INTRODUCTION:
Artificial Intelligence
means human-like behavior shown by computer systems and software. It means that
application or machine can understand human behavior dynamically and can adapt
a certain behavior according to the situation.
Artificial Intelligence (AI) traditionally speak of to
an artificial creation of human-like intelligence that can learn, reason, plan,
perceive, or process natural language. These behaviors allow AI to bring vast
socioeconomic prospects, while also posing ethical and socio-economic experiments.
[2]
The term itself was coined by Dartmouth College’s
John McCarthy in 1955 in a proposal to university researchers for its summer
research project on AI. According to the cognitive scientist Marvin Minsky, one
of the field’s most famous practitioners, AI is “the science of making machines
do things that would require intelligence if done by men.” [1]
Practically, artificial
intelligence represents a wide category of methodologies that teach computer to
adapt the behavior of an ‘intelligent’ human. Two terms “Machine Learning” and
“Deep Learning” are frequently used and seen when we discus about artificial
intelligence. Machine Learning refers
to the algorithms that are developed to tell a computer how to respond to a
certain event or activity. Deep Learning is
the type of machine learning which use the structure that is closely related to
human behavior. Deep-learning software attempts to simulate the activity in
layers of neurons in the neocortex, the wrinkly 80% of the brain where thinking
occurs. The software learns, in a very real sense, to recognize patterns in
digital representations of sounds, images, and other data. [1]
Artificial intelligence
has created a new revolution in the world of business and information
technology. Through artificial intelligence, our tasks have become frequent,
accurate, bug-free and effortless. Many of those tasks which required human
presence and efforts are now done by software and hardware tools using
artificial intelligence. One of the best examples in this perspective is plagiarism
detection software. Before the use of artificial intelligence, humans used to
check for the plagiarism manually. But with the evolution of technology and
introduction of AI, there have been a large number of software that detects
plagiarism within seconds of unlimited word count.
Even if artificial
intelligence induces thoughts of science fiction, artificial intelligence
already has many uses today, for example:
·
Filtering:
Email and text-messaging services use artificial intelligence to filter incoming
messages. User can mark a particular contact as ‘spam’ and next time that
contact tries to send message, your machine will automatically detect it.
·
Personalization:
Artificial intelligence has played a vital role in the world of sales and
advertisement. Online services like Amazon, Daraz, Foodpanda, Netflix etc.
learn from user’s previous experiences and display him the related content.
·
Fraud
Detection: Banks implement specialized algorithms
that detect if there is some strange activity and report it instantly.
·
Speech
Recognition: Applications use artificial intelligence
to optimize speech recognition functions. One of the most common examples is
assistant feature in our mobile phones. [2]
Everything has its
bright and dark sides but it depends on how we perceive it. Following are some
pros and cons of AI:
Advantages:
·
By the use of AI, human error is
reduced. Everything is programmed according to specialized algorithms and codes
and greater sense of precision is achieved.
·
Artificially intelligent system is able
to take risks because he got no emotion attached. A human being may think for a
moment before defusing a bomb but a robot doesn’t.
·
Human body needs rest. On average, a
human can work for 8-9 hours continuously. But services which are provided
through artificial intelligence are available 24x7.
·
Digital assistance is one of the most
useful services provided through AI. Even if relevant persons are not present
at customer support center, a user still gets response by Chabot.
·
Using AI combined with other
technologies we can make machines take decisions faster than a human and bring
out activities quicker. While taking a decision human will evaluate many
factors both emotionally and practically but AI-powered machine works on what
it is programmed and provides the outcomes in a faster way. [3]
Disadvantages:
·
Development of AI based application
requires a lot of resources including human efforts, expensive hardware devices
and latest technology.
·
AI is making humans lazy with
application automating majority of the tasks.
·
The use of AI has created unemployment
at larger scale. Robots have replaced the man at many important jobs and this
risk is increasing day by day.
·
An artificially intelligent system lacks
out of the box thinking due to the algorithms build on hard and fast rules. If
any unexpected event happens, that system is not able to respond correctly.
MATERIALS & METHODS:
It is commonly believed
that AI has become as mature as human brain. But the truth is that the scope of
problems solved by AI is narrow. AI based system cannot learn by their own. They are accomplished to implement a
specific task and support humans in solving demanding problems. AI can also go
wrong so we need to apply different testing techniques to ensure correct usability.
