wear
monitors your emotions in real time, and take pictures or videos automatically as you live your life.
Later, you can consult this log, and filter it by the most surprising, enjoyable or disgusting
experiences you had.”
●Actions: Externalization of mechanical work virtually and physically can serve as another step
towards the realization of a global brain.
One of the important developments in this direction is the emergence of microwork which is a
series of small tasks that have been broken out of a larger project, and can be completed via the
Internet by any worker with computer and Internet access [Rossotto 2011]. Microwork aggregators
include Amazon Mechanical Turk, ShortTask, TxtEagle and Clickworker; who outsource
microwork to anonymous users and provide supplementary income to global virtual workers. Lilly
Irani [Irani 2015] outlines “three key values around which Amazon and complementary
crowdsourcing companies have focused their design efforts: accuracy, speed, and scalability.”
While microwork depends so far on “Humans-as-a-Service”, future microwork systems shall
employ artificial agents as AI technologies become more mature.
While the existing and emerging cognitive technologies support the quest for a global brain, it is still
unclear how the evolution of these technologies should be and which types and parts of these technologies
should be used to create a global brain. Figure 8 shows an argumentative map that summarizes the current
state of the knowledge, examples, desirable and undesirable effects of local brain and cognitive
technologies.
2) A day in year 2060
Vidal [Vidal 2015] imagines what a day in 2060 could look like through a speculative science-fictional
story. The story shows more concretely what the impact of the cognitive technologies could be and how the
general intelligence could evolve. Vidal explains why the story is plausible through hints and references to
existing and emerging technologies:
“You're walking on the street and meet a Japanese woman. She starts to speak her native language,
but your augmented reality lenses automatically translate and display what she says. Akemi explains that
her GPS doesn't function well and asks if she can connect to yours to find her way. You accept the request,
but your augmented reality vision also display: “try something”. What happened in the background of your

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extended self in order to suggest this? In a fraction of a second your sensors and artificial agents did the
following. They took a picture of Akemi, from which an image-search was launched, along with
face-recognition.
Figure 8. The current reality of the local brain and related cognitive technologies [Vidal 2015]
Several webpages of her public profiles were found. This information was integrated to create a profile,
summarizing her professional, and to a lesser extent, personal interests. Additional visual and olfactory
sensors on your wearable clothes did notice unusual pupil dilatation and pheromone concentration.
Intellectual and bodily data concluded – on both sides, since Akemi did of course do a similar search – that
this encounter was an excellent love match. You could have configured your digital agents to give a you
better tip than “try something”, but you chose a low advice specificity profile, to leave some of life's
spontaneity. So, you indeed try something, and invite her to join you and your friends for swimming with
dolphins this afternoon. You share time and GPS coordinates and you are thrilled that she accepts the
invitation.
You run back home, cross the street without even looking at cars. A car brakes violently. You are
surprised to see a driver in it, and shout: “poor and dangerous biological human, buy yourself a
self-driving car, your reflexes are too slow!” Your emotional reaction was monitored and the automatic
legal decision making actually gives you a one bitcoin fine, because you should not have had crossed so
quickly the street in the first place, and you should not have had insulted the human driver, which had a
negative emotional impact on him. Your augmented reality informs you sympathetically: “I understand that
you felt upset and need more security. This security indeed implies that people should switch to stronger
human-machine symbiosis, but can you please be more careful next time? This transition towards
human-machine symbiosis is still in progress. The driver felt embarrassed and miserable about this

