How do politicians react to mathematics

17-year-old math genius: do politicians just need tutoring in math?

Mathematics is considered a "fear subject" in school. Basic knowledge of statistics could be useful, especially in the pandemic. A young math genius explains why many people don't like to deal with figures - and what could help against it.

Maximilian Janisch is 17 years old and is currently doing his master's degree in mathematics. We wanted to know from him what fascinates him so much about the subject - and how you can get others excited about it.

Mr. Janisch, we will keep up with the corona pandemicfaced mathematical concepts such as exponential growth or the calculus of probabilities. This is actually not higher mathematics, almost everyone has learned it at school. Nevertheless, in some parts of society there seems to be a lack of understanding for such relationships. How do you explain that?

Right! When I look at how some governments are reacting to the corona pandemic, not all seem to understand exponential growth. I do not know why that is. Maybe the education system is a little to blame. You could say: Mathematics has a bad reputation. A lot of friends of mine always say: "Oh, I was really bad at maths. I always hated the subject. Fortunately, I don't have to do that anymore." Math could be very useful here.

Math is considered an anxiety subject - where does it come from?

Mathematics missed entry into popular science. Physics, on the other hand, did it wonderfully: Many people are interested in black holes, the universe, the big bang and so on. But it is much less common for people to do math for fun.

Perhaps this is also because the subject was simply not made palatable to them. It is not always taught with joy, but is often relatively boring and too abstract. No wonder that people then think: "What do I need this for anyway? I have absolutely no pleasure in calculating any integrals." But I would say that is changing so slowly. There are more and more people who are interested in the knowledge that mathematics can provide.

Maximilian Janisch is known as a "child prodigy": at the age of just 17, he completed his master's degree in mathematics. (Source: manufacturer)

How do you imagine math lessons that are fun?

Let me give you a concrete example: There is a very popular YouTube channel called "3Blue1Brown". There are a lot of relevant math topics covered in it - from simpler things like complex numbers to Fourier transforms. This is partly higher mathematics, but in a visually very playful style and easy to understand. The videos have millions of viewers, which is impressive because it's math and not music videos (laughs).

Most of the videos mentioned are about how the ideas you deal with in maths came about. That is perhaps more interesting than when you hear: "That's just the way it is! Learn this formula by heart and in the test you have to use it 30 times."

They are considered highly gifted. Do you meet other people differently as a result?

This is an interesting question. But I didn't notice anything like that. I feel like a perfectly normal 17-year-old teenager. That I deal with this "strange" subject has relatively little influence on my environment. All the people I meet at the University of Zurich are also interested in mathematics. I don't attract any further attention.

Scientific work means asking questions. Is mathematics a science like any other in this regard? How does a mathematician do research?

Mathematics has a great advantage over all other sciences. In mathematics you have certainty. Naturally, this is not the case in any of the other sciences.

Take social science, for example. Heretical people like Richard Feynman, one of the most famous physicists of all time and Nobel Prize winner, claim that the social sciences are not science. Feynman was pretty radical and I don't necessarily share his opinion.

But it is true that social science is the least certain of all. People and societies behave in very complicated ways. Modeling that is very difficult. It's better in physics. The universe mostly behaves more regularly than humans. When I fire a cannon, I can calculate exactly how the bullet will behave.

Although the world kindly often behaves well enough along suitable mathematical models, unfortunately one has no certainty in physics either. No matter how beautiful a mathematical model or theory, you can have - if 100 years later it turns out that there is an experiment that does not correspond to my theory at all, you have to change everything or at least add a new idea to the theory . This has happened quite often in the past, for example Einstein's theory of relativity is a very elegant addition to Newton's theory of gravity.

On the other hand, if you've proven something in math, it's right and it's true forever, and you don't have to worry about getting it wrong after all.

Big data and artificial intelligence are two popular buzzwords in business. But are these really the topics of the future that they are often portrayed as?

In any case. I'm actually amazed that the AI ​​is coming so late. It's interesting: The math behind machine learning, for example, is not new math. In principle, this is a combination of analysis and probability theory. Both existed 50 years ago. But then there were no fast computers and no large amounts of data. Today we are at a point where computers are so fast and programming languages ​​so pleasant and abstract (i.e. user-friendly) that math can be implemented directly.

In which area of ​​life does this bring about the greatest upheavals?

I think the diagnosis and treatment of patients in medicine will change dramatically. In the future, patient data will be evaluated much better and perhaps better predictions made, for example in the case of diseases such as diabetes or cancer. This is where the greatest progress will be made.

What are your own career plans?

I am currently writing my master's thesis in mathematics. In it I deal with the Euler equations. These are the basic hydrodynamic equations that describe the behavior of water and other liquids. So that's going in the direction of theoretical physics. I will probably finish my master’s thesis in July. Then it goes on with the doctorate. I still have to decide which direction to go.

Either I am also doing my doctorate on the Euler equations. Or I am doing my doctorate in a completely different direction, namely in the direction of machine learning. That would be a mix of industry-related and academic work, theory and practice.

That sounds like a very fundamental decision.

The Euler equations are much more demanding from a mathematical point of view. The math behind it is very difficult. That already goes in the direction of the famous Millennium Problems. That's seven math problems that were written out by the Clay Institute. There's a million dollars for every problem you solve. This seems generous, but it is very economical when you see what is expected of you as a solution. Many people would say this is the hardest way to make a million dollars.

How are photos of black holes actually created? In this video lecture from the "Sparx" series by the big data company Trivadis, Maximilian Janisch explains the role the analysis of large amounts of data plays in space research and how mathematics helps us to better understand and visualize the universe.

Some of the problems have been unsolved for centuries. One problem has now been solved, by a Russian mathematician named Grigory Perelman. Interestingly enough, he declined the prize money.

But there are still six unsolved problems. One of them are the Navier-Stokes equations. This is a more general form of the Euler equations. So I'm very close to one of the toughest unsolved problems of all time. I definitely won't solve it, but I could maybe do a tiny bit to help us know a little more about this equation.

Machine learning is something completely different. Let's put it this way: It's the easier way to make a million dollars. It's more interesting to a lot of people, especially business. But mathematically it is probably not that demanding.

We wish you every success and thank you for talking to us.