ARTIFICIAL INTELLIGENCE NARRATIVES: AN OBJECTIVE PERSPECTIVE ON CURRENT DEVELOPMENTS
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A RTIFICIAL I NTELLIGENCE N ARRATIVES : A N O BJECTIVE
P ERSPECTIVE ON C URRENT D EVELOPMENTS
Noah Klarmann∗
Chair of Robotics, AI and Real-time Systems
Department of Informatics
arXiv:2103.11961v1 [cs.AI] 18 Mar 2021
Technical University of Munich
March 23, 2021
A BSTRACT
This work provides a starting point for researchers interested in gaining a deeper understand-
ing of the big picture of artificial intelligence (AI). To this end, a narrative is conveyed that
allows the reader to develop an objective view on current developments that is free from false
promises that dominate public communication. An essential takeaway for the reader is that
AI must be understood as an umbrella term encompassing a plethora of different methods,
schools of thought, and their respective historical movements. Consequently, a bottom-up
strategy is pursued in which the field of AI is introduced by presenting various aspects that
are characteristic of the subject. This paper is structured in three parts: (i) Discussion of
current trends revealing false public narratives, (ii) an introduction to the history of AI
focusing on recurring patterns and main characteristics, and (iii) a critical discussion on the
limitations of current methods in the context of the potential emergence of a strong(er) AI. It
should be noted that this work does not cover any of these aspects holistically; rather, the
content addressed is a selection made by the author and subject to a didactic strategy.
1 Today’s Perspectives on AI (Google™, Facebook™, or Amazon™). Commercially
viable business models are typically built on exceed-
ingly narrow models to be competitive as - similar
Undoubtedly, advances in AI have enormous impli- to humans - specialization usually leads to superior
cations on societies, politics, and industries, which performance. Products from big tech corporates are
has resulted in an increasing attention over the past ubiquitous in our everyday lives and may have led to
few years. The tremendous value from leveraging a widespread misunderstanding of what AI is. Truth
AI-based approaches is becoming more and more ap- to be told, it is exceptionally difficult to define the
parent in a wide variety of different areas, such as term intelligence on its own, which naturally also ap-
(i) drug discovery in medicine (an example is given plies to the term (AI). To complicate comprehension
by Ramsundar et al. [1]), (ii) theoretical research of the term "AI" even further, the societal view of AI is
where AI allows to systematically search hypothesis taken ad absurdum by unscientific debates or science
space to find unbiased, repeatable, and optimal re- fiction [5]. In addition, one can often observe unreal-
sults based on big data sets (for instance, see [2]), istic product advertisement when new products based
or (iii) transportation where researchers strive for on ordinary software development are sold as being
fully autonomous driving [3]. Especially data-driven based on advanced AI techniques, as "AI" is currently a
technologies are already transforming entire indus- very marketable keyword. Besides the confusion that
tries by improving processes or paving the way for is conveyed by public communication, the field of AI
fundamentally new business models [4]. It’s worth is difficult to understand as it comprises a wide vari-
noting that the huge popularity of AI is fueled by the ety of different schools of thought and methodologies.
fact that the world’s most successful companies sit on Furthermore, the public discussion is dominated by ex-
enormous data lakes of mostly personal data, form- tremes. On the one hand, AI evangelists appear who
ing powerful monopolies changing the way we live
∗
primary contact: noah.klarmann@tum.deArtificial Intelligence Narratives: An Objective Perspective on Current Developments
push away any concerns. On the other hand, there are lead to a loss of competitiveness. (ii) A second often
people who advocate an overly skeptical view on AI; raised issue is that advanced techniques are black-box
for example, by portraying a dystopian world in which models and hence, cannot be controlled and might
robots enslave humanity. It is obvious that all these sooner or later emerge towards something with an
mechanisms inevitably have an enormous impact on own will (see for instance [11]). While it is true that
the way AI is viewed today. For instance, young peo- many of the currently used methods are black-box
ple of generations "Y" and "Z" mostly associate AI with models (for example, machine learning), they are by
business models from big tech companies that exploit no means self-organizing or emergent (yet). It is also
AI, as this is the most visible aspect of their lifetime to worth noting that significant efforts are being made
date. At the same time, individuals from the "Boomer" by researchers to develop explainable AI to make use
and "X" generations are possibly more skeptical about of machine learning with more caution (especially
current developments in AI, primarily due to two dis- in safety-critical areas such as autonomous driving
tinct aspects that characterized the field of AI in the or in the medical field) or to derive knowledge from
20th century: (i) Reaching milestones usually caused machine learning models (an overview on this topic
disappointment when the anticipated technological can be found by Tjoa and Guan [15]).
