1. 1
Artificial Intelligence, Machine Learning, and Databases in the Information Age
Alejandro I. Arizpe
I. INTRODUCTION
In the coming years, an era of information will be further heralded. What will the future of
information be? Every single day, vast amounts of data is created and the means to interpret such data
are not limited, but they are inadequate. Research is being done for this very purpose; to understand and
interpret what exists in the world, to find new patterns, and to create novel ways of applying that
information in order to understand the world and the flow of interactions between man and machine.
What type of data is being generated and what does it tell us about the person, the culture, the
region where it is being generated? Assumptions can be made for this, but better methods can be utilized
to understand what is going on. Scientific evaluation needs to be done in the form of hypothesis and
experiments that can be tested and the replication of the results brought about by such experiments.
The applications are endless and it spans a multi-disciplinary approach to understand the basics
of what is going on, and the tools to aid humans in understanding patterns and make predictive analysis
is the utilization of artificial intelligence. First, in order to get and efficient or effective artificial
intelligence system, there must be methods created to help the artificial intelligence by the utilization of
machine learning by implementing algorithms that can detect patterns in data and form data structures to
access that data from databases, and then finding a way for the artificial intelligence system to
understand that information and present it in a relevant way that a human can understand it.
Thus, every field dealing with knowledge will be greatly impacted by such collaboration, and the
process of understanding will be incremented world-wide in places that have the infrastructure set to
support a worldwide interconnected network of computers, handheld devices, and other types of
machines that communicate with each other in the Internet of Things.
II. DATA STRUCTURES
First of all, what is it that makes artificial intelligence function, what does it require, how does it
do it, how far has it advanced, and what is it doing now? Those are all questions that come to mind when
thinking about this topic, but mainly, how is it done and what impact does computer science and
information technology have in that field of study? Where will this lead computer technology in the
future? A great portion of the field of artificial intelligence spans the understanding and utilization of
databases in various ways, and in order to understand databases, data structures are created to query or
retrieve information from a certain place and display it in a certain manner.
Hence, when looking in the field of computer science and memory, there lies a subject that deals
with the very important subject of data structures. What are data structures and what are they for? There
are a variety of different types of data structures built from databases which may contain seemingly
irrelevant pieces of data. These databases contain arrays and aggregates and may be structured in the
form lists, stacks, queues, and trees. They are all just different ways of organizing data. Arrays are
basically blocks or boxes that are in rows (records) and columns (attributes) and contain a piece of data
in each box (field). These records created may contain a piece of information, for instance, the name of
an employee, along with the address, phone number, etc., and these records are further subdivided into
the various other fields, which may contain other pieces of information relating to that employee or item

2. 2
of description. There are also lists, which contain a head (top) and a tail (bottom) and could be a list of
names or items. Stacks have a top and a bottom as well, but in stacks, information is removed or inserted
from the top only and it has a last-in first-out structure. In a queue, entries are removed from the head
and new entries are inserted at the tail, and queues follow a first-in first-out structure. Trees are a
collection of entries that branch out in a hierarchal manner creating nodes at each level of the hierarchy.
Tree structures are one of the most intriguing aspects of data structures since you can visually see data
being linked together in the nodes that come about, thus simulating a web of information that is tied
together, and may even look a little bit like the interconnected neurons of a brain.
What can these data structures help us do, how are they used, and what are they for? There are
many applications for data structures and how they are utilized in various ways to help people from
retrieval of information from a list, to doing searches on a web browser. Most of this data is stored in
databases and is indexed in such a way as to create easy access to it. Data structures are also very
important in the designing of efficient algorithms and the structuring or organization of software design.
All of this has allowed for the creation of the artificial intelligence construct known as IBM Watson,
which basically is a web of data structures interconnected by relating information to abstract data by the
utilization of “natural language.” There are many more implementations for data structures, one may ask
how the study and implementation of data structures will change the next phase of the World Wide Web.
In this context, databases and database systems are extremely important in this dynamic world
that we live in today. There are static databases that are for the easy retrieval of information stored
somewhere in a computer’s memory, and dynamic databases which store information that is ever
changing and not static, as with DNA. There have been many data mining techniques used for retrieval
and interpretation of the vast amounts of data available in the world. Whether it be from business,
society, or nature, these vast amounts of data all play an important role in how decisions are made in the
world and how certain patterns are interpreted.
III. DATABASES
Many business utilize database management systems, which are computer applications that allow
a user to interact with the system in order to retrieve data for the purpose of analyzing it. Depending on
the purpose of such systems, they can be used for the storage of information such as, student records
containing name, address, student ID, whether current or non-current, etc. Universities use such systems
to keep a record of everyone attending plus they also keep employee data. This way the system is
supposed to know when to send updates, when to sync something, etc.
Database management systems not always works as smoothly as they are supposed to work
theoretically. There are always issues with how information is stored, and information that may contain
duplicates, or information just plainly being synced wrong in the database system. These types of
systems are very specific in the way they are utilized to retrieve information, for instance, when
searching for a specific person, there may be duplicate records of that person. Sometimes, it may appear
there are duplicate records, but there may be more than one person named in the same way. Therefore,
extra fields representing information from other attributes are required for the identification of such
people: date of birth, telephone number, student ID, employee ID, address, etc. Most of the time, these
records can stay the same way, unless they are updated by the person of interest, as in the case with
updating phone numbers, addresses, e-mails, etc.

3. 3
Therefore, many issues arise from this malfunctioning systems that are poorly built. Surely,
nothing in this world is perfect, but to strive to create an efficient system of data retrieval is of upmost
important in this era and the coming years. Little by little, information technology is expanding at an
unprecedented rate. Little by little may be an understatement when thinking about the speed of the
growth of information technology. In a few decades, even undeveloped countries will need access to
these types of technologies to store information about their expanding populations with increasing wants
and needs.
Going back to poorly built systems, the University of Texas at Rio Grande Valley has a large
database containing information of past and current students, with new additions being made each or
every other day. Not to criticize the system to harshly, but the database management system employed
by the University of Texas at Rio Grande Valley is poorly built, with awful interfaces and duplicate
data, missing person records, or records that have not been updated.
When looking at the data stored in static databases, one has to take into account the data
contained in biological systems. The analysis and study of biological or psychological data can be more
complex than the analysis of records stored in business, university, or other types of databases. Thus the
question arises, what types of complex tools have been developed to interpret the more dynamic data,
and what has been done to keep track of patterns for such complex data sets created by the study of
biological systems? Hence the reason machine learning and genetic algorithms have been developed to
enhance the study in this area of study. The study of “Artificial Intelligence” greatly adds to the tools
and understanding of the more complex problems arising from databases and many other information
technology systems.
IV. TYPE OF DATA BEING CREATED AND ANALYZED
Across the world they type of data being generated comes in a wide variety of types. The most
mundane aspects of what a person does can generate a wide range of data which can be utilized for
predictive analytics tools. This type of data, for instance, can come in the form of what a person bought
at a grocery store on Friday, November 08, 2013 at 8:25PM, and the subsequent weeks after that until a
collection of a lot of purchase data is generated from that person for a year per se, until a table for a
database can be generated on a single person for a whole year. This can be done at the mass scales with
millions of people. That data being generated from the simple task of purchasing items at a grocery store
is astounding, and with this data and the utilization of database management systems, purchase patterns
can be detected into what a single person purchases, or how a whole community behaves in terms of
purchasing. In this way, a single person can be targeted for promotions into what they may enjoy buying
based on their purchase history and patterns of what and when they buy certain products, and special
promotions and coupons can be given to that person. On the mass scale, it can be detected what types of
things people usually buy on Fall at 8:25PM, in what area of the country, and in this way, the supply
chain of products going to certain places can be modified as to what types of sales are to be expected
from whichever area in the world they may come from.
All this generation of people data can be used by the government to target or track a specific
person that may be a threat based on their purchase history. The drawback of this is that in order to track
the purchase history of a person, that person would need to be using a type of debit or credit card in
order to obtain that specific information. If the person utilizes cash, that goes out the window. There are
other types of data which can be utilized, but not purchase data in that case. Still, patterns can be
extrapolated from past purchase behavior, or other types of behavior engaged by the person who is not

