 Informatica 41 (2017 ) 401–417 401

Artificial Intelligence in Life Extension: from Deep Learning to
Superintelligence
Mikhail  Batin and Alexey Turchin
Science for Life Extension Foundation, Prospect Mira 124 -15, Moscow, Russia
E-mail: alexeiturchin@gmail.com, http://scienceagainstaging.com/

Sergey Markov
ActiveBusinessCollection, Russia, Moscow, d.19 ul. Vavilova, Moscow 117997, Russia
E-mail: sergei.markoff@gmail.com, https://activebc.ru/

Alisa Zhila
IBM Watson, IBM Co rporation, 1 New Orchard Road, Armonk, NY 10504 -1722, USA
E-mail: alisa.zhila@gmail.com, https://www.ibm.com/watson/

David Denkenberger
Global Catastrophic Risk Institute; Tennessee State University
Alliance to Feed the Earth in Disasters; 3500 John A Mer ritt Blvd, Nashville, TN 37209, USA
E-mail: david.denkenberger@gmail.com, http://allfed.info/

Keywords:  artificial intelligence, life extension, aging, geroprotectors, biomarkers, machine learning
Received:  August 31 , 2017
In this paper we focus on the most efficacious AI applications for life extension and anti -aging at three
expected stages of AI development: narrow AI, AGI and superintelligence. First, we overview the
existing research and commercial work performed by a select number of startups and academic
projects. We find that at the current stage of “narrow” AI, the most promising areas for life extension
are geroprotector -combination discovery, detection of aging  biomarkers, and personalized anti -aging
therapy. These advances could help currently living people reach longevity escape velocity and survive
until more advanced AI appears. When AI comes close to human level, the main contribution to life
extension will  come from AI integration with humans through brain -computer interfaces, integrated AI
assistants capable of autonomously diagnosing and treating health issues, and cyber systems embedded
into human bodies. Lastly, we speculate about the more remote future , when AI reaches the level of
superintelligence and such life -extension methods as uploading human minds and creating
nanotechnological bodies may become possible, thus lowering the probability of human death close to
zero. We suggest that medical AI base d superintelligence could be safer than, say, military AI, as it may
help humans to evolve into part of the future superintelligence via brain augmentation, uploading, and a
network of self -improving humans. Medical AI’s value system is focused on human be nefit.
Povzetek: Prispevek opisuje najbolj učinkovite aplikacije umetne inteligence za podaljšanje življenjske
in delovne dobe od klasičnega strojnega učenja do superinteligence.
1 Introduction
The 2010s have shown a rapidly growing interest in
Artificial Intelligence (AI) technologies [63]. In recent
years, AI has appeared in top scientific ne ws sources, in
stories that have demonstrated that AI is “smarter” than
humans when it comes to playing a number of
boardgames [89] and word games [61], thus revealing
that AI is approaching a revolutionary point in its
development.
Investments in AI -related projects have increased
dramatically in the last few years. Global AI startup
financing reached US$5 billion in 2016 [76]. The current
market of AI in medicine is estimated at US$1.1 billion
and is expected to grow to US$9.1 billion in the next
decade [118] . Major IT companies including Google,
Facebook, IBM, Intel, and Microsoft nearly
simultaneously established biomedical subdivisions because their leadership sees great potential for AI in
healthcare. Based on the current rat e of development, it
is probable that AI will become a revolutionary
technology in healthcare in the upcoming decades.
AI has the potential to have the greatest impact on
the human life span through life -extension technologies,
but the means are underexpl ored. In this article we
investigate which AI technologies in healthcare are
likely to provide the best results in the quest for
increased life expectancy. There is a great number of
publications about the practical applications of existing
AI in medicine and healthcare. A recent review
performed by Ching, et al. [24] describes opportunities
and obstacles for the applications of deep learning in
medicine. Unlike their review, ours concentrates on
expected applications of different stag es of AI
402 Informatica 41 (2017 ) 401–417 M. Batin et al.

development to fight the main cause of death in humans,
aging. We demonstrate how gradual evolution of AI in
medicine will result in medically oriented beneficial
superintelligence able to produce indefinite life
extension.
The considered time span also distinguishes this
work from other analyses of benevolent AI, such as [16]
and [58], which immediately jump to the stage of
superin telligence, when AI will, by definition, be able to
solve most or all of our problems. As AI is constantly
evolving, we should determine how to use it most
efficiently during each stage of its development and look
at the period between now and superintelli gence. Only
by doing this will we be able to achieve the longest
possible life extension for currently -living human
beings.
In this article we outline a path for the application of
AI to life extension that yields increasing gains at each
step. We show th at analysis of aging biomarkers and
geroprotectors with the use of narrow AI will make the
largest impact on human life expectancy with a relatively
small investment. We also show how an increasing
amount of an individual’s healthcare data collected via
wearable devices (“wearables”) will feed the data -
crunching ability of AI and provide constant
personalized monitoring of that individual’s health on
ever-deeper levels, thus preventing illness at earlier
stages as well as repairing age -related damage. We al so
demonstrate how AI -powered robotics will gradually
become inner parts of the human body, resulting in
cyborgization  and high survivability. Our final point of
interest is integration of AI with the human brain via
neuroimplants to enable mind uploading.  See table 1 for
an outline of the expected evolution of the application of
medical AI in life extension.  The growth of AI’s ability for independent research
will be increasingly helpful in finding new technologies
to lower human mortality until AI reaches  the stage of
self-improvement. We expect that the development of
medical AI will at least partly offset the existential AI
risk [16] via intrinsic orientation of medical AI on
human benefit and AI’s closer integration with humans
via brain implants (see section 7.2).
This article is conceptually similar to the report on
the expected development of military AI [28], in which
the same three level s of the future of AI are considered.
The idea that AI will help us to make large gains in life
expectancy has been explored in works of futurists Ray
Kurzweil [58] and Robert A. Freitas Jr. [36], among
others.
This paper is structured as follows. In section 2, we
review the expected progress in AI, the levels of
development of AI, and the pr edicted timeline for the
corresponding advances. In section 3, we review the
current applications of AI to life extension, as developed
by select startups and academic projects. Prospective
near-future applications of AI to life extension and anti -
aging ar e outlined in section 4, which covers research
that is yet to be transferred from academia to the life -
extension industry. The expected effect of artificial
general intelligence (AGI) on life extension and
applications that it will enable are discussed in section 5.
The more distant future of AI, including
superintelligence and its effect on life expectancy, is
outlined in section 6. In section 7, we conclude our
overview with a discussion of the best strategies for
using AI to maximize the life span of the  currently living
generation.
2 AI d evelopment in the t wenty -first
century
2.1 AI d evelopment pace
Predictions about the development of AI have been
complicated by AI “winters,” periods of decline in
funding and enthusiasm due to the lack of breakthroughs.  Despite past “winters,” the advancement of AI
technologies has skyrocketed in recent years. We are
living in a very exciting moment, considering the overall
rise in enthusiasm for AI. According to one survey [16],
a majority of scientists believe that human -level AI, then
superintelligence, will be achieved before the end of the
twenty -first century. The current moment (2016 –2017),
is a period of accelerated AI development, fueled  partly
by the hype surrounding neural networks and machine
Table 1: Expected evolution of medical AI in life extension.

Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 403
learning. Dozens of startups are working to develop AGI,
and they are attracting substantial funding. Achievements
in the development of AI are doubling every year in such
areas as complexity in t ext understanding, speech and
visual recognition, and natural language conversation
[33].
If we extrapolate current trends in the performan ce
and capacity of neural networks, infrahuman (that is able
to most things that can do ordinary human being and may
work as a robotic brain; but some complex creative
activity is still beyond its abilities). AI could be achieved
as soon as the 2020s [93].
A recent, large poll of AI scientists [41] shows that
AI is expected to be able to master human language
around 2026 and, with 50 percent confidence, that
machines will exceed humans in every task by 2062.
If AGI appears soon enough, its impact will
overshadow that of the slower, decade -long research in
geroprotectors described below, and thus make them
obsolete even before their fruition, as AGI will provide
better solutions. Yet we cannot rely on the early AGI
scenario, as AI prediction is known to  be difficult.
In any case, two possible scenarios are:
- AGI will be achieved in the coming two decades;
- AGI will be achieved by the end of the twenty -first
century.
There is a big practical difference between these two
scenarios. In the first case , the majority of people living
today will be able to use AI for almost indefinite life
extension. In the second case, most currently living
people will be able to enjoy the benefits of AGI only if a
huge effort is made to take advantage of all intermediat e
life-extension technologies to help the current population
survive to see AGI achieved.
Aubrey de Grey named the situation of improving
life expectancy rate equal to the passage of time
“longevity escape velocity” [4]. The result would be
indefinite life expectancy (ignoring accidents, global
catastrophes, etc.). In t his paper we show that AI is the
main “game changer” that will help currently living
people reach longevity escape velocity, as its effects over
time will outweigh other known means of life extension.
AI is the most rapidly developing technology, and it
affects and accelerates the development of all other life -
extension technologies.
The exponential growth of AI, which is now
doubling with a period of one year, according to [33],
will potentially be able to compensate for the exponential
growth of the probability of human death because of
aging, which doubles every seven years [37], but there is
large lag of implementation of medical AI technology .
However, it is possible that AI growth will slow down, as
it happened several times before during AI winters, and
will be sigmoidal.
In [15], Nick Bostrom shows that each day of delay
in the achievement of superintelligent AI, which would
reverse aging, costs 100 thousand human lives.
The pace of the AI progress is very  uncertain but for
the purpose of this article, we are going to talk about
stages of AI development in a way that is agnostic to
timelines.  2.2 The t hree levels of the future of AI
development
In this section we clarify and enhance the classification
of the le vels of the prospective AI. These levels are often
mixed in AI discussion, which leads to confusion.
Narrow AI  (weak AI) is the level of a computer
program that achieves above -human performance in a
specific, narrow task [16]. For example, the tasks of MRI
scan recognition and facial recognition require two
differently trained systems, although the underlying
learning mechanism may be the same. Most existing AI
systems are consi dered narrow AI. The number of such
programs is growing rapidly due to the success of
machine learning and neural networks.
The difference between narrow AI and conventional
computer programs is the ability of the former to learn.
Autonomous cars employ a  good example narrow AI.
Such AI systems do not have full human capacity,
particularly in generalization.
Additionally, the majority of contemporary AI
systems need ongoing human supervision.
AGI  (human -level AI) is AI at the level of human
intelligence i n many areas. For example, there would
likely be communication in natural language,
understanding the context of most situations, as well as
performing most of the intellectual tasks that humans are
able to perform.
 Philosophical questions about the possi bility of
consciousness in AI are outside the scope of this
pragmatic definition. Ability to self -improve is an
obvious consequence of this level of AI development. As
a result, according to Nick Bostrom [16], an era of
human -level AI will be brief, as AGI with self -improving
abilities will soon evolve superintelligence. Robin
Hanson [45] adheres to the view that computer models —
emulations —of the human brain will dominate in the
future.
Superintelligence  is the level at which AI will
supersede humans in all aspects, overtaking the
intelligence of the entirety of human civilization. It will
be able to govern th e world, make scientific discoveries,
launch space exploration, and create accurate simulations
of the human past. Bostrom [16], Yampolskiy [113] ,
Yudkowsky [114] , and many other scientists expect its
eventual appearance.
3 The c urrent applications of AI in
healthcare and medical research
3.1 Growth of investments in healthcare
AI
In 2014 –16 the giants of the IT industry announced the
launch of biotechnology and life -extension projects
based on machine -learning techniques. Among those
projects are Google’s Calico, focusing on anti -aging;
Facebook’s Chan Zuckerberg Biohub, searching for
drugs for all diseases and creating an atlas of cells for
this task; IBM’s Watson Health, targeting healthcare in
404 Informatica 41 (2017 ) 401–417 M. Batin et al.

