Artificial Intelligence vs. Machine Learning vs. Deep Learning
Machine learning and artificial intelligence (AI) are all the rage these days — but
with all the buzzwords swirling around them, it's easy to get lost and not see
the difference between hype and reality. For example, just because an algorithm
is used to calculate information doesn’t mean the label "machine
learning" or "artificial intelligence" should be applied.
Before
we can even define AI or machine learning, though, I want to take a step back
and define a concept that is at the core of both AI and machine learning: algorithm.
What Is an Algorithm?
An
algorithm is a set of rules to be followed when solving problems. In machine
learning, algorithms take in data and perform calculations to find an answer.
The calculations can be very simple or they can be more on the complex side.
Algorithms should deliver the correct answer in the most efficient manner. What
good is an algorithm if it takes longer than a human would to analyze the data?
What good is it if it provides incorrect information?
Algorithms
need to be trained to learn how to classify and process information. The
efficiency and accuracy of the algorithm are dependent on how well the algorithm
was trained. Using an algorithm to calculate something does not automatically
mean machine learning or AI was being used. All squares are rectangles, but not
all rectangles are squares.
Unfortunately,
today, we often see the machine learning and AI buzzwords being thrown around
to indicate that an algorithm was used to analyze data and make a prediction.
Using an algorithm to predict an outcome of an event is not machine learning.
Using the outcome of your prediction to improve future predictions is.
Artificial Intelligence vs. Machine
Learning vs. Deep Learning
AI and machine
learning are often used interchangeably, especially in the realm of big
data. But these aren’t the same thing, and it is important to understand how
these can be applied differently.
Artificial
intelligence is a broader concept than machine learning, which addresses the
use of computers to mimic the cognitive functions of humans. When machines
carry out tasks based on algorithms in an “intelligent” manner, that is AI.
Machine learning is a subset of AI and focuses on the ability of machines to
receive a set of data and learn for themselves, changing algorithms as they
learn more about the information they are processing.
Training
computers to think like humans is achieved partly through the use of neural
networks. Neural networks are a series of algorithms modeled after the human
brain. Just as the brain can recognize patterns and help us categorize and
classify information, neural networks do the same for computers. The brain is
constantly trying to make sense of the information it is processing, and to do
this, it labels and assigns items to categories. When we encounter something
new, we try to compare it to a known item to help us understand and make sense
of it. Neural networks do the same for computers.
Benefits of neural networks:
·
Extract
meaning from complicated data
·
Detect
trends and identify patterns too complex for humans to notice
·
Learn
by example
·
Speed
advantages
Deep learning goes yet another level deeper and can be considered a subset of machine
learning. The concept of deep learning is sometimes just referred to as
"deep neural networks," referring to the many layers involved. A
neural network may only have a single layer of data, while a deep neural
network has two or more. The layers can be seen as a nested hierarchy of
related concepts or decision trees. The answer to one question leads to a set
of deeper related questions.
Deep
learning networks need to see large quantities of items in order to be trained.
Instead of being programmed with the edges that define items, the systems learn
from exposure to millions of data points. An early example of this is the
Google Brain learning to recognize cats after being shown over ten million
images. Deep learning networks do not need to be programmed with the criteria
that define items; they are able to identify edges through being exposed to
large amounts of data.
Data Is at the Heart of the Matter
Whether
you are using an algorithm, artificial intelligence, or machine learning, one
thing is certain: if the data being used is flawed, then the insights and
information extracted will be flawed. What is data cleansing?
“The
process of detecting and correcting (or removing) corrupt or inaccurate records
from a record set, table, or database and refers to identifying incomplete,
incorrect or irrelevant parts of the data and then replacing, modifying or
deleting the dirty or coarse data.”
And
according to the CrowdFlower Data Science report, data scientists spend the
majority of their time cleansing data — and surprisingly this is also their
least favorite part of their job. Despite this, it is also the most important
part, as the output can’t be trusted if the data hasn’t been cleansed.
For
AI and machine learning to continue to advance, the data driving the algorithms
and decisions need to be high-quality. If the data can’t be trusted, how can
the insights from the data be trusted?
Difference between Artificial Intelligence, Machine Learning
and Deep Learning
The
rise of autonomous vehicles, natural language processing, predictive
maintenance, and even chess robots has come with its own jargon. The terms
artificial intelligence, machine learning, and deep learning are fairly
familiar to people working in the field services sector. These technologies
make field service automation possible. They power field service software. But
what exactly distinguishes one from the other?
The Sky is the Limit
Think
of the evolution of artificial intelligence as an umbrella. Machine learning
and deep learning both fall under the umbrella of artificial intelligence. And
in that order. Without AI there would be no machine learning. And machine
learning has given birth to deep learning. However, it might be most logical to
turn that umbrella upside down, because with deep learning, the sky is the
limit.
One
easy way to understand the difference between these three types of intelligence
and learning is to draw a parallel to the age-old and very familiar analog
training and education process.
➔ Artificial intelligence is like
teaching a student directly the information that you want them to learn.
➔ Machine intelligence is like giving
a student a book and allowing them to learn and process the information on
their own.
➔ The process of deep learning is the
same as machine learning, except in this case the student is capable of
learning from mistakes made and constantly improving.
The
students in the case of AI, ML, and DL are machines. And the books are data. An
endless flow of data that is either fed to the machine, in the case of AI, or
that the machine retrieves from external sources like the Internet, sensors,
etc. in the case of ML and DL. Here is a more concrete explanation of each of
the three.
Artificial Intelligence (AI)
AI
is used to denote machines that imitate human cognitive abilities like problem
solving and learning or other skills that necessitate language and speech and
strategic thinking. AI applications make it possible for machines to perform
certain human tasks with the same skill level or better. And the era of big
data is making AI ever more crucial. With an infinite number of data points and
constant generation of new data, it will soon be impossible for the human mind
to sift, sort, analyze, assess and arrive at a logical conclusion. And this is
in regards to mundane tasks, like scheduling appointments, detecting software
errors or machine malfunctions, and managing a gig economy workforce. In fact,
many successful field service providers are already relying heavily on AI to
effectively navigate these tasks.
Machine Learning
Machine
learning is the next logical step in AI. Two realizations propelled ML forward:
the idea that machines could learn how to learn and the Internet. Teaching
machines is a cumbersome job. However, providing them access to the endless
source of data that is the Internet so they can learn for themselves has opened
up immeasurable opportunities. Like deep learning.
Deep Learning
Deep
learning differs from machine learning in that machines are capable of learning
beyond the data that is available to them. It involves the ability to analyze
and assess information to make logical conclusions, determine solutions, and
learn from errors. So the more data a machines receives, the more it is capable
of learning and the smarter it gets. And though the artificial neural networks
responsible for this technology have been around since the 1950s, extensive
developments in the last decade have starkly improved the learning curve. The
most common current applications are voice and image recognition. However, the
level of data analysis possible will make many predictive applications
possible. This includes anything from massive improvements in predictive
maintenance and safer autonomous vehicles, to predicting illnesses or
recidivism.
Where to Now?
Looking
at the strides made from the inception of AI in the 1950s to today, we can
track an obvious surge in developments and applications. More has occurred in
the past ten years than in the 50 preceding them. As more and more businesses
embrace the digital transformation and switch to automated processes, we will
see even more innovation in this field to meet a growing demand. The possibilities
are endless.
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