4. 4
utilizing the cash and predictions can be made. This is the essence of the study of artificial intelligence
with the gathering of intelligence in the area of national security and cyber security. Many government
agencies aim to do just that, and take actions based on the metadata being generated every day of
particular individuals of interest.
Other types of data generated by people come in the form of how they navigate the web or utilize
their browsers. Data can be generated in the form of typing tracking patterns and how long it takes a
certain person to type something, how long they press the key for, and how long it takes them to string
together certain letters to other letters in such a way as to develop a pattern that is specific to a particular
person. In this way, anywhere in the world where that person is, they can be tracked by how they move
their fingers across a keyboard. A person aware of such a thing can try to consciously change their
typing patterns, but once they are not conscious about it, old habits kick in and the person can be
tracked.
Also, what a person is searching for on the web is stored in databases for specific IP addresses
and can be tied to a person or an account of that specific machine in case the person logs in to a lot of
social media from one particular machine, it can be assumed the machine may belong to that particular
person, or maybe that person just uses that particular machine a lot. Then, maybe when they are typing
something or searching something in a browser, they type certain keywords that are being observed by a
simple type of artificial intelligence system, and over time of that person typing in those words for a
long time, that person can be identified as a threat base on the metadata gathered around that person, and
the government can take action to keep observing or do something.
V. TOOLS, TECHNIQUES, MACHINE LEARNING, AND DATA ANALYSIS IN AI
What are the tools being utilized for the means of predictive analysis of this data, and how do
they play in the role of machine learning and artificial intelligence? In New Zealand there was a tool
developed to analyze large amounts of data and to study databases and has been use by many
researchers to analyze patter, this tool is called the “Waikato Environment for Knowledge Analysis,”
also known as the WEKA. As Mark Hall put it, this tool was initially developed to “determine the
factors that contribute towards its successful application in the agricultural industries.” They also wanted
to know the factors or variables that impact the New Zealand economy, and wanted to find ways to
improve it.
This tool, WEKA, has proven to be very useful for research and with its interface, it simplifies
the way data is analyzed by providing the tools to develop algorithms to test the data or to train with
machined learning algorithms. It has been utilized to analyze purchasing data to understand customer’s
purchase patterns, and what to find out what may drive them to purchase certain items in certain days.
Companies like Wal-Mart have been doing this sort of thing for many years. It has even been
determined how to place items within the stores in order to get customers purchase those items together.
In order to find out that that technique worked, they had to analyze countless amounts of
customer data based on purchase patterns, during which time of the day and day of the week they were
purchase, the season, etc. In this way, stores have been able to improve sales. That is the power of
utilizing these tools utilized in machined learning or data mining. As T.K. Das mentioned, “The
common data mining tasks include classification, clustering, frequent pattern mining, association,
prediction and forecasting.” Another study demonstrated how the application of a GkM algorithm
managed to improve searches and recommendations within peer-to-peer networks. It is a very difficult

5. 5
task to achieve this sort of task within p2p networks since it is the users who are sharing the information,
and as such, they can choose to remove that information at any moment. It then becomes difficult to
make recommendations within this system, since the users serve as mini repositories of information that
can be accessed by other users, thereby creating the difficulty of making a recommendation. How is the
system going to do it and what is it going to base the recommendation on? Certainly it will not
recommend users who have that information. The researchers in this study were able to pinpoint cluster
of data, in their case they utilized music, and were able to use an algorithm that detected patterns in
popular music and what other music was closely related to it. Thereby, creating a sort of
recommendation system based on the categories of music instead of which person has what the user
wants or needs. Irad Ben-Gal says this about the algorithm used, “It uses the distance between co-shared
files as an item-similarity metric, mitigating the need for in-depth bit-level analysis of the files.”
Utilizing this simple algorithm, these researcher were able to succeed and demonstrate how machine
learning techniques can improve the searching and recommendation system within p2p networks,
thereby showing the possibility of what machine learning techniques can do within any realm where it is
difficult to find patterns and create recommendation systems based on the parameters imposed by the
users.
By looking at what is done through data mining, one can interpret how great it is to use these
tools that are available. These tools help with the visualization of this data as well in the form of the
clusters of interrelated data being created, and through visualization we can see how they communicate,
how tied the data is, and find the shortest path from data cluster to data cluster from the nodes being
created. The systems created in the future will dwarf the system in existence right now in this arena.
Every passing day, research is being conducted in the field of artificial intelligence dealing with the
search of information. There will come a day, when the Internet 3.0 will come about, and it will connect
all of the billions of devices connected to the network right now in an unprecedented way never
imagined before, but to state that is a lie of sorts.
There was a man that went by the name of Vannevar Bush who imagined such a world many
decades ago in the 1940s. Vannevar Bush said this, “There is a new profession of trail blazers, those
who find delight in the task of establishing useful trails through the enormous mass of the common
record.” The field of data mining and artificial intelligence owe a lot to the radical thinking of this one
man back in the 1940s. He believed there would come a time where looking for information would be
made relatively easy by the implementation of the Memex machine which he theorized. He believed
there would be people who are called trail blazers that would take care of all the associations between
the information of interest.
Bush believed that at the time he was alive there was just too much information available and it
was very hard for scientists and researchers to sift through all of that large amount of information and
data. Surprise surprise, even though Bush envisioned that about seven decades ago, that is the same
problem humanity is facing right now, but with orders of magnitude of more information to sift through.
The difference is, trail blazers still do not exist, and computers are even less able to associate data
through sound, images, and video the same way that Bush envisioned, but slowly, technology is heading
in that direction. Right now, all of the information being sift through on the web has links of associations
by the utilization of hyperlinks and hypertext, though that is not sufficient to create associations as Bush
envisioned. What is being researched right now is the creation of artificial intelligent agents that would
take care of “profession of trail blazers” that Bush believed would come about. The way these intelligent

6. 6
agents will do it, will be by similar ways envisioned by Bush and that is a subject of close research and
study.
This brings about the question? What other tools and techniques are used in this environment
that aid in the interpretation of data with all of its intricacies and what is it doing in the fields of machine
learning and other domains, such as “Expert Systems?” As Dr. Clarence N. W. Tan mentions in his
paper, “AI such as relational databases, frames and production systems which in turned, benefited many
other research areas in psychology, brain science, and applied expert systems.” In this case, we can see
the need for varying different tools that can be applied to the subject mentioned. In the areas of
psychology and brain science, different types of data being generated are used, and they are interpreted
using different machine learning tools that allow the people who study this areas carry out genetic
programming or the use of evolutionary computation.
VI. ARTIFICIAL INTELLIGENCE, STUDY OF PERCEPTION, AND IMPACT
Artificial intelligence is a subject that has been studied for several decades, since the 1950s as a
matter of fact, and has made great progress in the recent decade or two. Artificial intelligence is the
study of automating both simple and complex tasks, in a way, by imitating how biological life performs
such tasks, and studying those types of problem solving abilities, and trying to incorporate them in
machine learning. The main biological system of study that inspires the most in the creation of artificial
intelligence, is the human, and mainly the human brain. There is a drive that leads humans to want to
recreate themselves artificially, mainly curiosity and maybe the creation of efficient systems that require
little to no supervision to perform complex tasks.
How does artificial intelligence work? The goal of artificial intelligence is to obtain data from
the environment, and look at it in an abstract manner, much like a human or biological organisms
responds to it, and formulate a response/output from the input of such data, at least from the little that is
understood about it. Artificial intelligence could be built in anything from a robot to a computer
program, the applications are limitless.
What is difficult about this is that what may come natural or intuitively to a human or biological
system or organism is very difficult for a piece of software or algorithm to do. For instance, the act of
grabbing a ball, throwing it, bouncing it off a wall, and catching the ball may be pretty easy for a person
to do, but a computer needs to perform many calculations in order to do something like that. It may run
several thousand simulations of just that task to just get it right, and that is part of what some machine
learning algorithms do. It is similar to what a human does by trial and error, but it takes a machine
longer to run all those simulations to get a small movement just right in order to successfully perform
the task.
There are plenty of ways that artificial intelligence will change the world, but in order to be able
to do just that, various techniques have to be taken into account to know what works best when creating
intelligent system. Several techniques have already been mentioned, but the study of artificial
intelligence is not limited to just those techniques. The creation of machine learning algorithms and
creation or improvement of what is called as deep architectures are needed, along with various other
tools which the addition of each will enhance many of these different aspects of artificial intelligence.
Right now, an area of study within artificial intelligence is the creation of algorithmic tools that
will enable and aid computers and other machines in the identification of objects, and to truly

