Some of what we're learning suggests a balance between exposing children to new things, yet giving them a chance to repeatedly experience something they enjoy, which builds "chunks" of information in their brains.

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.

High fidelity is a rich experience, and you'll put up with terrible convenience to get it - maybe it's high cost, waiting in line, jumping through hoops. High convenience is the opposite - it's a commodity, but it's cheap and easy and ubiquitous. A great exclusive boutique shop is high fidelity; Wal-Mart is high convenience. Both are hard to establish in their own way. The thing to remember about sustaining either is that you can't sit still. Some other entity will always find a way to challenge your fidelity position or your convenience position.

Some people seem to have extreme natural wiring - a talent that seems to come out of nowhere. Like a music savant or prodigy. The uplifting news, though, is that many talented people don't have such natural wiring - but they forge a talent through thousands of hours of what's known as deliberate practice or deliberate performance.

Anybody can develop a certain amount of talent at something. However, the supremely talented - the superstars - are people who have married a gift of brain wiring to those thousands of hours of practice, usually in favorable circumstances.

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.

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.