How Soon Will You Lose Your Job To An AI Robot?

Written by Bernard Marr

We often read articles that pit man v machines — will the robots take our jobs, will AI take over and make us stupid — and yes, I’ve written some of these articles.

But as is so often true with predicting the future, the truth will likely not be an either or scenario, but rather shades of grey. Instead of asking who will win, we should think about a merger between man and machine to bring out the best in both.


An interesting example of this is chess. It has been a long time since IBM’s Deep Blue beat the grand chess master, but more recently hybrid teams (made up up people and AI) seem to have the edge over both people only and machine only teams.

From the book, The Signal and the Noise:

“In 2005, the online chess-playing site hosted what it called a ―freestyle chess tournament in which anyone could compete in teams with other players or computers. […] The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. […] Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.”

 This is an interesting case study for the future of computational and analytical work in general. There’s no doubt that humans are better at certain types of creative and abstract thinking, and may continue to be for a long time, while computers excel at detailed and complex analysis and computations.

But combine the two and you suddenly have a new powerhouse of computing to solve difficult problems. In the journal Science, researchers from Cornell University and the Human Computation Institute presented the unique idea of “human computation.”

In short, human computation is what’s known colloquially as “crowd sourcing;” a computational or analytical task is sent out in tiny micro-tasks to many different individuals, and the data are then stitched back together by a computer. The results are often more efficient and accurate than either humans or computers working alone could produce.

Google’s reCAPTCHA security feature is one common example of this. Websites use the tool to weed out spammers by prompting visitors to enter numbers from a photograph (often an address number on a building) which computers (bots) can’t read. Google then simultaneously uses the answers to collect wisdom from the crowds and improve their maps and street-view functions.

In another example, Cornell-based Alzheimer’s researchers are using an interactive tool on their website,, in which users play a game to help analyze data. The lead researcher believes that by deputizing the general public to help analyze data — through a simple online game that anyone can play — they can reduce time to treatment discovery from decades to just a few years.

As computers are becoming learning machines rather than simply doing machines, the fear is that they will replace humans. But it seems much more likely to me that these learning machines will help humans adapt to rapidly changing problems, environments, and systems, and we humans will continue to provide the creativity and ingenuity that the computers can’t match.

 Researches believe these AI/human teams may be the answer to solving the world’s most pressing and complex problems, including climate change, conservation and distribution of resources, controlling disease, and even fostering world peace or solving geopolitical problems.

The question, then, is not who will win: man or machine? Instead, it should be how can we work together to tackle the world’s biggest problems in entirely new and innovative ways?


Bernard Marr is a best-selling author & keynote speaker. His new book: ‘Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results