What is Machine Learning? — Learned from K-Drama Start-Up
By: Richardy Lobo' Sapan
Tags: Startup, Data Science, Kdrama, Artificial Intelligence, Machine Learning

This article is written inspired by LinkedIn post by Louis Owen on LinkedIn that also inspired by Korean Drama Start-Up, starring Bae Suzy as Seo Dal-Mi (OMG she’s so cute!) and Nam Joo-Hyuk as Nam Do-San. Here’s what Louis’ Post said:

*How K-Drama Explains Machine Learning* Recently, there’s a new K-Drama named “Start-Up”. It’s about young people passionately pursuing their dreams in the world of start-up companies. There’s one scene that really captures my attention where Do-San (the math-genius who is building a Computer Vision start-up) is trying to explain Machine Learning to Dal-Mi (Do-San’s crush) with a very unique analogy. Do-San:

“Let’s say we have a computer named Tarzan. He grew up on a deserted island, so he’s never seen a woman in his life. But one day, Jane arrives on the island. He gives her a ROCK, and she DOESN’T LIKE it. But when he gives her a FLOWER, it makes her HAPPY.” Dal-mi:

“I see. He’s never dated, so he doesn’t know what women like.” Do-San:

“That’s right. So Tarzan keeps on trying. He catches a SNAKE for her, but she DOESN’T like it. He gives her a CUTE RABBIT, and that makes her HAPPY. She seems DISPLEASED when he YELLS, but she LIKES it when he SMILES. While building such experiences, he learns how to win Jane’s heart.” Dal-Mi:

“I see. Hence the term, Machine Learning.” What do you think about this analogy? Please leave your thoughts in the comment :D

Really, What is Machine Learning?

Well. machine learning is branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving (IBM Cloud Education, 2020). Machine learning focuses on the development of computer algorithms that can access data and use it to learn for themselves.

Machine learning is just like Tarzan in Do-San’s analogy. The process of learning of Tarzan begins with observations of data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that provided. The primary aim is to allow the computers, in this case Tarzan, to learn automatically without human intervention or assistance and adjust actions accordingly.

A question then arises, why is machine learning important? Well, the answer is there are many problems that can be solved easily with machine learning. By gleaning insights from data — often in real time — organizations are able to work more efficiently or gain an advantage over competitors. All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results — even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities — or avoiding unknown risks (Hui Li, 2021).

How Machine Learning Works?

Photo by Arseny Togulev on Unsplash

Based on ibm.com, UC Berkeley breaks out how machine learning works into three parts.

A Decision Process: Based on some input data, which can be labelled or unlabeled, machine learning algorithms will make a prediction or classification. Your algorithm will produce an estimate about a pattern in the data. An Error Function: An error function serves to assess the prediction of the model. In the event that there are known examples, an error function can make a comparison to evaluate the accuracy of the model. A Model Optimization Process: A model optimization works very simple. In case the model can fit way better to the data points in the training set, at that point weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will rehash this assess and optimize process, updating weights autonomously until a threshold of accuracy has been met.

Machine Learning Methods

According to NVIDIA, machine learning classifiers fall into four primary categories .

1.) Supervised Machine Learning

Supervised machine learning is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. The dataset being used has been pre-labeled and classified by users to allow the algorithm to see how accurate its performance is.

2.) Unsupervised Machine Learning

In unsupervised machine learning, the raw dataset being used is unlabeled and an algorithm identifies patterns and relationships within the data without help from users.

3.) Semi-supervised Machine Learning

The dataset in semi-supervised machine learning contains structured and unstructured data, which guide the algorithm on its way to making independent conclusions. The combination of the two data types in one training dataset allows machine learning algorithms to learn to label the unlabeled data.

4.) Reinforcement Machine Learning

The dataset uses a “rewards/punishments” system, offering feedback to the algorithm to learn from its own experiences by trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.

Real-world Use Cases of Machine Learning

Based on sas.com, most industries working with large amounts of data have recognized the value of machine learning technology. Some industries that use machine learning are financial services, health care, oil and gas, government, retail and transportation. Below are some real-world cases of the use of machine learning

Real-world cases:

Digital Assistant: Siri, Google assistant, Yong-sil or Noon-gil from Start-Up

Photo by Miguel Tomás on Unsplash

Recommendations: Search engine recommendations (google, youtube, etc.)

Contextual online advertising

Chat bots

Fraud Detection

Cybersecurity

Medical image analysis

Self-diving cars: TARZAN from Start-Up

and many more.

REFERENCES:

IBM Cloud Education. (2020, July 15). What is Machine Learning? Ibm.com. https://www.ibm.com/cloud/learn/machine-learning

Machine Learning: What it is and why it matters. (2021). Sas.com. https://www.sas.com/en_us/insights/analytics/machine-learning.html

Shalian, Isha. NVIDIA Blog: Supervised Vs. Unsupervised Learning. (2018, August 2). The Official NVIDIA Blog. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/

What Is Machine Learning? — I School Online. (2020, June 26). UCB-UMT. https://ischoolonline.berkeley.edu/blog/what-is-machine-learning/

What is the Definition of Machine Learning? | Expert.ai. (2020, May 5). Expert.ai. https://www.expert.ai/blog/machine-learning-definition/