revolution did not occur. For example, IBM’s Deep
Despite the great success in employing AI methods,
Blue defeated the world grandmaster Garri Kasparov
there is a significant lack of understanding in society,
in 1997 [6] that lead to great attention in the scien-
as well as among scientists. To this end, this paper
tific world and also beyond. Apparently, it was a great
provides an objective overview of the field of AI by
surprise to the public when the agent that is able to
addressing the most important characteristics. The au-
master such an intellectual task as chess would not
thor is convinced that this bottom-up approach (start-
made a good lawyer or taxi driver. (ii) It has happened
ing from what AI encompasses) is the best strategy
several times that entire industries with questionable
from a didactic point of view. The alternative (a top-
AI-based business models have perished when high
down approach) would imply to introduce a high-level
expectations led to disappointment among society and
definition that has to be exceedingly broad/complex
stakeholders as extravagant promises remained unful-
and would hence be difficult to formulate/understand.
filled. Although skepticism and misconceptions are
In the next section, an introduction to the history of AI
evident, AI is undoubtedly playing an increasingly
is given. Different eras that were prevalent in the past
important role in our daily lives, leading to growing
as well as important events, such as Milestones, are
concerns about privacy [7], security [8], fairness [9],
presented. A look at the recent past shows that trends
or the threat posed by autonomous military agents
in AI are often temporary, may change significantly
[10]. Consequently, regulation and standardization of
over time, were adopted by other methods, or branch
AI methods are crucial to constrain the development
into different disciplines. In the subsequent section,
in a way that corresponds to mankind’s expectations
an outlook on possible future developments is given.
[11, 12]. In this context, two concerns commonly
appear: (i) Higher degrees of automation are asso-
ciated with job losses as machines substitute human
labor. In fact, a survey among researchers revealed 2 Characteristics of AI history
a predicted chance of 50% that AI will be superior
to human in almost every task within the next 42 A brief introduction to the history of AI is provided in
years (until 2063) and could replace all human labor this section. Besides conveying known facts, main
within the next 117 years (by 2138) [13]. However, it characteristics of AI are presented to support the
should be noted that substituted jobs do not necessar- reader in the comprehension of current developments
ily lead to unemployment in the short term. Miller and and to derive possible scenarios for the future. On a
Atkinson [14] advocate that the "job loss" argument high level, the history of AI can be categorized into
is based on a common fallacy, namely the claim that four phases, as shown in Figure 1. The four phases dif-
the number of jobs decreases with increasing automa- fer primarily in the contemporary conceptions of the
tion/productivity. In history however, no correlation respective researchers regarding the best approach
between productivity and unemployment can be iden- that could eventually lead to higher intelligence. Fur-
tified. The wrong assumption of job losses is based on thermore, Figure 2 shows a detailed timeline of the
the hypothesis that there is a limit amount of labor history of AI with a selection of different milestones
that does not rise with increasing productivity. This and events.
