Just in case you hadn’t noticed: The robots are coming.
Well, you probably had noticed. It’s hard to miss. Newspaper articles and blogs everywhere herald their arrival. The stories usually follow the same general formula: A catalogue of amazing things that robots (really, AI systems) can do. Debate about forced unemployment vs. people freed up to do more interesting work. And then some comments about whether it’s for the good or bad. You can generally infer the general tone from the choice of image accompanying the story: a cute Japanese robot with a plastic smile or a more sinister face with the Terminator’s jawline.
Of course, workplace automation has been around for almost as long as we have had workplaces. Each new wave of technology disrupts the employment landscape in its own particular way. However, this time it’s serious: It’s not just manual labor that’s under threat, but increasingly, we’re told, white-collar workers are being supplanted by machines. A recent McKinsey report has generated a wave of headlines: “Even the CEO’s job is susceptible to automation,” wrote Patrick Nelson at Network World; and Ana Swanson at the Washington Post reported, “How robots will even affect the jobs of people we thought were immune.”
Not surprisingly, a closer read of the McKinsey material gives a more nuanced appraisal than a headline can offer. Few of us will actually be replaced by a robot, but many workers will lose some part of their current jobs to automation. The McKinsey report states that up to 45 percent of the activities that employees perform today could be automated, but very few occupations will be automated entirely in the near or medium term. The things that require creativity and emotion remain uniquely in the domain of humans, and they are likely to remain there for quite some time.
It’s against this background that we see a steady increase in what is often referred to as robot journalism: machines writing news stories. So far, these applications rely on scenarios where the general ‘shape’ of a story remains pretty consistent because of the regularity of the data underlying those stories. For example, a report on stock performance is always going to mention the current stock price, what the stock price was at some particular point in the past, the price highs and lows, and perhaps something on volume or analyst sentiment. These reports often are found in the back pages of a newspaper, and are the stories that few journalists actually want to write, since they allow almost no space for creativity and emotion.
The reality is that today’s news-writing software is still a long way from replacing human journalists. Which is not to say that what a robot journalist produces isn’t useful; it surely is. Last month I spoke about robot journalism at the news:rewired conference. It was fascinating to see how most of the major news media are exploring how to get robots on staff. So while we’re going to see more robot journalists, it doesn’t necessarily mean fewer human journalists. A recent study by researchers at Oxford University and Deloitte explored how susceptible various jobs were to automation—journalists ranked 285th out of 366 jobs, with an ‘automation risk’ of just 8 percent.
But if you really want to see where the action is regarding automated writing, just look at it from a different angle. Rather than Natural Language Generation displacing people from their jobs, it’s offering a solution to the problem of shortage: A shortage of workers or expertise. And that story isn’t about news; it’s the Big Data story. You can take issue with the astounding numbers that are thrown around, but even if they’re out by a factor of 10, they’re still significant: by 2020, about 1.7MB of new information will be created every second for each person on earth. But less than 0.5 percent of all data is ever analyzed.
In 2011, McKinsey noted, “The United States alone faced a shortage of 140,000 to 190,000 people with analytical expertise, and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.” And subsequent reporting points to massive year-on-year leaps in demand.
Regardless of how few or how many people are displaced by robots, they are not all going to become data scientists. The only way this volume of data will be analyzed and explained is if we have machines doing it for us. And that’s the real calling and promise of Natural Language Generation (NLG). Interactive visualizations and dashboards don’t cut it; the meaning in all that data will be lost unless we call in the robots. But you need more than robot journalists, tirelessly churning out one formulaic story after another. You need articulate robots that use sophisticated NLG to construct fluent narratives from idiosyncratic data sets. So, accept the data reality, embrace automation, and find an NLG robot to explain the insights buried in your data.
About the Author:
Dr. Robert Dale, Chief Technology Officer and Chief Strategy Scientist at Arria NLG, is recognized as one of the world’s foremost experts in Natural Language Generation (NLG) research and Development, having authored or edited seven books and 160 papers on computational linguistics. He was a Professor in the Department of Computing, Director of the Centre for Language Technology at Macquarie University. He co-authored the seminal textbook “Building Natural Language Generation Systems” with Arria NLG Chief Scientist and co-founder Professor Ehud Reiter.
