Mahim Pritom’s Expanded Definition of Artificial Intelligence

TO: Prof. Jason Ellis

FROM: Mahim Pritom

DATE: October 21st, 2020

SUBJECT: Expanded Definition of Artificial Intelligence


The purpose of this 750-1000 Word Expanded Definition is to explore the definition of the term “Artificial Intelligence” which is a revolutionary invention of science in modern-day technology. I will be defining the term “Artificial Intelligence” in relation to machine learning and contextually how it operates in the different sections of the modern technology field. 

In this project, I will introduce Artificial Intelligence following several defining quotations from verified sources where I will discuss and compare those definitions from the authors. Next, I will discuss several quotations from a variety of sources where Artificial Intelligence is used in context. Finally, I will provide my own working definition of Artificial Intelligence after discussing all those quotations.


Author  J. F. SMOLKO in the article “ARTIFICIAL INTELLIGENCE” defines AI as, “Artificial intelligence (AI) is that division of science, particularly of computer science, which attempts to emulate and extend with programmed and fabricated elements the cognitive and affective functions of animals and human beings.”(Smolko, 2003, p.765). Author SMOLKO tries to define Artificial Intelligence as the emulation of cognitive and affective functions that animals and we, humans perform daily. However, he emphasized computer science since machines will think and perform as the human mind thinks by using algorithms which is programmable by AI. In another article “BUSINESS FINANCE”, authors  Mark Jon Snyder and Lisa Gueldenzoph Snyder define AI as, “Artificial Intelligence (AI) is the branch of computer science and engineering devoted to the creation of intelligent machines and the software to run them. This process is “artificial” because once it is programmed into the machine, it occurs without human intervention.”(Snyder et al., 2014, p.31). Comparing the two definitions, all of the authors mention computer science and programmable algorithms that machines will learn to think like human minds also known as Artificial Intelligence. The author from the first definition didn’t mention human intervention whereas the authors of the second definition did which means when machines adopt programmable AI, they can operate and run the software simultaneously without human interventions.


In a New York Times article “After the Pandemic, a Revolution in Education and Work Awaits” by  Thomas L. Friedman in an interview with Ravi Kumar, Artificial Intelligence is described as an automated system. “Now so many more people can play at that because you no longer need to know how to code to generate new software programs. Thanks to artificial intelligence, there is now “no-code software.’’ You just instruct the software to design some code for the application that you’ve imagined or need and, presto, it will spit it out.”(Friedman, 2020). Basically, the author gathered information about how AI is making our lives easier in the job sectors where AI will automate generate code according to the instruction given by the user whereas before we had to write all the codes from the scratch and as a human being, it takes up a lot of time to complete an application to run properly. In a blogpost “AI is Shaping the Future of Appointment Scheduling” posted by Ryan Williamson explains the importance of  AI in scheduling appointments. “AI-driven interface between the customer and the company can schedule appointments without human intervention to enable sending out confirmation emails, digital directions, etc. to help deliver a top-notch experience every time.”(Williamson, 2020). Ryan emphasizes an AI-driven interface where customers can schedule an appointment, collect basic pieces of information such as FAQ instead of calling the customer service, waiting in line, and manually schedule an appointment which is beneficial for both business and customers. Since an AI-driven interface can operate multiple tasks at the same time, the company can reduce labor and invest more in technology. In a CBS NEWS article “Facebook touts the use of artificial intelligence to help detect harmful content and misinformation” by Musadiq Bidar, he explains how Facebook is using AI to detect posts that violate the company’s policies and regulations. “Confronted with an onslaught of social media posts filled with misinformation and harmful content, Facebook said Tuesday it has begun to rely on artificial intelligence to boost its efforts to evaluate whether posts violate its policies and should be labeled or removed.”(Bidar, 2020). Billions of people use Facebook every day but not everyone follows the guidelines and policies. It is not possible for Facebook employees to manually detect and take down all those posts. Therefore, Facebook is using AI that can intelligently detect the violating posts and can track the devices from where the posts have been published all thanks to the programmable algorithms in combination with machine learning. Human eyes can make mistakes but we cannot escape with violations through the eyes of AI.