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situation, which is one of the reason why you had to pay this fine. I don't advice to make an appeal, it will
only cost more money and given that the situation was recorded by 10 different nearby sources, there is few
ambiguity, so the judgment has a 99.9% confidence. The bloodstream of the driver has also been checked
and it was perfectly clean, whereas your adrenaline levels were unusually high.” You understand this but
still wonder why human-driving cars are still allowed to circulate. Probably a lobby of the old-fashioned
Association for Biological Human Rights.
When you arrive home, a self-driving car just brought fresh cartridges, automatically ordered by
your 3D food printer. As soon as you plug the nutrient cartridges in, your 3D printer cooks for you, based
on inputs from nanobots floating in your bloodstream, which monitor the nutrients you need most. Your 3D
printer is furthermore configured to follow your preferences, in this case, to follow a paleo diet because
you decided to be in line with evolution. The animal protein supply is a mix of artificially grown meat, fish,
worms, and insect proteins. The food quantity is also higher than usual, because your printer anticipates
your sport activity planned in your agenda. Indeed, you gave access to your agenda to your printer. The
recipe is a new creation, because you've configured your printer to never print two times the same meal.
Life is too short and the world's diversity of cooking too great to eat two times the same meal.
When you arrive at the harbor, safety procedures are quick and simple, just to give your stem-cell
box, which could be used by the first-aid-kit on the boat. The boat is small, and no oxygen bottles are taken
on board. Instead, the trainer takes a suitcase containing syringes. Just before going into the water, the
trainer gives a shot to all participants. What is in the shot? Mechanical artificial red cells, providing a
4-hour in-vivo Self-Contained Underwater Breathing Apparatus (SCUBA). You and your friends dive in the
water, play and communicate with dolphins, thanks to the dolphin speaker interfaced with your
augmented-reality diving mask.
Suddenly, the boat radar displays an alert on your mask: “Shark approaching at high speed; no
time to swim back to the boat. Fight is the only option”. But you use your biological brain and think that
there must be another way. You remember that dolphins can sometimes fight a shark. You turn to the
dolphins hastily, set your dolphin speaker to beam a help signal, along with the 3D shape of a shark you
quickly downloaded. Fortunately, the dolphins understand your message, they do thank you, but get scared
and swim away! The AI advice was wise. You feel frustrated that AI was once again smarter than you.
Out of sea mist, the shape of a shark is coming. Some last-minute information is displayed on how
to fight a shark to you and your friends. You start to read them, but too late, the shark has chosen to attack
you. You see the shark's jaw dramatically expanding and... nothing. You lose consciousness.
You wake up on the boat, fully recovered. Akemi is looking at you. You ask her: “What happened?”
She explains that your friends managed to scare the shark by fighting him from multiple sides on its gills,
and that he finally released you. You ask: “But how come was I not wounded?” Akemi: “You actually
almost died! Your nano health bots detected your right kidney and your liver were critically failing. The
message was transmitted to the first-aid kit on the boat, and the 3D organ printer started to differentiate
your stem cells and printed at fast speed two new organs. I contacted a japanese surgeon [who is an]
expert in organ transfers for an urgent tele-operation. I gave him distant access to the first-aid robotic
surgery apparatus, and he could work with the printed organs. I hope you don't mind we chose a human
surgeon, we are still not confident enough with the cheaper fully robotic AI surgery.” Your health
insurance reckons that the incident could not have been avoided, and financially covers the whole
operation. The file is already closed.
You ask: “What about the shark?” Akemi continues: “Since it drunk on your blood, it will be
infected by artificial viruses. I guess you feel resented, but you know that global eco-regulations forbid to
reprogram them at a distance to kill the shark. However, thanks to this artificial virus infection, the shark is
now trackable and should not create any further incident to any diver with an augmented-reality diving
mask.” As you put back your augmented reality lenses, you look at your information feed, and see that you

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have been thanked by diving, surfing and fishing associations for successfully tracking an additional shark.
On the way back to the coast, you skim some news and learn that a bomb has exploded at the
headquarters of the Association for Biological Human Rights. The police has found out that the bomb was
printed directly through the local 3D printer of the association. The cyber-attack left traces distributed
around the globe. Police said the identity of the hacker is uncertain, and the debate rages whether it was
triggered by a human or a coalition of artificial agents. At the end of the day, you realize that AI agents
have done much for you today, and are in awe and grateful to them and your friends. You owe them all
your life.”
Many of the technologies mentioned in the story above are already developed, currently under
development, or are expected to be developed in the near future:
- Augmented reality vision [Perry 2017], [Rehman 2017], [Syberfeldt 2017]
- 3D food printing [Jan Sol 2015], [Sun 2015]
- Biological organ printing [Murphy 2014], [Visconti 2010], [Mironov 2009]
- Automatic speech translation [Yun 2014], [Stuker 2012]
- Artificial legal reasoning [Wong 1996], [Akalu 2004], [Nathan 1993], [Goto 2016],
[Thammaboosadee 2008]
- Distributed and decentralized digital currency [Tschorsch 2016], [Courtland 2012]
- Nanorobots [Hayakawa 2014], [Hamdi 2014], [Jia 2014], [Liu 2012]
- Dolphin speaker [Mishima 2013], [Mishima 2011]
- Artificial viruses [Wimmer 2009], [van Rijn 2016], [Mastrobattista 2006]
3) Global Brain Emergence
Vidal [Vidal 2015] tackles next the question of how the externalized cognitive parts will coordinate to form
a globally distributed intelligence, or a global brain

. He asserts that this is indeed a tough question:
“A superorganism such as an ant colony or a human body consists of simple elements, ants or cells. By
analogy, what will the elementary parts which compose the global brain be? Will the elementary parts be
humans? Machines? Biocyborgs? A mix of the three? Or should we focus on more abstract properties,
which can be embedded in different substrates? Should the elementary parts be living systems, performing
nineteen different functions on matter, energy and information? How intelligent should the parts be for the
global brain to function? Is Artificial General Intelligence (AGI) a requirement, or could the different parts
be differentiated and locally relatively stupid?”
Vidal assumes that the parts of the global brain will be the results of integration between human and
machine parts [Vidal 2015], [Froese 2014]. The machine parts shall represent externalized cognitive
functions of the human beings: “a human-machine symbiosis is suitable, since humans and machines have
complementary strengths and weaknesses. As long as this dichotomy stays, the human-machine symbiosis
will be the way for continuing techno-sociological progress.”

[Vidal 2015]
Figure 9 shows an argumentative map on the future of the global brain. The human-machine symbiosis path
is the most likely one with potential contributions from other biological cyborgs in the construction of the
global brain.

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Figure 9. The future reality of the global brain [Vidal 2015]
In an interview with Clément Vidal [Vidal 2015], Ted Goertzel proposes a map for the development of the
global brain:
“Although it is an approximation and a simplification, we can think of the global brain’s cognitive
development in three phases: reptilian, limbical and neocortical.
The reptilian brain reacts and controls primitive bodily functions such as hunger, fear, fight or flight. It
connects the nervous system to limbs and triggers instinctual behaviors. At the scale of the global brain,
this could correspond to a

planetary nervous system

. The main goal here is to deal with global risks, such
as tsunamis, epidemics, or volcanic eruptions, thanks to distributed sensors over the globe, connected to
effectors, which can be humans or machines.