Both SDLC (Software development Life Cycle) and STLC (Software Testing Life
Cycle) are different for artificially intelligent systems. The reason is that
AI involves training the machines to acquire, unlearn, and adjust definite
tasks throughout their lifespan; there is a particular need to train and test
them properly. Moreover, intelligent machines have machine learning
skills that will result in different results if the same function is called at
different instants in time. This demands new testing skills. [2]
·
Black Box Testing: In this type of testing, software is not tested on
the basis of coding structure. Rather, main focus is on certain types of input
and the expected outputs that are based on software requirements. This is also
called as Behavioral testing. [2]
·
A/B
testing: Many software companies practice A/B testing. They
provide two different versions of their software and investigate the user’s
reaction. This method is based on the scientific process and should be utilized
when developing AI aptitudes.[6]
·
Machine Learning and Neutral Networks: A neural network comprises of many lumps of “nodes” which are
arranged in layers. All the nodes are interconnected. These nodes are not
pre-programmed rather they are empty. Their processing parts are presented in
top layers where calculated result is returned. When the neural
network is exposed with an example in training it will analytically construct
itself so the different layers and nodes will process parts and aspects of the
input so the end result of all nodes will give the result that is given to the
network i-e label.[2]
·
Specialized Algorithms:
In normal systems, algorithm means coding techniques applied to it. But in the
case of artificially intelligent systems, the
algorithm is the product of the neural network but based strongly upon the
training data and the labels. So the algorithm is equal to code + training data
+ labels. [2]
·
Specific Input Testing: Different
types of input values can cause a mix of expected and unexpected outcomes. The
input values are important for the functional reliability, security, strength
and performance. An artificially intelligent system keeps on improving as more
and more data is provided as input. So this cause the system to change the
behavior as it proceeds. An AI tester should keep that fact under special
consideration. [6]
·
Test Oracle Problem: Test oracle problem occurs when tester is not
sure about expected result of specific use cases. It becomes really confusing
to determine whether the actual results are according to expected results or
not.[6]
RESULTS
AND INTERPRETATIONS:
Due to limitations in
training data and concepts, we see things too simple (reductionism) or only
from one point of view (bias). A granularity in the concepts could mean that
the system cannot generalize enough, thus the result is useless.
·
Selection Bias: If
the training data in the selection input has some important points missing,
then it can lead to the situation of selection bias. Consider an example of
mood-based recommendation system. If the information given to such a system is
incomplete, the results could be far away from expectations.
·
Confirmation Bias:
Willingness to verify Associate in attention hypothesis heavily
believed or invested with in will lead
to choosing or over-weighing information confirming the
thesis over manageable misrepresentations. Scientists,
politicians and merchandise developers might be prone
to this type of bias, even with the simplest of
intentions.
·
Under-fitting: As
we have learned earlier, artificially intelligent system improves itself as
more and more training data is given as input. If training data is not enough,
the system may lack many of its functionalities. For example, a system whose
job is to differentiate between the voices of different animals. If you don’t provide
voices of all the animals as training data, how can it respond according to
expectations in a real situation?
·
Over-fitting: Over-fitting happens
when the classification is too miscellaneous and
too assorted for the
purpose of the AI system. If we want to see designs and groupings, a high granularity
of labels compromises the outcome, making it impracticable because of its
vagueness.
·
Over-Confidence
in AI system: AI can perform some types of mental
activities way faster and accurate than a human brain can. The algorithm of AI
is not directly reachable or modifiable. From this the perception can be easily
drawn that AI cannot be judged by human standards. Intellectual laziness and
comfort can be an important motivation too for uncritically trusting AI. Take
the example of Google search. Human brains of thousands of people cannot match
the speed and accuracy of Google. Therefore, nobody questions the result and
algorithm behind it. [2]
DISCUSSION
& CONCLUSION:
We’re fast oncoming a time when even “Continuous Testing”
will be incapable of keeping pace with declining delivery cycle times,
increasing technical complexity, and rushing rates of change. We have already
starting to use basic forms of AI, but we need to continue the testing
evolution to achieve the efficiency needed for testing of robotics. We need to
learn the importance of smart work. Hard work alone is not sufficient always. Certifying quality in an era where software
will be processing an unbelievable number of data points in real time. For
example, now a day self-driving cars are about to introduced. Definitely, the
car will perform in real world. This means more complexities are on the way of
testing and quality assurance. Particularly, we can’t afford even a slight
error in this regard because it is not about failure of certain functionality;
it’s a matter of lives. So we need to keep pace with the upcoming technology
and should improve our testing standards. [6]
Another fear about artificial intelligence is that
in near future, it will completely take the role of humans and there will be
too much unemployment. This is true to a little extent but we don’t need to be
worry. As we have seen, when technology replaced the old conventions, new
opportunities also arrived. So we should look at the brighter side and should
try to adapt according to modernization.
REFERENCES:
1.
https://expertsystem.com/artificial-intelligence-software-definition/
3.
https://towardsdatascience.com/advantages-and-disadvantages-of-artificial-intelligence-182a5ef6588c
4.
https://testaing.com/testing-artificial-intelligence-ai-systems/
5.
https://www.researchgate.net/publication/337400746_Testing_Artificial_Intelligence
7.
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