Working Definition

From the above discussions, I think Artificial Intelligence is the counterfeit of the human mind which is programmable to apply as an algorithm into the machines to operate and run software without human interruption. AI is very important in my major(software development) since developers use the help of AI to generate automated codes that will provide time to the developers in the backend to debug and test the application while the frontend developers can modify those automated codes according to the requirements without starting from the scratch.


Bidar, M. (2020, August 12). Facebook touts use of artificial intelligence to help detect harmful content and misinformation. CBS News. 

Friedman, T. L. (2020, October 20). After the Pandemic, a Revolution in Education and Work Awaits. 

Williamson, R. (2020, October 6). AI is Shaping the Future of Appointment Scheduling. Data Science Central. 

SMOLKO, J. F. (2003). Artificial Intelligence. In New Catholic Encyclopedia (2nd ed., Vol. 1, pp. 765-766). Gale.

Snyder, M. J., & Snyder, L. G. (2014). Artificial Intelligence. In Encyclopedia of Business and Finance (3rd ed., Vol. 1, pp. 31-35). Macmillan Reference USA.

Summary of Huda et al.’s “Mobile-Based Driver Sleepiness Detection Using Facial Landmarks and Analysis of EAR Values”

TO: Professor Jason W. Ellis

FROM: Mahim M. Pritom

DATE: Sept 17, 2020

SUBJECT: 500-Word Summary

This memo is a 500-word summary of the article, “Mobile-Based Driver Sleepiness Detection Using Facial Landmarks and Analysis of EAR Values,” by Choirul Huda, Herman Tolle & Fitri Utaminingrum, researchers at Brawijaya University, Malang, Indonesia.  

The article discusses the facial recognition system capable of identifying closed eyes and generating results of the Eye Aspect Ratio(EAR) by analyzing Facial Landmark points with an accuracy rate of about 92.85%.

According to the studies, sleepiness while driving causes injuries from minor to life-threatening that results in death in both developed and developing countries. Even though automotive industries started to develop special devices designed to keep drivers awake while driving, only certain brands of vehicles such as Mercedes-Benz, BMW, Volvo have the technology leaving the people driving a vehicle equipped with simple technology still at risk of sleepiness driving-related injuries due to the expensive production cost.

Rahman et al. conducted a study to identify sleepiness based on eye blink analysis by using a webcam connected to a computer. He used the Viola-Jones method to detect the eye area of the face using the Haar-like feature algorithm that helps to expose the sleepy eyes and evaluating the frame of the closed-eyes caught on camera. Jacobe et al. conducted another study utilizing two models of Artificial Neural Network(ANN) to predict sleepiness in a driving simulation. Even though both studies gave satisfying results, there were problems such as converting the color of each frame, inadequate detection process, and obstacles to install the camera inside the car. Mohammad et al. utilized the Haar Cascade Classifier by using mobile to detect and observe the eye sclera both closed and opened situation to determine the color reflecting back. Although this method can generate high accuracy result, there are some limitations such as the driver’s face must be aimed directly towards the camera, needs further development to recognize eye areas precisely. Another method was proposed by Jabbar et al. to detect drowsiness by combining Deep Neural Networks and extraction of facial points. Although this system could identify the drowsy drivers by 81%, the application process of this method requires a computer with high specifications because of the complex learning process which is very expensive and time-consuming.

Soukupova and Cech used the EAR method to measure the facial landmark in the eye area using Euclidean Distance that helps to recognize the blinks using 6 different landmarks. Since the number of extraction points applied in this study was limited, the most influential points in closed eye detection are selected to speed up the computing process. The authors, on these limited extraction points, emphasizes, “The Facial Landmark method qualified to identify driver sleepiness accurately based on analysis of EAR (Eye Aspect Ratio) values utilizes the extraction of 4 points around the eye area” (Huda et al., 2020, p. 27). The tree Regression method is applied to complete the precision procedure of incompletely processed points that are produced during the extraction process. The library is used to determine the number of dots generated on the face. Face detection is utilized as the library to detect the extracted facial landmark dots using a face detector developed by Google. Soukupova and Cech assigned a threshold value of 0.20 compared with the EAR resulting in opened eyes if EAR>0.20 or closed eyes if EAR<0.20.


Huda, C., Tolle, H., & Utaminingrum, F. (2020). Mobile-Based Driver Sleepiness Detection Using Facial Landmarks and Analysis of EAR Values. International Journal of Interactive Mobile Technologies, 14(14), 16–30.