is a wrong assumption as savings due to increased
The first phase in Figure 1 Cybernetics (mainly be-
productivity recycles back to economy, rising the de-
tween 1943 - 1955) can be seen as the pioneering
mand for products that in turn lead to more jobs. In
stage before AI emerged. Cybernetics is defined as
addition, new jobs are also created in the prospering
the study of the fundamentals of the control and com-
automation/AI industry [14]. It is worth noting that
munication in animals and machines [18]. To this
a major risk for industrial ecosystems is to refuse the
end, closed control loops are assumed where an agent
highest possible level of automation, as this would
acts on an environment to reach a goal based on
2Artificial Intelligence Narratives: An Objective Perspective on Current Developments
Figure 1: Historical epochs of AI (extended and adapted from Russell and Norvig [16] and Launchbury [17])
the perceived feedback. The overall goal in cyber- solving a wide variety of tasks such as (i) logistics
netics was to create simple systems that mimic ba- problems, (ii) theorem proving, (iii) or tax statement
sic cognitive processes of brains. Models from this creation. Two important milestones of this time are as
epoch adopted ideas from a wide variety of different follows: (i) The Logic Theorist (1956) by Newell and
disciplines such as (i) (neuro-) biology, (ii) commu- Simon [26] is considered the first program that per-
nication/information theory, (iii) game theory, (iv) formed automatic reasoning. This was demonstrated
mathematics - particularly statistics -, (v) philosophy - by proving mathematical theorems. A mathematical
especially logic -, (vi) experimental psychology, and hypothesis serves as the starting point from which
(vii) linguistics [19]. Due to the absence of comput- a tree is spanned whereas each path is built by the
ers, simple electronic networks were mostly used to application of logical rules. If a goal was found - the
examine and verify theoretical considerations. An im- respective path describes the prove. (ii) R1 (1978)
portant milestone of this time (and an example for by McDermott [27] is seen as the first example of a
the adoption of biology) is the McCulloch and Pitts commercially exploited expert systems that was able
model that is today considered as the first Turing- to assist in the processing of new orders for computer
complete digital model of neurons [20]. A further systems. Systems based on hand-crafted knowledge
popular representative of this epoch is SNARC, which are restricted to perceive information in a way they
is considered the first neural network computer [16]. are programmed for and are limited regarding the
Although the cybernetics era was largely abandoned ability to learn new knowledge or to abstract new
after 1956, some of the today’s prevailing methods findings from existing knowledge [17]. It is worth
are significantly based on theoretical concepts of this noting that AI based on hand-crafted knowledge is by
phase. For instance, recently reached milestones in AI no means an abandoned field but is still a hot topic
are based on deep reinforcement learning adopting with milestones like IBM’s Watson2 that competed hu-
both, neural networks and control loops (for instance, man opponents in Jeopardy in 2011 (an introduced
Atari [21] or AlphaGo [22, 23]). to IBM’s Watson can be found in [28]).
Launchbury defined three waves of AI that occur since The second wave of AI (see Figure 1) denotes the
1956, whereby the third wave is a projection of fu- upcoming trend of statistical learning and mainly oc-
ture developments that will be covered in the next curred after 1986. Statistical learning is inherently
section. The first wave of AI (see Figure 1) is subject different from systems based on hand-crafted knowl-
to handcrafted knowledge and mainly occurred after edge as information is not stored in ontologies of sym-
the Dartmouth Conference (1956), a summer work- bolic data but as weights like the connection strength
shop that is considered being the founding event of between neurons in neural networks. Today’s statisti-
AI [24]. In this epoch, the emergence of computers cal models are exceptionally good at learning based
lead to the idea to express knowledge explicitly in on large data sets and can also generalize - to some
the form of human-readable symbols. In fact, the degree - beyond previously seen examples by recog-
theory of symbol manipulation originally dates back nizing learned patterns. Although the era of statistical
to 1944, where it was originally formulated by Herb learning has taken off after 1986, important theoreti-
Simon (as mentioned by Newell and Simon [25]). cal work took place much earlier. For example, a quite
In this phase, agents emerged that were capable of remarkable milestone was achieved by Samuel [29],
2
Note that IBM’s Watson also makes use of different methodologies such as statistical approaches but is mainly based on
symbolic AI.