7. 7
understand what an object is. The level of recognition in this area of study is still very low, recognizing
what something is, is an inherently difficult thing to do for computers. They can tell the difference
between simple objects right now, but artificial intelligence, at this moment, cannot abstract the context
of what an action is, what something is, or the meaning behind other types of natural language. It is now
beginning to understand to recognize patterns in written language and telling apart what each symbol is,
but extracting data from the natural world and turning it into something meaningful is still only
something in the realm of living creatures.
To study this type of recognition, in a study done by Honglak Lee, et al., it is mentioned, “Our
experiments show that the algorithm learns useful high-level visual features, such as object parts, from
unlabeled images of objects and natural scenes.” The way that Honglak Lee, et al. did this is by the
utilization and intense training of their system by utilization an algorithms that would train each of its
many upon many layers to identify what a three dimensional and moving object was. Every single layer
was trained under several different instances of an image and from various different angles. One could
say that images were dissected the same way it is done with magnetic resonance imaging machines are
doing when they take a look at the whole body of a patient, the only thing that was left out was the
insides, but a type of scan of objects was done.
From this sort of training sets generate, the system was able to interpolate what various
categories of objects were, even if they looked completely different. By now, it is not completely out of
the realm and based on the training set, the system can tell the difference between humans, and other
different categories of animals, such as elephants. They system is also able to distinguish what an
automobile is, even with all its different shapes and models. It was a complex study, but it was
successfully done. Now, the recognition system does not need to look at a whole image to know what it
is and then fit it into a category.
For most of these studies being done, a utilization of artificial neural networks have been done in
order to help these algorithms train. There are various different types of artificial neural networks and
many techniques used within them. Neural networks may consist of evolutionary computation
algorithms and genetic algorithms just to name a few, but what is a neural network? Why is it named a
neural network? The term came from neuroscience and the study of the brain and cognition. The brain is
made up of a network of billions of interconnected neurons connected through their axons and dendrites,
and in a way, the body of the neurons act as the nodes that store the data within them, and the
combinations and communications between all the neurons through their dendrites and by running
chemical reactions and electricity through synapse, the neurons convert that data into relevant
information. It is a very complicated process, and that is the reason why artificial neural networks are
named that way. These neural networks aim to mimic the inner workings of the brain through various
types of algorithms. As W. Daniel Hillis, the author of the book called ‘The Pattern on the Stone’ said,
“A neural network is a simulated network of artificial neurons. These simulation may be performed in
any kind of computer… a parallel computer is the most natural place to execute it.” This way, neural
networks aim to process data the same way that brains do through the input of information and therefore
creating an output.
In this same manner as the study done by Hongalk Lee, other studies have also been done in the
area of recognizing writing from many different languages by the utilization of an algorithm that after
training in the specific language, is able to distinguish the symbols which are manually written, on paper
per se. The study was called “Offline Handwriting Recognition with Multidimensional Recurrent Neural
Networks.” This study was carried out by Alex Graves and Jürgen Schmidhuber. What these researcher

8. 8
aimed to do and succeeded at, was to create an algorithm for the specific purpose of recognizing patterns
in hand written letters. This study show their success by creating a system that interpreted what the
symbols were, in the Arabic language, with 91.4% accuracy. Amazing how they were able to do this,
and how Graves and Schmudhuber said, “we would like to point out that our system is equally
applicable to unconstrained handwriting, and has been successfully applied to complete lines of English
text.” This demonstrates how their system is language independent, and they managed to achieve this by
training their system with machine learning techniques.
Hence, the study of artificial intelligence is also the study of nature itself. How can a computer
scientist tell what intelligence is, if he does not study the intelligences already of existence? One can see
how eventually, computers will be able to share intelligence on par with humans, but it will be a long
road ahead. Simplifying the human mind into simple general machine learning algorithms is an area of
study right now, but looking at the simple algorithms and how they interact with one another is not as
simple as it sounds. Many see the brain as a network of interconnected nodes, thus from this concept the
study of artificial neural networks evolved.
VII. ARTIFICIAL INTELLIGENCE IN SOFTWARE ENGINEERING
In recent years, there has been research going in the field of artificial intelligence pertaining to
software engineering and how to make the process of the software development lifecycle more efficient.
As described by the Meziane and Vadera, “Artificial intelligences techniques such as knowledge based
systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and
developers as the way to improve many of the software development activities.” It is a long road ahead
for artificial intelligence to be fully implemented in the role of software development since it still lacks
in many arenas, especially in the understanding of natural language since it is so ambiguous.
When gathering requirements in a software development project, the project manager or business
analyst has to gather requirements as to what the software product is supposed to do from a wide variety
of different stakeholders with different levels of education and understanding of the project at hand. It is
therefore difficult to know what exactly it is that is needed, but with artificial intelligence, great strides
have been taken forward in the form of genetic algorithms that interpret language and run various tests
until they pass on the most efficient algorithms to their “offspring.” In this way, with each run, artificial
intelligence becomes better at interpreting key aspects of natural language and is able to identify the
essence of the requirements needed for the software product.
Data mining methods along with a study of past cases that are similar in software development,
and the utilization of neural networks are aiding in the development of tools to better understand the
flow of activities and how they should be ordered in the most efficient way possible to finish the
software product within the schedule constraints that it has. The utilization of these methods has shown
that by implementing them, software projects have finished within the established schedule 85-86% of
the time. It is quite remarkable that this is so since most projects rarely finish within scheduled time
unless change requests are made to extend time due to the addition or removal of certain requirements or
resources. Study in this area is sure to increase the efficiency of projects and add to the tools that a
project manager uses in order to control the flow of activities, budgets, and other resources in the most
effective manner possible. In due time, autonomous AI may even replace the need for project managers,
and it will instruct technicians or specialist into what it is they should do and when.

9. 9
Research is being done in the development of artificial intelligence agents to aid in the process of
software development. As Jörg Rech and Klaus-Dieter Althoff mentioned about object oriented software
engineering agents, “As summarized in the Agent Technology Roadmap by the AgentLink network
future research is required to enable agent systems to adapt autonomously to communication languages,
behavioral patterns, or to support the reuse of knowledge bases [47].” It is indeed amazing how research
is progressing in that direction of developing these intelligent agents to support project managers in the
creation of and development of software products, thereby enhancing the software development
lifecycle. This intelligent agent “implements agent functions, which map percept sequences to actions,”
as described by T.K. Das. These intelligent agents will be able to adapt to any sort of environment for
which they are built and will have applications in many different industries.
Previously, intelligent agents were mentioned to act as “trail blazers,” as in Vennevar Bush’s
paper, but their applications are so much more than sifting through information. These intelligent agents
with the mix of expert systems will have their agent support human endeavors in many different fields.
Not only will they be software programs that provide tools for analysis. These software agents may even
learn to do the programming themselves. The field of software engineering will be aided by this fact of
the evolution of artificial intelligence technology.
This is not the only way artificial intelligence will impact the field of software engineering. Mark
Harman looks at the overlap between both fields, “Previous work on machine learning and SBSE also
overlaps through the use of genetic programming as a technique to learn/optimise.” What Harman
means by this, is that the applications in what he calls Search Based Software Engineering, overlap
when looking at probabilities and predictive analysis. It has been found that AI techniques, such as
genetic programming, have greatly impacted SBSE. The way it has done this is that it has helped with
“automatic bug fixing [24], [25] porting between platforms, languages and programming paradigms [26]
and trading functional and non-functional properties [27],” as mentioned by Mark Harman. As can be
clearly seen, slowly, artificial intelligence is beginning to take the tasks that required so many people
before into its own “metaphorical” hands, and single handedly is able to solve issues that may not have
been seen or predicted by quality assurance workers in working in the fields of software engineering.
VIII. ARTIFICIAL INTELLIGENCE IN WARFARE
It is indeed true that artificial intelligence will bring about a tremendous change in all fields, and
in a way that change began years ago with the creation of autonomous systems created by the military in
the form of drones created for vigilance and to attack enemy targets. It is another way of depersonalizing
close quarters combat in the battlefield, and a great way to gather intelligence for the terrain, bases, and
other targets from above. On land, autonomous robots are being created as well that can navigate a
variety of terrains to assist soldiers in carrying heavy loads, to attack enemy troops, or to disarm bombs.
Still, there is great controversy in the area of study. The research and creation of autonomous systems
for combat is still being studied and new methods are being created to bring about the time when drones
and other combat autonomous systems will not need the use of a human operator to access them
remotely to carry out the tasks of identifying and eliminating enemy targets.
The reality is, for every good thing that is invented by humanity, there will always be a bad
thing. In this area, it comes in the way of applications for warfare. What is autonomy in the battlefield?
It is hard to tell since even within the military branches, each branch defines autonomy by its own terms.
Some say full autonomy would be the ability to take all decisions of a machine in its own “hands.”
Others believe that autonomy comes in the way of just having the autonomous system carry out a

