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Artificial Intelligence and Analytics
February 2015
What is artificial intelligence?
Artificial intelligence concepts became popular in the late ‘90s, but the appeal waned due to limited applicability. At the time, it was
an idealistic notion and more than a little infeasible to implement. But with the birth of the data-driven economy at the turn of the
century, new age computational tools and digital transformations have been embraced by a wide variety of industries. And artificial
intelligence concepts have again found prominence and are being widely adopted as an alternative to traditional methodologies.
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Artificial Intelligence and Analytics | Dell Inc., 2015
Table of contents
About the author
Shatanjoy Ray is a senior advisor in the
Advanced Analytics practice at Dell Digital
Business Services. He has nine years of
experience in the analytics domain and has
extensively worked in the retail and financial
services industries,
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Introduction......................................................................................................................................1
Analytics is artificial intelligence in practice.............................................................................. 3
Functional architecture of artificial intelligence-enabled analytics..................................... 5
Potential applications of machine learning techniques in industries.................................. 6
Dell and artificial intelligence-driven analytics..........................................................................7
Artificial Intelligence and Analytics | Dell Inc., 2015
Analytics is artificial intelligence in practice
Artificial intelligence
is the science and
engineering of
making intelligent
computer programs
or machines.
Artificial intelligence and analytics
Analytics over the years has moved away
from traditional algorithms to machine
learning — embodying the true essence
of artificial intelligence. Practices and
algorithms based on machine learning
techniques are increasingly used to
aid scientific and critical business
decisions. With the advancement of
technology and statistical tools, there
is an emergence of several advanced
mathematical methods for optimization,
regression and classification using
machine learning-based logic. Machine
learning intelligence can be used in a
wide variety of fields for tasks such as:
Pattern recognition
Predictive modeling
Text mining and search
Genetic programming
Heuristics
Gaming
Speech recognition
Medical diagnostics
Credit card fraud detection
Several industries, such as healthcare
and banking and financial services, have
already started using machine learning-
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Artificial Intelligence and Analytics | Dell Inc., 2015
based analytics techniques in their
everyday practices.
A closer look reveals that many
traditional data mining algorithms and
statistical functions that employ iterative
analyses are being classified as machine
learning. However, the potential of
machine learning goes beyond simple
iterative algorithms. Emerging machine
learning algorithms like automated
neural networks, random forests
and adaptive boosters continuously
improve predictions and forecasts by
dynamically learning after each iteration.
This particular functionality brings this
group of analyses closer to artificial
intelligence.
Some of the most widely used artificial
intelligence-enabled analytics functions/
techniques include:
Decision trees: This method of learning
uses a decision tree as a predictive
model, which maps observations about
an item to conclusions about the item’s
target value.
Association rules: This method helps
discover interesting relations between
variables in large databases.
Artificial neural networks: This learning
algorithm, often referred to as a neural
network, is inspired by the structural and
functional aspects of biological neural
networks. Computations are structured
in terms of an interconnected group of
Several industries,
such as healthcare
and banking and
financial services,
have already started
using machine
learning-based
analytics techniques
in their everyday
practices.
artificial neurons, processing information
using a connectionist approach to
computation. Modern neural networks
are non-linear statistical data modeling
tools. They are usually used to model
complex relationships between inputs
and outputs, find patterns in data or
capture the statistical structure in an
unknown joint probability distribution
between observed variables.
Support vector machines (SVMs): This
training algorithm is a set of related
supervised learning methods used for
classification and regression. Given a set
of training examples, each marked as
belonging to one of two categories, an
SVM builds a model that predicts which
category a new example falls into.
Clustering: Cluster analysis is the
assignment of a set of observations
into subsets (called clusters) so that
observations within the same cluster
are similar according to pre-designated
criteria; and observations drawn from
different clusters are dissimilar. Different
clustering techniques make different
assumptions about the structure of the
data, often defined by some similarity
metric and evaluated, for example, by
internal compactness (similarity
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Artificial Intelligence and Analytics | Dell Inc., 2015
between members of the same cluster)
and separation between different
clusters. Clustering is a method of
unsupervised learning and a common
application of machine learning-based
statistical data analysis.
Bayesian networks: Also referred to
as a belief network or directed acyclic
graphical model, a Bayesian network
is a probabilistic graphical model that
represents a set of random variables
and their conditional independencies
via a directed acyclic graph. For
example, a Bayesian network could
represent the probabilistic relationships
between diseases and symptoms. Given
symptoms, the network can be used
to compute the probabilities of the
presence of various diseases.
Efficient algorithms exist that can
perform inference and learning in
Bayesian networks.
Random forests: This popular machine
learning technique is used for decision
trees and regressions that build a large
collection of de-correlated trees and
then averages them. On many problems,
the performance of random forests
is very similar to boosting and can be
simpler to train and tune.
Functional architecture of artificial intelligence-enabled analytics
Presentations
Business
decisions
Prediction layer
Prediction/rules
Feedback layer
Feedback
Recommendation layer
Recomendation
Prediction module
These algorithms and rules are
implemented and stored in the
recommendation layer. Whenever
new sets of data that needs to
be analyzed passes through
the recommendation layer,
recommendations are auto-generated
for business decision making.
Simultaneously, the model
performance is assessed through its
linkage with the feedback module. It
also suggests appropriate updates
to the model development module,
auto-learning with every run of the
recommendation module.
This automated, iterative and autolearning functionality of the AIE brings
artificial intelligence to life in motion
and practice.
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Artificial Intelligence and Analytics | Dell Inc., 2015
Model build
module
Training set
Feedback module
Recomendation
module
Test set
Decision set
Relevant business objects
Artificial Intelligence engine
The artificial intelligence engine (AIE) is
the brain behind any analytics program.