general; Intel’s large biotech section [52]; Microsoft’s
innovative cloud computations for new drug discovery;
and Apple’s platform for wearables and software for
health monitoring.
Not only big bus iness invests in healthcare research
and development; many startups are also making great
strides. It is estimated that in 2016, there were 106
startups that used AI in various areas of healthcare. The
number of mergers and acquisitions in healthcare AI
grew from less than 20 in 2012 to nearly 70 in 2016 [51].
Many startups promise almost unbelievable feats. A
collection of press releases for such companies comprises
hundreds of pages of breathtaking announcements and
lengthy enumerations, but most projects vanish within a
few years as the survival rate of startups is low [38]. In
order to attract investors, promises are often exaggerated.
However, these promises may be used to measure
general trends and expectations in the industry.
We can expect investment in AI to grow in the next
years if a new AI winter does not occur. The healthcare
sector is the largest potential source of funding for  AI
[11], as it is still a “deficit market” due to a large, unmet
demand for better health.
3.2 AI in m edical research
Even in scientific research, it is necessary to distinguish
between “advertising” statements that often exaggerate
achievements and real practical achievements. As to the
former, in 2009 it was stated that a robot called Adam
was able to formulate hypothes es and conduct
experiments on the yeast genome [95]. But there were no
subsequent publications on this device.
On the other hand, robots have indeed made
substantial contributions to the automation of laboratory
studies. For instance, robotic manipulators have
automated repetitive operations with test tubes [13].
Among the recent practical applications of AI is the
use of artificial neural networks for visual recognition of
brain scans, including reconstruction of the relationships
between biological neur ons in brain connections [25].
Several companies are using AI to accelerate their
research:
Gero  (formerly known as Quantum
Pharmaceuticals) employs the methods of physical
kinetics and the modern theory of dynamical systems to
model aging processes in complex biological regulatory
networks [27] aiming to develop novel anti -aging
therapies. To control the health effects of the future drugs
Gero team has applied a deep convolutional neural
network (CNN) to time series representing human
locomotor activity from wearable devices, which allowed
to produce a digital biomarker of aging [ 28]. This
biomarker now serves as the scientific basis for Gero
lifespan/health risks estimation app1 and could be used as
a metrics of health outcomes for wellness and life
insurance industries.
 Deep Genomics  is working on a system that will
allow study ing, predicting, and interpreting how genetic

1 https://itunes.apple.com/us/app/gero -lifespan/id1222911907  variations change important cellular processes such as
transcription, splicing, and so on. [119] .
Atomwise aims to reduce the cost of new -drug
development through the use of a supercomputer and a
database of molecular stru ctures to predict which
versions of a potential drug will work and which will not.
[120] .
There are many other companies and scientific
groups that use AI to accelerate their medical research,
and competition is fierce. Not all of them will survive.
3.3 AI in d iagnosis
Claims that AI has outperformed humans in various
narrow areas of healthcare have appeared since the 1980s
[18]. In the early days, such claims mostly referred  to
expert systems that were popular at the time. It was
difficult to translate such success into wider practice,
though —and this scaling issue has plagued AI research
from the beginning.
Yet humans are not much better. It was found that in
88% of cases a second opinion gives a different diagnosis
[104] . Of course, this estimate may be unrepresentative,
as only uncertain cases require additional evaluation, yet
it demonstrates uncertainty in human diagn ostics.
In April 2016, it was stressed by Mark Zuckerberg
that machine learning helps to make diagnosis more
accurate, inexpensive, and, perhaps most important,
quick [46]. For example, an app that tracks ch anges in
moles based on photos taken with a cell -phone camera
can replace expensive visits to a doctor. This software,
Total Body Photography , analyzes photos of moles in
comparison with images of 50 million malignant moles
using Israeli image recognition technology [88].
AI will be able to simulate biological processes in
the human body and use the resulting models for
prediction and diagnosis. This is done by using “big
data” —that is, by combining a vast amount of data
collected from wearables with the extensive data
accumulated in previous medical practice. In 2016, IBM
bought several corporations that had extensive data on an
enormous number of patients. One of these, Truven,
which alone has hundreds of millions of medical records,
has been bought for US$2.6 billion [26].
AI is also working with text and natural language,
which helps to handle scientific papers, medical records,
and patient complai nts, but it still has considerable
difficulty understanding human language [7].
IBM Watson for Oncology  is a cognitive -
computing system that can answer questions formulated
in a natural language (that is, in a human language). It
has access to various sources of data: encyclopedias,
databases of scientific articles, and knowledge
ontologies. Thanks to its huge computing power and
preprocessed sourc es, it can give accurate answers
questions it is asked.
Since 2013, IBM Watson has been used at the
Memorial Sloan Kettering Cancer Center to facilitate
decision -making about treatment of patients with lung
cancer. Its database is constantly updated with n ew
disease records.
Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 405
IBM Medical Sieve “is an ambitious long -term
exploratory grand challenge project to build a next
generation cognitive assistant with advanced multimodal
analytics, clinical knowledge and reasoning capabilities
that is qualified to assis t in clinical decision making in
radiology and cardiology” [50].
Google DeepMind (DM) Health is a Google
DeepMind subproject that applies AI technology to
healthcare [29]. In collaboration with the University
College London Hospital, DM will be involved in an
algorithm -development project for automated
distinguishing between healthy and cancerous tissues in
the head and neck area.
Babylon Health (iOS, Android)  is a mobile
application that allows a user to have an online
consultation with a British or Irish doctor [5].
Turbine.ai  is a team of scie ntists that formulate
personalized methods of treatment for any type of cancer
based on AI. [98].
Insilico Medicine  is another startup working on the
implementation of deep learning in drug discovery.
3.4 AI in b ioinformatics and modeling of
living organisms
Often artificial intelligence is thought of as something
that people have not experienced yet, and when it
becomes familiar and accessible, it stops being perceived
as AI and is perceived more as a mere "computational
method." A set of such computational methods  in biology
is called bioinformatics. The field of bioinformatics
consists of analysis of the genome, its changes, genome
linking to proteins, conformation of proteins, and the
evolution of living organisms in general.
The next step in the development of b ioinformatics is
simulation of living organisms. To make this happen, an
entity needs data on cellular processes, huge computing
power, and adequate biological models.
One of the first computer models of a living cell was
created at Stanford in 2012 [54]. It was the simplest
mycoplasma, with only 525 genes. However, Craig
Venter, who was working with the same mycoplasma in
2015, recognized that the functions of some 90 genes
were unknown, and therefore the completeness of the
model is in question [49]. Venter managed to crea te a
viable synthetic organism ( Mycoplasma mycoides  JCVI -
syn3.0), whose genome consists of 473 genes, but 149 of
them were not fully understood [117] .
Cell modeling cannot always be accurate, as it has
many levels of uncertainty , starting from the quantum
level and protein folding, Brownian motion, and so on.
Quantum computers may help with protein -folding
modeling in the future.
So far, the most advanced simulation of a
multicellular organism has been carried out on the
Caenorh abditis  elegans  worm [77]. The simulation
includes a model of its "brain," which consists of 302
neurons, and the connectome of which has been known
for a long time [110] . Some of its functions have been
put into the model, but full, correct modeling of its
behavior has not been achieved yet.  Modeling of a human cell is much more complex
than modeling of a mycoplasma cell because it includes
up to 40 times more genes, but such a mod el will allow
medication testing through computer simulation . It will
also allow preclinical testing on a variety of substances
as well as determining the positive effects of a particular
medication positive and how it works. Any divergence
from an experim ent will contribute to the model’s
improvement. For now, “organ -on-a-chip” works as a
proxy for in vitro  and in silico  research [80].
The next stage of this approach will be the modeling
of a particular human organs and then full body based on
its genome, epigenome, and data from medical analysis.
Such a model will enable precise calculation and
definition of a medical intervention when required [10].
Big companies are interested in cell modeling as
well. Chan Zuckerberg Biohub, for instance, has begun
work on t he atlas of all human cells [121] .
3.5 Merging c omputational biology, cell
programming, and AI
Cell programming is akin to bionanorobotics: making a
cell perform more and more complex tasks, including
calculations, guided moving, and most importantly,
protein creation in specified locations. One of the main
applications of the technology is drug delivery to fight
cancer.
However, to program cells, one needs to process
enormous amount of data about their DNA networks.
This is where AI and machine learning c ome in.
The Cellos project [47], which was presented to the
public in 2016, performs DNA -design aut omation for
new living organisms. It can calculate (and then
synthesize) a DNA sequence that corresponds to a certain
function carried out for specified cell types. Boolean
logic (commands such as “AND” and “OR”) can be used
in this function.
Molecula Maxi ma [69] is a similar platform, which
is positioned as a programming language for genetic
engineering.
It is worth mentioning DNA origami technology [6],
which allows the construction of different microscopic
mechanisms from DNA. It is enabled through a very
powerful system of computer -aided design that can
decompose a designed project into its component
elements (blocks), and then write the DNA c ode that will
guide self -assembly into a predetermined shape.
3.6 AI, w earables, and big data
There are hundreds of different medically oriented
wearables on the market, the explosion of which began
several years ago with fitness trackers such as Fitbit .
Other wearables include professional medical monitoring
devices, such as devices that track heart abnormalities.
The BioStampRC  sensor [122]  is a patch that can be
glued to different parts of a body, and it collects various
kinds of data and automatically loads them into the
cloud.
406 Informatica 41 (2017 ) 401–417 M. Batin et al.

