This entry may change over time, as the needs and purpose of my readers vary, but the core will remain the same.
I find myself wondering what technology must have “looked like” to folks just entering the industrial revolution. Strange engines running on magic and science, allowing one human being to do the work of 10 or 100? That sometimes detonated or otherwise disassembled, maiming or killing folks? The threat of all of the jobs we knew going to these new things? A society where the richest people could buy them and dominate the production industry? A world where knowledge of arcane engineering was the “new ticket” to riches while old industries crumbled to dust, sacrifices on the altar of progress?
But then…. the promise of more shoes made in a day by one person than ten could make in a week before. The ability to harvest more, plant more, and generate more food for the same labor. The promise of more knowledge, more communication, more time for learning and more dissemination of knowledge. A better world for everyone. Could both these visions be simultaneously true?
One thing I’d lay money on is that everyone felt the need for more knowledge. Whether you felt machine-fueled doom upon your livelihood or saw the dawn of a new era of prosperity, the world you’d have lived in was increasingly dominated by arcane knowledge – in particular, the knowledge of the forces of production. Today, one of these great forces is the analysis of data and production of numerical models. Take a look at the list of things my own professional career has touched on, and try to tell me it doesn’t sound like a list of desired skills for a “court wizard” in the middle ages:
- Prediction of weather patterns
- Production of automatons for manual labor
- Determination of the secret motivations of potential opponents
- Predicting the outcome of one choice vs. another on the part of a decision maker
- Detecting dangerous material or weapons even when hidden
Provide 2 professional references, must bring own wand, position open until next vernal equinox. Only now we’re called Scientists. And a lot of us were dug out of labs, professor positions and other gigs to answer the data-specific needs of this new age. “The more things change,” I guess.
This is getting long, so I’ll wrap it up for now. If there’s one fundamental I keep coming back to, it’s that knowledge is for everyone – and I mean everyone. And with the stakes raised sky-high in the new world, you need it. But chances are, you haven’t spent your whole life marinating in math books and code, and have little interest in slogging through piles of experimental methodology sections of papers to get to the heart of the matter. So I’m going to employ another bedrock principle I have, which is everything can be easy. So we’re going to take experimental results, contentious topics, and complex analyses and make them easy, understandable without leaving important things out. I’m not going to insult your intelligence by suggesting you don’t care about the details. The fact that you’re here means you probably do. So that’s the goal – all the specifics you want, starting from “no knowledge of the field” and building up how it all works. Explanation from soup to nuts, for your consideration and use. Hope it works!