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ARTIFICIAL INTELLIGENCE,
MACHINE LEARNING AND
DEEP LEARNING:
THE DIFFERENCE
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ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DEEP LEARNING
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AI:
AI is probably what you hear about most in the
media. It has become a catchy term, even spawning
movies and shows about AI and robots. A wellknown example of AI is IBM’s Watson system, which
beat two human champions on the television show
Jeopardy! in 2011. Watson is now used for other
purposes, including healthcare, and IBM has made
ARTIFICIAL
INTELLIGENCE
Watson available for a variety of problem-solving
tasks.
HISTORY
THE INTERNATIONAL DATA CORPORATION PREDICTS THAT:
HISTORY
AI research can be traced back more than half a
50%
of consumers will
regularly interact
with AI-based
services by 2018
75%
of developer teams
will include AI
functionality in at
least one application
40%
of digital
transformation
initiatives will be AI
supported by 2019
century. Alan Turing first sketched out his designs
for a machine that solves problems 80 years ago,
and he designed the Turing Test to see if a machine
Many people believe that AI has the unique potential
can be indistinguishable from humans in terms of its
to revolutionize not only technology, but also our
intelligence.
lives and our businesses. We already use AI in our
daily lives, perhaps without even realizing it. Google
WHAT IS IT?
uses it to improve search results, such as when your
search contains a typo and it asks you if you meant
By 2019, 40% of
#digitaltransformation
initiatives will be #AI supported
something else. Facebook uses facial recognition to
The goal of AI is to merge
the advantages of the
human brain and the
computer processor. AI is
able to learn from previous
situations.
suggest friends to tag when you post a photograph.
AI DEVICES MAKING OUR LIVES EASIER:
Mobile and virtual assistants, including
It can use past experiences and data to provide
insight and automate complex processes. AI
programs and robotic process automation are
advancing by leaps and bounds thanks to machine
learning, which enables them to perform and
replicate increasingly complicated tasks.
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86% of #ProjectManagers
would like support from #AI
in routine admin work
Amazon’s Echo, Apple’s Siri, Google’s
personal assistant and Windows’ Cortana
Online chatbots, such as customer
service bots and bots detecting harassing
comments
Smartphones
Wearable technology
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DEEP LEARNING
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AI is predicted to take over a great deal of
The Internet of Things (IoT) is technology that makes
administrative and technical project management
physical products mobile and virtual – and with
tasks in coming years. Right now, tasks including
instant connectivity. Connected devices are being
backups, job scheduling and password resets are
deployed already to make homes more secure, tasks
in the process of being automated. In a Harvard
more efficient and people healthier. But for a lot of
Business Review survey, 86% of project managers said
IoT devices, the traditional programming approach
that they would welcome AI support for administrative
does not make sense. Without AI technologies to
tasks. which can take up to 54% of their time. These
discover patterns and draw valuable insights from
administrative tasks include determining work
the enormous amounts of data from IoT devices,
schedules and a project’s budget. A Spiceworks survey
the IoT cannot reach its full potential. As such, the
of IT workers similarly found that most respondents
International Data Corporation predicts that by 2019,
believe that automation frees up their time to focus
AI capabilities will support 100% of all effective IoT
on strategy and innovation.
efforts.
The Internet of Things (IoT) is technology that makes
physical products mobile and virtual – and with
instant connectivity. Connected devices are being
deployed already to make homes more secure, tasks
more efficient and people healthier. But for a lot of
APPLICATIONS OF AI:
IoT devices, the traditional programming approach
Contactless emotion recognition
does not make sense. Without AI technologies to
Elderly assistance
discover patterns and draw valuable insights from
the enormous amounts of data from IoT devices,
the IoT cannot reach its full potential. As such, the
International Data Corporation predicts that by 2019,
AI capabilities will support 100% of all effective IoT
efforts.
Healthcare: The amount of data produced
by healthcare is staggering, and traditional
human and computational methods will
not be able to keep up. AI can be used to
save lives, prevent disease and create new
treatments.
Identifying criminals through facial recognition
Protecting endangered species
Voice recognition: Facebook and Google
are vying for the top spot in the voice
HOW IS IT DIFFERENT?
