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人工智能(AI)与机器学习(ML)与深度学习(DL).docx
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人工智能(AI)与机器学习(ML)与深度学习(DL).docx
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International Journal of Multidisciplinary Research and Publications
ISSN (Online): 2581-6187
117
Koffka Khan and Wayne Goodridge, “Artificial Intelligence (AI) versus Machine Learning (ML) versus Deep Learning (DL),” International
Journal of Multidisciplinary Research and Publications (IJMRAP), Volume 5, Issue 3, pp. 117-123, 2022.
Artificial Intelligence (AI) versus Machine Learning
(ML) versus Deep Learning (DL)
Koffka Khan
1
, Wayne Goodridge
1
1
Department of Computing and Information Technology, Faculty of Science and Agriculture, The University of the West Indies,
St. Augustine Campus, TRINIDAD AND TOBAGO
Email address: koffka.khan@gmail.com
Abstract— Due to the fact that businesses are embracing artificial
intelligence (AI), machine learning (ML), and deep learning (DL) to
create intelligent devices and apps, these technologies have emerged
as the most talked-about ones in today's business sector. And even
though these terminologies predominate in business conversations
throughout the globe, many individuals find it challenging to
distinguish between them. You may discover more about AI, machine
learning, and deep learning in this paper, as well as how they differ
from one another.
Keywords— Artificial Intelligence: AI: Machine Learning: ML:
Deep Learning: DL.
I.
INTRODUCTION
I'm sure we can all agree that one of the hottest trends in
today's market is machine learning. According to Gartner, by
2022, teams working on new application development projects
will need machine learning co-developers on their roster for at
least 40% of those projects [6]. Isn't it adorable to think about
the magnitude that these projects are predicted to bring in
about $3.9 trillion in revenue? global need for machine
learning is on the rise.
The five different topics that make up the machine learning
will be briefly discussed. We will begin with an introduction
to machine learning in this first topic. We'll talk about things
such as what precisely is machine learning? how does it vary
from artificial intelligence? and what types of applications
there are? The second topic focuses on statistics and
probability. We discuss sub-topics like descriptive and
inferential statistics. Supervised learning [22] is the third
topic. Well, supervised learning is a subset of machine
learning that mostly focuses on classification and regression-
type issues. It works with label data sets, and its algorithms
include random forest [24], decision tree [25], logistic
regression [23], and linear regression [8]. Unsupervised
learning [9] is the topic described later in this paper. It largely
focuses on using the algorithms that are a part of dealing with
unlabeled data sets. The fifth topic includes the k-means
method and the a priori algorithm. Here, reinforcement
learning [12] is at work. Finally, we will go into depth on the
Q-learning algorithm [11] and cover reinforcement learning.
As you are aware, we live in a world where both humans
and machines exist. Although humans have been growing and
picking up knowledge from the past for millions of years, the
age of machines and robots has only recently arrived in the
modern world. Typical machines act as though they need
programming before they will truly carry out your orders. But
what if the machine began to pick up knowledge on its own?
This is where machine learning enters the picture. Machine
learning is at the heart of many cutting-edge technological
developments in our day and age. And today, machine
learning is being used in many different ways, as evidenced by
Sophia [20], Apple Siri [10], and Tesla's self-driving
automobile [21].
What precisely is machine learning, then? Well, machine
learning is a branch of artificial intelligence that focuses on
creating systems that can learn from experience and make
predictions based on that experience, or data in the case of
machines. Machine learning empowers computers to act and
make data-driven decisions rather than relying on intuition.
These programmes are specifically coded to perform a specific
task, but they are also intended to learn and get better over
time when they are exposed to new information.
Let's address one of the main sources of misunderstanding
among people nowadays. They believe that all three—
artificial intelligence, machine learning, and deep learning—
are interchangeable, but this is untrue.
For the avoidance of doubt, artificial intelligence is the
ability of machines to perform tasks more intelligently. It
includes everything that makes the computer be as
functionally adaptive as possible. Use the well-known Turing
test to ascertain whether or not a computer is able to reason
similarly to a person. You are already pretty close to that if
you are speaking to Siri on your phone and she responds. As
I've already stated, machine learning is a branch of artificial
intelligence that is now being used. It is founded on the notion
that machines should be able to acquire data and learn from
previous experiences. It belongs within a category of artificial
intelligence. Is that related to the data set's pattern extraction.
Because of the advancements in computer science and parallel
computing, many of the algorithms involved have been known
for decades or even centuries, proving that the machine is
capable of more than just discovering the rules for optimal
behaviour. They are now capable of handling enormous data
quantities. A subset of machine learning called "deep
learning" uses comparable machine learning techniques. Deep
learning was used to improve accuracy in situations where
machine learning wasn't functioning up to par.
In Section II we outline Machine Learning as a prelude to
Section III which details AI, ML and DL. The conclusion is
given Section IV.
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