The separation of machine learning into three different approaches — supervised learning, unsupervised learning, and reinforcement learning — is standard (Lesson 3). In keeping with the course’s focus on journalism applications of ML, the example given for supervised learning is The Atlanta Journal-Constitution‘s deservedly famous investigative story about sex abuse of patients by doctors. Supervised learning was used to sort more than 100,000 disciplinary reports on doctors.
The example of unsupervised learning is one I hadn’t seen before. It’s an investigation of short-term rentals (such as Airbnb rentals) in Austin, Texas. The investigator used locality-sensitive hashing (LSH) to group property records in a set of about 1 million documents, looking for instances of tax evasion.
The main example given for reinforcement learning is AlphaGo (previously covered in this blog), but an example from The New York Times — How The New York Times Is Experimenting with Recommendation Algorithms — is also offered. Reinforcement learning is typically applied when a clear “reward” can be identified, which is why it’s useful in training an AI system to play a game (winning the game is a clear reward). It can also be used to train a physical robot to perform specified actions, such as pouring a liquid into a container without spilling any.
Also in Lesson 3, we find a very brief description of deep learning (it doesn’t mention layers and weights). and just a mention of neural networks.
“What you should retain from this lesson is fairly simple: Different problems require different solutions and different ML approaches to be tackled successfully.”—Lesson 3, Different approaches to Machine Learning
Lesson 4, “How you can use Machine Learning,” might be the most useful in this set of eight lessons. Its content comes (with permission) from work done by Quartz AI Studio — specifically from the post How you’re feeling when machine learning might help, by the super-talented Jeremy B. Merrill.
The examples in this lesson are really good, so maybe you should just read it directly. You’ll learn about a variety of unusual stories that could only be told when journalists used machine learning to augment their reporting.
“Machine learning is not magic. You might even say that it can’t do anything you couldn’t do — if you just had a thousand tireless interns working for you.”—Lesson 4, How you can use Machine Learning
(The Quartz AI Studio was created with a $250,000 grant from the Knight Foundation in 2018. For a year the group experimented, helped several news organizations produce great work, and ran a number of trainings for journalists. Then it was quietly disbanded in early 2020.)
Note (added April 4, 2022): The two links above to Quartz AI Studio content have been updated. The original domain, qz-dot-ai, was given up when, at renewal time, the price of all dot-ai domains had skyrocketed. Unfortunately, all the images have been lost, according to a personal communication from Merrill.
AI in Media and Society by Mindy McAdams is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Include the author’s name (Mindy McAdams) and a link to the original post in any reuse of this content.