Literature review for my topic progress of artificial intelligence and I want the article review for the articles . I attached the rubric. below


The first page = 20 points, other 10 pages = 8 points/each
Total: 20 + 10*8 = 100 points

TOPIC: ____________________________type it here________





1. Article Citation (APA format)

2. Topical Focus

3. Article Summary/Contribution to Field



Ethical Aspects of the Impact of AI: the Status of Humans in the Era
of Artificial Intelligence

Roman Rakowski1 & Petr Polak2 & Petra Kowalikova1

Accepted: 6 May 2021
# Springer Science+Business Media, LLC, part of Springer Nature 2021

On the one hand, AI is a functional tool for emancipating people from routine work tasks, thus expanding the possibilities of their
self-realization and the utilization of individual interests and aspirations through more meaningful spending of time. On the other
hand, there are undisputable risks associated with excessive machine autonomy and limited human control, based on the
insufficient ability to monitor the performance of these systems and to prevent errors or damage (Floridi et al. Minds &
Machines 28, 689–707, 2018). In connection with the use of ethical principles in the research and development of artificial
intelligence, the question of the social control of science and technology opens out into an analysis of the opportunities and risks
that technological progress can mean for security, democracy, environmental sustainability, social ties and community life, value
systems, etc. For this reason, it is necessary to identify and analyse the aspects of artificial intelligence that could have the most
significant impact on society. The present text is focused on the application of artificial intelligence in the context of the market
and service sector, and the related process of exclusion of people from the development, production and distribution of goods and
services. Should the application of artificial intelligence be subject to value frameworks, or can the application of AI be
sufficiently regulated by the market on its own?

Keywords AI . Big data . Datafication . Commodification of data . Digital ideology . Ethical aspects


We live in a period of digital turn, which is often referred to by
media, theorists and experts as the fourth industrial revolution
or Industry 4.0. The 4.0 concept was originally intended in
relation to the field of industry and production, in which there
will be such great changes that the whole social sphere will
subsequently change – as was the case during previous tech-
nological revolutions. The opposite is true; it is necessary to

talk more about the inconspicuous technological evolution
that is taking place at all levels of society, not just at the level
of the industry. The reach of modern technology has long
gone beyond research, development and manufacturing and
has completely dominated public and private life to the point
that 4.0 seems to be a society based on the interconnection of
technology, people and data (Big Data). However, this means
that new ethical and political challenges lie in the implemen-
tation of new technologies. On the one hand, technologies are
radically changing the environment in which we live, and on
the other hand, without us rea

■ Although one of the fundamental goals of AI is to
understand and develop intelligent systems that
have all the capabilities of humans, there is little
active research directly pursuing this goal. We pro-
pose that AI for interactive computer games is an
emerging application area in which this goal of
human-level AI can successfully be pursued. Inter-
active computer games have increasingly complex
and realistic worlds and increasingly complex and
intelligent computer-controlled characters. In this
article, we further motivate our proposal of using
interactive computer games for AI research, review
previous research on AI and games, and present
the different game genres and the roles that
human-level AI could play within these genres. We
then describe the research issues and AI techniques
that are relevant to each of these roles. Our conclu-
sion is that interactive computer games provide a
rich environment for incremental research on
human-level AI.

ver the last 30 years, research in AI has
fragmented into more and more spe-
cialized fields, working on more and

more specialized problems, using more and
more specialized algorithms. This approach
has led to a long string of successes with impor-
tant theoretical and practical advancements.
However, these successes have made it easy for
us to ignore our failure to make significant
progress in building human-level AI systems.
Human-level AI systems are the ones that you
dreamed about when you first heard of AI: HAL
from 2001, A Space Odyssey; DATA from Star Trek;
or CP30 and R2D2 from Star Wars. They are
smart enough to be both triumphant heroes
and devious villains. They seamlessly integrate
all the human-level capabilities: real-time
response, robustness, autonomous intelligent
interaction with their environment, planning,

communication with natural language, com-
monsense reasoning, creativity, and learning.

If this is our dream, why isn’t any progress
being made? Ironically, one of the major rea-
sons that almost nobody (see Brooks et al.
[2000] for one high-profile exception) is work-
ing on this grand goal of AI is that current
applications of AI do not need full-blown
human-level AI. For almost all applications,
the generality and adaptability of human
thought is not needed—specialized, although
more rigid and fragile, solutions are cheaper
and easier to develop. Unfortunately, it is
unclear whether the approaches that have
been developed to solve specific problems are
the right building blocks for creating human-
level intelligence. The thesis of this article is
that interactive computer games are the killer
application for human-level AI. They are the
application that will need human-level AI.
Moreover, they can provide the environments
for research on the right kinds of problem that
lead to the type of incremental and integrative
research needed to achieve human-level AI.

