Talking TED (“Understanding Analogy: Theory and Method”)

A few months ago the Information Sciences Institute here at USC invited me to talk at one of their weekly Natural Language Seminars. They knew I’d been working on theorizing and analyzing analogies digitally, and wanted to hear more.

It was an exciting but daunting opportunity. How would I speak to an audience that thought about language and procedures for studying it in a radically different way? Several years ago I gave a talk like this at a conference for the Association of Computational Algebra. It didn’t go over well.

This time, I decided to experiment with a TED-style talk. There’s been a lot of criticism of the TED format. Most of it centers on whether the talks are accurate and informational or simply entertainment. Some do seem to be the intellectual equivalent of cotton candy — tasty but evanescent. But they also, I think, are a model for how to talk to a wider audience and enlist interest across cultural, institutional, and disciplinary boundaries.

So I studied up. There’s Nancy Duarte’s TED talk on TED talks, and Chris Anderson, a TED coach, has also shared his recipe. I think it boils down to three things. First, use biography (yours or another’s) to tell a coherent story that centers on the problem you work on. Second, have a clear transition from the problem to your answer. And finally, emphasize why that answer is powerful — what it changes about how we see the problem, and what it might mean for others. To put it differently, they rely on an analogy drawn between a personal narrative and a larger problem.

Put this way, it’s a recipe that applies to most of the good talks that I’ve seen, except TED talks are more personal and less complex. You have to put yourself forward and abandon qualifications, hedges, and the basic acknowledgement that others have been working on similar problems, often more successfully.

Despite discomfort with the TED format, I’ve been trying to figure out how I can get my scholarship out to a wider audience, especially communities beyond academia. This seemed like a great opportunity to experiment.

So I sat down and hammered it out. Meg was out of town, which meant that most of the writing happened with my daughter in my lap, and we practiced with her in the baby bjorn (she’s my biggest fan).

The final title: “Understanding Analogy: Theory and Method.” The folks at ISI posted it here. It doesn’t quite live up to the billing, but it worked. My auditors generally agreed that analogies are an important feature of new ideas and that I’d found a new way of looking at them. And since that talk we’ve been talking about collaborating on a machine learning tool that finds analogies. I’m recruiting undergrads for some initial work this summer. It will be exciting to see where this leads.

Machine Grading

A friend of mine drew my attention to the NYTimes’ recent article on advanced in essay-grading software. It’s technology that will raise hackles at campuses around the country. The claim is that such programs are becoming sophisticated enough to grade college-level writing. Of course, their effectiveness is widely debated. The article helpfully includes a link to a study by Les Perelman which critiques the data being used to support such claims (he argues that sample size problems, confusion between distinct kinds of essays and grading systems, and loose assertions undermine the argument). The software is getting better, but it still doesn’t look like it can quite replicate the scores produced by human graders.

But such criticism is an argument at the margins. There is now clearly room for debate on both sides. Machines are comparable on standardized tests. The long-term trajectory is evident: if machines are roughly as effective as a force of part-time human graders, standardized tests will end up using the software to save money. They’ll keep some humans in the loop cross checking and validating, but the key incentives all point in the direction of greater automization. The reductive structures and simplistic arguments which we train students to replicate for these tests has laid the groundwork. We’ve already whittled essay writing into an algorithm.
Continue reading