Similar to wearables are medical implants. One
example is an implanted cardiac defibrillator (ICD) ,
which was been used to give an electric shock to restart
the heart and save a soccer player on the field [21].
It might be possible to improve the situation by
introducing AI trained on large amounts of data in order
to define the probabil ities of successful ICD therapy for a
particular patient in a particular case.
Final Frontier Medical Devices  produces devices
that can diagnose 90% of emergency situations at home.
[109] .
Nimb  is a wearable ring for requesting emergency
help. [123] .
Wearables can collect chemical signals from the skin
or electrical signals from the brain and heart. The next
stage in the development of wearables will involve
integrating them more closely with the human body and
reducing their size.
Wearables have improved clinical trials by
constantly measuring numerous parameters as well as
tracking whether drugs have been taken. AiCure  requires
taking a photo of a pill in a pati ent’s mouth [124] .
A general trend is that smartphones “absorb”
specialized gadget functions. This has happened with
fitness trackers, which are currently being replaced by
the Argus  app. Current smartphones can measure blood
oxygenation with their camera, replacing a US$50
monitoring gadget with a US$5 app.
Besides the cost savings, the body space limits the
number wearables that can be used at one time (setting
aside the inconvenience of keeping multiple device s
charged and updated). Hence, incorporating all wearables
into one device is reasonable. The future universal device
will likely combine a smartphone, medical device, and
brain -computer interface, and might well take a wearable
form such as glasses ( Googl e Glass , for example) or a
necklace.
Wearables will work together with different safety
systems, integrating with infrastructure and optimizing
the performance of smart homes [12], self -driving cars,
robot police, surveillance, drones, and the “Internet of
things,” provi ding a ubiquitous safety and healthcare net.
Even toilets can be made “smart,” analyzing biological
material every time you visit them [91], [116] . Google
has already patented a smart bathroom [59].
3.7 The problem of research data
verification: blockchain and evidence
systems
There is a reproducibility crisis medicine [53]. It is
explained by a number of statistical biases as well as
fraud and market pressure. Life -extension studies are
especially susceptible to fraud, as people are willing to
pay for “youth,” and it is not easy to make objective
measurements in such studies. By being able to work
through a large amount of patient data, AI will increase
the reliability o f results.
Experiment automation, experiment -procedure
recording, and the use of blockchain [70] to keep records secure could simplify verification processes and reduce
bias and fraud in the field.
4 Prospective applications of AI in
aging research
4.1 Fighting aging as the most efficient
means for life extension
It is widely understood nowadays that the purpose of
general healthcare is not only to treat certain diseases but
also to prolong healthy  human life span.
Different applications of AI in healthcare have
different effects on life expectancy. For e xample,
fighting rare diseases or advanced stages of cancer will
not yield much increase in total life expectancy over the
entire population.
The main causes of death in the US are circulatory
diseases (23.1% cardiac deaths, 5.1% stroke deaths),
cancer (22.5%), chronic lower respiratory disease
(5.6%),  and Alzheimer’s disease (3.6%). Combined,
these conditions cause 59.9% of all deaths in the United
States [44]. The probability of these diseases increases
exponentially according to the Gompertz law of mortality
[66, 67]. More than 75% of all deaths happen to people
of 65 years  of age or older [40].
As a result, some authors [105] , [115] say that aging
is the main cause of death and that if we are able to slow
the aging process, we will lower the probability of age -
related diseases and increase the healthy life span.
Experiments show that even simple interventions can
slow the aging process and thus delay the onset of deadly
diseases in and extend the healthy life span of the C.
elegans  worm [20], mice [66], and rats [87].
These life -extension experiments on animals have
involved relatively simple interventions, such as
administeri ng long -known drugs (metformin or
rapamycin, for example) or restricting caloric intake.
Such life -extending drugs are called geroprotectors [71].
 Unfortunately, studies of the life -extending effects
of geroprotectors on humans are scarce, although similar
interventions have often been used for other diseases
(treating diabetes with metformin, for example), hence
proving their safety. Altho ugh such studies could have
begun long ago, this has not happened, because of a
number of social and economic reasons. Naturally, such
experiments would require a lot of time (longitudinal
experiments take decades) and test groups would need to
be large.
Yet there is not the luxury of decades and centuries
for classical experiments, as people are dying now,
during our lifetime. There is a need to find ways to
extend human life —and prove that these inventions
work —in a shorter time. A well -recognized way to do
this is to find aging biomarkers  that will track that aging
is slowing before all participants of an experiment die.
In short, to slow the aging process, we must find
efficient geroprotectors and combinations of
geroprotectors; to prove that they work, we need to have
independently verified aging biomarkers.
Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 407
There are many other advanced ideas in the fight
against aging, including gene therapy, stem cell research,
and Strategies for Engineered Negligible Senescence
(SENS) [27]. However, in this section we will limit
ourselves to AI -based methods for creating efficient
geroprotectors and biomark ers.
There has been only one known attempt to use AI to
predict aging biomarkers, which involved training neural
networks on a large age -labeled sample of blood tests
[82].
4.2 Aging biomarkers as a computational
problem
Aging biomarkers are quantitative characteristics that
predict the future life expectancy of an organism based
on its current state [72]. They can be normalized to a
“biological age,” which can be older or younger than the
actual age. Future life expectancy is the difference
between the average median life expectancy for a
species2 and the biological age of an individual. Different
aging biomarkers have different predictive power [64].
For example, gray hair is a marker of aging, but it has
low correlation with mortality. Good aging biomarkers
should be causally connected to a potential cause of
death. Hair color is not causally connected to a potential
cause of death, as one could dye one’s hair without
affecting life expectancy. In contrast, blood pressure and
a number of genetic mutations are causally connected
with mort ality. Thus, they are better biomarkers for
aging. Since aging is a complex process, it cannot be
expressed by a single number; a large array of parameters
is needed to represent it. Aging biomarkers should also
be reversible: if the aging process has been  reversed, the
biomarkers’ respective characteristics should change
correspondingly (e.g., decrease in number).
There are two ways to find biomarkers: modeling of
aging processes, and statistics. As a side note, one could
also measure small changes in the Gompertz curve of
mortality, that is, use the number of deaths in a
population as an aging biomarker [79]. However, to
observe them, information about m illions of people
would be required.
With the help of modern wearables, it is possible to
record all the drugs and treatments received by a patient.
A huge number of patient records, along with
corresponding data on personal genetics, physical
movement, a nd lifetime behavioral activity, could be
collected and centralized. This would result in a cohort
study with better information supply and stronger
probative value. Collecting and interpreting this
information would likely require powerful AI.
One plausib le AI scenario in biomarker detection is
the use of unsupervised machine learning over a large set
of biomedical parameters that may lead to the discovery
of groups of parameters that correlate with biological
aging.

2 Technically the life expectancy should be at the biological age,
rather than at birth as is usually quoted.  Further, parameter -variance analysis wi ll help to
detect real aging biomarkers. For example, the company
Gero focuses on gene -stability networks [56].
Another applica tion of AI in the fight against aging
is in creating completely new geroprotectors by
analyzing cell models, aging models, and molecular
properties. Rather than drugs, the geroprotectors could be
genetic interventions, that is, insertions of new genes or
alterations in the expressions of existing genes
(epigenomics ).
Five hundred thousand British senior citizens have
donated their blood and anonymized their healthcare data
for use by Biobank, which is now sequencing their
genomes. Biobank will provide open  access to all the
resulting data, which will become an enormous data set
for various forms of machine -learning research [125] .
Especially promising is the search for genetic networks
of aging. Similar projects are taking place in Iceland [81]
and Estonia.
4.3 Geroprotector’s combinatorial
explosion
A number of medications can extend the life of a mouse
by slowing down its aging processes [57]. Most of these
medications, however, yield only a 10 –15% increase in
life span. In humans such medications would yield even
less, perhaps around 5%, as longer lives are more
difficult to ex tend, and they respond less to known
geroprotectors. But what if several geroprotectors are
combined? Results of a few studies on mice are
promising, as they show a multiplication of effects [96].
Rece nt research used a sophisticated testing
algorithm to identify three drugs that yield maximum life
extension in worms and flies [31]. While that algorithm
was designed manually, we expect that the best testing
scheme would involve AI -aided design of a range of
algorithm alternatives.
Although combining certain pairs of geroprotectors
works well enough, some geroprotector s are
incompatible with one another. Moreover, combining
them greatly reduces their effects. Hence, pairwise
testing of geroprotector combinations is needed to begin
with, followed by larger combinations. To test all
combinations of 10 geroprotectors would  require 1024
experiments, and for 20 geroprotectors the number of
experiments would be over a million, and that is for a
single dosage rate for each geroprotector. This is
virtually impossible, as there financing has been
unsuccessful for even simple test ing of one combination
on mice (see lifespan.io campaign [126] ).
The problem of searching in a n enormous space is
similar to that of playing a complex board game with a
huge search space, such as Go. The recent success of
AlphaGo [127]  promises that such a search could be
simplified. Consequently, a much smaller number of
experiments would need to be run to determine an
optimal geroprotector combination. The underlying
principle of AlphaGo is that the most promising
combinations are selected by a neural network trained on
a large number of previous games. Similarly, a neural
408 Informatica 41 (2017 ) 401–417 M. Batin et al.

