We learn more about how human brains work. And that leads us to ideas about how to make human brains work better.

I reluctantly signed up for a journalism major, thinking I needed a fall-back way to make money should my career as a novelist fail to take off. As I started to try on journalism, including doing internships and working at the campus paper, I found I actually liked it. So I started to want to be a journalist.

Eventually, we need to have computers that work differently from the way they do today and have for the past 60-plus years. We're capturing and generating increasingly massive amounts of data, but we can't make computers that keep up with it. One of the most promising solutions is to make computers that work more the way brains work.

Who "wins" and who "loses" depends on when you measure it.

Artificial intelligence uses a complex set of rules - algorithms - to get to a conclusion. A computer has to calculate its way through all those rules, and that takes a lot of processing. So AI works best when a small computer is using it on a small problem - your car's anti-lock brakes are based on AI. Or you need to use a giant computer on a big problem - like IBM using a room-size machine to compete against humans on Jeopardy in 2011.

Chunking makes our brains more efficient. The more you can chunk something, the faster and easier you can process it. Wayne Gretzky had chunked hockey like no one before or since. Talented people have supremely chunked whatever they become talented at doing.

Chunking is the ability of the brain to learn from data you take in, without having to go back and access or think about all that data every time. As a kid learning how to ride a bike, for instance, you have to think about everything you're doing. You're brain is taking in all that data, and constantly putting it together, seeing patterns, and chunking them together at a higher level. So eventually, when you get on a bike, your brain doesn't have to think about how to ride a bike anymore. You've chunked bike riding.