A BRIEF REVIEW OF THE DEVELOPMENT PATH OF ARTIFICIAL INTELLIGENCE AND ITS SUBFIELDS Krithiga G 1, Mohan V 2, Senthilkumar S 3 1, 2, Department of Electrical and Electronics Engineering, E.G.S. Pillay
Engineering College, Nagapattinam, Tamilnadu, India 3 Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
1. INTRODUCTION The
ability to solve any problem and evaluate huge amounts of data using abilities
like analytical thinking, logical reasoning, statistical understanding, and
mathematical or computational intelligence makes humans the most intelligent
species on the planet. Artificial Intelligence (AI) is a technology created for
machines and robots that imposes the ability to solve complicated issues in the
machines as equivalent to those that can be done by people. AI is built for
machines and robots with all these combinations of talents in mind. Systems
that combine powerful hardware and software with intricate databases and knowledge-based
processing models to mimic the features of efficient human decision-making are
referred to as AI
systems. Nowadays,
intelligence is no longer a characteristic of humans. Industry 4.0 is built on
AI and machine learning since Industry 3.0 created a critical demand for
automation and intelligent systems. AI aims to enable computers to exhibit
human-like thinking and intelligence without experiencing human-like fatigue.
AI is used in a variety of industries, including chatbots, autonomous vehicles,
speech recognition, and facial recognition. 1.1. History of Artificial Intelligence The
origins of AI can be traced back to ancient myths, tales, and legends of
man-made creatures that were given intellect or consciousness by master
craftsmen. Philosophers' attempts to characterise human thought as the
mechanical manipulation of symbols laid the groundwork for modern AI. Although
this idea has been around for a while, until 1950, no one knew about it. In the
year 1955, John McCarthy, the man credited with creating AI, first used the
word. John McCarthy famously described the field as "enabling a computer
to accomplish tasks that, when done by people, are supposed to involve
intelligence." The purpose of the definition was to give him the confidence
to continue his investigation without having to first defend a particular
philosophical interpretation of what the term "intelligence" implies.
McCarthy is regarded as one of the fathers of AI, along with Alan Turing, Allen
Newell, Herbert A. Simon, and Marvin Minsky. Alan argued that if humans can use
reason and the knowledge at hand to solve issues and arrive at judgements, then
machines can also do so. The wave of computers started slowly
over time. They improved over time, becoming quicker, more inexpensive, and
more data-storage capable. The fact that they were capable of abstract thought,
self-recognition, and Natural Language Processing was the best feature. With increased funding and algorithmic
tools, AI research took off again in 1980. Deep learning methods allow the
computer to learn from user experience. The technology was successfully
established after all the failed attempts, but the historic objectives weren't
reached until the 2000s Ertel and Black (2018). Despite a lack of government funding and public awareness
at the time, AI flourished. A list of significant developments in AI from Godel to the present is represented in Table 1. Table 1
1.2. ELEMENTS OF INTELLIGENCE Learning,
reasoning, problem-solving, perception, and linguistic intelligence are the
five fundamental facets of AI Littman et al. (2021). Software
developers and engineers have been able to produce a wide range of technologies
and services that users all over the world have grown to like and want through the use of these five fundamental components. Figure 1 shows the
Elements of Artificial Intelligence. Reasoning: It is the
process that enables us to offer the fundamental standards and principles for
making an assessment, a prediction, and a choice with regard
to any situation. There are two different sorts of reasoning. The first
is generalised reasoning, which is based on broad observable instances and
assertions. In this instance, the conclusion might not always be accurate. The
other is logical reasoning, which is founded on data, facts, precise claims,
and mentioned or witnessed occurrences. Learning : It is the
process of learning new information and developing existing skills from a
variety of sources, including books, real-life experiences, lessons from
experts, etc. The person gains knowledge in areas that he was previously
ignorant of, thanks to the learning. Not only do humans have the capacity for
learning, but some animals and artificially intelligent machines also do. Problem solving is the
process of determining the issue's root cause and looking for potential
solutions. This is accomplished by first understanding the issue, making a decision, and then researching several potential
solutions before choosing the best one. The best solution should be chosen
among those that are offered in order to solve the
problem effectively and quickly. Perception: It is the
process of gathering, deducing, selecting, and systematizing the pertinent
facts from the unfiltered input. Human perception is influenced by past
