FREE ESSAY ON ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

ABSTRACT
Current neural network technology is the most progressive of the artificial intelligence

systems today. Applications of neural networks have made the transition from laboratory 
curiosities to large, successful commercial applications. To enhance the security of
automated 
financial transactions, current technologies in both speech recognition and handwriting 
recognition are likely ready for mass integration into financial institutions.
RESEARCH PROJECT
TABLE OF CONTENTS
Introduction 1
Purpose 1
Source of Information 1
Authorization 1
Overview 2
The First Steps 3
Computer-Synthesized Senses 4
Visual Recognition 4
Current Research 5
Computer-Aided Voice Recognition 6
Current Applications 7
Optical Character Recognition 8
Conclusion 9
Recommendations 10
Bibiography 11
INTRODUCTION
? Purpose 
The purpose of this study is to determine additional areas where artificial intelligence

technology may be applied for positive identifications of individuals during financial 
transactions, such as automated banking transactions, telephone transactions , and home 
banking activities. This study focuses on academic research in neural network technology
. 
This study was funded by the Banking Commission in its effort to deter fraud.
Overview
Recently, the thrust of studies into practical applications for artificial intelligence 
have focused on exploiting the expectations of both expert systems and neural network 
computers. In the artificial intelligence community, the proponents of expert systems 
have approached the challenge of simulating intelligence differently than their
counterpart 
proponents of neural networks. Expert systems contain the coded knowledge of a human
expert 
in a field; this knowledge takes the form of if-then rules. The problem with this
approach 
is that people don't always know why they do what they do. And even when they can express
this 
knowledge, it is not easily translated into usable computer code. Also, expert systems
are 
usually bound by a rigid set of inflexible rules which do not change with experience
gained 
by trail and error. In contrast, neural networks are designed around the structure of a 
biological model of the brain. Neural networks are composed of simple components called 
neurons each having simple tasks, and simultaneously communicating with each other by 
complex interconnections. As Herb Brody states, Neural networks do not require an
explicit 
set of rules. The network - rather like a child - makes up its own rules that match the 
data it receives to the result it's told is correct (42). Impossible to achieve in expert

systems, this ability to learn by example is the characteristic of neural networks that
makes
them best suited to simulate human behavior. Computer scientists have exploited this
system 
characteristic to achieve breakthroughs in computer vision, speech recognition, and
optical
character recognition. Figure 1 illustrates the knowledge structures of neural networks 
as compared to expert systems and standard computer programs. Neural networks restructure

their knowledge base at each step in the learning process.
This paper focuses on neural network technologies which have the potential to increase
security 
for financial transactions. Much of the technology is currently in the research phase and
has 
yet to produce a commercially available product, such as visual recognition applications.

Other applications are a multimillion dollar industry and the products are well known,
like 
Sprint Telephone's voice activated telephone calling system. In the Sprint system the
neural 
network positively recognizes the caller's voice, thereby authorizing activation of his 
calling account.
The First Steps
The study of the brain was once limited to the study of living tissue. Any attempts at an

electronic simulation were brushed aside by the neurobiologist community as abstract
conceptions 
that bore little relationship to reality. This was partially due to the over-excitement
in 
the 1950's and 1960's for networks that could recognize some patterns, but were limited
in 
their learning abilities because of hardware limitations. In the 1990's computer
simulations 
of brain functions are gaining respect as the simulations increase their abilities to
predict 
the behavior of the nervous system. This respect is illustrated by the fact that many 
neurobiologists are increasingly moving toward neural network type simulations. One such

neurobiologist, Sejnowski, introduced a three-layer net which has made some excellent
predictions 
about how biological systems behave. Figure 2 illustrates this network consisting of
three 
layers, in which a middle layer of units connects the input and output layers. When the
network 
is given an input, it sends signals through the middle layer which checks for correct
output. 
An algorithm used in the middle layer reduces errors by strengthening or weakening
connections 
in the network. This system, in which the system learns to adapt to the changing
conditions, 
is called back-propagation. The value of Sejnowski's network is illustrated by an
experiment 
by Richard Andersen at the Massachusetts Institute of Technology. Andersen's team spent
years 
researching the neurons monkeys use to locate an object in space (Dreyfus and Dreyfus
42-61). 
Anderson decided to use a neural network to replicate the findings from their research.
They 
trained the neural network to locate objects by retina and eye position, then observed 
the middle layer to see how it responded to the input. The result was nearly identical to
what 
they found in their experiments with monkeys. 
Computer-Synthesized Senses
? Visual Recognition
The ability of a computer to distinguish one customer from another is not yet a reality.
But, recent breakthroughs in neural network visual technology are bringing us closer to
the time when computers will positively identify a person.
? Current Research
Studying the retina of the eye is the focus of research by two professors at the
California 
Institute of Technology, Misha A. Mahowald and Carver Mead. Their objective is to
electronically 
mimic the function of the retina of the human eye. Previous research in this field
consisted 
of processing the absolute value of the illumination at each point on an object, and
required 
a very powerful computer.(Thompson 249-250). The analysis required measurements be taken
over 
a massive number of sample locations on the object, and so, it required the computing
power of a 
massive digital computer to analyze the data.
The professors believe that to replicate the function of the human retina they can use a
neural 
network modeled with a similar biological structure of the eye, rather than simply using
massive 
computer power. Their chip utilizes an analog computer which is less powerful than the
previous 
digital computers. They compensated for the reduced computing power by employing a far
more 
sophisticated neural network to interpret the signals from the electronic eye. They
modeled the 
network in their silicon chip based on the top three layers of the retina which are the
best 
understood portions of the eye.(250) These are the photoreceptors, horizontal cells, and
bipolar cells.
The electronic photoreceptors, which make up the first layer, are like the rod and cone
cells in the eye. 
Their job is to accept incoming light and transform it into electrical signals. In the
second 
layer, horizontal cells use a neural network technique by interconnecting the horizontal
cells 
and the bipolar cells of the third layer. The connected cells then evaluate the estimated

