Over the years, we've talked a lot on Spark about the importance of computer programming and computer science education.

But as machine learning algorithms and computational models change, so too is the skill set required to teach machines to deal with not only binary variables, but with probability and uncertainty.

And that's why, in recent years, the number of physicists working in Silicon Valley has dramatically increased.

We're programming machines not to solve problems, but to learn from experience.

Chris Bishop predicted this decades ago. He's the director of Microsoft's research lab in Cambridge, UK. And he happens to be a physicist.

Chris Bishop "We're seeing a big revolution in computer science these days, [in] machine learning or artificial intelligence, and it's quite a fundamental change in the way we create software," he says.

And that revolution hinges the skills that physicists are trained in, and well-equipped to do.

As modes of computing evolve, such as quantum computing, physicists will become even more important, he adds.

Whereas in the past, where physics was divided into experimental and theoretical physics, Chris says that "computational" physics is rapidly growing as a third discipline.

Does this mean that traditional coders and software engineers are going to go extinct? No, he says. There will always be a need for the underlying code in computer science.

But he does suggest that students and teachers in schools perhaps shift away from learning specific coding languages, which constantly evolve and change anyway.

He suggests they could focus more on "an appreciation of the underlying principles of computation."

And take more physics classes.