Let’s start from evaluation. When have a computer that does something, we want to see if it does it well. So, this (“seeing if it does well”) is called evaluation. Evaluation is at the core of pretty much every computational technique, such as algorithm, model, system, device, etc. We want these things to work and improve people’s lives. And that…
MIRH: Motivate, Inspire, Relay personal experiences, Hold accountable >> four advantages human educators have in comparison to AI MOTIVATE: People still rely on other people to be motivated. Except if a person has strong intrinsic motivation, then they don’t need to be motivated. But many students need to be motivated and the generic “Hello, I am your friendly AI assistant…
Been playing chess recently. Chess is oddly similar to research. Thinking and writing require steps similar to moving pieces on board, with eventually the paper you’re writing being the board. Concepts and sentences are the pieces. You need to move them into the correct order (position) on the board. And there is a similar goal of making the opponent (reviewer)…
evaluation complexity increases when: *task is more subjective*you have more coders*you have more categories*you have more data to code chance-adjusted agreement (as opposed to percentage agreement) metrics typically aim to consider these factors in their calculation (though there’s no easy way to quantify “subjectivity” except after the calculation; a low score can be of indicative of the subjective nature of…
Research involves some interesting “back and forth” dynamics. Two of them are: 1. “Zoom in, zoom out” dance 2. Expand, simplify The first one deals with how to write paragraphs. For example: “There is little research on the effects of persona modality on users’ attention” (<= zoom out sentence = talks about the big picture). “However, of those studies that…
A quote from a panel discussion I participated recently:“AI didn’t replace thinking—it demanded more of it.” It is indeed our solemn duty, now more than ever, to ensure that the future generations get educated in thinking, whether logical, critical, or hypothetical. I don’t believe in the “calculator hypothesis” (=you don’t need to count because we have calculators; same way you…
Failing is painful in academia. Rejection hurts, mentally and almost physically. And you get plenty of it. You get it MUCH MORE than success. So, it’s very tough. We need to learn to cope with failing to survive. Here’s a simple formula: Recharge … regroup … retry 1. Recharge = rest enough, take some time off from the screen, eat,…
Three thoughts on students using LLMs to do their thinking. Of course, I’m not in favor of it. But it’s been hard to articulate why in a way that students would understand it. Think I found a good analogy for this in chess. If you watch chess videos, great moves often appear self evident after the player has made them,…
This is a snippet of an email I periodically send to my PhD students. “Let’s not break the head” is a good related quote, from my PhD student, Ilkka Kaate. Dear team, (cc other supervisors) you’ve all seen this place. You know you’re there when weird things start to happen: the research comes into your sleep. You can’t focus on…
When to defend. Now, there’s a thing with being a PhD student and a supervisor. There’s some information asymmetry: the supervisor has more experience and knowledge. So, should the student do everything the supervisor asks? No. Definitely not, because the supervisor is still a fallible human being. The goal is to somehow surpass the supervisor! (it has to be 🙂…