This is where models are developed
using historical and relevant datasets,
rules and prediction algorithms are
finalized, recommendations are
generated and decisions are made on
a continuous basis. The AIE has three
layers, as shown in the figure above:
the prediction layer, feedback layer
and recommendation layer. Historical
data enters the prediction layer, where
the model-building module develops
appropriate statistical models, validates
the robustness of the models and
creates the prediction algorithm or rules.
Potential applications of machine learning techniques in industries
The emergence of big data and the
Internet of Things (IoT) has led to a
sudden explosion in the volume, variety,
veracity and velocity of data. This not
only demands a transformation of
databases, but also the methodologies
needed to analyze this data. Machine
learning-based techniques (such as
clustering, associations, predictive and
prescriptive modeling and decision
trees) are increasingly being adopted to
solve these problems in the insurance,
healthcare, banking and
manufacturing industries.
For example, machine learning
techniques can be used to make it
easier for doctors to sort through
medical information in the form of
images and unstructured data (like notes
on a patient’s history and structured
laboratory test results). This technique
involves in-depth learning and helps
rapidly turn large amounts of data into
deep insights, as well as find subtle
patterns in the data invisible to the
naked eye. Images of importance,
needing further examination by the
practitioner, can be auto-flagged. This
saves a lot of time for the medical
practitioner, who otherwise would have
to review hundreds of images/data to
find the relevant ones. Iterative and
dynamic analyses of symptoms and lab
results can also help group ailments into
appropriate cluster of diseases, leading
to faster and more accurate treatment —
and transforming diagnostic healthcare.
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For banks and financial institutions,
machine learning can be used to detect
fraud, optimize credit limits and improve
risk management by performing multiple
iterations, dynamically improving model
predictions with every data iteration.
These optimized predictors can then
be used for superior forecasts and
predictions. This will help transform
the way risk assessments currently
performed, improving compliance
and governance.
In the field of insurance, telematics will
play a crucial role in determining how
insurance premiums can be calculated
in the future. Machine learning can help
business managers sift through tons of
data being generated by the sensors,
analyze associations and correlations
between the variables and appropriately
group drivers into various risk categories
by analyzing their driving performance.
This iterative and intelligent way of
partitioning and grouping at nearreal time will help enable a scientific,
accurate and dynamic premium
calculation method. Similarly, property
and casualty insurance premium
calculations could be transformed using
machine learning of sensor logs installed
in modern appliances.
The modern manufacturing sector is
at the threshold of embracing digital
transformation in a much bigger way —
the potential to store, analyze and draw
Artificial Intelligence and Analytics | Dell Inc., 2015
inferences from the multitude of data
is immense. Machine learning can act
as the brain behind any analytics-based
decision making, purely because of the
ability to analyze large datasets and
dynamically auto-improve predictions
and forecasts with every round of
iteration. And machine learning-based
techniques, such as random forests, can
help enable this shift toward
digital transformation.
In short, the potential for applications
of machine learning techniques in
analytics projects spans industries and
countless opportunities.
Dell and artificial intelligence-driven analytics
Dell has a good appreciation for
machine learning-based techniques —
not to mention in-depth experience
and expertise. Apart from having trained
data scientists in the field of machine
learning, Dell recently acquired StatSoft,
an analytics product company. Their
analytics tool STATISTICA has one of
the most advanced and best performing
neural networks applications in the
market, a module called STATISTICA
Automated Neural Networks. It offers
numerous unique advantages and has a
broad appeal, benefitting beginners and
neural network experts.
Experts have a wide selection of
network types and training algorithms
to choose from. And new users can
be guided, via the Automated Network
Search tool, through the necessary
procedures for creating neural networks.
STATISTICA automated neural networks
is a comprehensive, state-of-the-art,
powerful and extremely fast neural
network data analysis package.
Artificial intelligence-based analytics is
at the inflexion point of adoptability. Let
the in-house expertise of Dell Services
help your organization on this new and
exciting journey.
About Dell Digital Business Services
Dell Digital Business Services enables
digital transformation for customers
by taking a business-first approach.
Digital Business Services uses a robust
consulting methodology to create digital
strategy roadmaps for organizations,
enabling new revenue models,
exceptional customer engagement and
superior operational excellence. Our
services utilize digital technologies,
such as analytics, mobile, social media,
cloud and IoT, to deliver end-to-end
customer solutions.
For more information about any of
our service offerings, please visit
Dell.com/services or contact your
Dell representative.
Further reading
Hastie, Trevor, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second ed.
http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf.
“Introduction to Neural Networks.” Wolfram Mathematica Documentation Center.
http://reference.wolfram.com/applications/neuralnetworks/NeuralNetworkTheory/2.1.0.html.
Emanet, Nahit, Halil R Öz, Nazan Bayram and Dursun Delen. “A Comparative Analysis of Machine Learning Methods for Classification Type
Decision Problems in Healthcare.” Decision Analytics Journal, 2014. http://www.decisionanalyticsjournal.com/content/1/1/6.
Franks, Bill. “When Machine Learning Isn’t Learning.” International Institute for Analytics. April 10, 2014.
http://www.iianalytics.com/research/when-machine-learning-isnt-learning.
“STATISTICA Automated Neural Networks.” STATISTICA. http://www.statsoft.com/Products/STATISTICA/Automated-Neural-Networks.
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others. © 2015 Dell Inc. All rights reserved. Feb 2015 | D565-Artificial Intelligence and Analytics-whitepaper.indd | Rev. 1.0