When you need to solve a problem, it’s generally too late to learn how to solve the problem.
Contra Iron Man’s assertion “I learned that last night,” it’s simply not possible to become an expert in thermonuclear astrophysics overnight. (In all fairness to Tony Stark, he was being snarky back to someone mocking him, when he was the only one in the room who read the briefing packet). The superintelligence of characters like Tony Stark and Reed Richards are some of the most preposterous superpowers in the Marvel Universe, because they’re simply impossible to achieve: even if you ignore the fact that we can only process like 100 bits per second – and remember around 1 bit per second – and learn things in the zone of proximal development near things we already know – there’s too much information in a subject like astrophysics to absorb it in the few hours of effective concentration that one could muster for a single night. Take an area I know well: artificial intelligence. A popular treatment of AI, like Melanie Mitchell’s Artificial Intelligence, a Guide for Thinking Humans, is a nine hour audiobook, and drilling into a subarea is fractally just as large (a popular overview of deep learning, 8 hours – The Deep Learning Revolution; a technical overview of robotics, 1600 pages – The Springer Handbook of Robotics; and so on). You just can’t learn it overnight.
So how do you solve unprecedented problems when they arrive?
You learn ahead.
If you truly need to learn something esoteric to save the world, like thermonuclear astrophysics or the correct sequence of operators for the UNIX tar command, then it’s too late and you’re fucked. But if you have a hint of what your future problems might be – like knowing you may need to try a generative deep learning model to help solve a learning problem you’re working on – then you can read ahead on that problem before it arises. You may or may not need any specific skill that you train ahead on, but if you’ve got a good idea of the possibilities, you may have time to cover the bases.
Case in point: I’m working on a cover design for The Neurodiversiverse, and we’re going to have to dig into font choices soon. Even though I’ve been doing cover design for about ten years, graphic design for about thirty years, and art for about forty-five, this is calling for a level of expertise beyond my previous accomplishments, and I’m having to stretch. When we go into the Typographidome, it will be too late to learn the features that I need to pay attention to, so I’m reading ahead by working through the third edition of Thinking With Type, which is illuminating for me all sorts of design choices that previous books simply did not give me the tools to understand. I may not need all the information in that book, but it’s already given me some tools that help me understand the differences between potential font choices.
Alternately, you can work ahead.
If learning it per se isn’t the problem, you may be able to do pre-work that helps you solve it. Practice, if the problem is skill or conceptual variation; or contingency planning, if the problem is potential blockers. You can’t practice or plan for everything, but, again, you can cover many of the bases.
The other case in point: this entire blog post is a sneaky way to extend my blog buffer, using an idea I’ve already thought of to give me one more day ahead in the queue, leaving me adequate time and effort set aside to work on the series of posts that I plan to run next week. I don’t know what’s going to happen as I go into this interesting week of events … but I already know that I’m going to be crunched for time, and so if I complete my “blogging every day” series ahead of time, then I can focus next week on what I need to do, instead of scrambling every day to do a task that will detract from what I need to do in that day.
So: learn ahead, and work ahead. It can save you a lot of time and effort – and avert failures – later.
-the Centaur
Pictured: a bit of Thinking with Type, Third Edition.
