As the days grow shorter and the nights grow longer, it’s beginning to feel a lot like winter. But, will this cold season mean cold feet when it comes to AI investment and roll outs, as some are predicting — or in other words will this be another “AI Winter”?
There is no denying that there is, still, a lot of hype around AI. And with this hype comes inevitable disillusionment when some of the bold statements, commitments and trials don’t pan out as expected.
Many industries and companies experience ‘AI fails’ when projects aren’t properly planned out, are rushed through, are done for the wrong reasons, are not scalable, or are not supported by the correct infrastructures. Recently, for example, the automotive industry was dealt a blow when deep learning powered self-driving car experiments didn’t go to plan, setting progress back years.
Peaks and troughs
These peaks and troughs of enthusiasm and disappointment are characteristics of pretty much every major technological disruption in history, and part and parcel of the hype cycle, a concept famously created by IT analysts Gartner, whose basic graphical illustration helps to explain this phenomenon.
Some industries, and some companies operating within them, are further along the AI hype cycle than others. Arguably book publishing is at the very beginning of this process, so yet to experience a “peak of inflated expectation” let alone a “trough of disillusionment” or “AI Winter”, for that matter.
Early adopting cousins
Interestingly, one of the most advanced and progressive industries for innovative AI applications is the newspaper and magazine publishing industry. Our cousins have been experimenting and rolling out machine learning initiatives since 2013 when the Associated Press became an early adopter, automating formulaic business and sports reporting.
Two years later the New York Times implemented an AI project called Editor to help journalists reduce labour-intensive tasks such as research and fact-checking. In 2016, the Washington Post trialled “robot journalism” at the Rio Olympics using Heliograf software, which analysed data and produced news stories. And last year Reuters launched its News Tracer product, which uses machine learning to sift through social media outlets for legit breaking news. Finally, just a few days ago, Quartz announced the launch of the Quartz AI Studio, a new tool to help journalists around the world use machine learning to report their stories.
There are good reasons why newsrooms in particular have been so quick to innovate and experiment with AI, arguably reaching the “Plateau of Productivity” on Gartner’s hype cycle long before others. The tumultuous, cash-strapped sector has faced severe disruption in the form of migration to digital, changing consumer purchasing and reading habits, and a complete shake-up of the traditional business and revenue models which had existed for years (so not too dissimilar from the evolution of book publishing, but at breakneck speed). Pew Research reported that in the space of just 10 years newsroom employment at US newspapers dropped by nearly a quarter. There has never been more pressure on editorial teams to work more efficiently and deliver more with less resources.
In the face of such extreme circumstances and weakening financial conditions for media publishers, AI is clearly seen as a knight in shining armour, helping newsrooms to work harder, faster and smarter. And it just so happens that journalism, not traditionally seen as a hotbed of innovation, is the perfect testing ground for AI projects.
Lessons to learn
So, what can the book publishing industry learn from its cousins and their early adoption of AI technologies, given that we potentially have the benefit of a slower curve of disruption? If we look at where AI is being introduced in newsrooms, we can see most of the implementations are launched to boost efficiencies. Not necessarily to replace journalists on any meaningful scale, but to assist them in their roles, and take care of the more mundane and repetitive aspects of their roles, so they can focus on bigger and better things.
As Uber, Tesla and others within the automotive industry are learning, ambitious AI and machine learning projects can be incredibly risk averse and long, frustrating processes. Yet, as many newsrooms can now attest, workflow-based AI projects, which are innovative while scalable, useful and well-grounded can be incredibly effective and make all the difference. It’s realistic that the book industry will start to see AI applications rolling out over the next few years, and judging from the experiences of our cousins, these AI rollouts will be most successful when embedded in our workflows.