experiences, sensory organs, and contextual factors in the surrounding
environment. However, in terms of artificial intelligence perception, it is
logically obtained by the artificial sensor mechanism in conjunction with the
data. Linguistic Intelligence: It is the
phenomenon of a person's ability to use, comprehend, read, and write verbal
information in several languages. It is a fundamental part of every
communication between two or more people and is required for both logical and
analytical understanding. Figure 1
2. SUBFIELDS OF ARTIFICIAL INTELLIGENCE Many innovative computational intelligence algorithms have been
created throughout the AI development phase, including the Artificial Neural
Network (ANN), Fuzzy Logic (FL), and Support Vector Machine (SVM) Mohan et al. (2010). The classification of frequently used AIs is shown in Figure 2. Prediction, optimization, and diagnosis are the key three
application areas for AI in Power Electronics and Power system. Numerous
evolutionary and population-based optimization techniques, including the
genetic algorithm (GA), the particle swarm algorithm (PSO), and others, have
also been developed Chitrakala et al. (2017) As a result, it has become more common to combine intelligent
algorithms with optimization algorithms. Figure 2
2.1. SUPPORT VECTOR MACHINE One of the most well-liked supervised
learning algorithms SVM, is used to solve Classification and Regression
problems. However, it is largely employed in Machine Learning Classification
issues Krithiga and Mohan (2022). In order to make it simple to
place new data points in the appropriate category in the future, the goal of
the VM algorithm is to construct the best line or decision boundary that can
divide n-dimensional space into classes. A hyperplane is the name given to this
optimal decision boundary. SVM selects the extreme vectors and points that aid
in the creation of the hyperplane Mohan et al. (2017). Support vectors, which are used to represent these extreme
instances, form the basis for the SVM method. 2.2. ARTIFICIAL NEURAL NETWORK ANNs are a branch of AI inspired by
biology and fashioned after the brain. A computational network based on
biological neural networks, which create the structure of the human brain, is
typically referred to as an ANN Mohan and Senthilkumar
(2022). ANN also feature neurons that are linked to each other in
different layers of the networks, just as neurons are in a real brain. Nodes
are the name for these neurons. Figure 3 shows the simple ANN model, and Figure 4 shows the
classification of ANN. Figure
3
Figure 4
When learning is being done under
supervision, the objective that needs to be predicted is explicitly identified
in the training data. A CNN is a multilayer neural network that takes its
biological cues from the visual brain of animals. The design is especially
beneficial for applications that include image processing Senthilkumar et al. (2022) . Yann LeCun developed the
original CNN, whose architecture was centred on reading handwritten characters,
such as postal codes. Early layers in a deep network identify features (like
edges), while later layers reassemble this information into higher-level input
qualities. Figure 5 shows the basic block diagram of CNN. Figure 5
One
of the basic network architectures on which other deep learning architectures
are based is the RNN. A recurrent network may feature connections that feed
back into earlier layers in addition to purely feed-forward connections, which
is the main distinction between a standard multilayer network and a recurrent
network (or into the same layer). RNNs can keep track of previous inputs and
model problems over time thanks to this feedback. Hochreiter and Schimdhuber invented the LSTM in 1997, but it has gained
prominence as an RNN architecture for a variety of applications recently.
Products that you use every day, like smart phones, contain LSTMs. In order to achieve ground-breaking conversational voice
recognition, IBM used LSTMs in IBM Watson. The LSTM introduced the idea of a
memory cell in place of standard neuron-based neural network topologies Mohan et al. (2010). The memory
cell's ability to keep its value for a limited or unlimited period
of time depending on its inputs enables it to recall essential
information rather than just its most recent calculated value. The
gated recurrent unit, a simplified version of the LSTM, was introduced in 2014.
The output gate from the LSTM model is replaced with two gates in this model.
These are a reset gate and an update version of the LSTM, was introduced in
2014. The output gate from the LSTM model is replaced with two gates in this
model. These are a reset gate and an update gate. How much of the previous
cell's contents should be preserved is indicated by the update gate. The reset
gate specifies how to combine new input with the contents of the preceding
cell. Simply by changing the reset gate to 1 and the update gate to 0, a GRU
may represent a typical RNN Mohan et al. (2015). The GRU is
easier to understand, can be trained more rapidly, and has the potential to be
more effective when used. With more data, the LSTM can be more expressive and
produce better outcomes. Dr.
