Hope the following makes an interesting story for you all.
About the difference PhD mathematicians can make for Tesla (AI?) and as a result for TSLA.
In the past I have helped someone on a master thesis at the University of Technology in Delft.
A little engineering background (don’t get scared now):
Many engineering problems (maybe also AI solving, I would be interested to know, see the following story) result in having to numerically integrate differential equations and solve n equations with n unknowns with a computer.,
Engineering students learn how to solve that during their course in numerical analysis.
A well-known universal numerical integration method that is being taught is the Runge-Kutta method.
I helped a master student to solve an engineering problem on a personal computer, the personal computer being much to the chagrin of his professor.
‘There is a big Amdahl-computer of the university that can be used, why use such a small plaything?’, the professor said.
The answer of the master student involved: ‘Because they are the future.’
And if he could pull this off, he would have a predicting model.
Well, after having programmed the personal computer it was set to work.
A few hours later, we stopped the computer and analysed where it was.
It appeared that for it to finish one run, it would take a couple of million years to complete…
Looking on the internet, we found someone in the USA that for his PhD-thesis had tried something comparable: he had the same problems, but on a supercomputer.
Clearly Runge-Kutta was not the right method.
After several futile attempts with variations of the used method, we went to consult a professor in the mathematics department of the university.
He promised to look into it and we could come back in a few weeks.
When we came back, he told us we had a problem of stiff differential equations at hand. Ehhh, what??
A small variation of a variable in one equation resulted in a huge variation in another equation, making the set very unstable to solve.
In order to solve this type of differential equations, he explained that a so-called implicit method had to be used.
So, we went to the university library and got hold of the book the mathematics professor had told us about.
We reprogrammed the computer using an implicit method, pressed run and… after half an hour we had predicting results.
At the time the model being able to predict proved to be ground-breaking.
The experience taught me the importance of having access to PhD level people in an organization like Tesla, SpaceX, Google, etc.
They can make THE difference for us as investors.
I am very happy that Tesla has proven to be a huge magnet for the most talented people in the world.
And that, with their CEO showing the proverbial example, these most talented people work unrelentlessly to get things done.
About the difference PhD mathematicians can make for Tesla (AI?) and as a result for TSLA.
In the past I have helped someone on a master thesis at the University of Technology in Delft.
A little engineering background (don’t get scared now):
Many engineering problems (maybe also AI solving, I would be interested to know, see the following story) result in having to numerically integrate differential equations and solve n equations with n unknowns with a computer.,
Engineering students learn how to solve that during their course in numerical analysis.
A well-known universal numerical integration method that is being taught is the Runge-Kutta method.
I helped a master student to solve an engineering problem on a personal computer, the personal computer being much to the chagrin of his professor.
‘There is a big Amdahl-computer of the university that can be used, why use such a small plaything?’, the professor said.
The answer of the master student involved: ‘Because they are the future.’
And if he could pull this off, he would have a predicting model.
Well, after having programmed the personal computer it was set to work.
A few hours later, we stopped the computer and analysed where it was.
It appeared that for it to finish one run, it would take a couple of million years to complete…
Looking on the internet, we found someone in the USA that for his PhD-thesis had tried something comparable: he had the same problems, but on a supercomputer.
Clearly Runge-Kutta was not the right method.
After several futile attempts with variations of the used method, we went to consult a professor in the mathematics department of the university.
He promised to look into it and we could come back in a few weeks.
When we came back, he told us we had a problem of stiff differential equations at hand. Ehhh, what??
A small variation of a variable in one equation resulted in a huge variation in another equation, making the set very unstable to solve.
In order to solve this type of differential equations, he explained that a so-called implicit method had to be used.
So, we went to the university library and got hold of the book the mathematics professor had told us about.
We reprogrammed the computer using an implicit method, pressed run and… after half an hour we had predicting results.
At the time the model being able to predict proved to be ground-breaking.
The experience taught me the importance of having access to PhD level people in an organization like Tesla, SpaceX, Google, etc.
They can make THE difference for us as investors.
I am very happy that Tesla has proven to be a huge magnet for the most talented people in the world.
And that, with their CEO showing the proverbial example, these most talented people work unrelentlessly to get things done.
