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martyb writes:

For many years now, I've been following the blog, Knowing and Doing — Reflections of an Academic and Computer Scientist by University of Iowa college professor Eugene Wallingford. I admire his efforts at understanding his students' perspectives and his taking pains to try and help them to grow and understand what they are doing, meanwhile providing a solid foundation for future exploration.

I found this recent (August 7^th) entry, Some Advice for How To Think, and Some Personal Memories, to be especially interesting (emphasis from original):

I've been reading a bunch of the essays on David Chapman's Meaningness website lately, after seeing a link to one on Twitter. (Thanks, @kaledic.) This morning I read How To Think Real Good, about one of Chapman's abandoned projects: a book of advice for how to think and solve problems. He may never write this book as he once imagined it, but I'm glad he wrote this essay about the idea.

[...] Artificial intelligence has always played a useful role as a reality check on ideas about mind, knowledge, reasoning, and thought. More generally, anyone who writes computer programs knows this, too. You can make ambiguous claims with English sentences, but to write a program you really have to have a precise idea. When you don't have a precise idea, your program itself is a precise formulation of something. Figuring out what that is can be a way of figuring out what you were really thing about in the first place.

This is one of the most important lessons college students learn from their intro CS courses. It's an experience that can benefit all students, not just CS majors.

Chapman also includes a few heuristics for approaching the problem of thinking, basically ways to put yourself in a position to become a better thinker. Two of my favorites are:

Try to figure out how people smarter than you think.

Find a teacher who is willing to go meta and explain how a field works, instead of lecturing you on its subject matter.

This really is good advice. Subject matter is much easier to come by than deep understanding of how the discipline work, especially in these days of the web.

[...] Chapman's project is thinking about thinking, a step up the ladder of abstraction from "simply" thinking. An AI program must reason; an AI researcher must reason about how to reason.

This is the great siren of artificial intelligence, the source of its power and also its weaknesses: Anything you can do, I can do meta.

[more]

Have you ever stopped to think about how you think? Ever try to optimize your thinking processes? I often sense myself intuitively attempting to categorize new concepts in the fashion of "all A are B, but not all B are A"... in other words, finding encompassing abstractions that are proper supersets and subsets of other sets. What are the defining and distinguishing characteristics? How does this relate to other abstractions I've learned?

As a concrete example, when trying to get up to speed on a new programming project, I have found it helpful to make three passes through the documentation. On the first (rapid) read-through, I seek to identify the vocabulary and the higher-level interdependencies between the terms used. On the next pass, I read slower and seek a much stronger mental model of how everything is defined and interrelated. On the third pass, I read with a critical eye to clearly distinguish dependencies and assumptions that may, or may not, hold — I seek the outliers and corner cases.

So, my fellow Soylentils, how do you think you think?

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