Computer-Generated Forces
Given that our personal goal is to build human-
level AI systems, we have struggl

Müller, Vincent C. and Bostrom, Nick (2016), ‘Future progress in artificial intelligence:
A survey of expert opinion’, in Vincent C. Müller (ed.), Fundamental Issues of Artificial
Intelligence (Synthese Library; Berlin: Springer), 553-571.
[A short version of this paper appeared as (2014) ‘Future progress in artificial intelli-
gence: A poll among experts’, AI Matters, 1 (1), 9-11.]

Future Progress in Artificial Intelligence:
A Survey of Expert Opinion

Vincent C. Müller a,b & Nick Bostrom a

a) Future of Humanity Institute, Department of Philosophy & Oxford Martin School,
University of Oxford. b)Anatolia College/ACT, Thessaloniki

Abstract: There is, in some quarters, concern about high–level machine
intelligence and superintelligent AI coming up in a few decades, bring-
ing with it significant risks for humanity. In other quarters, these issues
are ignored or considered science fiction. We wanted to clarify what the
distribution of opinions actually is, what probability the best experts
currently assign to high–level machine intelligence coming up within a
particular time–frame, which risks they see with that development, and
how fast they see these developing. We thus designed a brief question-
naire and distributed it to four groups of experts in 2012/2013. The
median estimate of respondents was for a one in two chance that high-
level machine intelligence will be developed around 2040-2050, rising
to a nine in ten chance by 2075. Experts expect that systems will move
on to superintelligence in less than 30 years thereafter. They estimate
the chance is about one in three that this development turns out to be
‘bad’ or ‘extremely bad’ for humanity.

1. Introduction
Artificial Intelligence began with the “… conjecture that every aspect of learning or
any other feature of intelligence can in principle be so precisely described that a ma-
chine can be made to simulate it.” (McCarthy, Minsky, Rochester, & Shannon, 1955,
p. 1) and moved swiftly from this vision to grand promises for general human-level AI
within a few decades. This vision of general AI has now become merely a long-term
guiding idea for most current AI research, which focuses on specific scientific and en-
gineering problems and maintains a distance to the cognitive sciences. A small minori-
ty believe the moment has come to pursue general AI directly as a technical aim with
the traditional methods – these typically use the label ‘artificial general intelligence’
(AGI) (see Adams et al., 2012).

Future Progress in Artificial Intelligence: A Poll Among Experts 2/19

If general AI were to be achieved, this might also lead to superintelligence: “We
can tentatively define a superintelligence as any intellect that greatly exceeds the cognitive per-
formance of humans in virtually all domains of interest.” (Bostrom, 2014 ch. 2). One idea how
superintelligence might come about is that if we

oday’s AI systems can be remarkably effective. They can
solve planning and scheduling problems that are
beyond what unaided people can accomplish, sift

through mountains of data (both structured and unstruc-
tured) to help us find answers, and robustly translate speech
and handwriting into text. But these systems are carefully
crafted for specific purposes, created and maintained by
highly trained personnel who are experts in artificial intelli-
gence and machine learning. There has been much less
progress on building general-purpose AI systems, which
could be trained and tasked to handle multiple jobs. Indeed,
in my experience, today’s general-purpose AI systems tend to
skate a very narrow line between catatonia and attention
deficit disorder.

People and other mammals, by contrast, are not like that.
Consider dogs. A dog can be taught to do tasks like shaking
hands, herding sheep, guarding a perimeter, and helping a
blind person maneuver through the world. Instructing dogs
can be done by people who don’t have privileged access to
the internals of their minds. Dogs don’t blue screen. What if
AI systems were as robust, trainable, and taskable as dogs?
That would be a revolution in artificial intelligence.

In my group’s research on the companion cognitive archi-
tecture (Forbus et al. 2009), we are working toward such a
revolution. Our approach is to try to build software social
organisms. By that we mean four things:

First, companions should be able to work with people
using natural interaction modalities. Our focus so far has
been on natural language (for example, learning by reading
[Forbus et al. 2007; Barbella and Forbus 2011]) and sketch
understanding (Forbus et al. 2011).

Second, companions should be able to learn and adapt
over extended periods of time. This includes formulating
their own learning goals and pursuing them, in order to
improve themselves.