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The limbic system is involved in long-term memory, emotion and motivation. At a global scale, these
functions are performed by social networks, which weave a rather emotionally-driven network, and can
lead to spontaneous eruptions, such as the Arab Spring revolutions. Wikipedia would also be part of this
limbic development, acting as a dynamic and collective memory.
The neocortex is evolutionarily the most recent part of the brain, essential to human language, abstract
thinking, imagination or awareness. At the global brain scale, it corresponds to two challenges: building
global simulations, and fostering collective reasoning and decision-making. With the help of computer
simulations fed with big data, we start to better understand how to tackle global and complex issues. We
thus make wiser decisions, and this constitutes a kind of global imagination or dreaming faculty. The
process is indeed similar to the faculty of our minds to play possible scenarios, and to dreaming, when it
helps to consolidate the most important memories. I mentioned the importance of collective reasoning
through its externalization and distribution. If we use data both from distributed sensors and computer
simulations, and add effective methods for collective reasoning and decision making, it is certain that
smarter decisions will be made on a global scale.”
III.2 Memory and Experience Sharing: Opposition and Skepticism
In general, there are some issues and worries about the idea of having a global brain. First of all, there is
the fear that the global brain isn’t more than a hive mind where all members think and behave the same.
But Heylighen et al. [Heylighen 2002b] argue that this is not the case in the global brain as each member
should have its own personality, abilities and experiences in order to maximize its chance for survival.
Another issue is that members of the global brain may lose their freedom. Indeed, to make the global brain
effective and facilitate the communication and cooperation between its members, there should be a number
of rules and means of communication that should be agreed on by all members. However, this shouldn’t
decrease the freedom of people. Moreover, these rules should be built on the basis of democracy where no
one or group has more power or control than the others. This should prevent possible evil forces from
taking over the global brain as feared by some people [Goertzel 2001].
IV. ARTIFICIAL CONSCIOUSNESS: COPYING THE HUMAN CONSCIOUSNESS AND MEMORY TO THE
VIRTERNITY SPACE
Researchers, philosophers, and futurists have been intrigued by several questions [Prasad 2010] about
consciousness: What is consciousness? Is it a meta-physical or physical phenomenon? Is it possible to
capture the essential aspects of consciousness and upload them to the brains of robots or artificial human
clones? This last question is particularly challenging and it gave rise to the field of Artificial
Consciousness which seeks to “define that which would have to be synthesized were consciousness to be
found in an engineered artifact” [Aleksander 1995], [Buttazzo 2001], [Allen 2016], [Koch 2008], [Lehrer
2017]. Artificial consciousness models seek engineering implementation of the essential features of
consciousness, self-awareness, and mindfulness.
IV.1 Artificial Consciousness: Supporting Theories and Projects
Many scientists have been promoting the scheme of mind uploading where all important human traits, such

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as consciousness, emotions, and self-awareness, are faithfully transferred to robotic or virtual agents
equipped with sophisticated artificial brain circuitry [McCal 2011]. We summarize here some quotations
and efforts related to the realization of this goal.
A. Mind Uploading Efforts by Randal Koene
Randal Koene, a Dutch neuroscientist and neuro-engineer, is one of the most prominent supporters of mind
uploading [Piore 2014]. Koene justifies his deep interest:
“I got into this because I was interested in exploring not just the world, but eventually the universe. Our
current substrates, our biological bodies, have been selected to live in a particular slot in space and time.
But if we could get beyond that, we could tackle things we can’t currently even contemplate.”
To promote the goals of whole-brain emulation (WBE), mind uploading and substrate-independent minds
(SIM), Koene founded Carbon Copies Foundation <https://www.carboncopies.org/>, and
MindUploading.org.
Moreover, Koene co-organizes the Global Future 2045 International Congress <http://gf2045.com/>. This
congress addresses topics related to the realization of virtual and robotic eternal life such as:
“Project Avatar, Android robotics, Anthropomorphic telepresence, Neuroscience, Mind theory,
Neuroengineering, Brain-Computer Interfaces, Neuroprosthetics, Neurotransplantation, Long-range
forecasting, Future evolution strategy, Evolutionary transhumanism, Ethics, Bionic prostheses, Cybernetic
life-extension, Mid-century Singularity, Neo-humanity, Meta-intelligence, Cybernetic immortality,
Consciousness, Spiritual development, Science and Spirituality.”
B. Terasem Mind Uploading Experiment
Martine Rothblatt, an American lawyer, writer, and entrepreneur, founded Terasem Movement Inc. “to
educate the public on the practicality and necessity of greatly extending human life, consistent with
diversity and unity, via geoethical nanotechnology and personal cyberconsciousness, concentrating in
particular on facilitating revivals from biostasis.”
In 2012, Rothblatt wrote an article [Rothblatt 2012] in the International Journal of Machine Consciousness
where she proposed the design for a mind uploading experiment. The Terasem Null Hypothesis essentially
captures the idea that “a panel of psychologists will not believe a software-based mind is a continuation of,
or an analog of, a brain-based mind.”

