Should we insist our children learn to code?
We’ve seen major corporations declare that all their employees will learn to code. Should we insist our children do?
Machines won’t take all the jobs, despite the headlines. But organizations will compete based on the ability of humans and machines to work together, pitting machine + human vs. machine + human. Humans that can operate machines creatively will be highly sought after.
That leads to arguments like:
“All kids should code, whether they love it or not, and whether it comes easily or not. It’s like kids who might hate reading or math. Hating is not an option. They have to struggle through. It might not be “fun” for every kid. But it’s up to schools to make it easier, more accessible and more engaging. Letting kids graduate without coding skills is unfair.”
Here are two resources that I think will be helpful — and not too time consuming — to help come to your own opinion.
The first few chapters of Prediction Machines.
This book explains what Machine Learning is, without any jargon. It’s readable without any machine learning experience. Yet it is not dumbed down or glossing over anything important (I’ve worked with ML engineers since 2005 as a product manager and I’d suggest this book to anyone in the field as well).
This podcast interview with Professor Tyler.
Professor Tyler has published several books. But you might enjoy listening, rather than reading. He has a particular point of view that is well thought through. He explains that the future productivity of all humans will be based on ability to work with AI. All kinds of businesses, including creative ones, will be human + machine vs. human + machine. Humans trying to outcompete on their own will lose.
He also talks about the important skills to have in the future — such as critical thinking, creativity of ideas, and the tenacity to continually learn. He suggests we’ll need to learn new skills every 3–5 years. You can also read the transcript to get a sense of what he covers, if you prefer to read than listen.
Reading suggestions
I also liked these articles which explain some of the background. They set good context for the discussions on Future of Work:
Bain’s job, automation & inequality projections
https://www.fastcompany.com/40531968/how-demographics-automation-and-inequality-will-shape-the-next-decade
World Economic Forum
https://www.weforum.org/agenda/2017/12/future-of-work-community-soft-skills-jobs-robots/
McKinsey explains that this is not the only “labor upheaval”. There have been such upheavals before. (I’ll dig out the link).
Labor Department Statistics. I’ve copied them into a gSheet. Roughly 10% make $80k+ (and 1% make $150k+). Roughly 30% fall into each of the buckets: $45–80k, $30–45k, and < $30k.
I haven’t personally read it yet, but Weapons of Math Destruction is also highly recommended.
Codecademy
https://www.codecademy.com/learn/machine-learning
It might also be a good idea to play around with machine learning before forming a strong opinion. I like Codecademy as an entry point. There’s several things I love about their courses: the engaging user experience, no need to set up a coding environment because it is all browser-based, the content is bug-free, and the examples are fun and witty.