network ca n be trained to predict the biological effects of
chemicals based on knowledge of their properties
obtained from a comprehensive library of substances. A
similar computational approach is used for drug
discovery [92] and toxicity forecasting [103] . Toxcast  is
a large US -government -sponsored program designed to
use machine learning to pre dict the toxicity of different
chemicals [86].
To increase the number of useful outcomes of an
experiment, it is also necessary to record a vast number
of various vital parame ter measurements of an organism
(for instance, blood composition, physical movement,
EEG readings) during the process of geroprotector
testing. This would allow the discovery of aging
biomarkers during geroprotector testing.
Generally, the geroprotector -identification problem
can be reduced to the task of finding a global minimum
of a function of ten (or more) variables. A number of
efficient machine -learning algorithms are suited for such
a task.
The search for aging biomarkers can be pursued in a
similar  manner. From the mathematical point of view, it
is a search for the global minimum of the function of
many properties of an organism. The same process can
also be used to calculate specific gene interventions for
an individual human, in view of the genome
characteristics, age, and biomarkers.
Activities in this area are carried out by Gero,
Calico, the Buch Institute [19], and others. João Pedro de
Magalhães has used random -fores t machine learning to
predict the properties of life -extending compounds [9].
Additionally, several projects are searching in large
combination spaces by usin g neural networks designed
for other tasks:
- Project AtomNet [3] predicts  the properties of
chemical materials using convolutional neura l networks;
- E. Pyzer -Knapp et al. [83] are using a multilayer
neural network to predict the electrical properties of new
molecules;
- L. Rampasek and A. Goldenberg [84] are review ing
applications of neural -network project TensorFlow by
Google in computational biology;
- K. Myint and X. -Q. Xie are predicting ligand
properties using a fingerprint -based neural network [74].
4.4 AI, a ging, and personalized medicine
Aging can be view ed as the accumulation of errors and
lack of adequate regulation in a body by repair
mechanisms and the immune system [37]. Hence, in the
fight against aging, additional regulation is needed in the
form of medical therapy. Medical therapy consists of
tests (fo r instance, blood work, blood pressure readings,
medical scans), hypothesizing about causes of disease
(diagnosis), medical intervention, and in the case of an
incorrect hypothesis, subsequent correction based on new
observations.
This process is similar t o the scientific method, and
at its core it is an information -based process, that is, a
process of solving a particular computational task. This
means that it will benefit from more data and more intelligent processing, followed by a precise and targeted
intervention. Therefore, to cure a disease or rejuvenate a
body, it is helpful to collect a large amount of
information from that body, in order to construct a
detailed model of it. This will enable calculations for the
genetic interventions that will lead to recovery and
functional improvement.
It is now possible to obtain large amounts of data on
a body via full genome sequencing, thousands of
parameters of blood analysis, and analysis of the
transcriptome, metabolome, and other similar “ omics ”
(that is co mplex quantitative description of functions and
statistics of a type of organism’s elements). This is
achieved through continuous monitoring of food intake,
physical activity, and heart parameters via ECG, various
scans, and digital tomography. The rapid d ecline in the
cost of all these procedures (US$999 in 2016 for
complete sequencing of a genome [78]) has led to
individual humans becoming sources of big data. Now
we are faced with the question of how to interpret these
data to produce the best effects on human health by not
only diagnosing existing illnesses but also by predicting
future illnes ses and creating personalized aging profiles.
For this reason, there needs to be better means to derive
meaningful conclusions from this vast amount of data.
In the past, the following situation was typical: a
patient complains to a doctor about various ac hes and,
after having their blood pressure and temperature
measured, receives treatment with a single prescribed
medication. In this case the information exchange
between the patient and the doctor consisted of “just a
few bytes” and some intuitive impress ions of the doctor.
However, nowadays the information exchange may
consist of gigabytes of information at the same cost. For
the processing of this data stream, powerful data crunch
techniques are required.
During aging, a body gradually accumulates errors ,
and its natural repair systems begin to fail. The
information theory of aging could be designed to enable
therapies to correct all these errors, and this idea is at the
core of the Strategies for Engineered Negligible
Senescence (SENS) project [27].
AI may help humans to model aging by creating a
complex causal map of aging processes in a body [90]
and then personalizing the model.
Naturally, an organism’s body is able to solve most
of its problems locally without sending information
outside: cells know what to repair, and higher -level
attention is needed only when  they fail locally. An aging
body fails to solve its problems locally. Therefore, it may
be reasonable neither to extract information from the
body nor to direct therapy into the body, but rather to
introduce “AI helpers” inside the body, where they can
help solve problems as they appear. Implants and future
nanomedicine will be employed along these lines.
Another solution to the “messy problem of aging” is
growing completely new body parts and full bodies.
However, designing the immunogenic properties of such
parts and solving a complex “connection problem” will
require analysis of large amounts of information, which
will only be feasible if AI is employed.
Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 409
4.5 Narrow AI in medical -cost reduction
and affordable healthcare
Efficient and affordable healthcare wi ll be essential to a
global increase in life expectancy. Cheap mobile phones
solved the communication problem at the global scale by
operating as a standard solution. A similar kind of
solution must be sought in healthcare.
High -quality healthcare is very expensive. Nursing,
hospitals, drugs, tests, insurance, and highly paid
specialists all cost much money, and as a result,
advanced healthcare is out of reach for many people.
AI will provide less expensive services and make
them available to larger populat ion groups in developing
countries. Just as generic drugs can be taken in place of
expensive brand -name drugs, an AI -powered
consultation could provide diagnostics for people who
cannot afford a doctor.
Many people —for instance, those who search the
Intern et for answers to their medical questions —may be
less reluctant to consult an AI -powered specialist than a
real doctor.
The following instruments will make AI -based
healthcare an inexpensive alternative to hospitals:
- AI chatbots, such as the Babylon app  [5];
- Smartphones as a universal diagnostic implement
(they can be used to monitor heart rate, diet, physical
activity, oxygen saturation, mole changes, and so on);
- Home delivery of cheap generic drugs;
- Web -based medical expert systems.
4.6 Effects of narrow AI on life extension
Narrow AI will help unleash the full potential of life
extension, leading to dramatically slower aging. If
humans did not age, they could live hundreds of years
despite accidents (If we exclude age -dependent
component o f mortality by extrapolating of minimal
probability of death found in 10 years old American
girls, which is 0.000084 for a year [1], we will get life
expectancy of 5925 years. But increasing probability of
death with age lowers it to 81. Most of this death
probability increase comes from biological aging.) Yet
introduction of narrow AI into effective medical practice
could ta ke much longer than related advances in research
labs, possibly decades.
The present era of narrow AI might be long, lasting
until 2075 by pessimistic predictions [73]. However, this
time can be spent usefully, exploring aging biomarkers
and geroprotector combinations.
For those who are not able to survive until t he arrival
of radical life -extension technologies, narrow AI may
still play an important role by providing two main
backup options: cryonics  and digital immortality .
In cryonics, AI applications may, via wearables,
warn a patient’s cryonics organization of  the impending
death of that patient. Cryopreservation could be called
plan B, while plan A is to survive until the implantation
of life -extension technology.
Digital immortality [107]  is the concept of
preserving a human being’s data so that future AI will be able to reconstruct his or her model using DNA, video
recordings, and additional data gleaned from such
sources as social networks. It depends on certain
assumptions about AI’s capabilities, amounts of required
information, and the nature of human identity. AI could
help to collect and preserve data for digital immortality
and perform initial analysis of that data. Digital
immortality is plan C in achieving radical life extension.
An early arrival of advanced forms of AI may make
these three approaches obsolete before they are
implemented.
5 Prospective a pplications of AGI to
life extension
5.1 Personal robot physician
AGI may appear in the form of a human -mind upload
[23], [45], or as an infrahuman robotic brain [17] capable
of performing most human tasks. It will be Turing
complete [112] , meaning that it will be able to interact
conversationally approximately as well as a human.
There are numerous ways in which AGI may be
applied to life extension. In this secti on, we will explore
those that are likely to provide the biggest gains in life
expectancy.
Cheap and efficient AGI will enable accessible and
predictive personal healthcare. A plausible example is an
AI-based personal assistant that will be a combination o f
a healthcare researcher and personal physician and will
be able to provide personal treatment and early response
to symptoms. It will constantly monitor an individual’s
aging biomarkers and other life parameters, allowing
daily therapy adjustments. A pat ient will no longer need
to visit a clinic, get a prescription, have it filled at a
pharmacy, remember to take drugs at prescribed times,
try to determine whether her or she is feeling better, and
so on. A personal robot will simply utilize data gathered
from wearable monitoring systems to determine an ideal
drug combination, order it to be delivered, and then
prompt the patient to take a pill. The process of diagnosis
and cure will be as effortless and automated as an
upgrade of antivirus software on a per sonal computer.
The ability of AGI to comprehend human language
will lead to the possibility of “artificial scientists” that
are able to formulate hypotheses, organize experiments,
and publish results as scientific papers with less and less
help from huma ns. Combined with robotized labs and
less expensive equipment manufacturing, AGI will
accelerate scientific research in all fields, including life
extension.
Domestic medical robots and wearables will
automate clinical trials, reducing costs and accelerat ing
drug discovery by collecting data for clinical trails.
Currently, a clinical trial may cost hundreds of millions
of dollars because of legal and organizational issues.
Home robots will record patient activity, automating
clinical trials and making them  independent of large
medical companies via decentralization, which will
reduce their costs and improve data objectivity.
410 Informatica 41 (2017 ) 401–417 M. Batin et al.