In Rothblatt’s words, the Terasem Null Hypothesis is:
“Within a span of several decades worth of information technology growth at the Moore's Law or Kurzweil
Rate, databases populated via the open public participation websites LifeNaut.com and/or CyBeRev.org
with digital samples of participants' mannerisms, personality, recollections, feelings, beliefs, attitudes and
values (hereinafter referred to as "mind files"), and used as reference databases by software designed to
replicate and customize the functional characteristics of human minds (hereinafter referred to as
"mindware"), will not give rise to software-based minds that are recognized by a panel of psychologists as
equivalent to the matching brain-based minds of the participants, as determined by interviews with the

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software-based minds over a period of a year, and comparisons of impressions from such interviews with
the mannerisms, personality, recollections, feelings, beliefs, attitudes and values reflected in the matched
mind files of the original brain-based participants.”
The above verbose hypotheses were recast succinctly into Terasem Hypotheses:
(1) a conscious analog of a person may be created by combining sufficiently detailed data about the person
(a “mindfile”) using future consciousness software (“mindware”), and
(2) that such a conscious analog can be downloaded into a biological or nanotechnological body to provide
life experiences comparable to those of a typically birthed human.
Rothblatt calls the events in these hypotheses Transferred Consciousness (TC). To facilitate the collection
of “mindfile” data, two websites were created:
1. Cybernetic Beingness Revival (CyBeRev) project <https://www.cyberev.org/>. According to the
project website, “The CyBeRev project is part of a multi-decade experiment to test the
comparability of single person human consciousness with a digital representation of the same
person created by personality software that draws upon a database comprised of the original
person's digitized interactions, as may be assessed by expert psychological review.”
2. Lifenaut <https://www.lifenaut.com/>. This website offers to store clients’ genetic material, ready
for a transhuman future where “technology may be able to grow you a new body via ectogenesis
and your mindfile may be able to be downloaded into it, enabling you to live on indefinitely.”
[Zolfagharifard 2015].
Those two websites are used to collect the necessary ‘mindfile’ information that is necessary for testing the
Terasem Hypotheses. Table 1 shows the mind files that may be created using a wide variety of
autobiographical, human experience sampling tools based on CyBeRev.org and Lifenaut.com.
Another emergent service similar to CyBeRev.org and Lifenaut.com is Eterni.me which offers the
prospect of an eternal, albeit artificially generated, presence: Eterni.me collects almost everything that you
create during your lifetime, and processes this huge amount of information using complex artificial
intelligence algorithms. Then it generates a virtual YOU, an avatar that emulates your personality and can
interact with, and offer information and advice to your family and friends after you pass away. It’s like a
Skype chat from the past [Kuchler 2016].
The Terasem Movement Foundation also developed the social robot BINA48 (Breakthrough Intelligence
via Neural Architecture 48 where 48 exaflops is the speed at which the human brain probably computes).
This robot is an early demonstration of the potential for using "mindfiles" for transferring human
consciousness information to new forms [Stein 2012], [Andreae 2011].

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Table 1. Mind file tools for personality capture, storage of digital reflections of consciousness at
CyBeRev.org and Lifenaut.com public websites [Rothblatt 2012].
C. Mind Uploading is Crucial for Interstellar Travel and Preservation of Humanity
Dr. Keith B. Wiley, a computer scientist and a writer, wrote interesting arguments to emphasize the
criticality of computerized intelligence (CI) and mind uploading (MU) [Wiley 2015]. Figure 10 shows a
visual illustration of the line of reasoning starting from consciousness as the only phenomena that supports
metaphysical purpose. Wiley goes through intermediate arguments to conclude that storing consciousness
in digital form is the only way for interstellar travel and hence to preserve humanity. In Wiley’s words,
“Developing the technologies of CI (especially MU) is nearly the most important goal of our civilization.
Notably, this line of reasoning makes no mention of the more common and unrelated reason for pursuing
MU, that of extending individual lifespans. While that may be a worthy goal as well, it is a personal goal,
not a grand universal goal. I am concerned with insuring that the universe, reality, and existence preserve
fundamental purpose. This goal is met by maintaining consciousness in the form of conscious beings who

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escape extinction and maximize their conscious experiences. I am further concerned with insuring that
humanity retains its share of that purpose by preserving our species against extinction. The alternatives,
that the universe and existence could lose ultimate purpose at a needlessly early cosmic hour, or that
humanity might fade into obscurity, are too horrible to bare and cannot be allowed to transpire.”
Figure 10. Arguments for the criticality of mind uploading [Wiley 2015]
D. Consciousness Apps and Devices Promote Human Morality
James J. Hughes, the Executive Director of the Institute for Ethics and Emerging Technologies, and a
bioethicist and sociologist at Trinity College in Hartford, Connecticut, argues “How Conscience Apps and
Caring Computers will Illuminate and Strengthen Human Morality” [Hughes 2014]. In particular, Hughes
explains how apps and devices can help people strengthen core elements of self-control, caring, moral
cognition, mindfulness, and wisdom or intelligence.
As an example for self-control, “the psychologist Dan Ariely, for instance, designed the Conscience+ app,
which lists five arguments to resist temptation and five arguments to give in to temptation, in dozens of
situations, such as eating dessert, buying a new gadget, lying, and exercising.”

