With the devolving world order, seeking positive, engaging work, I wanted to learn a) what actually is machine learning for climate change and b) are there reasonable paths for us to dive in and contribute? To quote the call for action in a paper I cite heavily later:
Groundbreaking technologies have an impact, but so do well-constructed solutions to mundane problems.
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.
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? …
In this post, I address two questions:
I am intentionally avoiding a couple of hot topics in reinforcement learning that pertains to generalization/task transfer (such as meta-learning or other generalizable methods). I think that most generalization to date is proportional to data-distribution coverage of multiple tasks — not the ability for the same hyperparameters to solve numerically distinct tasks.
The good news: I think expanding our robot worldview can lead to robots that can generalize and help humans all the same. …