#  Artificial Intelligence: $70$ Years Down the Road

Lin Zhang
International Digital Economy Academy (IDEA)
<EMAIL_ADDRESS>
###### Abstract

Artificial intelligence (AI) has a history of nearly a century from its
inception to the present day. We have summarized the development trends and
discovered universal rules, including both success and failure. We have
analyzed the reasons from both technical and philosophical perspectives to
help understand the reasons behind the past failures and current successes of
AI, and to provide a basis for thinking and exploring future development.
Specifically, we have found that the development of AI in different fields,
including computer vision, natural language processing, and machine learning,
follows a pattern from rules to statistics to data-driven methods. In the face
of past failures and current successes, we need to think systematically about
the reasons behind them. Given the unity of AI between natural and social
sciences, it is necessary to incorporate philosophical thinking to understand
and solve AI problems, and we believe that starting from the dialectical
method of Marx is a feasible path. We have concluded that the sustainable
development direction of AI should be human-machine collaboration and a
technology path centered on computing power. Finally, we have summarized the
impact of AI on society from this trend.

##  1 Introduction

Since the concept of AI was introduced in the 1950s [MMRS06], researchers have
been striving to make machines intelligent, leading to important sub-fields
such as computer vision, natural language processing, and machine learning.
Despite ups and downs, AI technology has been rapidly advancing, especially in
the last two decades with the success of deep learning. However, the pursuit
of a comprehensive understanding of AI mechanisms and how to accelerate its
evolution continues. With more data, computing power, and minds than ever
before, it is time to review the development of AI, as the reasons behind the
ups and downs of its history are worth exploring. Otherwise, AI is likely to
repeat the tragedies of the past.

Here, we reflect on the development of AI over the years and how past
developments may extend into the future. We first analyze the technological
changes in the core areas of AI over the past century. Although the research
content and methods of sub-fields differ greatly, their development logic is
remarkably similar, and can be summarized as the path from rules to statistics
and then to data-driven approaches. The dilemma of AI development, from a
technical perspective, is fundamentally due to the lack of mathematical tools.
To further explore the reasons for this development model, we analyze it from
a philosophical perspective. Specifically, we analyze the problems of AI from
the perspectives of dialectics and pragmatism, and believe that AI needs to be
understood and solved from the perspectives of contradiction and practice
based on Marxist dialectics. Based on this, we believe that human-machine
interaction is the inevitable path of AI development, and that AI technology
should revolve around the development of computing power. Finally, we outline
the changes that AI brings to society.

##  2 A Brief History of AI Technology

AI research covers a wide range of fields, and we have chosen the most
representative research areas, including computer vision, natural language
processing, and machine learning, to understand their development history. We
will find that from a technical perspective, the dilemma of AI is
fundamentally due to the lack of mathematical ability, and we are still unable
to effectively model the complex real world. Ultimately, we rely on empirical
methods to compensate for the shortcomings of mathematics.

###  2.1 Computer Vision

Computer vision has a history of more than 60 years since its establishment in
the 1960s [Sha20], and it is a major branch of artificial intelligence. Its
core task is to understand the content of images. From the 1960s to the 2000s,
the development of computer vision can basically be attributed to a core idea:
structured combination. That is to say, past researchers believed that the
objects in images were composed of some basic structural units combined in a
certain way, and representative figures include David Marr [Mar82] and King-
Sun Fu. Specifically, Marr started from neuroscience and believed that the
process of visual recognition was completed through three progressive levels
of visual features, namely 2D (such as node, edge information, etc.), 2.5D
(the aggregation of 2D information into 3D contour information) and 3D
information. Marr’s computer vision theory imitated the process of human
vision. He believed that the calculation methods of the brain and the computer
were the same. The three-stage features he proposed were a simulation of human
vision, which had a dominant influence on the development of computer vision
for many years. Researchers have proposed a series of methods for different
stage features. For 2D information, representative works include the Scale-
invariant Feature Transform and the Canny edge detector for edge extraction,
to support downstream visual tasks such as classification, recognition, and
segmentation. The analysis of 2.5D and 3D visual information has become a key
research topic in the early development of computer vision. For example, the
first specialized computer vision paper (from Larry Roberts) analyzed the 3D
structural information of objects in images, and similar ideas appeared in the
cylinder structure research paradigm proposed by Thomas Binford [SB92] later
on. However, the limitations of this type of research were too great, and the
complexity of real visual objects made it difficult to complete them by simple
geometric combinations. With the success of SIFT feature extraction, this type
of research gradually withdrew from the historical stage.