Also, “MoralCompass
provides a flowchart of moral decision-making questions, and SeeSaw allows users to query other users

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about which action they should take in a situation.”
As people shift into digital lives through virtual eternity frameworks, such morality apps can be seamlessly
integrated to improve the overall human morality.
E. Practical Implications of Mind Uploading
Joe Strout enumerate several positive impacts of uploading on our daily lives [Strout 2014]:
“...people will be able to alter their shape and appearance, travel at the speed of light, live comfortably
throughout the solar system, and even dwell in artificial realities of their own design. It’s important to note,
however, that these differences are fundamentally superficial. We will laugh, cry, love, despair, strive for
goals, and sometimes fall short. We will care for our friends and family, seek reassurance when in doubt,
make music, work hard and take breaks, as people have done for thousands of years. In the end, despite all
the changes to our bodies, environments, and capabilities – we will still be human.”
F. The Enhanced Carnality of Post-Biological Life
Dr. Max More, philosopher, president and CEO of the Alcor Life Extension Foundation, sees enhanced
carnality, body image and body care in post-biological life [More 2014]:
“...it should be pointed out that post-biological beings are not truly disembodied. All thinking beings rely
ultimately on physical processes, even if those processes are distributed across numerous computational
devices. Even the most radical uploading scenarios do not require us to abandon bodies and senses. We
can have multiple prosthetic bodies, as well as any number of virtual bodies. Whatever senses are built into
the bodies we choose to inhabit, we will also be able to expand our perceptual range enormously by
connecting to external sensors. If we can accomplish the transition to post-biological being, the result will
not be mechanization and restriction; it will be greater richness of being and existential freedom.”
G. Qualia Surfing - Transfer between Different Substrates for Different Experiences
Dr. Richard Loosemore, a lecturer in the Department of Mathematical and Physical Sciences at Wells
College, explores the post-uploading scenario where one may want to switch between different substrates
to try different experiences [Loosemore 2014]:
“What will happen to human civilization when we can go qualia surfing – collecting new experiences by
transferring our consciousness back and forth between different substrates on a whim? As you wake up on
a typical morning, your choice of activities for the day might include: becoming a tiger and going off to the
jungle for some animal sex; changing into a body that can swim in the atmosphere of Jupiter; floating
naked in interplanetary space in a body adapted for hard vacuum, while you stargaze through telescopic
eyes that can see beyond the visible spectrum; swimming in a liquid-metal pool at noon on Mercury;
porting yourself into a six-inch-tall body so you can go on a Land of the Giants expedition; or perhaps
visiting a specially designed city where you check your memories at the door and live in a replica of, say,
Restoration London or Classical Athens, all the while oblivious to the fact that you are in a simulation.”
Loosemore coins the term “qualia surfing” to express and analyze this idea:

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“...at some point in the future people will freely transfer their consciousness into different substrates
(where a substrate is either a biological brain or a computer), or modify their existing substrate in various
ways, purely because they want to experience the sensations, feelings, points of view, or knowledge that
come with being another kind of living creature. I have labeled this “qualia surfing” because the term
qualia refers to the philosophically inexplicable core properties of our sensations that can be known only
to the sentient creature that experiences them – like the redness of the color red that is impossible to convey
to someone who is blind. In today’s world we seek novelty by finding new patterns or combinations of our
existing qualia – we go on vacation to new places and cultures so we can feel a new mix of colors, tastes,
sounds, and smells – but in the future we could modify the basic qualia that we experience, so we can know
what it is like to perceive the hue of ultraviolet light, or hear what a whale sounds like at infrasound
frequencies, or be inside a centaur’s body.”
H. Why I Want to be a Posthuman When I Grow Up?
In his essay “Why I Want to be a Posthuman When I Grow Up,” Dr. Nick Bostrom, Director of Future of
Humanity Institute at Oxford University, [Bostrom 2013] promotes extreme human enhancements as they
could result in “posthuman” modes of being. Bostrom defines a posthuman

as a being that has at least one
posthuman capacity

. By a posthuman capacity, he means a general central capacity greatly exceeding the
maximum attainable by any by any current human being. Some of these capacities are those of
●lifespan (e.g. capacities to remain fully healthy, active, and productive, both mentally and
physically),
●cognition (e.g. intellectual capacities, such as memory, deductive and analogical reasoning, and
attention, as well as capacities to understand and appreciate music, humor, eroticism, narration,
spirituality, mathematics), and
●emotion (e.g. capacities to enjoy life and to respond with appropriate affect to life situations).
Bostrom argues that some possible posthuman modes of being would be very good, and that it could be
very good for us to become posthuman.
Moreover, Bostrom lists and refutes some of the most common objections to posthumanism:
Objection #1: One might think that it would be bad for a person to be the only posthuman being
since a solitary posthuman would not have any equals to interact with.
Reply: It is not necessary that there be only one posthuman. The postulated posthuman reference society is
one that is adapted to its posthuman inhabitants in manners similar to the way current human society is
adapted to its human inhabitants. Bostrom also assumes that this reference society would offer many
affordances and opportunities to its posthuman inhabitants broadly analogous to those which
contemporary society offers humans. This diminishes the probability of a solitary posthuman.
Objection #2: Challenges that seemed interesting to the person while she was still human might become
trivial and therefore uninteresting to her when she acquires posthuman capacities. This could deprive
posthumans of the good of meaningful achievements.
Reply: The ability to appreciate what is more complex or subtle should not necessarily make it impossible
to appreciate simpler things. Somebody who has learnt to appreciate the music of the Austrian composer

