DAVID EPSTEIN: In a rapidly changing work world it's important to be a constant learner, to be able to change and evolve your skills. Especially when we're facing automation of certain types of work. So, I want you to think about a spectrum of work that gets automated. On one part of the spectrum is chess. Chess is based on rules. It's very clear. Patterns repeat. That is a great situation for a computer. Computers are really good at patterns which is why they made exponential progress in chess and now the chess app on your iPhone can beat the best human chess player in the world.

In the middle of the spectrum maybe think about self-driving cars. Self-driving cars we've made great progress. There are rules of the road so they're regular repeating patterns, but there are some significant challenges that remain. And on the far end of the spectrum we have something like say cancer research where IBM's Watson had a lot of hype but actually was underperformed at hype in such a way that when I talk to AI researchers some of them were worried that it would damage the reputation of AI in health research going forward. As one oncologist I talked to put it, the reason Watson destroyed at Jeopardy but failed in cancer research was because we know the answers to Jeopardy. So if you want to have skills that continue to be valuable you have to keep learning things and you have to be in some of these more amorphous fields almost.

So, I want to share one example of how this has played out in the past. When ATMs were created the thought was that this would do away with bank tellers for good, right. Bank tellers did repetitive transactions and so you wouldn't need them anymore. But, in fact, as more ATMS came online there were more jobs for bank tellers. What happened was that each branch needed fewer tellers so each branch of a bank became cheaper and banks opened more branches so there were more tellers. But the job of teller changed completely. It was no longer someone who could do repetitive transactions. Rather, they had to learn marketing skills and customer service and have this much wider array of broad skills because those broader skills and integrating different types of information are what's difficult for computers.

The psychologist Robin Hogarth characterized domains of learning as going from the kind to the wicked. Kind learning environments were areas where patterns repeated. There was a wealth of previous data. There were clear rules and feedback was apparent. And in those kinds of areas like chess computers really thrive. On the other end of the spectrum are wicked environments where not all the information is clear. Rules don't necessarily repeat. People aren't waiting for each other to take turns. Feedback may be delayed. If you get it at all it may be inaccurate. And human behavior is involved. Those are areas where computers don't do as well. They require a lot of the so-called soft skills. How to deal with human behavior and how to adjust to things that are changing in real time and interpret signals that are very difficult to quantify. That's an area that's very, very difficult for computers but humans have a huge advantage. So, those kinds of soft skills are really important and will be for a long time to come.