Third, companions should be able to maintain them-
selves. This does not mean a 24-hour, 7-day-a-week operation
— even people need to sleep, to consolidate learning. But


SPRING 2016 85Copyright © 2016, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602

Software Social Organisms:
Implications for Measuring

AI Progress

Kenneth D. Forbus

� In this article I argue that achieving
human-level AI is equivalent to learn-
ing how to create sufficiently smart soft-
ware social organisms. This implies
that no single test will be sufficient to
measure progress. Instead, evaluations
should be organized around showing
increasing abilities to participate in our
culture, as apprentices. This provides
multiple dimensions within which
progress can be measured, including
how well different interaction modali-
ties can be used, what range of domains
can be tackled, what human-normed
levels of knowledge they are able to
acquire, as well as others. I begin by
motivating the idea of software social
organisms, drawing on ideas from oth-


Minds and Machines (2020) 30:99–120

1 3

The Ethics of AI Ethics: An Evaluation of Guidelines

Thilo Hagendorff1

Received: 1 October 2019 / Accepted: 21 January 2020 / Published online: 1 February 2020
© The Author(s) 2020

Current advances in research, development and application of artificial intelligence
(AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a
number of ethics guidelines have been released in recent years. These guidelines
comprise normative principles and recommendations aimed to harness the “disrup-
tive” potentials of new AI technologies. Designed as a semi-systematic evaluation,
this paper analyzes and compares 22 guidelines, highlighting overlaps but also omis-
sions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also
examine to what extent the respective ethical principles and values are implemented
in the practice of research, development and application of AI systems—and how
the effectiveness in the demands of AI ethics can be improved.

Keywords Artificial intelligence · Machine learning · Ethics · Guidelines ·

1 Introduction

The current AI boom is accompanied by constant calls for applied ethics, which are
meant to harness the “disruptive” potentials of new AI technologies. As a result, a
whole body of ethical guidelines has been developed in recent years collecting prin-
ciples, which technology developers should adhere to as far as possible. However,
the critical question arises: Do those ethical guidelines have an actual impact on
human decision-making in the field of AI and machine learning? The short answer
is: No, most often not. This paper analyzes 22 of the major AI ethics guidelines and
issues recommendations on how to overcome the relative ineffectiveness of these

AI ethics—or ethics in general—lacks mechanisms to reinforce its own nor-
mative claims. Of course, the enforcement of ethical principles may involve

* Thilo Hagendorff

1 Cluster of Excellence “Machine Learning: New Perspectives for Science”, University
of Tuebingen, Tübingen, Germany

100 T. Hagendorff

1 3

reputational losses in the case of misconduct, or restrictions on memberships in
certain professional bodies. Yet altogether, these mechanisms are rather weak and
pose no eminent threat. Researchers, politicians, consultants, managers and activ-
ists have to deal with this essential weakness of ethics. However, it is also a reason
why ethics is so appealing to many AI companies and institutions. When companies
or research institutes formulate their own ethical guidelines, regularly incorporate
ethical considerations into their public relations work, or adopt ethically motivated

Long-Term Trends in the
Public Perception of Artificial Intelligence

Ethan Fast, Eric Horvitz,


Analyses of text corpora over time can reveal trends in be-
liefs, interest, and sentiment about a topic. We focus on views
expressed about artificial intelligence (AI) in the New York
Times over a 30-year period. General interest, awareness, and
discussion about AI has waxed and waned since the field was
founded in 1956. We present a set of measures that captures
levels of engagement, measures of pessimism and optimism,
the prevalence of specific hopes and concerns, and topics that
are linked to discussions about AI over decades. We find that
discussion of AI has increased sharply since 2009, and that
these discussions have been consistently more optimistic than
pessimistic. However, when we examine specific concerns,
we find that worries of loss of control of AI, ethical concerns
for AI, and the negative impact of AI on work have grown in
recent years. We also find that hopes for AI in healthcare and
education have increased over time.


Artificial intelligence will spur innovation and create oppor-
tunities, both for individuals and entrepreneurial companies,
just as the Internet has led to new businesses like Google and
new forms of communication like blogs and social network-
ing. Smart machines, experts predict, will someday tutor stu-
dents, assist surgeons and safely drive cars.
Computers Learn to Listen, and Some Talk Back. NYT, 2010
In the wake of recent technological advances in computer vi-
sion, speech recognition and robotics, scientists say they are
increasingly concerned that artificial intelligence technolo-
gies may permanently displace human workers, roboticize
warfare and make Orwellian surveillance techniques easier
to develop, among other disastrous effects.
Study to Examine Effects of Artificial Intelligence. NYT, 2014

These two excerpts from articles in the New York Times
lay out competing visions for the future of artificial intel-
ligence (AI) in our society. The first excerpt is optimistic
about the future of AI—the field will “spur innovation,” cre-
ating machines that tutor students or assist surgeons—while
the second is pessimistic, raising concerns about displaced
workers and dystopian surveillance technologies. But which
vision is more common in the public imagination, and how
have these visions evolved over time?