10. 10
particular task with very little intervention, and the only intervention required is the order given to it to
proceed.
What is scary about any of these, is the fact that artificial intelligence systems do not have the
capacity to carry out moral judgment, or any type of judgement by themselves for that manner. At least
for now and probably the coming decades, human judgement will still be needed in order for artificial
intelligence systems to distinguish between targets and who is a combatant or non-combatant. The ethics
that come into play in these types of systems for warfare are great indeed. Who will be to blame if the
artificial intelligence system injures an innocent? At least at this point, the system itself will not be to
blame, but would the engineers who built it, the designers, and the scientists giving it the training
datasets to “distinguish” objects?
In an article written by Noel Sharkey, this same issue is brought up. The truth is, autonomous
systems right now should not come into play at this moment. There have been studies made that show
that even humans have a hard time distinguishing targets when they access some of the autonomous
systems remotely. That is the truth about autonomy right now. It still requires human operators to
engage target by remotely controlling the aircraft used for espionage as well as for attack. The purpose
of autonomous machines was to get rid of enemy or terrorist leaders from afar, and in theory it would
work.
The paper mentions that for every one ring leader removed, there were fifty other casualties of
enemy combatants along with non-combatants. Non-combatants is just another word used for civilians
or regular people. How can an autonomous system tell who is the enemy if most of the terrorists or
enemy combatants dress just like regular civilians? What type of training data sets would allow for such
a distinction? Maybe one which includes training in distinguishing who or who does not have weapons,
and to determine the level of nervousness. In the United States, in many of the states, civilians can carry
weapons with permits, and they are not enemy targets. Would an artificial intelligence autonomous
system eliminate that civilian for the reason of carrying a weapon or because the civilian is nervous if it
is being interrogated. Many complex problems within the field of artificial intelligence would need to be
solved before a system has that level of autonomy. Even the tools required by that system would need to
change in order to determine who someone is or to find motives between actions.
Still it would be very difficult to tell. This is not computer science, but ethics do play an
important role when developing such systems. The author of the paper on autonomy, Noel Sharkey, says
this, “no one knows how all the complex algorithms working at faster than human speed will interact
and what devastation it may cause.” He mentions in the article how militaries across the world are
expanding their arsenal to include autonomous systems, and how the United States is the number one
country doing this. Government agencies along with computer scientists and engineers will have to work
very closely together in order to solve these issues before any massive deployment of such systems is
done, and it is not a matter of if it will be done or not, it is just a matter of when since most
industrialized nations are moving in the direction of autonomous systems for warfare.
On the other hand, semi-autonomous systems have begun to reach the market at an
unprecedented rate. They study of drones and the implications, impacts, and research have given way to
miniature drones, which in the military are utilized for surveillance, but with a few hundred dollars, not
more than a thousand, the drone market is taking off. People can now go into an electronic store and buy
these drones and utilize them for entertainment purpose or for more sinister reasons. These autonomous
systems are called miniature unmanned aerial vehicle, or miniature UAVs for short. The artificial

11. 11
intelligent aspect that comes into play within these system, is utilization of genetic algorithms that help
the micro UAVs remain stable when flying, plus the addition of integrated games in the form of
augmented reality which are displayed in their remote controls which contain a screen within them to
see where the drone is flying. The author, Pierre-Jean Bristeau, says this, “Combined into data fusion
algorithms, these devices have allowed to obtain relatively good results of state estimation and
stabilization on rotary wing.” Along with this, the human pilot is integrated within the loop, as with the
autonomous systems utilized for drone warfare. Since this device is connected online thought Wi-Fi,
neural network algorithms may come into play in the future development of these commercial drones
out in the market, which may aid the user with searches, looking for games the user may like, connect
them to social media, and maybe connect them to user preferences using data mining tools to analyze
behavior patterns into what they user may like.
IX. CONCLUSION
This brings up the perplexing question: How will it benefit humanity when the time of more
complex artificial intelligence comes? At first, artificial intelligence will serve as a tool that will aid
humanity with automation of very simple tasks that can be done more efficiently by the machine. Right
now, self-driving cars are being developed to remove the danger of tired drivers of the road. In the short-
run, it will be very beneficial and aid society, but in the long-run, many millions of unskilled jobs will be
replaced and massive retraining of the workforce will need to be implemented in a society with
decreasing job prospects. Certain limitations on artificial intelligence will need to be imposed to prevent
that from happening to the hundreds of millions of unskilled or uneducated people in developed and
developing countries, or they can just get educated or trained in something that is in demand, which may
be difficult to do if they do not have the means to pay for their reeducation or the background to find a
free opportunity for retraining.
The goal of this paper was to demonstrate the research happening in the areas of artificial
intelligence, machine learning, databases, along with the implications of such research and to show a
little on the ethical aspect of this subject. What the future may bring is unclear, but with all the research
that is happening mainly in the fields of genetic programming utilizing evolutionary computation and
genetic algorithms, great strides are being taken forward in creating systems that learn by themselves.
The information age is creating way for the visions of Vannevar Bush and exiting times are ahead.
This paper demonstrated what has been achieved and what is yet to be achieved and is mainly to
leave the reader intrigued or interested in the subject since it will impact most if not all fields of study in
the future. There will come a time when the autonomy created by computer science because of human
curiosity in wanting to understand intelligence and replicate it will come to be. It will bring about a great
era where efficiency will be heralded, but also bring the dangers of data mining user information and
more invasion of privacy will come to be. This will create an environment where systems will know
what the user wants or needs but at the cost of invasion of privacy. Systems for surveillance and warfare
will be improved by the development of better artificial intelligence techniques for machine learning,
but it will not be all bad, and many great thing will come to be.
X. REFERENCES
1. Alex Graves, Jürgen Schmidhuber. “Offline Handwriting Recognition with Multidimensional
Recurrent Neural Networks.” Advances in Neural Information Processing Systems. 2008

12. 12
2. Clarence N. W. Tan. “An Artificial Neural Networks Primer with Financial Applications
Examples in Financial Distress Predictions and Foreign Exchange Hybrid Trading System.”
School of Information Technology, Bond University, Gold Coast. 1999.
3. Farid Meziane and Sunil Vadera. “Artificial Intelligence in Software Engineering: Current
Developments and Future Prospects.” Information Science Reference. AGI Global - Artificial
Intelligence Applications for Improved Software Engineering Development: New Prospects,
2010, Chapter 14, pp. 278-299
4. Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng. “Convolutional Deep Belief
Networks for Scalable Unsupervised Learning of Hierarchical Representations.” Proceeding
ICML '09, Proceedings of the 26th Annual International Conference on Machine Learning,
Pages 609-616. June 2009.
5. Jörg Rech, Klaus-Dieter Althoff. “Artificial Intelligence and Software Engineering: Status and
Future Trends.” Special Issue on Artificial Intelligence and Software Engineering, K. 2004
6. Irad Ben-Gal, Yuval Shavitt, Ela Weinsberg, Udi Weinsberg. “Peer-to-peer information retrieval
using shared-content clustering.” Knowledge Information Systems. March 2013
7. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten.
“The WEKA Data Mining Software: An Update.” ACM SIGKDD Explorations Newsletter
Volume 11, Issue 1, June 2009
8. Mark Harman. “The Role of Artificial Intelligence in Software Engineering.” International
Conference on Software Engineering. Proceedings of the First International Workshop on
Realizing AI Synergies in Software Engineering. ACM Special Interest Group on Software
Engineering. June 2012
9. Noel Sharkey. "Automating Warfare: Lessons Learned from the Drones." Journal of Law,
Information and Science. 2012.
10. Pierre-Jean Bristeau, François Callou, David Vissière, Nicolas Petit. “The Navigation and
Control technology inside the AR.Drone micro UAV.” IFAC Proceedings Volumes. Volume 44,
Issue 1, Pages 1477–1484, January 2011.
11. T.K. Das. “Intelligent Techniques in Decision Making: A Survey.” Indian Journal of Science and
Technology, Vol 9, March 2016.
12. Vannevar Bush. “As We May Think.” The Atlantic Monthly, 176(1):101-108, July 1945.
13. W. Daniel Hillis. “Neural Networks.” The Pattern in the Stone. Computers That Learn and
Adapt, Chapter 8. 1998.

13. 13
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-
Barwińska, et al. “Hybrid Computing Using a Neural Network with Dynamic External Memory.”
Nature, Vol. 538, 27 October 2016.
This article comes to show how far we have come in the year 2016 in the field of artificial intelligence
and neural networks. The author of this article talks about the differential neural computer (DNC) and
how it is solving the problem of lack of external memory. As the author mentions, it consists of a
“neural network that can read from and write to an external memory matrix, analogous to the random-
access memory in a conventional computer.” What DNC tries to solve is an issues with traditional
neural networks and their lack of external memory to store data over time in order to interpret and learn
from it. The DNC is capable of learning to manipulate complex data structures from data stored within
the external memory it possesses, unlike the traditional neural network. The DNC is capable of
simulating natural language responses and even solve certain types of block problems.
The problem that was being faced by traditional neural networks was that as complex tasks were
assigned, it did not have the memory resources to carry out those tasks, therefore it has trouble learning
algorithms. The authors of this article mention this, “neural networks are remarkably adept at sensory
processing, sequence learning, and reinforcement learning… neural networks are limited in their ability
to represent variables and data structures.” This was the main issue that scientist studying this topic were
facing, but the solution lied in the addition of “read-write access to external memory.” In this way, these
neural networks began to train/learn from the information that was stored in that external memory. This
gave the DNC the ability to modify memory content through various iterations.
The author explains the application of these principals to increase memory capacity, and mentions how
this has allowed the DNC to mimic certain aspects of memory in the mammalian hippocampus,
resembling the processes that happen in a human brain, and especially the part of the brain that is
responsible for the creation of new neurons. This is truly remarkable, since by the study of artificial
neural networks and artificial intelligence, scientist are beginning to understand how the human brain
works and may eventually bring about an artificial intelligence system with human level cognition, but
that is merely an opinion and not mentioned in the article.
Based on this, the authors of this paper began to conduct basic reasoning experiments after the DNC had
trained on some data for a while. When the scientist provided basic stories or phrases and asked the
DNC questions, it would give a correct response to the questions with an average error rate of 3.8%.
This comes to show how little by little, AI and neural network systems are approaching human level
deduction based on simple but tricky questions. The author mentions that these responses utilized only
very basic vocabulary and were obtained from graphs of interconnected nodes of data sets that could
easily divide the response by the process of limiting the answer to two nodes containing the words
utilized in the experiment, which were “Playground” and “John.” The authors described it this way,
“…many important tasks faced by machine learning involve graph data, including parse trees, social
networks, knowledge graphs and molecular structures”. To test if the DNC could do it again, they
generated a random graph with different data and after retraining the DNC again on the new data, the
DNC was able to achieve a 98.8% accuracy after training on the data 1 million times. Previously, other
methods after 2 million tries only achieved a 37% accuracy. That is quite an accomplishment done by