Teuvo Kohonen created the
self-organised map (SOM), also referred to as the Kohonen
map, in 1982. SOM is an unsupervised neural network that divides the input data
set into smaller groups by lowering the input's dimensionality. SOMs differ
significantly from the conventional ANN in a number of
ways. Although it is unclear when auto encoders were created, LeCun discovered their first known application in 1987.
Three layers make up this particular ANN variant:
input, hidden, and output layers. 2.3. STOCHASTIC TECHNIQUES Numerous machine learning methods exhibit stochastic behaviour
and performance. Stochastic refers to a changeable process where the result
contains some element of uncertainty and randomness. It is a mathematical
phrase that is connected to "randomness" "probabilistic"
and can be used to contrast with "deterministic" thinking Krithiga et al. (2023).
To properly analyse the behaviour of various prediction models, it is necessary
to comprehend the stochastic character of machine learning algorithms, which is
a crucial machine learning core idea. An algorithm that keeps track of all potential options, each one
corresponding to a specific location in the problem's search space, is called
the population algorithm. Examples of population algorithms are the Intelligent
Water Drops Algorithm and the Artificial Immune System Algorithm. 2.4. Expert Systems Computer scientist Edward Feigenbaum, a
professor of computer science at Stanford University and the creator of
Stanford's Knowledge Systems Laboratory, invented the idea of expert systems in
the 1970s. Computer software known as an expert system uses AI techniques to
mimic the decision-making and actions of a person or group of people who have
knowledge and experience in a certain field Senthilkumar et al. (2023). Expert systems are often designed to support human experts
rather than replace them. Figure 6 shows the architecture of an expert system. Figure 6
2.5. FUZZY LOGIC A form of thinking that mimics human
reasoning is FL Masri et al. (2021). The
FL method mimics how humans make decisions by considering all middle-ground
options between the digital values YES and NO. A computer can interpret a
conventional logic block that receives precise input and outputs TRUE or FALSE,
which is similar to a human saying YES or NO. Fuzzy logic's creator, Lotfi Zadeh, noted that, in contrast to computers, human
decision-making involves a spectrum of options between YES and NO. Fuzzy logic
uses various degrees of input possibilities to produce a clear result. Figure 7 shows the block diagram of Fuzzy System. Figure 7
3. APPLICATIONS OF ARTIFICIAL INTELLIGENCE The world's many industries are changing
their tendencies as a result of AI. Numerous uses of
AI are changing our lives with the aid of technological breakthroughs. The
world is becoming digital as a result of the
integration of AI technologies with different applications and hardware Sy et al. (2018). It is capable of handling complicated problems in a
variety of fields, including many different industries, including robotics,
banking, gaming, chatbots, business, marketing, transportation, healthcare,
automotive, and business. The numerous applications of AI are making our daily
lives more effective and convenient. 4. FUTURE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE AI
is changing how individuals will act in practically every field. Big data, robotics,
and the Internet of Things are currently evolving technologies, and this will
continue to be the driving force behind them in the near
future. Technology moved to model- and algorithm-based machine learning
after such an evolutionary phase that began with "knowledge
representation" and lasted over many generations, was marked by periodic
inactivity, and became increasingly focused on observation, reasoning, and
generalization
Alkrimi et al. (2013). In a way that
was never before feasible, AI has now seized centre
stage, and there are no plans to do so anytime soon. Predictions
can improve the economic efficiency of common users by assisting individuals
and organizations in locating pertinent opportunities, products, and services,
linking producers and consumers, as AI becomes increasingly practical in
lower-data regimes. We anticipate that in the near future,
AI systems will take over a lot of boring and potentially hazardous duties Peter Norvig. In most situations, it is
not the algorithms themselves that are preventing these applications from
progressing, but rather the gathering and management of pertinent data and
their efficient integration into larger socio-technical systems. 5. CONCLUSION In the last five years, AI has advanced
remarkably and is now having an actual impact on society, institutions, and
culture. The key issues that have pushed the area since its inception in the
1950s, the ability of computer systems to do complex language and image
processing tasks, have come a long way. Although research and development teams
are utilising these developments and implementing them into applications that
will benefit society, the level of AI technology is still quite distant from
the field's founding aim of replicating fully human-like intelligence in
computers.
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