Fu believed that there was a higher-level grammar structure in the world, so
understanding images could be achieved by learning this grammar structure.
Compared to Marr’s three clear levels, Fu’s method was more general, and
therefore more difficult to design effective computational methods. Therefore,
there were few followers until the beginning of this century, when Song-Chun
Zhu [ZM06] made some progress in image segmentation using this kind of
thinking. However, subsequent research was still rare, mainly because it was
difficult to model such grammar structures.

The 2000s were a transitional period for computer vision. The mainstream
method was based on statistical models of hand-crafted features, and a series
of highly practical works were produced during this period, such as the Viola-
Jones algorithm [VJ01] for face detection, the histograms of oriented gradient
(HOG) descriptors [DT05] and its improved Deformable Parts Model (DPM)
algorithm [FGMR09] for pedestrian detection, and the Spatial Pyramid Matching
algorithm [LSP06] for image matching and recognition. It should be emphasized
that the success of these algorithms was based on the expert-designed features
for image understanding, generally utilizing the properties of gradient
distribution on the image. These studies gradually moved away from Marr’s
three-stage cognition. From a computable perspective, feature-based visual
research brought about significant performance improvements, which laid the
foundation for subsequent deep learning-based research, because the latter is
a data-driven feature rather than a hand-designed one.

The concept of deep learning [HOT06] was proposed by Geoffrey Hinton and his
students in 2006, but it was not until 2012 that AlexNet’s [KSH12] outstanding
performance on the ImageNet dataset attracted public attention. Deep learning
algorithms, represented by convolutional neural networks (CNNs), brought
computer vision into the era of deep learning, a new paradigm driven by data
and computing power. With the increase in model parameters, deep learning has
brought significant improvements in traditional computer vision tasks.
Applications such as face recognition have reached commercial-grade
capabilities. Finally, computer vision research converged on image
representation learning, from traditional key point localization and
representation to block-based representation learning represented by Vision
Transformer (ViT) [DBK+20], which completely overturned the visual theory
since Marr.

At this point, we may need to reflect on the ideological foundation of early
computer vision development. The way computers perceive things may not be the
same as what we consider to be the human visual process. In chronological
order, computer vision methods can be roughly divided into three stages: 1.
rule models established based on expert-designed visual processes; 2.
statistical models based on hand-crafted features designed by experts; 3.
representation learning paradigm driven by data and computing power. In terms
of performance, we observe an interesting phenomenon: the less prior knowledge
of humans is utilized in the development of computer vision, whether it is
rules or mathematics (or we consider rules as a simplified mathematical tool),
the more computing power and data are used. But this is not a phenomenon
unique to computer vision. We will find similar laws in other fields in the
future.

###  2.2 Natural Language Processing

The origin of natural language processing can be traced back to the early 20th
century, with figures such as Baudouin de Courtenay [RdCS72] and Ferdinand de
Saussure [dS83] approaching language as a system of symbols with inherent
rules, from a psychological and cognitive perspective. Over time, language
research gradually departed from the human and social context, abstracting the
study to rules of symbols, which had a profound impact on later natural
language processing. Generally speaking, natural language processing has gone
through three eras: rule-based, probabilistic statistical models, and deep
learning.

The first era was rule-based language analysis, which lasted from the 1950s to
the 1980s. The basic idea was to use expert-designed rule sets related to
specific domains to enable computers to understand natural language, with the
primary focus on syntax and semantics analysis. Noam Chomsky’s book ”Syntactic
Structures” [Cho57] was a representative work, which enabled computers to
analyze sentences according to given rules, such as constructing a syntactic
parse tree for a sentence. However, the complexity of the real world made the
artificial rules an abstract and simplified process, detached from the
practical application of language, and inadequate for understanding language
in real-world situations. Similarly to the study of combination rules in
computer vision, both fields depend entirely on experts’ abstraction of the
world, and their detachment from real situations makes it difficult to have
practical value.

In the 1970s, statistical methods gradually became the main tool in natural
language processing. Probability was used to represent words, rather than
artificially defined language rules. The core idea was that the probability of
a sentence equals the joint probability of each word that makes it up, based
on the Markov assumption on sentences. Representative works include the
general N-gram model [CT94] and the Hidden Markov Model (HMM) [JR91] used in
speech recognition. However, statistical models invariably require prior
assumptions, and then design models to fit the data, such as the typical
example of topic models. These assumptions may not match the real-world
situation, and the performance of the models is limited.

