Synthesis of Learning

I’m with some older family members and friends this weekend, and like most family gatherings I’m asked what am I doing in life. After telling them that I’m in school, they then asked what I was learning and it was amazing how in depth I could explain concepts like AI/ML. It was also great to see their interest in trying to understand it and clear up some confusion regarding AI/ML and the ethical implications. Below is some concepts that I refined after looking through my notes to take with me as go to future family gatherings and figure is a good start in my learning synthesis. I’ll finish it with some thoughts on how I want to approach my final paper:

Before we talk about Artificial Intelligence (AI) we need to understand the role of computers. Computers are nothing more than machine for following instructions and those instructions are what we call programs and algorithms. AI then seeks to make computers do the sorts of things that minds can do with two main aims: to get useful things done (technical) and to help answer questions about human beings and other living things (scientific). To do so though requires intelligence that must ultimately be reduced to simple explicit instructions for computers to process. Which presents the fundamental challenge of AI: Can you produce intelligent behavior simply by following list of instructions? 

A machine is said to have AI if it can interpret data, potentially learn from the data, and use that knowledge to adapt and achieve specific goals. The type of AI we are facing today is nothing like that of which is found in movies and science fiction novels. Today we are  struggling with the concept of narrow/weak AI in which scientist are trying to build computer programs that carry out task that currently requires thought. Some examples include filtering spam messages, recognizing faces in pictures, and creating usable translations. Each require a similar design processes in which they work because of machine learning (ML) which is a subset of AI. ML is used when we believe there is a relationship between observations of interest, but we do not know exactly how. Using artificial neural networks ML can be used to predict new cases of a certain instance through pattern recognition. Artificial neural networks are three layers connected as links that replicate the brain’s neural network. The first layer is the input layer that gives a numeric value to the input. The second layer is the hidden layer that classifies data and transfers inputs into the last layer the output layer. To do so the hidden layer applies a bias to the weighted input and an algorithm starts training the neural network using labeled data from a training set. The output is a result of the hidden layer’s interpretation i.e. learning from the training data. 

Though these programs or “set of instructions” do not understand decisions made but can simulate understanding, which can be dangerous when society puts trust in systems they do not understand. Some ethical concerns include the inherent bias in existing AI because of the biases in training data. Others revolve around privacy rights regarding AI uses in facial recognition as well as the destructive uses in new AI innovations in natural language processing like GPT-3. I think one topic for a final paper could be examining some of these concerns more thoroughly  specifically regarding facial recognition and the ideas behind big data and unregulated collection of all this data. At the same time I’m interested in the design aspects of cloud computing and AI with regard another big issue, the environmental impact. The amount of energy consumed through AI algorithms like GPT-3 is alarming as climate change becomes the focus of governments and corporations. 


Alapaydin, Ethem. 2016. Machine Learning-The New AI. MIT Press Essential Knowledge Series. Cambridge, MA: MIT Press.

Boden, Margaret. 2016. AI-Its Nature and Future. Great Britain: Oxford University Press.

CrashCourse. 2017. Machine Learning & Artificial Intelligence: Crash Course Computer Science #34. ———. 2019. What Is Artificial Intelligence? Crash Course AI #1.

Woodbridge, Micheal. 2020. A Brief History of Artificial Intelligence. 1st ed. New York: FlatIron Books.