My blog, CachesToCaches, hosted with Jekyll and served via GitHub Pages
When I first arrived at graduate school, one of my professors shared his experiences on managing students. He told us that his students tend to fall into three bins of roughly equal measure: one third of students earn their keep, one third don’t, and a final third produce enough work to fund both themselves and the underperformers. The implication was clear: Work hard and make sure you don’t end…
As an academic, I read a lot of papers. Part of my job is retaining what I read, since deeply understanding the work of others and building upon it is one way I come up with new research ideas. When it comes time to sit down and write a paper, I need to contextualize my ideas in a Related Work section: I include a discussion of other research that has touched upon similar problems or inspired my…
Five years ago, my life exploded in complexity. I had just started a new position in a new field. I was planning my wedding. And my inability to say NO to anyone and everyone had culminated in my serving on the board of three graduate student organizations. Inevitably, cracks began to form, and my finite brain started to lose track of tasks. My calendar was sufficient to ensure that I wouldn’t…
As a researcher at the intersection of Robotics and Machine Learning, the most surprising shift over my five years in the field is how quickly people have warmed to the idea of having AI impact their lives. Learning thermostats are becoming increasingly popular (probably good), digital voice assistants pervade our technology (probably less good), and self-driving vehicles populate our roads (about…
As an academic, I see a lot of talks. In general, good presentations tend to be based on a good slide deck; even very capable speakers have a tough time reaching their audience when their slides are a mess. One common pitfall I often see is that many researchers will take figures or diagrams directly from their papers, upon which the talk is usually based, and paste them into their slides. It’s…
At the end of every year, I like to take a look back at the different trends or papers that inspired me the most. As a researcher in the field, I find it can be quite productive to take a deeper look at where I think the research community has made surprising progress or to identify areas where, perhaps unexpectedly, we did not advance.
There’s a story I retell about an incredibly talented researcher friend of mine from time-to-time. Though the exact details elude me now, since it was a number of years ago, the story goes something like this:
I can’t tell you the number of articles I’ve read devoted to “debunking myths”. They try to communicate the author’s opinion by listing a set of negative examples, often with section headings labeled Myth #1, Myth #2, etc. At best, it’s an easy way of building up a straw-man argument, yet at worst, such an article confuses the reader, filling their screen with potentially contentious or confusing…
Summary: Recent ire from the media has focused on the high-power consumption of artificial neural nets (ANNs), yet popular discussion frequently conflates training and testing. Here, I aim to clarify the ways in which conversations involving the relative efficiency of ANNs and the human brain often miss the mark.
Summary: Machine learning must always balance flexibility and prior assumptions about the data. In neural networks, the network architecture codifies these prior assumptions, yet the precise relationship between them is opaque. Deep learning solutions are therefore difficult to build without a lot of trial and error, and neural nets are far from an out-of-the-box solution for most applications.