Since the advent of the deep learning era, natural language processing has
made remarkable breakthroughs, thanks to big data and large-scale models,
completely abandoning the rule-based and statistical model research paradigm.
Early classic works in this stage include Word2vec, which achieved a
breakthrough in word representation. From the perspective of model structure,
the success of Seq2seq [SVL14] made the model universality initially formed.
With the emergence of Transformer [VSP+17], universal models entered a new
stage, directly leading to large-scale pre-training, becoming the unified mode
of natural language processing. Recently, the appearance of ChatGPT has again
significantly improved the performance of natural language processing. We
notice that compared to the previous GPT-3 [BMR+20], the new version
fundamentally differs by introducing large-scale human feedback mechanisms
[LCvWH22], where human knowledge is directly fed back to the model to help it
iterate.

Similar to the development of computer vision, natural language processing has
undergone a development process from rules to data-driven approaches. However,
the uniqueness of language deserves our attention, and we must recognize that
language is one of the reasons why people become people. Language is not an
independent tool or system detached from humans. Using strong rules or data
statistics based on strong prior assumptions cannot explain human language,
making it difficult to understand human language.

###  2.3 Machine Learning

In 1952, Arthur Samuel proposed the concept of machine learning, which is ”a
field of study that gives computers the ability to learn without being
explicitly programmed.” He demonstrated the possibility of machines
outperforming humans by developing a checkers program. In pursuit of this
goal, the field of machine learning has experienced ups and downs over the
past seventy years, roughly divided into research directions based on neural
networks and statistics.

In 1957, Rosenblatt proposed the perceptron [Ros58], which can be considered
as the earliest prototype of today’s deep learning models and can be viewed as
a biomimetic structure of a neuron. In 1969, Marvin Minsky raised the famous
XOR problem, stating that the perceptron is ineffective on linearly
inseparable data distributions. Researchers of neural networks entered a
winter period until 1980, when Werbos proposed the multilayer perceptron
(MLP). This model includes the core component of today’s deep learning:
backpropagation (BP). At this point, the model level of deep learning has been
initially completed. In 2006, Hinton and others’ research on deep learning
once again received attention. After nearly twenty years of rapid development,
deep learning has completely changed research on AI in various fields. We need
to pay attention to the fact that computing power and data are the most
important variables in the current and past eras. The model itself is only a
small part of the reason for success because even a simple MLP model can
achieve high performance with enough computing power and data. Another
direction of machine learning is based on statistics. In the two decades after
the 1990s, statistical-based methods became the mainstream of machine
learning. Representative works such as Boosting [Fri01], Support Vector
Machine (SVM) [CV95], Ensemble Learning [SR18], Principal Component Analysis
[Jol86] were born. Although they have a solid mathematical foundation, their
performance cannot compete with today’s deep learning.

Fundamentally speaking, current mathematical tools are not enough to model the
real world. They are more of an abstraction and simplification of the real
world. Typically, these mathematical tools are based on strong assumptions,
and these empirical assumptions (guesses or judgments) cannot be rigorously
proven in a scientific manner. Therefore, these seemingly rigorous
mathematical tools are no different from expert rules in computer vision and
natural language processing. Therefore, from the beginning of modeling, we
must recognize the inadequacy of theory and rely on empirical judgments to
make up for it. This is the underlying logic of the success of deep learning,
which uses a data-driven approach to compensate for the inadequacy of
mathematical tools and directly learns models of the real world from data.

##  3 A Reflection from the Philosophy Perspective

###  3.1 The Necessity of Practice

Although artificial intelligence has been developing for nearly a century, it
still lacks a unified philosophical foundation. In particular, this discipline
has broken down the opposition between natural sciences and social sciences
since its inception, which means that it is itself a kind of unity. However,
this unity is only formal at present.

The fundamental question about artificial intelligence is still the same: what
is universal necessity? This universal necessity is actually the unity of the
world, which is in motion and universally connected. In other words, it is the
universality of contradictions. Truth or true knowledge is the unity of
universality and particularity, and knowledge of universal necessity is
obtained from limited experience. Sir Isaac Newton used mathematics to express
this universality, which is the ”Mathematical Principles of Natural
Philosophy” [NAJ29]. What is truth and what is universal necessity is the same
question. This was originally a problem for metaphysics to solve. Its
translation into metaphysics is not very accurate, and its original meaning is
ontology or first philosophy. In other words, why are universal conclusions
drawn from limited empirical materials reliable?

