How do Bots Write a Movie?

For Halloween, the Netflix Is A Joke YouTube channel (ran by Netflix) released a hilarious horror short written by bots. The short is called Mr. Puzzles Wants You to be Less Alive and is a 4 minute and 23 second film generated from over 400,000 hours of horror movie content. The ridiculousness that ensues is not quite self-aware enough to be satire and yet demonstrates enough horror tropes to make it seem intentional. If anything, this video is a refreshing reminder that bots are not quite ready to take over all of our content creation professions. 

The creator of the script for this video is Keaton Patti, a comedian who writes for Jimmy Kimmel Live! and happened to take an interest one day in an Artificial Intelligence (AI) field called Machine Learning. He specifically focused on comedy and movies due to his personal experience. The scripts he generated were too funny for Netflix to pass on and together, they created a few short films posted to YouTube. 

If you are not a tech person and/or have never worked with Machine Learning, this video might seem too out there to you and leave you with more questions about Machine Learning. If you are a tech person but have not worked with this type of AI, you might now be interested in trying it out. Regardless, if you are interested, here is a breakdown of what Patti did:

1. Gathered the scripts for hundreds of thousands of horror movies.
2. Imported them as text to a dataset using a data analysis software and/or language (typically Weka, Python, or R).
3. Chose from predefined algorithms to analyze the dataset and ran them with the data.
4. The program saved patterns found from all of the scripts and saved them to “predict” a new script.
5. Selected the best script out of the ones generated (if he used several algorithms). 

Here is a basic definition from Salem Press —one of the many databases you can access with a MCLS library card:

Machine learning is a branch of computer -science -algorithms that allow the computer to display behavior learned from past experience, rather than human instruction. Machine learning is essential to the development of artificial intelligence, but it is also applicable to many everyday computing tasks. Common examples of programs that employ machine learning include e-mail spam filters, optical character recognition, and news feeds on social networking sites that alter their displays based on previous user activity and preferences. - (2018). Machine learning. In R. F. Donald (Ed.), Principles of Programming & Coding. Salem Press.

There is more in Machine Learning to explore than just comedic videos, so if you want more, our library system has resources to help:

Machine Learning for Dummies by John Mueller

While machine learning expertise doesn't quite mean you can create your own Turing Test-proof android--as in the movie Ex Machina--it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models--and way, way more.

Introduction to Machine Learning by Michael L. Littman

Taught by Professor Michael L. Littman of Brown University, this course teaches about machine-learning programs and how to write them in the Python programming language. For those new to Python, a 'get-started' tutorial is included. The professor covers major concepts and techniques, all illustrated with real-world examples such as medical diagnosis, game-playing, spam filters, and media special effects.

Machine Learning in Python: Essential Techniques for Predictive Analysis by Michael Bowles

Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification.

Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.

Python Machine Learning: Unlock Deeper Insights Into Machine Learning with this Vital Guide to Cutting-edge Predictive Analytics by Sebastian Raschka

Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

– By Kim L, IT Department