Robotic drones with drugs and defibrillators will
provide assistance to people whose wearable systems
report an emergency. Domestic r obots will monitor the
health of a family, help with treatment, monitor medicine
consumption, act as physical -exercise instructors, and
predict disease. Additionally, they will provide
companionship for the elderly, which will also increase
life span.
5.2 Integration of m onitoring systems into
human bodies and nanomedicine
A person’s immune system maintains information on
such parameters as locations of body inflammation and
the types of viruses it is equipped to neutralize. This
information is beyond the contr ol of human
consciousness. The immune system can be trained with
vaccines, but information exchange between humans and
immune systems is limited. If a person could read the
immune system’s information and upload new
information into the system, then it wou ld be possible to
cure a large range of ailments, including autoimmune
diseases, infections, organ failure, tumors, and tissue
senescence. Ray Kurzweil expects communication to
appear in the 2020s [85]. The process will be similar to
current computerized automobile diagnostics. A system
of communication between an organism’s immune
system and a computer can be called a “humoral
interface” and would have much in common with a
neurointerface. It could be created with some form of
nano - or biotechnology, such as computer -programmed
cells.
The n ext step in this direction is artificial human
immune system management . Such a system may consist
of biological organisms, an individual’s own upgraded
cells [30], or micro robots circulating in an organism’s
blood. The following are the expected levels of a
nanotechnology -based upgrade of the human body:
1) In the first stage, the system w ill monitor
emerging diseases;
2) In the second stage, the system will assist in
treatment by killing bacteria, viruses, and cancer cells,
and by repairing vascular injuries;
3) In the advanced stages, the system will constantly
carry out body repair and treatment of aging;
4) In the final stage, these systems will transform
into nanomachines that will replace human cells, making
the human body completely artificial and immortal. This
will likely only happen when AI reaches the superhuman
level.
5.3 “The Upgra de Net”: a  path to
superintelligence through a network of
self-improving humans and humanlike
AI Systems
As Elon Musk famously tweeted, “Humans must merge
with machines or become irrelevant in AI age” [55].
Such a merger would require a powerful brain -
computer interface  (BCI), and we think that the be st
way to achieve this is through the implementation of a personal AI health assistant, which would be integrated
into human bodies and brains and focused on preserving
human lives.
Musk has also stated [102]  that he wants to
commercialize the AI health assistant with his Neuralink
project. Neuralink will begin by using a simple BCI to
treat depression and other mental illnesses. A simple BCI
may be used to control human emotions, preventing
mental -state-dependent types of violence such as road
rage and suicide. This will provide experience that can be
directed toward curing mental diseases with BCI, and
eventually proceeding to a stage of augmented humans ,
who could later be connected into a network of self -
improving humans.
In our opinion, there is another way of building a
network of self -improving humans, and it starts with the
creation of a medical social network:
First, new type of patient organizatio ns [42] will
need to be established to connect people who are
interested in the fight against aging [128] . These
organizations will essentially operate as social networks
for information exchange, mutual support, clinical trials,
crowdfunding, data collection for digital immortality,
civil science, aid in cryopreservation, and political
action.
Individual biohackers also could play important role
by self experimentation, like Elizabeth Parrish: they
could take higher risk experiments on themselves without
legal restriction and costs [68].
The next step will be the creation of a network for
direct interaction betw een the brains of human
participants, a so -called neuroweb  [60]. Information -
transmission mechanisms may  be implemented using
weak AI systems. The result of such a network will
effectively be a collective brain . Direct brain connection
may be confusing and inefficient, so a kind of AI firewall
may be required to control access to the information that
an indi vidual wants to share. Also, an AI dispatcher may
be needed to facilitate conversation by remembering
conversation’s lines, providing relevant links, illustrating
ideas, and so on. At a further stage of development, an
AGI-based virtual assistant connected  through BCI to a
human’s brain may work as a form of exocortex  [14].
The ultimate step is to merge with AI , which
implies blurring the boundaries between the biological
brain and the computer. This is  equivalent to achieving
practical immortality (if no global risks will happen),
because brain data will be easily backed up and, if
needed, restored. Effectively, human minds and
computer superintelligence will merge into a single
system. At the same time , people will be able to maintain
a preferred level of autonomy with regard to memory,
consciousness, and learned skills [34], [101] , [75].
Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 411
6 Superintelligence and the distant
future
6.1 Superintelligence finally solving
problems of aging and death
We can use trends and polls to predict narrow AI and
AGI. Superintelligence is by definiti on unpredictable.
For expectations of its arrival and what it will be able to
accomplish, we can refer to various futurists: Bostrom
[16], Yamploskiy [113] , Yudkowsky [114] , Kurzweil
[58], Vinge [106] , and Goertzel [39] all depict a future
dominated by global superintelligence.
According to these futurists, the arrival of
superhuman AI will enable solutions to the problems of
aging, curing presently incurable diseases, designing
universal medical nanorobots, and uploading an
individual’s consciousness into a computer network.
In the past, it took decades to accomplish complex,
globally valuable tasks such as the development of
modern aeronautics, wireless communication, and
noninvasive surgery; superintelligent AI will be able to
solve such problems very quickly, perhaps in momen ts.
With the arrival of superintelligent AI, achieving
practical immortality for the majority of people will
become feasible.
6.2 Simultaneous c reation of
superintelligence and advanced
nanotechnologies
K. Eric Drexler’s book Engines of Creation  [32] and
Robert A. Freitas Jr.’s Nanomedicine, Volume IIA:
Biocompatibility  [36] discuss nanotechnology  as
nanorobotics based on molecular manufacturing for
medical treatment and intervention. According to
Drexler, medical nanobots will:
 be self -replicating;
 be externally controlled;
 carry onboard computers;
 be capable of swarm be havior
 be cell sized;
 be capable of 3 -D printing organic structures;
and
be capable of sensing their environment and
navigating in it.
If such nanobots arrive before AGI, they will quickly
help us map the structure of the human brain and develop
technology  to create a very powerful supercomputer,
leading to the advent of AGI. On the other hand, if AGI
arrives first, it will create nanobots. The wait between
nanorobotics and AGI will likely be no more than a few
years.
Designing the first nanobot and control ling
nanorobotic swarms will be a huge computational task,
itself requiring the use of available AI.
When this technology matures, it may enable
relatively quick (hours to weeks) and seamless
replacement of living cells in a human body —with the
possible ex ception of the neurons responsible for personal experiences —with fully controlled
nanomachines by injecting a single self -replicating
nanobot. Such a nanotechnological body will not age as
it will be able constantly self -repair according to original
plan.
6.3 Superintelligence and the solution to
the consciousness problem: identity
copying
On the one hand, it will be difficult to develop full -
fledged AGI without first solving the problem of
consciousness. On the other hand, nanotechnology and
AGI will give us  the means to carry out various
experiments on the conscious brain and map its structure.
For example, investigation of qualia is feasible through a
gradual uploading process similar to the thought
experiment performed by David Chalmers [22]. This will
enable detection of the brain parts and internal processes
responsible for subjective experience.
There are two possible scenarios: either there is no
mystery here and the problem of uploading
consciousness to a com puter is purely informational, or
consciousness has a certain substrate. This substrate
could be a quantum process, continuity of causal
relationships, special particles, or similar —that provides
identity, and its preservation and transfer is a separate
technical task. In either case, the transfer of
consciousness to a new carrier is possible: an ordinary
computer can be used in the first scenario; the second
scenario will require a specialized computer, such as an
artificial neuron or a quantum computer [2].
This hypothetical consciousness -receptacle computer
will need to be extremely resistant to damage and have
advanced backing -up abilities in order to lower the risk
of death.
6.4 Usin g advanced forms of
superintelligence for the reconstruction
of the dead people
Cryonics  is the idea, introduced by Robert Chester
Ettinger and Jean Rostand [35] of using low temperatures
to preserve human bodies after death until it becomes
possible to return them to life. Currently around 250
people are cryopreserved by three cryocompanies [67].
At first, it was thought that bodies could be gradually
unfrozen upon the appearance of appropriate
technologies. Later it was thought that nanotechnology
could be used to repair d amage in thawing bodies [32]. A
more recent view is that bodies can be scanned without
thawing [65]. Advanced  tomography [48] or slicing [43]
would be employed, and the data from the scans would
be entered into a computer, where the human mind
would be reconstructed. Currently around 250 people are
cryopreserved by three cryocompanies [122] and
advanced nanotech created by AI could be used to s can
and upload their minds.
In addition, highly evolved superintelligence will be
able to reconstruct humans who lived in the past by
modeling their lives in a simulation. A reconstruction
412 Informatica 41 (2017 ) 401–417 M. Batin et al.