3Artificial Intelligence Narratives: An Objective Perspective on Current Developments
Figure 2: Most important phases and milestones in the field of AI (mostly inspired by Russell and Norvig [16]
and Launchbury [17]).
4Artificial Intelligence Narratives: An Objective Perspective on Current Developments
who wrote an agent that was able to play checkers at the application of neural networks in computer vision
world class level. In particular, the ability to learn was is not based on the identification of features in an
quite a remarkable feature at that time, as mentioned image. To the contrary, humans efficiently classify
by Buchanan [19]. The second important event was pictures by recognizing the whiskers of cats or the tail
the introduction of the perceptron model (1958) by of dogs.
Rosenblatt [30] that describes in its basic form a sin-
Another important impact was triggered by the adop-
gle neuron and hence, can be seen as the corner stone
tion of scientific methods and best practices, particu-
of modern deep neural networks. The perceptron
larly from the areas of mathematics and statistics [16].
model adopts major aspects of the aforementioned
Publishing new research results in appropriate math-
McCulloch and Walter Pitts model [20] and learns by
ematical formalization is of great benefit because (i)
applying the Hebbian learning rule (what fires together,
existing methods from other disciplines (for instance,
wires together) [31]. However, no learning scheme for
statistics, biology, neuroscience) can be more easily
multi-layered perceptrons (MLP) could be identified
adopted, (ii) researchers can better understand com-
at that time and even the simple task of learning a
plex concepts that are formulated in a common lan-
XOR gate could not be achieved. In 1986, the XOR
guage, and (iii) it is easier to adopt/build on existing
problem was solved for the first time by MLPs with
work.
an invention called backpropagation. Backpropaga-
tion exploits the chain rule to propagate the learning Big data is a trending topic that emerged in the
error through all layers of a deep neural network to noughties and has been keeping the AI community
infer learning gradients. These learning gradients on its toes ever since. Large quantities of structured
can then be used to perform gradient descent on the and unstructured data are generated primarily by the
weights that are the learnable connections between tremendous increase in devices and sensors connected
neurons. From that point on, the training of deep to the Internet (IoT - Internet of Things), as well as
neural networks became possible, which led to nu- by people feeding online services such as social me-
merous applications that have become an integral dia with their personal content. With the help of
part of modern life. About ten years after the intro- big data, companies are able to make objective, data-
duction of backpropagation, support vector machines3 based decisions without having to rely on intuition
were introduced by Cortes & Vapnik [32] and random and subjective experience. This enables faster and
forests by Tin Kam Ho [33]. Both methods have had more efficient decision making, which can result in
a great influence in the field of machine learning and significant economic value [35]. Applications based
are nowadays frequently exploited methods especially on big data are becoming increasingly complex and re-
in the field of data science. A general advantage of quire an ever-increasing amount of data. Hence, data
statistical methods compared to symbolic AI is the acquisition and automatic labeling techniques (for ex-
ability to express uncertainties that - among others ample, semi-supervised learning and active learning)
- lead to a great progress in the field of perception. became a critical factor for corporates [36].
For instance, Launchbury [17] stated that perception
Another influential trend is increasing openness. Even
based on statistical learning have had a great impact
leading commercial research teams are nowadays fre-
on the DARPA challenge4 whereas the following ex-
quently publishing state-of-the-art methodologies. Be-
ample was given: The capability of discriminating
sides communication via scientific publications, the
between a black rock and a shadow was not straight
respective code is often published in the form of open
forward by using conventional approaches but can be
source. An example is given by OpenAI™where open-
solved way more conveniently with statistical meth-
ness is a central aspect of the mission statement. But
ods. However, a disadvantage of statistical learning
where is the motivation towards openness coming
in comparison to symbolic-based methods is the in-
from? As mentioned by Bostrom [37], openness has
capability to reason. For example, a neural network
a positive impact on the overall reputation of the re-
is not able to answer why an image is classified in
spective corporation, might lead to more attractive
showing a cat or a dog. In this point, symbol-based
working conditions for researchers, and also lead to
systems are indeed advantageous as they are able to
automatic benchmarking. An obvious benefit of the
reason. For instance, by predicting a blood infection
emerging plethora of research papers and the corre-
using an expert system based on symbols5 one can
sponding source code is that researchers and com-
deduce the outcome by simply reviewing the decision
panies are able to quickly become proficient in the
process. A further weakness of statistical learning is
latest methodologies. For a more in-depth analysis of
the inability to abstract as the underlying methods
have no understanding of the matter. Consider that
3
Note, that various predecessor models of support vector machines were introduced much earlier.
