The NYC Media Lab and Bloomberg recently put out a White Paper based on information gleaned from their Machines + Man conference held earlier this year. The focus of the conference was on how “…data is changing the way media is produced, distributed and consumed.” As someone who has worked in many forms of media, including radio, TV, film, advertising, newspapers, magazines, blogs, website content, social media content — not to mention, I am also a media lawyer, ROSS figured I should take a look and see what the future holds.
The gist of the paper is that artificial intelligence not only provides super fast feedback on things like Facebook posts and digital ad campaigns, but it also speeds up how the media responds to the data — because with some AI systems, companies don’t have to react at all — AI does it for them.
For example, software can now be used to determine whether a movie should be funded or not, based on the script. No meetings, no pleading, no cocktails after dark with industry insiders. It’s AI doing the “pitching” now. According to Epagogix, the company that makes this kind of software, “Advanced Artificial Intelligence in combination with proprietary expert process enables Epagogix to provide studios, independent producers and investors with early analysis and forecasts of the Box Office potential of a script.”
And during this summer’s US Open in NYC, AI was choosing which tennis highlights would make it to air, on its own. It works this way: “Watson’s new system, called Cognitive Highlights, measures the volume of the crowd (which is known to be raucous), the commentators’ analysis, and the players’ reactions. It assigns each of those a score from 0 to 1, and then uses those inputs to determine an Overall Excitement score. A point that results in a player yelling and fist pumping, then, will generate a higher score than one in which she pats her racket against her leg.” (Inc.)
According to the White Paper, the photo bank Shutterstock — among many others — “uses image-recognition software to classify, categorize, and serve up millions of photos, videos, and music to customers around the world. The AI system is handsfree, which means that it ceaselessly optimizes user experiences and thus revenue for Shutterstock, and collects a massive amount of data and insights into consumer and business behavior for use in other departments. And, it is not uncommon.”
Indeed it is not.
As TechCrunch reported last year, image recognition is about to transform business. “Now, everyone from Google and Facebook to startups and universities use these open source picture sets to feed their machine learning beasts, but the big technology companies have the advantage of access to millions of user-labeled images from apps such as Google Photos and Facebook.
“Have you ever wondered why Google and Facebook let you upload so many pictures for free? It’s because those pictures are used to train their deep learning networks to become more accurate.” Google not only uses photos to train their AI system — but it developed an algorithm that can remove their watermarks as well, not that we could condone such actions, of course.
According to Variety, “YouTube has embraced deep learning powered by neural networks — which essentially means that the company is using algorithms that simulate the way the human brain works. These algorithms are being trained to make informed decisions about which videos to recommend… Google CEO Sundar Pichai recently told journalists at a press event that the shift to AI is as fundamental as the invention of the web or the smartphone. ‘We are at a seminal moment in computing. We are evolving from a mobile-first to an AI-first world.’”
“This sort of AI optimization process now occurs at every level throughout the media sector, and is the successor to predictive analytics. Whereas yesterday’s executives were content to have machines simply forecast, today’s management expects it to also take action.”–White Paper
We know AI systems already create original content like tweets and art [see Yes, Humans, you are responsible for your machines], but AI can also create news. According to Phil Napoli, a professor of Journalism at Northwestern University in a Rutgers University paper, “[…] the very existence of local news operations is, to some extent, being algorithmically dictated.” And, as the White Paper indicates: “This model is at the core of Narrative Science, a start-up based around a software package that can generate complete news stories once it is fed the core data on which the stories will be based (e.g., sporting event scores and statistics, company financial reports, housing data, survey data).”
If AI can create content on its own (or with little human intervention) and it can determine the success of that content really quickly, what does this mean for the media? You’ve already seen some big changes over the years. Advertisers who used to preciously guard their multi-million dollar Superbowl ads until the actual game, now release them online as soon as the hype begins. They know that the more people who see their ads, the faster they will go viral — even if they’re bad.
So conclusion number one on how AI affects the media? The media will invest very heavily in technology. As will just about every other company, if they’re smart.
Here are some stats showing how quickly the use of AI is growing: The International Data Corporation (IDC) forecasts “worldwide revenues for cognitive and artificial intelligence (AI) systems will reach $12.5 billion in 2017, an increase of 59.3% over 2016. Global spending on cognitive and AI solutions will continue to see significant corporate investment over the next several years, achieving a compound annual growth rate (CAGR) of 54.4% through 2020 when revenues will be more than $46 billion.”
And conclusion number two? The media will not only invest in AI, it will also learn to adapt and that’s pretty good news for most who work in the sector.
Despite the ever-present doom and gloom for the media, employment in that sector is expected to rise, not fall or disappear. The White Paper concludes that “Teams will face dramatic shuffling, reorganization, and reskilling, and may actually increase in headcount in the near term: According to the Bureau of Labor Statistics’ most recent projections, employment in the media and communications industry is projected to rise 4% between 2014 and 2024.” More specifically, “Fewer roles as media buyer and media planner, SEO specialist, website usability and conversion optimization specialists, email marketing specialists… may be required,” says Deloitte University Press. “Instead, these employees will launch new data science teams and work cross-functionally with content, product, technology, and distribution business units.”
In other words, AI will keep a lot of people busy.