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Schoenberg may still delight in simple folk songs, even bird songs. A fan of the French artist Cézanne may
still enjoy watching a sunrise. Even if it were impossible for posthuman beings to appreciate some simple
things, they could compensate by creating new cultural riches.
Objection #3: A sense of vulnerability, dependence, and limitedness can sometimes add to the
value of a life or help a human being grow as a person, especially along moral or spiritual
dimensions.
Reply: A posthuman could be vulnerable, dependent, and limited. A posthuman could also be able to grow
as a person in moral and spiritual dimensions without those extrinsic spurs that are sometimes necessary to
affect such growth in humans.
Objection #4: A capacity obtained through a technological shortcut would not have the same value
as one obtained through self-discipline and sacrifice.
Reply: Bostrom argues that the possession of posthuman capacities could be extremely valuable
even where the capacities are effortlessly obtained. This is still consistent with the fact that
achieving a capacity through a great expenditure of blood, sweat, and tears would further
increase its value. It is unlikely that we could in practice become posthuman other than via
recourse to advanced technology.
Objection #5: Posthuman talent sets the stage for posthuman failure. Having great potential might
make for a great life if the potential is realized and put to some worthwhile use, but it could
equally make for a tragic life if the potential is wasted. It is better to live well with modest capacities
than to live poorly with outstanding capacities.
Reply: We do not lament that a human is born talented on grounds that it is possible that she will
waste her talent. It is not clear why posthuman capacity would be any more likely to be wasted
than human capacity. Bostrom stipulates that “the posthuman reference society would offer
affordances and opportunities to its posthuman inhabitants broadly analogous to those that contemporary
society offers humans. If posthumans are more prone to waste their potential, it must
therefore be for internal, psychological reasons. But posthumans need not be any worse than
humans in regard to their readiness to make the most of their lives.”
I. Life Expansion Media
Dr. Natasha Vita-More is a leading expert on human enhancement and emerging and speculative
technologies. She is also Professor at the University of Advancing Technology and the Chairwoman of the
of the Board of Directors of Humanity+, an international organization which advocates the ethical use of
emerging technologies to enhance human capacities. Vita-More [Vita-More 2013] investigates life
expansion means for “increasing the length of time a person is alive and diversifying the matter in which a
person exists.”

On the one hand, for the core elements of life, time, and matter, life expansion addresses the
issues of “how to regenerate biological cells, extend personal identity, and preserve the brain, whether
through cryonics, connectomics, or computations.”

On the other hand, for the media, life expansion
becomes “an issue of the semi-biological and non-biological substrates we might exist within (whether
virtual, synthetic, and/or computational), the potential of a connective mind, what we might look like and
what form we might take, and how to sustain our human species as a whole.”

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Vita-More argues that life expansion is important for the prolongation of life in multiple instances, “For
life expansion, the idea is to stay alive. Life in and of itself is necessary, to be sure, but it is you – your
personhood – the “now” of being alive, and the continuation of the instances of “living”, that denotes life.
Thus, any and all instances of life could be experienced in a posthuman virtual, synthetic, and/or
computational matter.”
In an interview at the London Futurist Forum 2014 [Przegalinska 2014], Vita-More described Primo
Posthuman

which is both a theoretical and practical whole body prosthetic, whose prototype was developed
to answer problems of cellular breakdown, disease and the finality of death. Figure 11 shows some of the
engineering features of the Primo Posthuman

prototype.
Figure 11. Primo Posthuman: A whole body prosthetic for life expansion [Przegalinska 2014]
J. Transavatars: Enhancement of Human Abilities in Virtual Worlds
Dr. William Bainbridge, an American sociologist who served as the Director, Human-Centered Computing,
at the National Science Foundation, suggests that enhancements of human abilities can be accomplished in
several ways without necessarily modifying the person’s biological body [Bainbridge 2013a]. One way is
to use avatars in virtual worlds and, in future, we are likely to have the option of teleoperation of personal
robots or other completely new alternatives.
Enhancement means increasing the effectiveness of a person in taking action, but avatars show that it can
also mean “an altered form of consciousness that expands opportunities for experiences, and escape from
the conventional system of moral constraints.”

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Bainbridge reviews several prominent examples of massively multiplayer online games (MMOs) (e.g.
World of Warcraft

, Star Wars Galaxies

, and EverQuest II

) and virtual worlds (e.g. Second Life

). Bainbridge
highlights five important ways in which these virtual environments can bring transhumanist visions close to
reality:
1. Subjectivity. The experience of virtually transcending human ordinary limitations “feels real” in
some positive sense (e.g. giving pleasure, inspiration, suspense, etc.).
2. Consequentiality. Through avatars, one’s actions in the virtual world have unusual consequences outside
it, whether by influencing other people, in terms of economic resources that can be transferred across the
reality boundary, or in terms of ideas and information that can be applied in other contexts.
3. Prototype. Whatever significance gameworlds or virtual environments may have today, they are early
steps toward much more significant future developments, for example technology in which a person’s
avatar is a powerful robot in the material world, rather than a mere image on a computer screen.
4. Education. Through operating an avatar in one of the more sophisticated current gameworlds or virtual
environments, human beings gain valuable skills that enhance their abilities in the material world, for
example in learning how to use geographic information systems, real-time teamwork groupware systems,
graphic computer programming languages, and inventory information systems.
5. Transference. Aspects of a person’s identity are offloaded onto multiple avatars, including
semi-autonomous agents possessing a measure of artificial intelligence, which can function as the same
person simultaneously, when such a person is not currently logged into a computer, and even after the
person’s death.
Bainbridge sees the fifth of these as “clearly the most radical, yet the first syllable of transference suggests
some connection to transhumanism, and to transcendence, thus evoking the word transavatar.”
K. Multiplex Personality: Using Multiple Avatars for One Individual
Part of the human condition has been the equation: one body=one person