4
The DARPA Grand Challenge was a competition for unmanned land vehicles sponsored by the Defense Advanced Research
Projects Agency (DARPA) of the US Department of Defense.
5
Mycin, invented by Feigenbau, Buchanan, and Shortliffe in 1973 [34] was an expert system that was able to diagnose
blood infections.
5Artificial Intelligence Narratives: An Objective Perspective on Current Developments
the impact of openness on AI, readers are directed to and an entire industry collapsed after stakeholders
Bostrom [37]. discontinued funding schemes [16].
After reviewing historical and current movements In the following, another way to distinguish AI sys-
with all their successes and glory, the following para- tems based on the underlying motivation for creating
graph introduces a very essential aspect in the history the system is presented. Four different schools of
of AI, namely the recurring AI winters. The term AI thought can be identified (adapted from Russell and
winter refers to the hype cycles in which high expec- Norvig [16]): (1) Thinking humanly: In the think-
tations were built up that eventually remained unful- ing humanly approach, the human brain functions
filled. During the hype phases, the fear of missing are focused. To gather insights from the brain, dif-
out initially prevailed, was accompanied by enormous ferent methodologies like determining own thoughts
private and public financial agendas, and eventually by introspection, psychological experiments, or by
led to periods of depression. A basic understanding of observing the brain in action (MRI, fMRI, EEG) are
the reasons that lead to AI winters is important as the employed. The overall idea and ultimate goal is to
awareness could save us from repeating past mistakes derive a theory of the mind with the purpose to create
or to be misled by ongoing hype phases. However, an artificial system that is capable of human thinking.
AI, with its false narratives, coupled with the human Note, researchers in this field do not intend to build
psyche that seems to inherently crave hype, is a sus- systems that show optimal behavior in specific tasks.
ceptible nexus that could be incorrigible in this regard. Rather, researchers are aiming for a model similar
Most references differentiate in two major AI winters. to the imperfect human, in which the actual human
The first AI winter took place mainly after 1969 and thought process is mimicked regardless of the per-
was mainly subject to three problems [16]: (i) It was formance. Truth to be told, brains are exceptionally
understood that systems based on symbolic knowl- complex and far from being understood and it is not
edge, rules, and logic are incapable to understand the yet possible to model/simulate a human brain. How-
subject matter - or in other words to build a common ever, many disciplines exploit results from cognitive
sense. In this context, the Chinese Room Argument by science, such as neuro-physical findings that lead to
Searle [38] states that a digital computer cannot gain developments in the field of computer vision [16]. (2)
any understanding of the challenge by simply follow- Acting humanly: The creation of agents that behave
ing a syntax in a program. Moreover, changing the like humans can be achieved without the necessity to
syntax according to adapt to new challenges requires rebuild what lead to this behavior and is hence differ-
a deep understanding of the syntax itself. A popular ent from the thinking humanly approach. The agent’s
example of a natural language processing agent that capability of acting humanly can be evaluated in the
fails to understand context is the famous translation popular Turing test published in the seminal Paper by
of The spirit is willing but the flesh is weak to the vodka Turing [40] (originally coined as the imitation game).