reliability of the other cells and give a weighted average of the potentials of the cells

around it. Nearby cells are given the most weight and far cells less weight.(251) 
This technique is very important to this process because of the dynamic nature of image 
processing. If the image is accepted without testing its probable accuracy, the
likelihood 
of image distortion would increase as the image changed.
The silicon chip that the two professors developed contains about 2,500 pixels—
photoreceptors 
and their associated image-processing circuitry. The chip has circuitry that allows a
professor 
to focus on each pixel individually or to observe the whole scene on a monitor. The
professors 
stated in their paper, The behavior of the adaptive retina is remarkably similar to that
of 
biological systems (qtd in Thompon 251).
The retina was first tested by changing the light intensity of just one single pixel
while the 
intensity of the surrounding cells was kept at a constant level. The design of the neural
network 
caused the response of the surrounding pixels to react in the same manner as in
biological retinas. 
They state that, In digital systems, data and computational operations must be converted
into 
binary code, a process that requires about 10,000 digital voltage changes per operation.

Analog devices carry out the same operation in one step and so decrease the power
consumption 
of silicon circuits by a factor of about 10,000 (qtd in Thompson 251). 
Besides validating their neural network, the accuracy of this silicon chip displays the
usefulness 
of analog computing despite the assumption that only digital computing can provide the
accuracy 
necessary for the processing of information.
As close as these systems come to imitating their biological counterparts, they still
have a long 
way to go. For a computer to identify more complex shapes, e. g., a person's face, the
professors 
estimate the requirement would be at least 100 times more pixels as well as additional
circuits 
that mimic the movement-sensitive and edge-enhancing functions of the eye. They feel it
is possible 
to achieve this number of pixels in the near future. When it does arrive, the new
technology will 
likely be capable of recognizing human faces.
Visual recognition would have an undeniable effect on reducing crime in automated
financial transactions. 
Future technology breakthroughs will bring visual recognition closer to the recognition
of individuals, 
thereby enhancing the security of automated financial transactions.
? Computer-Aided Voice Recognition
Voice recognition is another area that has been the subject of neural network research. 
Researchers have long been interested in developing an accurate computer-based system
capable 
of understanding human speech as well as accurately identifying one speaker from
another.
? Current Research
Ben Yuhas, a computer engineer at John Hopkins University, has developed a promising
system for 
understanding speech and identifying voices that utilizes the power of neural networks.
Previous attempts 
at this task have yielded systems that are capable of recognizing up to 10,000 words, but
only when each 
word is spoken slowly in an otherwise silent setting. This type of system is easily
confused by back 
ground noise (Moyne 100).
Ben Yuhas' theory is based on the notion that understanding human speech is aided, to
some small degree, 
by reading lips while trying to listen. The emphasis on lip reading is thought to
increase as the 
surrounding noise levels increase. This theory has been applied to speech recognition by
adding a 
system that allows the computer to view the speaker's lips through a video analysis
system while 
hearing the speech.
The computer, through the neural network, can learn from its mistakes through a training
session. Looking 
at silent video stills of people saying each individual vowel, the network developed a
series of 
images of the different mouth, lip, teeth, and tongue positions. It then compared the
video images 
with the possible sound frequencies and guessed which combination was best. 
Yuhas then combined the video recognition with the speech recognition systems and input a
video frame 
along with speech that had background noise. The system then estimated the possible sound
frequencies 
from the video and combined the estimates with the actual sound signals. After about 500
trial runs the 
system was as proficient as a human looking at the same video sequences.
This combination of speech recognition and video imaging substantially increases the
security factor by 
not only recognizing a large vocabulary, but also by identifying the individual customer
using the system.
? Current Applications
Laboratory advances like Ben Yuhas' have already created a steadily increasing market in
speech recognition. 
Speech recognition products are expected to break the billion-dollar sales mark this year
for the first time. 
Only three years ago, speech recognition products sold less than $200 million (Shaffer,
238).
Systems currently on the market include voice-activated dialing for cellular phones, made
secure by their 
recognition and authorization of a single approved caller. International telephone
companies such as Sprint 
are using similar voice recognition systems. Integrated Speech Solution in Massachusetts
is investigating 
speech applications which can take orders for mutual funds prospectuses and account
activities (239).
? Optical Character Recognition
Another potential area for transaction security is in the identification of handwriting