(except in rare pathological cases
such as multiple personality neurosis or split personality). Now we can already see that one individual may
have many different avatars, which is a step along the way to possibly becoming a multiplex or protean
personality

.
Dr. William Bainbridge [Bainbridge 2013a] outlines several benefits of having multiple avatars in
gameworlds. These benefits are quite important and valid for living in virtual eternity environments:
“1. Division of labor.

Two avatars of different classes have a greater range of abilities than either
one of them, so players often have more than one in order to perform different practical functions at
different times. Among other uses, this gives a player the opportunity to join temporary teams that need
different specialties. For example, a player may have two avatars, one a tank and the other a healer,
logging into one or the other depending upon the momentary needs of a team that is in the process of
assembling. Even without other players, division of labor can be an important motivation for having two

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avatars.
2. Diversity of experience.

Avatars of different races often enter the virtual world in different geographic
regions, experiencing a different set of initial conditions and completing different missions. Different
classes experience even the same quest and territory in a different way. Game designers encourage this
diversity of experiences, because they want players to persist in subscribing to the gameworld, effectively
combining many games into one to accomplish this commercial goal. Very popular games like World of
Warcraft run many instances of the game simultaneously, on different servers, which may be fundamentally
different in their rules, as well as being inhabited by different players who give each a distinct quality.
3. Multiple affiliations.

Most gameworlds incorporate sophisticated groupware systems not only to allow
temporary teams to cooperate, but also to support long-term guilds or similar groups, many of which
survive for years.
4. Achievement of perfection.

It is said that practice makes perfect, but online gameworlds have a second
way through which players may develop superior avatars, namely using resources obtained elsewhere to
give a low-level avatar the highest-quality gear, which usually consists of armor, weapons, and
consumable resources like magic potions. In most gameworlds, high-level avatars experience much higher
incomes in terms of virtual resources, and are permitted to transfer this wealth to lower-level avatars.
5. Trial and error.

Most gameworlds set narrow limits on how much an avatar can be changed after it has
been created. [...] Thus, after creating an avatar and running it for a few hours, a player may decide it has
the wrong qualities, and start a new one from scratch with a better understanding of what decisions to
make. However, in many gameworlds there is no particular reason to erase the earlier character. Some
large fraction of the low-level avatars in such gameworlds are really failed experiments, but they do
continue to exist.
6. Multiboxing.

A few rare players are so avid and so skilled that they use two computers and two
subscription accounts to run two avatars simultaneously. I have done this multiboxing myself in both World
of Warcraft and Second Life, partly to study closely the technical details of how avatars interact with each
other, but also to be able to collect more data during important events.”
These features of gameworlds would make the realization of virtual eternity worlds more efficient, reliable,
and fault tolerant.
L. Posthumous Avatars or Ancestor Veneration Avatars
Dr. William Bainbridge [Bainbridge 2013b, Bainbridge 2013c] also considers how users can build
posthumous avatars

in virtual worlds and develop unique relationships with their human users,
“It is possible at the present time to create virtual representations of deceased loved ones, and inhabit them
as a way of expressing reverence and of dealing with one’s own feelings of loss, as demonstrated by [our]
study in which 18 Ancestor Veneration Avatars (AVAs) were created. Most obviously, this can be done in
massively multiplayer online role-playing games and comparable non-game virtual worlds. The identity of
any individual person contains fragments of other people, most especially members of one’s family. In
addition, people play a variety of roles, adopting identities temporarily that are more or less distinct from

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each other. Furthermore, a number of social scientists and commentators have suggested that individuals
have become protean or multiplex, as rapid social change, multiculturalism, and the division of labor have
eroded the functionality of unified identities. Finally, secularization has undercut traditional religious ways
of managing feelings toward deceased relatives. A remarkable deduction from these observations is that
many people should consider playing the role of a deceased loved one through an avatar in an online
gameworld, as a form of emotionally satisfying ancestor veneration.”
In particular, Bainbridge created several ancestor veneration avatars (AVA) for deceased members of his
family [Bainbridge 2013a],
“Reflecting on my own life and family, I have created virtual representations of 17 deceased members of
my family in a research project on ancestor veneration avatars (AVAs). One creates an avatar somehow
related to the deceased relative, and then has that relative’s real personality in mind while operating the
avatar, as I did for over 700 hours with Maxrohn, the posthumous avatar representing my deceased uncle
Max Rohn.”
IV.2 Artificial Consciousness: Opposition and Skepticism
Several scientists have justifiable doubts about the limitations of how far we can go with creating artificial
agents with digital brains that possess the full range of human cognitive abilities including self-awareness,
sentience, and consciousness.
A. Could human-like artificial consciousness be created or manifested in AI agents?
Bobby Azarian, a cognitive neuroscientist at George Mason University and a science writer at multiple
media outlets, argues that such agents are considered to be strong