Copyright c© 2017, Association for the Advancement of Artificial
Intelligence ( All rights reserved.

Understanding public concerns about AI is important, as
these concerns can translate into regulatory activity with po-
tentially serious repercussions (Stone, P. et al. 2016). For
example, some have recently suggested that the government
should regulate AI development to prevent existential threats
to humanity (Guardian 2014). Others have argued that racial
profiling is implicit in some machine learning algorithms, in

Birth of Industry 5.0:
Making Sense of Big Data with Artificial Intelligence,

‘‘The Internet of Things’’ and Next-Generation
Technology Policy

Vural Özdemir

and Nezih Hekim


Driverless cars with artificial intelligence (AI) and automated supermarkets run by collaborative robots (cobots)
working without human supervision have sparked off new debates: what will be the impacts of extreme
automation, turbocharged by the Internet of Things (IoT), AI, and the Industry 4.0, on Big Data and omics
implementation science? The IoT builds on (1) broadband wireless internet connectivity, (2) miniaturized
sensors embedded in animate and inanimate objects ranging from the house cat to the milk carton in your smart
fridge, and (3) AI and cobots making sense of Big Data collected by sensors. Industry 4.0 is a high-tech strategy
for manufacturing automation that employs the IoT, thus creating the Smart Factory. Extreme automation until
‘‘everything is connected to everything else’’ poses, however, vulnerabilities that have been little considered to
date. First, highly integrated systems are vulnerable to systemic risks such as total network collapse in the event
of failure of one of its parts, for example, by hacking or Internet viruses that can fully invade integrated systems.
Second, extreme connectivity creates new social and political power structures. If left unchecked, they might
lead to authoritarian governance by one person in total control of network power, directly or through her/his
connected surrogates. We propose Industry 5.0 that can democratize knowledge coproduction from Big Data,
building on the new concept of symmetrical innovation. Industry 5.0 utilizes IoT, but differs from predecessor
automation systems by having three-dimensional (3D) symmetry in innovation ecosystem design: (1) a built-in
safe exit strategy in case of demise of hyperconnected entrenched digital knowledge networks. Importantly,
such safe exists are orthogonal—in that they allow ‘‘digital detox’’ by employing pathways unrelated/unaf-
fected by automated networks, for example, electronic patient records versus material/article trails on vital
medical information; (2) equal emphasis on both acceleration and deceleration of innovation if diminishing
returns become apparent; and (3) next generation social science and humanities (SSH) research for global
governance of emerging technologies: ‘‘Post-ELSI Technology Evaluation Research’’ (PETER). Importantly,
PETER considers the technology opportunity costs, ethics, ethics-of-ethics, framings (epistemology), inde-
pendence, and reflexivity of SSH research in technology policymaking. Industry 5.0 is poised to harness
extreme automation and Big Data with safety, innovative technology policy, and responsible implementation
science, enabled by 3D symmetry in innovation ecosystem design.

Keywords: artificial intelligence, Big Data, Industry 5.0, Internet of Things, technology policy

Independent Writer and Researc



Technical and Humanities Students’ Perspectives
on the Development and Sustainability of Artificial
Intelligence (AI)

Vasile Gherhes, 1,* ID and Ciprian Obrad 2 ID

1 Department of Communication and Foreign Languages, Politehnica University of Timis, oara,
300006 Timis, oara, Romania

2 Department of Sociology, West University of Timis, oara, 300223 Timis, oara, Romania;
* Correspondence:; Tel.: +40-721-022-440

Received: 17 July 2018; Accepted: 24 August 2018; Published: 28 August 2018

Abstract: This study investigates how the development of artificial intelligence (AI) is perceived by
the students enrolled in technical and humanistic specializations at two universities in Timisoara.
It has an emphasis on identifying their attitudes towards the phenomenon, on the connotations
associated with it, and on the possible impact of artificial intelligence on certain areas of the social life.
Moreover, the present study reveals the students’ perceptions on the sustainability of these changes
and developments, and therefore aims to reduce the possible negative impact on consumers, and at
anticipate the changes that AI will produce in the future. In order to collect the data, the authors have
used a quantitative research method. A questionnaire-based sociological survey was completed by
928 students, with a representation error of only ±3%. The analysis has shown that a great number of
respondents have a positive attitude towards the emergence of AI, who believe it will influence society
for the better. The results have also underscored underlying differences based on the respondents’
type of specialization (humanistic or technical), and their gender.