would be based on a subject’s informational traces. It is
called “d igital immortality” [108] .
For global resurrection of the dead [123],
superintelligence may perform a large -scale simulation
of the past [124]. Then, based on all the data about the
past, it will reconstruct everyone who ever  lived.
7 Discussion: s trategies for applying
AI to life extension
7.1 Problems of AI application in
healthcare
In 1979, a rule -based expert system could make a
diagnosis better than human doctors [18]. Since then,
decades have passed, and yet a large -scale AI revolution
still has not happened in healthcare. Most modern
medical systems are still based on extremely simple
algorithms, for example, if the he art rate is more than X,
execute Y  [8].
Brandon Ballinger [8] wrote that one major obstacle
is the majority of “cheap” easily available data sets is not
labeled, but machine -learning algorithms mostly require
labeled data for training. For example, there is a lot of
cardiac data, but it is not clear what disease it is
associated with or what the patient’s vital parameters
were. To obtain labele d data, it might be necessary to
conduct costly and potentially harmful experiments on
humans. Currently, this problem is being approached by
unsupervised learning algorithms, which do not require
labeled data, but their performance is still behind that of
the supervised systems.
In addition, there are regulatory issues regarding the
utilization of AI in healthcare, as well as disputes about
risk allocation and insurance payments between startups
and hospitals. AI can easily be migrated into an
individual’s  smartphone, but getting it into a doctor’s
office is more complicated, not to mention the intricacies
of accounting for AI in insurance payment systems.
One can imagine that the modest pace of
advancement of AI applications in healthcare in recent
decade s might be disappointing to the authors of the first
edition of Artificial Intelligence in Medicine , which was
published back in 1982 [97]. Yet, due to substantial
increase in  computing power, availability of “cheap”
digitized data, advanced data -analysis algorithms, and
new regulations, we finally seem to find ourselves at the
dawn of the rapid development of AI in healthcare.
Privacy issues regarding personal data create a tr ade-
off for AI development. On one hand, the greater the
amount of open data, the easier it is to train AI
algorithms. (Sharing one’s personal health data may
cause unpredictable harm to the individual, however.) On
the other hand, if only anonymized data is available,
important vital parameters and data points will be lost.
The patient organizations discussed in section 5.3 may
understand the importance of providing open access to
personal data, as doing so would help train AI for
healthcare.  7.2 AI in m edicin e, and AI safety
Issues of AI safety, on both local and global levels, are
beyond the scope of this work. We want to emphasize
just two points of intersection of AI in healthcare and AI
safety:
Medical AI is aimed at the preservation of human
lives, wherea s, for example, military AI is generally
focused on human destruction. If we assume that AI
preserves the values of its creators, medical AI should be
more harmless.
The development of such types of medical AI as
neuroimplants will accelerate the developme nt of AI in
the form of a distributed social network consisting of
self-upgrading people. Here, again, the values of such an
intelligent neuroweb will be defined by the values of its
participant “nodes,” which should be relatively safer than
other routes t o AI. Also, AI based on human uploads
may be less probable to go into quick unlimited self -
improvement, because of complex and opaque structure.
If the orthogonality of values and intelligence thesis
[16] has some exceptions, medical AI may be safer than
military AI.
On the other way, medical AI may increase the risks
as it will open the way to the neuromorphic AI, which is
regarded dangerous [16], or it will be under less control
than military AI, and could run into explosive run -away
self-improvement.
The Upgrade Net discussed above may become a
useful instrument in solving the AI safety problem, as  the
growing collective human intelligence could operate as a
global police force, identifying potential terrorist
behavior and other threats.
The safety will come from intrinsic value alignment
of human uploads [94], combined with superintelligence
power of the whole net which will be able to find and
prevent appearance of other types of potentially
dangerous AI systems, as well as exterminate the need of
creation of such systems. Turchin addressed this quest ion
in greater details in [99].
7.3 Surviving to s ee AGI: personalized,
age-dependent strategies
The older a person gets, the lower his or her chances of
surviving into the e ra of AGI and powerful life -extension
technologies. Fortunately, it is not necessary to wait until
superintelligence arises. In order for an individual’s life
expectancy to be increased indefinitely, that individual
must stay alive only until the moment wh en average life
expectancy begins increasing by more than a year each
year, at which point longevity escape velocity will be
achieved [27].
However, the chances that a person will be able to
benefit from life extension significantly increase if that
person has better access to upcoming technologies by, for
instance, living in a developed country, having financial
security, or being foresighted enough to research and
utilize those technologies when first available.
In order to increase and spread the benefits of
medical AI in the future, it will be necessary to increase
Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 413
people’s awareness and encou rage them to exercise all
available means for life extension. As part of this
strategy, we promote participation in patient
organizations committed to fighting aging, signing up for
cryonics, and sharing and collecting digital immortality
data.
8 Conclusion
This work is an overview of the existing and prospective
AI applications that the authors consider the most
promising and beneficial for life extension and antiaging.
We have considered a wide range of problems with the
current state of the research and th e industry, the most
promising prospective applications of AI, and strategies
to increase public awareness in order to ensure maximal
life-extension opportunities for everyone.
Based on related work, we have reviewed the
expected stages of the development  of AI in the near
future, and estimated when the most advanced levels will
arrive.
Further, we have presented an overview of the
current AI -based healthcare projects of certain for -profit
companies of various scales. These projects include IBM
Watson He althcare, Google Calico, and DeepMind
Health, as well as the research projects of certain
academic groups and nonprofit organizations.
We have shown that the exponential growth of AI’s
capabilities makes it more likely that AI could help fight
the exponent ial increase of the probability of a human
being’s mortality over time, and that AI could help a
person to reach longevity escape velocity before
superintelligence is achieved. It may help millions or
maybe even billions of people to “survive until
immorta lity,” and thus rescue their life from impending
death. Some of the authors explored this topic in greater
detail in the article “Fighting aging as an effective
altruism case: the model of impact” [100] .
We have emphasized the importance of establishing
patient organizations to spread awareness of the subjects
of life ext ension, voluntary patient data collection, early
adoption of medical AI technologies, and the eventual
formation of a “neuroweb” with the arrival of advanced
forms of AI.
9 Acknowledgements
We thank Anastasia Egorova, Maxim Cholin, Sergei
Shegurin, Alexan dra Alexeeva, and Dmitriy Shakhov for
interesting discussions that helped in the preparation of
the article.
Conflict of interest:  No.
Research funding : No.
10 References
[1] Actuarial  Life Table. 2017. Actuarial Life Table.
Retrieved August 24, 2017 from
https://www.ssa.gov/oact/STATS/table4c6.html
[2] Victor Yu Argonov. 2012. Neural Correlate of
Consciousness in a Single Electron: Radical Answer to “Quantum Theories of Consciousness.”
NeuroQuantology 10, 2 (2012). Retrieved from
http://neuroquantology.com/index.php/journal/articl
e/view/548
[3] Atomnet. 2017. Introducing AtomNet – Drug
design with convolutional neural networks.
Retrieved from
http://www.atomwise.com/introducing -atomnet/
[4] DN Au brey. 2004. Escape velocity: why the
prospect of extreme human life extension matters
now. PLoS Biol. 2, 6 (2004), e187.
[5] Babylon Health. 2017. Retrieved from
https://www.babylonhealth.com/
[6] Xiao -chen Bai, Thomas G Martin, Sjors HW
Scheres, and Hendrik Die tz. 2012. Cryo -EM
structure of a 3D DNA -origami object. Proc. Natl.
Acad. Sci. 109, 49 (2012), 20012 –20017.
[7] Katherine Bailey. 2017. Conversational AI and the
road ahead. Retrieved from
https://techcrunch.com/2017/02/25/conversational -
ai-and-the-road-ahead /
[8] Brandon Ballinger. 2016. Three Challenges for
Artificial Intelligence in Medicine. Retrieved from
https://blog.cardiogr.am/three -challenges -for-
artificial -intelligence -in-medicine -dfb9993ae750
[9] Diogo G. Barardo, Danielle Newby, Daniel
Thornton, Taravat Ghafourian, João Pedro de
Magalhães, and Alex A. Freitas. 2017. Machine
learning for predicting lifespan -extending chemical
compounds. Aging (2017).
[10] David J Barnes and Dominique Chu. 2010.
Introduction to modeling for biosciences. Springer
Science & Busin ess Media.
[11] K. Belcher. 2016. From $600 M to $6 Billion,
Artificial Intelligence Systems Poised for Dramatic
Market Expansion in Healthcare. Frost & Sullivan.
Retrieved August 24, 2017 from
https://ww2.frost.com/news/press -releases/600 -m-
6-billion -artifici al-intelligence -systems -poised -
dramatic -market -expansion -healthcare/
[12] Jamie Bennett, Osvaldas Rokas, and Liming Chen.
2017. Healthcare in the Smart Home: A Study of
Past, Present and Future. Sustainability 9, 5 (2017),
840.
[13] Kent S Boles, Krishna Kannan, J ohn Gill, Martina
Felderman, Heather Gouvis, Bolyn Hubby, Kurt I
Kamrud, J Craig Venter, and Daniel G Gibson.
2017. Digital -to-biological converter for on -demand
production of biologics. Nat. Biotechnol. 35 672 –
675 2017 (2017).
[14] Tamara Bonaci, Jeffrey Herro n, Charlie Matlack,
and Howard Jay Chizeck. 2014. Securing the
exocortex: A twenty -first century cybernetics
challenge. 1 –8.
[15] N. Bostrom. 2003. Astronomical waste: The
opportunity cost of delayed technological
development. Utilitas 15, 3 (2003), 308 –314.
[16] N. Bostrom. 2014. Superintelligence. Oxford
University Press, Oxford.
414 Informatica 41 (2017 ) 401–417 M. Batin et al.