by optical 
character recognition systems (OCR). In conventional OCR systems the program matches each
letter in a 
scanned document with a pre-arranged template stored in memory. Most OCR systems are
designed specifically 
for reading forms which are produced for that purpose. Other systems can achieve good
results with 
machine printed text in almost all font styles. However, none of the systems is capable
of recognizing 
handwritten characters. This is because every person writes differently. 
Nestor, a company based in Providence, Rhode Island has developed handwriting recognition
products based 
on developments in neural network computers. Their system, NestorReader, recognizes
handwritten characters 
by extracting data sets, or feature vectors, from each character. The system processes
the input 
representations using a collection of three by three pixel edge templates (Pennisi, 23).
The system then 
lays a grid over the pixel array and pieces it together to form a letter. Then the
network discovers 
which letter the feature vector most closely matched. The system can learn through trial
and error, 
and it has an accuracy of about 80 percent. Eventually this system will be able to
evaluate all symbols 
with equal accuracy.
It is possible to implement new neural-network based OCR systems into standard large
optical systems. 
Those older systems, used for automated processing of forms and documents, are limited to
reading typed 
block letters. When added to these systems, neural networks improve accuracy of reading
not only typed 
letters but also handwritten characters. Along with automated form processing, neural
networks will 
analyze signatures for possible forgeries.
Conclusion
Neural networks are still considered emerging technology and have a long way to go toward
achieving their 
goals. This is certainly true for financial transaction security. But with the current
capabilities, 
neural networks can certainly assist humans in complex tasks where large amounts of data
need to be analyzed. 
For visual recognition of individual customers, neural networks are still in the simple
pattern matching 
stages and will need more development before commercially acceptable products are
available. Speech 
recognition, on the other hand, is already a huge industry with customers ranging from
individual computer 
users to international telephone companies. For security, voice recognition could be an
added link to the 
chain of pre-established systems. For example, automated account inquiry, by telephone,
is a popular method 
for customers to determine the status of existing accounts. With voice identification of
customers, an 
option could be added for a customer to request account transactions and payments to
other institutions.
For credit card fraud detection, banks have relied on computers to identify suspicious
transactions. 
In fraud detection, these programs look for sudden changes in spending patterns such as
large cash withdrawals 
or erratic spending. The drawback to this approach is that there are more accounts
flagged for possible 
fraud than there are investigators. The number of flags could be dramatically reduced
with optical character 
recognition to help focus investigative efforts.
It is expected that the upcoming neural network chips and add-on boards from Intel will
add blinding speed 
to the current network software. These systems will even further reduce losses due to
fraud by enabling 
more data to be processed more quickly and with greater accuracy.
Recommendations
Breakthroughs in neural network technology have already created many new applications in
financial transaction 
security. Currently, neural network applications focus on processing data such as loan
applications, and 
flagging possible loan risks. As computer hardware speed increases and as neural networks
get smarter, 
real-time neural network applications should become a reality. Real-time processing means
the network 
processes the transactions as they occur. 
In the mean time,
1. Watch for advances in visual recognition hardware / neural networks. When available,
commercially produced 
visual recognition systems will greatly enhance the security of automated financial
transactions. 
2. Computer aided voice recognition is already a reality. This technology should be
implemented in automated 
telephone account inquiries. The feasibility of adding phone transactions should also be
considered. 
Cooperation among financial institutions could result in secure transfers of funds
between banks when 
ordered by the customers over the telephone.
3. Handwriting recognition by OCR systems should be combined with existing check
processing systems. 
These systems can reject checks that are possible forgeries. Investigators could
follow-up on the 
OCR rejection by making appropriate inquiries with the check writer.
Bibliography
BIBLIOGRAPHY
Winston, Patrick. Artificial Intelligence. Menlo Park: Addison-Wesley Publishing, 1988.
Welstead, Stephen. Neural Network and Fuzzy Logic in C/C++. New York: Welstead, 1994.
Brody, Herb. Computers That Learn by Doing. Technology Review August 1990: 42-49.
Thompson, William. Overturning the Category Bucket. BYTE January 1991: 249-50+.
Hinton, Geoffrey. How Neural Networks Learn from Experience. Scientific American
September 1992: 145-151.
Dreyfus, Hubert., and Stuart E. Dreyfus. Why Computers May Never Think Like People.
Technology Review January 1986: 42-61.
Shaffer, Richard. Computers with Ears. FORBES September 1994: 238-239.