Keywords: artificial intelligence; sustainable development; technology; perceptions

1. Introduction

We live in a constantly changing world, marked by profound transformations that are generated
by technological advancements, happening at an unprecedented pace. The incredible technological
changes taking place every day are also due to the great number of programs carried out by an
increasing number of researchers in universities, and various organizations. We are now witnessing.
and wondering about the ways in which technology impacts society—such as ways that could
lead to sustainable growth in the economy, in culture, and in our life expectancy [1]. We use so
much technology daily that we have grown dependent on it. We use it to communicate, to learn,
to travel, to do business, etc. Technology has simplified the access to many of the tools needed in the
above-mentioned areas of interest.

It has become obvious that the continuous technological evolution is vital to the contemporary society.
Nevertheless, it is difficult to quantify the way in which each new technology has affected our lives,
and the way in which it is going to influence our future. Although technology solves many problems,
it also generates new

Machine learning & artificial intelligence in the quantum domain

Vedran Dunjko

Institute for Theoretical Physics, University of Innsbruck, Innsbruck 6020, Austria
Max Planck Institute of Quantum Optics, Garching 85748, Germany

Hans J. Briegel

Institute for Theoretical Physics, University of Innsbruck Innsbruck 6020, Austria
Department of Philosophy, University of Konstanz, Konstanz 78457, Germany

Abstract. Quantum information technologies, on the one side, and intelligent learning
systems, on the other, are both emergent technologies that will likely have a transforming
impact on our society in the future. The respective underlying fields of basic research –
quantum information (QI) versus machine learning and artificial intelligence (AI) – have
their own specific questions and challenges, which have hitherto been investigated largely
independently. However, in a growing body of recent work, researchers have been prob-
ing the question to what extent these fields can indeed learn and benefit from each other.
QML explores the interaction between quantum computing and machine learning, inves-
tigating how results and techniques from one field can be used to solve the problems of
the other. In recent time, we have witnessed significant breakthroughs in both directions
of influence. For instance, quantum computing is finding a vital application in providing
speed-ups for machine learning problems, critical in our “big data” world. Conversely,
machine learning already permeates many cutting-edge technologies, and may become
instrumental in advanced quantum technologies. Aside from quantum speed-up in data
analysis, or classical machine learning optimization used in quantum experiments, quan-
tum enhancements have also been (theoretically) demonstrated for interactive learning
tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works
exploring the use of artificial intelligence for the very design of quantum experiments,
and for performing parts of genuine research autonomously, have reported their first
successes. Beyond the topics of mutual enhancement – exploring what ML/AI can do
for quantum physics, and vice versa – researchers have also broached the fundamental
issue of quantum generalizations of learning and AI concepts. This deals with questions
of the very meaning of learning and intelligence in a world that is fully described by
quantum mechanics. In this review, we describe the main ideas, recent developments,
and progress in a broad spectrum of research investigating machine learning and artificial
intelligence in the quantum domain.


I. Introduction 3
A. Quantum mechanics, computation and information processing 4
B. Artificial intelligence and machine learning 7

1. Learning from data: machine learning 9
2. Learning from interaction: reinforcement learning 11
3. Intermediary learning settings 12
4. Putting it all together: the agent

Progress in Retinal and Eye Research xxx (xxxx) xxx

Please cite this article as: Cristina González-Gonzalo, Progress in Retinal and Eye Research,

Available online 10 December 2021
1350-9462/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (

Trustworthy AI: Closing the gap between development and integration of AI
systems in ophthalmic practice

Cristina González-Gonzalo a, b, *, Eric F. Thee c, d, Caroline C.W. Klaver c, d, e, f, Aaron Y. Lee g,
Reinier O. Schlingemann h, i, Adnan Tufail j, k, Frank Verbraak h, Clara I. Sánchez a, l

a Eye Lab, qurAI Group, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands
b Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
c Department of Ophthalmology, Erasmus Medical Center, Rotterdam, the Netherlands
d Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
e Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the Netherlands
f Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
g Department of Ophthalmology, School of Medicine, University of Washington, Seattle, WA, USA
h Department of Ophthalmology, Amsterdam University Medical Center, Amsterdam, the Netherlands
i Department of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland
j Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
k Institute of Ophthalmology, University College London, London, United Kingdom
l Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, the Netherlands


Artificial intelligence
Deep learning
Machine learning
Ophthalmic care


An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by
the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of
patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap
between development and integration of AI systems in ophthalmic practice. This work focuses on the importance
of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along
the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including
those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and
considerations to address those aspects or challenges, and define the roles and responsibilities of the different
stakeholders involved in AI for ophthalmic care, i.e., AI d

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