is good but the meat is rotten. (ii) Computational com- An agent passes the Turing test if an interrogator is
plexity was not yet known and problems did not sim- not able to discriminate between an artificial agent
ply scale by employing faster CPUs and more memory that acts humanly and a real human. For this purpose,
due to chain branching effects when the problem size an agent needs to master a variety of different disci-
increases. (iii) As mentioned above, it was recognized plines like natural language processing, knowledge
that perceptrons - initially seen as universal function representation, automated reasoning (to make use
approximators - are not capable of solving a trivial of the stored knowledge), and machine learning (to
XOR gate as already mentioned above. The first AI adapt to specific situations by detecting patterns). A
winter culminated in a document by Lighthill [39] that further escalation of the standard Turing test is the
was a commissioned report to evaluate the achieve- total Turing test that includes visual interaction be-
ments of previous funding agendas. The report con- tween the agent and the interrogator requiring the
cluded with a devastating assessment regarding the agent to employ computer vision and embodiment.
made progress in that time. In particular, the combina- (3) Thinking rationally: Deriving irrefutable conclu-
torial explosion of solving real-world problems is men- sions from a knowledge representation is referred
tioned as a major reason to be pessimistic regarding to as thinking rationally. It is obvious that human
further funding schemes. As a result of the Lighthill thinking is irrational and hence, this approach is dif-
report, funding schemes were suspended. The second ferent from the thinking humanly approach. In the
AI winter (around 1987) is associated with the for- context of AI, thinking rationally is often associated
mation of a bubble that formed after first successes with using formal, symbolic representation of knowl-
of expert systems were presented and hundreds of edge and to derive conclusions by applying logical
companies with extravagant promises emerged. The rules. An example for thinking in a rational way is
winter started after it was realized that the capabilities to reason as follows [16]: Socrates is a man; all men
of these expert systems were limited. Many compa- are mortal; therefore, Socrates is mortal. As noted by
nies failed to deliver with what they have promised Russell and Norvig [16], the main challenges of the
so-called logicism approach are the representation of
6Artificial Intelligence Narratives: An Objective Perspective on Current Developments
informal or uncertain knowledge. So far, demonstra- tial application can be found by Krizhevsky, Sutskever,
tion of basic principles is limited to trivial problems and Hinton [41]), it requires an enormous amount
as real-world complexity requires a vast amount of of high-quality data that must be accurately labeled.
data that is difficult to formalize in an automated way. In contrast, a model that learns key aspects of the
(4) Acting rationally: Agents that act rationally do not car only needs to understand what a tire is and how
mimic human thinking/acting but instead focus on to recognize it. This differs from today’s brute force
acting in an optimal way towards an (expected) out- methods where every scenario/view must be fed to
come/goal. Note that acting rationally is more general the model (at least in a similar way as the target sce-
than thinking rationally as the irrefutable inference is nario) during training. It is worth mentioning that a
just one possibility to determine how to act rationally. model identifying key aspects of an image is similar to
For instance, reinforcement learning can be shown to the way human learn: Instead of pixel-wise feeding an
converge to optimal solutions without the ability to image to the brain, human learn basic characteristics
reason at all. The acting rationally approach is more like recognizing the dot of the letter "i" [17]. More-
attractive for engineers building real-world applica- over, such a methodology would address major short-
tions than mimicking biological systems that involve falls of current practices: (i) Tremendous reduction
complex and often unknown aspects. An often-used of the required data as learning key aspects consumes
analogy in this context (for instance in [16]) is the less data than current deep learning methods. The
fact that aviation engineers initially studied how birds reasoning behind this is as follows: A human learns
achieve flying. Being inspired by having to wings did the most important aspects of a car by seeing a few
eventually not led to aircrafts that are exact mechani- examples and identifying similarities. Intuitively, this
cal copies of birds. This analogy might also apply in is much more efficient than presenting every possible
the field of AI where agents are usually created to sup- scenario/view in a training dataset. (ii) Abandon-
port humans in their daily lives rather than creating ing black-box models from which no explanation can
exact copies. It is worth mentioning that there are ac- be derived. Consider that a model recognizing the
tive communities in all four schools of thought, which whiskers of a cat can explain why a picture shows a
can differ drastically in terms of employed methods, cat rather than a dog.
assumptions, and opinions.
