Machine learning extracts the magic dust out of data.
Ralf Herbrich, Director of Machine Learning Science at Amazon
Recently members of our development team here at Twipe attended the AWS Summit in Berlin and came back with new insights on machine learning. Coupled with the call from Benedict Evans at GEN Summit to change the way we talk about artificial intelligence, it’s time to make sure we’re all on the same page when it comes to AI, ML, and all the other important acronyms you hear in every conversation about the future of news these days. (For an introduction to machine learning, including a one sentence definition and a brief explanation of the different types of machine learning, read our guide available here).
Joris Gielen, Jasmien Lismont, Bram Hendrickx, and Lies Tambeur have answered some of the common questions you might have.
What can non-technical people do with machine learning?
There are different levels of what you can do with machine learning, and it’s important that all types of profiles can access the systems. In fact, a good machine learning system requires strong skills in three different areas: development, business, and stats. It’s hard to find one person with all three skills, so different tools have been developed to help users benefit from machine learning, no matter their background. One such tool is SageMaker, a fully managed platform that allows anyone to easily build, train, and deploy machine learning models at any scale.
What do we need to consider when working with machine learning?
There can be unintended consequences with machine learning, as machines can learn things that we didn’t foresee and they lack the intuition that humans have. An example of this is the case of a robot which was told to learn to walk with the least contact with the ground, as this is how cheetahs are able to run very fast. However the robot learned to turn over and crawl on his back, as this way his feet didn’t touch the ground at all. While he fulfilled the task he was asked to do, this wasn’t what the intended outcome had been!
It is also important to understand the tradeoff between efficiency and quality when developing an algorithm, as the one with the best result might not be the most optimal for your solution.
How will machine learning impact the media industry?
Last year McKinsey released a report on the impact of machine learning on the media industry, including the most likely use cases. We’ll begin to see even more hyper-personalised advertising, price and product offerings, and recommender systems. We’ll also see journalists being able to quickly spot new trends in consumption patterns, such as identifying viral content before it goes viral. Reducing reader churn will become more of a data-driven process (something we’re working on at Twipe now!).
What are newspapers doing with machine learning today?
The New York Times announced this year they have launched “nytDEMO“, a cross-functional team that will build data and technology solutions for brands, using the same tools and insights that power the newspaper itself. Their first machine learning project was “Project Feels”, which looked at how reader emotional response to articles influenced engagement. Now they’ve launched perspective targeting, which allows advertisers to target their media against content predicted to evoke certian reader sentiments, such as self-confidence or adventurousness.
Schibsted is also busy using machine learning in a variety of ways. In the newsroom itself they’ve implemented automatic tag suggestions for tagging articles, while for readers machine learning is used to personalise the front page and the content they see on the website.
Neue Zürcher Zeitung has built a flexible paywall using machine learning, so that is is personalised to the individual based on hundreds of criteria. In the three years since they built this, they’ve increased their conversion rate by fivefold, with 2.5% of people who view the payment message becoming subscribers. The system looks at data including reading history, device and time of day to alter the paywall message. Looking at reading history helps to better communicate the value a subscription would offer.
Of course, News UK is also working on a machine learning project right now with us at Twipe. “JAMES, your digital butler” will use machine learning to gradually get to know the habits, interests, and preferences of readers. He will expose them to relevant content in editions–current and past–in readers’ preferred formats, channels, times, and frequencies. This will increase reader satisfaction and engagement and ultimately accelerate subscription growth, enabling JAMES to transform conversion and engagement strategies by moving from segmented to highly individualised interactions with readers. To learn more about this project, make sure to attend ConTech in London this November, where Twipe CEO Danny Lein will be discussing our learnings from this project.
What is the future of machine learning?
Every day, every minute even, there is more and more data available to us, with the amount of data we’re able to access only growing in the future. We don’t know yet what we don’t know in regards to machine learning–as Benedict Evans explained at GEN Summit, no one was thinking of car hailing apps like Uber when cellphones were first developed.
We’re just at the start of what’s possible with machine learning.
Glenn Gore, Chief Architect for Amazon Web Services
That’s why it is important now to think about the scalability of our machine learning systems, so they’re able to grow with the influx of data in the future.
How can I implement more machine learning in our working processes now?
We asked our team for their advice to publishers wanting to work more with machine learning, here’s what they had to say.
I would advise publishers to first start with a descriptive analytics project, so taking a better look at your readers and understanding how they’re reading for example. Then you can move on to larger projects, such as creating a predictive model to predict churn.
Machine learning might sound scary and difficult at first, but you don’t need to be afraid. Decide what you want to extract from your data and give it a try. It might not be easy at first but I guarantee you: you will learn a lot, and you’ll quickly discover what works and what doesn’t.
No matter your technical background, you can succeed with machine learning if you pick the right tools. In the beginning, make sure to choose tools that will allow you to control the level of complexity.
My advice to publishers wanting to work with machine learning is simple: focus on your data. The first step is to collect lots of data, then you can work on analysing and cleaning your data.
The KDD Conference this August in London will be a good resource as well for diving deeper into machine learning. We recommend attending this event August 19-23 for all publishers who are interested in learning from leading experts in the world of applied data mining. On August 20th, there will be a day dedicated to data science for journalism and media, we hope to see you there!
This article was written by Mary-Katharine Phillips, Media Innovation Analyst at Twipe from 2017 – 2021.