Assignment 1.3 Technical Definitions – Machine Learning

Assignment 1.3 Definitions

Introduction

The purpose of this assignment is to use technical writing skills to explain a complex technical term to target audience with very little or no related background. In this post, I will provide three types of definitions: parenthetical, sentence and expanded definitions.  I will use my own knowledge base with supplement from online resources to define the technical term.

Audience

I’m writing this post for the purpose to define Machine Learning, a widely used but complex term in Computer Science to a non-technical audience.

Parenthetical Definition

Machine Learning:a field of Computer Science that teaches computer program to learn from experience.

Sentence Definition

Machine Learning is a field of Computer Science that teaches computer program learn from experience through data analysis in order to make predictions.

Expanded Definition

Machine Learning is a field in Computer Science that allows computer program to learn from experience through constructing and adapting algorithmic models to improve predictions base on data analysis.

Etymology

In 1952, Arthur Samuel, a computer scientist designed a checker game to play against itself. Incrementally, the program learnt from different patterns and strategies generated from every move in all matches and were able to win over Arthur, his designer. Through developing the first checker game, Arthur coined the term “machine learning” by disproved the general pre-assumption that computer cannot perform task without giving detailed instructions (McCarthy and Feigenbaum 10).

Description and Operating Principle

 

                      FIGURE 1. Work Flow of Machine Learning Process (Pant)

To better understand how machine learning works, we can refer to Figure 1, which illustrated the 6 steps, as explained in detail sequentially:

Step One(1a and 1b): Gather and Prepare Data: We gather dataset for both training and testing our predictive model, in this case, we want to set up a framework that the computer can learn from experience

Step Two(3a and 5): Develop Model – We construct algorithmic model tailor for the dataset, similar to pick a specific formula to solve a mathematic problem

Step Three(2a): Training – We then train and adapt our model by feeding data. In this case, think about the linear equation we learn to solve, y = mx + b, given the input x and output y and y intercept b, based on the data, our model incrementally learn from predicting the slope m.

Step Four(3b): Evaluation – After the training, we evaluate the effectiveness of our model by testing it against the testing dataset, to see if it’s able to predict a slope accurately given all the input and output values.

Step Five(3c): Parameter Tuning – When we train our model, we have set up some basic assumption, given the discrepancy we have discovered in evaluation, we can adjust this assumption/parameters accordingly to generate a better model to make prediction

Step Six: Prediction – Once we finished tuning the model, we can feed production data into the model to make prediction.

Implementation and Application of Machine Learning in Real Life

Machine learning are widely implemented and applied to any place where data can be generated and collected, following are some examples:

• Netflix uses machine learning to produce different types of content based on data collected from users (“Ramachandram and Flint”).
• Google Health leverages on machine learning to analyze medical data to facilitate early discovery and detection of diseases, at the same time, provide insights for insurance coverage (CBS Insights).
• Google’s Deepmind developed the first computer program, AlphaGo, to defeat professional Go champion (Deepmind).
• Google’s CallJoy is utilizing sophisticated machine learning agents to answer calls for small business owners. Business can have virtual agent answering calls and making bookings and appointment according customer’s needs over phone calls (Summers).
• American Express, a credit card company, using machine learning with data analysis to detect fraud in credit card transactions in real time (Marr)

Works Cited

“AlphaGo: The Story so Far.” Deepmind, deepmind.com/research/case-studies/alphago-the-story-so-far. Accessed 4 June 2020.

“How Google Plans To Use AI To Reinvent The $3 Trillion US Healthcare Industry.” CBS Insights, www.cbinsights.com/research/report/google-strategy-healthcare/. Accessed 4 June 2020.

“Machine Learning Workflow.” Google Cloud, 29 May 2020, cloud.google.com/ai-platform/images/ml-workflow.svg. Accessed 4 June 2020.

Marr, Bernard. “27 Incredible Examples Of AI And Machine Learning In Practice.” Forbes, Forbes Magazine, 12 Dec. 2018, www.forbes.com/sites/bernardmarr/2018/04/30/27-incredible-examples-of-ai-and-machine-learning-in-practice/#740d1c097502. Accessed 4 June 2020.

McCarthy, John, and Ed Feigenbaum. “Arthur Samuel: Pioneer in Machine Learning.” AI Magazine, 1990, pp. 10–11.

Pant, Ayush. “Machine Learning Workflow.” Workflow of a Machine Learning Project, 10 Jan. 2019, towardsdatascience.com/workflow-of-a-machine-learning-project-ec1dba419b94. Accessed 4 June 2020.

Ramachandran, Shalini, and Joe Flint. “At Netflix, Who Wins When It’s Hollywood vs. the Algorithm?” The Wall Street Journal, Dow Jones & Company, 10 Nov. 2018, www.wsj.com/articles/at-netflix-who-wins-when-its-hollywood-vs-the-algorithm-1541826015. Accessed 4 June 2020.

Summers, Bob. “CallJoy’s New Agent Helps Small Businesses Answer Calls.” Google, Google, 12 Nov. 2019, www.blog.google/technology/area-120/calljoy-small-business-virtual-phone-system/. Accessed 4 June 2020.