TIME Magazine debuted to the world in 1923, with former Speaker of the House Joseph G. Cannon featured on its cover. It was a historic occasion — the introduction of a monolith that helped shape traditional media.
Media was relatively simple then, and continued to be through the expansion of cable TV networks in the 1970s and 1980s.
The model looked something like this: Develop a piece of content and distribute it to an audience, with the entire audience experiencing the same thing, at the same time.
Then the World Wide Web graced us with its presence in the 1990s. Startups like America Online (AOL) laid the foundation for everyday people to access the Internet, while enabling the first wave of instantaneous, on-demand content.
As the Internet evolved through the 2000s, media and technology companies envisioned a future that transcended the limits of on-demand content. They prophesied a future in which on-demand content became personalized, and they created it.
Tech titans like Yahoo! learned how to predict audience preferences in real-time. Media vanguards like The New York Times and The Wall Street Journal adopted recommendation engines using algorithmic techniques such as content-based filtering and collaborative filtering.
These suggest fresh content based on reading history, and what similar users have read, too.
With these personalization tools, relevance became the new normal within an industry accustomed to irrelevance. Some would argue that these were the early days of technological innovation for both traditional and digital native media companies.
Meanwhile, a team at Google was leveraging decades of research to realize a breakthrough in artificial intelligence (AI) — the capacity for computer systems to perform human tasks such as decision-making, image recognition, and language translation.
AI’s potential to reshape our world is what inspired me, in part, to become an entrepreneur. While I believe it will transform most industries — including transportation, retail, and healthcare, among others — I’m most excited about its application within the media industry.
Imagine news articles that write themselves. Envision bots that identify fake news before it’s widely disseminated.
These things will be possible because of a subset of AI called deep learning, in which computers learn human-behavior without being explicitly programmed.
Prior to deep learning, computers had to be programmed to exhibit human behavior.
For example, if you wanted a computer to learn how to write a news article, you’d have to figure out the behaviors of a journalist, and manually input those behaviors into the computer. This was a painfully time-consuming process.
In contrast, deep learning allows us to feed data into an artificial neural network — a computer system modeled after the human brain — and use algorithms to help the computer learn autonomously.
For example, in 2012, a team at Google fed images from 10 million YouTube videos into an artificial neural network. The system identified distinct objects within the images, such as cats (apparently there are tons of cat videos on YouTube).
Why was this such a big deal? Because no one told the computer what a cat actually looks like. It learned on its own.
The opportunities to leverage this technology within the media industry will be enormous. As publishers are squeezed for ad dollars and face the duopoly that is Facebook and Google, AI will help them stay competitive in an era of dominant social platforms.
To that end, here are a few applications of AI that media companies should consider.
Automation in the newsroom
This is all about making work more efficient.
Imagine you’re a journalist or editor tasked with writing photo captions. Eventually, you’ll be able transition this task to machine intelligence. A computer system will be able to identify the contents of an image and write the caption for you.
In fact, a recent study by AI researchers shows that humans prefer computer-generated captions 25% of the time they’re displayed.
The technology isn’t perfect, but as its accuracy continues to improve, survey results will increasingly favor computer-generated outputs.
Similarly, AI can’t write nuanced, eloquent news articles yet, but it’s getting close. For example, the Associated Press (AP) is already leveraging deep learning to automate news articles that have structured data, such as corporate earnings reports and baseball game recaps.
Ultimately, automation will enable media companies to shift resources to higher value initiatives, and that will be good for both consumers and editorial teams.
Publishers that detect emotion
We’ve known for some time that technology can be cognitively intelligent. However, Affectiva — an AI company born out of MIT’s Media Lab — is helping it become emotionally intelligent, too. And that’s fascinating to me.
What if your mobile device could detect how you feel and publishers could combine these emotional indicators with viewing history to improve content recommendations?
Powered by emotion recognition software, a streaming service like Netflix could analyze the look on your face to understand your feelings and serve up content to fit your mood.
Sad? Here’s a ‘feel good’ movie. Energized? Check out this action flick. Silly? Maybe you’ll enjoy this comedy.
Recommendations across content types
The opportunity to enhance recommendations extends beyond emotionally intelligent software. If you’ve been around the industry, you’ve heard time spent being referred to as the new “currency” of advertising.
Ad buyers want to know how much time people spend on your content, what percentage of video ads they complete — that sort of thing.
One way to maximize time spent would be to suggest content across multiple formats. For example, Fortune Magazine could recommend its Most Powerful Women podcast series to users who read articles about women CEOs on Fortune.com.
AI could discover similarities between text, video, and audio assets, and guide users through content that meets their interests across different formats.
It’s something to consider as the industry shifts to “video first,” and experiences continued buzz in audio and podcasting.
Smarter content management
AI will improve our ability to recommend content, but what about our ability to distribute it?
With numerous platforms to find audience at scale — including Facebook, YouTube, Twitter, and Snapchat — it can be difficult to determine the optimal distribution strategy.
This reality, coupled with the fact that many publishers rely on these platforms for over 40% of their traffic, creates a problem in legitimate need of a solution.
Content management systems like WordPress VIP and Drupal are fantastic, but what if they could publish content across multiple platforms, and predict when and where (i.e. what platform) to publish in order to maximize referral traffic?
What if they could predict virality? AI will make these things possible.
Cultivating a smarter workforce
AI can also be leveraged to build a more knowledgeable workforce, which is what we’re currently focused on at my company, NewsCart.
Communications teams are commonly bogged down by the manual process of discovering and sharing news with their employees. The ability to share the right information at the right time, thus enabling better business decisions, can be a difficult undertaking.
It doesn’t have to be this way, though. AI will be able to automate this process and predict what type of news and information will be most valuable to your employees at any given time — whether you’re a team of 50 or 50,000.
We believe that information is only as valuable as the ability to access, understand, and share it at scale, and we’re excited about using AI to make that easier for media companies, and ultimately, the world at-large.
What are some other applications of AI that will shape the future of media? I’d love to hear from you.