[17] N. Bostrom and A. Sandberg. 2008. Whole brain
emulation: a roadmap. Lanc. Univ. Accessed
January 21, (2008), 2015.
[18] Bruce G Buchanan and Edward Hance Shortliffe.
1984. Rule -based expert  systems. Addison -Wesley
Reading, MA.
[19] Buck Institute. Home | The Buck Institute for
Research on Aging. Retrieved August 24, 2017
from https://www.buckinstitute.org/
[20] Filipe Cabreiro, Catherine Au, Kit -Yi Leung, Nuria
Vergara -Irigaray, Helena M Cochemé, Ta hereh
Noori, David Weinkove, Eugene Schuster, Nicholas
DE Greene, and David Gems. 2013. Metformin
retards aging in C. elegans by altering microbial
folate and methionine metabolism. Cell 153, 1
(2013), 228 –239.
[21] Hugo Campos. 2009. Soccer player Anthony Van
Loo survives a sudden cardiac arrest (SCA) when
his ICD fires. Retrieved from
https://www.youtube.com/watch?v=DU_i0ZzIV5U
[22] D. Chalmers. 1996. The Conscious Mind. Oxford
University Press, New York.
[23] William P Cheshire JR. 2015. The Sum of All
Thoughts: Pro spects of Uploading the Mind to a
Computer. Ethics Med. 31, 3 (2015), 135.
[24] Travers Ching, Daniel S Himmelstein, Brett K
Beaulieu -Jones, Alexandr A Kalinin, Brian T Do,
Gregory P Way, Enrico Ferrero, Paul -Michael
Agapow, Wei Xie, and Gail L Rosen. 2017.
Opportunities And Obstacles For Deep Learning In
Biology And Medicine. bioRxiv (2017). Retrieved
from
http://www.biorxiv.org/content/early/2017/05/28/14
2760
[25] M Coppock. 2017. Researchers Are Using Neural
Networks To Get Better At Reading Our MindsR
MINDS. Retrieved from
https://www.digitaltrends.com/computing/researche
rs-use-neural -network -algorithms -for-more -
accurate -brain -scans/
[26] Brad Darrow. 2016. Why IBM Is Dropping $2.6
Billion on Truven Health. Retrieved from
http://fortune.com/2016/02/18/ibm -truven -health -
acquisition/
[27] Aubrey De Grey and Michael Rae. 2007. Ending
aging: The rejuvenation breakthroughs that could
reverse human aging in our lifetime. St. Martin’s
Press.
[28] Stephan De Spiegeleire, Matthijs Maas, and Tim
Sweijs. 2017. Artificial intelligence  and the future
of defence. Retrieved from
http://www.hcss.nl/sites/default/files/files/reports/A
rtificial%20Intelligence%20and%20the%20Future
%20of%20Defense.pdf
[29] Deepmind. 2017. Helping clinicians get patients
from test to treatment, faster. Retrieved fro m
https://deepmind.com/applied/deepmind -health/
[30] Linda Delacey. 2017. Cyborg step? Scientists
engineer bioelectric cells. New Atlas. Retrieved
from http://newatlas.com/cyborg -technology -
scientists -create -bioelectric -hybrid -cells/47481/  [31] Tesfahun Dessale, Kr ishna Chaithanya Batchu,
Diogo Barardo, Li Fang Ng, Vanessa Yuk Man
Lam, Markus R Wenk, Nicholas S Tolwinski, and
Jan Gruber. 2017. Slowing ageing using drug
synergy in C. elegans. bioRxiv (2017), 153205.
[32] Drexler. 1986. E.: Engines of Creation. Anchor
Press.
[33] [33] Petter Eckersley and Nasser Yomna.
2017. Measuring the progress of AI research.
Retrieved from https://www.eff.org/ai/metrics
[34] Douglas C Engelbart. 1962. Augmenting human
intellect: a conceptual framework (1962). Pack.
Randall JORDAN Ken Multimed.  Wagner Virtual
Real. N. Y. WW Nort. Co. (1962), 64 –90.
[35] Robert CW Ettinger and Jean Rostand. 1965. The
prospect of immortality. Sidgwick and Jackson.
[36] Robert A Freitas Jr. 2003. Nanomedicine, Vol. IIA:
Biocompatibility. Landes Biosci. Georget. USA
(2003).
[37] Leonid A Gavrilov and Natalia S Gavrilova. 2001.
The reliability theory of aging and longevity. J.
Theor. Biol. 213, 4 (2001), 527 –545.
[38] Carmine Giardino, Xiaofeng Wang, and Pekka
Abrahamsson. 2014. Why early -stage software
startups fail: a behavioral fr amework. 27 –41.
[39] Goertzel. Should Humanity Build a Global AI
Nanny to Delay the Singularity Until It’s Better
Understood? J. Conscious. Stud. 19 No 1 –2 2012
Pp 96 –111. Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=
10.1.1.352.3966&rep=rep 1&type=pdf
[40] Yelena Gorina, Donna Hoyert, Harold Lentzner,
and Margie Goulding. 2006. Trends in Causes of
Death among Older Persons in the United States.
Retrieved from
https://www.cdc.gov/nchs/data/ahcd/agingtrends/06
olderpersons.pdf
[41] K. Grace. 2017. When Will AI Exceed Human
Performance? Evidence from AI Experts. Retrieved
from https://arxiv.org/pdf/1705.08807.pdf
[42] N Guillamón, M Armayones, E Hernández, and B
Gómez -Zúñiga. 2010. The role of patient
organizations in participatory medicine: Can virtual
healt h communities help participatory medicine
accomplish its objectives. J. Particip. Med. 2,
(2010), e21.
[43] Gwern. 2017. Plastination versus Cryonics. (2017).
Retrieved from https://www.gwern.net/plastination
[44] Nichois Hannah. 2017. The top 10 leading causes of
death in the United States. Retrieved from
http://www.medicalnewstoday.com/articles/282929.
php
[45] R Hanson. 2016. The Age of Em: Work, Love, and
Life when Robots Rule the Earth. Oxford
University Press.
[46] Erika Check Hayden. 2016. A $3 -billion plan to
cure d isease. (2016).
[47] Erika Check Hayden. 2016. Biology software
promises easier way to program living cells. Nat.
News (2016). Retrieved from Biology software
promises easier way to program living cells
Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 415
[48] Kenneth J Hayworth. 2012. Electron imaging
technology fo r whole brain neural circuit mapping.
Int. J. Mach. Conscious. 4, 1 (2012), 87 –108.
[49] Clyde A Hutchison, Ray -Yuan Chuang, Vladimir N
Noskov, Nacyra Assad -Garcia, Thomas J Deerinck,
Mark H Ellisman, John Gill, Krishna Kannan,
Bogumil J Karas, and Li Ma. 2016 . Design and
synthesis of a minimal bacterial genome. Science
351, 6280 (2016), aad6253.
[50] IBM. 2017. Medical Sieve. Retrieved from
http://researcher.watson.ibm.com/researcher/view_
group.php?id=4384
[51] CB Insights. 2016. From Virtual Nurses to Drug
Discovery:  65+ Artificial Intelligence Startups in
Healthcare. CB Insights (2016).
[52] Intel. 2017. Intel healthcare overview. Retrieved
from
http://www.intel.com/content/www/us/en/healthcar
e-it/healthcare -overview.html
[53] John PA Ioannidis. 2005. Contradicted and initia lly
stronger effects in highly cited clinical research.
Jama 294, 2 (2005), 218 –228.
[54] Jonathan R Karr, Jayodita C Sanghvi, Derek N
Macklin, Miriam V Gutschow, Jared M Jacobs,
Benjamin Bolival, Nacyra Assad -Garcia, John I
Glass, and Markus W Covert. 2012. A  whole -cell
computational model predicts phenotype from
genotype. Cell 150, 2 (2012), 389 –401.
[55] Arjun Kharpal. 2017. Elon Musk: Humans must
merge with machines or become irrelevant in AI
age. CNBC. Retrieved from
http://www.cnbc.com/2017/02/13/elon -musk -
humans -merge -machines -cyborg -artificial -
intelligence -robots.html
[56] Valeria Kogan, Ivan Molodtsov, Leonid I
Menshikov, Robert J Shmookler Reis, and Peter
Fedichev. 2015. Stability analysis of a model gene
network links aging, stress resistance, and
negligible senescence. Sci. Rep. 5, (2015), 13589.
[57] Maria Konovalenko. 2016. Longevity Cookbook:
Combinations of Life Extending Drugs. Retrieved
from
https://medium.com/@mariakonovalenko/longevity
-cookbook -combinations -of-life-extending -drugs -
d092feb64c46
[58] Ray Kurzwe il. 2006. Singularity is Near. Viking.
[59] Christian de Looper. 2016. Google’s smart
bathroom patent puts sensors in your toilet, tub, and
mirror. Digital trends. Retrieved July 16, 2017 from
https://www.digitaltrends.com/home/google -smart -
bathroom -patent/
[60] Pavel Luksha. 2014. NeuroWeb Roadmap: Results
of Foresight & Call for Action. Retrieved from
https://www.slideshare.net/PavelLuksha/neuroweb -
roadmap -preliminary
[61] John Markoff. 2011. Computer Wins on
“Jeopardy!” Retrieved from
http://www.nytimes.com/2011/02/ 17/science/17jeo
pardy -watson.html?pagewanted=all  [62] Albert W Marshall and Ingram Olkin. 2015. A
bivariate Gompertz –Makeham life distribution. J.
Multivar. Anal. 139, (2015), 219 –226.
[63] Gianluka Mauro. 2016. Six graphs to understand
the state of Artificial Int elligence academic
research. Retrieved from https://blog.ai -
academy.com/six -graphs -to-understand -the-state-
of-ai-academic -research -3a79cac4c9c2
[64] Steve Hill May. 2017. The Need for Better Aging
Biomarkers. Life Ext. Advocaс Found. (2017).
Retrieved from htt p://www.leafscience.org/aging -
biomarkers/
[65] Ralf C Merkle. 1994. The molecular repair of the
brain. Cryonics Mag. 15, (1994).
[66] Richard A Miller, David E Harrison, Clinton M
Astle, Elizabeth Fernandez, Kevin Flurkey, Melissa
Han, Martin A Javors, Xinna Li, N ancy L Nadon,
and James F Nelson. 2014. Rapamycin ‐mediated
lifespan increase in mice is dose and sex dependent
and metabolically distinct from dietary restriction.
Aging Cell 13, 3 (2014), 468 –477.
[67] Ole Martin Moen. 2015. The case for cryonics. J.
Med. Eth ics (2015), medethics -2015.
[68] Dara Mohammadi and Nicola Davis. 2016. Can this
woman cure ageing with gene therapy? The
Observer. Retrieved November 14, 2017 from
http://www.theguardian.com/science/2016/jul/24/eli
zabeth -parrish -gene -therapy -ageing
[69] Molecula Maxima. 2017. Retrieved from
https://moleculamaxima.com/
[70] Megan Molteni. 2017. Blockchain Could Be the
Answer to Health Care’s Electronic Record Woes.
Wired. Retrieved July 16, 2017 from
https://www.wired.com/2017/02/moving -patient -
data-messy -blockchain -help/
[71] A. Moskalev, Elizaveta Chernyagina, Vasily
Tsvetkov, Alexander Fedintsev, Mikhail
Shaposhnikov, Vyacheslav Krut’ko, Alex
Zhavoronkov, and Brian K. Kennedy. 2016.
Developing criteria for evaluation of geroprotectors
as a key stage toward translation to  the clinic.
Aging Cell 15, 3 (June 2016), 407 –415.
DOI:https://doi.org/10.1111/acel.12463
[72] AA Moskalev and MA Batin. 2011. Biomarkers of
aging and aging -related pathologies. Dep. Bioeng.
Bioinforma. MV Lomonosov Mosc. State Univ.
(2011), 63.
[73] Vincent C Mü ller and N. Bostrom. 2016. Future
progress in artificial intelligence: A survey of
expert opinion. In Fundamental issues of artificial
intelligence. Springer, 553 –570.
[74] Kyaw Z Myint and Xiang -Qun Xie. 2015. Ligand
Biological Activity Predictions Using Fing erprint -
Based Artificial Neural Networks (FANN -QSAR).
Artif. Neural Netw. (2015), 149 –164.
[75] Miguel AL Nicolelis. 2014. Brain -to-Brain
Interfaces: When Reality Meets Science Fiction.
[76] Joshua Ogawa. 2017. Global AI startup financing
hit $5bn in 2016. Retrie ved from
http://asia.nikkei.com/Business/Trends/Global -AI-
startup -financing -hit-5bn-in-2016
416 Informatica 41 (2017 ) 401–417 M. Batin et al.