14. 14
the DNC. In the last experiment, the DNC learned to make a plan and solve a puzzle involving blocks, in
order to do this, the DNC was trained on reinforcement learning in the same way it is done with animals,
and it was able to succeed at the task.
Alex Graves, Jürgen Schmidhuber. “Offline Handwriting Recognition with Multidimensional Recurrent
Neural Networks.” Advances in Neural Information Processing Systems. 2008
This article is mainly about the training of neural networks to recognize actual handwriting, and the
main focus is on the handwritten Arabic language. The applications of this are great. In this manner,
instead of having a digital copy of a book that doesn’t need much in the way of pattern recognition,
since typing is already integrated into the system and the format into which they created the file, the
research being done by the authors on this, in a way, recognize pictures. The system would have to
understand how a symbol looks through various form of personal handwriting from many different
people, and then interpret what the symbol is through training of the neural network.
The author mentions this about what is happening with their neural network, “with sophisticated
preprocessing techniques used to extract the image features and sequential models such as the HMMs
used to provide the transcriptions,” are what are mainly used today. The acronym HMM just stands for
the “Hidden Markov Model.” The drawback of using this system as the authors mention, it would be
that this method would have to be used independently for each language and is not a one size fits all
solution.
The different techniques used for this research paper, as mentioned by Graves and Schmidhuber, are
“multidimensional recurrent neural networks and connectionist temporal classification.” With this being
said, the way this works is not necessarily that the system or neural network understands a language, but
it learns to recognize patterns in the way letters are written and interconnected. What better way to test
this than with the utilization of the Arabic language which just flow as if it were all one string pieced
together.
Previously, the authors had worked on a system that would do something similar, but it was an “online”
version as they call it. This method works differently in that it trains to recognize patterns from offline
pixel data of the letters of interest. This system would work language independently and as mentioned
before, it would not have to train for each particular language, but more on the pattern recognition aspect
of processing “raw pixel data” on images. The challenge of this, as mentioned by the authors, would be
that instead of a one-dimensional approach that works on assigning IDs, they would need to use a
“multidimensional recurrent neural network” approach since offline handwriting is inherently different
than what is done online. What the authors of this paper are aiming to do by following this approach, is
to convert two dimensional data with multiple layers to a one dimensional form that would be easier for
the neural network to understand. Also, a “multidimensional long short-term memory” are required to
activate each memory cell to “store and retrieve” information.
The last component of how this must be done is to create a hierarchal structure that will aid in the
process of creating a solution to this problem. In this way, they can create different levels or stages, and
in each level, an input would have to be done in order to create something for the next level up the
hierarchy, thereby creating “global” features at the highest levels in the hierarchy. All of these processes
are repeated many times in order to better train the neural network to recognize this global features that
certain patterns share.

15. 15
After experiments were run to see how well it worked, the authors competed against other handwriting
recognition neural nets, and they found that theirs worked very well when giving the task of recognizing
the postal codes and match them to their respective Tunisian towns in the Arabic language. The
utilization of this system not only works well for Arabic, but is language independent.
Cesar Bravo, Luigi Saputelli, Francklin Rivas, et al. “State of the Art of Artificial Intelligence and
Predictive Analytics in the E&P Industry: A Technology Survey.” Society of Petroleum Engineers.
March 2012.
The author of this article starts of by mentioning how artificial intelligence is used in the E&P
(Exploration and Production) industry, but it is not prevalent and mainly pilot programs are being run in
the industry to study its usefulness. He mentions how data mining and neural networks are what is
popular right now in the industry, and in a way, this paper is mainly to attract the attention of asset
managers and other personnel to tell them what is available in the industry, and how it can improve
performance and create more value. The authors of this article say this, “AI is an area of great interest in
the E&P industry, mainly in applications related to production control and optimization, proxy model
simulation, and virtual sensing.” This just comes to show how AI is becoming an important aspect of
many different industries and not just in the field of computer science and information technology.
Engineers in many fields along with other types of analysts are paying more attention to the tools that AI
can provide them in order to create better performance in the areas of analytics and operations, and
provide more relevant information for decision making.
The field of Artificial Intelligence and Predictive Analytics (AIPA) are the ones that have the greater
impact in this area, but as mentioned by the author, it has been greatly neglected in the area of petroleum
exploration, and he mentions that engineers are the ones who see the value in having such tools, but
management does not. Computational intelligence, and more specifically neural networks help with
parallel computation and provide good results, but are have been shun into bad light since as the author
mentions, “it is difficult to determine exactly why a neural net produces a particular result.” Hence, the
main reason why some aspects of AI have not been looked well upon and have not been adopted. The
author touches the concepts of fuzzy logic which aid in pattern recognition, sensors, prediction, etc.
Also, different aspect of evolutionary computation are touched in this paper: genetic algorithms,
machine learning, and intelligent agents. The field of evolutionary computation is an interesting one,
since it tries to mimic the biological process of evolution and learning and applies it to artificial
intelligence principles. The author places a bigger emphasis on intelligent agents and their ability to
support the user with large amounts of information gathered from their surrounding environment and aid
with the process of decision making. These intelligent agent help with workflow, the dispatch of oil, and
in reservoir simulations, as mentioned by the author.
Other AI systems are defined and explained, but the author also focuses a little more attention on Expert
Systems, which have been implemented since the 1970s in a variety of different fields requiring specific
knowledge about procedures in a particular domain. In the oil industry, this comes in the form of drilling
operations which require human expert knowledge of that domain. AI also allows for virtual
environments where the drill can be accessed remotely in order for better penetration precision. These
virtual environments also allow for operators of drilling equipment to train in them in order to train
without damaging real equipment.

16. 16
The rest of the article the authors focus on the levels of awareness of artificial intelligence and business
intelligence application in the oil industry. It shows how educators are the ones that have the highest
awareness of this technology, and business executives second, consultants third, and engineers are part
of the group that would benefit the most, but have the lowest level of awareness and usage.
Farid Meziane and Sunil Vadera. “Artificial Intelligence in Software Engineering: Current
Developments and Future Prospects.” Information Science Reference. AGI Global - Artificial
Intelligence Applications for Improved Software Engineering Development: New Prospects, 2010,
Chapter 14, pp. 278-299
In this paper, the author speaks about the impact of artificial intelligence and the role it plays in the
software development life cycle and how various techniques in artificial intelligence are used for this
purpose. As described by the Meziane and Vadera, “Artificial intelligences techniques such as
knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many
researchers and developers as the way to improve many of the software development activities.” It is
indeed true that the utilization of these techniques will aid future development of the software for the
coming generations.
Planning methods for the full software development lifecycle play an integral role in the areas of
software engineering that pertain to requirements analysis, design, testing, scheduling, and estimation of
budgets for software development. It is important to know how the development process will flow, and
artificial intelligence aids in the research of finding new ways to be more productive in this arena. New
methods in project management and software engineering are mentioned in this paper. The role that each
of these methods plays is very important, and they all have their advantages and disadvantages.
Data mining along with case based reasoning have been mentioned to help project meet deadlines about
85% of the time. The way CBR works is by the analysis of past cases, which may have been similar, by
utilization of data mining methods, such as association rule mining to determine “popular patterns” that
aid with project management.
Natural language is an aspect of requirements analysis that can hinder the software design phase in
software engineering. When developing a software product, requirements need to be gathered from all
the stakeholders in the software project. These stakeholders may not understand the software
development process completely and come from a wide variety of different backgrounds, so a common
link needs to be established in order to understand what the stakeholder wants or needs. The study of
natural language in artificial intelligence has been a very difficult problem to solve, but nonetheless it
aids in the software development lifecycle by removing natural language ambiguity in the gathering of
requirements. This way, AI aids with any “incompleteness,” prioritization of requirements and tasks,
and helps manage requirements and model problems, as mentioned by the authors of this paper.
Research on neural networks has shown that neural networks can be utilized in the form of forecasting
what areas are of risk in software engineering by the utilization of past information and 39 areas of risk
were identified and further subdivided for analysis. After running some tests using some algorithms to
teach the neural network to identify risks, the tests showed that the neural network was 86% successful
in identifying risks.
In this paper, it is mentioned that genetic algorithms are utilized in the study of the factors that constrain
software projects, such as scheduling. After a genetic algorithm was used to study over 400 projects, the