Another often criticized aspect of today’s AI appli-
cations is the limited range of problems that can
be solved by a specific model. As mentioned be-
3 Future Developments fore, agents that can beat chess world champions
tremendously fail in automating driving; vice versa,
Projecting future developments in AI is an intriguing autonomous driving agents are usually very bad at
subject. On the one hand, there is growing concern playing chess. In this context, a classification regard-
and fear about loss of privacy, autonomous military ing AI methods that expresses the agent’s capability to
agents, or that humanity will become obsolete. On generalize on a wide variety of different problems can
the other hand, disruptive new technologies could be made as follows: (1) Artificial Narrow Intelligence
emerge that will change our daily lives for the better. (ANI - also referred to as "weak" AI) comprises systems
A third scenario one can envision is that commercial that are capable of solving specific tasks like playing
exploitation of current methods is completed and a video games, driving cars, recognition of speech et
third AI winter ensues. The future of AI is difficult to cetera. Even though super-human performance can be
predict and an accurate projection cannot be given achieved in various scenarios, ANI agents do not gen-
in this work. However, an overview is provided of eralize on experience to solve tasks that are different
possible scenarios that might emerge to address the from the original task. (2) Artificial General Intelli-
shortcomings of current methods. In this context, gence (AGI - also referred to as "strong" or human-level
Launchbury [17] predicts that the next era (the third AI): AGI encompasses systems that are on the human
wave of AI - see Figure 1) will be driven by models level. Agents within the AGI paradigm can solve new,
that are capable to understand context. As mentioned unforeseen tasks by generalizing on their experience.
above, statistical models cannot explain why an image It is difficult to define what the attribute "human level"
is classified in showing a car or not. Consider a model means. Human level intelligence might be defined as
that is able to identify the most unique aspects of a car, the capability to autonomously set and pursue goals,
such as four wheels, an antenna, multiple windows, et reason on complex matters, to comprehend language,
cetera. Such a model would be substantially different to perceive and make sense of the environment, to
from currently applied convolutional neural network solve problems, or to make decisions under uncer-
(CNN). A CNN starts by detecting edges, assembles tainty. One could require that an AGI agent must be
these edges into more complex features such as circles integrated into the real world and mimic the sensori-
and rectangles, reduces the information into several motor capabilities of humans, for instance, to interact
dense layers, and finally activates a single neuron that socially with other humans. However, current meth-
decides whether there is a car in the image or not. ods cannot generalize, a fact that has recently led
While this approach is overly successful (an influen-
7Artificial Intelligence Narratives: An Objective Perspective on Current Developments
to a series of conferences on AGI [42]. (3) Artificial intelligence? An influential idea by Brooks [47] is
Super Intelligence (ASI): ASI encompasses methods that agents need to have an embodiment in order
that go beyond human intelligence and is mentioned to interact with the real world by making use of the
here only for the sake of completeness. Discussions re- physical grounding. It is argued that the grounding,
garding ASI are often accompanied by complex philo- for instance for symbols, is extraordinarily difficult to
sophical implications. Although ASI is frequently the automated. According to Brooks, grounding of sym-
subject of science fiction, it is also discussed on a sci- bols is not necessary as the physical representation of
entific level for example by Bostrom [12] or Tegmark the environment is sufficient and the agent just needs
[11]. to perceive it. But does embodiment necessary lead
to the emergence of human-level AI? Let us consider
Today’s methods are in the field of ANI. It has become
a robot that learns to perform a task in a production
the holy grail in AI to overcome this narrowness, while
line (for example based on reinforcement learning).
it is still unknown what could lead to higher gener-
The agent has to learn the task from a large amount
ality. Suppose for now that we follow the "human
of experience that is sampled from the environment.