[77] Open worm. 2017. Retrieved from
http://www.openworm.org/
[78] Alexandra Ossola. 2015. Your Full Genome Can Be
Sequenced and Analyzed For Just $1,000. Pop. Sci.
(2015).  Retrieved from
http://www.popsci.com/cost -full-genome -
sequencing -drops -to-1000
[79] L. Piantanelli, G. Rossolini, A. Basso, A.
Piantanelli, M. Malavolta, and A. Zaia. 2001. Use
of mathematical models of survivorship in the study
of biomarkers of aging: the ro le of heterogeneity.
Mech. Ageing Dev. 122, 13 (September 2001),
1461 –1475. DOI:https://doi.org/10.1016/S0047 -
6374(01)00271 -8
[80] Alessandro Polini, Ljupcho Prodanov, Nupura S
Bhise, Vijayan Manoharan, Mehmet R Dokmeci,
and Ali Khademhosseini. 2014. Organs -on-a-chip: a
new tool for drug discovery. Expert Opin. Drug
Discov. 9, 4 (2014), 335 –352.
[81] Alison Proffitt. 2013. NextCODE Health Launches
deCODE’s Clinical Genomics Platform. Retrieved
July 16, 2017 from http://www.bio -itworld.com
[82] Evgeny Putin, Polina Mamo shina, Alexander
Aliper, Mikhail Korzinkin, Alexey Moskalev,
Alexey Kolosov, Alexander Ostrovskiy, Charles
Cantor, Jan Vijg, and Alex Zhavoronkov. 2016.
Deep biomarkers of human aging: application of
deep neural networks to biomarker development.
Aging 8, 5 (2016), 1021.
[83] Edward O Pyzer ‐Knapp, Kewei Li, and Alan
Aspuru‐Guzik. 2015. Learning from the harvard
clean energy project: The use of neural networks to
accelerate materials discovery. Adv. Funct. Mater.
25, 41 (2015), 6495 –6502.
[84] Ladislav Rampasek and Anna Goldenberg. 2016.
Tensorflow: Biology’s gateway to deep learning?
Cell Syst. 2, 1 (2016), 12 –14.
[85] Lidia Ramsey. 2016. Futurist Ray Kurzweil wants
to use tiny robots in our bloodstream to fight
disease and live forever. Business insider. Retrieved
from  http://www.businessinsider.com/ray -kurzweil -
on-nanobots -and-the-immune -system -2016 -4
[86] Ann M Richard, Richard S Judson, Keith A Houck,
Christopher M Grulke, Patra Volarath, Inthirany
Thillainadarajah, Chihae Yang, James Rathman,
Matthew T Martin, and John F Wambaugh. 2016.
ToxCast chemical landscape: paving the road to
21st century toxicology. Chem. Res. Toxicol. 29, 8
(2016), 1225 –1251.
[87] Arlan Richardson, Steven N Austad, Yuji Ikeno,
Archana Unnikrishnan, and Roger J McCarter.
2016. Significant life extens ion by ten percent
dietary restriction. Ann. N. Y. Acad. Sci. 1363, 1
(2016), 11 –17.
[88] A Rosenberg and JH Meyerle. 2017. Total -body
photography in skin cancer screening: the clinical
utility of standardized imaging. Cutis 99, 5 (2017),
312.
[89] J. Russell. 201 7. After beating the world’s elite Go
players, Google’s AlphaGo AI is retiring.
TechCrunch. Retrieved from https://techcrunch.com/2017/05/27/googles -
alphago -ai-is-retiring/
[90] Andrew D Rutenberg, Arnold B Mitnitski, Spencer
Farrell, and Kenneth Rockwood. 201 7. Unifying
ageing and frailty through complex dynamical
networks. ArXiv Prepr. ArXiv170606434 (2017).
[91] Aaron Saenz. 2009. Smart Toilets: Doctors in Your
Bathroom. Singularity Hub. Retrieved July 16,
2017 from
https://singularityhub.com/2009/05/12/smart -
toilets-doctors -in-your-bathroom/
[92] Marwin HS Segler, Thierry Kogej, Christian
Tyrchan, and Mark P Waller. 2017. Generating
focussed molecule libraries for drug discovery with
recurrent neural networks. ArXiv Prepr.
ArXiv170101329 (2017).
[93] V. Shakirov. 2016. Review of state -of-the-arts in
artificial intelligence with application to AI safety
problem. ArXiv Prepr. ArXiv160504232 (2016).
Retrieved from https://arxiv.org/abs/1605.04232
[94] Carl Shulman. 2010. Whole brain emulation and the
evolution of superorganisms . Mach. Intell. Res.
Inst. Work. Pap. Httpintelligence OrgfilesWBE -
Superorgs Pdf (2010).
[95] Andrew Sparkes, Wayne Aubrey, Emma Byrne,
Amanda Clare, Muhammed N Khan, Maria Liakata,
Magdalena Markham, Jem Rowland, Larisa N
Soldatova, and Kenneth E Whelan. 2010 . Towards
Robot Scientists for autonomous scientific
discovery. Autom. Exp. 2, 1 (2010), 1.
[96] Randy Strong, Richard A Miller, Clinton M Astle,
Robert A Floyd, Kevin Flurkey, Kenneth L
Hensley, Martin A Javors, Christiaan
Leeuwenburgh, James F Nelson, and En nio Ongini.
2008. Nordihydroguaiaretic acid and aspirin
increase lifespan of genetically heterogeneous male
mice. Aging Cell 7, 5 (2008), 641 –650.
[97] Peter Szolovits. 1982. Artificial intelligence in
medicine. Westview Press Boulder, CO.
[98] Turbine AI. 2017. R etrieved from Turbine.ai
[99] A. Turchin and D. Denkenberger. 2017. Global
Solutions of the AI Safety Problem.
[100]  A. Turchin, D. Denkenberger, E. Milova, A.
Egorova, and M. Batin. 2017. Fighting aging as an
effective altruism case: the model of impact.
[101]  Valentin  Turchin and Cliff Joslyn. 1990.
Communications: The Cybernetic Manifesto (Part
I). Kybernetes 19, 2 (1990), 63 –64.
[102]  Tim Urban. 2017. Neuralink and the Brain’s
Magical Future. Retrieved from
http://waitbutwhy.com/2017/04/neuralink.html
[103]  ORD US EPA. 2015. Tox icity Forecasting. US
EPA. Retrieved July 16, 2017 from
https://www.epa.gov/chemical -research/toxicity -
forecasting
[104]  Monica Van Such, Robert Lohr, Thomas
Beckman, and James M Naessens. 2017. Extent of
diagnostic agreement among medical referrals. J.
Eval. Cl in. Pract. (2017).
[105]  Jan Vijg and Aubrey DNJ De Grey. 2014.
Innovating aging: promises and pitfalls on the road
Artificial Intelligence in Life Extension...  Informatica 41 (2017 ) 401–417 417
to life extension. Gerontology 60, 4 (2014), 373 –
380.
[106]  Vernor Vinge. 1993. Technological singularity.
30–31.
[107]  Gian Volpicelli. 2016. This Transhuman ist
Records Everything Around Him So His Mind Will
Live Forever. Vice.Motherboard. Retrieved from
https://motherboard.vice.com/en_us/article/4xangw/
this-transhumanist -records -everything -around -him-
so-his-mind -will-live-forever
[108]  Gian Volpicelli. 2016. This T ranshumanist
Records Everything Around Him So His Mind Will
Live Forever. Motherboard. Retrieved August 24,
2017 from
https://motherboard.vice.com/en_us/article/4xangw/
this-transhumanist -records -everything -around -him-
so-his-mind -will-live-forever
[109]  Brian Wan g. 2017. Final Frontier Medical
Devices won the tricorder xprize with device that
can diagnose 90% of ER situations at home.
Retrieved from
https://www.nextbigfuture.com/2017/04/final -
frontier -medical -devices -won-the-tricorder -
xprize.html
[110]  John G White, Eil een Southgate, J Nichol
Thomson, and Sydney Brenner. 1986. The structure
of the nervous system of the nematode
Caenorhabditis elegans: the mind of a worm. Phil
Trans R Soc Lond 314, (1986), 1 –340.
[111]  Zongli Xu and Jack A Taylor. 2013. Genome -
wide age -related DNA methylation changes in
blood and other tissues relate to histone
modification, expression and cancer.
Carcinogenesis 35, 2 (2013), 356 –364.
[112]  R. Yampolskiy. 2013. Turing test as a defining
feature of AI -completeness. Artif. Intell. Evol.
Comput. Metaheuristics (2013), 3 –17.
[113]  R. Yampolskiy. 2015. Artificial
Superintelligence: a Futuristic Approach. CRC
Press.
[114]  E. Yudkowsky. 2008. Artificial Intelligence as a
Positive and Negative Factor in Global Risk, in
Global Catastrophic Risks. Oxford University
Press: Oxford, UK.
[115]  Alex Zhavoronkov and Bhupinder Bhullar.
2015. Classifying aging as a disease in the context
of ICD -11. Front. Genet. 6, (2015).
[116]  2013. Intelligent Toilet Monitors Your Health.
iStep Blog by ifm efector. Retrieved July 16, 2017
from
http:/ /istep.ifmefector.com/2013/04/09/intelligent -
toilet -monitors -your-health/
[117]  2016. JCVI: First Minimal Synthetic Bacterial
Cell Designed and Constructed by Scientists at
Venter Institute and Synthetic Genomics, Inc.
Retrieved July 16, 2017 from
http://www.jcv i.org/cms/press/press -releases/full -
text/article/first -minimal -synthetic -bacterial -cell-
designed -and-constructed -by-scientists -at-venter -
institute -an [118]  2017. Global Artificial Intelligence in Medicine
Market. Retrieved from
https://www.mordorintelligence.com /industry -
reports/artificial -intelligence -in-medicine -market
[119]  2017. Deep genomics. Retrieved from
https://www.deepgenomics.com/
[120]  2017. Atomwise. Retrieved from
http://www.atomwise.com/
[121]  2017. Biohub cell atlas. Retrieved from
https://czbiohub.org/projects/cel l-atlas/
[122]  2017. Biostamp. Retrieved from
https://www.mc10inc.com/our -products/biostamprc
[123]  2017. Nimb: A Smart Ring That Helps You Feel
Safe And Sound. Retrieved from
https://www.kickstarter.com/projects/1629204423/n
imb-a-smart -ring-that-keeps -you-safe-and-sound
[124]  2017. Artificial Intelligence for Continuous
Patient  Monitoring. Retrieved from
https://aicure.com/
[125]  2017. GSK and Regeneron to mine gene data
from 500,000 Britons. Reuters. Retrieved July 16,
2017 from http://uk.reuters.com/article/uk -health -
genes -gsk-regeneron -pharms -idUKKBN16U01O
[126]  2017. Lifespan.io. Retrieved July 16, 2017 from
https://www.lifespan.io/
[127]  2017. AlphaGo. Retrieved from
https://deepmind.com/research/alphago/
[128]  2017. Open Longevity. Retrieved from
http://openlongevity.org/

418 Informatica 41 (2017 ) 401–417 M. Batin et al.

Mikhail Batin
Alexey Turchin
Markov Sergey
Alisa Zhila
David Denkenberger