The best teams for robotics are not all computer scientists — they have electrical & mechanical engineers, computer scientists, robots, and more to fill the cracks. This post is an exploration of how different ways of thinking contribute in robotics — and by extension to many software engineering projects.

How would you summarize the overarching conceptual theme of your undergraduate major?

This was originally posted on my free newsletter on robotics & automation, Democratizing Automation.

Seeing models

I don’t characterize EE primarily by circuit design nor nano-fabrication. It took me a long time to figure out what was different between my degree in electrical engineering (EE) and a similar computer science degree. So many of my classmates get software engineer (SWE) jobs regardless, are we really different? I know the courses are different, but there’s a chance that the courses teach the same concepts in a different curriculum and timeline.

So, one asks, how are EEs different? They learn to see the world in models. All of the different tasks we engage in have a different set of assumptions and tools. We need to be careful with and understand where the model we are using is true and where we may be hurting performance (in the form of uncertainty). Circuit design has a model in the form of a Cadence library for a specific fabrication run, microelectromechanical systems have models normally grounded in physics (but many other things effect the final device), signal processing uses models to determine what information they see and send, and more.

The mathematics of the major, such as Fourier transforms, wavelets, electromagnetism, and more, are different sets of models. These analyses require a large underpinning of mathematics to teach (linear algebra, multi-variable calculus, and probability) when compared to deep learning (import keras). The rigor of learning these contained systems makes EEs view machine learning models with their limitations in scope.

This way of seeing the world in models is becoming even more valuable as every company tries to throw machine learning models at new problems. My lab group is almost entirely EE-type people, and the scientific, constrained way of thinking will help in way more systems than traditional EE design spaces.

Maybe send this to a friend who did EE and you had no idea what that meant?

#future #software-engineering #engineering #machine-learning

Seeing the world in models in the age of machine learning
1.10 GEEK