To solve this problem, philosophers have made remarkable efforts. G.W.F. Hegel
reconstructed metaphysics from the absolute, regarding truth as the alienation
and return of absolute spirit. Specifically, Hegel’s absolute is something
that can only be grasped by pure thought, or it is pure thought itself.
However, in reality, people always start from the limited to understand this
infinite world. The so-called practice is endless, and understanding is
endless, which negates Hegel’s understanding of truth. Regarding artificial
intelligence, Zhao Nanyuan proposed the theory of the generalized evolution of
knowledge, which should demonstrate the universality and unity of knowledge.
However, he said that the expansion of knowledge itself can prove its own
effectiveness, which is equivalent to saying nothing. Moreover, the lack of
unity in natural science also negates his conclusion.

Half a century ago, Mao Tse-tung’s theory of practice and theory of
contradictions solved the fundamental problem above from a philosophical level
[Tt51]. He attributed the answer to this problem to practice being the only
criterion for testing truth at the practical level. The universality and
immediacy of practice are the basis of the universality and unity of
knowledge. His philosophical theory is based on the epistemology of Marxist
dialectical materialism, which places practice in the first position, and
believes that human cognition cannot be separated from practice. It rejects
all erroneous theories that deny the importance of practice and make cognition
separate from practice. We must emphasize the dependence of theory on
practice, and the foundation of theory is practice, which in turn serves
practice. The judgment of whether knowledge or theory is true is not based on
subjective feelings, but on how the results of social practice are objectively
determined. The standard of truth can only be social practice, and the
viewpoint of practice is the first and fundamental viewpoint of dialectical
materialism epistemology. Therefore, artificial intelligence is no exception
and needs to be based on practice.

We can make a speculation here: the establishment of causality cannot be
obtained by logical reasoning and mathematical statistics. The theory of
practice provides the standard of truth, and its essence and causality are
actually the same problem, both of which involve universality and necessity.
The establishment of universality and necessity is ultimately determined by
practice, with confirmation, refutation, and revision. Therefore, the
establishment of causality cannot be obtained by logical reasoning and
mathematical statistics. In addition, if mathematics and logic are used as the
basis of causality, it is tantamount to saying that there is no world without
mathematics and logic. Causality is the interaction of things, one thing
causes another. Therefore, there are two prerequisites for the establishment
of causality: 1. things exist; 2. there is a connection between things.
Discovering and revealing this connection is the task of science. Mathematics
and logic can be used to express causality, but they cannot become the main
factor and say that causality exists because of mathematics and logic. The
establishment of causality is ultimately determined by practice, and it should
be noted that practice is infinite, and only the endless process of practice
verification can prove its universality.

###  3.2 Why will our artificial intelligence be doomed to fail?

Why have our past AI attempts always failed? Because researchers have been
eager to establish a knowledge system independent of humans, that is, to
create a new intelligent species without human participation, allowing
machines to make judgments independently of humans, whether based on rules or
statistics and data-driven, fundamentally this is the vision. Specifically, AI
simulates the thinking process of human beings from intuitive cognition to
rational cognition, and mechanizes and mathematizes a part of human’s actual
thinking process. However, in the past, we focused on the conceptual deduction
of this thinking process, such as Marr’s visual process and Saussure’s
language symbols mentioned in Chapter 2, abstracting this thinking process.
But this system did not incorporate human feedback into the development
framework of AI technology. Instead, it treated humans as humans and machines
as machines, as two independent systems (subject-object binary opposition), or
as humans being equivalent to machines themselves (mechanical monism). In
fact, machine activity is essentially mathematical reasoning without self-
awareness, while practice is a conscious human activity. This subject-object
separation system without human feedback also lacks practice, and machines
cannot produce knowledge, let alone human-like intelligence. Therefore, human
participation is an essential part of AI. ChatGPT is an accidental event in
technological development, but it is a necessary result of historical
development, because only by systematically incorporating human factors into
the AI system can AI move towards inevitable success.