AI agents while all of the agents
developed so far are essentially weak

AI agents, i.e., non-sentient machines that have no conscious states,
no mind, and no subjective awareness [Azarian 2016]. Dr. Azarian points to the “Hard Problem of
Consciousness”, namely, how physical phenomena, such as biochemical and electrical processes, create
sensation and unified experience.
Other neuroscientists, such as Sam Harris, admit that the mere definition and realization of consciousness is
challenging [Harris 2011] and even the decision whether a machine is conscious or not is an “open
question.”
B. No uploading with mind transfer and identity preservation is possible
Patrick Hopkins, Professor of Philosophy at Millsaps College, argues that mind uploading will not succeed
because no transfer of mind occurs and the personal identity isn’t preserved [Hopkins 2012]:
“...while the technology may be feasible, uploading will not succeed because it in fact does not "transfer" a
mind at all and will not preserve personal identity. Transhumanist hopes for such transfer ironically rely
on treating the mind dualistically and inconsistently with materialism as the functional equivalent of a soul,
as is evidenced by a careful examination of the language used to describe and defend uploading. In this
sense, transhumanist thought unwittingly contains remnants of dualistic and religious concepts.

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They say - and this is the whole point of uploading, the whole point of its connection to immortality and
transcendence - that a specific, intact mind can be "transferred" (moved) from one embodiment to another.
If one wants to say that, in spite of the language used, we should not take metaphors of transfer and
movement too seriously, we are still left with the critical, literal, claim that the mind in the computer is the
same mind as the mind in the original brain. If that is not the case, then uploading is pointless in terms of
immortality or enhancement or transcendence. If the specific mind that is "in" or produced by a brain is not
the very same specific mind that is "in" or produced by the computer, then immortality has not been
achieved, a person's life has not been saved, and uploading fails to satisfy its original promise.
In fact, the very hope for immortality actually requires these spatial and motion properties of minds not to
be metaphors at all, but to be literally true.
The real question in uploading is whether uploading procedures maintain the identity of the specific mind
throughout the process. The spatial and motion language of transference assumes it does, but at the cost of
treating minds like ghosts.”
C. Is there a way to get our mind out of our brain? Uploading can be destructive with no
guaranteed outcomes.
Furthermore, Hopkins [Hopkins 2012] summarizes similar skepticism by Hans Moravec [Moravec 1988]
who asks the question “Is there a way to get our mind out of our brain?"
Moravec’s answer describes “a potential process in which a robot brain surgeon microscopically scans the
layers of your brain, constructs a 3-D chemical map, writes a program modeling the neural tissue
behavior, and then installs and activates the program in a computer. Checking the functionality of the
program by allowing you to switch over to the simulation periodically, you are able to fine-tune the
program so that it matches what your original neural tissue could do in terms of movement, sensation and
cognition. As more and more of the simulation is tested, the brain cells originally responsible for those
activities are removed. Eventually your entire brain is destroyed and your body dies, but your
consciousness is described as having shifted perspective to the computer. This kind of process is sometimes
referred to as "destructive uploading" since the brain is destroyed as the simulation takes over.”
Assuming that the technology is feasible and would perform as described. The question here is what is
accomplished in this procedure? This is what Moravec thinks is accomplished:
- “Though you have not lost consciousness, or even your train of thought, your mind has been
removed from the brain and transferred to a machine
- Ultimately your brain would die and your mind would find itself entirely in the computer
- You may choose to move your mind from one computer to another that is more technically
advanced.
- The program can also be copied to a future equivalent of magnetic tape. Then, if the machine you
inhabit is fatally clobbered, the tape can be read into a blank computer.
- As a computer program, your mind can travel over information channels”
The objections about transferring mind, consciousness, and self-awareness are indeed significant and they
need to be addressed seriously from technological, philosophical, and ethical points of view.


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V. CONCLUSION
This paper covered the technical advancements, as well as contrasting opinions about three futuristic
endeavors regarding the capture and transfer of human memories and emotions into virtual eternal worlds.
The first endeavor focuses on creating digital brains that are equivalent functionally and structurally to
human brains. The second endeavor seeks to scale up individual digital brains into global brains that share
knowledge, thoughts, and reasoning among large populations. The third endeavor is uploading or
transferring individual human mind, thoughts, and consciousness into artificial robotic or virtual
counterparts. While huge advancements have been made and several assisting technologies have been
emerging for the last few decades, several technical, social, and ethical challenges still need to be addressed
before the goal of eternal life in virtual worlds can be attained.
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ABOUT THE AUTHOR
Muhammad A. Rushdi was born in Urbana, Illinois. He received B.Sc. (2001) and M.Sc. (2005) degrees
in Biomedical and Systems Engineering and a B.Sc. degree in Mathematics (2003) from Cairo University,
Giza, Egypt. He received M.Sc. (2012) and Ph.D. (2013) degrees in Computer and Information Science and
Engineering from the University of Florida at Gainesville, FL, USA. He is currently an assistant professor
at the Department of Biomedical Engineering and Systems at Cairo University, Giza, Egypt. He has been
teaching courses on artificial intelligence, robotics, biomedical signal processing, measurements and
instrumentation, optimization, algorithms, and numerical methods. His research interests include artificial
intelligence, machine learning, image processing, computer vision, computational modeling and applied
mathematics. He received research and development support from EACEA, ITIDA, Flat6Labs, and French
Tech Ticket.