17. 17
system learned to identify the order at which aspects and activities of software development should be
performed in order to optimize the schedule, thus creating a higher quality products that meet the target
completion date. Utilizing this approach can be costly, but by the end of the software development
lifecycle, the benefits would outweigh the costs and the efficiency would of this approach would save
money.
Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng. “Convolutional Deep Belief Networks
for Scalable Unsupervised Learning of Hierarchical Representations.” Proceeding ICML '09,
Proceedings of the 26th Annual International Conference on Machine Learning, Pages 609-616. June
2009.
This research paper deals with the issues of image recognition and creating machine learning algorithms
that are better equipped for such task. The authors mention “hierarchical generative models such as deep
belief networks” to aid with this issue. As of the moment the article was written, there is success in the
recognition of two dimensional images, but recognition of three dimensional images is something that is
more difficult to do. To solve this issue, the authors are implementing a “convolutional deep belief
network,” as mentioned by them. This helps them recognize images by looking at the different layers
and different parts of the image. To be successful at this, the authors are using what they call
“probabilistic max-pooling” to help them with the issue of the representation of many layers. All of this
allows their algorithms to detect a wide variety of objects after it has trained for a bit.
The authors conduct their research by doing a top-down and bottom-up approach to the scanning of
visual data sets which contain many levels of hierarchies and layers. All of this helps the algorithms they
use to better process an image and to understand the type of object it is, even if not all parts of the object
are visible. The authors say this, “In particular, the deep belief network (DBN) (Hinton et al., 2006) is a
multilayer generative model where each layer encodes statistical dependencies among the units in the
layer below it; it is trained to (approximately) maximize the likelihood of its training data.” This deep
belief network has been successful in identifying motion data as well, and what is best about it is that it
can learn in an “unsupervised” manner based on all the training data given to it.
Utilizing the techniques mentioned by the authors, this convolutional deep belief network can even
detect full sized objects, even if not all parts of the object are displayed. In order to do this, it has to take
into account the variation between objects and learn from that data. In order to do this, it must have been
shown data of a particular object from many different angles and instances, and trained from it in order
to recognize what a person or whichever object is supposed to be.
The authors say this, “Hinton et al. (2006) proposed an efficient algorithm for training deep belief
networks, by greedily training each layer (from lowest to highest) as an RBM using the previous layer’s
activations as inputs.” From this information, one can infer that in order for their system to work, they
trained the system by implementing data sets for each and every layer it needed to train for. In order to
do this, algorithms needed to be in place to take into account the variations between layers, since the
system needed to train on two-dimensional multi-layered images, much like what magnetic resonance
imaging system does.
To succeed at this, they utilized a “restricted Boltzmann machine (RBM)” along with “deep belief
network (DBN). The authors of this paper believed this was not enough, so they developed a
“convolutional RBM (CRBM)” and utilized “probabilistic max-pooling” and stacked CRBMs in order to
“learn high-level representation.” Very complex indeed, but the authors believe that doing this would

18. 18
improve “detection layers” required for the system to process and understand what the object of the
images may be. Max-pooling shrank the layers at the higher layers in order to “address scalability
issues.” After training all the layers, it learned to identify and recognize the many different types of
objects on that data that it trained for and could distinguish the different categories in which those
objects belonged in. The algorithm utilized by the authors demonstrated that it could identify various
types of complex objects which were never labeled, and they believe this algorithm to be scalable.
Hsinchun Chen, Roger H. L. Chiang, Veda C. Storey. “Business Intelligence and Analytics: From Big
Data to Big Impact.” MIS Quarterly Vol. 36 No. 4, pp. 1165-1188, December 2012.
The area of business intelligence has grown a large amount in the last few years, mainly because of
improvements and new research being done in the fields of “Big Data” and business analytics. The
author introduces the subject of business intelligence and analytics as a growing field, with there being a
shortage of hundreds of thousands of people required for this field by 2018, and a shortage of millions of
data savvy managers by the same time frame.
There has been a tremendous growth in the field of “Big Data,” with very large datasets containing
thousands or millions of terabytes being generated that require specialized or specific tools and
techniques to be able to analyze and turn into something useful. The author mentions BI&A 1.0, 2.0, and
3.0, which consecutively have a foundation in data management and warehousing, web-based content
analytics, and mobile and sensor based analytics. For BI&A 1.0, there was and still is a need for data For
mining tools, visualization, and analysis of data structures. For BI&A 2.0, google analytics came into
play to analyze user behavior in the areas of how the user browsed the internet, what they bought, etc.
All this was done to understand user behavior in order to drive sales. The author mentions this about
web analytics, “Web site design, product placement optimization, customer transaction analysis, market
structure analysis, and product recommendations can be accomplished through web analytics.” For
BI&A 3.0, research is still underway to understand all the data being generated by the billions of mobile
devices out in the market. It is estimated that by 2020 there will be about 10 billion devices in use across
the world, and that is a great opportunity in Big Data to study user behavior patterns across the world on
a massive scales, since there will be an addition of data from developing countries added to the mix.
The article mentions the great impact Big Data and analytics is having various fields and how ethics is
also coming into play in the area of privacy. Many of these tools utilize consumer generated information
to predict behavior patterns, or how a product will sale, or what the “rating” of a movie will be. In order
to do all of this, major privacy issues come into play, since this is all user generated data. E-commerce
websites benefit a great deal from this since they can better understand their consumers and now know
how to approach them with product the customer is more likely to buy based on previous data generated
by purchase patterns and web browsing patterns.
Along with the study of purchase patterns and browsing behavior, data analytics is being done on social
media and networks, invading even more privacy. This way, patterns on what a person believes or their
opinions are being mined and targeted in order to understand how, for instance, the user will vote for a
particular candidate, what the user likes to read, what types of social media groups he has joined or
liked. The author goes on to mention how the health industry is also affected by big data analytics and
how the user on average will produce about 4 terabytes of data in this area alone. How is it affecting
healthcare, users, and companies? With all the information being generated, the users’ health will come
into play in that it will be easy to access from anywhere, and a lot of places will have access to those
records. People are afraid of this since maybe insurance companies will have access to this information

19. 19
and drive premiums up or deny insurance to a potential customer based on his health records, genetics,
how prone they are to disease, etc. What is good about this is that it makes it easier to track outbreaks or
disease patterns coming from certain regions. This allows scientists and doctors to track down the
reasons why certain outbreaks or diseases are more prevalent in certain regions of a community or the
world. This helps them plan for the future and better prepare the logistics of how they will engage the
areas affected and in what seasons those patterns are more prevalent.
Iebelin Kaastra and Milton Boyd. “Designing a Neural Network for Forecasting Financial and Economic
Time Series.” ELSEVIER Science, Neurocomputing. 1996
When looking at neural networks, many people think only about computers and the web, but neural
networks can be applied to anything as long as you give them a good training set. In the case of this
paper published in 1996, it is about neural network being designed for the purposes of forecasting
financial and economic data, and this paper talks about how to do it. It also mentions the drawbacks of
them, and how there is still much trial and error involved. For this specific purpose, the paper mentions
an eight step procedure in order to be able to do this.
The authors mention, at the time of the article, how the financial services are the second largest investors
in the study of neural networks. They then provide the parameters required for the creation of a neural
networks for the purposes of financial and economic analysis of times series, and they provide a training
set along with the parameters and the topology or structure that it possesses. They mention how neural
networks are “universal approximators” and how they function better than expert systems. It is true that
neural networks can be trained in pattern recognition, but that same reason is why many people of that
time did not look well upon them. It takes too much time in order to train them, and a lot of iterations
and trial and error have to be done in order to get it just right.
Backpropagation (BP) neural networks are mentioned and how they are built by a web of interconnected
nodes which store knowledge. This BP network is created by many hidden layers of inputs and outputs.
It is mentioned how neural networks and linear regression models are similar in the solving and trying to
minimize of least squares problems. How these networks are structured is similar to how behavior trees
are structured with their different nodes and responses to input thereby creating an output. What is
different about the network mentioned in the article, is that it seems it follows a bottom-up approach
instead of a top-down one.
The other also lists the eight steps which are required for the building of a neural network for financial
and economic forecasting. These eight steps are: “variable selection, data collection, data preprocessing,
(training, testing, and validation sets), neural network paradigms, evaluation criteria, neural network
training, and implementation.”
What is complex when creating a neural network for these purposes, is the use of many different
variables which behave non-linearly. The market never behaves the same way, and the relationships
between the differing markets need to be understood in order to train the neural network to respond to
the changes in these variables. They began to study and compare how the different markets, both
national and international, behaved and saw if they followed economic theory, and also how the prices
behaved and tried to see which approach was better when providing the training. They used the “Box-
Jenkins model for forecasting” for this purpose. After that, the authors mentioned the need to obtain
reliable data for the purposes of training, and this data must not have been used before to eliminate the
possibility of looking at something in hindsight. The authors also come to explain data preprocessing