By 2019, #AI capabilities
will support 100% of all
effective #IoT efforts
AI is the idea of making machines
think more like humans. It is
enabled by machine learning,
which teaches computers to act
without explicit programming.
recognition field.
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ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DEEP LEARNING
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MACHINE
LEARNING
HISTORY
WHAT IS IT?
Machine learning turns the computer-human
dynamic inside out, in a way.
MACHINE LEARNING USES:
With machine learning,
we get a computer to
learn from data and
find insights rather than
just following explicit
instructions, as in the
traditional programming
model.
Machine learning is a subset of AI that developed around
Advances in understanding the human genome
Biomedical engineering
Facebook determining the stories that
appear in your News Feed
Microsoft’s Skype Translator
Self-driving cars and autonomous vehicles
The traditional idea of big data has lost favor
recently, with AI the next logical thing; it has a more
sophisticated way of making decisions based on vast
1980. It has become more powerful in recent years due to
advances in deep learning and neural networks, as well as
We are teaching the machines instead of
the unprecedented availability of vast amounts of data.
commanding them; we are guiding them instead of
controlling them. Machine learning relies on large
amounts of data and the repetition of this data so that
amounts of data. Companies are starting to integrate
the power of big data analytics with machine learning
and complex algorithms, which brings opportunities
as well as data management challenges.
the computer or system can learn. The computer
is shown thousands or millions of data pieces.
Machine learning can fuel predictive analytics, which
Algorithms start to detect data, and the machine
is like telling the future with your data. It can help you
is then able to make a prediction or categorize
to identify hidden patterns in data and to use data
information without explicit programming.
in micro-segmenting the market and customizing
your products. It can be used in everything from
fraud detection to helping parents to manage
TWO MAJOR TYPES OF MACHINE LEARNING:
their children’s online activities to increasing our
productivity.
Supervised learning is about
predicting trends. Examples
and logistic regression
(classification).
Unsupervised learning is about
classifying or categorizing data,
using clustering techniques
and Principle Component
Analysis, among others.
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HOW IS IT DIFFERENT?
include linear regression
Machine learning is the science that
leads to machines learning without
being explicitly programmed. AI
focuses on getting machines to
make more human-like decisions.
Machine learning accelerates AI
development.
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DEEP LEARNING
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It refers to neural networks being able to conduct
HISTORY
more complex algorithms and to make more refined
Deep learning is a relatively young field; it
predictions and categorizations.
is basically a subset or branch of machine
learning. It relies on neural networks, which
are computational models of interconnected
Deep learning studies deep neural networks, which
processing elements. Neural networks got their
are neural networks with multiple layers. Data is
name from their similarities to the human brain’s
entered, and after several layers, models figure out
neurons and their connections.
how to solve a given problem or execute a particular
task. The neural networks transform data with each
layer. Other deep learning architectures include
convolutional neural networks and deep belief
networks.
DEEP
LEARNING
Deep learning is
a set of algorithms
in machine learning
that lead us closer
to real AI.
HOW IS IT DIFFERENT?
Deep learning models automatically capture
patterns in huge datasets, whereas traditional
machine learning becomes untenable with its
manual extraction. Deep learning could almost
be considered machine learning with an extra
boost.
CONSIDERATIONS FOR THE FUTURE OF AI, MACHINE
LEARNING AND DEEP LEARNING
AI, machine learning and deep learning can uncover
great business opportunities:
#DeepLearning could be
considered #MachineLearning
with an extra boost
improve the
automate tedious
save time and money
accuracy of
administrative
with better predictions
tasks
Find out more about AI, machine learning
predictions
and smarter decision
and deep learning at www.ciklum.com or
making
our blog at www.ciklum.com/blog.
For more information on what AI, machine
They also come with their own challenges.
learning and deep learning can do for your
The success in getting machines to learn and make
company, contact the experts at Ciklum
predictions is also one of the downsides. Just as
today!
a lot of aspects of the human brain remain beyond our
understanding, we don’t know how exactly
the machines accomplish these tasks.
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ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DEEP LEARNING
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