Most of us interact with artificial intelligence (AI) in multiple shapes and forms on a daily basis – though we are often completely unaware of it. From the recommendations we receive on Amazon or Netflix, to the AI-driven camera software used to improve the photos we take on our smartphones, AI powers a lot of the services we consume on a daily basis. Even the map and sat nav applications we use rely on AI. Company chatbots are a more obvious use of AI, and can now be found on nearly every company website you visit. In fact, it's been predicted that 80% of companies will be using chatbots this year.

The robots are coming – for jobs. This is the plain, cold, hard fact we now face as we head towards the third decade of the 21st Century. The technology-driven world in which we now live is one filled with promise – cars that drive themselves, algorithms that respond to customer service inquiries, automated business intelligence on tap. Yet, this brave new world is also filled with challenges. For even as AI and automation increase productivity and improve our lives, their widespread adoption means that many work activities humans currently perform will soon be displaced – if they haven't been already. What this doesn't mean, however, is that there will be a shortage of jobs in the future.

In 2015, Uber opened a research facility around the corner from Carnegie Mellon University's National Robotics Engineering Center in a move positioned as a partnership between the two organizations. Within months, dozens of faculty members had left their positions for full-time roles at Uber, draining the center of much of its talent. Other major tech companies have followed a similar path – in 2018, Facebook launched AI labs in Seattle and Pittsburgh headed by former professors. These stories provide a window into a tug-of-war that's been playing out between the tech industry and academia. Keen to build products and services that use AI and machine learning, tech firms and other businesses have been hiring away researchers and professors from universities, creating a shortage of academics who can teach the next generation of data scientists. The proportion of computer science PhDs who stay in academia has reached a "historic low," the Computing Research Association has said.

I see the citizen data scientists as this emerging group,

"It's never too late," said Rigby. "People are working later and, unlike previous generations, are sometimes having two or three careers in a lifetime. It doesn't even have to be as radical as that: as we've seen, the overall feeling is that AI will enhance existing jobs so, as long as workers remain open to embracing that, they can utilise the freed up time to add back to the business in other areas.