20. 20
and everything that is required to be successful with that. Details aside, the authors went on to explain
all the eight steps required in great detail all the way to implementation of a working neural network.
This paper was mainly done to train researchers in how to build a good neural network, and they
provided many concrete examples of how to do it and what each step along the way meant. It is also
meant to illustrate the advantages of possessing a neural network when analyzing trends and how they
serve well as a tool for research in order to better understand how the markets move.
Irad Ben-Gal, Alexandra Dana, Niv Shkolnik, Gonen Singer. “Efficient Construction of Decision Trees
by the Dual Information Distance Method.” Quality Technology & Quantitative Management, Vol. 11,
No. 1, pp. 133-147, 2014
The authors of this paper talk about the NP-Hard problem faced in Big Data analytics and how they
want to find a way to create better “efficient decision and classification trees.” These decision trees help
understand the decision making process using past knowledge. To deal with the issues of NP Hard
problems, the author mentions many heuristics have been suggested, and those suggestions are all
greedy by nature. What the author means by greedy is the utilization of greedy algorithms to solve
problems. Greedy algorithms are used to find optimal solutions but fail when the problem at hand is too
complex for it to solve.
In this paper, the authors talk about the dual information distance (DID) to solve the issue of creating an
efficient tree, but using this method also faces a few problems. They mention that the DID method takes
into account the orthogonality between partitions that are selected. By orthogonality, the author means
the use of combinations that bring about consistency in the results.
They also mention that most decision trees utilize a top-down approach and branch out at each level of
hierarchy. Look ahead trees are also mentioned, and they say they are efficient but very time consuming
to utilize for very big values. They compare them to simple greedy trees and they say look ahead trees
“have less appealing convergence properties,” as mentioned by the authors.
The goal of this paper is to show that improvement in the construction of greedy trees can be used to
solve the problem of noise, and that the DID approach helps solve just that. The authors of this paper say
this, “We show that a good balance between these two distance components leads to a tractable, yet a
“less greedy” construction approach.” What they aim to do is to find the shortest path between graphs of
partitions.
It is very difficult to understand what is happening in this paper, but mainly they want to make better
decision trees to analyze the partition of data sets. These partitions are data that contain multiple
members and branch out and “hold a separate sub-data set.” Most of the article explains how this is
happening mathematically using different types of formulas and algorithms, one of which is the Rohklin
Method which is utilized to measure the distance between the partitions.
The authors then utilized the DID approach and used what they call, “Korf’s Learning Real Time
Algorithm” as a classification method. After running their algorithms they began to measure the
orthogonality between attributes. This is supposed to allow them to see the relation between restricted
partitions in this problem they are facing.
Later the authors ran experiments and analysis of partitioning all the way to the “leaf” of the trees to test
their DID methods. To do this they utilized various datasets, test sets, and training sets to run their

21. 21
experiments. As the author mentions, their goal was to search, “for the most promising weights
combinations that could result primarily in a relatively small tree with a lower average depth, yet
relatively high classification accuracy.” What they found was that their DID algorithm performed better
than the other algorithms in some instances, but for other purposes it fared worse, and most of the time it
ranked in second. The DID method has many applications and can save money, they mention that
utilizing this method can reduce the time used to treat patients in the healthcare setting.
Irad Ben-Gal, Yuval Shavitt, Ela Weinsberg, Udi Weinsberg. “Peer-to-peer information retrieval using
shared-content clustering.” Knowledge Information Systems. March 2013
This paper is about how using peer-to-peer clustering algorithms help users find content faster, but how
these cluster algorithms work is utilizing “power-law node degree distribution” graphs. The goal of
clustering algorithms is to find the shortest distance between the clusters in order to find the information
of want faster. The author mentions the deficiency which power-law may face and proposes another
simple clustering algorithm which may function better for the purposes of finding information in peer-
to-peer networks.
The articles explain how users find information by looking for “query strings” of data that may have
meta-data tags attached to them for easier retrieval of the information they are looking for. The
drawback of looking for information this way, is that it makes it difficult for the user to find what they
need if they there are blanks in the data or if they misspell something, or if something has to be searched
through a specific genre. The reason for this is that they query the information in the form of strings, and
that way, what they are looking for has to be spelled correctly without any missing spaces. It is an
inefficient system of searching that is in place.
The author mentions this in the article, “the large amount of noise which is attributed to user-generated
content, the power-law structure of the network, and the extreme sparseness of the network which is the
result of the abundance of users and shared content.” Systems in place in other search engines may not
work well in these peer-to-peer networks since the algorithms do not work well when users are the ones
sharing and uploading the information, which may be searchable for a short amount of time due to the
nature of peer-to-peer sharing. The search system in these repositories cannot recommend the users
much and is hard for it to show the user what they are searching for, hence creating more difficulty.
What the author and colleagues propose for this is to “validate the applicability of clustering by using a
real-world recommender system.” This way, they are able to form a simple algorithms which in the
authors’ words, is “scalable” and better able to help the user in finding what they are looking for. The
way they do this, as the author mentions, is by using “the distance between co-shared files as an item-
similarity metric, mitigating the need for in-depth bit-level analysis of the files.” The aim is to find a
way to help the user find what they need by making searches use “smaller search dimensions.”
What they do to test this is to use an algorithm to conduct searches of the over 531 thousand songs in
whatever peer-to-peer network where they are conducting their experiment. The authors say this, “The
graph holds a total of 531k songs, performed by more than 100k artists covering over 3,600 genres and
have more than 1 billion interconnecting links.” The algorithm the authors propose to test and compare
against the other algorithms currently of use is the use of the GkM algorithm on power-law graphs. In
order to achieve good searches and recommendations, this algorithm has to work well on such a massive

22. 22
graph containing numerous clusters. The GkM algorithm is then run and compared against other
algorithms currently of use in these peer-to-peer networks: “Graclus, MCL and Metis.” Measures of how
the clusters are related to one another, also how big or small cluster are, how trends are represented
within the clusters, and how similar songs are clustered together are made. What the authors found was,
“The only measure in which the k-means was found inferior to the GkM was the diversity of
popularity.” In most other aspects, the algorithms performed better than the GkM, but still more
improvements and research need to be made in this topic. The authors did show that their algorithm,
though not optimal, did show potential in creating a recommendation system for each user to tailor to
their tastes in whatever it is they are searching for and making it easier for them to find it.
Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R. Curran. “Learning multilingual
named entity recognition from Wikipedia.” Artificial Intelligence, Volume 194, Pages 151-175, January
2013.
What this article talks about is the recognition of words in various different language and how the
authors achieve a silver standard in recognition, but they aspire to have a gold standard, and they
compare their approach to various others to see if it is on par. The medium through which they do this is
Wikipedia. They mention how Wikipedia uses named entity recognition (NER) to sift through the text
and structure of the website, and the aim of the authors is to improve this approach to make something
more efficient.
NER is still a great tool that is used in natural language processing (NLP) as mentioned by the authors of
this paper. The authors mention how most recent research is being done in the area of making language
independent systems to recognize speech and other language patterns, and the authors see this as
inefficient. They mention how it is too costly and time consuming to do this type of research, and it
hinders language specific NER research. They give the valid point that creating such a language-
independent system can generalize language too much and not pay close attention to the intricacies of
each language and what is actually meant when something is said or written in a particular language. It
basically does not understand the context of what is meant.
The authors use links in Wikipedia to transform them in to NE annotations, which are “silver standard
corpora” as the authors put it, and it does not meet gold standards, but they say it is good enough for
NER training. The languages the authors used to carry out their research were, “English, German,
French, Polish, Italian, Spanish, Dutch, Portuguese and Russian.” They used a “hierarchal classification
scheme” and labeled thousands of articles in the English language. Then they used a technique to
“project” the labels into the rest of the other languages. They then train a tagger derived from their silver
standard and note that it performs as well as the gold standard.
Gazetteers are utilized to “cluster words in unstructured text” and this way, they test how well these
gazetteers improve the performance of the NER. Many other types of annotations with varying standards
are utilized in this articles to test their system and show how it is better to have a language specific NE
for the purposes of looking for and understanding information from Wikipedia. They go on to explain
the process they used utilizing each of the 9 languages, and for each of those languages they utilized
native speakers or fluent speakers who learned the language to test if their system worked for each
specific language. They utilized associations to create mapping in order to create a training data set for
their system. The authors said this, “a Wikipedia-trained model does not outperform CoNLL training,
but combining automatic and gold-standard annotations in training exceeds the gold-standard model
alone.”