###  3.3 The Necessity of Human-Machine Collaboration

In the past, the philosophy of artificial intelligence was based on the
subject-object dualism, where knowledge was the basis of knowledge, rather
than practice. However, practice cannot be separated from feedback. Thus,
implementing artificial intelligence requires human feedback, rather than
simply allowing machines to independently achieve intelligence. Therefore,
human-machine collaboration is inevitable, as it combines human cognitive
abilities with machine capabilities, where human cognitive abilities are
determined by human practice. Thus, human-machine collaboration is the
necessity of artificial intelligence, rather than simply machine intelligence.

However, we must pay attention to the effectiveness of feedback. Taking
recommendation systems as an example, as one of the most successful
applications of artificial intelligence, the biggest problem is that users do
not have true freedom of choice and decision-making. Although users can choose
”dislike” or ”not needed” in form, it is actually the imposition of the
system’s will on humans, which does not conform to the practice theory in
reality.

###  3.4 The Necessity of Computing Power

We need to recognize the role of computers. Without computers, there would be
no artificial intelligence. Both computing power and algorithms are essential
for computation. In terms of computers, we have never demanded more than the
computing advantage and cannot demand more. This is the reason why artificial
intelligence development has faced problems, as we often demand more from
computers than just computation, such as independent consciousness. The
development of artificial intelligence technology must always be centered on
computing power, even if there are more powerful computing tools in the
future, such as quantum computers. We can analyze the fundamental
contradictions of AI in two aspects: first, computing power is constant, so we
can only use human knowledge to improve the computational performance of
intelligent machines; second, human knowledge is too complex and difficult to
teach machines through computation, so computing power can continue to expand.
The result is that with the expansion of computing power, a certain level can
be reached, such as when quantum computers mature, and the part of human
knowledge that cannot currently be achieved through computation may become
possible. Thus, the chain of human knowledge, algorithms, and computing power
can spiral upwards. Therefore, in light of these real contradictions,
development centered on computing power is an inevitable choice.

##  4 Artificial Intelligence and Future Society

The ”practice” of artificial intelligence is to participate in the social
production and life practice of human beings, that is, it is a part of human
social production and life practice. Artificial intelligence acts as a tool
and means in social production and life. From this perspective, it has no
essential difference from the mechanical machines invented by humans before,
which is its universality. Artificial intelligence is the product of deepening
and upgrading of labor division, but unlike previous technological
innovations, it has its own particularity. AI represents the replacement of
mental labor for physical labor, creative labor for simple repetitive labor,
and the shift from being controlled by the production process to controlling
the entire production process, marking a new leap in human social
productivity. This creates a more solid material basis for higher and more
comprehensive human development and ultimately produces a more efficient and
harmonious social form.

Artificial intelligence is rooted in human social production and life
practice, and human social production and life practice are the fundamental
driving force for promoting the research and development of artificial
intelligence. Therefore, we can predict that population size and production
scale will give practical advantages, and societies with this form will win in
the evolution of artificial intelligence.

In the era of artificial intelligence technology, what is the role of human
beings? First of all, we need to answer a question: what can humans do and
what can’t they do? The human brain can use conceptual thinking to process
experience and generate new knowledge, but the data that the human brain can
process is limited. Secondly, we need to answer another question: what can
machines do and what can’t they do? We all know that machines can perform
efficient calculations, and anything that can be calculated can be done using
a computer. However, machines cannot practice, so they cannot judge the truth
of knowledge and may even forge knowledge. Therefore, future humans need to
constantly provide new knowledge, ultimately serving three aspects: 1.
increase computing power; 2. improve algorithms; 3. provide feedback to
machines. Future social division of labor will also revolve around these three
points. On one hand, it will eliminate old divisions of labor, and on the
other hand, it will deepen new divisions of labor, especially in the
development of intellectual labor. Artificial intelligence undoubtedly will
expand the scope, breadth, and depth of social division of labor.

##  5 Conclusion

This article summarizes the technological development and evolution of
artificial intelligence over the past century from both technical and
philosophical perspectives. We found that the development paths of different
directions are generally similar, and the profound philosophical logic behind
the failures and successes has never been valued in the field of AI, nor has
it been systematically analyzed. It should be noted that AI is the combination
of natural science and social science, not just machine intelligence, but an
extension of human intelligence. Therefore, it is necessary to understand AI
from different perspectives in order to avoid blind development. We call for
attention to the establishment of the philosophical foundation of AI, and the
urgent need for a unified and time-tested philosophical system in the AI field
to ultimately guide the development of technology.

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