thinking" approach and are convinced that general-
After the learning phase, the agent is looking up the
ity at the human level requires reverse engineering
action policy in a function that correlates status-action
of the human brain. The first question that arises
pairs with a predefined performance metric. Although
is whether computers are inherently capable of per-
such an agent might be economically reasonable, it
forming the processes that occur in a brain. Or, in
is important to realize that the agent is not building
other words, whether mental processes are substrate-
an understanding of the scenario and does not grasp
independent and, if so, do not require a human brain
which actions (causes) lead to which effects. The
[11]. The Computational Theory of Mind, formulated
robot in the production line follows a specific syntax
in a early version by Putnam [43], raises the hypothe-
that fits a statistical model but can never come up
ses that brains can be seen as information processing
with new suggestion for the program itself such as an
systems and can be mimicked by computers. This
improvement of the reinforcement learning algorithm.
theory is controversial and it is also argued that hu-
In other words, it has no self-organizational capabil-
man reasoning is different from computation as it is
ities and hence, cannot emerge. We can conclude
not algorithmic [44, 45]. For instance, Weizenbaum
that although the robotic arm has an embodiment, no
[46] pointed out that human thinking cannot be im-
stronger intelligence emerges. A further escalation of
itated by machines, since it relies on having some
the embodiment theory is that a computer will never
sort of wisdom to understand the whole picture and
gain human-level intelligence as they are not part of
make the right decisions accordingly. However, if the
the world like being integrated in a culture, having a
Computational Theory of Mind holds, we can con-
childhood et cetera (Fjelland [48], Judea Pearl [49],
clude that any Turing complete system can represent
and Dreyfus and Dreyfus [50]).
a human-level intelligence given the right methodolo-
gies. Consequently, the most obvious choice to reach
human-level AI is to reverse engineer the brain. How-
ever, a whole brain simulation is difficult to achieve 4 Concluding Remarks
mainly due to two reasons. (i) The brain is exceed-
ingly complex and not fully understood. Attempts to This paper presents a bottom-up approach to intro-
simulate the brain range from early approaches in cy- duce AI as an umbrella term that encompasses a
bernetics (for example by McCulloch and Pitts [20]), plethora of different eras, narratives, methods, and
over simplistic MLPs, to advanced non-stationary, bio- schools of thought. The key message of the first sec-
inspired approaches (such as spiking neural networks tion is that the public portrayal of AI is flawed. Prod-
that account for synaptic plasticity). (ii) Computa- ucts from big tech corporates changed our daily live
tional power is not yet sufficient to simulate the brain and fueled a widespread misunderstanding of what
and it is not yet known how computational intense "AI" comprises beyond applications in social media or
the undertaking is. However, various estimates on the e-commerce. A critical view on public communication
computational power necessary to emulate the brain is recommended to the reader to maintain an objec-
exists (for instance by Tegmark [11] and Russell and tive view on current developments. In this context,
Norvig [16]). Most of the projections imply that the two common arguments are examined: AI will lead to
computational resources will be available in this cen- job losses and AI is emergent. Both arguments are of-
tury. It is also not clear whether an accurate computer ten passionately debated, whereas the author believes
model of a human brain will lead to something like that a more factual view would be appropriate. The
consciousness. Moreover, one might question which second section presents a brief history of AI. It is evi-
of the technologies might be the right path to higher dent that AI encompasses a variety of different aspects
intelligence. Or to put it like this: Are more complex that tremendously complicate the understanding of
symbol manipulation systems or more data in statisti- the underlying terminology. In this regard, the AI
cal approaches are eventually leading to human-level term is often reduced to the "acting rationally" school
of thought, as this is the most visible aspect today. An-
8Artificial Intelligence Narratives: An Objective Perspective on Current Developments
other addressed subject are AI winters that occurred org/pdf/1412.7584v1 (visited on 2020-11-
regularly in the past. From a historical point of view, 16).
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