Original article was published on Artificial Intelligence on Medium
“Technology has never been a net destroyer of jobs,” says Stuart Frankel, CEO of Narrative Science. “Look at almost every technology job that exists in any enterprise today. None of those jobs existed twenty years ago, and most of them probably didn’t even exist ten years ago.”
For the moment, anyway, rather than being a total takeover of human jobs by robots, the problem is that there are a lot of vacant job posts and not enough skilled people to fill them. With the rise of data-driven business, the demand for tech talent is increasing across the board.
For instance, in 2016, cyber-economy researcher Cybersecurity Ventures reported that the cybersecurity unemployment rate was at zero — and that in fact, there’s a shortage of more than one million experts across the world. Similar tech-employment areas, such as software development and data science, aren’t faring any better and are dealing with their own talent gap. The need for more experts in tech jobs will continue to grow as artificial intelligence finds its way into even more domains.
“I believe that governments should ensure that coding is valued as highly as English, math, and science, if we are to ensure that we can maximize this boom in opportunities that artificial intelligence will provide us,” Lobo says.
Recent years have seen a number of government-led projects as well as initiatives by the private sector to help fulfill the need for tech talent. Former President Barack Obama’s TechHire project is an example: It includes a $100 million grant meant to pave the way for more people into tech jobs, including those who don’t have higher-education certifications.
We’re also seeing the development of massive open online courses (MOOCs) from institutions such as Coursera and Big Data University — free online education for technical skills that are in high demand. Coding boot camps, institutions that teach applicants computer programming in a short amount of time, have also risen in popularity. At the same time, companies such as AT&T are helping their employees adapt to the future of employment.
As the pace of artificial intelligence development picks up, skill and expertise requirements will change just as rapidly. Not even software development will stay the same in the future and will shift from coding toward training AI algorithms.
A REVOLUTION IN HUMAN-COMPUTER INTERACTION
Many of the people who are losing their jobs to AI do not have the skills and knowledge to enter tech jobs, and training them requires considerable time. Fortunately, in this respect, artificial intelligence can help solve a problem that might be largely its own making. AI is already promising to revolutionize education in many ways, including personalizing and optimizing the learning experience. This means it’ll take less time to learn new skills.
“Humans will be able to retrain into other industries quicker than ever before, giving them a maximum flexibility to react to the changes in the job market,” Lobo says. “Why can’t a truck driver be able to move into a career in coding within months?”
Where AI can’t soften the learning curve, it’ll be able to break down the complexity of tasks and make them simpler, enabling more people to enter jobs that once required years of education and training.
One noteworthy development is Natural Language Processing and Generation (NLP/NLG), the branch of artificial intelligence that has to do with understanding and producing human language scripts. NLP and NLG are redefining the way we interact with computers, removing hurdles and barriers to perform tasks and making us much more efficient at our jobs.
“NLG is an enabling and augmentation technology,” says Narrative Science’s Frankel. “When combined with human skills, NLG can produce results that far exceed what either group could achieve alone. I think Excel is a great analogy to NLG. When Lotus 123 and Excel first came out, there were lots of dire predictions about the future of accountants and financial analysts, but we quickly learned that these tools weren’t going to replace analysts. In fact, the analysts turned into super analysts and businesses started hiring them in droves. The same thing is happening with NLG.”
Narrative Science integrates NLG into business intelligence (BI) platforms to provide users with Intelligent Narratives, insightful, conversational communications packed with audience-relevant information that provide complete transparency into how analytic decisions are made. The technology, Frankel explains, is helping enable a broader group of people to do their jobs without requiring a specialized set of skills such as data science.
“This means less technical folks or people at any analytical skillset can use these BI tools, instantly get the insights they need, and ultimately, do their jobs better,” he says.
NLP, on the other hand, makes it much easier for people to interface with analytics tools and data sources. You can already see this in platforms such as IBM Watson Analytics, where natural language commands are making it easier to query data sources. This can pave the way for people with mathematical skills to enter data science jobs without having to go through lengthy programming courses.
NLP is also helping make sense of large corpuses of unstructured knowledge, including articles, books, and whitepapers, organizing them into data that is queryable and usable by machines. This can make software and services much more efficient at helping human experts.
Alex Linden, the researcher at Gartner, believes this can help create more efficient knowledge graphs — loosely structured data repositories that power AI engines. “AI/NLP can help create a real knowledge industry,” he says. But he adds, “We are still in its absolute infancy.”