23. 23
When the authors began this research, they selected the nine languages above since they are the
languages that contain the most articles within Wikipedia. They then worked with these languages and
the articles in order to develop a classification system as well. They then classified the articles using a
one language approach, and later they utilized a multi-language approach as well for classification, and
several iterations were done. The article goes on to explain other aspects of this research, but the main
point of the article is that they have still much to learn when doing multi-lingual classification. The goal
is to improve search and classification of articles in Wikipedia for those languages which possesses the
lowest level or resources or less articles in Wikipedia. The authors say this, “We exploit this knowledge
to derive enormous and accurate NE-annotated corpora for a variety of domains and languages.”
Jörg Rech, Klaus-Dieter Althoff. “Artificial Intelligence and Software Engineering: Status and Future
Trends.” Special Issue on Artificial Intelligence and Software Engineering, K. 2004
This article shows the systems of artificial intelligence that are being implemented and the relation
artificial intelligence and software engineering share. Research in this arena is being done to improve the
software tools people utilize to meet their needs. The utilization of “Software Agents,” which are better
known as bots or chat-boxes like Siri are being utilized to aid the user when using a software product
with artificial intelligence. These bots, in a way, understand natural language or what the user may be
trying to do by picking up on the sound patterns and words the person is saying, and comparing to other
users’ previous responses to similar organization of words.
The author goes on to explain that the main motivation of artificial intelligence research is to understand
natural intelligence by trying to build intelligent machines and not the other way around. Artificial
Intelligence research spans a wide variety of fields from biological, psychological, computer science, to
engineering. All of this is necessary in order to understand how to best build an AI, and within the field
of AI there are many other fields that specialize in certain aspects of it in order to improve that specific
area.
The creation and study of agent oriented software engineering (AOSE) is an interesting one. In this area,
the purpose is to create software agents that understand natural language in order to communicate better
with the user and understand what the user wants to do. To the project manager in the software
engineering project, the author mentions it helps solve more complex problems and aids with
“development, verification, validation, and maintenance.” These software agents are known to aid
companies in making menial tasks more efficient and effective by employing these agents to take care of
such tasks. This frees employees’ time and allows them focus their efforts on more complicated
problems that need to be taken care of.
There are Knowledge-Based Systems (KBS), and as the author says about how this field is divided,
“KBS: the cognitive layer (human-oriented, rational, informal), the representation layer (formal,
logical), and the implementation layer (machine-oriented, data structures and programs).” These are
used to describe the interrelationships and levels of communication between the KBS, and it is
interesting to see how each one interacts with the other. These KBS aid software engineers in providing
better and more intelligent tools and methods to aid in the software development lifecycle process.
As indicated by the author of this paper, there is a type of Artificial Intelligence that is being develop
called, “Computational Intelligence (CI)” and it “plays an important role in research about software
analysis or project management as well as knowledge discovery in databases or machine learning.” It is
truly astounding how far Artificial Intelligence technology has come and aided in the field of software

24. 24
engineering. With CI, the ability to simulate development process comes into play and this helps with
understanding if any changes are needed in the software development project in order to create change
requests, and it is easier to understand why they are made. Computational artificial intelligence utilizes
neural networks, data mining, fuzzy logic and such in order to aid in a more effective manner the
management of software projects by helping with estimates and discovering defects.
The author also mentions Ambien Intelligence (AmI) and how this type of AI is being developed to
understand the more complex human emotions. This is in order to create a more stable working
environment by providing the worker understanding and help them to relax. It takes into account the
users’ needs and habits. This also involves an interdisciplinary approach to understand what is best.
Jun Zhang, Zhi-Hui Zhang, Ying Lin, Ni Chen, et al. “Evolutionary Computation Meets Machine
Learning: A Survey.” IEEE Computational Intelligence Magazine. November 2011
This article begins by talking about evolutionary computation and the many different areas of study
within it and how evolutionary algorithms share a common definition with evolutionary computation.
What is evolutionary computation? In the area of machine learning and artificial intelligence, it is the
study of biological systems, especially mammalian organisms, and applying biological principles and
processes to machine learning. Many scientists believe that it is the best method to arrive to artificial
intelligence with the capacity to reason similar to the type of intelligence a human processes.
The evolutionary computation algorithm starts with population initialization, then it asks the question to
terminate, and if it does not terminate, it goes through several iterations of in the following order “fitness
evaluation selection, population reproduction and evaluation, algorithm adaptation (optional), and local
search (optional),” and it does this until it meets the requirements to terminate, therefore ending the
iterations. (That example of the algorithms was obtained from a diagram within the article.) This
algorithm therefore enhances machine learning, and vice versa, machine learning techniques also
enhance evolutionary computation algorithms. This machine learning technique therefore enhances
search function as mentioned by the authors of this article.
The study of these algorithms are not as simple as they seem in the diagrams. Machine learning
techniques incorporate a wide variety of complex methods including statistical analysis, regression, and
clustering analysis to name a few. Most evolutionary computation/machine learning algorithms contain
a combination of the steps mentioned above from the diagram described above. Some of the opposition-
based learning techniques mentioned greatly resemble the principles of how genetic algorithms function
by selecting the best solutions to problems and then running iterations of the best.
In the second part of the article, the authors go into more detail into what processes happen within each
of the steps in the first diagram in the article, and all the different methods to represent each portion are
mentioned, and those include more statistical methods, along with Gaussian distribution, genetic
algorithms, and cluster analysis, and the picking of one representative from a specific cluster from all
clusters to be evaluated by the objective function. It is mentioned how the different methods are used in
the subsequent generations to improve multi-objective machine learning. Along with all that the authors
are doing, they also set parameters so the algorithms adapt to them. These adaptation algorithms
improve the “search process and adaptively control the algorithm’s parameters.” In this way, cluster
analysis is done to select and divide what they call “chromosomes” to create evolutionary states that are
determined by the amount of chromosomes and the information of “fitness” from other groups. All of
this is done to allow for mutations, and in a way it mimics real biological evolutionary process

25. 25
happening in the planet that take millions of years, but since this is done in machines and with
programming techniques, it still takes time, but it happens at a much faster rate. This way machine
learning is expanding at an unprecedented rate.
In the last part of the paper, the authors talk about where this research is going and where it may lead,
and how new areas of research are being opened up to improve machine learning since research is still
lacking in some places. For instance in the enhancement of evolutionary computation operators to
increase performance. Also, research still needs to be done in the area of machine learning evolutionary
computation algorithms to “automatically determine their parameters.” Many other areas of research are
available in the field of machine learning and evolutionary computation algorithms, complex as they
may be, they will solve future computation problems in the fields of computer science and IT.
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten. “The
WEKA Data Mining Software: An Update.” ACM SIGKDD Explorations Newsletter Volume 11, Issue
1, June 2009
This paper talks about the utilization of the software called WEKA, short for Waikato Environment for
Knowledge Analysis, utilized for data mining and how far it has come in the years since its inception.
WEKA is an open source software that is available to all who wish to use it. This software is utilized by
researchers in both academia and business and it has come a long way since it was developed from
scratch in 2003. There have been several updates of this software and the contributions it has made are
numerous, with its great client base.
The purpose of this software product is to aid researchers with the creation or interpretation of
algorithms and the development of new algorithms for machine learning purposes. People create raw
data, and that data needs to be understood in order to put it to good use. That is where WEKA comes
into play with its ease of use and tools it contains to aid the researcher in generating useful information.
It helps researcher be able to make predictions about a variety of different things, such as purchase
patterns, in order to create experiments that may deal with prediction of behavior with the change in
certain types of variables.
The WEKA project was originally developed in 1993 in the country of New Zealand in order to help
understand how the economy moves and what is necessary to make improvements to it. The goal was to
improve machine learning, and as the author mentions, “determine the factors that contribute towards its
successful application in the agricultural industries.” At first, WEKA only supported C and Prolog, but
as time went on, the ability to use other programming languages was implemented and since then,
WEKA has continued to evolve into what it is today, a tool that facilitates research for scientists. In
2006, the Pentaho Corporation became the biggest sponsor of WEKA and it has been running strong
since its inception.
The WEKA workbench User Interface is knows as Explorer, and this is where the user interacts with the
various tools in the WEKA system. This user interface has various panels the user can use for the
different types of data mining tasks the user may engage in. The second panel of the WEKA UI is where
the user conducts classification and regression algorithms in order to extrapolate data in a relevant
manner. The author says this, “By default, the panel runs a cross-validation for a selected learning
algorithm on the dataset that has been prepared in the Preprocess panel to estimate predictive
performance.” The WEKA UI also allows for the use of clustering algorithms so the user can run a
cluster analysis and also allows for association rule mining. The last aspect of the WEKA UI is the